Genome-wide association analyses for growth, carcass composition and meat quality traits of Iberian pigs fattened in free open air

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García-Casco, Manuel Ramón, Miguel A. Delgado-Gutiérrez, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7515242/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study aimed to identify genomic regions and candidate genes associated with body composition, and meat quality traits in Iberian pigs fattened in Montanera . A genome-wide association study (GWAS) was conducted on 528 pigs for 29 phenotypic traits using genomic data from the GGP Porcine HD Array. After quality control, 526 animals and 35,894 SNPs were retained for the association analysis. Despite the limitations of the genotyping chip used, which lacked coverage for Iberian-specific variants, the GWAS performed with GCTA software identified 165 SNPs significantly associated with 11 traits. Among these, 145 SNPs were clustered into 25 quantitative trait loci (QTL) regions. Five QTL were identified for ham yield, containing genes such as KCNIP4 , ZNF438 , and PID1 . Eight QTL were associated with loin yield, with genes like PREX2 , MATN2 , RSPO1 , and PDE4B . One QTL was associated with shear force, and 16 QTL were related to fatty acid composition. Genes linked to these traits included ELOVL6 , associated with myristic and palmitic acids, and ADCY9 and ROBO1 , associated with linoleic acid. Overall, these results provide novel genomic insights and markers that could enhance selection strategies in Iberian pig breeding programs, while highlighting the need for improved genomic tools tailored to local breeds. Biological sciences/Biotechnology Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics Biological sciences/Molecular biology GWAS autochthonous breed fatty acids premium cuts polymorphisms Figures Figure 1 Figure 2 Figure 3 Introduction The Iberian pig, a native breed of the Iberian Peninsula, is distinguished for the exceptional quality of its meat products, particularly premium cuts such as ham, shoulders and loins, in both dry-cured and fresh forms, all of which achieve high economic values in the market. This excellence is primarily due to the traditional extensive farming system, known as Montanera , which take advantages of the natural resources of the Dehesa forests such as acorns and grass [ 1 ]. The relative importance of the Iberian purebred pig production has been reduced in favor of crossbred with Duroc, due to their higher growth rates and lean meat accumulation. Despite this, the Iberian purebred pig, which has undergone limited selective pressure compared to commercial/cosmopolitan breeds, retains its exceptional quality and offers significant potential for genetic improvement, which could enhance its competitiveness in the market. Currently, the objectives of the public breeding programme for the Iberian purebred, managed by the Spanish Association of Iberian Pig Breeders (AECERIBER), include the evaluation of carcass composition with regard to its premium cuts, such as shoulders, hams, and loins, as well as intramuscular fat, a crucial factor in the quality of meat products [ 2 ]. For this breed, the genetic evaluations have been traditionally carried out using a BLUP-Animal model. Fortunately, the development and implementation of genomic analysis techniques in breeding programs has made it possible to know more about the genetic basis of traits of economic interest, as well as to improve the accuracy of estimates. And in the particular case of the Iberian pig, genetic evaluations could be complemented with genomic information to carry out a marker-assisted selection, which would be especially important given that carcass components usually show negative genetic correlations with quality traits [ 3 , 4 ]. In Palma-Granados et al. [ 5 ], we proposed four single nucleotide polymorphisms (SNPs), PRKAG3_rs319678464G > C , FASN_rs331694510G > A, ACACA_rs340781986C > T and CAST_rs196949783G > A , which were significantly associated with the fatty acid (FA) profile and showed no antagonistic effects on premium cuts. Other SNPs of interest were also identified, PRKAG3_rs1108399077G > A , ELOVL6_rs3473714672A > G , CAPN1_rs81358667G > A , MTTP_rs335896411T > C and NR6A1_rs326780270T > C , which exhibited antagonistic pleiotropic effects between premium cuts and meat quality [ 5 ], highlighting the need for further research on additional genetic markers. Genomic tools allow for more comprehensive and accurate searches for genetic markers. Among these, Genome-Wide Association Studies (GWAS) are a powerful and widely used approach that utilizes SNPs as molecular genetic markers at the genome-wide level for the identification of Quantitative Trait Loci (QTL) regions and the discovery candidate genes associated with target traits, based on the hypothesis that these markers are in linkage disequilibrium with the QTLs [ 6 , 7 ]. In pigs, GWAS studies have allowed the identification of many QTLs associated with various traits of interest, according to the Pig QTL Database (Pig QTLdb; https://www.animalgenome.org/cgi-bin/QTLdb/SS/index ). More specifically, in Iberian pigs Pena et al. [ 8 ] detected key regions on SSC4 and SSC7 related to FA composition. Similarly, Crespo-Piazuelo et al. [ 9 ] reported nine significant regions related to FA traits and six additional regions associated with intramuscular fat (IMF), distributed across different chromosomes and identified in various Iberian crosses. In contrast, the study by Amaral et al. [ 10 ], focusing on Iberian pigs of the Alentejano strain, mainly identified orphan SNPs, with no clear evidence of well-defined genomic regions associated with fat composition traits. Although there are some previous GWAS studies carried out in crossbreeding between Iberian and other commercial lines, specific genetic studies for pure Iberian pigs reared by the traditional fattening system are still limited. This lack of scientific knowledge, together with the economic importance of the Iberian pig as well as its condition as a very robust animal adapted to a harsh environment, underline the importance of promoting studies that help to know more about the genetic basis of productive, quality and resilience traits, as well as to contribute to the sustainability and competitiveness of Iberian pig production system. Therefore, the aim of this study was to conduct a genome-wide association study to identify genomic regions as well as possible QTLs and candidate genes associated with performance, carcass composition, and meat quality traits in purebred Iberian pigs fed under a Montanera system. Results GWAS analyses and genomic regions A total of 526 pigs passed quality control, and a subset of 35.894 SNPs from the initial 68,516 with adequate genotyping quality were selected for the GWAS studies. The different association analyses revealed a total of 165 SNPs statistically associated (q-value < 0.1) with some of the traits, with only 11 out of the 29 traits analysed being significantly associated with at least one SNP. Among the 165 SNPs, 71 were located into intragenic regions (1 was a synonymous variant, 3 were 3’UTR variants, 2 were non-coding transcript exon variants, and 65 were intronic) and 80 mapped to intergenic regions. Additionally, there were 9 upstream and 5 downstream genetic variants (Supplementary Table S1 , S2 y S3). Considering the QTL data, 145 SNPs of these 165 SNPs were located into 25 QTL distributed along 13 autosomes. Of these, 60 SNPs were mapped to genes, corresponding to a total of 39 different genes that contained at least one significant SNP. QTL for growth and carcass composition No SNPs were found to be associated with growth or the shoulder yield, whereas significant association were found for ham and loin yields, with no overlapping genomic regions observed between these two traits. For ham yield, 38 significantly associated SNPs were detected (Fig. 1 and Supplementary Table S1 ), 35 of which were located within five QTL regions (Table 1 ). These QTL were distributed across SSC6 (QTL4), SSC8 (QTL10 and QTL11), SSC10 (QTL12), and SSC15 (QTL13). Together, these regions comprise a total of 21 annotated genes (Supplementary Table S4), among which five contained significant SNPs (Supplementary Table S1 ). Among the identified QTL, regions with significant SNPs located in genes potentially related to muscle development include QTL11 (SSC8:15.83-16.40Mb), where four significant SNPs were mapped to the Potassium Voltage-Gated Channel Interacting Protein 4 ( KCNIP4 ) gene, and QTL13 (SSC15:129.90-130.34Mb), which contained one SNP located in the Phosphotyrosine Interaction Domain Containing Protein 1 ( PID1 ) gene. Functional enrichment analyses of these genes did not reveal strong enrichment for any specific biological pathways or molecular functions. Table 1 Description of the significant QTLs affecting ham and loin yields. Trait Region Genomic position (Mb) nº SNP 1 Genes 2 SNP 3 Freq 4 b (se) 5 q-value Loin yield, % QTL1 4:35.70-39.18 7 RGS22 , MATN2 rs80950146G > A 0.18 0.08 (0.020) 0.079 Loin yield, % QTL2 4:55.89–55.91 2 ZNF704 rs80926181G > A; rs80992100C > A 0.33 0.07 (0.016) 0.031 Loin yield, % QTL3 4:64.49–75.17 15 NCOA2 , PREX2 , CYP7B1 , CHD7 , NSMAF rs80784264G > A 0.11 0.14 (0.025) 5.65x10 − 4 Ham yield, % QTL4 6:73.01–73.02 3 rs81341242A > G 0.06 -0.52 (0.123) 0.093 Loin yield, % QTL5 6:66.25–66.48 2 rs81476483A > G 0.11 0.10 (0.026) 0.079 Loin yield, % QTL6 6:82.66–83.56 3 PDIK1L rs81476037G > A 0.09 0.11 (0.028) 0.079 Loin yield, % QTL7 6:87.01–90.88 7 PUM1 , TRIM62 rs337558398G > A 0.10 0.12 (0.027) 0.058 Loin yield, % QTL8 6:92.89–95.69 7 GRIK3 , RSPO1 , RHBDL2 rs81390114A > G 0.23 0.08 (0.019) 0.030 Loin yield, % QTL9 6:146.22-147.87 7 PDE4B , CACHD1 rs80893734G > A 0.12 0.11 (0.023) 0.026 Ham yield, % QTL10 8:6.19–12.22 19 ZNF518B , CC2D2A rs81306997G > A 0.22 0.34 (0.067) 0.019 Ham yield, % QTL11 8:15.83–16.40 7 KCNIP4 rs328356965A > G; rs346092903C > A; rs321745578G > A; rs81476740A > G 0.20 0.30 (0.068) 0.054 Ham yield, % QTL12 10:41.57–41.97 2 ZNF438 rs81424140G > A 0.11 0.36 (0.089) 0.095 Ham yield, % QTL13 15:129.90-130.34 4 PID1 rs328167887A > G 0.48 0.25 (0.061) 0.095 1 Number of significant SNPs within the QTL 2 Genes within the QTL containing significant SNPs 3 The most significant SNP within the QTL (based on q-value) 4 Reference allele frequency 5 SNP effect and standard error Regarding loin yield, a total of 54 significant SNPs were associated with this trait (Fig. 1 and Supplementary Table S2), 50 of which were located within eight identified QTL regions, and distributed across SSC4 (QTL1 to QTL3) and SSC6 (QTL5 to QTL9) (Table 1 ). These regions comprise 145 annotated genes (Supplementary Table S4), 18 of which contained significant SNPs (Supplementary Table S2). Functional annotation of these genes highlighted biological processes related to lipid and steroid metabolism, transferase activity, zinc ion binding, and ubiquitination and proteasomal degradation. Genes linked to lipid and steroid metabolism were predominantly found in QTL3, QTL7, and QTL9. Among these, QTL3 (SSC4:64.49-75.17Mb) harbored 15 associated SNPs, spanning 39 genes. One of these SNPs mapped to the Cytochrome P450 family 7 subfamily B member 1 ( CYP7B1 ), a key enzyme involved in cholesterol catabolism and steroid hormone biosynthesis. Additionally, two significant SNP were detected, one of them mapping to the Phosphatidylinositol-3,4,5-trisphosphate-dependent Rac exchange factor 2 ( PREX2) and the other to the Neutral Sphingomyelinase Activation Associated Factor ( NSMAF ), both implicated in intracellular signalling and potentially in muscle development. Genes involved in transferase activity, zinc ion binding, and the ubiquitin-proteasome pathway were found in QTL1, QTL3, QTL6, and QTL7. For example, QTL7 (SSC6:87.01-90.88Mb) contains several genes with relevant functions, including Tripartite Motif Containing 62 ( TRIM62 ), in which a significant SNP was located and that is directly involved in the ubiquitination pathway. QTL for meat quality A total of 73 SNPs were significantly associated with different backfat fatty acid (FA) (Fig. 2 and Supplementary Table S3), and two other SNPs showed significant associations with shear force. In contrast, no significant associations were observed between SNPs and the remaining analysed quality traits, including IMF, moisture, protein content, instrumental meat colour, and water loss. None of the SNPs or QTL identified for meat quality overlapped with those found for ham and loin yields. The two SNPs associated with shear force were mapped to QTL14 (SSC2:6.77-7.05Mb), with one of them located to the Speedy/RINGO Cell Cycle Regulator Family Member C ( SPDYC ) gene (Table 2 ). Table 2 Description of the significant QTLs affecting shear force and fatty acid proportions (%). Trait Region Genomic position (Mb) nº SNP 1 Genes 2 SNP 3 Freq 4 b (se) 5 q-value Shear Force QTL14 2:6.77–7.05 2 SPDYC rs81357736A > C 0.47 -0.51 (0.104) 0.03 C18:2, PUFA QTL15 3:38.12–38.95 3 ADCY9 rs81236837A > G 0.27 0.18 (0.041) 0.028 C18:2, PUFA QTL16 3:44.43–44.63 3 MERTK rs81228599A > G , rs81304124G > A 0.25 0.17 (0.041) 0.072 C18:2, PUFA QTL17 3:51.02–51.06 2 rs81370348A > C , rs81370345G > A 0.28 0.17 (0.040) 0.056 C20:1 QTL18 7:115.65-117.04 7 rs321344396C > A 0.05 0.13 (0.027) 0.016 C14:0, C16:0, C16:1 QTL19 8:109.92-113.86 19 ELOVL6 , EMPEP, MCUB , RRG , SEC24B, LEF1 rs336391861C > A 0.3 -0.33 (0.062) 2.51x10 − 3 C14:0, C16:0, C18:1, C20:1 QTL20 