Journal of Applied Genetics

, Volume 59, Issue 3, pp 335–344 | Cite as

Genome-wide association study on chicken carcass traits using sequence data imputed from SNP array

  • Shuwen Huang
  • Yingting He
  • Shaopan Ye
  • Jiaying Wang
  • Xiaolong Yuan
  • Hao Zhang
  • Jiaqi Li
  • Xiquan Zhang
  • Zhe ZhangEmail author
Animal Genetics • Original Paper


Chicken carcass traits are economically important for the chicken industry. Detecting which genes affect chicken carcass traits is of great benefit to the genetic improvement of this important agricultural species. To investigate the genetic mechanism of carcass traits in chickens, we carried out a genome-wide association study (GWAS). A total of 435 Chinese indigenous chickens were phenotyped for carcass weight (CW), eviscerated weight with giblets (EWG), and eviscerated weight (EW) after slaughter at 91 days and were genotyped using a 600-K single nucleotide polymorphism (SNP) genotyping array. Twenty-four birds were selected for sequencing, and the 600 K SNP panel data were imputed to sequence data with the 24 birds as the reference. Univariate GWASs were performed with GEMMA software using the whole genome sequence data imputed from SNP chip data. Finally, 3, 25, and 63 suggestively significant SNPs were identified to be associated with carcass weight (CW), eviscerated weight with giblets (EWG), and eviscerated weight (EW), respectively. Six candidate genes, RNF219, SCEL, MYCBP2, ETS1, APLP2, and PRDM10 were detected. SCEL and MYCBP2 were potentially associated with these three traits, RNF219 and APLP2 were potentially associated with EWG and EW, and ETS1 and PRDM10 were only potentially associated with EWG and EW, respectively. Compared with forefathers’ research, 10 reported QTLs associated with CW were located within a 5-Mb distance near the SNPs with P value lower than 1×10−5. This study enriched the knowledge of the genetic mechanisms of chicken carcass traits.


Chicken GWAS Imputation Sequence data Carcass traits 



This study was funded by the National Natural Science Foundation of China (31772556), the earmarked fund for China Agriculture Research System (CARS-41), the S&T Planning Project of Guangdong (2015A020209159), and the Pearl River S&T Nova Program of Guangzhou (201506010027).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

The experimental procedures used in this study met the guidelines of the Animal Care and Use Committee of the South China Agricultural University (SCAU) (Guangzhou, People’s Republic of China). All animal experiments of this study were approved by the Animal Care and Use Committee of the SCAU with approval number SCAU#0017. All efforts were made to minimize animal suffering.

Supplementary material

13353_2018_448_MOESM1_ESM.docx (3.6 mb)
ESM 1 (DOCX 3679 kb)


