Genome-wide association study identifies loci and candidate genes for meat quality traits in Simmental beef cattle
- 839 Downloads
Improving meat quality is the best way to enhance profitability and strengthen competitiveness in beef industry. Identification of genetic variants that control beef quality traits can help breeders design optimal breeding programs to achieve this goal. We carried out a genome-wide association study for meat quality traits in 1141 Simmental cattle using the Illumina Bovine HD 770K SNP array to identify the candidate genes and genomic regions associated with meat quality traits for beef cattle, including fat color, meat color, marbling score, longissimus muscle area, and shear force. In our study, we identified twenty significant single-nucleotide polymorphisms (SNPs) (p < 1.47 × 10−6) associated with these five meat quality traits. Notably, we observed several SNPs were in or near eleven genes which have been reported previously, including TMEM236, SORL1, TRDN, S100A10, AP2S1, KCTD16, LOC506594, DHX15, LAMA4, PREX1, and BRINP3. We identified a haplotype block on BTA13 containing five significant SNPs associated with fat color trait. We also found one of 19 SNPs was associated with multiple traits (shear force and longissimus muscle area) on BTA7. Our results offer valuable insights to further explore the potential mechanism of meat quality traits in Simmental beef cattle.
KeywordsPrinciple Component Analysis Meat Quality Significant SNPs Meat Quality Trait Meat Color
This work was supported by Cattle Breeding Innovative Research Team (cxgc-ias-03), the 12th “Five-Year” National Science and Technology Support Project (2011BAD28B04) Basic Research Fund Program, the National High Technology Research and Development Program of China (863 Program 2013AA102505-4), Chinese Academy of Agricultural Sciences Fundamental Research Budget Increment Projects (2013ZL031 and 2014ZL006), Chinese Academy of Agricultural Sciences Foundation (2014ywf-yb-4), Beijing Natural Science Foundation (6154032), and the National Natural Science Foundations of China (31472079, 31372294, 31402039 and 31201774).
HJG and JYL conceived and designed the experiments. JWX and QX conducted the experiments. BZ and YW analyzed the data. XG, YC, LPZ, and LYX helped conduct the experiments. HJG and JYL supervised the study. JWX wrote the manuscript. All authors have read and approved the final manuscript
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodological) 57:289–300Google Scholar
- Cesar A, Regitano L, Mourao G, Tullio R, Lanna D, Nassu R, Mudado M, Oliveira P, do Nascimento M, Chaves A, Alencar M, Sonstegard T, Garrick D, Reecy J, Coutinho L (2014a) Genome-wide association study for intramuscular fat deposition and composition in Nellore cattle. BMC Genet. doi: 10.1186/1471-2156-15-39 Google Scholar
- Cesar A, Regitano L, Mourao G, Tullio R, Lanna D, Nassu R, Mudado M, Oliveira P, do Nascimento M, Chaves A, Alencar M, Sonstegard T, Garrick D, Reecy J, Coutinho L (2014b) Genome-wide association study for intramuscular fat deposition and composition in Nellore cattle. BMC Genet. doi: 10.1186/1471-2156-15-39 Google Scholar
- Jin C, Zhang L, Xian Y, Liu X, Wu Y, Zhang F, Zhu J, Zhang G, Chen C, Gong R, Yuan J, Tian L, Wang G, Cheng Z (2014) The SORL1 polymorphism rs985421 may confer the risk for amnestic mild cognitive impairment and Alzheimer’s disease in the Han Chinese population. Neurosci Lett 563:80–84CrossRefPubMedGoogle Scholar
- Lambert AS, Grybek V, Francou B, Esterle L, Bertrand G, Bouligand J, Guiochon-Mantel A, Hieronimus S, Voitel D, Soskin S, Magdelaine C, Lienhardt A, Silve C, Linglart A (2014) Analysis of AP2S1, a calcium-sensing receptor regulator, in familial and sporadic isolated hypoparathyroidism. J Clin Endocrinol Metab 99:E469–E473CrossRefPubMedGoogle Scholar
- Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, Gore MA, Buckler ES, Zhang Z (2012) GAPIT: genome association and prediction integrated tool. Bioinformatics (Oxford, England) 28, 2397-2399Google Scholar
- McClure MC, Morsci NS, Schnabel RD, Kim JW, Yao P, Rolf MM, McKay SD, Gregg SJ, Chapple RH, Northcutt SL, Taylor JF (2010) A genome scan for quantitative trait loci influencing carcass, post-natal growth and reproductive traits in commercial Angus cattle. Anim Genet 41:597–607CrossRefPubMedGoogle Scholar
- McClure MC, Ramey HR, Rolf MM, McKay SD, Decker JE, Chapple RH, Kim JW, Taxis TM, Weaber RL, Schnabel RD, Taylor JF (2012) Genome-wide association analysis for quantitative trait loci influencing Warner-Bratzler shear force in five taurine cattle breeds. Anim Genet 43:662–673CrossRefPubMedPubMedCentralGoogle Scholar
- Sosa MS, Lopez-Haber C, Yang C, Wang H, Lemmon MA, Busillo JM, Luo J, Benovic JL, Klein-Szanto A, Yagi H, Gutkind JS, Parsons RE, Kazanietz MG (2010) Identification of the Rac-GEF P-Rex1 as an essential mediator of ErbB signaling in breast cancer. Mol Cell 40:877–892CrossRefPubMedPubMedCentralGoogle Scholar
- Takasuga A, Watanabe T, Mizoguchi Y, Hirano T, Ihara N, Takano A, Yokouchi K, Fujikawa A, Chiba K, Kobayashi N, Tatsuda K, Oe T, Furukawa-Kuroiwa M, Nishimura-Abe A, Fujita T, Inoue K, Mizoshita K, Ogino A, Sugimoto Y (2007) Identification of bovine QTL for growth and carcass traits in Japanese Black cattle by replication and identical-by-descent mapping. Mamm Genome 18:125–136CrossRefPubMedGoogle Scholar
- Tizioto PC, Gromboni CF, Nogueira AR, de Souza MM, Mudadu Mde A, Tholon P, Rosa Ado N, Tullio RR, Medeiros SR, Nassu RT, Regitano LC (2014) Calcium and potassium content in beef: influences on tenderness and associations with molecular markers in Nellore cattle. Meat Sci 96:436–440CrossRefPubMedGoogle Scholar
- Yamashita H, Goto C, Tajima R, Koparal AT, Kobori M, Ohki Y, Shitara K, Narita R, Toriyama K, Torii S, Niimi T, Kitagawa Y (2008) Cryptic fragment alpha4 LG4-5 derived from laminin alpha4 chain inhibits de novo adipogenesis by modulating the effect of fibroblast growth factor-2. Dev Growth Differ 50:97–107CrossRefPubMedGoogle Scholar
- Yang W, Guo Z, Huang C, Duan L, Chen G, Jiang N, Fang W, Feng H, Xie W, Lian X, Wang G, Luo Q, Zhang Q, Liu Q, Xiong L (2014) Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat Commun 5:5087CrossRefPubMedPubMedCentralGoogle Scholar
- Zhou Z, Sheng X, Zhang Z, Zhao K, Zhu L, Guo G, Friedenberg SG, Hunter LS, Vandenberg-Foels WS, Hornbuckle WE, Krotscheck U, Corey E, Moise NS, Dykes NL, Li J, Xu S, Du L, Wang Y, Sandler J, Acland GM, Lust G, Todhunter RJ (2010) Differential Genetic Regulation of Canine Hip Dysplasia and Osteoarthritis. PLoS ONE 5:e13219CrossRefPubMedPubMedCentralGoogle Scholar