Identification of potential biomarkers associated with meat tenderness in Hanwoo (Korean cattle): an expression quantitative trait loci analysis

Identification of potential biomarkers associated with meat tenderness in Hanwoo (Korean cattle): an expression quantitative trait loci analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification of potential biomarkers associated with meat tenderness in Hanwoo (Korean cattle): an expression quantitative trait loci analysis Yoonji Chung, Sun Sik Jang, Dong Hun Kang, Yeong Kuk Kim, Hyun Joo Kim, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2013149/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 Meat tenderness is considered the most important trait contributing to beef quality, level of consumer satisfaction, willingness to pay premium prices, and industry profit. Genomic selection method would be helpful for genetic improvement of traits with low heritability and are difficult to measure. The identification of genes that affect beef tenderness can promote efficient genomic prediction in breeding programs. We performed statistical analysis of associations between longissimus dorsi muscle tenderness and gene expression in 20 Hanwoo cattle, using Warner-Bratzler shear force (WBSF) and RNAseq data, respectively. We found 166 core genes with significant regression coefficient. In expression quantitative trait loci (eQTL) analysis, using the core genes and 777,962 SNPs for 20 individuals, we found 6 core genes ( ASAP1 , CAPN5 , ELN , SUMF2 , TTC8 , and MGAT4A ) regulated by 16 cis-eQTL SNPs. The variants within 5 kb of the transcription start site or transcription termination site of these core genes were significant (p < 0.01). Notably, we found that a cis-eQTL SNP of the ELN gene contained an MFZ1 binding site in its putative promoter region. These findings provide a useful information for genomic prediction using additive and non-additive genetic effects in prediction model. Biological sciences/Biotechnology/Genomics Biological sciences/Genetics/Agricultural genetics Biological sciences/Genetics/Gene expression Biological sciences/Genetics/Genetic markers Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The breeding and production program of Hanwoo cattle (Hanwoo beef cattle) consist of a bull selection program using genetic selection for production traits and high-concentration feeding system for high marbling meat 1 – 4 . The system mainly has focused on increasing genetic potentials for breeding goal traits such as carcass weight and marbling score because beef consumption per capita has increased more than five times compared to 10 years ago with Korea’s economic growth and westernization of eating habits 3 , 5 . As a result, the production system produced Hanwoo beef with very high intramuscular fat (IMF) contents. However, over times, there are changes in Korean consumers preference about meat characteristics, and the consumers are eager to eat Hanwoo beef with the highly eating quality giving a delicious and juicy eating experience, without compromising health problems such as coronary heart disease 6 , 7 . Eating quality is affected by sensory traits such as tenderness, juiciness, and flavor likeness 8 . Among these traits, meat tenderness is considered most important for beef quality, consumer satisfaction level, and industry profit. Beef tenderness is analyzed in terms of the Warner-Bratzler shear force (WBSF) which is negatively correlated with IMF contents in Hanwoo beef 9 . Meat texture (toughness and tenderness) is affected by collagen contents and beef connective tissue 10 , 11 . IMF deposition in connective tissue can directly influence tenderness through the breakdown of connective tissue 12 . Therefore, improvements in meat tenderness have been prioritized to provide beef consumers with a pleasurable eating experience. Meat tenderness could be included in breeding program as another breeding objective trait. However, tenderness in beef is a complex trait that is very difficult to measure and has a low heritability, so genomic selection programs using causal genetic factors would be useful to increase the genetic improvement of this trait 13 . To identify the causality of genetic factors (genes or variants) on meat tenderness, genetic and proteomic data have mainly been used in cattle breeding program. For example, two-dimensional electrophoresis (2DE) showed that genotypes of the CAPN4751 and UOGCAST affect the expression of protein related to muscle metabolism in 155 Nellore cattle 14 . A meta-proteomics analysis by comparing tender and tough meat using 2DE identified 21 major protein biomarkers regardless of the treatments and techniques 15 . Moreover, genetic variants and RNA sequencing data have been used to find QTLs or differentially expressed genes (DEGs). Co-expression analysis for tenderness in Longissimus thoracis muscle of Nellore cattle found three hub-genes 16 . In multi-breed Angus-Brahman population, genotyping and RNA-seq evaluation identified four differentially expression genes and four splicing genes as expression QTL (eQTL) and splicing QTL (sQTL) key regulators associated with meat quality in beef 17 . Multi-omics data analyses have shown that the significant eQTL SNPs are associated with cattle traits, but all eQTL is not located in the obvious regulatory regions including enhancers and regulatory element 17 , 18 . It is necessary to confirm whether the eQTL SNPs contribute biologically to modulate gene expression and phenotype. For example, two variants in 5’ regulatory region of AGPAT3 were reported as potential causal mutations because they affect the transcription activity of AGPAT3 associated with contents of milk fatty acid in dairy cattle 19 . Therefore, identification of eQTLs as causal genetic factors needs to be more accurate to increase selection efficiency in terms of the regulatory role of genetic factors. This study performed eQTL analysis to identify associations between omics data and WBSF in the longissimus dorsi muscle of Hanwoo cattle (Fig. 1 created by Biorender). (1) Putative core genes were detected through analysis of associations between WBSF and gene expression levels, in accordance with the method established by Liu et al. 20 . (2) Cis-eQTL SNPs regulating the gene expression of core genes are identified as key factors. (3) We also confirm whether eQTL SNPs have potentially functional roles involved in gene expression regulation or not. In the Hanwoo population, functional analyses of meat tenderness have focused on DEG detection; factors that control gene expression have been unclear. The present study thus aimed to identify key factors that can be used to predict phenotype changes. Materials And Methods Ethical statement The experimental cattle muscle tissue sampling and genotypic procedures followed the standards established by the Committee for Accreditation of Laboratory Animal Care at National Institute of Animal Science (NIAS) in South Korea. The institutional Animal Care and Use Committee (IACUC) of NIAS, RDA approved this experiment (permit No. 2015-164). This study complied with the ARRIVE guidelines. Animals and phenotype assessment In total, 20 Hanwoo cattle (all 30 months of age) were included in this study; samples were obtained from the longissimus dorsi muscles of cattle raised in beef feedlots at the Hanwoo Experimental Station of the National Institute of Animal Science, Rural Development Administration in Pyeongchang, Republic of Korea. The experimental procedures were conducted in accordance with the guidelines of the Animal Care and Use Committee (ethics committee approval number: 2015 − 150). All methods are reported in accordance with ARRIVE guidelines ( https://arriveguidelines.org ) for the reporting of animal experiments. In accordance with the method described by Wheeler et al. 21 , the WBSF of cooked steaks ( longissimus dorsi muscle) was measured. At 48 h post-slaughter, sliced steaks (approximately 80 g; 2.5 cm) were placed into polyethylene bags. They were preheated in a water bath at 80°C for 40 min until the internal temperature of the steak reached 70°C. Samples were cooked and then cooled in running water at room temperature for 30 min. At least six to eight representative core samples (diameter, 1.27 cm) were removed from each steak in a parallel arrangement. WBSF values were determined using an Instron Universal Testing machine (Instron Corporation, Canton, MA, USA) with the following operating parameters: load cell of 50 kg and crosshead speed of 200 mm/min. The WBSF value indicated the mean force required to shear each core. WBSF values (means and standard deviations) are shown in Additional file 1: Supplementary Table S1. Muscle Biopy Methods Hanwoo steers were restrained in a hydraulic squeeze chute, hair was removed from the biopsy site, and a local anesthetic (lidocaine HCl; 20 mg/mL; 8 mL per biopsy site) was administered. A sterile cloth drape was placed over the biopsy site and a 1-cm incision was made with a scalpel. A sterile Bergstrom biopsy needle (6 mm) was used to obtain the tissue (0.5 g) from the longissimus muscle. The incision was closed with veterinary tissue glue and sprayed with a topical antibiotic followed immediately by application of a spray-on aluminum bandage. All steers were monitored for swelling 24 h and 48 h after the biopsy. Bergstrom biopsy needle (custom made) following procedures described previously in the research of Dunn et al., 2003 22 ; Pampusch et al., 2008 23 ; Winterholler et al., 2008 24 ; Parr et al., 2014 25 . RNA data mRNA was extracted from the longissimus dorsi muscle tissues of individual cattle using TRIzol Reagent (Invitrogen, Carlsbad, CA, USA), in accordance with the manufacturer’s instructions. The quality and quantity of RNA were analyzed by automated capillary gel electrophoresis using a Bioanalyzer 2100 system with RNA 6000 Nano kit (Agilent Technologies, Dublin, Ireland). To avoid genomic DNA contamination, the isolated RNA was treated with 0.1% RNase-free DNase1 (Ambion, Inc., Austin, TX, USA). Approximately 2 µg of total RNA from each sample were utilized to construct paired-end sequencing complementary DNA (cDNA) libraries using an Illumina TrueSeq Preparation Kit, in accordance with the manufacturer’s instructions (Illumina, San Diego, CA, USA). RNA sequencing was performed on a Hiseq 2000 Illumina platform to obtain paired-end reads for 100-base pair (bp) sequences. Genomic data and quality control Genomic DNA was extracted from longissimus dorsi muscle samples using the DNeasy Blood & Tissue Kit (Qiagen, Valencia, CA, USA); DNA concentration and purity were assessed using a NanoDrop 1000 (Thermo Fisher Scientific, Wilmington, DE, USA). All animals were genotyped with the Illumina Bovine SNP777 BeadChip (777K) platform (UMD3.1). We used SNP chip data, which initially contained 777,962 SNPs for 20 individuals. For eQTL analysis, genomic data for 20 samples were filtered in accordance with the quality control procedures in PLINK1.9 software. At the SNP level, we excluded SNPs