Identification of eQTL using different sets of single nucleotide polymorphisms associated with carcass and body composition traits in pigs

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Brito, Simara Larissa Fanalli, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3254185/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jan, 2024 Read the published version in BMC Genomics → Version 1 posted 8 You are reading this latest preprint version Abstract Background Mapping expression quantitative trait loci (eQTL) in skeletal muscle tissue in pigs is crucial for understanding the relationship between genetic variations and phenotypic expression of carcass traits. Therefore, the primary objective of this study was to evaluate the impact of different sets of single nucleotide polymorphisms (SNP), including those pruned for linkage disequilibrium (LD), derived from SNP chip arrays and RNA-seq data from liver, brain, and skeletal muscle tissues on the identification of eQTL in the Longissimus lumborum tissue, associated with carcass and body composition traits in Large White pigs. SNPs identified from muscle mRNA were combined with SNPs identified in brain and liver tissue transcriptomes, as well as SNPs from the GGP Porcine 50K array. Cis- and trans-eQTL were identified based on the skeletal muscle gene expression level, followed by functional genomic analyses and statistical associations with carcass and body composition traits in Large White pigs. Results The number of cis- and trans-eQTL identified across different sets of SNPs (scenarios) ranged from 261 to 2,539 and from 29 to 13,721, respectively. Furthermore, 6,180 genes were modulated by eQTL in at least one of the scenarios evaluated. The eQTL identified were not significantly associated with carcass and body composition traits based on the association analyses but were significantly enriched for many traits in the "Meat and Carcass" type QTL. The scenarios with the highest number of cis- (n = 304) and trans- (n = 5,993) modulated genes were the unpruned and LD-pruned SNP set scenarios, identified in the mRNA of muscle. These genes include 84 transcription factor coding genes. Conclusions After LD pruning, the set of SNPs identified based on the transcriptome of the skeletal muscle tissue of pigs resulted in the highest number of genes modulated by eQTL. Most eQTL are of the trans type and are involved in genes influencing complex traits in pigs, such as transcription factors and enhancers. Furthermore, the incorporation of SNPs from other genomic regions to the SNPs identified in the porcine skeletal muscle transcriptome contributed to the identification of eQTL that were not identified based on the porcine skeletal muscle transcriptome alone. Sus scrofa expression quantitative trait loci transcriptomic LD pruning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Developing effective breeding strategies and genetic improvement programs are paramount for improving the long-term sustainability of livestock production. In this context, there is a need to determine the impact of genomic variants on gene expression and phenotypic variability related to production and environmental efficiency traits, such as feed efficiency, carcass yield, live weight, and body composition [ 1 ]. Genome-wide association studies (GWAS) based on single nucleotide polymorphism (SNP) information and production efficiency and meat quality traits have been extensively explored in recent years [ 2 , 3 ]. These studies have contributed to the understanding of the genetic architecture of complex traits in pigs, but most studies have been based primarily on SNPs located in intronic and intergenic regions. Therefore, the use of SNPs obtained from transcriptome sequencing could provide additional information about the SNPs located in transcribed regions of the genome, which have a greater likelihood of being more functionally relevant with greater influence on the phenotypic expression of complex traits [ 4 – 6 ]. Genetic markers (e.g., SNPs) located within coding regions of the genome are more likely to change the level of global gene expression in the most diverse tissues of living organisms. For example, a missense variant could result in the alteration of a codon that encodes a certain amino acid and, consequently, lead to changes in protein synthesis and in the functionality of these proteins in various tissues and physiological processes of organisms [ 7 , 8 ]. When a SNP is in the promoter region of a gene or 3' untranslated region (3'UTR), it can alter the level of gene expression and affect post-transcriptional regulations [ 7 ]. Thus, these variants may result in phenotypic differences (e.g., carcass trait, body composition) among individuals in a population. Due to the reduced genetic variability in livestock populations, SNPs located throughout the genome are in moderate to high linkage disequilibrium (LD) [ 9 – 11 ] and, therefore, could have similar effects on a given trait. So, it is common to perform SNP or tag-SNP pruning based on LD thresholds to eliminate SNPs capturing similar quantitative trait loci (QTL) effects in GWAS, in which only one representative SNP of the LD block is maintained to reduce the total number of statistical tests [ 7 , 12 – 15 ]. Not performing LD pruning could result in more false positives and decrease the statistical power of the analyses [ 16 – 18 ]. The SNPs originating from the transcriptome could be in greater proximity and, therefore, in greater LD among themselves. Thus, the level of LD among the studied variants is an important element to be considered in expression QTL (eQTL) identification studies based on transcriptome sequencing data [ 17 , 19 , 20 ]. The integration of SNPs from transcriptome sequencing data from different tissues (e.g., skeletal muscle, liver, brain) with other data sources such as genotyping SNP arrays (e.g., GGP-50K genotyping) can provide complementary information about genomic variability related to gene expression in specific tissues such as the skeletal muscle – a key tissue for pork production. The combination of SNPs obtained through sequencing the RNA from different biological tissues and data sources (i.e., sequencing, genotyping) could enable a more accurate identification of eQTL that would not be detected by analyzing variants from the skeletal muscle tissue alone. In addition to data integration, it is important to evaluate alternative statistical approaches, such as LD pruning and quality control parameters (e.g., minor allele frequency, genotyping call rate, and variants with extreme departure from the Hardy-Weinberg equilibrium expectations), to adjust the initial data structure and reduce potential biases in the results due to the presence of closely linked or low-quality variants [ 9 , 12 , 21 – 25 ]. We hypothesize that different combinations of SNPs obtained from different biological tissues (e.g., skeletal muscle, liver, and brain) and data sources (GGP-50K genotyping and RNA-seq) may affect the identification of eQTL associated with carcass and body composition traits in pigs. Therefore, our primary objectives were to: 1) evaluate the impact of different SNP-set combinations (including LD pruning) derived from SNP chip arrays and RNA-seq data from liver, brain, and skeletal muscle tissues on the identification of eQTL associated with carcass and body composition traits in Large White pigs; and, 2) investigate candidate genes and biological processes associated with the phenotypic expression of these traits. The phenotypic traits evaluated in this study included slaughter weight (SW; in kg), cold carcass yield as a percentage of the slaughter weight (CCY, in %), loin eye area measured by ultrasound (LEA; in cm²), backfat thickness measured by ultrasound (BFT; in cm), and intramuscular fat content in percentage (IMF, in %). Results Phenotypes, genotypes, and scenarios The descriptive statistics of the phenotypic traits evaluated in the study are presented in Table 1 and were previously described by Almeida et al. [ 26 ]. Table 1 Descriptive statistics of the traits included in the association studies, which were partially described by Almeida et al. [ 26 ]. TRAIT N Mean Minimum Maximum CV SD SW (kg) 72 132.7 107 160 8.24 10.93 CCY (%) 72 69.9 66.4 73 1.76 1.23 LEA (cm 2 ) 72 44.3 23.4 57.2 11.69 5.17 BFT (cm) 72 14.7 9.9 23.1 17.21 2.53 IMF (%) 72 2.6 0.2 8.38 52.26 1.23 SW (kg): Slaughter weight in kg; CCY (%): Cold carcass yield as a percentage of the slaughter weight; LEA (cm²): Ultrasound-based loin eye area measured between the 10th and 11th ribs; BFT (cm): backfat thickness measured by ultrasound at the 10th rib; IMF (%): Intramuscular fat content in percentage; N: Number of records; CV (%): Coefficient of variation; SD: Phenotypic standard deviation. The set of SNPs analyzed in this study were derived from RNA-seq data from brain, liver, and skeletal muscle tissues from 72 pigs and from the genotyping of these same animals with the GeneSeek Genomic Profiler Porcine 50K (GGP-50K) SNP chip array. A total of 50,697 SNPs were obtained from the GGP-50K SNP chip array as well as 2,650,720, 1,816,600, and 4,404,053 SNPs (before quality control) obtained from RNA-seq data of skeletal muscle ( Longissimus lumborum ), liver (right lobe of the liver), and brain (a portion of the middle region of the frontal lobe) tissues, respectively, of 72 Large White pigs. The quality control used for filtering out the SNPs identified from the RNA-seq data considered a Phred score (QUAL) equal or greater than 30 (QUAL ≥ 30) and coverage depth (DP) equal or greater than 10 (DP ≥ 10). Only bi-allelic variants from the Sus scrofa autosomal chromosomes SSC1 to SSC18 were included in further analyses. Thus, 1,609,081, 915,828, and 2,649,856 SNPs from skeletal muscle, liver, and brain tissues, respectively, remained in the dataset. Additional quality control filters included removing SNPs with minor allele frequency (MAF) lower than 5%, variants with genotyping rate lower than 95% (more than 5% missing), and extreme departure from Hardy-Weinberg equilibrium (HWE; p-value lower than 10 − 6 ). After that, 74,812, 50,932, and 117,330 SNPs from the skeletal muscle, liver, and brain tissues, and 30,872 SNPs from the GGP-50K array from 72 animals were considered for further analyses. After quality control of RNA-Seq data, total of 15,090 genes were expressed in the skeletal muscle of the 72 animals, which were normalized and represented as transcripts per million (TPM), and the expression was also normalized before fitting the linear models. All the SNP datasets were combined for the identification of cis- and trans-eQTL in the skeletal muscle tissue. For that, we considered the scenario with only the SNPs found in the skeletal muscle transcriptome as the base scenario, and subsequently, added the SNPs from the brain and liver transcriptomes and from the 50K SNP chip array. Hence, the SNPs from the RNA-seq data of the brain and liver tissues and the SNPs from the 50K SNP chip panel were used alone or combined with the SNPs from the skeletal muscle, which resulted in four scenarios: (S1) only the SNPs from the GGP-50K; (S2) SNPs from the RNA-seq data of the skeletal muscle (baseline scenario); (S3) SNPs from the GGP-50K plus the SNPs of the RNA-seq data of the skeletal muscle; (S4) SNPs from the GGP-50K plus the SNPs of RNA-seq data of the skeletal muscle, liver, and brain. Subsequently, the SNP sets from the four scenarios were LD pruned considering an r² threshold of 0.70, which resulted in four additional scenarios: (S5) SNPs from the GGP-50K after LD pruning; (S6) SNPs from the RNA-seq data of the skeletal muscle after LD pruning; (S7) SNPs from the GGP-50K plus the SNPs from the RNA-seq data of the skeletal muscle after LD pruning; (S8) SNPs from the GGP-50K plus the SNPs from the RNA-seq data of the skeletal muscle, liver, and brain after LD pruning. The number of SNPs before and after quality control for all scenarios are described in Table 2 . Table 2 Number of single nucleotide polymorphisms (SNPs) before and after the quality control for each of the scenarios evaluated. Dataset Number of SNPs (before quality control) Number of SNPs after quality control and prior to LD pruning Number of SNPs after LD pruning SNPs from the GGP-50K SNP chip array 50,697 30,872 (S1) 9,210 (S5) SNPs from the RNA-seq data of the skeletal muscle 2,591,269 74,812 (S2) 18,933 (S6) SNPs from the GGP-50K SNP chip array plus the SNPs from the RNA-seq data of the skeletal muscle 2,701,417 104,699 (S3) 30,037 (S7) SNPs from the GGP-50K SNP chip array plus the SNPs from the RNA-seq data of the skeletal muscle, liver, and brain tissues 6,675,049 135,996 (S4) 105,870 (S8) GGP-50K: SNPs from the GeneSeek Genomic Porcine 50K medium density genotyping array; LD: Linkage disequilibrium; RNA-seq: RNA sequencing; S1-S8: scenarios 1 to 8. Identification of eQTLs across scenarios For the cis- and trans-eQTL identification analyses, genomic windows of up to 1 Mb upstream from the beginning of the regulated gene and 1M downstream from the end of the regulated gene were considered for the cis (local) effect, and more than 1 Mb of the regulated gene for the trans (distant) effect. These analyzes were performed for each of the eight scenarios aiming to identify eQTL based on the gene expression level in the skeletal muscle tissue. Considering a False Discovery Rate (FDR) of 1%. The number of eQTL associations identified were: S1 and S5 = There were no significant cis- or trans-eQTL; S2: cis-eQTL = 2,538 and trans-eQTL = 2,752; S3: cis-eQTL = 2,355 and trans-eQTL = 1,719; S4: cis-eQTL = 2,256 and trans-eQTL = 43; S6: cis-eQTL = 291 and trans-eQTL = 13,721; S7: cis-eQTL = 231 and trans-eQTL = 6,754; and, S8: cis-eQTL = 646 and trans-eQTL = 29, as shown in Fig. 1 . The unique count of eQTL (considering a single SNP count) in scenarios S2, S3, and S4 ranged from 2,066 to 2,247 for cis-eQTL and 43 to 379 for trans-eQTL. In the scenarios with LD pruning (S6, S7, and S8), the number of cis-eQTL ranged from 223 to 612 while the number of trans-eQTL ranged from 29 to 403, considering a single SNP count. The number of genes regulated by cis- and trans-eQTL in scenarios S2, S3, and S4 ranged from 159 to 304 and from 8 to 1,965, respectively. The number of genes regulated by cis- and trans-eQTL in scenarios S6, S7, and S8 ranged from 109 to 185 and from 6 to 5,993, respectively. Table 3 shows the overlap between the significant cis- and trans-eQTL. The table diagonal represents the number of eQTL identified, whereas the values above the diagonal indicate the percentage of overlapping eQTL among the scenarios and the values below the diagonal represent the number of overlapping eQTL across datasets. Table 3 . Description of the percentage and count of cis- and trans-eQTL (expression quantitative trait loci) identified across the scenarios represented by different set of SNPs associated with expression level of skeletal muscle of Large White pigs. SCENARIO Cis-eQTL Trans-eQTL S2 S3 S4 S6 S7 S8 S2 S3 S4 S6 S7 S8 Cis-eQTL S2 2,247 100% 57% 100% 99% 59% 27% 26% 0% 18% 22% 0% S3 2,061 2,065 56% 11% 100% 58% 25% 24% 0% 15% 19% 0% S4 1,184 1,152 2,066 5% 4% 25% 13% 12% 5% 4% 5% 0% S6 223 219 95 223 95% 14% 9% 10% 0% 12% 16% 0% S7 182 183 81 174 183 13% 7% 8% 0% 9% 13% 0% S8 361 352 156 88 82 612 2% 1% 0% 2% 3% 0% Trans-eQTL S2 103 96 50 34 27 7 379 100% 60% 30% 46% 69% S3 76 71 36 29 23 4 291 291 58% 7% 36% 66% S4 0 0 2 0 0 0 26 25 43 7% 32% 31% S6 72 60 18 50 37 9 120 3 93 403 94% 14% S7 58 49 13 41 34 9 121 94 3 249 264 14% S8 0 0 0 0 0 0 20 19 9 4 4 29 eQTL: expression quantitative trait loci; SNPs: single nucleotide polymorphisms; S2: SNPs from the SNP calling of RNA-seq data of the skeletal muscle; S3: SNPs from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle; S4: SNPs from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle, liver, and brain tissues; S6: SNPs from the SNP calling of RNA-seq data of the skeletal muscle after linkage disequilibrium (LD) pruning; S7: SNPs from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle after LD pruning, and, S8: SNPs from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle, liver, and brain tissues after LD pruning. The table diagonal represents the number of eQTL identified, whereas the values above the diagonal indicate the percentage of overlapping eQTL among the scenarios and the values below the diagonal represent the number of overlapping eQTL across datasets. The cis- and trans-eQTL of scenarios S3 and S7 overlapped by 94 to 100% with scenarios S2 and S6. The results of the cis- and trans-eQTL for all scenarios are presented in Additional File 1. Figures 2 a-d illustrate the eQTL distribution across the autosomal chromosomes for cis- and trans-eQTL for scenarios S2, S4, S6, and S8, respectively. The diagonal line formed refers to the cis-eQTL distribution, and the vertical points refer to the trans-eQTL. The Y-axis represents the gene order in relation to chromosome position in the pig reference genome, and the X-axis represents the SNP order in relation to chromosome position in the pig genome. Association of eQTL with carcass and body composition traits A total of 2,547, 2,107, 576, and 641 eQTL (cis- and trans-eQTL) were identified for the scenarios S2, S4, S6, and S8, respectively. These eQTL were subsequently used for the association analyses with SW, CCY, LEA, BFT, and IMF. The effects of initial body weight (28.44 ± 2.95kg) and treatment [basal diet with 1.5% degummed soybean oil, basal diet with 3% soybean oil, basal diet with 3% canola oil and basal diet with 3% fish oil from crooked sardines ( Cetengraulis edentulus )] were adjusted as continuous covariate and categorical fixed effects, respectively. No significant (FDR < 0.05) or suggestive (0.05 ≤ FDR < 0.10) associations were identified between the eQTL identified and SW, CCY, LEA, BFT, and IMF for the scenarios S2, S4, S6, and S8. The genomic inflation factor (lambda value - λ) ranged from 0.9 to 1.10, indicating that population structure was properly accounted for in the analyses. eQTL annotation For scenarios S2, S4, S6, and S8, 2,547, 2,107, 576, and 165 variants (cis- and trans-eQTL) were analyzed, respectively. A total of 1,044 (41.0%), 834 (39.6%), 390 (67.7%), and 68 (41.2%) variants were classified as new variants for scenarios S2, S4, S6, and S8, respectively. Most of these new variants were located within long non-coding RNA (lncRNA) and protein coding genes. Figures 3 a-d show the most severe predicted consequences of cis- and trans-eQTL for each scenario. The Additional File 2 shows the complete Variant Effect Predictor (VEP) annotation for all cis- and trans-eQTL. eQTL and QTL overlap enrichment analyses To search for overlapping genomic position between the eQTL herein identified and QTL previously reported to be associated with meat and carcass quality and other production traits in pigs was performed using the Genomic Annotation in Livestock for positional candidate LOci (GALLO, [ 27 ]) R package. This R package annotates and shows the graphical visualization of QTL enrichment analyses. The annotation and enrichment analyses of the eQTL from each of the scenario’s tested (S2, S4, S6, and S8) resulted in 31,023 QTL previously reported in the PigQTLdb database (release 47) [ 28 ], considering a window of up to 100kb downstream and upstream of the genomic coordinates of the cis- and trans-eQTL. The initial number of SNPs in a single count were 2,247 for cis-eQTL and 379 for trans-eQTL in scenario S2; 2,066 of cis-eQTL and 43 of trans-eQTL in scenario S4; 223 cis-eQTL and 403 trans-eQTL for scenario S6; and 612 cis-eQTL and 29 trans-eQTL for scenario S8. The QTL resulting from the annotation were enriched using a hypergeometric test to reduce potential bias in the results. For scenarios S2, S4, S6, and S8, the traits ‘loin muscle area’, ‘average backfat thickness’, and ‘abdominal fat weight’ from the QTL list of the “Meat and Carcass” type were enriched. The traits ‘carcass weight (hot)’, ‘fat-cuts percentage’, ‘linoleic acid content’, ‘backfat above muscle dorsi’, ‘subcutaneous fat area’, and ‘muscle protein percentage’ were also enriched for cis- and trans-eQTL in scenarios S2 and S6, and only for cis-eQTL in scenarios S4 and S8. The traits ‘fat weight (total)’ and ‘polyunsaturated fatty acid content’ were enriched for cis- and trans-eQTL in S2. The traits ‘total body fat tissue linear’ and ‘loin eye area linear’ were enriched for cis-eQTL in S6, and trans-eQTL in S2. Additionally, ‘carcass weight (cold)’ was enriched for cis-eQTL in S2, S4, and S8, and for trans-eQTL in S6. For the “Production” QTL type, the traits ‘average daily gain’ and ‘body weight (slaughter)’ were enriched for cis- and trans-eQTL in scenarios S2 and S6 and only for cis-eQTL in scenarios S4 and S8. The QTL type enriched with the SNP markers of the predominant significant eQTL was “Meat and Carcass,” followed by “Health” across all scenarios. The top 10 significant traits in the Meat and Carcass QTL type enrichment analyses for cis- and trans-eQTL from scenario S2 are shown in Figs. 4 and 5 . More details about the enrichment results are shown in Additional File 3. Gene Ontology (GO), annotation and metabolic pathways The genes (counted uniquely) regulated by cis- and trans-eQTL, identified in scenarios S2 (cis = 304, trans = 1,965), S4 (cis = 159, trans = 8), S6 (cis = 185, trans = 5,993), and S8 (cis = 109, trans = 6) were used for Gene Ontology (GO), gene annotation, and Metabolic Pathway (MP) analyses. The same gene set was used for functional enrichment analyses. These analyzes were performed to understand the biological mechanisms influenced by candidate genes regulated by cis- and trans-eQTL. To investigate eQTL associated with possible gene regulation mechanisms, we applied a filter on the annotation description of the genes modulated by cis- and trans-eQTL in each of the scenarios evaluated. We used key terms such as transcription factors, inhibitors, co-regulators, chromatin modelers and remodelers, histone acetylators, modifiers, RNA binding, repressors, and other