12:0.623–0.715 5 NARF , CYBC1 rs331568234A > G , rs322685807A > G 0.47 -0.07 (0.012) 1.67x10 − 3 C18:2, PUFA QTL21 13:169.82-170.04 2 CADM2 rs341320025A > G , rs81441284C > A 0.41 0.16 (0.036) 0.043 C18:2, PUFA QTL22 13:172.62-176.42 9 ROBO1 rs324938004C > A 0.33 0.19 (0.037) 0.019 C20:1 QTL23 14:128.72-129.82 2 CACUL1 , BAG3 rs329389257A > G 0.37 -0.05 (0.012) 0.092 C16:0 QTL24 15:50.37–50.47 2 UNC5D rs80943094G > A , rs80930489A > G 0.03 -0.62 (0.160) 0.099 C16:0, SFA QTL25 15:72.72–72.83 2 SCN9A rs341238670A > G, rs81453508G > A 0.04 -0.66 (0.146) 0.018 C16:0 QTL26 15:115.18-118.02 5 SPAG16 rs81454499C > A 0.3 -0.32 (0.070) 0.017 1 Number of significant SNPs within the QTL 2 Genes within the QTL containing significant SNPs 3 The most significant SNP within the QTL (based on q-value) 4 Reference allele frequency 5 SNP effect and standard error Table 3 Phenotypic data of Iberian pigs. Covariates n Mean Max Min SD CV 1 BW pre- Montanera , kg 524 107 150 69 13.5 12.7 2 Weight gain Montanera , kg 524 61.5 96.0 21.5 11.7 19.0 Weight final, kg 523 168 227 129 14.5 8.6 Growth and carcass composition n Mean Max Min SD CV ADG, kg/d 521 590 1034 178 122 20.6 Carcass Weight, kg 526 135 191 104 12.2 9.0 Carcass yield, % 523 80.6 86.8 75.6 1.78 2.23 Ham Weight, kg 475 21.98 29.48 17.32 1.77 8.06 Shoulder Weight, kg 395 14.96 20.52 12.12 1.22 8.18 Loin Weight, kg 524 3.20 4.36 2.25 0.35 10.97 Ham yield, % 475 16.41 19.56 14.27 0.88 5.36 Shoulder yield, % 395 11.22 13.87 9.00 0.69 6.17 Loin yield, % 527 2.38 3.32 1.69 0.26 10.79 Premium cuts yield, % 392 30.09 35.45 25.63 1.53 5.09 Meat quality n Mean Max Min SD CV Intramuscular fat (IMF), % 526 5.41 18.84 1.56 2.21 40.86 Moisture, % 526 71.15 75.11 61.19 1.68 2.37 Protein, % 526 21.53 24.33 18.03 0.87 4.05 Shear force, N 514 4.41 11.83 1.72 1.56 35.26 Thawing loss, % 518 6.18 17.83 0.48 4.16 67.23 Cooking loss, % 515 20.69 44.77 9.74 4.36 21.06 L* 516 38.55 48.97 29.89 3.13 8.12 a* 514 10.62 14.92 7.35 1.22 11.51 b* 517 5.82 8.55 2.79 1.08 18.57 Fatty Acids (%) n Mean Max Min SD CV C12:0 499 0.063 0.085 0.046 0.006 10.0 C14:0 499 1.264 1.593 0.972 0.096 7.6 C16:0 499 20.54 23.16 17.02 1.04 5.1 C16:1 499 2.164 3.204 1.564 0.249 11.5 C17:0 499 0.331 0.594 0.222 0.054 16.3 C17:1 499 0.345 0.564 0.222 0.054 15.6 C18:0 499 9.521 12.378 1.228 1.016 10.7 C18:1 499 55.05 59.96 50.95 1.51 2.75 C18:2 499 8.357 10.080 6.400 0.597 7.1 C18:3 499 0.507 0.849 0.187 0.084 16.5 C20:0 498 0.176 0.284 0.124 0.023 13.1 C20:1 499 1.680 2.316 1.230 0.178 10.6 SFA 499 31.89 36.63 25.60 1.88 5.9 MUFA 499 59.24 64.22 54.68 1.63 2.7 PUFA 499 8.86 10.65 6.78 0.64 7.2 1 Body weight at the start of Montanera 2 Weight gain during Montanera As mention above, a total of 73 significant SNP-FA associations were identified, involving individual FA such as myristic acid (C14:0), palmitic acid (C16:0), palmitoleic acid (C16:1), oleic acid (C18:1), linoleic acid (C18:2), eicosenoic acid (C20:1), as well as the sums of saturated (SFA) and polyunsaturated (PUFA) fatty acids. Among them, 32 SNPs exhibited pleiotropic effects, influencing more than one fatty acid. Moreover, 60 of these 73 SNPs were located within 16 QTL associated to the FA profile, distributed across SSC3, SSC7, SSC8, SSC12, SSC13, SSC14, and SSC15. Among the significant SNPs located within QTLs, 31 were annotated within 22 candidate genes. Regarding SFA proportions, C14:0 and C16:0 were associated with QTL19 and QTL20, while C16:0 and total SFA were linked to QTL25. C16:0 was also associated to QTL24 and QTL26. Monounsaturated fatty acids (MUFA) showed fewer associations, some of which overlapped with those observed for SFA. The most abundant FA in the backfat profile, C18:1, was associated only with QTL20; C16:1 with QTL19; and C20:1 with QTL18, QTL20 and QTL23. These QTL regions contained 34 annotated genes (Supplementary Table S5), 14 of which harboured at least one significant SNP (Supplementary Table S3). Functional annotation clustering in DAVID did not reveal enrichment for any specific pathways or molecular functions. Among these regions, QTL19 (SSC8:109.92-113.86Mb) displayed the highest number of associated SNPs (19), including one within ELOVL Fatty Acid Elongase 6 ( ELOVL6 ), responsible for the elongation of medium-chain FAs. Concerning the C18:2 and PUFA proportions, associations were found in five QTL distributed across SSC3 (QTL15, QTL16 and QTL17) and SSC13 (QTL21 and QTL22). These regions contained 44 annotated genes, eight of which harbored eleven significant SNPs (Supplementary Table S3). Functional annotation analysis revealed enrichment of serpin-domain proteins, linked to serine protease inhibition. Among the QTLs associated with PUFA, QTL22 (SSC13:172.62-176.42Mb) and QTL15 (SSC3:38.12-38.95Mb) stand out for presenting significant SNPs mapped within genes related to lipid metabolism. QTL22 harboured the highest number of associated SNPs (9), including four mapped to the Roundabout Guidance Receptor 1 ( ROBO1 ) gene, while QTL15 included one significant SNP annotated within Adenylate Cyclase 9 ( ADCY9 ) gene, both involved in cell signaling and developmental processes. Discussion The Iberian pig breeding programs are mainly based on the use of classical genetic evaluation methods that include phenotypic data for growth, premium cuts, and IMF as well as pedigree information. The incorporation of genetic markers may significantly enhance the effectiveness of selection, similar to what has been observed in commercial pig breeds. Thus, genomic information will not only improve the accuracy of EBVs, but could also help to avoid potential antagonistic effects between productive traits and those related to meat quality [ 11 ], by enabling the identification of genetic markers with pleiotropic favourable effects for both type of traits [ 12 ]. This type of analysis, which combines productive and meat quality traits and aims to know more about the genetic basis of these traits and the possible existence of antagonistic effects, is especially valuable for the Iberian pork industry in order to maintain its differentiated position in a high-end food market. Within this context, the main aim of this study was to identify SNPs and chromosomal regions associated with the most relevant economic traits in the Iberian pig raised in Montanera. The present study has identified several QTLs and putative genes associated with traits of economic interest for the Iberian pig breeding program. Among them, several regions were identified for the first time in pigs as associated with ham yield, such as QTL4 (SSC6:82.66-83.56Mb) and QTL12 (SSC10:41.57-41.97Mb), and with loin yield, as QTL1 (SSC4:35.70-39.18Mb), QTL2 (SSC4:55.89-55.91Mb), and QTL3 (SSC4:64.49-75.17Mb). Other regions, such as those located on SSC6 and on SSC8, had already been reported in previous studies. For instance, Martínez-Montes et al. [ 13 ] examined several Iberian crosses involving Landrace, Duroc, and Pietrain breeds, and identified a total of 89 SNPs and 15 significant QTL regions associated with productive and quality traits. Among their key findings, two chromosomal regions associated with shoulder (SSC6:71.90-86.76Mb) and ham weight (SSC6:80.62-112.49Mb) were reported, which partially overlap with QTL6, QTL7, and QTL8 in our study, but with the particularity that in our study these regions were exclusively associated with loin percentage. We also identified the TRIM63 gene mapped within QTL6, which has been also proposed as a candidate gene by Martinez-Montes et al. [ 13 ], and a significant SNP ( rs337558398G > A ) mapped to the TRIM62 gene, located in QTL7. Both TRIM63 and TRIM62 encode proteins with ubiquitin-protein transferase activity [ 14 ]. Ubiquitination is crucial for removing damaged or misfolded proteins through the proteasome system, and in muscle, it plays a key role in tissue remodelling and regeneration during atrophy, injury, or growth [ 15 ]. An expression-GWAS study using the same Iberian crosses mention above [ 16 ] identified QTL regions on SSC6 (80.52-84.33Mb) and SSC8 (8.93-11.96Mb) associated with the expression of Protein Tyrosine Phosphatase 4A2 ( PTP4A2 ) and Transmembrane Anterior Posterior Transformation 1 ( TAPT1 ) genes, respectively. The PTP4A2 gene, found in our QTL7, encodes a phosphatase involved in cell proliferation and migration, which may play a role in muscle growth or regeneration [ 17 ]. The TAPT1 gene, located in our QTL10, is involved in skeletal development and extracellular matrix [ 18 ]. Whereas the GGP chip did not include any SNPs located in PTP4A2 gene, the three SNPs mapped to TAPT1 showed not significant associated with primal cut yields. One interesting SNP associated to ham is rs328167887A > G (QTL13, Table 1 ) with a MAF of 0.48 and a SNP effect (b) of 0.25 ± 0.06, corresponding to a change of approximately 1.52% relative to the trait mean. This SNP is mapped to PID1 gene, which has been previously associated with loin muscle depth in Duroc × (Landrace × Yorkshire) pigs [ 19 ] and encodes for a protein that reduces insulin sensitivity and is highly expressed in key insulin-responsive tissues such as adipose tissue, skeletal muscle, and myocardium [ 20 ]. Other relevant SNPs associated with ham percentage are those located in QTL11, where four SNPs (MAF = 0.20 and b = 0.30 ± 0.07), were identified and mapped to the KCNIP4 gene, proposed as a candidate gene for growth traits in chickens [ 21 ] and rabbits [ 22 ], and that has also been suggested as a regulator of muscle growth and fat deposition in sheep [ 23 ]. According to the Pig QTLdb database, only four QTL have been reported for loin yield, and none overlap with those identified in our study. In contrast, a larger number of QTL associated with loin weight match the regions we identified for loin yield. These include QTL3, identified in an Iberian × Landrace intercross [ 24 ]; QTL5, reported in a Duroc × Pietrain cross [ 25 ]; QTL6, QTL7, and QTL8, detected in Duroc × Pietrain crosses [ 25 , 26 ]; as well as QTL8 and QTL9, found in (Pietrain × Large White) × Landrace populations [ 27 ]. For the loin yield, the rs80862639A > G SNP, with a MAF of 0.04 and located in QTL3 (SSC4:64.49-75.17Mb), had the highest additive effect, b = 0.15 ± 0.04, which represents an increase of ~ 6.3% relative to the trait mean. This SNP maps to the PREX2 gene, involved in insulin signalling and cell migration, key processes in muscle regeneration and adaptation [ 28 ]. Other SNPs with positive effects on loin yield but low allelic frequencies for favourable alleles value (0.04–0.09) were detected in genes related to muscle development. For instance, three significant SNPs in QTL1 ( rs321093035G > A , rs80996716A > C and rs80828796G > A ) were mapped to RGS22 gene, involved in ubiquitination. Another SNP in QTL3, rs80937006A > G were mapped to CHD7 gene, a gene linked to growth regulation and liver expression in commercial pig populations (Duroc, Landrace, and Large White) [ 29 ]. Finally, rs80959844A > C was located in the NSMAF gene (in QTL3), which has been associated with carcass weight, bone density, and growth in Red Angus cattle [ 30 ]. Despite their low MAF (< 0.10), these SNPs may offer valuable genetic potential for selection. Other remarkable SNPs had intermediate MAF values (0.10–0.50) and moderate additive effects (b ~ 0.06–0.07) could also be interesting for genetic selection. For example, rs81382111A > C , mapped to the CYP7B1 gene (QTL3), has been previously associated with meat pH in pigs [ 31 ], a trait affecting meat colour, water-holding capacity, and tenderness. Additional promising variants include rs80829499A > G and rs80787763A > G in MATN2 (QTL1), a gene linked to the number of ribs in crossbred pigs [ 32 ]. In addition, there are six highlighted SNPs within the PDE4B gene (QTL9), which encodes a phosphodiesterase that regulates cellular concentrations of cyclic nucleotides [ 33 ]. Genomic regions containing this gene have been reported to be associated with backfat thickness in the Duroc pig breed [ 34 ], while other genes of the same family have been linked with muscle development. No significant SNPs were found for meat quality traits such as IMF, protein, water loss and instrumental colour, which contrasts with the results observed in the other porcine breeds and the Pig QTLdb. In the present study, we identified only one SNP, rs319898417G > A , associated with some of these traits, specifically with shear force. This SNP was mapped to the SPDYC gene (QTL14), which is involved in maintaining spindle-assembly checkpoint integrity and proper chromosome segregation during mitosis [ 35 ]. Previous studies carried out in this same population of Iberian barrows identified SNPs associated with this trait such as CAPN1 _ rs81358667G > A and CASP3 _ rs319658214G > T [ 36 ] as well as CAST _ rs196949783G > A and ELOVL6 _ rs3473714672A > G [ 5 ]. However, none of these SNPs matched those genotyped by the GGP Porcine chip. Furthermore, this GGP chip contained no SNPs mapped to the CASP3 and CAST genes, despite scientific evidence linking these genes to quality traits in pigs [ 37 , 38 ]. Although CAPN1 and ELOVL6 genes are located within QTL2 and QTL19, respectively, in the present study SNPs mapping to these genes were either removed in the filtering/QC stage or showed no statistical significance for any meat quality trait. IMF is a key factor affecting the organoleptic properties of meat [ 39 ], and together with MUFA, like C18:1, the exceptional quality of Iberian pig products. However, in this work GWAS did not reveal significant SNPs