with a Minor Allele Frequency < 0.01, a Hardy-Weinberg equilibrium p-value 0.1. After the completion of quality control, 20 individuals and 576,549 SNPs remained. Detection of core genes FastQC v0.11.5 software 26 was used to assess raw read quality. Adaptor sequences and reads with low-quality bases were removed by Cutadapt v1.16 software 27 and Trimmomatic v0.36 software 28 , respectively. TopHat v2.1.1 software 29 and Bowtie2 v2.2.9 software 30 were used to align pre-processed RNA sequences to the bovine reference genome Bos_taurus.UMD3.1 in the Ensembl database. Mapped read counts were measured by HTSeq v0.91 software 31 ; genes with both CPM (gene) ≤ 1 and rowSum (CPM) < 10 were excluded from analysis because of low expression. Read counts were normalized by the trimmed mean of M-value method using the edgeR v.3.22.3 package 32 in R software. Many RNAseq studies use a generalized linear model, which regards gene expression as a response and groups divided by phenotype as explanatory variables, to identify genes affected by a trait. Nevertheless, quantitative traits can be conceptually regarded as a response variable because of ambiguity when explanatory variables are used as response variables 33 . We utilized the following linear regression model to detect core genes: $$\mathbf{y}={\mathbf{E}\mathbf{x}\mathbf{p}}_{{i}}+{\mathbf{e}}_{{i}}$$ where \(\mathbf{y}\) is a vector of shear force on 20 Hanwoo cattle; \({\mathbf{E}\mathbf{x}\mathbf{p}}_{{i}}\) is a vector of the normalized gene expression for the i th gene ( i = 1, …, m), and \(\mathbf{e}\) is a vector of residual effects. No fixed effects were used for WBSF because of the lowest Akaike information criterion value in the null model (Additional file 1: Supplementary Table S2). eQTL analysis using core genes A linear regression model was used to determine the association between SNP genotype (0, 1, 2) and the expression patterns of core genes. For eQTL analysis, read counts were transformed to CPM, then normalized by the trimmed mean of M-value method using the edgeR package 32 . A cis-eQTL was defined as an SNP located within 5 kb of the transcription start sites (TSS) or transcription termination sites (TTS) of an annotated gene; cis- and trans-eQTL SNPs were separately identified using a threshold for statistical significance (p < 0.01). Transcription factor binding site (TFBS) enrichment analysis To identify potential transcription factors (TFs) that act as regulators of candidate genes, we carried out a TF binding site (TFBS) enrichment analysis using the R package TFBSTools v1.26.0 with the JASPAR2020 database 34 , 35 . mRNA sequence information was obtained from the ENSEMBL database, and sequences within 1 kb upstream of the transcription start site were regarded as potential TF binding sites. We calculated binding scores for 37 position weight matrices corresponding to vertebrate TFs, using the corresponding sequence for each candidate gene. The effect of each sequence on a TF PWM by calculating its PWM score and then compared this score to a pre-determined minimum score threshold (0.9). After multiple testing using the Benjamini-Hochberg p-value adjustment method (adjusted p-value < 0.05), we searched TFs. Results Detection of potential core genes To show the suitability of ordinary linear regression model in RNA-seq, we checked the normality assumptions of the residuals (Additional file 2: Supplementary Fig. S1). The Shapiro-Wilk’s test was performed on all genes. No significant genes were detected under the significance level (p-value < 0.01). The association study using WBSF and gene expression was conducted to detect the significant genes modulating the phenotype. After filtering out genes with low expression among 24,596 genes, 13,360 genes were used for detecting core genes. We found significant WBSF-related 166 genes in the proposed regression model (p-value < 0.01). Here, 135 genes have positive coefficients, and 31 genes were negatively associated with WBSF (Additional file 3: Supplementary Table S3). Expression QTL detection We examined the cis-eQTL variants within 5 kb on either side of TSS and TTS of each core gene to find the putative variants regulating the transcription of core gene. Applying a threshold (p-value < 0.01) for gene-based association with WBSF, we identified 16 cis-eQTLs that were each significantly associated with expression levels of six mRNA-coding genes including ASAP1 (ArfGAP With SH3 Domain, Ankyrin Repeat And PH Domain 1), CAPN5 (Calpain 5), ELN (Elastin), SUMF2 (Sulfatase Modifying Factor 2), TTC8 (Tetratricopeptide Repeat Domain 8) , and MGAT4A (Alpha-1,3-Mannosyl-Glycoprotein 4-Beta-N-Acetylglucosaminyltransferase A) . There were 13 intron variants and 3 upstream gene variants (Table 1 and Additional file 2: Supplementary Fig. S2). The expression levels of ASAP1, ELN, CAPN5, SUMF2, and TTC8 were all positively correlated with WBSF (negatively correlated with tenderness) and were downregulated in minor allele variants, except for TTC8 (Fig. 2A and B). However, the expression level of MGAT4A was negatively correlated with WBSF and increased in minor alleles (Fig. 2A and C). (A) The X-axis represents log2 TMM normalized gene expression, and Y-axis represents WBSF values for Hanwoo cattle. (B, C) The X-axis represents cis-eQTL genotypes, and Y-axis represents log2 TMM normalized gene expression for core genes. The blue lines and grey area indicate estimated fit line and standard error, respectively. We identified two alleles that were significant cis-eQTLs for ASAP1 ( rs110751858 and rs135397766) found decreases in gene expression in the animals that have minor allele of rs110751858 (p-value = 7.96E-03) as well as rs135397766 (p-value = 7.96E-03), and the negative regression coefficient (CV; coefficient value = -0.31). We also identified two cis-eQTLs (rs111004978, p-value = 9.47E-03 and rs133370240, p-value = 9.47E-03) in the upstream region of ELN, modulated the expression levels (CV = -0.6). We found rs41772707 (p-value = 5.78E-03) and rs110465445 (p-value = 1.70E-03) in CAPN5 and SUMF2 , respectively. The minor alleles tended to decrease the expression of the both cis-eQTL gene, CAPN5 (CV = -0.33) and SUMF2 (CV = -0.34). For TTC8 gene, two SNPs (rs135354635 and rs136767380) have statistical significance (p-value = 7.42E-03 for both SNPs). In particular, the minor alleles of both SNPs upregulated TTC8 expression (CV = 0.26). In the analysis of cis-eQTL and MGAT4A expression, seven significant eQTL SNPs were identified. Three variants (rs109601924, rs110541800, and rs137222564) and two variants (rs109048556 and rs136765473) had the positive regression coefficient in the MGAT4A expression (CV = 0.29, p-value = 1.03E-04, and CV = 0.17, p-value = 3.9E-03), indicating the expression tends to increase in the minor alleles. In addition, rs109687823, rs134437911 and rs134260466 had also positive slopes (0.19, 0.26 and 0.15, respectively). Some cis-eQTLs were in each Linkage disequilibrium (LD) block, and the colour scheme is according to the Haploview r2 scheme. Numbers in the red cell represent pairwise r2-value (%) between the corresponding SNPs. For ASAP1 (ArfGAP With SH3 Domain, Ankyrin Repeat And PH Domain 1), two cis eQTLs, rs110751858 and rs135397766, were identified in a LD block within 6kb (A). For ELN (Elastin) gene, rs111004978 and rs133370240 have the same regression coefficients and were in a LD block within 9 kb (B). For TTC8 (Tetratricopeptide Repeat Domain 8), two variants, rs135354635 and rs136767380, were detected as cis-eQTLs and were linked within 2 kb. For MGAT4A (Alpha-1,3-Mannosyl-Glycoprotein 4-Beta-N-Acetylglucosaminyltransferase A), Figures D and E represent linked cis-eQTLs within each LD block, 3 kb and 1 kb, respectively. Some cis-eQTLs were in each linkage disequilibrium (LD) block; the color scheme was established in accordance with the Haploview r 2 scheme (Fig. 3). Numbers in red cells represent pairwise r 2 -values (%) between corresponding SNPs. For ASAP1 , two cis eQTLs, rs110751858 and rs135397766, were identified in an LD block within 6 kb (Fig. 3A). For ELN , rs111004978 and rs133370240 had identical regression coefficients and were in an LD block within 9 kb (Fig. 3B). For TTC8 , rs135354635 and rs136767380 were identified as cis-eQTLs and were in an LD block within 2 kb (Fig. 3C). For MGAT4A , two LD groups contained three variants (rs109601924, rs110541800, and rs137222564) and two variants (rs109048556 and rs136765473) were within 3 kb and 1 kb, respectively (Fig. 3D and E). The r 2 -values of SNPs in green boxes are 1. Prediction of transcription factor targeting core genes We examined putative TFBS within the upstream 1 kb sequences of six core genes as promoter regions using the R package TFBSTools and the JASPAR2020 database; we identified 26 TFs in the genes, including CAPN5 (18 TFs), SUMF2 (20 TFs), ELN (17 TFs), ASAP1 (21 TFs), TTC8 (21 TFs), and MGAT4A (21 TFs) (Additional file 3: Supplementary Table S4). Moreover, 13 TFs were found in all six core genes. We then investigated the overlap between the 16 eQTL SNPs and the 26 TFs; we found a variant upstream of the ELN gene. This variant was rs133370240, which is located within a TFBS where myeloid zinc finger 1 (MZF-1; a transcription factor in the Krüppel family of zinc finger proteins) is bound (Table 2 and Fig. 4). This figure indicates a cis-eQTL (rs133370240) of ELN gene in MZF1 binding site. The information content matrix (ICM) represents motif in the biological sequence and contains the weights associated with the occurrence of each nucleotide at the given position in a pattern, calculating from a raw position frequency matrix (PFM). In the sequence logo, each position gives the information content obtained for each nucleotide, and the higher of the letter corresponding to a nucleotide, the larger the information and higher probability of getting that nucleotide at that position. Discussion Tenderness in Hanwoo meat is an important trait in terms of eating quality in a beef cattle breeding program, however, there are limited studies on the discovery of causal genetic variants. This study aimed to identify functional genetic factors (genes and variants) associated with meat tenderness using eQTL analysis with RNA-seq data, WBSF measurement, and TF/TFBS database. We identified 6 core genes and 16 eQTL SNPs that affected meat tenderness. In particular, the major allele variants of eQTL SNPs associated with ASAP1 , ELN , CAPN5 , and SUMF2 had been shown to be correlated with an increase in expression of these genes and WBSF (toughness), and the decreased expression levels of MGAT4A containing major alleles of eight SNPs in the introns tended to have toughness, suggesting that we need to select animals with the minor allele of the SNPs to improve tenderness of beef. In agreement with our finding, a SNP in intron 13 of ASAP1 was reported to be associated with meat quality in terms of WBSF and backfat, and three SNPs associated with WBSF were also found in CAPN5 in beef cattle 32,33 . Notably, we found that