possible genes related to gene regulation. Furthermore, we also annotated the genes where the identified eQTL are located. More details of the gene annotation of the regulated genes found in scenarios S2 to S8 are presented in Additional File 4. The most significant MP in S2, considering the genes regulated in cis-eQTL type was ‘Chemical carcinogenesis’ (ssc05204). No GO terms were enriched for this gene set. For the genes regulated in trans-eQTL class in S2, the most enriched GO terms were the biological process (BP) ‘Small GTPase-mediated signal transduction’ (GO:0007264), the molecular function (MF) ‘calcium ion binding’ (GO:0005509), and the cellular component (CC) ‘cell leading edge’ (GO:0031252), and the most significant MP was ‘Adherens junction’ (ssc04520). For S4, only two MP were enriched, the first and most significant MP was ‘Drug metabolism’ (ssc00982) considering the genes regulated in cis, with no GO terms enriched for the trans regulated genes. For genes regulated by trans-eQTL, there was no significant MP, CC, BP, or MF. For S6, four MP were enriched and the most significant was ‘Ovarian steroidogenesis’ (ssc04913) in cis. On the other hand, in trans, the most enriched GO terms for the S6 scenario were the BP ‘circulatory system development’ (GO:0072359), the CC ‘extrinsic membrane component’ (GO:0019898), and the MF ‘identical protein binding’ (GO:0042802), and the MP ‘Proteoglycans in cancer’ (ssc05205). There were no significant GO terms or MP for S8. The most enriched GO terms and MP are shown in Table 4 and further details of the enrichment analyses for the GO domains of BP, MF, CC, and MP are presented in the Additional File 4. Table 4 Description of the most enriched gene ontology (GO) and metabolic pathways (MP) terms across the evaluated scenarios. Gene Set Scenario Action Term Description FDR ssc05204 S2 Cis MP Chemical carcinogenesis 4E-05 GO:0007264 S2 Trans BP Small GTPase mediated signal transduction 1E-04 GO:0031252 CC Cell leading edge 1E-04 GO:0005509 MF Calcium ion binding 4E-05 ssc04520 MP Adherens junction 2E-08 ssc00982 S4 Cis MP Drug metabolism 5E-02 ssc04913 S6 Cis MP Ovarian steroidogenesis 4E-04 GO:0072359 S6 Trans BP Circulatory system development 3E-11 GO:0019898 CC Extrinsic component of membrane 1E-04 GO:0042802 MF Identical protein binding 7E-05 ssc05205 MP Proteoglycans in cancer 5E-08 MP: Metabolic pathways; BP: Biological process; CC: Cellular component; MF: Molecular function; FDR: False Discovery Rate; S2: SNPs from the SNP calling of RNA-seq data of the skeletal muscle; S4: SNPs from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle, liver, and brain tissues; S6: SNPs from the SNP calling of RNA-seq data of the skeletal muscle after linkage disequilibrium pruning (r² > 0.7); cis: genes locally modulated by eQTL used for the enrichment analyses; trans: genes distantly modulated by eQTL used for the enrichment analyses. Comparing up to 100 GO terms and the most enriched MP between S2 and S6 trans-eQTL, there was an overlap of 71.4% of CC terms, 71.4% of MF, and 86.1% of BP. The overlapping results of MP from S2 and S6 revealed 28% and 82.5% of similarity in cis- and trans-eQTL, respectively. There was a 100% overlap of the significant MP in cis-eQTL from the S4 and the top twenty most significant pathways in cis-eQTL for S2. Genes modulated by eQTL A total of 457 genes were found to be associated with the eQTL from the different scenarios. The scenarios with SNPs only from the skeletal muscle transcriptome (S2 and S6) enabled the identification of more genes modulated both in cis and in trans. Scenarios with LD-pruned SNPs also identified more modulated genes. Additionally, the scenario S6 presented the largest number of overlapping genes modulated with the other scenarios, that is, genes modulated in cis, or trans identified in scenario S6 were frequently identified in other scenarios. In S2, the trans-eQTL located in the genes encoding CEBZB (zeta CCAAT enhancer binding protein), eIF2B (eukaryotic translation initiation factor 2B subunit alpha), TSTD3 (sulfur thiosulphate transferase domain containing 3), TMEM245 (Transmembrane protein 245), and OXCT1 (3-oxoacid CoA-transferase 1) were identified. These trans-eQTL were associated with expression of genes involved in regulatory mechanisms, such as transcription factors, chromatin modifiers, primers, and bindings. Key transcription factors identified include BCLAF1 , E2F8 , ELF1 , ELK3 , ETS1 , ETV6 , GABPB1 , TCF12 , TCF4 , GTF3C1 , MYT1L , SREBF2 , YY1 , ETS1 , SOX7 , FAP2A , and GTF3C5 . In addition, cis-eQTL identified in S2 modulate the genes NAT10 (RNA cytidine acetyltransferase), YIPF2 ( Protein yipf2 isoform x3; member 2), EARS2 (Probable glutamate—tRNA ligase, mitochondrial isoform x1; glutamyl-tRNA synthetase 2; Belongs to the class-I aminoacyl-tRNA synthetase family), GBA (Glucosylceramidase precursor; Sus scrofa glucosidase), MTERF3 (Transcription termination factor 3, mitochondrial isoform x2), EMG1 (ENSSSCP00000026081), SYMPK (Symplekin isoform x1), and THYN1 (Thymocyte nuclear protein 1 isoform x1). These genes are modulated by cis-eQTL predicted to 3’UTR, 5’UTR, downstream, upstream, and missense variants. The incorporation of SNPs from the 50K SNP chip array resulted in a lower number of significant modulated genes (FDR < 0.01) between scenarios S2 and S3, as well as between S6 and S7, with a pattern inversely to the increase in the number of SNPs. However, the combination of SNPs from the SNP array with skeletal muscle sequencing SNPs (S3 and S7 scenarios) enabled the identification of 13 specific variants of the GGP-50K associated with 19 genes, including the Zic family member 5 ( ZIC5 ) identified exclusively in the S3 scenario in cis. This gene contains a variant from the GGP-50K panel (rs81431697). In addition, in the S7 scenario, in trans, there were also genes modulated exclusively by variants derived from the GGP-50K SNP panel dataset, including SLC7A1, TRAPPC9, ENSSSCG00000034462, GOLT1A, ENSSSCG00000018018, ENSSSCG00000024765, TTC23 , and ENSSSCG00000009523 . The other genes identified in scenarios S3 and S7, modulated by SNPs from the GGP-50K SNP panel dataset, were identified in S6, however, modulated by variants identified in the skeletal muscle transcriptome. Lastly, in scenarios S4 and S8, there were no significant eQTL derived from the GGP-50K SNP panel dataset. The cis-eQTL, detected only in liver or brain tissue (not identified in the transcriptome of skeletal muscle tissue and GGP-50K), modulated only four genes in S4 (not identified in S2, S3, S6 and S7), including CACNG5 (calcium voltage-gated channel auxiliary subunit gamma 5), IK (IK cytokine), RBM46 (RNA binding motif protein 46), and ZNF821 (zinc finger protein 821), which were cis modulated. Only IK and ZNF821 were identified in S8 and cis modulated. DISCUSSION Currently, most of the GWAS in livestock studies for meat quality traits used a set of SNPs located primarily in non-coding genomic regions. However, Next Generation Sequencing (NGS) technology has enabled the discovery of thousands of SNPs across the whole transcriptome, which might not be present in the SNP genotyping arrays. Based on the limitation of previous GWAS, in this study we evaluated the impact of combining different sets of SNPs from medium-density genotyping commercial arrays (i.e., GGP-50K) and SNPs identified in the transcriptome of pig brain, liver, and skeletal muscle tissues (with and without LD pruning) on the identification of cis- and trans-eQTL and their association with carcass and body composition traits in Large White pigs. In addition, enrichment analyses were performed using the gene lists identified across the scenarios to reveal GO terms and MP in which these genes are involved. The SNPs were used to analyze the identification of cis- and trans-eQTL. Identification of cis- and trans-eQTL in different scenarios The combination of SNP datasets and LD pruning resulted in eight scenarios that were used to identify cis- and trans-eQTL. Considering the gene expression level in the skeletal muscle transcriptome, 15,090 genes were identified. The number of cis- and trans-eQTL in all scenarios is within the expected ranges reported in the literature. For instance, Liu et al. [ 29 ] detected 10,693 cis-eQTL and 10,961 trans-eQTL in the Longissimus dorsi muscle of 189 crossbred pigs from Duroc sires x Luchuan dams. Liu et al. [ 30 ] reported 3,054 eQTL, including 1,283 cis-eQTL and 1,771 trans-eQTL in skeletal muscle from F2 White Duroc x Erhualian pigs. Besides of the tissue sampled, there are several differences between the aforementioned studies and ours, which may explain the variability in the number of cis- and trans-eQTL identified herein. These differences include the technique used for measuring gene expression (such as RT-qPCR, RNA-seq, and microarray), sequencing coverage depth, breed (e.g., Duroc, Luchuan, Erhualian, Large White, or crossbred animals), sample size, statistical models, covariates used for adjusting the phenotypes, level of correction for population stratification, initial number of SNPs and genes considered (SNP x gene interactions), quality control measures applied to SNPs and genes, method and thresholds used for multiple testing correction, and the significance levels. In this study, there was a decrease in the number of eQTL associations in cis- and trans-eQTL as the number of SNPs increased, possibly due to the greater stringency of correction for multiple tests and the reduced sample size, as suggested by Huang et al. [ 31 ]. The reduction of significant eQTL by increasing the weight of the correction method may be due to a greater removal of false positives [ 31 ]. Thus, when incorporating other SNPs from other genomic regions, such as those identified in the liver and brain, it is important to consider stricter thresholds. However, such approaches are necessary to capture variants from other genomic regions, which may contribute to a better understanding of the cellular mechanisms. Furthermore, by keeping the threshold constant for the identification of cis- and trans-eQTL, scenarios with a higher number of tests (S4) may indicate those that have a greater impact on gene regulation. Inclusion of SNP from different genomic context to identify eQTL It was found that in S3 and S7, the combination of SNPs from a 50K SNP panel with the skeletal muscle sequencing SNPs allowed the detection of cis- and trans-eQTL exclusive to genotyping, in addition to genes that were not detected in other scenarios. The eQTL were identified in scenarios S3 and S7, specifically derived from SNPs from the GGP-50K SNP panel dataset. Among these, the rs81431697 eQTL showed modulation in cis action of the ZIC5 gene, which is involved in cell differentiation [ 32 ]. In scenarios S4 and S8, we also identified unique local and distant eQTL that modulate genes, which were not identified in the other scenarios and from SNPs derived from muscle sequencing (S2, S3, S6, and S7). These genes are related to bioprocesses ( CACNG5 ) [ 33 ], regulation of the immune system and autoimmune disorders ( IK ) [ 34 ], and developmental disorders ( RBM46) [ 35 ]. Additionally, the ZNF821 gene encodes a protein involved in the regulation of the structure and function of DNA (GO:1990837) [ 36 ]. Thus, verifying the combination of all SNPs allowed the identification of genes, not identified in other scenarios, modulated by eQTL not identified by the sequencing of skeletal muscle tissue transcriptome. However, the scenarios containing the combinations of all SNPs contributed to the identification of genes cis modulated by eQTL not present in the transcriptome of the skeletal muscle of pigs, allowing the detection of variants located in different gene contexts in relation to the target tissue. The scenarios S4 and S8, presented the lowest number of genes modulated by trans-eQTL (8 and 6), that is, the approach adopted for these scenarios is not indicated for detecting distant effects of variants on gene modulation in the skeletal muscle of pigs. Modulated genes by cis- and trans-eQTL and regulatory mechanisms Some trans-eQTL identified in the genes CEBZB, eIF2B, TSTD3, TMEM245 , and OXCT1 , in scenario S2 modulating gene encoding transcription factors, include BCLAF1, E2F8, ELF1, ELK3, ETS1, ETV6, GABPB1, TCF12, TCF4, GTF3C1, MYT1L, SREBF2, YY1, ETS1, SOX7, FAP2A , and GTF3C5 . This indicates potential indirect regulatory interactions between the genes containing the eQTL and these transcription factors, by trans modulation. These genes play important roles in the phenotypic expression of traits such as carcass, body composition, and meat quality. BCLAF1 is involved in the regulation of muscle growth in homologues [ 43 ]. E2F8 is involved in the regulation of cell cycle progression [ 44 ], and ELF1 is involved in the regulation of gene expression [ 45 , 46 ]. Additionally, chromatin modifiers identified in this study, such as GABPB1 , TCF12 , and GTF3C1 , are known to play a role in regulating gene expression [ 47 – 49 ]. Genes such as MYT1L , SREBF2 , and YY1 also play important roles in regulating gene expression [ 50 – 57 ], and they may interact with each other, such as enhancer CEBPZ and eIF2B to regulate the expression of genes involved in protein synthesis, potentially impacting skeletal muscle mass. OXCT1 , on the other hand, can interact with other genes to regulate the expression of genes involved in muscle development [ 58 , 59 ], potentially affecting meat quality. Other possible mechanisms are highlighted, such as the cis action, which is when a cis-eQTL modulates the expression of genes nearby. The variants predicted by VEP indicate possible consequences, such as changes in 3'UTR, downstream gene, upstream gene, and missense regions. These consequences may imply changes in amino acids, molecular affinity, tridimensional structure, or mRNA stability, all of which can affect gene expression regulation. Changes in the expression of genes like TPM1 , and ARL14EP could influence regulation of cell growth, thus muscle growth and carcass weight [ 60 ]. Genes involved in energy metabolism, like GLUT4 and CPT1A , have also been identified. For example, GLUT4 is involved in glucose uptake by cells and CPT1A is involved in the production of ketone bodies from fatty acids [ 53 , 61 , 62 ]. Thus, alterations in the expression of these genes can lead to changes in carcass and body composition traits. The impact of linkage disequilibrium pruning on eQTL identification Based on the observed pattern of the scenarios based on LD pruning (S6, S7, and S8), and the fact that more SNPs in a database implies in an increased number of statistical tests and greater weight in the FDR correction, the cis-eQTL in S8 are the only ones that behaved differently in all scenarios, as their numbers increased. In all other cases, including trans-eQTL, the detection sensitivity of eQTL decreased with the relative increase of SNPs. Although cis-eQTL are more easily found [ 29 ] they were not predominant in this study. Pruning for LD also had a significant impact on the identification of eQTL, as genetic variations that are linked may also be associated with differences in gene expression levels. Therefore, LD pruning is important as it allows the removal of linked genetic variants that may confound the results of gene expression analyses [ 9 , 12 , 15 , 16 , 21 , 37 , 38 ]. LD pruning reduces the number of variants considered in the analyses, which can increase the accuracy of the results by reducing collinearity among SNPs [ 15 , 21 ]. The numerical difference in the total number of cis- and trans-eQTL identified in S2 was 214, whereas in the equivalent scenario subjected to LD pruning (S6) this difference was 13,430 eQTL. A similar pattern was observed between scenarios S3 and S7. However, despite this abrupt difference, when considering the unique eQTL, these differences were reduced. A notable decrease in the unique count of the cis-eQTL from S2 (2,247) to S6 (223) was observed, indicating that LD pruning, despite reducing the numerical count of cis-eQTL and their unique genomic coordinates, favored the identification of trans-eQTL. It is also worth mentioning that the cis effect adopted in these analyses refers to the "local" effect, as explained by Hasin-Brumshtein et at. [ 39 ]. These cis-eQTL are defined by the distance of up to 1Mb from the regulated gene, indicating that these SNP are originally closer, and thereby more susceptible to be pruned for LD. This could explain the reduction in the number of cis-eQTL from S2 to S6. It was also observed that most of the genes modulated by eQTL unique to GGP-50k (scenarios S3 and S7) were also modulated by eQTL from scenario S6. This indicates that LD pruning may contribute to increasing the detection ability of the adopted model, which would explain part of the overlapping of modulated genes in the scenarios enriched with genotyping SNPs, such as S3 and S7. As some of the cis and trans eQTL were associated with several genes, genes associated with several eQTL simultaneously were also observed. The scenarios with SNPs from skeletal muscle sequencing of pigs identified the greatest number of genes. Additionally, it demonstrated significant overlap in functional analyses with the LD unpruned scenarios, despite having a smaller set of initial SNPs. This suggests that LD pruning can effectively balance the stringency of FDR correction. These observations highlight the intrinsic relationship between pruning for LD and FDR in sensibility of the correction to multiple tests from the results. eQTL associations with carcass and body composition traits The cis- and trans-eQTL identified in each of the scenarios were used for the GWAS analyses with carcass and body composition traits. However, there were no significant variants (FDR < 0.05) or any trends (0.05 ≤ FDR < 0.10) for the tested traits. According to Yang et al. [ 40 ], MLMA is directly related to the proportion of samples to the number of SNPs and a small number of markers reduces the analysis power of the MLMA model. The lack of significance in our analyses may be related to the low sample size (72 pigs). Moreover, the absence of LD pruning in the cis- and trans-eQTL identified in scenarios S2 and S4 may have limited the analyses’ power. In this regard, for future analyses, the adoption of models that can address such issues of sample limitations and size is recommended. For example, the use of Bayesian analysis can be recommended for analyses with limited data, as it allows for the incorporation of prior information about model parameters, which can help mitigate the uncertainty caused by the reduced datasets [ 41 ] and a recent possible application is the analysis of TWAS (Transcriptome-Wide Association Studies), as described by Dai et al. [ 4 ] and Li and Ritchie [ 5 ]. Despite the lack of significant associations between cis- and trans-eQTL with carcass traits and body composition, a substantial number of overlapping eQTL with previously reported QTL related to pork meat and carcass traits were identified. This overlap with QTL provides valuable insights into potential regulatory interactions of cis- and trans-eQTLs and gene mechanisms that may influence the carcass and body composition traits. The incorporation of SNPs from brain and liver tissues transcriptomes, as well as genotyping SNPs, into the skeletal muscle SNP dataset was helpful in identifying genes not identified solely based on SNPs from skeletal muscle sequencing. However, this increases the FDR correction level as discussed earlier. LD pruning was found to increase the number of eQTL identified, with good concordance with functional enrichment analyses. LD pruning requires caution in its application, as it can lead to the loss of information on potentially important variants. Furthermore, different approaches to combine SNPs can lead to different tested hypotheses. These combinations can increase false positives, or those that are difficult to explain, in addition to inflating errors and increasing the weight of the correction, as already mentioned. For this reason, they can be indicated as complementary analyses, for cases where data already exist or for specific purposes. However, the combination or LD pruning approach to be used depends mainly on the hypothesis to be tested, in addition to factors such as data availability and sample size. In addition, the eQTL identified in this study can be used in future analyses of gene regulation, cis- and trans-eQTL-regulated genes, gene co-expression networks, and data integration. However, it is important to note that the sample size used in the study was a limiting factor in some analyses, such as GWAS, and associations with the tested phenotypes. CONCLUSIONS The LD-pruned SNPs (r²>0.7) identified in the transcriptome of the skeletal muscle tissue of pigs, resulted in the highest number of genes modulated by eQTL, mainly in trans, among them, several are involved in gene regulation related to complex traits of pig, such as transcription factors and enhancers. In addition, combinations of SNPs identified in the transcriptome of skeletal muscle with SNPs identified in the transcriptome of brain and liver tissues, and also the SNPs from genotyping, were effective approaches in the identification of eQTL modulating genes, not modulated by eQTL identified in the pig skeletal muscle transcriptome. New functional candidate variants associated with the level of gene expression in skeletal muscle were identified in all scenarios. Interestingly, the integration of the 50K genotyping data resulted in gene associations not discovered in other scenarios. It is emphasized that the identification of several candidate functional variants associated with the level of gene expression in porcine muscle that had not been previously reported. Methods Experiment The transcriptome as well as the carcass and body composition data used in this study were previously described by our team [ 26 , 63 , 64 ]. In brief, 72 genetically lean male immunocastrated pigs of the Large White breed with negative genotypes for