for IMF. In contrast, previous research on the same population identified SNPs and genes linked to IMF content, such as ELOVL6:g.112186423A > G , FASN_rs331694510G > A [ 40 ], ADIPOQ_rs3476515794T > G and CAST_rs196949783G > A [ 5 ]. As mention before for other genes, the GGP chip lacked SNPs located in ADIPOQ gene, and included only one in FASN gene, which did not pass quality control filters. Moreover, although the FASN gene is situated less than 1 Mb away from the QTL20, no SNPs in this region were retained after filtering, probably due to the absence of informative markers for this specific breed. In general, no new QTL were identified for FA. Ramayo-Caldas et al. [ 41 ] performed a GWAS analysis on 32 traits related to FA profiles in IMF in an Iberian × Landrace backcross population, identifying 813 significant SNPs across 43 regions, of which only a few regions overlapped with ours in the present study. For example, a region on SSC15 (50-50.4Mb) similar to our QTL24 was associated with C16:0. Another region on SSC8 (103.8-107.5Mb), close to our QTL19, was also associated with C16:0. Muñoz et al. [ 42 ] observed a QTL on SSC8 between 83.84 and 126.88 Mb on Iberian × Landrace pigs, similar to our QTL19, and associated with C14:0, C16:0 and C16:1 proportions. In Korean native black pigs, similar SSC8 regions (85.3-114.8Mb and 109.8-114.1Mb), have been reported to be associated to C16:0 and C16:1 [ 43 ]. One important gene in this region is ELOVL6 , involved in fatty acid elongation, and affecting the C14:0 and C16:0 levels. As mentioned earlier, the ELOVL6_rs3473714672A > G SNP has been linked to quality traits in Iberian pigs, including FAs proportions [ 5 ]. Of the two SNPs mapping to the ELOVL6 gene included on the GGP chip, rs339079803C > T failed quality control, while rs336391861C > A (MAF = 0.30, b=-0.33 ± 0.06) was significantly associated to C14:0 and C16:0. Regarding the most important FA, C18:1, significant associations were identified with PRKAG3_rs319678464G > C , FASN_rs331694510G > A , ELOVL6_rs347371467A > G , and ACACA_rs340781986C > T SNPs [ 5 ]. However, in the present study, only QTL20 showed a significant association with C18:1, but no genes were annotated within this QTL. The FASN gene, coding for fatty acid synthase, lies close to QTL20, but lacks SNPs on the GGP genotyping chip. Similarly, although ELOVL6 gene is annotated within our QTL19 region, neither the region nor its SNPs were associated with C18:1. Moreover, no SNPs located in the PRKAG3 gene were present in the GGP chip, and none of the seven SNPs mapped to the ACACA gene were found to be associated with this FA. In contrast to the C18:1, several SNPs and QTL regions were found to be significantly associated with the C18:2 fatty acid, which also impacts total PUFA content due to C18:2 is the major PUFA component. While PUFA have health benefits, they are more susceptible to oxidation, increasing rancidity in cured products. Essential fatty acids, such as C18:2 and linolenic acid (C18:3) come mainly from diet, and therefore, their genetic associations are likely to be related to transport or conversion rather than fatty acids biosynthesis. These regions are on SSC3 and SSC13, where relevant genes such as ADCY9 and ROBO1 , respectively, were identified. A significant SNP, rs81236837A > G (MAF = 0.26, b = 0.16 ± 0.04), was identified in ADCY9 gene. The ADCY9 gene, in QTL15, is involved in the cyclic AMP signalling pathway, previously associated with meat pH in other studies [ 44 ]. The ADCY9 has been also associated with increased fat, better feed efficiency, and growth [ 45 ]. Four significant SNPs were located within the ROBO1 gene (MAF = 0.32–0.36, b = 0.15–0.19). ROBO1 regulates cell migration and vascular endothelial growth factor signalling [ 46 ], essential for lipid membranes organization. Moreover, ROBO1 has been previously linked to subcutaneous fat deposition in pigs by transcriptomic studies [ 47 ]. Overall, the SNPs and candidate genes identified in our previous studies appear to be more relevant for genetic selection based on major FA (C16:0, C18:0, and C18:1) than those detected in the present work. As mentioned throughout the discussion, this may have been due in part to the lack of coverage of the GGP chip used in the present study, as well as the lack of agreement with the low-density chip used in previous studies. A relevant aspect of this study is the use in a local breed, the Iberian pig, of a commercial genotyping chip developed for lean cosmopolitan pig breeds. The original PorcineSNP60 Beadchip was designed based on SNP information from breeds such as Duroc, Landrace, Large White, Pietrain, as well as wild boar populations [ 49 ]. Consequently, the selection of SNPs was optimized for variants segregating in those populations, which may limit the ability of GGP chip to capture the differentiated genetic background of traditional autochthonous breeds [ 49 ] like the Iberian pig, with a distinct evolutionary history than cosmopolitan breeds [ 50 ]. Although a subset of SNPs on the chip segregates in the Iberian population, a considerable proportion remains monomorphic or non-informative. Unlike some commercial breeds, the Iberian pig exhibits lower linkage disequilibrium (LD) decay [ 49 ], resulting in larger LD blocks and, in principle, a reduced need for high-density marker coverage. Thus, the main limitation is the lack of markers selected specifically for this breed. Moreover, important genomic regions and candidate genes implicated in productive and quality traits appear to be underrepresented or absent from the GGP chip, limiting its effectiveness. To partially address this limitation, we applied more permissive filtering criteria for MAF, allowing the retention of rare variants that may be functionally important in the Iberian population but would typically be excluded in studies of commercial breeds. Notably, among the 165 SNPs significantly associated with the evaluated traits, 40 of them had a MAF below 0.1, and 30 of these were found to be associated with traits of economic interest. Although such low-frequency variants are often missed by standard genome-wide arrays, they may represent a significant and underexplored component of the genetic architecture of complex traits, as shown in human studies [ 51 ]. Their targeted selection, despite their current low prevalence, could contribute meaningfully to genetic improvement. Overall, further SNP discovery tailored to the genetic background of the Iberian pig could help to optimize the genomic tools available for this breed and fully capture the genetic variation relevant to it. In this study, no pleiotropic SNPs or QTL were identified between traits related to productive performance and those involved in meat quality. This suggests that the proposed candidate regions could be used independently to improve each specific trait, minimizing potential antagonistic effects and allowing for more targeted selection strategies. In particular, several SNPs and genomic regions associated with ham and loin traits emerged as promising, which is especially relevant considering that carcass composition remains one of the major limitations of the Iberian pig. Although the contribution of the GGP chip to quality traits such as fatty acid composition was limited, in contrast to previous studies [ 5 , 36 , 40 ], our results highlight new avenues for genetic improvement within the breed. Nevertheless, the proposed markers for ham and loin should be validated in a larger number of animals before their implementation in selection programs, along with functional characterization of the candidate genes and an evaluation of the long-term effects of marker-assisted selection. Overall, despite the technical limitations of the current genotyping platform, the findings of this study represent a valuable contribution with a potential application in the breeding program of the Iberian pig. Methods Animals Blood sampling was performed exclusively as part of routine livestock management practices complying with Spanish Royal Decree 53/2013 and Directive 2010/63/EU, which exclude routine agricultural practices from the requirement for prior approval by an ethics committee. The animals included in this study belonged to the SRC line of Iberian pig and were part of the breeding program of the company Sánchez Romero Carvajal S.A., which has been under development since 2011 [ 52 ]. In a previous study by Palma-Granados et al. [ 5 ], 1520 pigs from this program were genotyped using a small SNP panel in order to evaluate their association with carcass composition and meat quality traits. To deepen the identification of genomic regions and genes associated with traits of interest, 528 pigs from this population were selected for high-density genotyping (60K) and a GWAS on the same traits previously analysed was conducted. Detailed information on the management system and feeding regime of these 528 pigs is documented in Palma-Granados et al. [ 5 ], along with the phenotypic data used in the present study. The selection criteria guaranteed representation across different production batches and slaughter dates. The 528 pigs were selected from different fattening groups (pigs managed together in the grazing of acorns in Montanera ) and slaughtered between 2017 and 2022 in a commercial slaughterhouse, grouped into 10 slaughter batches, with an average age of 517 days (SD = 37 days) and an average body weight (BW) of 167 kg (SD = 19 kg). The combination of fattening group and slaughter batches resulted in 22 distinct production batches. The phenotypic data used in this study were collected following the procedures described in Sánchez-Esquiliche et al. [ 53 ] for muscle protein and moisture composition, and in Palma-Granados et al. [ 5 ], for the remaining traits, allowing the reuse of previously reported measurements. Table 1 summarizes these phenotypic traits, including means and standard deviations. The data included growth parameters, carcass components related to premium cuts (ham, shoulder, loin), and quality traits such as loin biochemical composition (content in IMF, protein and moisture), FA composition, water loss, shear force, and instrumental colour of the meat. Genotypic data The 528 animals were genotyped with the platform GGP Porcine HD Array (Illumina, Inc.) containing 68,516 SNPs. Genomic DNA was extracted from the blood using a standard sucrose-proteinase K method. Genotyping was carried out at the Servei Veterinari de Genètica Molecular (UAB, Barcelona, Spain). PLINK 1.9 [ 54 ] software was used to visualize, edit, apply standard quality controls (QC) and extract genotyping data for subsequent GWAS analyses. Regarding to QC, those markers that showed minor allele frequency (MAF) < 0.01 or call rate < 90% were removed. Additionally, individuals with more than 10% missing genotypes were excluded from analysis. The SNPs located in sex chromosomes were also excluded. After QC, two animals were removed and a total of 35,894 SNP markers on 18 autosomes remained. The distribution of the SNPs in windows of 1 Mb that passed the filtering criteria is represented in Fig. 3 . GWAS analysis The genome-wide association analysis was performed using the GCTA software [ 55 ], applying a mixed linear model association analysis with a leave-one-chromosome-out approach (MLMA-LOCO). The model used was: y ​ = a + bx + fF + g− + e where y is the vector of phenotypes, a is the intercept, b is the additive effect (fixed effect) of the candidate SNP and x is the SNP genotype indicator variable coded as 0, 1 or 2, f includes the production batch (22 levels), treated as a fixed effect and covariates that were different depending on the trait analysed and F its corresponding incidence matrix, g − represents the accumulated effect of all SNPs excluding those located on the chromosome containing the tested SNP, and e represents the residual effects. The variance of g⁻ (var(g⁻)) is re-estimated each time a chromosome is excluded from the calculation of the genomic relationship matrix (GRM). A summary of the traits analysed as well as the fixed effects and covariates included in the different models is provided in the Supplementary Table S1 . We used the q-value library in R [ 56 ] to correct multiple testing. A SNP was considered as significantly associated to a trait when it displayed a q-value lower than 0.1. Region analysis and Candidate genes Putative QTL regions were defined as two or more consecutive SNPs significantly associated with each phenotypic trait, separated by less than 2 Mb. QTL regions that were identical or overlapping between traits were considered as a single one. Gene annotation of QTL regions was conducted using the Biomart tool from the Porcine ENSEMBL database, Sscrofa11.1. In order to identify potential candidate genes, a functional gene annotation analysis was carried out with the DAVID online annotation database ( https://davidbioinformatics.nih.gov/ ). Additionally, the identified QTL regions were compared against previously reported loci in the Pig QTLdb ( https://www.animalgenome.org/cgi-bin/QTLdb/SS/ontrait?trait_ID=718 ), in order to assess whether the associations found in this study overlapped with known QTL. Declarations Acknowledgements We thank to Sánchez Romero Carvajal (SRC) staff for their technical support, especially to Luisa Ramírez and Fernando Gómez-Carballar. Data availability statement (mandatory) The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Additional Information (including a Competing Interests Statement) The authors declare no competing interests. Funding This work was funded by CON15-078, CON17-125 and CON19-281 (INIA-SRC) and IDI-20171141 (CDTI and FEDER) and IDI-20220528 (CDTI) grants. Author contributions Conceptualization: J.M. García-Casco, M. Muñoz Formal analysis: M. Muñoz, P. Palma-Granados, M. Ramón Funding acquisition: J.M. García-Casco, F. Sánchez-Esquiliche, A. Márquez Investigation: J.M. García-Casco, M. Muñoz, P. Palma-Granados, F. Sánchez-Esquiliche, M. Delgado-Gutierrez, M. Ramón Methodology: J.M. García-Casco, M. Muñoz Project administration: J.M. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7515242","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":523776226,"identity":"26dce380-3d43-4b17-ab6a-dddd6a14331b","order_by":0,"name":"Patricia Palma-Granados","email":"data:image/png;base64,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","orcid":"","institution":"INIA-CSIC","correspondingAuthor":true,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Palma-Granados","suffix":""},{"id":523776227,"identity":"a4c34b27-aa56-42bc-a624-475db7943882","order_by":1,"name":"Juan M. 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15:34:43","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":183899,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7515242/v1/d58f69f8cf9e59afaae61aa2.html"},{"id":92730224,"identity":"7141a22e-9f5f-432f-a1e1-2c942ddb49e8","added_by":"auto","created_at":"2025-10-03 15:34:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":500030,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plots of the GWAS for ham and loin percentages in Iberian pigs fattened in \u003cem\u003eMontanera\u003c/em\u003e. The y-axis represents the -log\u003csub\u003e10 \u003c/sub\u003eof the p-value, and the x-axis indicates the chromosomal position of each SNP included in the GWAS. A horizontal threshold line corresponds to the –log\u003csub\u003e10\u003c/sub\u003e of the p-value at which the corresponding q-value equals to 0.1.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7515242/v1/647682ae3d27a13547d08e86.png"},{"id":92730229,"identity":"e594a399-fbd6-46a2-947e-33db00cd9a73","added_by":"auto","created_at":"2025-10-03 15:34:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":466347,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plots of the GWAS for main FAs in backfat of Iberian pigs fattened in \u003cem\u003eMontanera\u003c/em\u003e. The y-axis represents the -log\u003csub\u003e10\u003c/sub\u003e of the p-value, and the x-axis indicates the chromosomal position of each SNP included in the GWAS. A horizontal threshold line corresponds to the –log\u003csub\u003e10\u003c/sub\u003e of the p-value at which the corresponding q-value equals to 0.1.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7515242/v1/447275e9231bf7f7155f76ff.png"},{"id":92730237,"identity":"1eecd7d7-80e2-4953-9104-85e2c1a9fb53","added_by":"auto","created_at":"2025-10-03 15:34:43","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":449514,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of filtered SNPs (n=35,894) on autosomal chromosomes within a 1 Mb window size. The horizontal axis shows the chromosome length (Mb). Colour index indicates the number of SNPs by window.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7515242/v1/e9ebc8ad43b5e7338dbeeaf7.jpeg"},{"id":94985531,"identity":"6b39df51-ebf5-4256-8dfb-4ea164a2155a","added_by":"auto","created_at":"2025-11-03 06:58:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2620279,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7515242/v1/cd97b96e-0f17-489b-817f-2242b8177448.pdf"},{"id":92731232,"identity":"f21136b6-b2f4-4830-a2d5-c7069144e472","added_by":"auto","created_at":"2025-10-03 15:50:43","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":49401,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7515242/v1/2596d2c995659214af6fcd29.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide association analyses for growth, carcass composition and meat quality traits of Iberian pigs fattened in free open air","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Iberian pig, a native breed of the Iberian Peninsula, is distinguished for the exceptional quality of its meat products, particularly premium cuts such as ham, shoulders and loins, in both dry-cured and fresh forms, all of which achieve high economic values in the market. This excellence is primarily due to the traditional extensive farming system, known as \u003cem\u003eMontanera\u003c/em\u003e, which take advantages of the natural resources of the \u003cem\u003eDehesa\u003c/em\u003e forests such as acorns and grass [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe relative importance of the Iberian purebred pig production has been reduced in favor of crossbred with Duroc, due to their higher growth rates and lean meat accumulation. Despite this, the Iberian purebred pig, which has undergone limited selective pressure compared to commercial/cosmopolitan breeds, retains its exceptional quality and offers significant potential for genetic improvement, which could enhance its competitiveness in the market. Currently, the objectives of the public breeding programme for the Iberian purebred, managed by the Spanish Association of Iberian Pig Breeders (AECERIBER), include the evaluation of carcass composition with regard to its premium cuts, such as shoulders, hams, and loins, as well as intramuscular fat, a crucial factor in the quality of meat products [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. For this breed, the genetic evaluations have been traditionally carried out using a BLUP-Animal model. Fortunately, the development and implementation of genomic analysis techniques in breeding programs has made it possible to know more about the genetic basis of traits of economic interest, as well as to improve the accuracy of estimates. And in the particular case of the Iberian pig, genetic evaluations could be complemented with genomic information to carry out a marker-assisted selection, which would be especially important given that carcass components usually show negative genetic correlations with quality traits [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In Palma-Granados et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], we proposed four single nucleotide polymorphisms (SNPs), \u003cem\u003ePRKAG3_rs319678464G\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/em\u003e, \u003cem\u003eFASN_rs331694510G\u0026thinsp;\u0026gt;\u0026thinsp;A, ACACA_rs340781986C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/em\u003e and \u003cem\u003eCAST_rs196949783G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e, which were significantly associated with the fatty acid (FA) profile and showed no antagonistic effects on premium cuts. Other SNPs of interest were also identified, \u003cem\u003ePRKAG3_rs1108399077G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e, \u003cem\u003eELOVL6_rs3473714672A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e, \u003cem\u003eCAPN1_rs81358667G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e, \u003cem\u003eMTTP_rs335896411T\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/em\u003e and \u003cem\u003eNR6A1_rs326780270T\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/em\u003e, which exhibited antagonistic pleiotropic effects between premium cuts and meat quality [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], highlighting the need for further research on additional genetic markers.\u003c/p\u003e\u003cp\u003eGenomic tools allow for more comprehensive and accurate searches for genetic markers. Among these, Genome-Wide Association Studies (GWAS) are a powerful and widely used approach that utilizes SNPs as molecular genetic markers at the genome-wide level for the identification of Quantitative Trait Loci (QTL) regions and the discovery candidate genes associated with target traits, based on the hypothesis that these markers are in linkage disequilibrium with the QTLs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In pigs, GWAS studies have allowed the identification of many QTLs associated with various traits of interest, according to the Pig QTL Database (Pig QTLdb; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.animalgenome.org/cgi-bin/QTLdb/SS/index\u003c/span\u003e\u003cspan address=\"https://www.animalgenome.org/cgi-bin/QTLdb/SS/index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). More specifically, in Iberian pigs Pena et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] detected key regions on SSC4 and SSC7 related to FA composition. Similarly, Crespo-Piazuelo et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] reported nine significant regions related to FA traits and six additional regions associated with intramuscular fat (IMF), distributed across different chromosomes and identified in various Iberian crosses. In contrast, the study by Amaral et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], focusing on Iberian pigs of the Alentejano strain, mainly identified orphan SNPs, with no clear evidence of well-defined genomic regions associated with fat composition traits. Although there are some previous GWAS studies carried out in crossbreeding between Iberian and other commercial lines, specific genetic studies for pure Iberian pigs reared by the traditional fattening system are still limited. This lack of scientific knowledge, together with the economic importance of the Iberian pig as well as its condition as a very robust animal adapted to a harsh environment, underline the importance of promoting studies that help to know more about the genetic basis of productive, quality and resilience traits, as well as to contribute to the sustainability and competitiveness of Iberian pig production system.\u003c/p\u003e\u003cp\u003eTherefore, the aim of this study was to conduct a genome-wide association study to identify genomic regions as well as possible QTLs and candidate genes associated with performance, carcass composition, and meat quality traits in purebred Iberian pigs fed under a \u003cem\u003eMontanera\u003c/em\u003e system.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eGWAS analyses and genomic regions\u003c/h2\u003e\u003cp\u003eA total of 526 pigs passed quality control, and a subset of 35.894 SNPs from the initial 68,516 with adequate genotyping quality were selected for the GWAS studies. The different association analyses revealed a total of 165 SNPs statistically associated (q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1) with some of the traits, with only 11 out of the 29 traits analysed being significantly associated with at least one SNP.\u003c/p\u003e\u003cp\u003eAmong the 165 SNPs, 71 were located into intragenic regions (1 was a synonymous variant, 3 were 3\u0026rsquo;UTR variants, 2 were non-coding transcript exon variants, and 65 were intronic) and 80 mapped to intergenic regions. Additionally, there were 9 upstream and 5 downstream genetic variants (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, S2 y S3). Considering the QTL data, 145 SNPs of these 165 SNPs were located into 25 QTL distributed along 13 autosomes. Of these, 60 SNPs were mapped to genes, corresponding to a total of 39 different genes that contained at least one significant SNP.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eQTL for growth and carcass composition\u003c/h3\u003e\n\u003cp\u003eNo SNPs were found to be associated with growth or the shoulder yield, whereas significant association were found for ham and loin yields, with no overlapping genomic regions observed between these two traits. For ham yield, 38 significantly associated SNPs were detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), 35 of which were located within five QTL regions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These QTL were distributed across SSC6 (QTL4), SSC8 (QTL10 and QTL11), SSC10 (QTL12), and SSC15 (QTL13). Together, these regions comprise a total of 21 annotated genes (Supplementary Table S4), among which five contained significant SNPs (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Among the identified QTL, regions with significant SNPs located in genes potentially related to muscle development include QTL11 (SSC8:15.83-16.40Mb), where four significant SNPs were mapped to the \u003cem\u003ePotassium Voltage-Gated Channel Interacting Protein 4\u003c/em\u003e (\u003cem\u003eKCNIP4\u003c/em\u003e) gene, and QTL13 (SSC15:129.90-130.34Mb), which contained one SNP located in the \u003cem\u003ePhosphotyrosine Interaction Domain Containing Protein 1\u003c/em\u003e (\u003cem\u003ePID1\u003c/em\u003e) gene. Functional enrichment analyses of these genes did not reveal strong enrichment for any specific biological pathways or molecular functions.\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\u003eDescription of the significant QTLs affecting ham and loin yields.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" 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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\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\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGenomic position (Mb)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u0026ordm; SNP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGenes\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSNP\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFreq\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eb (se)\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eq-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoin yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4:35.70-39.