other genes (e.g., ELN , SUMF2 , TTC8 , and MGAT4A ) were related to IMF deposition through glucose metabolism in various organisms, including pigs, cattle, chickens, and mouse 34-39 . In particular, 4 SNPs found in several exons of TTC8 have been associated with IMF in pigs 40 ; SUMF2 expression is associated with the glycogen contents of the longissimus dorsi muscle in pigs 39 and with ketosis in dairy cows 41 . The discovery of genes that are closely linked to IMF deposition implies that meat tenderness is generally positively associated with IMF content or marbling 42 . Additionally, a high level of concentrate diet is fed to Hanwoo cattle for accumulation of IMF and high marbling scores 5 . Of 16 cis-eQTL SNPs associated with 6 genes for meat tenderness, we found a putative causal variant (rs133370240) overlapping MZF1 TFBS in the upstream region of ELN that is one of the extracellular matrix components and contributes to determining Hanwoo meat’s tenderness 43,44 . The tendency towards upregulation of expression in a variant (G) is due to allele-specific binding of transcription factor. That is, the major allele has more TF-binding affinity at the MZF1 TFBS compared to the minor allele (A), which also affects gene expression and meat tenderization 45 . Furthermore, 13 common TFs for all putative core genes are involved in muscle development and maintenance. For example, PAX2 contributes to recruitment of histone methyltransferase complexes to the promoter regions of genes, including Myf4 and Myod1 in muscle satellite cells 46,47 , and SOX5 has been associated with the regulation of myogenic progenitor cells of pigs as well as related to QTL for meat quality 48,49 Many variants may accompany the effects of beneficial variants because of LD (i.e., genetic hitchhiking), in which the frequency of nearby linked alleles tends to increase along with the selected gene; this results in selective sweeps when a positively selected allele becomes frequent 50 . This phenomenon creates difficulty in distinguishing between causal variants and nearby non-functional variants 51 . Here, we showed that two or more eQTL SNPs within the same gene belong to the same LD. In this context, we also need to identify potential regulatory SNPs in introns that could alter transcriptional regulation; such SNPs have the potential to cause meat tenderness because intronic variants could also be one of regulatory elements 52-54 . Promoter-proximal introns and intron splicing could stimulate transcription by enhancing RNA polymerase II initiation or histone modification 53-55 . Intron-mediated enhancement has a strong effect on mRNA accumulation 55,56 . We acknowledge that the small sample size is a limitation of our multi-omics analysis. Therefore, we used core genes to detect eQTLs due to adjusted p-value. The biological functions of core DEGs and cis-eQTLs can be investigated using loss- and gain-of-function methods, or genome editing; it would be informative to elucidate trans-eQTLs to explain the effects of putative core genes for meat tenderness. Conclusion We revealed cis-eQTL variants associated with meat tenderness in Hanwoo cattle by utilizing gene expression data (RNAseq) as a bridge between phenotype (WBSF) and genotypic variants (SNPs); we identified a putative causal variant located in the TFBS for MFZ1 within the upstream region of ELN . Moreover, all cis-eQTL SNPs in the core genes exhibit strong LD. Our findings would provide useful information for genomic selection. Declarations Acknowledgments This work was partly supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2019R1F1A1057605). Funding This study was supported by the “Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ012687)” Rural Development Administration, Republic of Korea. Author contributions These authors contributed equally: Yoonji Chung and Sun Sik Jang Authors and Affiliations Division of Animal and Dairy Science, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon, 34134, South Korea Yoonji Chung, Yeong Kuk Kim, Inchul Choi and Seung Hwan Lee Hanwoo Research Institute, National Institute of Animal Science, RDA, Pyeongchang, South Korea Sun Sik Jang & Hyun Joo Kim Department of Beef Science, Korea National University of Agriculture and Fisheries, Wanju, 54874, South Korea Dong Hun Kang, Ki Yong Chung School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia Hyun Joo Kim Contributions Y.C.(Yoonji Chung), S.S.J.(Sun Sik Jang), D.H.K.(Dong Hun Kang), Y.K.K.(Yeong Kuk Kim), H.J.K.(Hyun Joo Kim), K.Y.C.(Ki Yong Chung), I.C.(Inchul Choi) and S.H.L.(Seung Hwan Lee) Y.C., S.S.J, I.C and S.H.L conceived the idea. I.C and S.H.L directed and supervised the study as corresponding authors. S.S.J, D.H.K and K.Y.C organized the database. Y.C. and S.S.J wrote the first draft of the manuscript. Y.C., S.S.J., I.C., S.H.L., Y.K.K. and H.J.K. contributed to manuscript revision. All authors contributed to read and approved the submitted version. Y.C and S.S.J. contributed equally to this work. Corresponding authors Correspondence to Inchul Choi and Seung Hwan Lee Ethics declarations Competing interests The authors declare no competing interests. Availability of data and materials The data (RNAseq data) analyzed in this study were obtained from the marbling fineness project in Hanwoo Research Institute of the National Institute of Animal Science in Republic of Korea. The datasets generated during the current study are available from the corresponding author on reasonable request. Additional information Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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Genetics 175 , 1395-1406 (2007). Qanbari, S. et al. Classic selective sweeps revealed by massive sequencing in cattle. PLoS genetics 10 , e1004148 (2014). Shaul, O. How introns enhance gene expression. The international journal of biochemistry & cell biology 91 , 145-155 (2017). Rojano, E., Seoane, P., Ranea, J. A. & Perkins, J. R. Regulatory variants: from detection to predicting impact. Briefings in bioinformatics 20 , 1639-1654 (2019). Rose, A. B. Introns as gene regulators: a brick on the accelerator. Frontiers in genetics 9 , 672 (2019). Dwyer, K., Agarwal, N., Gega, A. & Ansari, A. Proximity to the Promoter and Terminator Regions Regulates the Transcription Enhancement Potential of an Intron. Frontiers in Molecular Biosciences 8 (2021). Gallegos, J. E. & Rose, A. B. The enduring mystery of intron-mediated enhancement. Plant Science 237 , 8-15 (2015). Tables Table 1 Significant cis-eQTL variants Gene name rs id Allele a Chromosome Position (bp) Type of variant Coef b P-value ASAP1 rs110751858 rs135397766 G/A A/G 14 11330635 11333062 Intron variant -0.31 -0.31 7.96E-03 7.96E-03 ELN rs111004978 rs133370240 G/A G/A 25 33824086 33825442 Upstream gene variant -0.61 -0.61 9.48E-03 9.48E-03 CAPN5 rs41772707 C/A 15 57261958 Intron variant -0.33 5.78E-03 SUMF2 rs110465445 G/A 25 27990277 Upstream gene variant -0.34 1.70E-03 TTC8 rs135354635 rs136767380 A/C G/A 10 101628209 101628784 Intron variant 0.26 0.26 7.42E-03 7.42E-03 MGAT4A rs109601924 rs110541800 rs137222564 rs109048556 rs136765473 rs109687823 rs134437911 rs134260466 G/A G/A A/G A/G A/G A/C G/A G/A 11 3828573 3830575 3832150 3834852 3835362 3833726 3817768 3889038 Intron variant 0.29 0.29 0.29 0.17 0.17 0.19 0.26 0.15 1.03E-04 1.03E-04 1.03E-04 3.93E-03 3.93E-03 3.22E-03 3.89E-03 8.54E-03 a Major allele/minor allele b Regression coefficient was obtained from linear model A total of 15 cis-eQTL SNPs had significance with p-value < 0.01. Among them, 3 or 12 SNPs was on upstream or intron region of six genes; ASAP1 , ELN , CAPN5 , SUMF2, TTC8 and MGAT4A . The genes were regarded as candidate genes modulating phenotype of WBSF. Table 2 Common TFs for all core genes TF Number of TFBS ASAP1 ELN CAPN5 SUMF2 TTC8 MGAT4A Arnt 136 7 1 10 21 16 Ahr-Arnt 176 25 5 13 25 43 MZF1 (var. 2) 138 44 6 15 24 49 Pax2 724 62 16 24 111 221 SOX9 178 4 1 5 40 78 Sox17 229 9 3 11 31 125 SRY 307 16 3 20 96 110 Sox5 353 13 5 17 91 196 Hand1-Tcf3 170 31 8 10 12 37 HLTF 3075 265 78 161 543 1166 Nobox 271 10 5 16 66 104 ZNF354C 1058 174 30 57 119 197 Arid3a 548 24 4 38 150 311 Additional Declarations No competing interests reported. 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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-2013149","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":148295681,"identity":"c7506c9c-4c95-4f55-a482-a52a6576b6b0","order_by":0,"name":"Yoonji Chung","email":"","orcid":"","institution":"Chungnam National University","correspondingAuthor":false,"prefix":"","firstName":"Yoonji","middleName":"","lastName":"Chung","suffix":""},{"id":148295682,"identity":"4bcebe0c-5113-43b2-908b-152837f0cb1b","order_by":1,"name":"Sun Sik Jang","email":"","orcid":"","institution":"National Institute of Animal Science, RDA","correspondingAuthor":false,"prefix":"","firstName":"Sun","middleName":"Sik","lastName":"Jang","suffix":""},{"id":148295683,"identity":"5d6c751a-94e9-47b6-8075-172b15434184","order_by":2,"name":"Dong Hun Kang","email":"","orcid":"","institution":"Korea National University of Agriculture and Fisheries","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"Hun","lastName":"Kang","suffix":""},{"id":148295684,"identity":"0962e3fe-0fd2-4ba6-8d71-39b095608d19","order_by":3,"name":"Yeong Kuk Kim","email":"","orcid":"","institution":"Chungnam National University","correspondingAuthor":false,"prefix":"","firstName":"Yeong","middleName":"Kuk","lastName":"Kim","suffix":""},{"id":148295687,"identity":"47162fce-e6f5-477e-8a96-2e9053a64761","order_by":4,"name":"Hyun Joo Kim","email":"","orcid":"","institution":"University of New England","correspondingAuthor":false,"prefix":"","firstName":"Hyun","middleName":"Joo","lastName":"Kim","suffix":""},{"id":148295688,"identity":"51180f00-494b-4ede-bd8a-31f79fb8ac48","order_by":5,"name":"Ki Yong Chung","email":"","orcid":"","institution":"Korea National University of Agriculture and Fisheries","correspondingAuthor":false,"prefix":"","firstName":"Ki","middleName":"Yong","lastName":"Chung","suffix":""},{"id":148295689,"identity":"aa7925d8-501b-469b-ba7c-d2ba34172c30","order_by":6,"name":"Inchul Choi","email":"","orcid":"","institution":"Chungnam National University","correspondingAuthor":false,"prefix":"","firstName":"Inchul","middleName":"","lastName":"Choi","suffix":""},{"id":148295692,"identity":"924e3fec-4630-422e-9219-82eedf61feec","order_by":7,"name":"Seung Hwan Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYDACdiCWqLCBcROI0MIMxBZn0kjVUtlymAQt8s3MBx/cbDgvby6RwPjhB0NaPkEtBofZkg1n7rhtuHNGArNkD0OOZQNBLcw8ZtKSZ24nGNxIYJBmYKgwIMJh/N9//207B9LC/JsoLQyHedgYJNsOgLSwAW3JIawF6BdjCYkzyYYbzjxss+wxSCPCYe3NDz9IVNjJGxxPPnzjR0UyEQ5DAMYGoKWkaBgFo2AUjIJRgBMAAMh7NzbPB3NXAAAAAElFTkSuQmCC","orcid":"","institution":"Chungnam National University","correspondingAuthor":true,"prefix":"","firstName":"Seung","middleName":"Hwan","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2022-08-30 09:29:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2013149/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2013149/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":28575750,"identity":"bb8878d5-70ae-4621-886d-208197d06256","added_by":"auto","created_at":"2022-11-02 18:08:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":178535,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the study protocol\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2013149/v1/baad4be4c890a3ef528ba8ae.png"},{"id":28575752,"identity":"0d606834-9081-4150-8a9c-4c8f2a2a92c0","added_by":"auto","created_at":"2022-11-02 18:08:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":457028,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression QTL plots of significant core genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The X-axis represents log2 TMM normalized gene expression, and Y-axis represents WBSF values for Hanwoo cattle. (B, C) The X-axis represents cis-eQTL genotypes, and Y-axis represents log2 TMM normalized gene expression for core genes. The blue lines and grey area indicate estimated fit line and standard error, respectively.