the homozygous halothane gene (NN) were randomly assigned to one of four dietary treatments with six replicate pens per treatment and three pigs per pen. Treatments consisted of diets supplemented with 1.5% degummed soybean oil or 3% oil from soybean oil, or 3% canola oil, or 3% fish oil from crooked sardines ( Cetengraulis edentulus ). All animals had ad libitum access to feed and water throughout the experimental period (98 days). The average initial body weight (BW) was 28.44 ± 2.95 kg, and the average age was 71 ± 1.8 days. The pigs were fed a basal diet formulated to meet or exceed the nutritional requirements for growing and finishing pigs [ 42 ]. Collection of samples and phenotypes After 12-h fasting period, the pigs were slaughtered with an average BW of approximately 132.7 kg. The skeletal muscle ( Longissimus lumborum ) between the 10th and 11th ribs, liver (right lobe of the liver), and brain (portion of the middle region of the frontal lobe) samples were collected within at most 30 minutes after bleeding, immediately frozen in liquid nitrogen, and then stored at -80° C in an ultra-freezer. These samples were used for total mRNA extraction. In addition, carcass and body compositions phenotypes were collected before and after slaughter, including SW, CCY, LEA, BFT, and IMF [ 26 ]. Total RNA extraction and mRNA sequencing The RNA extraction from the tissue of skeletal muscle, brain, and liver samples, quality control of the RNA-seq data, counting, and normalization are described by Silva et al. [ 43 ] and Fanalli et al. [ 44 , 45 ]. The sequencing analyses were performed at the Genomics Center from ‘Luiz de Queiroz’ College of Agriculture (ESALQ), Piracicaba, São Paulo, Brazil. Quality control of RNA-seq data, counting and normalization The quality of RNA-seq was checked using the FastQC software v. 0.11.8 [ 46 ]. Sequencing adapters and low complexity reads were removed by Trim Galore 0.6.5 software [ 47 ]. Reads with a minimum length of 70 bases and a Phred score greater than 33 were kept after trimming and were aligned and mapped to the porcine reference genome ( Sus scrofa 11.1) [ 48 ] using the assembly available at Ensembl (Release 102) [ 49 ]. Alignment, mapping, and sorting (by genomic coordinates) were performed using the STAR v. 2.7.6a software [ 50 ]. The dataset used is available in the European Nucleotide Archive (ENA) repository (EMBL-EBI), under the accession PRJEB52665 (brain tissue) [ www.ebi.ac.uk/ena/data/view/PRJEB52665 ]; PRJEB50513 [ www.ebi.ac.uk/ena/data/view/PRJEB50513 ] (liver tissue); and PRJEB52629 (skeletal muscle tissue - Longissimus lumborum ) [ www.ebi.ac.uk/ena/data/view/PRJEB52629 ]. Identification of SNPs in RNA-seq data For the variant calling analysis for each tissue, the Genome Analysis Toolkit (GATK, v. 4.1.9.0) was used in the Genomic Variant Call Format (GVCF) mode [ 51 , 52 ]. Genome coverage for each of the BAM files was calculated using SAMtools (v. 1.9) [ 53 , 54 ]. The HaplotypeCaller algorithm [ 51 , 52 ] was used to call the variants individually, generating GVCF files for each sample. These files were then merged using the CombineGVCF tool [ 51 , 52 ], and the joint genotyping analysis was performed using the GenotypeGVCF [ 51 , 52 ]. In the end, a VCF file with all samples genotyped was generated. DNA extraction and genotyping The extraction of the genomic DNA of the 72 animals was performed from 30 mg of liver tissue that was macerated in liquid nitrogen, transferred to a 1.5mL microtube, and then processed according to the procedures protocol suggested by the manufacturer of the HighPrep™ Blood & Tissue DNA Plus Kit (MagBio Genomics, London, UK) which uses nucleic acid isolation technology based on magnetic beads. Subsequently, the DNA obtained was evaluated for quality and quantity by readings in the NanoDrop 2000 nano-spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) at three different wavelengths 230 nm, 260 nm, and 280 nm. The integrity of the DNA extracted from the samples was evaluated by means of Ultra-Violet (UV) light visualization of the electrophoresis run on a 1.5% agarose gel [w/vol] and in Tris-borate-EDTA buffer with GelRed fluorescent staining (Biotium, Hayward, CA, USA). After, DNA evaluation and quantification, an aliquot of approximately 1,000 ng was sent to the NEOGEN company (Pindamonhangaba, SP, Brazil) which performed the genotyping using the first generation GeneSeek Genomic Profiler (GGP) Porcine 50K the medium-density array chip, with approximately 50,915 SNPs. After that, the results were received in the Illumina raw format and converted to PLINK 1.9 [ 55 ] “ped” and “map” formats using a python algorithm [ https://github.com/bioinformatics-ptp/Zanardi/blob/master/Zanardi.py ]. The individual identification (iid) and family identification (fid) referring to the animals were updated by PLINK 1.9 [ 55 ], based on the sampling index present in the “sample map” file, so that the animal identifications coincided with the other data files. In addition, the genomic coordinates (position and chromosome) were updated for the latest version of the Illumina GGP Porcine 50K-24 v2 chip ( www.illumina.com/products/by-type/microarray-kits/ggp-porcine.html ). Data from SNPs from all tissues and 50K animal genotyping were merged (--bmerge) using PLINK 1.9 [ 55 ]. Quality Control After the variant calling, to reduce the false discovery rate, variants were filtered by the SNP for variant quality score (QUAL) equal or greater than 30 ( Pred score, Sanger/Illumina 1.9 + encoding) [ 53 , 56 , 57 ] and total depth of coverage (DP) equal or greater than 10, using BCFtools v. 1.9. [ 25 , 53 ]. Then, we filtered only SNPs of the autosomal chromosomes from 1 to 18 and biallelic SNPs using PLINK 1.9 software [ 55 , 58 ]. Quality filters were used for variants with low MAF (--maf) 0.05, variants with a 0.95 genotyping call rate (0.05 missing) (--geno), and variants with extreme departure from the Hardy-Weinberg equilibrium test (--hwe) with p-value less than 10 − 6 [ 25 , 58 – 60 ]. Files were also generated without quality filters to perform LD pruning considering a r² threshold of 0.70 (--indep-pairphase) in the PLINK 1.9 software [76]. The parameters used for LD pruning were: '--indep-pairphase 50 5 0.7', that is, a window size equal to 50 SNPs, a window offset every 5 SNPs per step, and a correlation threshold (r²) paired equal to 70%. Thus, the pairs of SNPs in each window of 50 SNPs, with a square correlation greater than 70% were noted and one of the SNPs of that pair was removed, later, the window was shifted by 5 SNPs, and the procedure was repeated, until none of these correlated pairs (r²>0.7) remained [ 55 ]. The genomic datasets pruned for LD were subsequently filtered for MAF, missing call rate, and extreme departure from HWE as described before. Scenarios To identify the SNPs that modulate the level of gene expression in the skeletal muscle of pigs, the SNPs identified from the brain and liver transcriptome data and 50K genotyping were combined with the skeletal muscle transcriptome SNP dataset, which generated four datasets (scenarios). These datasets were subjected to LD pruning, which resulted in four additional scenarios. These combinations were performed to investigate the impact of adding SNPs from different tissues and SNP identification methods to SNPs derived from skeletal muscle transcriptome sequencing data on the identification of cis- and trans-eQTL in muscle. Furthermore, LD pruning was performed for all combinations of SNPs, aiming to elucidate the implications of using this technique in the eQTL mapping, according to different combinations of SNPs from different tissues and methods for SNP identification. These scenarios are: (S1) SNP set from the GGP-50K; (S2) SNP set from the SNP calling of RNA-seq data of the skeletal muscle; (S3) SNP set from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle; (S4) SNP set from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle, liver, and brain tissues; (S5) SNP set from the GGP-50K after LD pruning; (S6) SNP set from the SNP calling of RNA-seq data of the skeletal muscle after LD pruning; (S7) SNP set from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle after LD pruning; and, (S8) SNP set from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle, liver, and brain tissues after LD pruning. Identification of eQTLs The MatrixeQTL package [ 61 ] of the R statistical program was used to identify associations between the SNPs from different scenarios and the gene expression level of the skeletal muscle tissue. The window for cis-eQTL (local effect) was defined as up to 1 Mb upstream from the start of transcription and up to 1Mb downstream from the end of the regulated gene. The other combinations were considered as trans-eQTL. To deal with gene expression outliers, the data was transformed to a normal distribution based on the mean, preserving the relative rank, which is one of the solutions accepted by the consortium ‘GTEx’ (Genotype-Tissue Expression) [ 62 ]. The Matrix eQTL package tests the linear association between each marker (SNP) and gene assuming the genotype effect as additive, performs a separate test for each pair (marker and gene), and corrects for multiple testing by calculating the false discovery rate (FDR) [ 63 , 64 ]. The fixed linear regression model fitted was: \(\text{G}= {\beta }\text{*}\text{s}+\text{P}\text{C}+\text{S}\text{B}\text{W}+\text{S}\text{I}\text{R}\text{E}+\text{T}\text{R}\text{E}\text{A}\text{T}+\text{ϵ}\) where \(\text{G}\) = is the gene expression level in normalized transcripts per million (TPM), \({\beta }\) = SNP allelic substitution effect, \(s\) is the genetic marker covariate, coded as 0 (homozygous for the reference allele), 1 (heterozygous), and 2 (homozygous for the reference alternative), \(\text{P}\text{C}\) = the first 10 principal components to correct for potential population stratification (principal components explained a total of 28% on the variance-standardized relationship matrix), \(\text{S}\text{B}\text{W}\) = starting body weight, \(\text{S}\text{I}\text{R}\text{E}\) = is a dummy variable that represents the sire effect, TREAT = is a dummy variable that represents the treatments effect, and \(ϵ\) = the random residuals with \(\text{ϵ}\sim \text{i}.\text{i}.\text{d}.\text{N}\left(0,{{\sigma }}^{2}\right)\) . For both cis- and trans-eQTL, an FDR level of 0.01 was considered. The estimated effect size (slope coefficient) and the genetic variance explained by the markers were also estimated according to the MatrixeQTL package [ 61 ]. The scatter plots were done using the R ggplot2 package [ 65 ]. The genomic coordinates of the eQTL data and associated genes were converted to mega base pairs (Mb) and sorted by chromosome and position. Then, the eQTL and gene position orders were used to plot the graphs with the X-axis referring to the order of the SNPs and the Y-axis referring to the order of the initial position of the genes. Association with carcass and body composition traits After identifying the eQTL, significant SNPs were selected and subsequently these SNPs were associated with the traits of interest. The association between the SNPs in the eQTL for all scenarios (S2, S3, S4, S6, S7, and S8) with the phenotypes was performed using a Mixed Linear Model Association (MLMA) [ 40 ] in the GCTA software [ 66 ], considering the effects identified for each phenotype and fitting the genomic relationship matrix to account for population stratification and polygenic effects. The model fitted was: \(\text{y}= \text{a} + {\beta }\text{*}\text{x} + \text{S}\text{B}\text{W} + \text{T}\text{R}\text{E}\text{A}\text{T} + \text{G} + \text{ϵ}\) where \(\text{y}\) is the phenotypic record of the carcass and body composition trait evaluated, \(\text{a}\) is the overall mean, \({\beta }\) is the additive effect (fixed effect) of the SNP being tested for potential association with the phenotype, \(\text{x}\) is the indicator variable of the SNP genotype coded as 0 (homozygous for the reference allele), 1 (heterozygous), or 2 (homozygous for the reference alternative), SBW = systematic effect of starting body weight (as a linear covariate), TREAT = represents the systematic treatments effect (fixed effects), \(\text{g}\) is the polygenic effect (random effect), that is, the cumulative effect of all SNPs (as captured by the genomic relationship matrix), and \(\text{ϵ}\) is the residual effect. The carcass and body composition traits evaluated were SW, CCY, LEA, BFT, and IMF. The values resulting from the association analyses were corrected for multiple tests using the FDR method [ 31 , 63 ], and the significance level adopted was 5% while we considered as indicative the FDR value between 5% and 10%. Prediction of the effects of cis- and trans-eQTL identified in each of the scenarios Based on the significant cis- and trans-eQTL variants (FDR < 0.01) identified in scenarios S2, S4, S6, and S8, the functional consequence analyses of these variants (cis plus trans) for each of these scenarios were predicted using the VEP tool (Ensembl release 109 - Feb 2023 © EMBL-EBI) [ 67 ] and considering the Sus scrofa 11.1assembly genome. The distance to transcription that the VEP assigned for the upstream/downstream consequence was of up to 5,000 bp and the other configurations were kept the default of the web interface ( www.ensembl.org/info/docs/tools/vep/online/index.html ) [ 68 ]. The command line used was “./vep --appris --biotype --buffer_size 5000 --check_existing --distance 5000 --mane --sift b --species sus_scrofa --symbol --transcript_version --tsl --cache --input_file [input_data] --output_file [output_file]”, where input_data is the VCF file with the cis- and trans-eQTL from each scenario. Annotation and functional enrichment of eQTL The GALLO package [ 27 ] was used to perform the QTL annotation of the SNPs identified as cis-eQTL and trans-eQTL in scenarios S2, S4, S6, and S8. The eQTL annotation was performed using known QTL data obtained from the PigQTLdb database (version 47 - pigSS11) [ 28 ], considering a window of up to 100kb downstream and upstream of the genomic coordinates of the cis- and trans-eQTL for each scenario. An enrichment analysis was performed using a hypergeometric test based on the "qtl_enrich" function from the GALLO package [ 27 ], with the objective of reducing the bias of overrepresented traits. The QTL enrichment test was performed using traits annotated within the candidate regions (window of up to ± 100kb of the eQTL) from the QTL database, considering 18 autosomes. From this information, the hypergeometric test estimate allows us to determine whether the number of records observed for a specific trait in the 18 pig autosomes is greater than what would be expected by chance. Gene annotation, GO and metabolic pathways for the genes Annotations of genes close to the cis- and trans-eQTL were performed in each of the scenarios (S2, S4, S6 and S8), considering a window of 100kb downstream and upstream of each eQTL. The adopted reference position was the genomic coordinate of each of the cis- and trans-eQTL, for each scenario. Data from the gene annotation of the species Sus scrofa (Assembly ' Sscrofa 11.1'; genome-build-accession GCA_000003025.6; available at: [ https://ftp.ensembl.org/pub/release-106/gtf/sus_scrofa/ ]) were extracted from the Ensembl platform (Ensembl release 106 - Jul 2022) [ 49 ] in the ".gtf" format (gene transfer format). Among the genes, only those regulated by cis- and trans-eQTL (FDR < 0.01) were selected for the Gene Ontology and Metabolic Pathway analyses aiming to understand the functional roles of the genes related to the eQTL. These analyzes were performed using the WebGestaltR package [ 69 ], where counted genes uniquely modulated by cis- and trans-eQTL in each scenario (S2, S4, S6, S8) were used to identify biological processes (BP), molecular functions (MF), cellular components (CC), and metabolic pathways (MP). The enrichment method adopted was ORA (Over-Representation Analyses), while the other settings were the package default for each set of genes regulated by cis and trans-eQTL, for each scenario separately. Abbreviations 3'UTR - 3' untranslated region variant 5’UTR - 5’ untranslated region variant BFT - Backfat thickness measured by ultrasound in cm BP - Biological Process CC - Cellular Component CCY - Cold carcass yield in percentage of the slaughter weight CV - Coefficient of variation DNA – Deoxyribonucleic acid DP - Total coverage depth EMBL-EBI - European Molecular Biology Laboratory-European Bioinformatics Institute ENA - European Nucleotide Archive eGWAS - Expression Genome-Wide Association Study eQTL - Expression Quantitative Trait Loci FDR - False Discovery Rate GGP-50K - GeneSeek Genome Porcine medium density SNPs from SNP array GO - Gene Ontology GRM - Genomic Relationship Matrix between the pair of animals GVCF - Genomic Variant Calling Format HWE - Hardy-Weinberg Exact balance test Kb - Kilobase (1,000 base pairs) LD - Linkage disequilibrium LEA - Loin eye area measured by ultrasound in cm 2 MAF - Minor allele frequency Mb - Mega base pair MF - Molecular function IMF - Muscle fat content in percentage MLMA - Mixed Linear Model Association MP - Metabolic pathways N - Number ORA - Over Representation Analyses PC - Principal components QTL - Quantitative trait loci QUAL - Phred score RNA - Ribonucleic acid RNA-seq - RNA sequencing RT-PCR - Real Time Polymerase Chain Reaction RT-qPCR - Real Time Quantitative Polymerase Chain Reaction r 2 - Correlation SBW - Initial body weight SD – Phenotypic standard deviation SM - Skeletal muscle SNP - Single nucleotide polymorphism SSC1 - Sus scrofa chromosome 1 SSC18 - Sus scrofa chromosome 18 SW - Slaughter weight in kg TPM - Transcripts per million UV – Ultra-violet light VEP - Variant Effect Predictor VCF - Variant calling format Declarations Ethics approval All animal procedures were conducted in accordance with the Guide for the Care and Use of Agricultural Animals in Agricultural Research and Teaching [70] and approved by the Animal Care and Use Committee of the Luiz de Queiroz College of Agriculture (University of São Paulo, Piracicaba, SP, Brazil, protocol: 2018.5.1787.11.6 and number CEUA 2018-28). This study was also performed in compliance with the ARRIVE guidelines. Consent for publication Not applicable. Availability of data and materials The dataset (s) supporting the conclusions of this article is (are) included within the article and its Additional Files 1 to 4. The dataset used is available in the European Nucleotide Archive (ENA) repository (EMBL-EBI), under accession PRJEB52665 (brain tissue) [www.ebi.ac.uk/ena/data/view/PRJEB52665]; PRJEB50513 [www.ebi.ac.uk/ena/data/view/PRJEB50513] (liver); and PRJEB52629 (skeletal muscle) [www.ebi.ac.uk/ena/data/view/PRJEB52629]. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This study was supported by the São Paulo Research Foundation (FAPESP, Grant numbers: 2017/25180-2, 2021/11261-6, 2021/01694-2, 2020/10042-6, 2022/10643-5, 2022/10780-2, 2023/02067-7), the Brazilian National Council for Scientific and Technological Development (CNPq) that provided a researcher fellowship to A. S. M. Cesar (303165/2022-7). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001, and the Goiás Research Foundation (FAPEG process number: 202110267000074). Authors' contributions Conceptualization, FAOF, LFB, and ASMC; writing-original draft preparation, FAOF, LFB, BSV, and ASMC; writing-review and editing, FAOF, LFB, SLF, JLG, BPMS, MCD, FNC, CSO, LEN, ICG, JDF, GCMM, BSV, LLC, VVA, and ASMC; supervision, ASMC and LFB; funding acquisition, VVA and ASMC. All authors have read and agreed to the published version of the paper. Acknowledgements We thank the collaborative efforts between the University of São Paulo, Iowa State University, Federal University of Goiás, and Purdue University. We are also grateful to DB Genética Suína (Patos de Minas, MG, Brazil) for providing the animals, housing, feeds, and employees, who helped in carrying out this research. Authors and Affiliations Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, 13416-000, SP, Brazil. Felipe André Oliveira Freitas, Janaína Lustosa Gonçales, Bruna Pereira Martins da Silva, Fernanda Nery Ciconello, Camila Sabino de Oliveira, Lucas Echevarria Nascimento, Izally Carvalho Gervásio, Julia Dezen Gomes, Luiz Lehmann Coutinho, Aline Silva Mello Cesar. Department of Animal Sciences, Purdue University, West Lafayette, 47907, IN, USA. Luiz F. Brito. Faculty of Animal Science and Food Engineering, University of São Paulo, Pirassununga, 13635-900, SP, Brazil. Simara Larissa Fanalli, Mariah Castro Durval, Bárbara Silva-Vignato. Department of Animal Science, Federal University of Goiás, Goiânia, GO, 74690-900, Brazil. Vivian Vezzoni de Almeida. Unit of Animal Genomics, GIGA-R and Faculty of Veterinary Medicine, University of Liège (B34), 4000, Liège, Belgium. Gabriel Costa Monteiro Moreira. Corresponding author Correspondence to Aline Silva Mello Cesar, [email protected] . References Delpuech E, Aliakbari A, Labrune Y, Fève K, Billon Y, Gilbert H, et al. Identification of genomic regions affecting production traits in pigs divergently selected for feed efficiency. Genetics Selection Evolution. 2021;53:49. Ellen E, van der Sluis M, Siegford J, Guzhva O, Toscano M, Bennewitz J, et al. Review of Sensor Technologies in Animal Breeding: Phenotyping Behaviors of Laying Hens to Select Against Feather Pecking. Animals. 2019;9:108. Ramayo-Caldas Y, Mármol-Sánchez E, Ballester M, Sánchez JP, González-Prendes R, Amills M, et al. 