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eRGS22\u003c/em\u003e, \u003cem\u003eMATN2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers80950146G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.08 (0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoin yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4:55.89\u0026ndash;55.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eZNF704\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers80926181G\u0026thinsp;\u0026gt;\u0026thinsp;A; rs80992100C\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.07 (0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoin yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4:64.49\u0026ndash;75.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eNCOA2\u003c/em\u003e, \u003cem\u003ePREX2\u003c/em\u003e, \u003cem\u003eCYP7B1\u003c/em\u003e, \u003cem\u003eCHD7\u003c/em\u003e, \u003cem\u003eNSMAF\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers80784264G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.14 (0.025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.65x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHam yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6:73.01\u0026ndash;73.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers81341242A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.52 (0.123)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoin yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6:66.25\u0026ndash;66.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers81476483A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.10 (0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoin yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6:82.66\u0026ndash;83.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ePDIK1L\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers81476037G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.11 (0.028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoin yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6:87.01\u0026ndash;90.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ePUM1\u003c/em\u003e, \u003cem\u003eTRIM62\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers337558398G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.12 (0.027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoin yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6:92.89\u0026ndash;95.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eGRIK3\u003c/em\u003e, \u003cem\u003eRSPO1\u003c/em\u003e, \u003cem\u003eRHBDL2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers81390114A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.08 (0.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoin yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6:146.22-147.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ePDE4B\u003c/em\u003e, \u003cem\u003eCACHD1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers80893734G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.11 (0.023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHam yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8:6.19\u0026ndash;12.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eZNF518B\u003c/em\u003e, \u003cem\u003eCC2D2A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers81306997G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.34 (0.067)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHam yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8:15.83\u0026ndash;16.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eKCNIP4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers328356965A\u0026thinsp;\u0026gt;\u0026thinsp;G; rs346092903C\u0026thinsp;\u0026gt;\u0026thinsp;A; rs321745578G\u0026thinsp;\u0026gt;\u0026thinsp;A; rs81476740A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.30 (0.068)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHam yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10:41.57\u0026ndash;41.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eZNF438\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers81424140G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.36 (0.089)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHam yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15:129.90-130.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ePID1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers328167887A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.25 (0.061)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e1\u003c/sup\u003eNumber of significant SNPs within the QTL\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e2\u003c/sup\u003eGenes within the QTL containing significant SNPs\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e3\u003c/sup\u003eThe most significant SNP within the QTL (based on q-value)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e4\u003c/sup\u003eReference allele frequency\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e5\u003c/sup\u003eSNP effect and standard error\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRegarding loin yield, a total of 54 significant SNPs were associated with this trait (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table S2), 50 of which were located within eight identified QTL regions, and distributed across SSC4 (QTL1 to QTL3) and SSC6 (QTL5 to QTL9) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These regions comprise 145 annotated genes (Supplementary Table S4), 18 of which contained significant SNPs (Supplementary Table S2). Functional annotation of these genes highlighted biological processes related to lipid and steroid metabolism, transferase activity, zinc ion binding, and ubiquitination and proteasomal degradation. Genes linked to lipid and steroid metabolism were predominantly found in QTL3, QTL7, and QTL9. Among these, QTL3 (SSC4:64.49-75.17Mb) harbored 15 associated SNPs, spanning 39 genes. One of these SNPs mapped to the \u003cem\u003eCytochrome P450 family 7 subfamily B member 1\u003c/em\u003e (\u003cem\u003eCYP7B1\u003c/em\u003e), a key enzyme involved in cholesterol catabolism and steroid hormone biosynthesis. Additionally, two significant SNP were detected, one of them mapping to the \u003cem\u003ePhosphatidylinositol-3,4,5-trisphosphate-dependent Rac exchange factor\u003c/em\u003e 2 (\u003cem\u003ePREX2)\u003c/em\u003e and the other to the \u003cem\u003eNeutral Sphingomyelinase Activation Associated Factor\u003c/em\u003e (\u003cem\u003eNSMAF\u003c/em\u003e), both implicated in intracellular signalling and potentially in muscle development. Genes involved in transferase activity, zinc ion binding, and the ubiquitin-proteasome pathway were found in QTL1, QTL3, QTL6, and QTL7. For example, QTL7 (SSC6:87.01-90.88Mb) contains several genes with relevant functions, including \u003cem\u003eTripartite Motif Containing 62\u003c/em\u003e (\u003cem\u003eTRIM62\u003c/em\u003e), in which a significant SNP was located and that is directly involved in the ubiquitination pathway.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eQTL for meat quality\u003c/h3\u003e\n\u003cp\u003eA total of 73 SNPs were significantly associated with different backfat fatty acid (FA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table S3), and two other SNPs showed significant associations with shear force. In contrast, no significant associations were observed between SNPs and the remaining analysed quality traits, including IMF, moisture, protein content, instrumental meat colour, and water loss. None of the SNPs or QTL identified for meat quality overlapped with those found for ham and loin yields. The two SNPs associated with shear force were mapped to QTL14 (SSC2:6.77-7.05Mb), with one of them located to the \u003cem\u003eSpeedy/RINGO Cell Cycle Regulator Family Member C\u003c/em\u003e (\u003cem\u003eSPDYC\u003c/em\u003e) gene (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\u003eDescription of the significant QTLs affecting shear force and fatty acid proportions (%).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\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\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGenomic position (Mb)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u0026ordm; SNP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGenes\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSNP\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFreq\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eb (se)\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eq-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShear Force\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2:6.77\u0026ndash;7.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSPDYC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers81357736A\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.51 (0.104)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC18:2, PUFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3:38.12\u0026ndash;38.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eADCY9\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers81236837A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.18 (0.041)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC18:2, PUFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3:44.43\u0026ndash;44.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eMERTK\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers81228599A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e, \u003cem\u003ers81304124G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.17 (0.041)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC18:2, PUFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3:51.02\u0026ndash;51.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers81370348A\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/em\u003e, \u003cem\u003ers81370345G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.17 (0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC20:1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7:115.65-117.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers321344396C\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.13 (0.027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC14:0, C16:0, C16:1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8:109.92-113.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eELOVL6\u003c/em\u003e, \u003cem\u003eEMPEP, MCUB\u003c/em\u003e, \u003cem\u003eRRG\u003c/em\u003e, \u003cem\u003eSEC24B, LEF1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers336391861C\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.33 (0.062)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.51x10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC14:0, C16:0, C18:1, C20:1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12:0.623\u0026ndash;0.715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eNARF\u003c/em\u003e, \u003cem\u003eCYBC1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers331568234A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e, \u003cem\u003ers322685807A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.07 (0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.67x10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC18:2, PUFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13:169.82-170.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eCADM2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers341320025A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e, \u003cem\u003ers81441284C\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.16 (0.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC18:2, PUFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13:172.62-176.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eROBO1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers324938004C\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.19 (0.037)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC20:1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14:128.72-129.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eCACUL1\u003c/em\u003e, \u003cem\u003eBAG3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers329389257A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.05 (0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC16:0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15:50.37\u0026ndash;50.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eUNC5D\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers80943094G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e, \u003cem\u003ers80930489A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.62 (0.160)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC16:0, SFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15:72.72\u0026ndash;72.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSCN9A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers341238670A\u0026thinsp;\u0026gt;\u0026thinsp;G, rs81453508G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.66 (0.146)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC16:0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15:115.18-118.