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2013149/v1/5c2814e8a7d679351b1237ff.png"},{"id":28575753,"identity":"f63d297d-afeb-4c9d-b540-95fad3894c2b","added_by":"auto","created_at":"2022-11-02 18:08:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":154125,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ecis-eQTLs in each LD block\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSome cis-eQTLs were in each Linkage disequilibrium (LD) block, and the colour scheme is according to the Haploview r2 scheme. Numbers in the red cell represent pairwise r2-value (%) between the corresponding SNPs. For ASAP1 (ArfGAP With SH3 Domain, Ankyrin Repeat And PH Domain 1), two cis eQTLs, rs110751858 and rs135397766, were identified in a LD block within 6kb (A). For ELN (Elastin) gene, rs111004978 and rs133370240 have the same regression coefficients and were in a LD block within 9 kb (B). For TTC8 (Tetratricopeptide Repeat Domain 8), two variants, rs135354635 and rs136767380, were detected as cis-eQTLs and were linked within 2 kb. For MGAT4A (Alpha-1,3-Mannosyl-Glycoprotein 4-Beta-N-Acetylglucosaminyltransferase A), Figures D and E represent linked cis-eQTLs within each LD block, 3 kb and 1 kb, respectively.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2013149/v1/1965dc98ac546e78288a2a63.png"},{"id":28576322,"identity":"8d147232-f6cf-44ff-aca3-dc29df013951","added_by":"auto","created_at":"2022-11-02 18:24:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133540,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscription factor binding site about \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eELN\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e gene\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2013149/v1/8193231cf9855d414adfc26d.png"},{"id":32507090,"identity":"17d9cdc2-eca3-4ad7-924b-df8fdeb15c13","added_by":"auto","created_at":"2023-02-06 06:29:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1118502,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2013149/v1/daa712bd-59fa-45cb-be3d-9712131d7fe6.pdf"},{"id":28575751,"identity":"19efe517-95e0-4050-bc63-783dbcdd2fc3","added_by":"auto","created_at":"2022-11-02 18:08:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14260,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-2013149/v1/ce61767dc311d267900e9b34.docx"},{"id":28575756,"identity":"8fbefc4f-466d-4ced-a88b-6ce3c5be9a14","added_by":"auto","created_at":"2022-11-02 18:08:08","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":429960,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-2013149/v1/50502d14e91558f7e6076eb1.docx"},{"id":28576022,"identity":"aa39ed91-820f-4d85-a88d-c2fc5cd696f0","added_by":"auto","created_at":"2022-11-02 18:16:08","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":947319,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2013149/v1/c22d972329064c67c4b329dd.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of potential biomarkers associated with meat tenderness in Hanwoo (Korean cattle): an expression quantitative trait loci analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe breeding and production program of Hanwoo cattle (Hanwoo beef cattle) consist of a bull selection program using genetic selection for production traits and high-concentration feeding system for high marbling meat \u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The system mainly has focused on increasing genetic potentials for breeding goal traits such as carcass weight and marbling score because beef consumption per capita has increased more than five times compared to 10 years ago with Korea\u0026rsquo;s economic growth and westernization of eating habits \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. As a result, the production system produced Hanwoo beef with very high intramuscular fat (IMF) contents. However, over times, there are changes in Korean consumers preference about meat characteristics, and the consumers are eager to eat Hanwoo beef with the highly eating quality giving a delicious and juicy eating experience, without compromising health problems such as coronary heart disease \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEating quality is affected by sensory traits such as tenderness, juiciness, and flavor likeness \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Among these traits, meat tenderness is considered most important for beef quality, consumer satisfaction level, and industry profit. Beef tenderness is analyzed in terms of the Warner-Bratzler shear force (WBSF) which is negatively correlated with IMF contents in Hanwoo beef \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Meat texture (toughness and tenderness) is affected by collagen contents and beef connective tissue \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. IMF deposition in connective tissue can directly influence tenderness through the breakdown of connective tissue \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Therefore, improvements in meat tenderness have been prioritized to provide beef consumers with a pleasurable eating experience.\u003c/p\u003e \u003cp\u003eMeat tenderness could be included in breeding program as another breeding objective trait. However, tenderness in beef is a complex trait that is very difficult to measure and has a low heritability, so genomic selection programs using causal genetic factors would be useful to increase the genetic improvement of this trait \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo identify the causality of genetic factors (genes or variants) on meat tenderness, genetic and proteomic data have mainly been used in cattle breeding program. For example, two-dimensional electrophoresis (2DE) showed that genotypes of the \u003cem\u003eCAPN4751\u003c/em\u003e and \u003cem\u003eUOGCAST\u003c/em\u003e affect the expression of protein related to muscle metabolism in 155 Nellore cattle \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. A meta-proteomics analysis by comparing tender and tough meat using 2DE identified 21 major protein biomarkers regardless of the treatments and techniques \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Moreover, genetic variants and RNA sequencing data have been used to find QTLs or differentially expressed genes (DEGs). Co-expression analysis for tenderness in \u003cem\u003eLongissimus thoracis\u003c/em\u003e muscle of Nellore cattle found three hub-genes \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In multi-breed Angus-Brahman population, genotyping and RNA-seq evaluation identified four differentially expression genes and four splicing genes as expression QTL (eQTL) and splicing QTL (sQTL) key regulators associated with meat quality in beef \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMulti-omics data analyses have shown that the significant eQTL SNPs are associated with cattle traits, but all eQTL is not located in the obvious regulatory regions including enhancers and regulatory element \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. It is necessary to confirm whether the eQTL SNPs contribute biologically to modulate gene expression and phenotype. For example, two variants in 5\u0026rsquo; regulatory region of \u003cem\u003eAGPAT3\u003c/em\u003e were reported as potential causal mutations because they affect the transcription activity of \u003cem\u003eAGPAT3\u003c/em\u003e associated with contents of milk fatty acid in dairy cattle \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Therefore, identification of eQTLs as causal genetic factors needs to be more accurate to increase selection efficiency in terms of the regulatory role of genetic factors.\u003c/p\u003e \u003cp\u003eThis study performed eQTL analysis to identify associations between omics data and WBSF in the \u003cem\u003elongissimus dorsi\u003c/em\u003e muscle of Hanwoo cattle (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e created by Biorender). (1) Putative core genes were detected through analysis of associations between WBSF and gene expression levels, in accordance with the method established by Liu et al. \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. (2) Cis-eQTL SNPs regulating the gene expression of core genes are identified as key factors. (3) We also confirm whether eQTL SNPs have potentially functional roles involved in gene expression regulation or not. In the Hanwoo population, functional analyses of meat tenderness have focused on DEG detection; factors that control gene expression have been unclear. The present study thus aimed to identify key factors that can be used to predict phenotype changes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cem\u003eEthical statement\u003c/em\u003e\u003c/p\u003e \n\u003cp\u003eThe experimental cattle muscle tissue sampling and genotypic procedures followed the standards established by the Committee for Accreditation of Laboratory Animal Care at National Institute of Animal Science (NIAS) in South Korea. The institutional Animal Care and Use Committee (IACUC) of NIAS, RDA approved this experiment (permit No. 2015-164). This study complied with the ARRIVE guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnimals and phenotype assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn total, 20 Hanwoo cattle (all 30 months of age) were included in this study; samples were obtained from the \u003cem\u003elongissimus dorsi\u003c/em\u003e muscles of cattle raised in beef feedlots at the Hanwoo Experimental Station of the National Institute of Animal Science, Rural Development Administration in Pyeongchang, Republic of Korea. The experimental procedures were conducted in accordance with the guidelines of the Animal Care and Use Committee (ethics committee approval number: 2015\u0026thinsp;\u0026minus;\u0026thinsp;150). All methods are reported in accordance with ARRIVE guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arriveguidelines.org\u003c/span\u003e\u003c/span\u003e) for the reporting of animal experiments.