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Review of the Third Edition of the Guide for the Care and Use of Agricultural Animals in Research and Teaching. J Am Assoc Lab Anim Sci. 2012;51:298. Additional Declarations No competing interests reported. Supplementary Files AdditionalFile1.xlsx Additional File 1. File format XLSX. Title: "Cis- and trans-eQTL identified based on different scenarios of genomic data from pigs". Description: This file presents the results of the expression quantitative trait loci (eQTL) analyses for different scenarios (FDR<0.01). It includes information on the eQTL identified in each scenario, providing insights into the genetic variants associated with gene expression regulation. Furthermore, the information of the VEP is included to facilitate to understand the information about the cis- and trans-eQTL. AdditionalFile2.xlsx Additional File 2. File format XLSX. Title: "Variant Effect Prediction for cis- and trans-eQTL identified in the skeletal muscle of pigs with threshold of 1% from the FDR". Description: This file provides Variant Effect Prediction (VEP) information for all cis- and trans-eQTL (FDR<0.01) identified in the scenarios S2, S3, S4, S6, S7, and S8. It includes detailed annotations and predictions on the functional consequences of genetic variants associated with gene expression regulation in the scenarios (FDR<0.01). AdditionalFile3.xlsx Additional File 3. File format XLSX. Title: "Annotation of the genes regulated by the cis- and trans-eQTL based on different scenarios". Description: This file contains the annotation of the genes regulated by the eQTL found in scenarios S2, S3, S4, S6, S7, and S8. It provides information on the biological functions, description, and other relevant annotations for the identified genes. AdditionalFile4.xlsx Additional File 4. File format XLSX. Title: "Annotation and enrichment for the modulated genes by eQTL identified in the skeletal muscle of pigs". Description: This file contains the results of gene enrichment analyses, including enriched gene sets, pathways, and functional categories associated with the eQTL and their regulated genes. This file also provides modulated gene annotations, including gene symbols, chromosomal locations, gene descriptions, and other relevant information. Cite Share Download PDF Status: Published Journal Publication published 02 Jan, 2024 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Major revision 07 Oct, 2023 Reviews received at journal 19 Sep, 2023 Reviewers agreed at journal 19 Sep, 2023 Reviewers invited by journal 11 Sep, 2023 Editor assigned by journal 06 Sep, 2023 Editor invited by journal 06 Sep, 2023 Submission checks completed at journal 06 Sep, 2023 First submitted to journal 11 Aug, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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of São Paulo","correspondingAuthor":true,"prefix":"","firstName":"Aline","middleName":"Silva Mello","lastName":"Cesar","suffix":""}],"badges":[],"createdAt":"2023-08-11 04:59:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3254185/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3254185/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-023-09863-8","type":"published","date":"2024-01-02T15:01:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":42945530,"identity":"3ddcd874-7a10-47be-acff-4d8c47853b6b","added_by":"auto","created_at":"2023-09-11 14:42:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22057,"visible":true,"origin":"","legend":"\u003cp\u003eExpression quantitative trait loci (eQTL) associations identified for combinations of SNPs pruned and unpruned for linkage disequilibrium (LD). S2: SNPs from the SNP calling of RNA-seq data of the skeletal muscle; S3: SNPs from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle; S4: SNPs from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle, liver, and brain tissues; S6: SNPs from the SNP calling of RNA-seq data of the skeletal muscle after LD pruning; S7: SNPs from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle after LD pruning; and, S8: SNPs from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle, liver, and brain tissues after LD pruning.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3254185/v1/2dffc54a8acc7c500d98197b.png"},{"id":42942364,"identity":"1227ef54-97b5-4215-bfd2-66d876cc405f","added_by":"auto","created_at":"2023-09-11 14:26:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21219,"visible":true,"origin":"","legend":"\u003cp\u003eExpression quantitative trait loci (eQTL) distribution across the autosomal chromosomes for cis- and trans-eQTL for scenarios S2, S4, S6, and S8 (Figures 2a, 2b, 2c, and 2d, respectively). The blue lines separate the chromosomes, the Y-axis represents the gene order in relation to chromosome position in the pig genome, and the X-axis represents the SNP order in relation to chromosome position in the pig genome. \u003cstrong\u003eFigure 2a.\u003c/strong\u003e S2: SNPs from the SNP calling of RNA-seq data of the skeletal muscle, \u003cstrong\u003eFigure 2b.\u003c/strong\u003e S4: SNPs from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle, liver, and brain tissues, \u003cstrong\u003eFigure 2c.\u003c/strong\u003e S6: SNPs from the SNP calling of RNA-seq data of the skeletal muscle after linkage disequilibrium (LD) pruning, and \u003cstrong\u003eFigure 2d.\u003c/strong\u003e S8: SNPs from the SNP calling of RNA-seq data of the skeletal muscle after LD pruning.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3254185/v1/e9a1d31e9d113194c8e2407e.png"},{"id":42944261,"identity":"2f72cd17-d56c-46a6-b071-c27d032aa2e3","added_by":"auto","created_at":"2023-09-11 14:34:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56213,"visible":true,"origin":"","legend":"\u003cp\u003eThe most severe consequences predicted by the Variant Effect Predictor (VEP) in scenarios S2 (Figure 3a), S4 (Figure 3b), S6 (Figure 3c), and S8 (Figure 3d). VEP: Variant Effect Predictor;UTR: untranslated region. 3’UTR: a UTR variant of the 3' UTR; 5’UTR: a UTR variant of the 5' UTR; UTR variant: A transcript variant that is within the UTR; Other: variants non-coding transcript exon, splice polypyrimidine tract, intron intergenic, intron, non-coding transcript, splice region, synonymous, splice acceptor, splice polypyrimidine tract, splice region, intron, splice donor, stop lost, splice donor region, intron, splice polypyrimidine tract, intron, non-coding transcript, splice region, 3’UTR, stop retained, missense, splice region.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3254185/v1/eaa070754087f225d62b2be6.png"},{"id":42942367,"identity":"f0f99af5-8447-48fa-be59-45113e4a9d36","added_by":"auto","created_at":"2023-09-11 14:26:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":9258,"visible":true,"origin":"","legend":"\u003cp\u003eTop 10 significant traits in Meat and Carcass QTL-type enrichment analyzes for cis-eQTL identified in porcine skeletal muscle. The area of the bubbles represents the number of observed QTL for that class, while the color represents the p-value scale (the darker the color, the most significant the p-value). Additionally, the X-axis shows the richness factor for each QTL, representing the ratio of number of QTL and the expected number of that QTL.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3254185/v1/eed046c826745d9813d0b897.png"},{"id":42942369,"identity":"2e435576-96b6-4e4b-84aa-32aeef7a3fbd","added_by":"auto","created_at":"2023-09-11 14:26:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":9832,"visible":true,"origin":"","legend":"\u003cp\u003eTop 10 significant traits in Meat and Carcass QTL-type enrichment analyzes for trans-eQTL identified in porcine skeletal muscle. The area of the bubbles represents the number of observed QTLs for that class, while the color represents the p-value scale (the darker the color, the most significant are the p-values). Additionally, the X-axis shows the richness factor for each QTL, representing the ratio of number of QTL and the expected number of that QTL.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3254185/v1/673f3f2c24fafc13a9b5814f.png"},{"id":49316130,"identity":"a7b71885-d937-484a-b021-8f6e967af803","added_by":"auto","created_at":"2024-01-08 15:11:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":949853,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3254185/v1/5c7df093-1351-48a4-914f-4a58d66e06d6.pdf"},{"id":42944264,"identity":"1d723311-b6cf-4485-8444-725ca16de4bf","added_by":"auto","created_at":"2023-09-11 14:34:02","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9034413,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File 1. File format XLSX. Title: \"Cis- and trans-eQTL identified based on different scenarios of genomic data from pigs\". Description: This file presents the results of the expression quantitative trait loci (eQTL) analyses for different scenarios (FDR\u0026lt;0.01). It includes information on the eQTL identified in each scenario, providing insights into the genetic variants associated with gene expression regulation. Furthermore, the information of the VEP is included to facilitate to understand the information about the cis- and trans-eQTL.\u003c/p\u003e","description":"","filename":"AdditionalFile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3254185/v1/61a88d9014dd08f345da1cde.xlsx"},{"id":42942372,"identity":"69bd0695-75bf-4f18-961e-03d887f17d13","added_by":"auto","created_at":"2023-09-11 14:26:02","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3120089,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File 2. File format XLSX. Title: \"Variant Effect Prediction for cis- and trans-eQTL identified in the skeletal muscle of pigs with threshold of 1% from the FDR\". Description: This file provides Variant Effect Prediction (VEP) information for all cis- and trans-eQTL (FDR\u0026lt;0.01) identified in the scenarios S2, S3, S4, S6, S7, and S8. It includes detailed annotations and predictions on the functional consequences of genetic variants associated with gene expression regulation in the scenarios (FDR\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"AdditionalFile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3254185/v1/4c45cb499d5a991219a54460.xlsx"},{"id":42944263,"identity":"d357574a-3da4-4a05-a6df-58d6ecd5b099","added_by":"auto","created_at":"2023-09-11 14:34:02","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":64724,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File 3. File format XLSX. Title: \"Annotation of the genes regulated by the cis- and trans-eQTL based on different scenarios\". Description: This file contains the annotation of the genes regulated by the eQTL found in scenarios S2, S3, S4, S6, S7, and S8. It provides information on the biological functions, description, and other relevant annotations for the identified genes.\u003c/p\u003e","description":"","filename":"AdditionalFile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3254185/v1/8bd453b1faf91f6994ff0e11.xlsx"},{"id":42942374,"identity":"13046c78-da38-4f39-bd7b-a8f74f411993","added_by":"auto","created_at":"2023-09-11 14:26:02","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":461394,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File 4. File format XLSX. Title: \"Annotation and enrichment for the modulated genes by eQTL identified in the skeletal muscle of pigs\". Description: This file contains the results of gene enrichment analyses, including enriched gene sets, pathways, and functional categories associated with the eQTL and their regulated genes. This file also provides modulated gene annotations, including gene symbols, chromosomal locations, gene descriptions, and other relevant information.\u003c/p\u003e","description":"","filename":"AdditionalFile4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3254185/v1/fd0e1fe089c5481a7b25665f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of eQTL using different sets of single nucleotide polymorphisms associated with carcass and body composition traits in pigs","fulltext":[{"header":"Background","content":"\u003cp\u003eDeveloping effective breeding strategies and genetic improvement programs are paramount for improving the long-term sustainability of livestock production. In this context, there is a need to determine the impact of genomic variants on gene expression and phenotypic variability related to production and environmental efficiency traits, such as feed efficiency, carcass yield, live weight, and body composition [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Genome-wide association studies (GWAS) based on single nucleotide polymorphism (SNP) information and production efficiency and meat quality traits have been extensively explored in recent years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These studies have contributed to the understanding of the genetic architecture of complex traits in pigs, but most studies have been based primarily on SNPs located in intronic and intergenic regions. Therefore, the use of SNPs obtained from transcriptome sequencing could provide additional information about the SNPs located in transcribed regions of the genome, which have a greater likelihood of being more functionally relevant with greater influence on the phenotypic expression of complex traits [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenetic markers (e.g., SNPs) located within coding regions of the genome are more likely to change the level of global gene expression in the most diverse tissues of living organisms. For example, a missense variant could result in the alteration of a codon that encodes a certain amino acid and, consequently, lead to changes in protein synthesis and in the functionality of these proteins in various tissues and physiological processes of organisms [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. When a SNP is in the promoter region of a gene or 3' untranslated region (3'UTR), it can alter the level of gene expression and affect post-transcriptional regulations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Thus, these variants may result in phenotypic differences (e.g., carcass trait, body composition) among individuals in a population.\u003c/p\u003e \u003cp\u003eDue to the reduced genetic variability in livestock populations, SNPs located throughout the genome are in moderate to high linkage disequilibrium (LD) [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and, therefore, could have similar effects on a given trait. So, it is common to perform SNP or tag-SNP pruning based on LD thresholds to eliminate SNPs capturing similar quantitative trait loci (QTL) effects in GWAS, in which only one representative SNP of the LD block is maintained to reduce the total number of statistical tests [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Not performing LD pruning could result in more false positives and decrease the statistical power of the analyses [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The SNPs originating from the transcriptome could be in greater proximity and, therefore, in greater LD among themselves. Thus, the level of LD among the studied variants is an important element to be considered in expression QTL (eQTL) identification studies based on transcriptome sequencing data [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe integration of SNPs from transcriptome sequencing data from different tissues (e.g., skeletal muscle, liver, brain) with other data sources such as genotyping SNP arrays (e.g., GGP-50K genotyping) can provide complementary information about genomic variability related to gene expression in specific tissues such as the skeletal muscle \u0026ndash; a key tissue for pork production. The combination of SNPs obtained through sequencing the RNA from different biological tissues and data sources (i.e., sequencing, genotyping) could enable a more accurate identification of eQTL that would not be detected by analyzing variants from the skeletal muscle tissue alone. In addition to data integration, it is important to evaluate alternative statistical approaches, such as LD pruning and quality control parameters (e.g., minor allele frequency, genotyping call rate, and variants with extreme departure from the Hardy-Weinberg equilibrium expectations), to adjust the initial data structure and reduce potential biases in the results due to the presence of closely linked or low-quality variants [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe hypothesize that different combinations of SNPs obtained from different biological tissues (e.g., skeletal muscle, liver, and brain) and data sources (GGP-50K genotyping and RNA-seq) may affect the identification of eQTL associated with carcass and body composition traits in pigs. Therefore, our primary objectives were to: 1) evaluate the impact of different SNP-set combinations (including LD pruning) derived from SNP chip arrays and RNA-seq data from liver, brain, and skeletal muscle tissues on the identification of eQTL associated with carcass and body composition traits in Large White pigs; and, 2) investigate candidate genes and biological processes associated with the phenotypic expression of these traits. The phenotypic traits evaluated in this study included slaughter weight (SW; in kg), cold carcass yield as a percentage of the slaughter weight (CCY, in %), loin eye area measured by ultrasound (LEA; in cm\u0026sup2;), backfat thickness measured by ultrasound (BFT; in cm), and intramuscular fat content in percentage (IMF, in %).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypes, genotypes, and scenarios\u003c/h2\u003e \u003cp\u003eThe descriptive statistics of the phenotypic traits evaluated in the study are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and were previously described by Almeida et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of the traits included in the association studies, which were partially described by Almeida et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRAIT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSW (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCY (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEA (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBFT (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSW (kg): Slaughter weight in kg; CCY (%): Cold carcass yield as a percentage of the slaughter weight; LEA (cm\u0026sup2;): Ultrasound-based loin eye area measured between the 10th and 11th ribs; BFT (cm): backfat thickness measured by ultrasound at the 10th rib; IMF (%): Intramuscular fat content in percentage; N: Number of records; CV (%): Coefficient of variation; SD: Phenotypic standard deviation.\u003c/p\u003e \u003cp\u003eThe set of SNPs analyzed in this study were derived from RNA-seq data from brain, liver, and skeletal muscle tissues from 72 pigs and from the genotyping of these same animals with the GeneSeek Genomic Profiler Porcine 50K (GGP-50K) SNP chip array. A total of 50,697 SNPs were obtained from the GGP-50K SNP chip array as well as 2,650,720, 1,816,600, and 4,404,053 SNPs (before quality control) obtained from RNA-seq data of skeletal muscle (\u003cem\u003eLongissimus lumborum\u003c/em\u003e), liver (right lobe of the liver), and brain (a portion of the middle region of the frontal lobe) tissues, respectively, of 72 Large White pigs.\u003c/p\u003e \u003cp\u003eThe quality control used for filtering out the SNPs identified from the RNA-seq data considered a \u003cem\u003ePhred\u003c/em\u003e score (QUAL) equal or greater than 30 (QUAL\u0026thinsp;\u0026ge;\u0026thinsp;30) and coverage depth (DP) equal or greater than 10 (DP\u0026thinsp;\u0026ge;\u0026thinsp;10). Only bi-allelic variants from the \u003cem\u003eSus scrofa\u003c/em\u003e autosomal chromosomes SSC1 to SSC18 were included in further analyses. Thus, 1,609,081, 915,828, and 2,649,856 SNPs from skeletal muscle, liver, and brain tissues, respectively, remained in the dataset. Additional quality control filters included removing SNPs with minor allele frequency (MAF) lower than 5%, variants with genotyping rate lower than 95% (more than 5% missing), and extreme departure from Hardy-Weinberg equilibrium (HWE; p-value lower than 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e). After that, 74,812, 50,932, and 117,330 SNPs from the skeletal muscle, liver, and brain tissues, and 30,872 SNPs from the GGP-50K array from 72 animals were considered for further analyses. After quality control of RNA-Seq data, total of 15,090 genes were expressed in the skeletal muscle of the 72 animals, which were normalized and represented as transcripts per million (TPM), and the expression was also normalized before fitting the linear models.\u003c/p\u003e \u003cp\u003eAll the SNP datasets were combined for the identification of cis- and trans-eQTL in the skeletal muscle tissue. For that, we considered the scenario with only the SNPs found in the skeletal muscle transcriptome as the base scenario, and subsequently, added the SNPs from the brain and liver transcriptomes and from the 50K SNP chip array. Hence, the SNPs from the RNA-seq data of the brain and liver tissues and the SNPs from the 50K SNP chip panel were used alone or combined with the SNPs from the skeletal muscle, which resulted in four scenarios: (S1) only the SNPs from the GGP-50K; (S2) SNPs from the RNA-seq data of the skeletal muscle (baseline scenario); (S3) SNPs from the GGP-50K plus the SNPs of the RNA-seq data of the skeletal muscle; (S4) SNPs from the GGP-50K plus the SNPs of RNA-seq data of the skeletal muscle, liver, and brain. Subsequently, the SNP sets from the four scenarios were LD pruned considering an r\u0026sup2; threshold of 0.70, which resulted in four additional scenarios: (S5) SNPs from the GGP-50K after LD pruning; (S6) SNPs from the RNA-seq data of the skeletal muscle after LD pruning; (S7) SNPs from the GGP-50K plus the SNPs from the RNA-seq data of the skeletal muscle after LD pruning; (S8) SNPs from the GGP-50K plus the SNPs from the RNA-seq data of the skeletal muscle, liver, and brain after LD pruning. The number of SNPs before and after quality control for all scenarios are described in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of single nucleotide polymorphisms (SNPs) before and after the quality control for each of the scenarios evaluated.