Abstract
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.
Similar content being viewed by others
References
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):352–358
Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21(2):263–265
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):973–979
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):264–268
Chen J, He T (2012) Factors affecting the accuracy of genotype imputation in populations from several maize breeding programs. Nat Struct Biol 9(10):729–733
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):39–47
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):361–369
Goddard ME, Hayes BJ (2009) Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat Rev Genet 10(6):381–391
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):1509–1518
Hayes BJ, Bowman PJ, Daetwyler HD, Kijas JW, Jh VDW (2012) Accuracy of genotype imputation in sheep breeds. Anim Genet 43(1):72
Hu ZL, Park CA, Reecy JM (2016) Developmental progress and current status of the animal QTLdb. Nucleic Acids Res 44(Database issue):D827–D833
Joazeiro CA, Weissman AM (2000) RING finger proteins: mediators of ubiquitin ligase activity. Cell 102(5):549–552
Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14):1754–1760
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):e61172
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, 2006
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):1297–1303
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):739–745
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):1006–1016
Pritchard JK, Przeworski M (2001) Linkage disequilibrium in humans: models and data. Am J Hum Genet 69(1):1–14
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):559–575
Sargolzaei M, Chesnais JP, Schenkel FS (2014) A new approach for efficient genotype imputation using information from relatives. BMC Genomics 15(1):478
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):373–389
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):e1000477
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):6314–6326
Turner SD (2014) qqman: an R package for visualizing GWAS results using QQ and manhattan plots. bioRxiv 005165
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):1091–1099
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):661–669
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):500–506
Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88(1):76–82
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):30
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):746
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):521–525
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):833–838
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):72–80
Zhou X, Stephens M (2012) Genome-wide efficient mixed model analysis for association studies. Nat Genet 44(7):821–824
Zhou J, Hidaka K, Futcher B (2000) The Est1 subunit of yeast telomerase binds the Tlc1 telomerase RNA. Mol Cell Biol 20(6):1947–1955
Funding
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).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
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.
Additional information
Communicated by: Maciej Szydlowski
Electronic supplementary material
ESM 1
(DOCX 3679 kb)
Rights and permissions
About this article
Cite this article
Huang, S., He, Y., Ye, S. et al. Genome-wide association study on chicken carcass traits using sequence data imputed from SNP array. J Appl Genetics 59, 335–344 (2018). https://doi.org/10.1007/s13353-018-0448-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13353-018-0448-3