\u003c/p\u003e\n\u003cp\u003eIn accordance with the method described by Wheeler et al. \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, the WBSF of cooked steaks (\u003cem\u003elongissimus dorsi\u003c/em\u003e muscle) was measured. At 48 h post-slaughter, sliced steaks (approximately 80 g; 2.5 cm) were placed into polyethylene bags. They were preheated in a water bath at 80\u0026deg;C for 40 min until the internal temperature of the steak reached 70\u0026deg;C. Samples were cooked and then cooled in running water at room temperature for 30 min. At least six to eight representative core samples (diameter, 1.27 cm) were removed from each steak in a parallel arrangement. WBSF values were determined using an Instron Universal Testing machine (Instron Corporation, Canton, MA, USA) with the following operating parameters: load cell of 50 kg and crosshead speed of 200 mm/min. The WBSF value indicated the mean force required to shear each core. WBSF values (means and standard deviations) are shown in Additional file 1: Supplementary Table S1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMuscle Biopy Methods\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHanwoo steers were restrained in a hydraulic squeeze chute, hair was removed from the biopsy site, and a local anesthetic (lidocaine HCl; 20 mg/mL; 8 mL per biopsy site) was administered. A sterile cloth drape was placed over the biopsy site and a 1-cm incision was made with a scalpel. A sterile Bergstrom biopsy needle (6 mm) was used to obtain the tissue (0.5 g) from the longissimus muscle. The incision was closed with veterinary tissue glue and sprayed with a topical antibiotic followed immediately by application of a spray-on aluminum bandage. All steers were monitored for swelling 24 h and 48 h after the biopsy. Bergstrom biopsy needle (custom made) following procedures described previously in the research of Dunn et al., 2003 \u003csup\u003e22\u003c/sup\u003e; Pampusch et al., 2008 \u003csup\u003e23\u003c/sup\u003e; Winterholler et al., 2008 \u003csup\u003e24\u003c/sup\u003e; Parr et al., 2014 \u003csup\u003e25\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRNA data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003emRNA was extracted from the \u003cem\u003elongissimus dorsi\u003c/em\u003e muscle tissues of individual cattle using TRIzol Reagent (Invitrogen, Carlsbad, CA, USA), in accordance with the manufacturer\u0026rsquo;s instructions. The quality and quantity of RNA were analyzed by automated capillary gel electrophoresis using a Bioanalyzer 2100 system with RNA 6000 Nano kit (Agilent Technologies, Dublin, Ireland). To avoid genomic DNA contamination, the isolated RNA was treated with 0.1% RNase-free DNase1 (Ambion, Inc., Austin, TX, USA). Approximately 2 \u0026micro;g of total RNA from each sample were utilized to construct paired-end sequencing complementary DNA (cDNA) libraries using an Illumina TrueSeq Preparation Kit, in accordance with the manufacturer\u0026rsquo;s instructions (Illumina, San Diego, CA, USA). RNA sequencing was performed on a Hiseq 2000 Illumina platform to obtain paired-end reads for 100-base pair (bp) sequences.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGenomic data and quality control\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted from \u003cem\u003elongissimus dorsi\u003c/em\u003e muscle samples using the DNeasy Blood \u0026amp; Tissue Kit (Qiagen, Valencia, CA, USA); DNA concentration and purity were assessed using a NanoDrop 1000 (Thermo Fisher Scientific, Wilmington, DE, USA). All animals were genotyped with the Illumina Bovine SNP777 BeadChip (777K) platform (UMD3.1). We used SNP chip data, which initially contained 777,962 SNPs for 20 individuals. For eQTL analysis, genomic data for 20 samples were filtered in accordance with the quality control procedures in PLINK1.9 software. At the SNP level, we excluded SNPs with a Minor Allele Frequency\u0026thinsp;\u0026lt;\u0026thinsp;0.01, a Hardy-Weinberg equilibrium p-value\u0026thinsp;\u0026lt;\u0026thinsp;1E-04, and a genotype missing rate\u0026thinsp;\u0026gt;\u0026thinsp;0.1. After the completion of quality control, 20 individuals and 576,549 SNPs remained.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDetection of core genes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFastQC v0.11.5 software \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e was used to assess raw read quality. Adaptor sequences and reads with low-quality bases were removed by Cutadapt v1.16 software \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and Trimmomatic v0.36 software \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, respectively. TopHat v2.1.1 software \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and Bowtie2 v2.2.9 software \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e were used to align pre-processed RNA sequences to the bovine reference genome Bos_taurus.UMD3.1 in the Ensembl database. Mapped read counts were measured by HTSeq v0.91 software \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e; genes with both CPM (gene)\u0026thinsp;\u0026le;\u0026thinsp;1 and rowSum (CPM)\u0026thinsp;\u0026lt;\u0026thinsp;10 were excluded from analysis because of low expression. Read counts were normalized by the trimmed mean of M-value method using the edgeR v.3.22.3 package \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e in R software.\u003c/p\u003e\n\u003cp\u003eMany RNAseq studies use a generalized linear model, which regards gene expression as a response and groups divided by phenotype as explanatory variables, to identify genes affected by a trait. Nevertheless, quantitative traits can be conceptually regarded as a response variable because of ambiguity when explanatory variables are used as response variables \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. We utilized the following linear regression model to detect core genes:\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equa\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\mathbf{y}={\\mathbf{E}\\mathbf{x}\\mathbf{p}}_{{i}}+{\\mathbf{e}}_{{i}}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mathbf{y}\\)\u003c/span\u003e\u003c/span\u003e is a vector of shear force on 20 Hanwoo cattle; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mathbf{E}\\mathbf{x}\\mathbf{p}}_{{i}}\\)\u003c/span\u003e\u003c/span\u003e is a vector of the normalized gene expression for the \u003cem\u003ei\u003c/em\u003eth gene (\u003cem\u003ei\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1, \u0026hellip;, m), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mathbf{e}\\)\u003c/span\u003e\u003c/span\u003e is a vector of residual effects. No fixed effects were used for WBSF because of the lowest Akaike information criterion value in the null model (Additional file 1: Supplementary Table S2).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eeQTL analysis using core genes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA linear regression model was used to determine the association between SNP genotype (0, 1, 2) and the expression patterns of core genes. For eQTL analysis, read counts were transformed to CPM, then normalized by the trimmed mean of M-value method using the edgeR package \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. A cis-eQTL was defined as an SNP located within 5 kb of the transcription start sites (TSS) or transcription termination sites (TTS) of an annotated gene; cis- and trans-eQTL SNPs were separately identified using a threshold for statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTranscription factor binding site (TFBS) enrichment analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo identify potential transcription factors (TFs) that act as regulators of candidate genes, we carried out a TF binding site (TFBS) enrichment analysis using the R package TFBSTools v1.26.0 with the JASPAR2020 database \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. mRNA sequence information was obtained from the ENSEMBL database, and sequences within 1 kb upstream of the transcription start site were regarded as potential TF binding sites. We calculated binding scores for 37 position weight matrices corresponding to vertebrate TFs, using the corresponding sequence for each candidate gene. The effect of each sequence on a TF PWM by calculating its PWM score and then compared this score to a pre-determined minimum score threshold (0.9). After multiple testing using the Benjamini-Hochberg p-value adjustment method (adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), we searched TFs.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eDetection of potential core genes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo show the suitability of ordinary linear regression model in RNA-seq, we checked the normality assumptions of the residuals (Additional file 2: Supplementary Fig. S1). The Shapiro-Wilk\u0026rsquo;s test was performed on all genes. No significant genes were detected under the significance level (p-value \u0026lt; 0.01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe association study using WBSF and gene expression was conducted to detect the significant genes modulating the phenotype. After filtering out genes with low expression among 24,596 genes, 13,360 genes were used for detecting core genes. We found significant WBSF-related 166 genes in the proposed regression model (p-value \u0026lt; 0.01). Here, 135 genes have positive coefficients, and 31 genes were negatively associated with WBSF (Additional file 3: Supplementary Table S3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eExpression QTL detection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe examined the cis-eQTL variants within 5 kb on either side of TSS and TTS of each core gene to find the putative variants regulating the transcription of core gene. Applying a threshold (p-value \u0026lt; 0.01) for gene-based association with WBSF, we identified 16 cis-eQTLs that were each significantly associated with expression levels of six mRNA-coding genes including \u003cem\u003eASAP1\u0026nbsp;\u003c/em\u003e(ArfGAP With SH3 Domain, Ankyrin Repeat And PH Domain 1), \u003cem\u003eCAPN5\u0026nbsp;\u003c/em\u003e(Calpain 5), \u003cem\u003eELN\u0026nbsp;\u003c/em\u003e(Elastin), \u003cem\u003eSUMF2\u0026nbsp;\u003c/em\u003e(Sulfatase Modifying Factor 2), \u003cem\u003eTTC8\u0026nbsp;\u003c/em\u003e(Tetratricopeptide Repeat Domain 8)\u003cem\u003e,\u003c/em\u003e and \u003cem\u003eMGAT4A\u0026nbsp;\u003c/em\u003e(Alpha-1,3-Mannosyl-Glycoprotein 4-Beta-N-Acetylglucosaminyltransferase A)\u003cem\u003e.\u003c/em\u003e There were 13 intron variants and 3 upstream gene variants (Table 1 and Additional file 2: Supplementary Fig. S2).