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of SNPs (before quality control)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of SNPs after quality control and prior to LD pruning\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of SNPs after LD pruning\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNPs from the GGP-50K SNP chip array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50,697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30,872 (S1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,210 (S5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNPs from the RNA-seq data of the skeletal muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,591,269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74,812 (S2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18,933 (S6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNPs from the GGP-50K SNP chip array plus the SNPs from the RNA-seq data of the skeletal muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,701,417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104,699 (S3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30,037 (S7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNPs from the GGP-50K SNP chip array plus the SNPs from the RNA-seq data of the skeletal muscle, liver, and brain tissues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,675,049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135,996 (S4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105,870 (S8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGGP-50K: SNPs from the GeneSeek Genomic Porcine 50K medium density genotyping array; LD: Linkage disequilibrium; RNA-seq: RNA sequencing; S1-S8: scenarios 1 to 8.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of eQTLs across scenarios\u003c/h2\u003e \u003cp\u003eFor the cis- and trans-eQTL identification analyses, genomic windows of up to 1 Mb upstream from the beginning of the regulated gene and 1M downstream from the end of the regulated gene were considered for the cis (local) effect, and more than 1 Mb of the regulated gene for the trans (distant) effect. These analyzes were performed for each of the eight scenarios aiming to identify eQTL based on the gene expression level in the skeletal muscle tissue. Considering a False Discovery Rate (FDR) of 1%. The number of eQTL associations identified were: S1 and S5\u0026thinsp;=\u0026thinsp;There were no significant cis- or trans-eQTL; S2: cis-eQTL\u0026thinsp;=\u0026thinsp;2,538 and trans-eQTL\u0026thinsp;=\u0026thinsp;2,752; S3: cis-eQTL\u0026thinsp;=\u0026thinsp;2,355 and trans-eQTL\u0026thinsp;=\u0026thinsp;1,719; S4: cis-eQTL\u0026thinsp;=\u0026thinsp;2,256 and trans-eQTL\u0026thinsp;=\u0026thinsp;43; S6: cis-eQTL\u0026thinsp;=\u0026thinsp;291 and trans-eQTL\u0026thinsp;=\u0026thinsp;13,721; S7: cis-eQTL\u0026thinsp;=\u0026thinsp;231 and trans-eQTL\u0026thinsp;=\u0026thinsp;6,754; and, S8: cis-eQTL\u0026thinsp;=\u0026thinsp;646 and trans-eQTL\u0026thinsp;=\u0026thinsp;29, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe unique count of eQTL (considering a single SNP count) in scenarios S2, S3, and S4 ranged from 2,066 to 2,247 for cis-eQTL and 43 to 379 for trans-eQTL. In the scenarios with LD pruning (S6, S7, and S8), the number of cis-eQTL ranged from 223 to 612 while the number of trans-eQTL ranged from 29 to 403, considering a single SNP count. The number of genes regulated by cis- and trans-eQTL in scenarios S2, S3, and S4 ranged from 159 to 304 and from 8 to 1,965, respectively. The number of genes regulated by cis- and trans-eQTL in scenarios S6, S7, and S8 ranged from 109 to 185 and from 6 to 5,993, respectively.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the overlap between the significant cis- and trans-eQTL. The table diagonal represents the number of eQTL identified, whereas the values above the diagonal indicate the percentage of overlapping eQTL among the scenarios and the values below the diagonal represent the number of overlapping eQTL across datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Description of the percentage and count of cis- and trans-eQTL (expression quantitative trait loci) identified across the scenarios represented by different set of SNPs associated with expression level of skeletal muscle of Large White pigs.\u003c/p\u003e\n\u003ctable style=\"width: 105%;border: none;margin-left:5.4pt;border-collapse:collapse;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width:16.08%;border:none;border-top:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eSCENARIO\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width:44.52%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:none;border-right:dashed windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eCis-eQTL\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width:38.72%;border:none;border-top:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eTrans-eQTL\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border:none;padding:0in 0in 0in 0in;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;text-align:justify;line-height:200%;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:7.76%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eS2\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eS3\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.74%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eS4\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eS6\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eS7\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.62%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:dashed windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eS8\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eS2\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eS3\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eS4\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eS6\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eS7\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:6.66%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eS8\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width:10.06%;border:none;border-bottom:dashed windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eCis-eQTL\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.0%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003eS2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.76%;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003e2,247\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e100%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.74%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e57%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e100%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e99%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.62%;border:none;border-right:dashed windowtext 1.0pt;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e59%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e27%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e26%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e18%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e22%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:6.66%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:6.0%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003eS3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.76%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e2,061\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003e2,065\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.74%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e56%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e11%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e100%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.62%;border:none;border-right:dashed windowtext 1.0pt;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e58%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e25%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e24%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e15%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e19%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:6.66%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:6.0%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003eS4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.76%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e1,184\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e1,152\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.74%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003e2,066\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e5%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e4%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.62%;border:none;border-right:dashed windowtext 1.0pt;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e25%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e13%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e12%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e5%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e4%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e5%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:6.66%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:6.0%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003eS6\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.76%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e223\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e219\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.74%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e95\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003e223\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e95%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.62%;border:none;border-right:dashed windowtext 1.0pt;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e14%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e9%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e10%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e12%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e16%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:6.66%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:6.0%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003eS7\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.76%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e182\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e183\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.74%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e81\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e174\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003e183\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.62%;border:none;border-right:dashed windowtext 1.0pt;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e13%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e7%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e9%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e13%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:6.66%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:6.0%;border:none;border-bottom:dashed windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003eS8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.76%;border:none;border-bottom:dashed windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e361\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;border:none;border-bottom:dashed windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e352\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.74%;border:none;border-bottom:dashed windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e156\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:dashed windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e88\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;border:none;border-bottom:dashed windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e82\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.62%;border-top:none;border-left:none;border-bottom:dashed windowtext 1.0pt;border-right:dashed windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003e612\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:dashed windowtext 1.0pt;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:dashed windowtext 1.0pt;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e1%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:dashed windowtext 1.0pt;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:dashed windowtext 1.0pt;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:dashed windowtext 1.0pt;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e3%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:6.66%;border:none;border-bottom:dashed windowtext 1.0pt;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width:10.06%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;\"\u003eTrans-eQTL\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.0%;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003eS2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.76%;border:none;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e103\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;border:none;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.74%;border:none;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;border:none;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e27\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.62%;border:none;border-right:dashed windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e7\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003e379\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e100%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e60%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e30%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e46%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:6.66%;border:none;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e69%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:6.0%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003eS3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.76%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e76\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e71\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.74%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e29\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e23\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.62%;border:none;border-right:dashed windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e291\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003e291\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e58%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e7%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e36%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:6.66%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e66%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:6.0%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003eS4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.76%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.74%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.62%;border:none;border-right:dashed windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e26\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e25\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003e43\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e7%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e32%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:6.66%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e31%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:6.0%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003eS6\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.76%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e72\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e60\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.74%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e37\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.62%;border:none;border-right:dashed windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e120\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e93\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003e403\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e94%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:6.66%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e14%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:6.0%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003eS7\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.76%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e58\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e49\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.74%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e13\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e41\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.62%;border:none;border-right:dashed windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e121\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e94\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e249\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003e264\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:6.66%;background:#DDEBF7;padding:0in 5.4pt 0in 5.4pt;height:14.9pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e14%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:6.0%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:15.65pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003eS8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.76%;border:none;border-bottom:solid windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:15.65pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;border:none;border-bottom:solid windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:15.65pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.74%;border:none;border-bottom:solid windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:15.65pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:solid windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:15.65pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.94%;border:none;border-bottom:solid windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:15.65pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.62%;border-top:none;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:dashed windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:15.65pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:solid windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:15.65pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e20\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:solid windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:15.65pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e19\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:solid windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:15.65pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:solid windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:15.65pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:6.54%;border:none;border-bottom:solid windowtext 1.0pt;background:#FCE4D6;padding:0in 5.4pt 0in 5.4pt;height:15.65pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:6.66%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:15.65pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;line-height:normal;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"font-size:13px;\"\u003e29\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n \u003cp\u003eeQTL: expression quantitative trait loci; SNPs: single nucleotide polymorphisms; S2: SNPs from the SNP calling of RNA-seq data of the skeletal muscle; S3: SNPs from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle; S4: SNPs from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle, liver, and brain tissues; S6: SNPs from the SNP calling of RNA-seq data of the skeletal muscle after linkage disequilibrium (LD) pruning; S7: SNPs from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle after LD pruning, and, S8: SNPs from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle, liver, and brain tissues after LD pruning. The table diagonal represents the number of eQTL identified, whereas the values above the diagonal indicate the percentage of overlapping eQTL among the scenarios and the values below the diagonal represent the number of overlapping eQTL across datasets.\u003c/p\u003e \u003cp\u003eThe cis- and trans-eQTL of scenarios S3 and S7 overlapped by 94 to 100% with scenarios S2 and S6. The results of the cis- and trans-eQTL for all scenarios are presented in Additional File 1. Figures\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-d illustrate the eQTL distribution across the autosomal chromosomes for cis- and trans-eQTL for scenarios S2, S4, S6, and S8, respectively. The diagonal line formed refers to the cis-eQTL distribution, and the vertical points refer to the trans-eQTL. The Y-axis represents the gene order in relation to chromosome position in the pig reference genome, and the X-axis represents the SNP order in relation to chromosome position in the pig genome.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation of eQTL with carcass and body composition traits\u003c/h3\u003e\n\u003cp\u003eA total of 2,547, 2,107, 576, and 641 eQTL (cis- and trans-eQTL) were identified for the scenarios S2, S4, S6, and S8, respectively. These eQTL were subsequently used for the association analyses with SW, CCY, LEA, BFT, and IMF. The effects of initial body weight (28.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95kg) and treatment [basal diet with 1.5% degummed soybean oil, basal diet with 3% soybean oil, basal diet with 3% canola oil and basal diet with 3% fish oil from crooked sardines (\u003cem\u003eCetengraulis edentulus\u003c/em\u003e)] were adjusted as continuous covariate and categorical fixed effects, respectively. No significant (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) or suggestive (0.05\u0026thinsp;\u0026le;\u0026thinsp;FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.10) associations were identified between the eQTL identified and SW, CCY, LEA, BFT, and IMF for the scenarios S2, S4, S6, and S8. The genomic inflation factor (lambda value - λ) ranged from 0.9 to 1.10, indicating that population structure was properly accounted for in the analyses.