  1. Atzmon G, Ronin YI, Korol A, Yonash N, Cheng H, Hillel J (2006) QTLs associated with growth traits and abdominal fat weight and their interactions with gender and hatch in commercial meat-type chickens. Anim Genet 37(4):352–358CrossRefPubMedGoogle Scholar
  2. Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21(2):263–265CrossRefPubMedGoogle Scholar
  3. Bredrup C, Johansson S, Bindoff LA, Sztromwasser P, Krakenes J, Mellgren AE, Bruras KR, Lind O, Boman H, Knappskog PM, Rodahl E (2015) High myopia-excavated optic disc anomaly associated with a frameshift mutation in the MYC-binding protein 2 gene (MYCBP2). Am J Ophthalmol 159(5):973–979CrossRefPubMedGoogle Scholar
  4. Champliaud MF, Baden HP, Koch M, Jin W, Burgeson RE, Viel A (2000) Gene characterization of sciellin (SCEL) and protein localization in vertebrate epithelia displaying barrier properties. Genomics 70(2):264–268CrossRefPubMedGoogle Scholar
  5. Chen J, He T (2012) Factors affecting the accuracy of genotype imputation in populations from several maize breeding programs. Nat Struct Biol 9(10):729–733Google Scholar
  6. Druet T, Macleod IM, Hayes BJ (2014) Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions. Heredity 112(1):39–47CrossRefPubMedGoogle Scholar
  7. Gao X, Starmer J, Martin ER (2008) A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet Epidemiol 32(4):361–369CrossRefPubMedGoogle Scholar
  8. Goddard ME, Hayes BJ (2009) Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat Rev Genet 10(6):381–391CrossRefPubMedGoogle Scholar
  9. Havenstein GB, Ferket PR, Qureshi MA (2003) Carcass composition and yield of 1957 versus 2001 broilers when fed representative 1957 and 2001 broiler diets. Poult Sci 82(10):1509–1518CrossRefPubMedGoogle Scholar
  10. Hayes BJ, Bowman PJ, Daetwyler HD, Kijas JW, Jh VDW (2012) Accuracy of genotype imputation in sheep breeds. Anim Genet 43(1):72CrossRefPubMedGoogle Scholar
  11. Hu ZL, Park CA, Reecy JM (2016) Developmental progress and current status of the animal QTLdb. Nucleic Acids Res 44(Database issue):D827–D833CrossRefPubMedGoogle Scholar
  12. Joazeiro CA, Weissman AM (2000) RING finger proteins: mediators of ubiquitin ligase activity. Cell 102(5):549–552CrossRefPubMedGoogle Scholar
  13. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14):1754–1760CrossRefPubMedPubMedCentralGoogle Scholar
  14. Liu R, Sun Y, Zhao G, Wang F, Wu D, Zheng M, Chen J, Zhang L, Hu Y, Wen J (2013) Genome-wide association study identifies loci and candidate genes for body composition and meat quality traits in Beijing-You chickens. PLoS One 8(4):e61172CrossRefPubMedPubMedCentralGoogle Scholar
  15. Madsen, P., P. Sørensen, G. Su, L. H. Damgaard, H. Thomsen and R. Labouriau (2006) DMU—a package for analyzing multivariate mixed models. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil, 13–18 August, 2006Google Scholar
  16. Mckenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20(9):1297–1303CrossRefPubMedPubMedCentralGoogle Scholar
  17. Nassar MK, Goraga ZS, Brockmann GA (2012) Quantitative trait loci segregating in crosses between New Hampshire and White Leghorn chicken lines: II. Muscle weight and carcass composition. Anim Genet 43(6):739–745CrossRefPubMedGoogle Scholar
  18. Pertile SFN, Zampar A, Petrini J, Gaya LDG, Rovadoscki GA, Ramírezdíaz J, Ferraz JBS, Michelan Filho T, Mourão GB (2014) Correlated responses and genetic parameters for performance and carcass traits in a broiler line. Am J Hum Genet 15(4):1006–1016Google Scholar
  19. Pritchard JK, Przeworski M (2001) Linkage disequilibrium in humans: models and data. Am J Hum Genet 69(1):1–14CrossRefPubMedPubMedCentralGoogle Scholar
  20. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, De Bakker PI, Daly MJ (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3):559–575CrossRefPubMedPubMedCentralGoogle Scholar
  21. Sargolzaei M, Chesnais JP, Schenkel FS (2014) A new approach for efficient genotype imputation using information from relatives. BMC Genomics 15(1):478CrossRefPubMedPubMedCentralGoogle Scholar
  22. Siegel DA, Huang MK, Becker SF (2002) Ectopic dendrite initiation: CNS pathogenesis as a model of CNS development. Int J Dev Neurosci 20(3–5):373–389CrossRefPubMedGoogle Scholar
  23. Spencer CC, Su Z, Donnelly P, Marchini J (2009) Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip. PLoS Genet 5(5):e1000477CrossRefPubMedPubMedCentralGoogle Scholar
  24. Thinakaran G, Kitt CA, Roskams AJ, Slunt HH, Masliah E, Von KC, Ginsberg SD, Ronnett GV, Reed RR, Price DL (1995) Distribution of an APP homolog, APLP2, in the mouse olfactory system: a potential role for APLP2 in axogenesis. J Neurosci 15(10):6314–6326CrossRefPubMedGoogle Scholar
  25. Turner SD (2014) qqman: an R package for visualizing GWAS results using QQ and manhattan plots. bioRxiv 005165Google Scholar
  26. Van Kaam JB, Groenen MA, Bovenhuis H, Veenendaal A, Vereijken AL, Van Arendonk JA (1999) Whole genome scan in chickens for quantitative trait loci affecting carcass traits. Poult Sci 78(8):1091–1099CrossRefPubMedGoogle Scholar
  27. von Koch CS, Zheng H, Chen H, Trumbauer M, Thinakaran G, van der Ploeg LH, Price DL, Sisodia SS (1997) Generation of APLP2 KO mice and early postnatal lethality in APLP2/APP double KO mice. Neurobiol Aging 18(6):661–669CrossRefGoogle Scholar
  28. Wang W, Tao Z, Wang J, Zhang G, Wang Y, Zhang Y, Zhang J, Li G, Qian X, Han K (2015) Genome-wide association study of 8 carcass traits in Jinghai Yellow chickens using specific-locus amplified fragment sequencing technology. Poult Sci 95(3):500–506CrossRefPubMedCentralGoogle Scholar
  29. Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88(1):76–82CrossRefPubMedPubMedCentralGoogle Scholar
  30. Ye S, Yuan X, Lin X, Gao N, Luo Y, Chen Z, Li J, Zhang X, Zhang Z (2018) Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population. J Anim Sci Biotechnol 9(1):30CrossRefPubMedPubMedCentralGoogle Scholar
  31. Yi G, Shen M, Yuan J, Sun C, Duan Z, Liang Q, Dou T, Ma M, Lu J, Guo J (2015) Genome-wide association study dissects genetic architecture underlying longitudinal egg weights in chickens. BMC Genomics 16(1):746CrossRefPubMedPubMedCentralGoogle Scholar
  32. Zerehdaran S, Vereijken ALJ, Van Arendonk JAM, Van der Waaijt EH (2004) Estimation of genetic parameters for fat deposition and carcass traits in broilers. Poult Sci 83(4):521–525CrossRefPubMedGoogle Scholar
  33. Zerehdaran S, Vereijken ALJ, Arendonk JAM, Waaij EHVD (2005) Effect of age and housing system on genetic parameters for broiler carcass traits. Poult Sci 84(6):833–838CrossRefPubMedGoogle Scholar
  34. Zhang Z, Xu ZQ, Luo YY, Zhang HB, Gao N, He JL, Ji CL, Zhang DX, Li JQ, Zhang XQ (2017) Whole genomic prediction of growth and carcass traits in a Chinese quality chicken population. J Anim Sci 95(1):72–80PubMedGoogle Scholar
  35. Zhou X, Stephens M (2012) Genome-wide efficient mixed model analysis for association studies. Nat Genet 44(7):821–824CrossRefPubMedPubMedCentralGoogle Scholar
  36. Zhou J, Hidaka K, Futcher B (2000) The Est1 subunit of yeast telomerase binds the Tlc1 telomerase RNA. Mol Cell Biol 20(6):1947–1955CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Institute of Plant Genetics, Polish Academy of Sciences, Poznan 2018

Authors and Affiliations

  1. 1.Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal ScienceSouth China Agricultural UniversityGuangzhouChina

Personalised recommendations