\u003c/p\u003e\n\u003cp\u003eThe expression levels of\u003cem\u003e\u0026nbsp;ASAP1, ELN, CAPN5, SUMF2,\u003c/em\u003e and\u003cem\u003e\u0026nbsp;TTC8\u003c/em\u003e were all positively correlated with WBSF (negatively correlated with tenderness) and were downregulated in minor allele variants, except for \u003cem\u003eTTC8\u003c/em\u003e (Fig. 2A and B). However, the expression level of \u003cem\u003eMGAT4A\u003c/em\u003e was negatively correlated with WBSF and increased in minor alleles (Fig. 2A and C).\u003c/p\u003e\n\u003cp\u003e(A) The X-axis represents log2 TMM normalized gene expression, and Y-axis represents WBSF values for Hanwoo cattle. (B, C) The X-axis represents cis-eQTL genotypes, and Y-axis represents log2 TMM normalized gene expression for core genes. The blue lines and grey area indicate estimated fit line and standard error, respectively.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We identified two alleles that were significant cis-eQTLs for \u003cem\u003eASAP1 (\u003c/em\u003ers110751858 and rs135397766) found decreases in gene expression in the animals that have minor allele of rs110751858 (p-value = 7.96E-03) as well as rs135397766 (p-value = 7.96E-03), and the negative regression coefficient (CV; coefficient value = -0.31).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also identified two cis-eQTLs (rs111004978, p-value = 9.47E-03 and rs133370240, p-value = 9.47E-03) in the upstream region of \u003cem\u003eELN,\u003c/em\u003e modulated the expression levels (CV = -0.6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe found rs41772707 (p-value = 5.78E-03) and rs110465445 (p-value = 1.70E-03) in \u003cem\u003eCAPN5\u003c/em\u003e and \u003cem\u003eSUMF2\u003c/em\u003e, respectively. The minor alleles tended to decrease the expression of the both cis-eQTL gene, \u003cem\u003eCAPN5\u003c/em\u003e (CV = -0.33) and \u003cem\u003eSUMF2\u003c/em\u003e (CV = -0.34).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor \u003cem\u003eTTC8\u003c/em\u003e gene, two SNPs (rs135354635 and rs136767380) have statistical significance (p-value = 7.42E-03 for both SNPs). In particular, the minor alleles of both SNPs upregulated \u003cem\u003eTTC8\u003c/em\u003e expression (CV = 0.26).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the analysis of cis-eQTL and \u003cem\u003eMGAT4A\u003c/em\u003e expression, seven significant eQTL SNPs were identified. Three variants (rs109601924, rs110541800, and rs137222564) and two variants (rs109048556 and rs136765473) had the positive regression coefficient in the \u003cem\u003eMGAT4A\u003c/em\u003e expression (CV = 0.29, p-value = 1.03E-04, and CV = 0.17, p-value = 3.9E-03), indicating the expression tends to increase in the minor alleles. In addition, rs109687823, rs134437911 and rs134260466 had also positive slopes (0.19, 0.26 and 0.15, respectively).\u003c/p\u003e\n\u003cp\u003eSome cis-eQTLs were in each Linkage disequilibrium (LD) block, and the colour scheme is according to the Haploview r2 scheme. Numbers in the red cell represent pairwise r2-value (%) between the corresponding SNPs. For ASAP1 (ArfGAP With SH3 Domain, Ankyrin Repeat And PH Domain 1), two cis eQTLs, rs110751858 and rs135397766, were identified in a LD block within 6kb (A). For ELN (Elastin) gene, rs111004978 and rs133370240 have the same regression coefficients and were in a LD block within 9 kb (B). For TTC8 (Tetratricopeptide Repeat Domain 8), two variants, rs135354635 and rs136767380, were detected as cis-eQTLs and were linked within 2 kb. For MGAT4A (Alpha-1,3-Mannosyl-Glycoprotein 4-Beta-N-Acetylglucosaminyltransferase A), Figures D and E represent linked cis-eQTLs within each LD block, 3 kb and 1 kb, respectively.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Some cis-eQTLs were in each linkage disequilibrium (LD) block; the color scheme was established in accordance with the Haploview r\u003csup\u003e2\u003c/sup\u003e scheme (Fig. 3). Numbers in red cells represent pairwise r\u003csup\u003e2\u003c/sup\u003e-values (%) between corresponding SNPs. For \u003cem\u003eASAP1\u003c/em\u003e, two cis eQTLs, rs110751858 and rs135397766, were identified in an LD block within 6 kb (Fig. 3A). For \u003cem\u003eELN\u003c/em\u003e, rs111004978 and rs133370240 had identical regression coefficients and were in an LD block within 9 kb (Fig. 3B). For \u003cem\u003eTTC8\u003c/em\u003e, rs135354635 and rs136767380 were identified as cis-eQTLs and were in an LD block within 2 kb (Fig. 3C). For \u003cem\u003eMGAT4A\u003c/em\u003e,\u0026nbsp;two LD groups contained three variants (rs109601924, rs110541800, and rs137222564) and two variants (rs109048556 and rs136765473) were within 3 kb and 1 kb, respectively (Fig. 3D and E).\u0026nbsp;The\u0026nbsp;r\u003csup\u003e2\u003c/sup\u003e-values of SNPs in green boxes are 1.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003ePrediction of transcription factor targeting core genes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe examined putative TFBS within the upstream 1 kb sequences of six core genes as promoter regions using the R package TFBSTools and the JASPAR2020 database; we identified 26 TFs in the genes, including \u003cem\u003eCAPN5\u003c/em\u003e (18 TFs), \u003cem\u003eSUMF2\u003c/em\u003e (20 TFs), \u003cem\u003eELN\u003c/em\u003e (17 TFs), \u003cem\u003eASAP1\u003c/em\u003e (21 TFs), \u003cem\u003eTTC8\u003c/em\u003e (21 TFs), and \u003cem\u003eMGAT4A\u003c/em\u003e (21 TFs) (Additional file 3: Supplementary Table S4). Moreover, 13 TFs were found in all six core genes. We then investigated the overlap between the 16 eQTL SNPs and the 26 TFs; we found a variant upstream of the \u003cem\u003eELN\u003c/em\u003e gene. This variant was rs133370240, which is located within a TFBS where myeloid zinc finger 1 (MZF-1; a transcription factor in the Kr\u0026uuml;ppel family of zinc finger proteins) is bound (Table 2 and Fig. 4).\u003c/p\u003e\n\u003cp\u003eThis figure indicates a cis-eQTL (rs133370240) of ELN gene in MZF1 binding site. The information content matrix (ICM) represents motif in the biological sequence and contains the weights associated with the occurrence of each nucleotide at the given position in a pattern, calculating from a raw position frequency matrix (PFM). In the sequence logo, each position gives the information content obtained for each nucleotide, and the higher of the letter corresponding to a nucleotide, the larger the information and higher probability of getting that nucleotide at that position.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTenderness in Hanwoo meat is an important trait in terms of eating quality in a beef cattle breeding program, however, there are limited studies on the discovery of causal genetic variants. This study aimed to identify functional genetic factors (genes and variants) associated with meat tenderness using eQTL analysis with RNA-seq data,\u0026nbsp;WBSF measurement, and TF/TFBS database.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe identified 6 core genes and 16 eQTL SNPs that affected meat tenderness. In particular, the major allele variants of eQTL SNPs associated with \u003cem\u003eASAP1\u003c/em\u003e, \u003cem\u003eELN\u003c/em\u003e, \u003cem\u003eCAPN5\u003c/em\u003e, and \u003cem\u003eSUMF2\u003c/em\u003e had been shown to be correlated with an increase in expression of these genes and WBSF (toughness), and the decreased expression levels of \u003cem\u003eMGAT4A\u003c/em\u003e containing major alleles of eight SNPs in the introns tended to have toughness, suggesting that we need to select animals with the minor allele of the SNPs to improve tenderness of beef.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn agreement with our finding, a SNP in intron 13 of \u003cem\u003eASAP1\u003c/em\u003e was reported to be associated with meat quality in terms of WBSF and backfat, and three SNPs associated with WBSF were also found in \u003cem\u003eCAPN5\u003c/em\u003e in beef cattle\u0026nbsp;\u003csup\u003e32,33\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eNotably, we found that other genes (e.g., \u003cem\u003eELN\u003c/em\u003e, \u003cem\u003eSUMF2\u003c/em\u003e, \u003cem\u003eTTC8\u003c/em\u003e, and \u003cem\u003eMGAT4A\u003c/em\u003e) were related to IMF deposition through glucose metabolism in various organisms, including pigs, cattle, chickens, and mouse\u0026nbsp;\u003csup\u003e34-39\u003c/sup\u003e. In particular, 4 SNPs found in several exons of \u003cem\u003eTTC8\u003c/em\u003e have been associated with IMF in pigs\u0026nbsp;\u003csup\u003e40\u003c/sup\u003e; \u003cem\u003eSUMF2\u003c/em\u003e expression is associated with the glycogen contents of the\u0026nbsp;\u003cem\u003elongissimus dorsi\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003emuscle in pigs\u0026nbsp;\u003csup\u003e39\u003c/sup\u003e and with ketosis in dairy cows\u0026nbsp;\u003csup\u003e41\u003c/sup\u003e. The discovery of genes that are closely linked to IMF deposition implies that meat tenderness is generally positively associated with IMF content or marbling\u0026nbsp;\u003csup\u003e42\u003c/sup\u003e. Additionally, a high level of concentrate diet is fed to Hanwoo cattle for accumulation of IMF and high marbling scores\u0026nbsp;\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOf 16 cis-eQTL SNPs associated with 6 genes for meat tenderness, we found a putative causal variant (rs133370240) overlapping MZF1 TFBS in the upstream region of \u003cem\u003eELN\u003c/em\u003e that is one of the extracellular matrix components and contributes to determining Hanwoo meat\u0026rsquo;s tenderness\u0026nbsp;\u003csup\u003e43,44\u003c/sup\u003e. The tendency towards upregulation of expression in a variant (G) is due to allele-specific binding of transcription factor. That is, the major allele has more TF-binding affinity at the MZF1 TFBS compared to the minor allele (A), which also affects gene expression and meat tenderization\u0026nbsp;\u003csup\u003e45\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, 13 common TFs for all putative core genes are involved in muscle development and maintenance. For example, PAX2 contributes to recruitment of histone methyltransferase complexes to the promoter regions of genes, including Myf4 and Myod1 in muscle satellite cells\u0026nbsp;\u003csup\u003e46,47\u003c/sup\u003e, and SOX5 has been associated with the regulation of myogenic progenitor cells of pigs as well as related to QTL for meat quality\u0026nbsp;\u003csup\u003e48,49\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eMany variants may accompany the effects of beneficial variants because of LD (i.e., genetic hitchhiking), in which the frequency of nearby linked alleles tends to increase along with the selected gene; this results in selective sweeps when a positively selected allele becomes frequent\u0026nbsp;\u003csup\u003e50\u003c/sup\u003e. This phenomenon creates difficulty in distinguishing between causal variants and nearby non-functional variants\u0026nbsp;\u003csup\u003e51\u003c/sup\u003e. Here, we showed that two or more eQTL SNPs within the same gene belong to the same LD. In this context, we also need to identify potential regulatory SNPs in introns that could alter transcriptional regulation; such SNPs have the potential to cause meat tenderness because intronic variants could also be one of regulatory elements\u0026nbsp;\u003csup\u003e52-54\u003c/sup\u003e. Promoter-proximal introns and intron splicing could stimulate transcription by enhancing RNA polymerase II initiation or histone modification\u0026nbsp;\u003csup\u003e53-55\u003c/sup\u003e. Intron-mediated enhancement has a strong effect on mRNA accumulation\u0026nbsp;\u003csup\u003e55,56\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe acknowledge that the small sample size is a limitation of our multi-omics analysis. Therefore, we used core genes to detect eQTLs due to adjusted p-value. The biological functions of core DEGs and cis-eQTLs can be investigated using loss- and gain-of-function methods, or genome editing; it would be informative to elucidate trans-eQTLs to explain the effects of putative core genes for meat tenderness.