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eeQTL annotation\u003c/h2\u003e \u003cp\u003eFor scenarios S2, S4, S6, and S8, 2,547, 2,107, 576, and 165 variants (cis- and trans-eQTL) were analyzed, respectively. A total of 1,044 (41.0%), 834 (39.6%), 390 (67.7%), and 68 (41.2%) variants were classified as new variants for scenarios S2, S4, S6, and S8, respectively. Most of these new variants were located within long non-coding RNA (lncRNA) and protein coding genes. Figures\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-d show the most severe predicted consequences of cis- and trans-eQTL for each scenario. The Additional File 2 shows the complete Variant Effect Predictor (VEP) annotation for all cis- and trans-eQTL.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eeQTL and QTL overlap enrichment analyses\u003c/h2\u003e \u003cp\u003eTo search for overlapping genomic position between the eQTL herein identified and QTL previously reported to be associated with meat and carcass quality and other production traits in pigs was performed using the Genomic Annotation in Livestock for positional candidate LOci (GALLO, [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]) R package. This R package annotates and shows the graphical visualization of QTL enrichment analyses. The annotation and enrichment analyses of the eQTL from each of the scenario\u0026rsquo;s tested (S2, S4, S6, and S8) resulted in 31,023 QTL previously reported in the PigQTLdb database (release 47) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], considering a window of up to 100kb downstream and upstream of the genomic coordinates of the cis- and trans-eQTL. The initial number of SNPs in a single count were 2,247 for cis-eQTL and 379 for trans-eQTL in scenario S2; 2,066 of cis-eQTL and 43 of trans-eQTL in scenario S4; 223 cis-eQTL and 403 trans-eQTL for scenario S6; and 612 cis-eQTL and 29 trans-eQTL for scenario S8. The QTL resulting from the annotation were enriched using a hypergeometric test to reduce potential bias in the results.\u003c/p\u003e \u003cp\u003eFor scenarios S2, S4, S6, and S8, the traits \u0026lsquo;loin muscle area\u0026rsquo;, \u0026lsquo;average backfat thickness\u0026rsquo;, and \u0026lsquo;abdominal fat weight\u0026rsquo; from the QTL list of the \u0026ldquo;Meat and Carcass\u0026rdquo; type were enriched. The traits \u0026lsquo;carcass weight (hot)\u0026rsquo;, \u0026lsquo;fat-cuts percentage\u0026rsquo;, \u0026lsquo;linoleic acid content\u0026rsquo;, \u0026lsquo;backfat above muscle dorsi\u0026rsquo;, \u0026lsquo;subcutaneous fat area\u0026rsquo;, and \u0026lsquo;muscle protein percentage\u0026rsquo; were also enriched for cis- and trans-eQTL in scenarios S2 and S6, and only for cis-eQTL in scenarios S4 and S8. The traits \u0026lsquo;fat weight (total)\u0026rsquo; and \u0026lsquo;polyunsaturated fatty acid content\u0026rsquo; were enriched for cis- and trans-eQTL in S2. The traits \u0026lsquo;total body fat tissue linear\u0026rsquo; and \u0026lsquo;loin eye area linear\u0026rsquo; were enriched for cis-eQTL in S6, and trans-eQTL in S2. Additionally, \u0026lsquo;carcass weight (cold)\u0026rsquo; was enriched for cis-eQTL in S2, S4, and S8, and for trans-eQTL in S6. For the \u0026ldquo;Production\u0026rdquo; QTL type, the traits \u0026lsquo;average daily gain\u0026rsquo; and \u0026lsquo;body weight (slaughter)\u0026rsquo; were enriched for cis- and trans-eQTL in scenarios S2 and S6 and only for cis-eQTL in scenarios S4 and S8.\u003c/p\u003e \u003cp\u003e The QTL type enriched with the SNP markers of the predominant significant eQTL was \u0026ldquo;Meat and Carcass,\u0026rdquo; followed by \u0026ldquo;Health\u0026rdquo; across all scenarios. The top 10 significant traits in the Meat and Carcass QTL type enrichment analyses for cis- and trans-eQTL from scenario S2 are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003e. More details about the enrichment results are shown in Additional File 3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene Ontology (GO), annotation and metabolic pathways\u003c/h2\u003e \u003cp\u003eThe genes (counted uniquely) regulated by cis- and trans-eQTL, identified in scenarios S2 (cis\u0026thinsp;=\u0026thinsp;304, trans\u0026thinsp;=\u0026thinsp;1,965), S4 (cis\u0026thinsp;=\u0026thinsp;159, trans\u0026thinsp;=\u0026thinsp;8), S6 (cis\u0026thinsp;=\u0026thinsp;185, trans\u0026thinsp;=\u0026thinsp;5,993), and S8 (cis\u0026thinsp;=\u0026thinsp;109, trans\u0026thinsp;=\u0026thinsp;6) were used for Gene Ontology (GO), gene annotation, and Metabolic Pathway (MP) analyses. The same gene set was used for functional enrichment analyses. These analyzes were performed to understand the biological mechanisms influenced by candidate genes regulated by cis- and trans-eQTL.\u003c/p\u003e \u003cp\u003eTo investigate eQTL associated with possible gene regulation mechanisms, we applied a filter on the annotation description of the genes modulated by cis- and trans-eQTL in each of the scenarios evaluated. We used key terms such as transcription factors, inhibitors, co-regulators, chromatin modelers and remodelers, histone acetylators, modifiers, RNA binding, repressors, and other possible genes related to gene regulation. Furthermore, we also annotated the genes where the identified eQTL are located. More details of the gene annotation of the regulated genes found in scenarios S2 to S8 are presented in Additional File 4.\u003c/p\u003e \u003cp\u003eThe most significant MP in S2, considering the genes regulated in cis-eQTL type was \u0026lsquo;Chemical carcinogenesis\u0026rsquo; (ssc05204). No GO terms were enriched for this gene set. For the genes regulated in trans-eQTL class in S2, the most enriched GO terms were the biological process (BP) \u0026lsquo;Small GTPase-mediated signal transduction\u0026rsquo; (GO:0007264), the molecular function (MF) \u0026lsquo;calcium ion binding\u0026rsquo; (GO:0005509), and the cellular component (CC) \u0026lsquo;cell leading edge\u0026rsquo; (GO:0031252), and the most significant MP was \u0026lsquo;Adherens junction\u0026rsquo; (ssc04520). For S4, only two MP were enriched, the first and most significant MP was \u0026lsquo;Drug metabolism\u0026rsquo; (ssc00982) considering the genes regulated in cis, with no GO terms enriched for the trans regulated genes. For genes regulated by trans-eQTL, there was no significant MP, CC, BP, or MF. For S6, four MP were enriched and the most significant was \u0026lsquo;Ovarian steroidogenesis\u0026rsquo; (ssc04913) in cis. On the other hand, in trans, the most enriched GO terms for the S6 scenario were the BP \u0026lsquo;circulatory system development\u0026rsquo; (GO:0072359), the CC \u0026lsquo;extrinsic membrane component\u0026rsquo; (GO:0019898), and the MF \u0026lsquo;identical protein binding\u0026rsquo; (GO:0042802), and the MP \u0026lsquo;Proteoglycans in cancer\u0026rsquo; (ssc05205). There were no significant GO terms or MP for S8. The most enriched GO terms and MP are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and further details of the enrichment analyses for the GO domains of BP, MF, CC, and MP are presented in the Additional File 4.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of the most enriched gene ontology (GO) and metabolic pathways (MP) terms across the evaluated scenarios.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Set\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essc05204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChemical carcinogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0007264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTrans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmall GTPase mediated signal transduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0031252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCell leading edge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0005509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCalcium ion binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essc04520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdherens junction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essc00982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDrug metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essc04913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOvarian steroidogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0072359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTrans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCirculatory system development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3E-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0019898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExtrinsic component of membrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0042802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIdentical protein binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essc05205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProteoglycans in cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMP: Metabolic pathways; BP: Biological process; CC: Cellular component; MF: Molecular function; FDR: False Discovery Rate; S2: SNPs from the SNP calling of RNA-seq data of the skeletal muscle; S4: SNPs from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle, liver, and brain tissues; S6: SNPs from the SNP calling of RNA-seq data of the skeletal muscle after linkage disequilibrium pruning (r\u0026sup2; \u0026gt; 0.7); cis: genes locally modulated by eQTL used for the enrichment analyses; trans: genes distantly modulated by eQTL used for the enrichment analyses.\u003c/p\u003e \u003cp\u003eComparing up to 100 GO terms and the most enriched MP between S2 and S6 trans-eQTL, there was an overlap of 71.4% of CC terms, 71.4% of MF, and 86.1% of BP. The overlapping results of MP from S2 and S6 revealed 28% and 82.5% of similarity in cis- and trans-eQTL, respectively. There was a 100% overlap of the significant MP in cis-eQTL from the S4 and the top twenty most significant pathways in cis-eQTL for S2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGenes modulated by eQTL\u003c/h2\u003e \u003cp\u003eA total of 457 genes were found to be associated with the eQTL from the different scenarios. The scenarios with SNPs only from the skeletal muscle transcriptome (S2 and S6) enabled the identification of more genes modulated both in cis and in trans. Scenarios with LD-pruned SNPs also identified more modulated genes. Additionally, the scenario S6 presented the largest number of overlapping genes modulated with the other scenarios, that is, genes modulated in cis, or trans identified in scenario S6 were frequently identified in other scenarios.\u003c/p\u003e \u003cp\u003eIn S2, the trans-eQTL located in the genes encoding \u003cem\u003eCEBZB\u003c/em\u003e (zeta CCAAT enhancer binding protein), \u003cem\u003eeIF2B\u003c/em\u003e (eukaryotic translation initiation factor 2B subunit alpha), \u003cem\u003eTSTD3\u003c/em\u003e (sulfur thiosulphate transferase domain containing 3), \u003cem\u003eTMEM245\u003c/em\u003e (Transmembrane protein 245), and \u003cem\u003eOXCT1\u003c/em\u003e (3-oxoacid CoA-transferase 1) were identified. These trans-eQTL were associated with expression of genes involved in regulatory mechanisms, such as transcription factors, chromatin modifiers, primers, and bindings. Key transcription factors identified include \u003cem\u003eBCLAF1\u003c/em\u003e, \u003cem\u003eE2F8\u003c/em\u003e, \u003cem\u003eELF1\u003c/em\u003e, \u003cem\u003eELK3\u003c/em\u003e, \u003cem\u003eETS1\u003c/em\u003e, \u003cem\u003eETV6\u003c/em\u003e, \u003cem\u003eGABPB1\u003c/em\u003e, \u003cem\u003eTCF12\u003c/em\u003e, \u003cem\u003eTCF4\u003c/em\u003e, \u003cem\u003eGTF3C1\u003c/em\u003e, \u003cem\u003eMYT1L\u003c/em\u003e, \u003cem\u003eSREBF2\u003c/em\u003e, \u003cem\u003eYY1\u003c/em\u003e, \u003cem\u003eETS1\u003c/em\u003e, \u003cem\u003eSOX7\u003c/em\u003e, \u003cem\u003eFAP2A\u003c/em\u003e, and \u003cem\u003eGTF3C5\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIn addition, cis-eQTL identified in S2 modulate the genes \u003cem\u003eNAT10\u003c/em\u003e (RNA cytidine acetyltransferase), \u003cem\u003eYIPF2 (\u003c/em\u003eProtein yipf2 isoform x3; member 2), \u003cem\u003eEARS2\u003c/em\u003e (Probable glutamate\u0026mdash;tRNA ligase, mitochondrial isoform x1; glutamyl-tRNA synthetase 2; Belongs to the class-I aminoacyl-tRNA synthetase family), \u003cem\u003eGBA\u003c/em\u003e (Glucosylceramidase precursor; \u003cem\u003eSus scrofa\u003c/em\u003e glucosidase), \u003cem\u003eMTERF3\u003c/em\u003e (Transcription termination factor 3, mitochondrial isoform x2), \u003cem\u003eEMG1\u003c/em\u003e (ENSSSCP00000026081), \u003cem\u003eSYMPK\u003c/em\u003e (Symplekin isoform x1), and \u003cem\u003eTHYN1\u003c/em\u003e (Thymocyte nuclear protein 1 isoform x1). These genes are modulated by cis-eQTL predicted to 3\u0026rsquo;UTR, 5\u0026rsquo;UTR, downstream, upstream, and missense variants.\u003c/p\u003e \u003cp\u003eThe incorporation of SNPs from the 50K SNP chip array resulted in a lower number of significant modulated genes (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between scenarios S2 and S3, as well as between S6 and S7, with a pattern inversely to the increase in the number of SNPs. However, the combination of SNPs from the SNP array with skeletal muscle sequencing SNPs (S3 and S7 scenarios) enabled the identification of 13 specific variants of the GGP-50K associated with 19 genes, including the Zic family member 5 (\u003cem\u003eZIC5\u003c/em\u003e) identified exclusively in the S3 scenario in cis. This gene contains a variant from the GGP-50K panel (rs81431697). In addition, in the S7 scenario, in trans, there were also genes modulated exclusively by variants derived from the GGP-50K SNP panel dataset, including \u003cem\u003eSLC7A1, TRAPPC9, ENSSSCG00000034462, GOLT1A, ENSSSCG00000018018, ENSSSCG00000024765, TTC23\u003c/em\u003e, and \u003cem\u003eENSSSCG00000009523\u003c/em\u003e. The other genes identified in scenarios S3 and S7, modulated by SNPs from the GGP-50K SNP panel dataset, were identified in S6, however, modulated by variants identified in the skeletal muscle transcriptome.\u003c/p\u003e \u003cp\u003eLastly, in scenarios S4 and S8, there were no significant eQTL derived from the GGP-50K SNP panel dataset. The cis-eQTL, detected only in liver or brain tissue (not identified in the transcriptome of skeletal muscle tissue and GGP-50K), modulated only four genes in S4 (not identified in S2, S3, S6 and S7), including \u003cem\u003eCACNG5\u003c/em\u003e (calcium voltage-gated channel auxiliary subunit gamma 5), \u003cem\u003eIK\u003c/em\u003e (IK cytokine), \u003cem\u003eRBM46\u003c/em\u003e (RNA binding motif protein 46), and \u003cem\u003eZNF821\u003c/em\u003e (zinc finger protein 821), which were cis modulated. Only \u003cem\u003eIK\u003c/em\u003e and \u003cem\u003eZNF821\u003c/em\u003e were identified in S8 and cis modulated.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eCurrently, most of the GWAS in livestock studies for meat quality traits used a set of SNPs located primarily in non-coding genomic regions. However, Next Generation Sequencing (NGS) technology has enabled the discovery of thousands of SNPs across the whole transcriptome, which might not be present in the SNP genotyping arrays. Based on the limitation of previous GWAS, in this study we evaluated the impact of combining different sets of SNPs from medium-density genotyping commercial arrays (i.e., GGP-50K) and SNPs identified in the transcriptome of pig brain, liver, and skeletal muscle tissues (with and without LD pruning) on the identification of cis- and trans-eQTL and their association with carcass and body composition traits in Large White pigs. In addition, enrichment analyses were performed using the gene lists identified across the scenarios to reveal GO terms and MP in which these genes are involved. The SNPs were used to analyze the identification of cis- and trans-eQTL.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of cis- and trans-eQTL in different scenarios\u003c/h2\u003e \u003cp\u003eThe combination of SNP datasets and LD pruning resulted in eight scenarios that were used to identify cis- and trans-eQTL. Considering the gene expression level in the skeletal muscle transcriptome, 15,090 genes were identified. The number of cis- and trans-eQTL in all scenarios is within the expected ranges reported in the literature. For instance, Liu et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] detected 10,693 cis-eQTL and 10,961 trans-eQTL in the \u003cem\u003eLongissimus dorsi\u003c/em\u003e muscle of 189 crossbred pigs from Duroc sires \u003cem\u003ex\u003c/em\u003e Luchuan dams. Liu et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] reported 3,054 eQTL, including 1,283 cis-eQTL and 1,771 trans-eQTL in skeletal muscle from F2 White Duroc \u003cem\u003ex\u003c/em\u003e Erhualian pigs. Besides of the tissue sampled, there are several differences between the aforementioned studies and ours, which may explain the variability in the number of cis- and trans-eQTL identified herein. These differences include the technique used for measuring gene expression (such as RT-qPCR, RNA-seq, and microarray), sequencing coverage depth, breed (e.g., Duroc, Luchuan, Erhualian, Large White, or crossbred animals), sample size, statistical models, covariates used for adjusting the phenotypes, level of correction for population stratification, initial number of SNPs and genes considered (SNP x gene interactions), quality control measures applied to SNPs and genes, method and thresholds used for multiple testing correction, and the significance levels.\u003c/p\u003e \u003cp\u003eIn this study, there was a decrease in the number of eQTL associations in cis- and trans-eQTL as the number of SNPs increased, possibly due to the greater stringency of correction for multiple tests and the reduced sample size, as suggested by Huang et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The reduction of significant eQTL by increasing the weight of the correction method may be due to a greater removal of false positives [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Thus, when incorporating other SNPs from other genomic regions, such as those identified in the liver and brain, it is important to consider stricter thresholds. However, such approaches are necessary to capture variants from other genomic regions, which may contribute to a better understanding of the cellular mechanisms. Furthermore, by keeping the threshold constant for the identification of cis- and trans-eQTL, scenarios with a higher number of tests (S4) may indicate those that have a greater impact on gene regulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInclusion of SNP from different genomic context to identify eQTL\u003c/h2\u003e \u003cp\u003eIt was found that in S3 and S7, the combination of SNPs from a 50K SNP panel with the skeletal muscle sequencing SNPs allowed the detection of cis- and trans-eQTL exclusive to genotyping, in addition to genes that were not detected in other scenarios. The eQTL were identified in scenarios S3 and S7, specifically derived from SNPs from the GGP-50K SNP panel dataset. Among these, the \u003cem\u003ers81431697\u003c/em\u003e eQTL showed modulation in cis action of the \u003cem\u003eZIC5\u003c/em\u003e gene, which is involved in cell differentiation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn scenarios S4 and S8, we also identified unique local and distant eQTL that modulate genes, which were not identified in the other scenarios and from SNPs derived from muscle sequencing (S2, S3, S6, and S7). These genes are related to bioprocesses (\u003cem\u003eCACNG5\u003c/em\u003e) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], regulation of the immune system and autoimmune disorders (\u003cem\u003eIK\u003c/em\u003e) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and developmental disorders (\u003cem\u003eRBM46)\u003c/em\u003e [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Additionally, the \u003cem\u003eZNF821\u003c/em\u003e gene encodes a protein involved in the regulation of the structure and function of DNA (GO:1990837) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Thus, verifying the combination of all SNPs allowed the identification of genes, not identified in other scenarios, modulated by eQTL not identified by the sequencing of skeletal muscle tissue transcriptome. However, the scenarios containing the combinations of all SNPs contributed to the identification of genes cis modulated by eQTL not present in the transcriptome of the skeletal muscle of pigs, allowing the detection of variants located in different gene contexts in relation to the target tissue. The scenarios S4 and S8, presented the lowest number of genes modulated by trans-eQTL (8 and 6), that is, the approach adopted for these scenarios is not indicated for detecting distant effects of variants on gene modulation in the skeletal muscle of pigs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eModulated genes by cis- and trans-eQTL and regulatory mechanisms\u003c/h2\u003e \u003cp\u003eSome trans-eQTL identified in the genes \u003cem\u003eCEBZB, eIF2B, TSTD3, TMEM245\u003c/em\u003e, and \u003cem\u003eOXCT1\u003c/em\u003e, in scenario S2 modulating gene encoding transcription factors, include \u003cem\u003eBCLAF1, E2F8, ELF1, ELK3, ETS1, ETV6, GABPB1, TCF12, TCF4, GTF3C1, MYT1L, SREBF2, YY1, ETS1, SOX7, FAP2A\u003c/em\u003e, and \u003cem\u003eGTF3C5\u003c/em\u003e. This indicates potential indirect regulatory interactions between the genes containing the eQTL and these transcription factors, by trans modulation. These genes play important roles in the phenotypic expression of traits such as carcass, body composition, and meat quality. \u003cem\u003eBCLAF1\u003c/em\u003e is involved in the regulation of muscle growth in homologues [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. \u003cem\u003eE2F8\u003c/em\u003e is involved in the regulation of cell cycle progression [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and \u003cem\u003eELF1\u003c/em\u003e is involved in the regulation of gene expression [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Additionally, chromatin modifiers identified in this study, such as \u003cem\u003eGABPB1\u003c/em\u003e, \u003cem\u003eTCF12\u003c/em\u003e, and \u003cem\u003eGTF3C1\u003c/em\u003e, are known to play a role in regulating gene expression [\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenes such as \u003cem\u003eMYT1L\u003c/em\u003e, \u003cem\u003eSREBF2\u003c/em\u003e, and \u003cem\u003eYY1\u003c/em\u003e also play important roles in regulating gene expression [\u003cspan additionalcitationids=\"CR51 CR52 CR53 CR54 CR55 CR56\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], and they may interact with each other, such as enhancer \u003cem\u003eCEBPZ\u003c/em\u003e and \u003cem\u003eeIF2B\u003c/em\u003e to regulate the expression of genes involved in protein synthesis, potentially impacting skeletal muscle mass. \u003cem\u003eOXCT1\u003c/em\u003e, on the other hand, can interact with other genes to regulate the expression of genes involved in muscle development [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], potentially affecting meat quality.