Document Type : Research Paper

Authors

1 Ph.D. Student, Department of Animal Science, Faculty of Agriculture, Tarbiat Modares university, Tehran, Iran

2 Assistant Professor, Department of Animal Science, Faculty of Agriculture, Tarbiat Modares university, Tehran, Iran

3 Associated Professor, Department of Animal Science, Faculty of Agriculture, Tarbiat Modares university, Tehran, Iran

Abstract

Understanding the genetic control of growth traits is one of the most important breeding goals in poultry breeding. In order to estimate the genomic heritability of growth traits, we used Illumnia 60K chicken SNP Beadchip in a chicken F2 resource population derived from the reciprocal cross between Arian line and Azerbaijan indigenous chicken. The genomic heritability was estimated through genomic relationship matrix for body weights and Shank lengths at different ages 1,3,5,7 and 9 weeks. To investigate the relationship between allele frequency and genomic heritability estimated for BW7 explained by markers, SNPs were classified into five groups of MAF (0 – 0.1, 0.1 –0.2, 0.2– 0.3, 0.3–0.4 and 0.4–0.5). To estimate the genomic heritability, five models were fitted accounting for the similarity relationship matrix within each of the five MAF groups, respectively. The genomic heritability estimations ranged from 0.43 to 0.27 for bw1 and bw9, and from 0.46 to 0.12 for Shl1 and Shl9, respectively. The estimated genomic correlations between BW and ShL at different ages were moderately high. Estimated heritabilities were 0.15, 0.3, 0.17, 0.26 and 0.27 for each of the five MAF groups, respectively. Interestingly, heritability estimates revealed highest value for MAF group (0. 1 to 0. 2). Genomic heritability estimated here can contribute to a better understanding of the genetic control of growth traits in broiler chickens. In addition, using these findings can accelerate the genetic progress in the breeding programs.

Keywords

Abdollahi-Arpanahi, R., Pakdel, A., Nejati-Javaremi, A., Moradi Shahrbabak, M., Morota, G., Valente, B.D.,  Kranis, A., Rosa, G.J.M. and Gianola, D. (2014). Dissection of additive genetic variability for quantitative traits in chickens using SNP markers. Journal of Animal Breeding and Genetics, 1–11
Demeure, O., Duclos, M. J., Bacciu, N., Mignon, G. L., Filangi, O., Pitel, F., Boland, A., Lagarrigue, S.,Cogburn, L. A., Simon, J., Roy, P. L. and Bihan-Duval, E.L. (2013). Genome wide interval mapping using SNPs identifies new QTL for growth, body composition and several physiological variables in an F2 intercross between fat and lean chicken lines. Genetic Selection Evolution, 45: 36.
Gonzalez, F., Rekaya R. and Aggrey, S.E. (2015). Genetic analysis of bone quality traits and growth in a random mating broiler population. Poultry Science, 94:883-889.
Goddard, M. and Hayes, B.J. (2009). Mapping genes for complex traits in domestic animals and their use in breeding programs. Nature reviews genetics, 10: 381-391.
Gu, X.R., Feng, C.G., Ma, L., Song, C., Wang, Y.Q., Da, Y., Li, H., Chen, K., Ye, S., Ge, C., Hu, X. and Li, N. (2011). Genome-wide association study of body weight in chicken F2 resource population. PLoS One, 6(7): e21872.
Havenstein, G.B., Ferket. P.R. and Qureshi, M.A. (2003). Growth, livability and feed conversion   of 1957 versus 2001 broilers when fed representative 1957 and 2001 broiler diets. Poultry Science. 82:1500–1508.
 Javanrouh, A., Banabazi., M.H.,  Esmaeilkhanian, S., Amirinia, C., Seyedabadi, H.R. and Emrani, H.(2006). blood cells. The 57th Annual Meeting of the European Association for Animal Production. Antalya,Turkey.
Khanyile, K.S., Dzomba, E.F. and Muchadeyi F.C. (2015). Population genetic structure, linkage disequilibrium and effective population size of conserved and extensively raised village chicken populations of Southern Africa. Frontiers in Genetics 6(13) 1-11.
Kruglyak, L. (1999). Prospects for whole-genome linkage disequilibrium mapping of common disease genes. Nature Genetics 22: 139-144.
Liu, T., QU, H., Luo, C., Shu, D., Wang, J., Lund, M.S. and Su, G. (2014). Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens. BMC Genetics 15:110
Nejati-Javaremi A., Smith C. and Gibson, J.P. (1997) Effect of total allelic relationship on accuracy of evaluation and response to selection. Journal of Animal science, 75, 1738 – 1745.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., de Bakker, P.I., Daly, M.J. and Sham, P.C. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses.The American Journal of Human Genetics,  81(3):559-75.
Schork N.J. (2001) Genome partitioning and whole-gen-ome analysis. Advances in Genetics, 42, 299 – 322.
Simeone, R., Misztal, I., Aguilar, I. and Legarra, A.(2011). Evaluation of the utility of diagonal elements of the genomic relationship matrix as a diagnostic tool to detect mislabelled genotyped animals in a broiler chicken population.  Journal of Animal Breeding and Genetics,128(5):386-93.
Shariatmadari, F.(2012). Plans of feeding broiler chickens. World's Poultry Science Journal, 68:21-30
Speed, D., Hemani, G., Johanson, Michael R, Balding and David, J. (2012). Improved Heritability Estimation from Genome-Wide SNPs. American Journal of Human Genetics. 91, 1011-1021. 
Sun, Y.F., Liu, R.R., Zheng, M.Q., Zhao, G.P., Zhang, L., Wu, D., Hu, Y.D., Li, P. and Wen J. (2013). Genome-wide Association Study on Shank Length and Shank Girth in Chicken. Chinese Journal of Animal and Veterinary Sciences, 44:358–365.
Xu, Y., and Wu, J.(2014). A linkage information based method for imputing missing diploid genotypes. https://CRAN.R-project.org/package=linkim
Yang, J.,Lee,S.H., Goddard,M.E. and  Visscher, P.M. (2011).GCTA: a  tool for  Genome wide  Complex  Analysis. American Journal of Human Genetics. 88(1): 76-82
VanRaden,F.R.(2008). Efficient methods to compute genomic predictions. Journal of dairy science, 91, 4414-23.
Van Goor, A., Bolek, K., Ashwell, C., Persia, M.E., Rothschild, M.F., Schmidt, C. and Lamont, S.J. (2015). Identification of quantitative trait loci for body temperature, body weight, breast yield, and digestibility in an advanced intercross line of chickens under heat stress. Genetic Selection Evolution, 47:96-109.
Wimmer, V. Auinger, H.J., Albrecht, T. and Schoen C.C. (2016). synbreed: a framework for the analysis of genomic prediction data using R. Bioinformatics 1;28(15):2086-7.