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe revealed cis-eQTL variants associated with meat tenderness in Hanwoo cattle by utilizing gene expression data (RNAseq) as a bridge between phenotype (WBSF) and genotypic variants (SNPs); we identified a putative causal variant located in the TFBS for MFZ1 within the upstream region of \u003cem\u003eELN\u003c/em\u003e. Moreover, all cis-eQTL SNPs in the core genes exhibit strong LD. Our findings would provide useful information for genomic selection.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partly supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2019R1F1A1057605).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the \u0026ldquo;Cooperative Research Program for Agriculture Science \u0026amp; Technology Development (Project No. PJ012687)\u0026rdquo; Rural Development Administration, Republic of Korea.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese authors contributed equally: Yoonji Chung and Sun Sik Jang\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDivision of Animal and Dairy Science, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon, 34134, South Korea\u003c/p\u003e\n\u003cp\u003eYoonji Chung, Yeong Kuk Kim, Inchul Choi\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand Seung Hwan Lee\u003c/p\u003e\n\u003cp\u003eHanwoo Research Institute, National Institute of Animal Science, RDA, Pyeongchang, South Korea\u003c/p\u003e\n\u003cp\u003eSun Sik Jang\u0026nbsp;\u0026amp; Hyun Joo Kim\u003c/p\u003e\n\u003cp\u003eDepartment of Beef Science, Korea National University of Agriculture and Fisheries, Wanju, 54874, South Korea\u003c/p\u003e\n\u003cp\u003eDong Hun Kang, Ki Yong Chung\u003c/p\u003e\n\u003cp\u003eSchool of Environmental and Rural Science, University of New England, Armidale, NSW, Australia\u003c/p\u003e\n\u003cp\u003eHyun Joo Kim\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eContributions\u003c/p\u003e\n\u003cp\u003eY.C.(Yoonji Chung), S.S.J.(Sun Sik Jang), D.H.K.(Dong Hun Kang), Y.K.K.(Yeong Kuk Kim), H.J.K.(Hyun Joo Kim), K.Y.C.(Ki Yong Chung), I.C.(Inchul Choi) and S.H.L.(Seung Hwan Lee)\u003c/p\u003e\n\u003cp\u003eY.C., S.S.J, I.C and S.H.L conceived the idea. I.C and S.H.L directed and supervised the study as corresponding authors. S.S.J, D.H.K and K.Y.C organized the database. Y.C. and S.S.J wrote the first draft of the manuscript. Y.C., S.S.J., I.C., S.H.L., Y.K.K. and H.J.K. contributed to manuscript revision. All authors contributed to read and approved the submitted version. Y.C and S.S.J. contributed equally to this work.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Corresponding authors\u003c/p\u003e\n\u003cp\u003eCorrespondence to Inchul Choi and Seung Hwan Lee\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data (RNAseq data) analyzed in this study were obtained from the marbling fineness project in Hanwoo Research Institute of the National Institute of Animal Science in Republic of Korea. The datasets generated during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s Note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePark, B., Choi, T., Kim, S. \u0026amp; Oh, S.-H. National genetic evaluation (system) of Hanwoo (Korean native cattle). \u003cem\u003eAsian-Australasian journal of animal sciences\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 151 (2013).\u003c/li\u003e\n\u003cli\u003eLee, S. 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W.\u003cem\u003e et al.\u003c/em\u003e Pax7 activates myogenic genes by recruitment of a histone methyltransferase complex. \u003cem\u003eNature cell biology\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 77-84, doi:https://doi.org/10.1038/ncb1671 (2008).\u003c/li\u003e\n\u003cli\u003eMa, G.\u003cem\u003e et al.\u003c/em\u003e Cloning, expression, and bioinformatics analysis of the sheep CARP gene. \u003cem\u003eMolecular and cellular biochemistry\u003c/em\u003e \u003cstrong\u003e378\u003c/strong\u003e, 29-37 (2013).\u003c/li\u003e\n\u003cli\u003eNonneman, D.\u003cem\u003e et al.\u003c/em\u003e Genome-wide association of meat quality traits and tenderness in swine. \u003cem\u003eJournal of Animal Science\u003c/em\u003e \u003cstrong\u003e91\u003c/strong\u003e, 4043-4050 (2013).\u003c/li\u003e\n\u003cli\u003eMcVean, G. The structure of linkage disequilibrium around a selective sweep. \u003cem\u003eGenetics\u003c/em\u003e \u003cstrong\u003e175\u003c/strong\u003e, 1395-1406 (2007).\u003c/li\u003e\n\u003cli\u003eQanbari, S.\u003cem\u003e et al.\u003c/em\u003e Classic selective sweeps revealed by massive sequencing in cattle. \u003cem\u003ePLoS genetics\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, e1004148 (2014).\u003c/li\u003e\n\u003cli\u003eShaul, O. How introns enhance gene expression. \u003cem\u003eThe international journal of biochemistry \u0026amp; cell biology\u003c/em\u003e \u003cstrong\u003e91\u003c/strong\u003e, 145-155 (2017).\u003c/li\u003e\n\u003cli\u003eRojano, E., Seoane, P., Ranea, J. A. \u0026amp; Perkins, J. R. Regulatory variants: from detection to predicting impact. \u003cem\u003eBriefings in bioinformatics\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 1639-1654 (2019).\u003c/li\u003e\n\u003cli\u003eRose, A. B. Introns as gene regulators: a brick on the accelerator. \u003cem\u003eFrontiers in genetics\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 672 (2019).\u003c/li\u003e\n\u003cli\u003eDwyer, K., Agarwal, N., Gega, A. \u0026amp; Ansari, A. Proximity to the Promoter and Terminator Regions Regulates the Transcription Enhancement Potential of an Intron. \u003cem\u003eFrontiers in Molecular Biosciences\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e (2021).\u003c/li\u003e\n\u003cli\u003eGallegos, J. E. \u0026amp; Rose, A. B. The enduring mystery of intron-mediated enhancement. \u003cem\u003ePlant Science\u003c/em\u003e \u003cstrong\u003e237\u003c/strong\u003e, 8-15 (2015).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":" \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 \u003cdiv class=\"SimplePara\"\u003eSignificant cis-eQTL variants\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eGene name\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ers id\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eAllele\u003csup\u003ea\u003c/sup\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eChromosome\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003ePosition (bp)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eType of variant\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eCoef\u003csup\u003eb\u003c/sup\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eP-value\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eASAP1\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ers110751858\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003ers135397766\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eG/A\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003eA/G\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e11330635\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e11333062\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eIntron variant\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.31\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e-0.31\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e7.96E-03\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e7.96E-03\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eELN\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ers111004978\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003ers133370240\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eG/A\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003eG/A\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e25\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e33824086\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e33825442\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eUpstream gene variant\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.61\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e-0.61\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e9.48E-03\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e9.48E-03\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eCAPN5\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ers41772707\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eC/A\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e15\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e57261958\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eIntron variant\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.33\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.78E-03\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eSUMF2\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ers110465445\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eG/A\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e25\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e27990277\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eUpstream gene variant\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.34\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.70E-03\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eTTC8\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ers135354635\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003ers136767380\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eA/C\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003eG/A\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e101628209\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e101628784\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eIntron