\u003c/p\u003e \u003cp\u003eOther possible mechanisms are highlighted, such as the cis action, which is when a cis-eQTL modulates the expression of genes nearby. The variants predicted by VEP indicate possible consequences, such as changes in 3'UTR, downstream gene, upstream gene, and missense regions. These consequences may imply changes in amino acids, molecular affinity, tridimensional structure, or mRNA stability, all of which can affect gene expression regulation. Changes in the expression of genes like \u003cem\u003eTPM1\u003c/em\u003e, and \u003cem\u003eARL14EP\u003c/em\u003e could influence regulation of cell growth, thus muscle growth and carcass weight [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Genes involved in energy metabolism, like \u003cem\u003eGLUT4\u003c/em\u003e and \u003cem\u003eCPT1A\u003c/em\u003e, have also been identified. For example, \u003cem\u003eGLUT4\u003c/em\u003e is involved in glucose uptake by cells and \u003cem\u003eCPT1A\u003c/em\u003e is involved in the production of ketone bodies from fatty acids [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Thus, alterations in the expression of these genes can lead to changes in carcass and body composition traits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eThe impact of linkage disequilibrium pruning on eQTL identification\u003c/h2\u003e \u003cp\u003eBased on the observed pattern of the scenarios based on LD pruning (S6, S7, and S8), and the fact that more SNPs in a database implies in an increased number of statistical tests and greater weight in the FDR correction, the cis-eQTL in S8 are the only ones that behaved differently in all scenarios, as their numbers increased. In all other cases, including trans-eQTL, the detection sensitivity of eQTL decreased with the relative increase of SNPs. Although cis-eQTL are more easily found [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] they were not predominant in this study.\u003c/p\u003e \u003cp\u003ePruning for LD also had a significant impact on the identification of eQTL, as genetic variations that are linked may also be associated with differences in gene expression levels. Therefore, LD pruning is important as it allows the removal of linked genetic variants that may confound the results of gene expression analyses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. LD pruning reduces the number of variants considered in the analyses, which can increase the accuracy of the results by reducing collinearity among SNPs [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe numerical difference in the total number of cis- and trans-eQTL identified in S2 was 214, whereas in the equivalent scenario subjected to LD pruning (S6) this difference was 13,430 eQTL. A similar pattern was observed between scenarios S3 and S7. However, despite this abrupt difference, when considering the unique eQTL, these differences were reduced. A notable decrease in the unique count of the cis-eQTL from S2 (2,247) to S6 (223) was observed, indicating that LD pruning, despite reducing the numerical count of cis-eQTL and their unique genomic coordinates, favored the identification of trans-eQTL. It is also worth mentioning that the cis effect adopted in these analyses refers to the \"local\" effect, as explained by Hasin-Brumshtein et at. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These cis-eQTL are defined by the distance of up to 1Mb from the regulated gene, indicating that these SNP are originally closer, and thereby more susceptible to be pruned for LD. This could explain the reduction in the number of cis-eQTL from S2 to S6.\u003c/p\u003e \u003cp\u003eIt was also observed that most of the genes modulated by eQTL unique to GGP-50k (scenarios S3 and S7) were also modulated by eQTL from scenario S6. This indicates that LD pruning may contribute to increasing the detection ability of the adopted model, which would explain part of the overlapping of modulated genes in the scenarios enriched with genotyping SNPs, such as S3 and S7.\u003c/p\u003e \u003cp\u003eAs some of the cis and trans eQTL were associated with several genes, genes associated with several eQTL simultaneously were also observed. The scenarios with SNPs from skeletal muscle sequencing of pigs identified the greatest number of genes. Additionally, it demonstrated significant overlap in functional analyses with the LD unpruned scenarios, despite having a smaller set of initial SNPs. This suggests that LD pruning can effectively balance the stringency of FDR correction. These observations highlight the intrinsic relationship between pruning for LD and FDR in sensibility of the correction to multiple tests from the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eeQTL associations with carcass and body composition traits\u003c/h2\u003e \u003cp\u003eThe cis- and trans-eQTL identified in each of the scenarios were used for the GWAS analyses with carcass and body composition traits. However, there were no significant variants (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) or any trends (0.05\u0026thinsp;\u0026le;\u0026thinsp;FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.10) for the tested traits. According to Yang et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], MLMA is directly related to the proportion of samples to the number of SNPs and a small number of markers reduces the analysis power of the MLMA model. The lack of significance in our analyses may be related to the low sample size (72 pigs). Moreover, the absence of LD pruning in the cis- and trans-eQTL identified in scenarios S2 and S4 may have limited the analyses\u0026rsquo; power. In this regard, for future analyses, the adoption of models that can address such issues of sample limitations and size is recommended. For example, the use of Bayesian analysis can be recommended for analyses with limited data, as it allows for the incorporation of prior information about model parameters, which can help mitigate the uncertainty caused by the reduced datasets [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and a recent possible application is the analysis of TWAS (Transcriptome-Wide Association Studies), as described by Dai et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and Li and Ritchie [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the lack of significant associations between cis- and trans-eQTL with carcass traits and body composition, a substantial number of overlapping eQTL with previously reported QTL related to pork meat and carcass traits were identified. This overlap with QTL provides valuable insights into potential regulatory interactions of cis- and trans-eQTLs and gene mechanisms that may influence the carcass and body composition traits.\u003c/p\u003e \u003cp\u003eThe incorporation of SNPs from brain and liver tissues transcriptomes, as well as genotyping SNPs, into the skeletal muscle SNP dataset was helpful in identifying genes not identified solely based on SNPs from skeletal muscle sequencing. However, this increases the FDR correction level as discussed earlier. LD pruning was found to increase the number of eQTL identified, with good concordance with functional enrichment analyses. LD pruning requires caution in its application, as it can lead to the loss of information on potentially important variants. Furthermore, different approaches to combine SNPs can lead to different tested hypotheses. These combinations can increase false positives, or those that are difficult to explain, in addition to inflating errors and increasing the weight of the correction, as already mentioned. For this reason, they can be indicated as complementary analyses, for cases where data already exist or for specific purposes. However, the combination or LD pruning approach to be used depends mainly on the hypothesis to be tested, in addition to factors such as data availability and sample size. In addition, the eQTL identified in this study can be used in future analyses of gene regulation, cis- and trans-eQTL-regulated genes, gene co-expression networks, and data integration. However, it is important to note that the sample size used in the study was a limiting factor in some analyses, such as GWAS, and associations with the tested phenotypes.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe LD-pruned SNPs (r²\u0026gt;0.7) identified in the transcriptome of the skeletal muscle tissue of pigs, resulted in the highest number of genes modulated by eQTL, mainly in trans, among them, several are involved in gene regulation related to complex traits of pig, such as transcription factors and enhancers. In addition, combinations of SNPs identified in the transcriptome of skeletal muscle with SNPs identified in the transcriptome of brain and liver tissues, and also the SNPs from genotyping, were effective approaches in the identification of eQTL modulating genes, not modulated by eQTL identified in the pig skeletal muscle transcriptome.\u003c/p\u003e \u003cp\u003eNew functional candidate variants associated with the level of gene expression in skeletal muscle were identified in all scenarios. Interestingly, the integration of the 50K genotyping data resulted in gene associations not discovered in other scenarios. It is emphasized that the identification of several candidate functional variants associated with the level of gene expression in porcine muscle that had not been previously reported.\u003c/p\u003e "},{"header":"Methods","content":"\u003ch2\u003eExperiment\u003c/h2\u003e\u003cp\u003eThe transcriptome as well as the carcass and body composition data used in this study were previously described by our team [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. In brief, 72 genetically lean male immunocastrated pigs of the Large White breed with negative genotypes for the homozygous halothane gene (NN) were randomly assigned to one of four dietary treatments with six replicate pens per treatment and three pigs per pen. Treatments consisted of diets supplemented with 1.5% degummed soybean oil or 3% oil from soybean oil, or 3% canola oil, or 3% fish oil from crooked sardines (\u003cem\u003eCetengraulis edentulus\u003c/em\u003e). All animals had ad libitum access to feed and water throughout the experimental period (98 days). The average initial body weight (BW) was 28.44 ± 2.95 kg, and the average age was 71 ± 1.8 days. The pigs were fed a basal diet formulated to meet or exceed the nutritional requirements for growing and finishing pigs [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eCollection of samples and phenotypes\u003c/h2\u003e\u003cp\u003eAfter 12-h fasting period, the pigs were slaughtered with an average BW of approximately 132.7 kg. The skeletal muscle (\u003cem\u003eLongissimus lumborum\u003c/em\u003e) between the 10th and 11th ribs, liver (right lobe of the liver), and brain (portion of the middle region of the frontal lobe) samples were collected within at most 30 minutes after bleeding, immediately frozen in liquid nitrogen, and then stored at -80° C in an ultra-freezer. These samples were used for total mRNA extraction. In addition, carcass and body compositions phenotypes were collected before and after slaughter, including SW, CCY, LEA, BFT, and IMF [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eTotal RNA extraction and mRNA sequencing\u003c/h2\u003e\u003cp\u003eThe RNA extraction from the tissue of skeletal muscle, brain, and liver samples, quality control of the RNA-seq data, counting, and normalization are described by Silva et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and Fanalli et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The sequencing analyses were performed at the Genomics Center from ‘Luiz de Queiroz’ College of Agriculture (ESALQ), Piracicaba, São Paulo, Brazil.\u003c/p\u003e\u003ch2\u003eQuality control of RNA-seq data, counting and normalization\u003c/h2\u003e\u003cp\u003eThe quality of RNA-seq was checked using the FastQC software v. 0.11.8 [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Sequencing adapters and low complexity reads were removed by Trim Galore 0.6.5 software [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Reads with a minimum length of 70 bases and a \u003cem\u003ePhred\u003c/em\u003e score greater than 33 were kept after trimming and were aligned and mapped to the porcine reference genome (\u003cem\u003eSus scrofa\u003c/em\u003e 11.1) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] using the assembly available at Ensembl (Release 102) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Alignment, mapping, and sorting (by genomic coordinates) were performed using the STAR v. 2.7.6a software [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe dataset used is available in the European Nucleotide Archive (ENA) repository (EMBL-EBI), under the accession PRJEB52665 (brain tissue) [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ebi.ac.uk/ena/data/view/PRJEB52665\u003c/span\u003e\u003cspan address=\"http://www.ebi.ac.uk/ena/data/view/PRJEB52665\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]; PRJEB50513 [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ebi.ac.uk/ena/data/view/PRJEB50513\u003c/span\u003e\u003cspan address=\"http://www.ebi.ac.uk/ena/data/view/PRJEB50513\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e] (liver tissue); and PRJEB52629 (skeletal muscle tissue - \u003cem\u003eLongissimus lumborum\u003c/em\u003e) [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ebi.ac.uk/ena/data/view/PRJEB52629\u003c/span\u003e\u003cspan address=\"http://www.ebi.ac.uk/ena/data/view/PRJEB52629\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eIdentification of SNPs in RNA-seq data\u003c/h2\u003e\u003cp\u003eFor the variant calling analysis for each tissue, the Genome Analysis Toolkit (GATK, v. 4.1.9.0) was used in the Genomic Variant Call Format (GVCF) mode [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Genome coverage for each of the BAM files was calculated using SAMtools (v. 1.9) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The HaplotypeCaller algorithm [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] was used to call the variants individually, generating GVCF files for each sample. These files were then merged using the CombineGVCF tool [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], and the joint genotyping analysis was performed using the GenotypeGVCF [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In the end, a VCF file with all samples genotyped was generated.\u003c/p\u003e\u003ch2\u003eDNA extraction and genotyping\u003c/h2\u003e\u003cp\u003eThe extraction of the genomic DNA of the 72 animals was performed from 30 mg of liver tissue that was macerated in liquid nitrogen, transferred to a 1.5mL microtube, and then processed according to the procedures protocol suggested by the manufacturer of the HighPrep™ Blood \u0026amp; Tissue DNA Plus Kit (MagBio Genomics, London, UK) which uses nucleic acid isolation technology based on magnetic beads. Subsequently, the DNA obtained was evaluated for quality and quantity by readings in the NanoDrop 2000 nano-spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) at three different wavelengths 230 nm, 260 nm, and 280 nm. The integrity of the DNA extracted from the samples was evaluated by means of Ultra-Violet (UV) light visualization of the electrophoresis run on a 1.5% agarose gel [w/vol] and in Tris-borate-EDTA buffer with GelRed fluorescent staining (Biotium, Hayward, CA, USA). After, DNA evaluation and quantification, an aliquot of approximately 1,000 ng was sent to the NEOGEN company (Pindamonhangaba, SP, Brazil) which performed the genotyping using the first generation GeneSeek Genomic Profiler (GGP) Porcine 50K the medium-density array chip, with approximately 50,915 SNPs.\u003c/p\u003e\u003cp\u003eAfter that, the results were received in the Illumina raw format and converted to PLINK 1.9 [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] “ped” and “map” formats using a python algorithm [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/bioinformatics-ptp/Zanardi/blob/master/Zanardi.py\u003c/span\u003e\u003cspan address=\"https://github.com/bioinformatics-ptp/Zanardi/blob/master/Zanardi.py\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. The individual identification (iid) and family identification (fid) referring to the animals were updated by PLINK 1.9 [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], based on the sampling index present in the “sample map” file, so that the animal identifications coincided with the other data files. In addition, the genomic coordinates (position and chromosome) were updated for the latest version of the Illumina GGP Porcine 50K-24 v2 chip (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.illumina.com/products/by-type/microarray-kits/ggp-porcine.html\u003c/span\u003e\u003cspan address=\"http://www.illumina.com/products/by-type/microarray-kits/ggp-porcine.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Data from SNPs from all tissues and 50K animal genotyping were merged (--bmerge) using PLINK 1.9 [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eQuality Control\u003c/h2\u003e\u003cp\u003eAfter the variant calling, to reduce the false discovery rate, variants were filtered by the SNP for variant quality score (QUAL) equal or greater than 30 (\u003cem\u003ePred\u003c/em\u003e score, Sanger/Illumina 1.9 + encoding) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] and total depth of coverage (DP) equal or greater than 10, using BCFtools v. 1.9. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Then, we filtered only SNPs of the autosomal chromosomes from 1 to 18 and biallelic SNPs using PLINK 1.9 software [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Quality filters were used for variants with low MAF (--maf) 0.05, variants with a 0.95 genotyping call rate (0.05 missing) (--geno), and variants with extreme departure from the Hardy-Weinberg equilibrium test (--hwe) with p-value less than 10\u003csup\u003e− 6\u003c/sup\u003e [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e–\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Files were also generated without quality filters to perform LD pruning considering a r² threshold of 0.70 (--indep-pairphase) in the PLINK 1.9 software [76]. The parameters used for LD pruning were: '--indep-pairphase 50 5 0.7', that is, a window size equal to 50 SNPs, a window offset every 5 SNPs per step, and a correlation threshold (r²) paired equal to 70%. Thus, the pairs of SNPs in each window of 50 SNPs, with a square correlation greater than 70% were noted and one of the SNPs of that pair was removed, later, the window was shifted by 5 SNPs, and the procedure was repeated, until none of these correlated pairs (r²\u0026gt;0.7) remained [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The genomic datasets pruned for LD were subsequently filtered for MAF, missing call rate, and extreme departure from HWE as described before.\u003c/p\u003e\u003ch2\u003eScenarios\u003c/h2\u003e\u003cp\u003eTo identify the SNPs that modulate the level of gene expression in the skeletal muscle of pigs, the SNPs identified from the brain and liver transcriptome data and 50K genotyping were combined with the skeletal muscle transcriptome SNP dataset, which generated four datasets (scenarios). These datasets were subjected to LD pruning, which resulted in four additional scenarios. These combinations were performed to investigate the impact of adding SNPs from different tissues and SNP identification methods to SNPs derived from skeletal muscle transcriptome sequencing data on the identification of cis- and trans-eQTL in muscle. Furthermore, LD pruning was performed for all combinations of SNPs, aiming to elucidate the implications of using this technique in the eQTL mapping, according to different combinations of SNPs from different tissues and methods for SNP identification. These scenarios are: (S1) SNP set from the GGP-50K; (S2) SNP set from the SNP calling of RNA-seq data of the skeletal muscle; (S3) SNP set from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle; (S4) SNP set from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle, liver, and brain tissues; (S5) SNP set from the GGP-50K after LD pruning; (S6) SNP set from the SNP calling of RNA-seq data of the skeletal muscle after LD pruning; (S7) SNP set from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle after LD pruning; and, (S8) SNP set from the GGP-50K plus the SNP calling of RNA-seq data of the skeletal muscle, liver, and brain tissues after LD pruning.