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSPAG16\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ers81454499C\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.32 (0.070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e1\u003c/sup\u003eNumber of significant SNPs within the QTL\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e2\u003c/sup\u003eGenes within the QTL containing significant SNPs\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e3\u003c/sup\u003eThe most significant SNP within the QTL (based on q-value)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e4\u003c/sup\u003eReference allele frequency\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e5\u003c/sup\u003eSNP effect and standard error\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePhenotypic data of Iberian pigs.\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=\"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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eCovariates\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCV\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eBW pre-\u003cem\u003eMontanera\u003c/em\u003e, kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eWeight gain \u003cem\u003eMontanera\u003c/em\u003e, kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight final, kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eGrowth and carcass composition\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCV\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADG, kg/d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarcass Weight, kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarcass yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHam Weight, kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShoulder Weight, kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoin Weight, kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHam yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShoulder yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoin yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremium cuts yield, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eMeat quality\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCV\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntramuscular fat (IMF), %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e40.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoisture, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProtein, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShear force, N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThawing loss, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e67.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCooking loss, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eL*\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ea*\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eb*\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eFatty Acids (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCV\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC12:0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC14:0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC16:0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC16:1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC17:0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC17:1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC18:0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC18:1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC18:2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC18:3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC20:0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC20:1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMUFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePUFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e1\u003c/sup\u003eBody weight at the start of \u003cem\u003eMontanera\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e2\u003c/sup\u003eWeight gain during \u003cem\u003eMontanera\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs mention above, a total of 73 significant SNP-FA associations were identified, involving individual FA such as myristic acid (C14:0), palmitic acid (C16:0), palmitoleic acid (C16:1), oleic acid (C18:1), linoleic acid (C18:2), eicosenoic acid (C20:1), as well as the sums of saturated (SFA) and polyunsaturated (PUFA) fatty acids. Among them, 32 SNPs exhibited pleiotropic effects, influencing more than one fatty acid. Moreover, 60 of these 73 SNPs were located within 16 QTL associated to the FA profile, distributed across SSC3, SSC7, SSC8, SSC12, SSC13, SSC14, and SSC15. Among the significant SNPs located within QTLs, 31 were annotated within 22 candidate genes.\u003c/p\u003e\u003cp\u003eRegarding SFA proportions, C14:0 and C16:0 were associated with QTL19 and QTL20, while C16:0 and total SFA were linked to QTL25. C16:0 was also associated to QTL24 and QTL26. Monounsaturated fatty acids (MUFA) showed fewer associations, some of which overlapped with those observed for SFA. The most abundant FA in the backfat profile, C18:1, was associated only with QTL20; C16:1 with QTL19; and C20:1 with QTL18, QTL20 and QTL23. These QTL regions contained 34 annotated genes (Supplementary Table S5), 14 of which harboured at least one significant SNP (Supplementary Table S3). Functional annotation clustering in DAVID did not reveal enrichment for any specific pathways or molecular functions. Among these regions, QTL19 (SSC8:109.92-113.86Mb) displayed the highest number of associated SNPs (19), including one within \u003cem\u003eELOVL Fatty Acid Elongase 6\u003c/em\u003e (\u003cem\u003eELOVL6\u003c/em\u003e), responsible for the elongation of medium-chain FAs.\u003c/p\u003e\u003cp\u003eConcerning the C18:2 and PUFA proportions, associations were found in five QTL distributed across SSC3 (QTL15, QTL16 and QTL17) and SSC13 (QTL21 and QTL22). These regions contained 44 annotated genes, eight of which harbored eleven significant SNPs (Supplementary Table S3). Functional annotation analysis revealed enrichment of serpin-domain proteins, linked to serine protease inhibition. Among the QTLs associated with PUFA, QTL22 (SSC13:172.62-176.42Mb) and QTL15 (SSC3:38.12-38.95Mb) stand out for presenting significant SNPs mapped within genes related to lipid metabolism. QTL22 harboured the highest number of associated SNPs (9), including four mapped to the \u003cem\u003eRoundabout Guidance Receptor 1\u003c/em\u003e (\u003cem\u003eROBO1\u003c/em\u003e) gene, while QTL15 included one significant SNP annotated within \u003cem\u003eAdenylate Cyclase 9\u003c/em\u003e (\u003cem\u003eADCY9\u003c/em\u003e) gene, both involved in cell signaling and developmental processes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe Iberian pig breeding programs are mainly based on the use of classical genetic evaluation methods that include phenotypic data for growth, premium cuts, and IMF as well as pedigree information. The incorporation of genetic markers may significantly enhance the effectiveness of selection, similar to what has been observed in commercial pig breeds. Thus, genomic information will not only improve the accuracy of EBVs, but could also help to avoid potential antagonistic effects between productive traits and those related to meat quality [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], by enabling the identification of genetic markers with pleiotropic favourable effects for both type of traits [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This type of analysis, which combines productive and meat quality traits and aims to know more about the genetic basis of these traits and the possible existence of antagonistic effects, is especially valuable for the Iberian pork industry in order to maintain its differentiated position in a high-end food market. Within this context, the main aim of this study was to identify SNPs and chromosomal regions associated with the most relevant economic traits in the Iberian pig raised in \u003cem\u003eMontanera.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe present study has identified several QTLs and putative genes associated with traits of economic interest for the Iberian pig breeding program. Among them, several regions were identified for the first time in pigs as associated with ham yield, such as QTL4 (SSC6:82.66-83.56Mb) and QTL12 (SSC10:41.57-41.97Mb), and with loin yield, as QTL1 (SSC4:35.70-39.18Mb), QTL2 (SSC4:55.89-55.91Mb), and QTL3 (SSC4:64.49-75.17Mb). Other regions, such as those located on SSC6 and on SSC8, had already been reported in previous studies. For instance, Mart\u0026iacute;nez-Montes et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] examined several Iberian crosses involving Landrace, Duroc, and Pietrain breeds, and identified a total of 89 SNPs and 15 significant QTL regions associated with productive and quality traits. Among their key findings, two chromosomal regions associated with shoulder (SSC6:71.90-86.76Mb) and ham weight (SSC6:80.62-112.49Mb) were reported, which partially overlap with QTL6, QTL7, and QTL8 in our study, but with the particularity that in our study these regions were exclusively associated with loin percentage. We also identified the \u003cem\u003eTRIM63\u003c/em\u003e gene mapped within QTL6, which has been also proposed as a candidate gene by Martinez-Montes et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and a significant SNP (\u003cem\u003ers337558398G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e) mapped to the \u003cem\u003eTRIM62\u003c/em\u003e gene, located in QTL7. Both \u003cem\u003eTRIM63\u003c/em\u003e and \u003cem\u003eTRIM62\u003c/em\u003e encode proteins with ubiquitin-protein transferase activity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Ubiquitination is crucial for removing damaged or misfolded proteins through the proteasome system, and in muscle, it plays a key role in tissue remodelling and regeneration during atrophy, injury, or growth [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAn expression-GWAS study using the same Iberian crosses mention above [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] identified QTL regions on SSC6 (80.52-84.33Mb) and SSC8 (8.93-11.96Mb) associated with the expression of \u003cem\u003eProtein Tyrosine Phosphatase 4A2\u003c/em\u003e (\u003cem\u003ePTP4A2\u003c/em\u003e) and \u003cem\u003eTransmembrane Anterior Posterior Transformation 1\u003c/em\u003e (\u003cem\u003eTAPT1\u003c/em\u003e) genes, respectively. The \u003cem\u003ePTP4A2\u003c/em\u003e gene, found in our QTL7, encodes a phosphatase involved in cell proliferation and migration, which may play a role in muscle growth or regeneration [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The \u003cem\u003eTAPT1\u003c/em\u003e gene, located in our QTL10, is involved in skeletal development and extracellular matrix [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Whereas the GGP chip did not include any SNPs located in \u003cem\u003ePTP4A2\u003c/em\u003e gene, the three SNPs mapped to \u003cem\u003eTAPT1\u003c/em\u003e showed not significant associated with primal cut yields.\u003c/p\u003e\u003cp\u003eOne interesting SNP associated to ham is \u003cem\u003ers328167887A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e (QTL13, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) with a MAF of 0.48 and a SNP effect (b) of 0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06, corresponding to a change of approximately 1.52% relative to the trait mean. This SNP is mapped to \u003cem\u003ePID1\u003c/em\u003e gene, which has been previously associated with loin muscle depth in Duroc \u0026times; (Landrace \u0026times; Yorkshire) pigs [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and encodes for a protein that reduces insulin sensitivity and is highly expressed in key insulin-responsive tissues such as adipose tissue, skeletal muscle, and myocardium [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Other relevant SNPs associated with ham percentage are those located in QTL11, where four SNPs (MAF\u0026thinsp;=\u0026thinsp;0.20 and b\u0026thinsp;=\u0026thinsp;0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07), were identified and mapped to the \u003cem\u003eKCNIP4\u003c/em\u003e gene, proposed as a candidate gene for growth traits in chickens [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and rabbits [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and that has also been suggested as a regulator of muscle growth and fat deposition in sheep [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAccording to the Pig QTLdb database, only four QTL have been reported for loin yield, and none overlap with those identified in our study. In contrast, a larger number of QTL associated with loin weight match the regions we identified for loin yield. These include QTL3, identified in an Iberian \u0026times; Landrace intercross [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]; QTL5, reported in a Duroc \u0026times; Pietrain cross [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; QTL6, QTL7, and QTL8, detected in Duroc \u0026times; Pietrain crosses [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]; as well as QTL8 and QTL9, found in (Pietrain \u0026times; Large White) \u0026times; Landrace populations [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor the loin yield, the \u003cem\u003ers80862639A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e SNP, with a MAF of 0.04 and located in QTL3 (SSC4:64.49-75.17Mb), had the highest additive effect, b\u0026thinsp;=\u0026thinsp;0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04, which represents an increase of ~\u0026thinsp;6.3% relative to the trait mean. This SNP maps to the \u003cem\u003ePREX2\u003c/em\u003e gene, involved in insulin signalling and cell migration, key processes in muscle regeneration and adaptation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Other SNPs with positive effects on loin yield but low allelic frequencies for favourable alleles value (0.04\u0026ndash;0.09) were detected in genes related to muscle development. For instance, three significant SNPs in QTL1 (\u003cem\u003ers321093035G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e, \u003cem\u003ers80996716A\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/em\u003e and \u003cem\u003ers80828796G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e) were mapped to \u003cem\u003eRGS22\u003c/em\u003e gene, involved in ubiquitination. Another SNP in QTL3, \u003cem\u003ers80937006A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e were mapped to \u003cem\u003eCHD7\u003c/em\u003e gene, a gene linked to growth regulation and liver expression in commercial pig populations (Duroc, Landrace, and Large White) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Finally, \u003cem\u003ers80959844A\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/em\u003e was located in the \u003cem\u003eNSMAF\u003c/em\u003e gene (in QTL3), which has been associated with carcass weight, bone density, and growth in Red Angus cattle [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Despite their low MAF (\u0026lt;\u0026thinsp;0.10), these SNPs may offer valuable genetic potential for selection.