variant\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.26\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e0.26\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e7.42E-03\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e7.42E-03\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eMGAT4A\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ers109601924\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003ers110541800\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003ers137222564\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003ers109048556\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003ers136765473\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003ers109687823\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003ers134437911\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003ers134260466\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eG/A\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003eG/A\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003eA/G\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003eA/G\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003eA/G\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003eA/C\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003eG/A\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003eG/A\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e11\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e3828573\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e3830575\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e3832150\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e3834852\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e3835362\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e3833726\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e3817768\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e3889038\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eIntron variant\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.29\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e0.29\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e0.29\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e0.17\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e0.17\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e0.19\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e0.26\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e0.15\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.03E-04\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e1.03E-04\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e1.03E-04\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e3.93E-03\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e3.93E-03\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e3.22E-03\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e3.89E-03\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e8.54E-03\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003eMajor allele/minor allele\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003eRegression coefficient was obtained from linear model\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eA total of 15 cis-eQTL SNPs had significance with p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Among them, 3 or 12 SNPs was on upstream or intron region of six genes; \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eASAP1\u003c/span\u003e, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eELN\u003c/span\u003e, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eCAPN5\u003c/span\u003e, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eSUMF2, TTC8\u003c/span\u003e and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eMGAT4A\u003c/span\u003e. The genes were regarded as candidate genes modulating phenotype of WBSF.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\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 \u003cdiv class=\"SimplePara\"\u003eCommon TFs for all core genes\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTF\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNumber of TFBS\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eASAP1\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eELN\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eCAPN5\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eSUMF2\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eTTC8\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eMGAT4A\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eArnt\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e136\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e7\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e21\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e16\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAhr-Arnt\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e176\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e25\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e13\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e25\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e43\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMZF1 (var. 2)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e138\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e44\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e15\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e24\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e49\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePax2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e724\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e62\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e16\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e24\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e111\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e221\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSOX9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e178\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e40\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e78\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSox17\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e229\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e11\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e31\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e125\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSRY\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e307\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e16\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e96\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e110\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSox5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e353\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e13\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e17\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e91\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e196\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHand1-Tcf3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e170\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e31\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e8\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e12\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e37\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHLTF\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3075\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e265\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e78\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e161\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e543\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e1166\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNobox\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e271\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e16\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e66\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e104\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eZNF354C\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1058\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e174\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e30\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e57\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e119\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e197\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eArid3a\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e548\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e24\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e38\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e150\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e311\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\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":"","lastPublishedDoi":"10.21203/rs.3.rs-2013149/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2013149/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMeat tenderness is considered the most important trait contributing to beef quality, level of consumer satisfaction, willingness to pay premium prices, and industry profit. Genomic selection method would be helpful for genetic improvement of traits with low heritability and are difficult to measure. The identification of genes that affect beef tenderness can promote efficient genomic prediction in breeding programs. We performed statistical analysis of associations between \u003cem\u003elongissimus dorsi\u003c/em\u003e muscle tenderness and gene expression in 20 Hanwoo cattle, using Warner-Bratzler shear force (WBSF) and RNAseq data, respectively. We found 166 core genes with significant regression coefficient. In expression quantitative trait loci (eQTL) analysis, using the core genes and 777,962 SNPs for 20 individuals, we found 6 core genes (\u003cem\u003eASAP1\u003c/em\u003e, \u003cem\u003eCAPN5\u003c/em\u003e, \u003cem\u003eELN\u003c/em\u003e, \u003cem\u003eSUMF2\u003c/em\u003e, \u003cem\u003eTTC8\u003c/em\u003e, and \u003cem\u003eMGAT4A\u003c/em\u003e) regulated by 16 cis-eQTL SNPs. The variants within 5 kb of the transcription start site or transcription termination site of these core genes were significant (p \u0026lt; 0.01). Notably, we found that a cis-eQTL SNP of the \u003cem\u003eELN\u003c/em\u003e gene contained an MFZ1 binding site in its putative promoter region. These findings provide a useful information for genomic prediction using additive and non-additive genetic effects in prediction model.\u003c/p\u003e","manuscriptTitle":"Identification of potential biomarkers associated with meat tenderness in Hanwoo (Korean cattle): an expression quantitative trait loci analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-02 18:08:03","doi":"10.21203/rs.3.rs-2013149/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":"a077dc7c-e621-4aab-b83c-1e185d66e8ff","owner":[],"postedDate":"November 2nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":16610585,"name":"Biological sciences/Biotechnology/Genomics"},{"id":16610586,"name":"Biological sciences/Genetics/Agricultural genetics"},{"id":16610587,"name":"Biological sciences/Genetics/Gene expression"},{"id":16610588,"name":"Biological sciences/Genetics/Genetic markers"}],"tags":[],"updatedAt":"2023-02-06T06:29:26+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-02 18:08:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2013149","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2013149","identity":"rs-2013149","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}