\u003c/p\u003e\u003ch2\u003eIdentification of eQTLs\u003c/h2\u003e\u003cp\u003eThe MatrixeQTL package [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] of the R statistical program was used to identify associations between the SNPs from different scenarios and the gene expression level of the skeletal muscle tissue. The window for cis-eQTL (local effect) was defined as up to 1 Mb upstream from the start of transcription and up to 1Mb downstream from the end of the regulated gene. The other combinations were considered as trans-eQTL. To deal with gene expression outliers, the data was transformed to a normal distribution based on the mean, preserving the relative rank, which is one of the solutions accepted by the consortium ‘GTEx’ (Genotype-Tissue Expression) [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The Matrix eQTL package tests the linear association between each marker (SNP) and gene assuming the genotype effect as additive, performs a separate test for each pair (marker and gene), and corrects for multiple testing by calculating the false discovery rate (FDR) [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. The fixed linear regression model fitted was:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{G}= {\\beta }\\text{*}\\text{s}+\\text{P}\\text{C}+\\text{S}\\text{B}\\text{W}+\\text{S}\\text{I}\\text{R}\\text{E}+\\text{T}\\text{R}\\text{E}\\text{A}\\text{T}+\\text{ϵ}\\)\u003c/span\u003e \u003c/span\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{G}\\)\u003c/span\u003e\u003c/span\u003e = is the gene expression level in normalized transcripts per million (TPM), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }\\)\u003c/span\u003e\u003c/span\u003e = SNP allelic substitution effect, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(s\\)\u003c/span\u003e\u003c/span\u003eis the genetic marker covariate, coded as 0 (homozygous for the reference allele), 1 (heterozygous), and 2 (homozygous for the reference alternative), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{P}\\text{C}\\)\u003c/span\u003e\u003c/span\u003e= the first 10 principal components to correct for potential population stratification (principal components explained a total of 28% on the variance-standardized relationship matrix), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{S}\\text{B}\\text{W}\\)\u003c/span\u003e\u003c/span\u003e = starting body weight, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{S}\\text{I}\\text{R}\\text{E}\\)\u003c/span\u003e\u003c/span\u003e = is a dummy variable that represents the sire effect, TREAT = is a dummy variable that represents the treatments effect, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(ϵ\\)\u003c/span\u003e\u003c/span\u003e = the random residuals with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{ϵ}\\sim \\text{i}.\\text{i}.\\text{d}.\\text{N}\\left(0,{{\\sigma }}^{2}\\right)\\)\u003c/span\u003e\u003c/span\u003e. For both cis- and trans-eQTL, an FDR level of 0.01 was considered.\u003c/p\u003e\u003cp\u003eThe estimated effect size (slope coefficient) and the genetic variance explained by the markers were also estimated according to the MatrixeQTL package [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The scatter plots were done using the R ggplot2 package [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The genomic coordinates of the eQTL data and associated genes were converted to mega base pairs (Mb) and sorted by chromosome and position. Then, the eQTL and gene position orders were used to plot the graphs with the X-axis referring to the order of the SNPs and the Y-axis referring to the order of the initial position of the genes.\u003c/p\u003e\u003ch2\u003eAssociation with carcass and body composition traits\u003c/h2\u003e\u003cp\u003eAfter identifying the eQTL, significant SNPs were selected and subsequently these SNPs were associated with the traits of interest. The association between the SNPs in the eQTL for all scenarios (S2, S3, S4, S6, S7, and S8) with the phenotypes was performed using a Mixed Linear Model Association (MLMA) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] in the GCTA software [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], considering the effects identified for each phenotype and fitting the genomic relationship matrix to account for population stratification and polygenic effects. The model fitted was:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{y}= \\text{a} + {\\beta }\\text{*}\\text{x} + \\text{S}\\text{B}\\text{W} + \\text{T}\\text{R}\\text{E}\\text{A}\\text{T} + \\text{G} + \\text{ϵ}\\)\u003c/span\u003e \u003c/span\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{y}\\)\u003c/span\u003e\u003c/span\u003e is the phenotypic record of the carcass and body composition trait evaluated, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{a}\\)\u003c/span\u003e\u003c/span\u003e is the overall mean, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }\\)\u003c/span\u003e\u003c/span\u003e is the additive effect (fixed effect) of the SNP being tested for potential association with the phenotype,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{x}\\)\u003c/span\u003e\u003c/span\u003e is the indicator variable of the SNP genotype coded as 0 (homozygous for the reference allele), 1 (heterozygous), or 2 (homozygous for the reference alternative), SBW = systematic effect of starting body weight (as a linear covariate), TREAT = represents the systematic treatments effect (fixed effects), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{g}\\)\u003c/span\u003e\u003c/span\u003e is the polygenic effect (random effect), that is, the cumulative effect of all SNPs (as captured by the genomic relationship matrix), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{ϵ}\\)\u003c/span\u003e\u003c/span\u003e is the residual effect. The carcass and body composition traits evaluated were SW, CCY, LEA, BFT, and IMF.\u003c/p\u003e\u003cp\u003eThe values resulting from the association analyses were corrected for multiple tests using the FDR method [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], and the significance level adopted was 5% while we considered as indicative the FDR value between 5% and 10%.\u003c/p\u003e\u003ch2\u003ePrediction of the effects of cis- and trans-eQTL identified in each of the scenarios\u003c/h2\u003e\u003cp\u003eBased on the significant cis- and trans-eQTL variants (FDR \u0026lt; 0.01) identified in scenarios S2, S4, S6, and S8, the functional consequence analyses of these variants (cis plus trans) for each of these scenarios were predicted using the VEP tool (Ensembl release 109 - Feb 2023 © EMBL-EBI) [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] and considering the \u003cem\u003eSus scrofa\u003c/em\u003e 11.1assembly genome. The distance to transcription that the VEP assigned for the upstream/downstream consequence was of up to 5,000 bp and the other configurations were kept the default of the web interface (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ensembl.org/info/docs/tools/vep/online/index.html\u003c/span\u003e\u003cspan address=\"http://www.ensembl.org/info/docs/tools/vep/online/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The command line used was “./vep --appris --biotype --buffer_size 5000 --check_existing --distance 5000 --mane --sift b --species sus_scrofa --symbol --transcript_version --tsl --cache --input_file [input_data] --output_file [output_file]”, where input_data is the VCF file with the cis- and trans-eQTL from each scenario.\u003c/p\u003e\u003ch2\u003eAnnotation and functional enrichment of eQTL\u003c/h2\u003e\u003cp\u003eThe GALLO package [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] was used to perform the QTL annotation of the SNPs identified as cis-eQTL and trans-eQTL in scenarios S2, S4, S6, and S8. The eQTL annotation was performed using known QTL data obtained from the PigQTLdb database (version 47 - pigSS11) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], considering a window of up to 100kb downstream and upstream of the genomic coordinates of the cis- and trans-eQTL for each scenario. An enrichment analysis was performed using a hypergeometric test based on the \"qtl_enrich\" function from the GALLO package [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], with the objective of reducing the bias of overrepresented traits. The QTL enrichment test was performed using traits annotated within the candidate regions (window of up to ± 100kb of the eQTL) from the QTL database, considering 18 autosomes. From this information, the hypergeometric test estimate allows us to determine whether the number of records observed for a specific trait in the 18 pig autosomes is greater than what would be expected by chance.\u003c/p\u003e\u003ch3\u003eGene annotation, GO and metabolic pathways for the genes\u003c/h3\u003e\u003cp\u003eAnnotations of genes close to the cis- and trans-eQTL were performed in each of the scenarios (S2, S4, S6 and S8), considering a window of 100kb downstream and upstream of each eQTL. The adopted reference position was the genomic coordinate of each of the cis- and trans-eQTL, for each scenario. Data from the gene annotation of the species \u003cem\u003eSus scrofa\u003c/em\u003e (Assembly '\u003cem\u003eSscrofa\u003c/em\u003e11.1'; genome-build-accession GCA_000003025.6; available at: [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ftp.ensembl.org/pub/release-106/gtf/sus_scrofa/\u003c/span\u003e\u003cspan address=\"https://ftp.ensembl.org/pub/release-106/gtf/sus_scrofa/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]) were extracted from the Ensembl platform (Ensembl release 106 - Jul 2022) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] in the \".gtf\" format (gene transfer format).\u003c/p\u003e \u003cp\u003eAmong the genes, only those regulated by cis- and trans-eQTL (FDR \u0026lt; 0.01) were selected for the Gene Ontology and Metabolic Pathway analyses aiming to understand the functional roles of the genes related to the eQTL. These analyzes were performed using the WebGestaltR package [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], where counted genes uniquely modulated by cis- and trans-eQTL in each scenario (S2, S4, S6, S8) were used to identify biological processes (BP), molecular functions (MF), cellular components (CC), and metabolic pathways (MP). The enrichment method adopted was ORA (Over-Representation Analyses), while the other settings were the package default for each set of genes regulated by cis and trans-eQTL, for each scenario separately.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e3\u0026apos;UTR - 3\u0026apos; untranslated region variant\u003c/p\u003e\n\u003cp\u003e5\u0026rsquo;UTR - 5\u0026rsquo; untranslated region variant\u003c/p\u003e\n\u003cp\u003eBFT - Backfat thickness measured by ultrasound in cm\u003c/p\u003e\n\u003cp\u003eBP - Biological Process\u003c/p\u003e\n\u003cp\u003eCC - Cellular Component\u003c/p\u003e\n\u003cp\u003eCCY - Cold carcass yield in percentage of the slaughter weight\u003c/p\u003e\n\u003cp\u003eCV - Coefficient of variation\u003c/p\u003e\n\u003cp\u003eDNA \u0026ndash; Deoxyribonucleic acid\u003c/p\u003e\n\u003cp\u003eDP - Total coverage depth\u003c/p\u003e\n\u003cp\u003eEMBL-EBI - European Molecular Biology Laboratory-European Bioinformatics Institute\u003c/p\u003e\n\u003cp\u003eENA - European Nucleotide Archive\u003c/p\u003e\n\u003cp\u003eeGWAS - Expression Genome-Wide Association Study\u003c/p\u003e\n\u003cp\u003eeQTL - Expression Quantitative Trait Loci\u003c/p\u003e\n\u003cp\u003eFDR - False Discovery Rate\u003c/p\u003e\n\u003cp\u003eGGP-50K - GeneSeek Genome Porcine medium density SNPs from SNP array\u003c/p\u003e\n\u003cp\u003eGO - Gene Ontology\u003c/p\u003e\n\u003cp\u003eGRM - Genomic Relationship Matrix between the pair of animals\u003c/p\u003e\n\u003cp\u003eGVCF - Genomic Variant Calling Format\u003c/p\u003e\n\u003cp\u003eHWE - Hardy-Weinberg Exact balance test\u003c/p\u003e\n\u003cp\u003eKb - Kilobase (1,000 base pairs)\u003c/p\u003e\n\u003cp\u003eLD - Linkage disequilibrium\u003c/p\u003e\n\u003cp\u003eLEA - Loin eye area measured by ultrasound in cm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eMAF - Minor allele frequency\u003c/p\u003e\n\u003cp\u003eMb - Mega base pair\u003c/p\u003e\n\u003cp\u003eMF - Molecular function\u003c/p\u003e\n\u003cp\u003eIMF - Muscle fat content in percentage\u003c/p\u003e\n\u003cp\u003eMLMA - Mixed Linear Model Association\u003c/p\u003e\n\u003cp\u003eMP - Metabolic pathways\u003c/p\u003e\n\u003cp\u003eN - Number\u003c/p\u003e\n\u003cp\u003eORA - Over Representation Analyses\u003c/p\u003e\n\u003cp\u003ePC - Principal components\u003c/p\u003e\n\u003cp\u003eQTL - Quantitative trait loci\u003c/p\u003e\n\u003cp\u003eQUAL - \u003cem\u003ePhred\u003c/em\u003e score\u003c/p\u003e\n\u003cp\u003eRNA - Ribonucleic acid\u003c/p\u003e\n\u003cp\u003eRNA-seq - RNA sequencing\u003c/p\u003e\n\u003cp\u003eRT-PCR - Real Time Polymerase Chain Reaction\u003c/p\u003e\n\u003cp\u003eRT-qPCR - Real Time Quantitative Polymerase Chain Reaction\u003c/p\u003e\n\u003cp\u003er\u003csup\u003e2\u003c/sup\u003e - Correlation\u003c/p\u003e\n\u003cp\u003eSBW - Initial body weight\u003c/p\u003e\n\u003cp\u003eSD \u0026ndash; Phenotypic standard deviation\u003c/p\u003e\n\u003cp\u003eSM - Skeletal muscle\u003c/p\u003e\n\u003cp\u003eSNP - Single nucleotide polymorphism\u003c/p\u003e\n\u003cp\u003eSSC1 - \u003cem\u003eSus scrofa\u003c/em\u003e chromosome 1\u003c/p\u003e\n\u003cp\u003eSSC18 - \u003cem\u003eSus scrofa\u003c/em\u003e chromosome 18\u003c/p\u003e\n\u003cp\u003eSW - Slaughter weight in kg\u003c/p\u003e\n\u003cp\u003eTPM - Transcripts per million\u003c/p\u003e\n\u003cp\u003eUV \u0026ndash; Ultra-violet light\u003c/p\u003e\n\u003cp\u003eVEP - Variant Effect Predictor\u003c/p\u003e\n\u003cp\u003eVCF - Variant calling format\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval\u003c/p\u003e\n\u003cp\u003eAll animal procedures were conducted in accordance with the Guide for the Care and Use of Agricultural Animals in Agricultural Research and Teaching [70]\u0026nbsp;and approved by the Animal Care and Use Committee of the Luiz de Queiroz College of Agriculture (University of S\u0026atilde;o Paulo, Piracicaba, SP, Brazil, protocol: 2018.5.1787.11.6 and number CEUA 2018-28). This study was also performed in compliance with the ARRIVE guidelines.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe dataset (s) supporting the conclusions of this article is (are) included within the article and its Additional Files 1 to 4. The dataset used is available in the European Nucleotide Archive (ENA) repository (EMBL-EBI), under accession PRJEB52665 (brain tissue) [www.ebi.ac.uk/ena/data/view/PRJEB52665]; PRJEB50513 [www.ebi.ac.uk/ena/data/view/PRJEB50513] (liver); and PRJEB52629 (skeletal muscle) [www.ebi.ac.uk/ena/data/view/PRJEB52629].\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by the S\u0026atilde;o Paulo Research Foundation (FAPESP, Grant numbers: 2017/25180-2, 2021/11261-6, 2021/01694-2, 2020/10042-6, 2022/10643-5, 2022/10780-2, 2023/02067-7), the Brazilian National Council for Scientific and Technological Development (CNPq) that provided a researcher fellowship to A. S. M. Cesar (303165/2022-7). This study was financed in part by the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior - Brazil (CAPES) - Finance Code 001, and the Goi\u0026aacute;s Research Foundation (FAPEG process number: 202110267000074).\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eConceptualization, FAOF, LFB, and ASMC; writing-original draft preparation, FAOF, LFB, BSV, and ASMC; writing-review and editing, FAOF, LFB, SLF, JLG, BPMS, MCD, FNC, CSO, LEN, ICG, JDF, GCMM, BSV, LLC, VVA, and ASMC; supervision, ASMC and LFB; funding acquisition, VVA and ASMC. All authors have read and agreed to the published version of the paper.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe thank the collaborative efforts between the University of S\u0026atilde;o Paulo, Iowa State University, Federal University of Goi\u0026aacute;s, and Purdue University. We are also grateful to DB Gen\u0026eacute;tica Su\u0026iacute;na (Patos de Minas, MG, Brazil) for providing the animals, housing, feeds, and employees, who helped in carrying out this research.\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eLuiz de Queiroz College of Agriculture, University of S\u0026atilde;o Paulo, Piracicaba, 13416-000, SP, Brazil.\u003c/p\u003e\n\u003cp\u003eFelipe Andr\u0026eacute; Oliveira Freitas, Jana\u0026iacute;na Lustosa Gon\u0026ccedil;ales, Bruna Pereira Martins da Silva, Fernanda Nery Ciconello, Camila Sabino de Oliveira, Lucas Echevarria Nascimento, Izally Carvalho Gerv\u0026aacute;sio, Julia Dezen Gomes, Luiz Lehmann Coutinho, Aline Silva Mello Cesar.\u003c/p\u003e\n\u003cp\u003eDepartment of Animal Sciences, Purdue University, West Lafayette, 47907, IN, USA.\u003c/p\u003e\n\u003cp\u003eLuiz F. Brito.\u003c/p\u003e\n\u003cp\u003eFaculty of Animal Science and Food Engineering, University of S\u0026atilde;o Paulo, Pirassununga, 13635-900, SP, Brazil.\u003c/p\u003e\n\u003cp\u003eSimara Larissa Fanalli, Mariah Castro Durval, B\u0026aacute;rbara Silva-Vignato.\u003c/p\u003e\n\u003cp\u003eDepartment of Animal Science, Federal University of Goi\u0026aacute;s, Goi\u0026acirc;nia, GO, 74690-900, Brazil.\u003c/p\u003e\n\u003cp\u003eVivian Vezzoni de Almeida.\u003c/p\u003e\n\u003cp\u003eUnit of Animal Genomics, GIGA-R and Faculty of Veterinary Medicine, University of Li\u0026egrave;ge (B34), 4000, Li\u0026egrave;ge, Belgium.\u003c/p\u003e\n\u003cp\u003eGabriel Costa Monteiro Moreira.\u003c/p\u003e\n\u003cp\u003eCorresponding author\u003c/p\u003e\n\u003cp\u003eCorrespondence to Aline Silva Mello Cesar, [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDelpuech E, Aliakbari A, Labrune Y, F\u0026egrave;ve K, Billon Y, Gilbert H, et al. 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J Am Assoc Lab Anim Sci. 2012;51:298.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sus scrofa, expression quantitative trait loci, transcriptomic, LD pruning","lastPublishedDoi":"10.21203/rs.3.rs-3254185/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3254185/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMapping expression quantitative trait loci (eQTL) in skeletal muscle tissue in pigs is crucial for understanding the relationship between genetic variations and phenotypic expression of carcass traits. Therefore, the primary objective of this study was to evaluate the impact of different sets of single nucleotide polymorphisms (SNP), including those pruned for linkage disequilibrium (LD), derived from SNP chip arrays and RNA-seq data from liver, brain, and skeletal muscle tissues on the identification of eQTL in the \u003cem\u003eLongissimus lumborum\u003c/em\u003e tissue, associated with carcass and body composition traits in Large White pigs. SNPs identified from muscle mRNA were combined with SNPs identified in brain and liver tissue transcriptomes, as well as SNPs from the GGP Porcine 50K array. Cis- and trans-eQTL were identified based on the skeletal muscle gene expression level, followed by functional genomic analyses and statistical associations with carcass and body composition traits in Large White pigs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe number of cis- and trans-eQTL identified across different sets of SNPs (scenarios) ranged from 261 to 2,539 and from 29 to 13,721, respectively. Furthermore, 6,180 genes were modulated by eQTL in at least one of the scenarios evaluated. The eQTL identified were not significantly associated with carcass and body composition traits based on the association analyses but were significantly enriched for many traits in the \"Meat and Carcass\" type QTL. The scenarios with the highest number of cis- (n\u0026thinsp;=\u0026thinsp;304) and trans- (n\u0026thinsp;=\u0026thinsp;5,993) modulated genes were the unpruned and LD-pruned SNP set scenarios, identified in the mRNA of muscle. These genes include 84 transcription factor coding genes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAfter LD pruning, the set of SNPs identified based on the transcriptome of the skeletal muscle tissue of pigs resulted in the highest number of genes modulated by eQTL. Most eQTL are of the trans type and are involved in genes influencing complex traits in pigs, such as transcription factors and enhancers. Furthermore, the incorporation of SNPs from other genomic regions to the SNPs identified in the porcine skeletal muscle transcriptome contributed to the identification of eQTL that were not identified based on the porcine skeletal muscle transcriptome alone.\u003c/p\u003e","manuscriptTitle":"Identification of eQTL using different sets of single nucleotide polymorphisms associated with carcass and body composition traits in pigs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-11 14:25:57","doi":"10.21203/rs.3.rs-3254185/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-10-08T02:31:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-09-19T12:38:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10a14a33-f92b-41f0-b697-9993dc14d741","date":"2023-09-19T10:12:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-09-12T00:55:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-09-06T15:29:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-09-06T11:59:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-09-06T11:52:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2023-08-11T04:55:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8ac2087d-06d2-40e6-be28-98017bf0e179","owner":[],"postedDate":"September 11th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-01-08T15:11:29+00:00","versionOfRecord":{"articleIdentity":"rs-3254185","link":"https://doi.org/10.1186/s12864-023-09863-8","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2024-01-02 15:01:55","publishedOnDateReadable":"January 2nd, 2024"},"versionCreatedAt":"2023-09-11 14:25:57","video":"","vorDoi":"10.1186/s12864-023-09863-8","vorDoiUrl":"https://doi.org/10.1186/s12864-023-09863-8","workflowStages":[]},"version":"v1","identity":"rs-3254185","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3254185","identity":"rs-3254185","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}