\u003c/p\u003e\u003cp\u003eOther remarkable SNPs had intermediate MAF values (0.10\u0026ndash;0.50) and moderate additive effects (b\u0026thinsp;~\u0026thinsp;0.06\u0026ndash;0.07) could also be interesting for genetic selection. For example, \u003cem\u003ers81382111A\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/em\u003e, mapped to the \u003cem\u003eCYP7B1\u003c/em\u003e gene (QTL3), has been previously associated with meat pH in pigs [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], a trait affecting meat colour, water-holding capacity, and tenderness. Additional promising variants include \u003cem\u003ers80829499A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e and \u003cem\u003ers80787763A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e in \u003cem\u003eMATN2\u003c/em\u003e (QTL1), a gene linked to the number of ribs in crossbred pigs [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, there are six highlighted SNPs within the \u003cem\u003ePDE4B\u003c/em\u003e gene (QTL9), which encodes a phosphodiesterase that regulates cellular concentrations of cyclic nucleotides [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Genomic regions containing this gene have been reported to be associated with backfat thickness in the Duroc pig breed [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], while other genes of the same family have been linked with muscle development.\u003c/p\u003e\u003cp\u003eNo significant SNPs were found for meat quality traits such as IMF, protein, water loss and instrumental colour, which contrasts with the results observed in the other porcine breeds and the Pig QTLdb. In the present study, we identified only one SNP, \u003cem\u003ers319898417G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e, associated with some of these traits, specifically with shear force. This SNP was mapped to the \u003cem\u003eSPDYC\u003c/em\u003e gene (QTL14), which is involved in maintaining spindle-assembly checkpoint integrity and proper chromosome segregation during mitosis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Previous studies carried out in this same population of Iberian barrows identified SNPs associated with this trait such as \u003cem\u003eCAPN1\u003c/em\u003e_\u003cem\u003ers81358667G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e and \u003cem\u003eCASP3\u003c/em\u003e_\u003cem\u003ers319658214G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/em\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] as well as \u003cem\u003eCAST\u003c/em\u003e_\u003cem\u003ers196949783G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e and \u003cem\u003eELOVL6\u003c/em\u003e_\u003cem\u003ers3473714672A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, none of these SNPs matched those genotyped by the GGP Porcine chip. Furthermore, this GGP chip contained no SNPs mapped to the \u003cem\u003eCASP3\u003c/em\u003e and \u003cem\u003eCAST\u003c/em\u003e genes, despite scientific evidence linking these genes to quality traits in pigs [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Although \u003cem\u003eCAPN1\u003c/em\u003e and \u003cem\u003eELOVL6\u003c/em\u003e genes are located within QTL2 and QTL19, respectively, in the present study SNPs mapping to these genes were either removed in the filtering/QC stage or showed no statistical significance for any meat quality trait.\u003c/p\u003e\u003cp\u003eIMF is a key factor affecting the organoleptic properties of meat [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and together with MUFA, like C18:1, the exceptional quality of Iberian pig products. However, in this work GWAS did not reveal significant SNPs for IMF. In contrast, previous research on the same population identified SNPs and genes linked to IMF content, such as \u003cem\u003eELOVL6:g.112186423A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e, \u003cem\u003eFASN_rs331694510G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], \u003cem\u003eADIPOQ_rs3476515794T\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e and \u003cem\u003eCAST_rs196949783G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As mention before for other genes, the GGP chip lacked SNPs located in \u003cem\u003eADIPOQ\u003c/em\u003e gene, and included only one in \u003cem\u003eFASN\u003c/em\u003e gene, which did not pass quality control filters. Moreover, although the \u003cem\u003eFASN\u003c/em\u003e gene is situated less than 1 Mb away from the QTL20, no SNPs in this region were retained after filtering, probably due to the absence of informative markers for this specific breed.\u003c/p\u003e\u003cp\u003eIn general, no new QTL were identified for FA. Ramayo-Caldas et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] performed a GWAS analysis on 32 traits related to FA profiles in IMF in an Iberian \u0026times; Landrace backcross population, identifying 813 significant SNPs across 43 regions, of which only a few regions overlapped with ours in the present study. For example, a region on SSC15 (50-50.4Mb) similar to our QTL24 was associated with C16:0. Another region on SSC8 (103.8-107.5Mb), close to our QTL19, was also associated with C16:0. Mu\u0026ntilde;oz et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] observed a QTL on SSC8 between 83.84 and 126.88 Mb on Iberian \u0026times; Landrace pigs, similar to our QTL19, and associated with C14:0, C16:0 and C16:1 proportions. In Korean native black pigs, similar SSC8 regions (85.3-114.8Mb and 109.8-114.1Mb), have been reported to be associated to C16:0 and C16:1 [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. One important gene in this region is \u003cem\u003eELOVL6\u003c/em\u003e, involved in fatty acid elongation, and affecting the C14:0 and C16:0 levels. As mentioned earlier, the \u003cem\u003eELOVL6_rs3473714672A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e SNP has been linked to quality traits in Iberian pigs, including FAs proportions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Of the two SNPs mapping to the \u003cem\u003eELOVL6\u003c/em\u003e gene included on the GGP chip, \u003cem\u003ers339079803C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/em\u003e failed quality control, while \u003cem\u003ers336391861C\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e (MAF\u0026thinsp;=\u0026thinsp;0.30, b=-0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06) was significantly associated to C14:0 and C16:0.\u003c/p\u003e\u003cp\u003eRegarding the most important FA, C18:1, significant associations were identified with \u003cem\u003ePRKAG3_rs319678464G\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/em\u003e, \u003cem\u003eFASN_rs331694510G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e, \u003cem\u003eELOVL6_rs347371467A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e, and \u003cem\u003eACACA_rs340781986C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/em\u003e SNPs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, in the present study, only QTL20 showed a significant association with C18:1, but no genes were annotated within this QTL. The \u003cem\u003eFASN\u003c/em\u003e gene, coding for fatty acid synthase, lies close to QTL20, but lacks SNPs on the GGP genotyping chip. Similarly, although \u003cem\u003eELOVL6\u003c/em\u003e gene is annotated within our QTL19 region, neither the region nor its SNPs were associated with C18:1. Moreover, no SNPs located in the \u003cem\u003ePRKAG3\u003c/em\u003e gene were present in the GGP chip, and none of the seven SNPs mapped to the \u003cem\u003eACACA\u003c/em\u003e gene were found to be associated with this FA.\u003c/p\u003e\u003cp\u003eIn contrast to the C18:1, several SNPs and QTL regions were found to be significantly associated with the C18:2 fatty acid, which also impacts total PUFA content due to C18:2 is the major PUFA component. While PUFA have health benefits, they are more susceptible to oxidation, increasing rancidity in cured products. Essential fatty acids, such as C18:2 and linolenic acid (C18:3) come mainly from diet, and therefore, their genetic associations are likely to be related to transport or conversion rather than fatty acids biosynthesis. These regions are on SSC3 and SSC13, where relevant genes such as \u003cem\u003eADCY9\u003c/em\u003e and \u003cem\u003eROBO1\u003c/em\u003e, respectively, were identified. A significant SNP, \u003cem\u003ers81236837A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e (MAF\u0026thinsp;=\u0026thinsp;0.26, b\u0026thinsp;=\u0026thinsp;0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04), was identified in \u003cem\u003eADCY9\u003c/em\u003e gene. The \u003cem\u003eADCY9\u003c/em\u003e gene, in QTL15, is involved in the cyclic AMP signalling pathway, previously associated with meat pH in other studies [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The \u003cem\u003eADCY9\u003c/em\u003e has been also associated with increased fat, better feed efficiency, and growth [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Four significant SNPs were located within the \u003cem\u003eROBO1\u003c/em\u003e gene (MAF\u0026thinsp;=\u0026thinsp;0.32\u0026ndash;0.36, b\u0026thinsp;=\u0026thinsp;0.15\u0026ndash;0.19). \u003cem\u003eROBO1\u003c/em\u003e regulates cell migration and vascular endothelial growth factor signalling [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], essential for lipid membranes organization. Moreover, \u003cem\u003eROBO1\u003c/em\u003e has been previously linked to subcutaneous fat deposition in pigs by transcriptomic studies [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOverall, the SNPs and candidate genes identified in our previous studies appear to be more relevant for genetic selection based on major FA (C16:0, C18:0, and C18:1) than those detected in the present work. As mentioned throughout the discussion, this may have been due in part to the lack of coverage of the GGP chip used in the present study, as well as the lack of agreement with the low-density chip used in previous studies. A relevant aspect of this study is the use in a local breed, the Iberian pig, of a commercial genotyping chip developed for lean cosmopolitan pig breeds. The original PorcineSNP60 Beadchip was designed based on SNP information from breeds such as Duroc, Landrace, Large White, Pietrain, as well as wild boar populations [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Consequently, the selection of SNPs was optimized for variants segregating in those populations, which may limit the ability of GGP chip to capture the differentiated genetic background of traditional autochthonous breeds [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] like the Iberian pig, with a distinct evolutionary history than cosmopolitan breeds [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Although a subset of SNPs on the chip segregates in the Iberian population, a considerable proportion remains monomorphic or non-informative. Unlike some commercial breeds, the Iberian pig exhibits lower linkage disequilibrium (LD) decay [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], resulting in larger LD blocks and, in principle, a reduced need for high-density marker coverage. Thus, the main limitation is the lack of markers selected specifically for this breed. Moreover, important genomic regions and candidate genes implicated in productive and quality traits appear to be underrepresented or absent from the GGP chip, limiting its effectiveness. To partially address this limitation, we applied more permissive filtering criteria for MAF, allowing the retention of rare variants that may be functionally important in the Iberian population but would typically be excluded in studies of commercial breeds. Notably, among the 165 SNPs significantly associated with the evaluated traits, 40 of them had a MAF below 0.1, and 30 of these were found to be associated with traits of economic interest. Although such low-frequency variants are often missed by standard genome-wide arrays, they may represent a significant and underexplored component of the genetic architecture of complex traits, as shown in human studies [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Their targeted selection, despite their current low prevalence, could contribute meaningfully to genetic improvement. Overall, further SNP discovery tailored to the genetic background of the Iberian pig could help to optimize the genomic tools available for this breed and fully capture the genetic variation relevant to it.\u003c/p\u003e\u003cp\u003eIn this study, no pleiotropic SNPs or QTL were identified between traits related to productive performance and those involved in meat quality. This suggests that the proposed candidate regions could be used independently to improve each specific trait, minimizing potential antagonistic effects and allowing for more targeted selection strategies. In particular, several SNPs and genomic regions associated with ham and loin traits emerged as promising, which is especially relevant considering that carcass composition remains one of the major limitations of the Iberian pig. Although the contribution of the GGP chip to quality traits such as fatty acid composition was limited, in contrast to previous studies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], our results highlight new avenues for genetic improvement within the breed. Nevertheless, the proposed markers for ham and loin should be validated in a larger number of animals before their implementation in selection programs, along with functional characterization of the candidate genes and an evaluation of the long-term effects of marker-assisted selection. Overall, despite the technical limitations of the current genotyping platform, the findings of this study represent a valuable contribution with a potential application in the breeding program of the Iberian pig.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eAnimals\u003c/h2\u003e\u003cp\u003eBlood sampling was performed exclusively as part of routine livestock management practices complying with Spanish Royal Decree 53/2013 and Directive 2010/63/EU, which exclude routine agricultural practices from the requirement for prior approval by an ethics committee.\u003c/p\u003e\u003cp\u003eThe animals included in this study belonged to the SRC line of Iberian pig and were part of the breeding program of the company S\u0026aacute;nchez Romero Carvajal S.A., which has been under development since 2011 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In a previous study by Palma-Granados et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], 1520 pigs from this program were genotyped using a small SNP panel in order to evaluate their association with carcass composition and meat quality traits. To deepen the identification of genomic regions and genes associated with traits of interest, 528 pigs from this population were selected for high-density genotyping (60K) and a GWAS on the same traits previously analysed was conducted. Detailed information on the management system and feeding regime of these 528 pigs is documented in Palma-Granados et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], along with the phenotypic data used in the present study. The selection criteria guaranteed representation across different production batches and slaughter dates. The 528 pigs were selected from different fattening groups (pigs managed together in the grazing of acorns in \u003cem\u003eMontanera\u003c/em\u003e) and slaughtered between 2017 and 2022 in a commercial slaughterhouse, grouped into 10 slaughter batches, with an average age of 517 days (SD\u0026thinsp;=\u0026thinsp;37 days) and an average body weight (BW) of 167 kg (SD\u0026thinsp;=\u0026thinsp;19 kg). The combination of fattening group and slaughter batches resulted in 22 distinct production batches.\u003c/p\u003e\u003cp\u003eThe phenotypic data used in this study were collected following the procedures described in S\u0026aacute;nchez-Esquiliche et al. [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] for muscle protein and moisture composition, and in Palma-Granados et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], for the remaining traits, allowing the reuse of previously reported measurements. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes these phenotypic traits, including means and standard deviations. The data included growth parameters, carcass components related to premium cuts (ham, shoulder, loin), and quality traits such as loin biochemical composition (content in IMF, protein and moisture), FA composition, water loss, shear force, and instrumental colour of the meat.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGenotypic data\u003c/h3\u003e\n\u003cp\u003eThe 528 animals were genotyped with the platform GGP Porcine HD Array (Illumina, Inc.) containing 68,516 SNPs. Genomic DNA was extracted from the blood using a standard sucrose-proteinase K method. Genotyping was carried out at the Servei Veterinari de Gen\u0026egrave;tica Molecular (UAB, Barcelona, Spain). PLINK 1.9 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] software was used to visualize, edit, apply standard quality controls (QC) and extract genotyping data for subsequent GWAS analyses. Regarding to QC, those markers that showed minor allele frequency (MAF)\u0026thinsp;\u0026lt;\u0026thinsp;0.01 or call rate\u0026thinsp;\u0026lt;\u0026thinsp;90% were removed. Additionally, individuals with more than 10% missing genotypes were excluded from analysis. The SNPs located in sex chromosomes were also excluded. After QC, two animals were removed and a total of 35,894 SNP markers on 18 autosomes remained. The distribution of the SNPs in windows of 1 Mb that passed the filtering criteria is represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eGWAS analysis\u003c/h3\u003e\n\u003cp\u003eThe genome-wide association analysis was performed using the GCTA software [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], applying a mixed linear model association analysis with a \u003cem\u003eleave-one-chromosome-out\u003c/em\u003e approach (MLMA-LOCO). The model used was:\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ey\u003csub\u003e​\u003c/sub\u003e = a\u0026thinsp;+\u0026thinsp;bx\u0026thinsp;+\u0026thinsp;fF\u0026thinsp;+\u0026thinsp;g\u0026minus; + e\u003c/h2\u003e\u003cp\u003ewhere \u003cem\u003ey\u003c/em\u003e is the vector of phenotypes, \u003cem\u003ea\u003c/em\u003e is the intercept, \u003cem\u003eb\u003c/em\u003e is the additive effect (fixed effect) of the candidate SNP and \u003cem\u003ex\u003c/em\u003e is the SNP genotype indicator variable coded as 0, 1 or 2, \u003cem\u003ef\u003c/em\u003e includes\u003c/p\u003e\u003cp\u003ethe production batch (22 levels), treated as a fixed effect and covariates that were different depending on the trait analysed and \u003cem\u003eF\u003c/em\u003e its corresponding incidence matrix, \u003cem\u003eg\u0026thinsp;\u0026minus;\u003c/em\u003e\u0026thinsp;represents the accumulated effect of all SNPs excluding those located on the chromosome containing the tested SNP, and \u003cem\u003ee\u003c/em\u003e represents the residual effects. The variance of g⁻ (var(g⁻)) is re-estimated each time a chromosome is excluded from the calculation of the genomic relationship matrix (GRM). A summary of the traits analysed as well as the fixed effects and covariates included in the different models is provided in the Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. We used the q-value library in R [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] to correct multiple testing. A SNP was considered as significantly associated to a trait when it displayed a q-value lower than 0.1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRegion analysis and Candidate genes\u003c/h2\u003e\u003cp\u003ePutative QTL regions were defined as two or more consecutive SNPs significantly associated with each phenotypic trait, separated by less than 2 Mb. QTL regions that were identical or overlapping between traits were considered as a single one.\u003c/p\u003e\u003cp\u003eGene annotation of QTL regions was conducted using the Biomart tool from the Porcine ENSEMBL database, Sscrofa11.1. In order to identify potential candidate genes, a functional gene annotation analysis was carried out with the DAVID online annotation database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://davidbioinformatics.nih.gov/\u003c/span\u003e\u003cspan address=\"https://davidbioinformatics.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Additionally, the identified QTL regions were compared against previously reported loci in the Pig QTLdb (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.animalgenome.org/cgi-bin/QTLdb/SS/ontrait?trait_ID=718\u003c/span\u003e\u003cspan address=\"https://www.animalgenome.org/cgi-bin/QTLdb/SS/ontrait?trait_ID=718\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), in order to assess whether the associations found in this study overlapped with known QTL.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank to Sánchez Romero Carvajal (SRC) staff for their technical support, especially to Luisa Ramírez and Fernando Gómez-Carballar.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement (mandatory)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information (including a Competing Interests Statement)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by CON15-078, CON17-125 and CON19-281 (INIA-SRC) and IDI-20171141 (CDTI and FEDER) and IDI-20220528 (CDTI) grants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: J.M. García-Casco, M. Muñoz\u003c/p\u003e\n\u003cp\u003eFormal analysis: M. Muñoz, P. Palma-Granados, M. Ramón\u003c/p\u003e\n\u003cp\u003eFunding acquisition: J.M. García-Casco, F. Sánchez-Esquiliche, A. Márquez\u003c/p\u003e\n\u003cp\u003eInvestigation: J.M. García-Casco, M. Muñoz, P. Palma-Granados, F. Sánchez-Esquiliche, M. Delgado-Gutierrez, M. Ramón\u003c/p\u003e\n\u003cp\u003eMethodology: J.M. García-Casco, M. Muñoz\u003c/p\u003e\n\u003cp\u003eProject administration: J.M. García-Casco\u003c/p\u003e\n\u003cp\u003eResources: J.M. García-Casco\u003c/p\u003e\n\u003cp\u003eSupervision: J.M. García-Casco, M. Muñoz\u003c/p\u003e\n\u003cp\u003eVisualization: M. Muñoz, P. Palma-Granados, M. RamónWriting – original draft: P. Palma-Granados, J.M. García-Casco, M. Muñoz\u003c/p\u003e\n\u003cp\u003eWriting – review \u0026amp; editing: \u0026nbsp;P. Palma-Granados, J. M. García-Casco, M. Muñoz, M. 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Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://github.com/jdstorey/qvalue\u003c/span\u003e\u003cspan address=\"http://github.com/jdstorey/qvalue\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"GWAS, autochthonous breed, fatty acids, premium cuts, polymorphisms","lastPublishedDoi":"10.21203/rs.3.rs-7515242/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7515242/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to identify genomic regions and candidate genes associated with body composition, and meat quality traits in Iberian pigs fattened in \u003cem\u003eMontanera\u003c/em\u003e. A genome-wide association study (GWAS) was conducted on 528 pigs for 29 phenotypic traits using genomic data from the GGP Porcine HD Array. After quality control, 526 animals and 35,894 SNPs were retained for the association analysis. Despite the limitations of the genotyping chip used, which lacked coverage for Iberian-specific variants, the GWAS performed with GCTA software identified 165 SNPs significantly associated with 11 traits. Among these, 145 SNPs were clustered into 25 quantitative trait loci (QTL) regions. Five QTL were identified for ham yield, containing genes such as \u003cem\u003eKCNIP4\u003c/em\u003e, \u003cem\u003eZNF438\u003c/em\u003e, and \u003cem\u003ePID1\u003c/em\u003e. Eight QTL were associated with loin yield, with genes like \u003cem\u003ePREX2\u003c/em\u003e, \u003cem\u003eMATN2\u003c/em\u003e, \u003cem\u003eRSPO1\u003c/em\u003e, and \u003cem\u003ePDE4B\u003c/em\u003e. One QTL was associated with shear force, and 16 QTL were related to fatty acid composition. Genes linked to these traits included \u003cem\u003eELOVL6\u003c/em\u003e, associated with myristic and palmitic acids, and \u003cem\u003eADCY9\u003c/em\u003e and \u003cem\u003eROBO1\u003c/em\u003e, associated with linoleic acid. Overall, these results provide novel genomic insights and markers that could enhance selection strategies in Iberian pig breeding programs, while highlighting the need for improved genomic tools tailored to local breeds.\u003c/p\u003e","manuscriptTitle":"Genome-wide association analyses for growth, carcass composition and meat quality traits of Iberian pigs fattened in free open air","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 15:34:38","doi":"10.21203/rs.3.rs-7515242/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f65191f-615d-4c55-b37c-52adb086b707","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55670712,"name":"Biological sciences/Biotechnology"},{"id":55670713,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":55670714,"name":"Biological sciences/Genetics"},{"id":55670715,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2025-10-31T11:24:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 15:34:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7515242","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7515242","identity":"rs-7515242","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}