Broiler chicken; Genomic prediction; High density panel; Imputation; Whole-genomic sequence; Animal Science and Zoology; Veterinary (all); General Veterinary
Abstract :
[en] Traditionally, breeding values are estimated based on phenotypic and pedigree information using the numerator relationship (A) matrix. With the availability of genomic information, genome-wide markers can be included in the estimation of breeding values through genomic kinship. Genomic information allows a more accurate estimation of breeding values compared to the breeding values estimated by phenotypic and pedigree information. However, the density of genomic information used can impact the cost of implementation. The aim of this study was to compare the rank, accuracy, and bias of estimated breeding values (EBV) for organs [heart (HRT), liver (LIV), gizzard (GIZ), lungs (LUN)] and carcass [breast (BRST), drumstick (DRM) and thigh (THG)] weight traits in a broiler population using pedigree-based BLUP (PBLUP) and single-step genomic BLUP (ssGBLUP) methods using various densities of SNP and variants imputed from whole-genome sequence (WGS). For both PBLUP and ssGBLUP, heritability estimates varied from low (LUN) to high (HRT, LIV, GIZ, BRST, DRM and THG). Regression coefficient values of EBV on genomic estimated breeding values (GEBV) were similar for both the high density (HD) and WGS sets of SNPs, ranging from 0.87 to 0.99 across scenarios. Results show no benefit of using WGS data compared to HD array data using an unweighted ssGBLUP. Our results suggest that 10% of the content of the HD array can yield unbiased and accurate EBV.
Disciplines :
Genetics & genetic processes Animal production & animal husbandry
Author, co-author :
Salvian, Mayara; Department of Animal Science, University of São Paulo, Piracicaba, Brazil
The authors would like to thank Embrapa Swine & Poultry for providing the genotype, phenotype and pedigree data. We also thank the Animal Biotechnology Laboratory at University of São Paulo (ESALQ-USP) for sequencing and for genotyping the animals. The authors would like to acknowledge the collaborative efforts among Embrapa, University of São Paulo, CNPq and FAPESP for the support, and University of Nebraska – Lincoln; especially the Animal Science Department and the Holland Computer Center (HCC) for providing resources to perform the analysis. We also thank CNPq for the Research Productivity Grant granted to professors Gerson Barreto Mourão and Luiz Lehmann CoutinhoThe authors would like to thank Embrapa Swine & Poultry for providing the genotype, phenotype and pedigree data. We also thank the Animal Biotechnology Laboratory at University of São Paulo (ESALQ-USP) for sequencing and for genotyping the animals. The authors would like to acknowledge the collaborative efforts among Embrapa, University of São Paulo, CNPq and FAPESP for the support, and University of Nebraska – Lincoln; especially the Animal Science Department and the Holland Computer Center (HCC) for providing resources to perform the analysis. We also thank CNPq for the Research Productivity Grant granted to professors Gerson Barreto Mourão and Luiz Lehmann CoutinhoThis study was supported by the Brazilian Agricultural Research Corporation – Embrapa (project number 01.11.07.002.04.02) and by the thematic project (2014/08704–0) from São Paulo Research Foundation (FAPESP). The TT Reference Population was funded by the National Council of Scientific and Technological Development (CNPq) grant number 481755/2007–1 from the Brazilian Government. G.C.M. Moreira received fellowships from FAPESP, grants 14/21380–9 (in cooperation agreement with CAPES), and 16/00569–1. M. Salvian received a fellowship from CNPq, grants 206489/2017-0. L.L. Coutinho and M.C. Ledur are recipients of productivity fellowship from CNPq.
Aguilar, I., Misztal, I., Johnson, D.L., Legarra, A., Tsuruta, S., Lawlor, T.J., Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J. Dairy Sci., 2010, 10.3168/jds.2009-2730.
Al Kalaldeh, M., Gibson, J., Duijvesteijn, N., Daetwyler, H.D., MacLeod, I., Moghaddar, N., et al. Using imputed whole-genome sequence data to improve the accuracy of genomic prediction for parasite resistance in Australian sheep. Genet. Sel. Evol., 51, 2019, 32, 10.1186/s12711-019-0476-4.
Argentão, C., Filho, T.M., Marques, J.L.B., Souza, E.M., Eler, J.P., Ferraz, J.B.S., Genetic and phenotypic parameters of growth and carcass traits of a male line of broilers raised in tropical conditions. Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, 2002, 2–5.
Boschiero, C., Moreira, G.C.M., Gheyas, A.A., Godoy, T.F., Gasparin, G., Mariani, P., et al. Genome-wide characterization of genetic variants and putative regions under selection in meat and egg-type chicken lines. BMC Genom., 19, 2018, 83, 10.1186/s12864-018-4444-0.
Browning, B.L., Browning, S.R., A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am. J. Hum. Genet., 84(2), 2008, 10.1016/j.ajhg.2009.01.005.
Clark, S.A., Hickey, J.M., Van Der Werf, J.H.J., Different models of genetic variation and their effect on genomic evaluation. Genet. Sel. Evol., 43(1), 2011, 18, 10.1186/1297-9686-43-18.
Deeb, N., Cahaner, A., Genotype-by-environment interaction with broiler genotypes differing in growth rate. 3. Growth rate and water consumption of broiler progeny from weight-selected versus nonselected parents under normal and high ambient temperatures. Poult. Sci. 81 (2002), 293–301, 10.1093/ps/81.3.293.
Druet, T., Macleod, I.M., Hayes, B.J., Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions. Heredity 112:1 (2014), 39–47, 10.1038/hdy.2013.13 (Edinb).
Fornari, M.B., Zanella, R., Ibelli, A.M.G., Fernandes, L.T., Cantão, M.E., Thomaz-Soccol, V., et al. Unraveling the associations of osteoprotegerin gene with production traits in a paternal broiler line. Springerplus 3 (2014), 1–8.
Frischknecht, M., Meuwissen, T.H.E., Bapst, B., Seefried, F.R., Flury, C., Garrick, D., et al. Short communication: genomic prediction using imputed whole-genome sequence variants in Brown Swiss Cattle. J. Dairy Sci. 101 (2018), 1292–1296, 10.3168/jds.2017-12890.
Gaya, L.G., Ferraz, J.B.S., Rezende, F.M., Mourão, G.B., Mattos, E.C., Eler, J.P., et al. Heritability and genetic correlation estimates for performance and carcass and body composition traits in a male broiler line. Poult. Sci. 85:5 (2006), 837–843, 10.1093/ps/85.5.837.
Habier, D., Fernando, R.L., Dekkers, J.C.M., Genomic selection using low-density marker panels. Genetics 182 (2009), 343–353.
Heidaritabar, M., Calus, M.P.L., Megens, H.J., Vereijken, A., Groenen, M.A.M., Bastiaansen, J.W.M., Accuracy of genomic prediction using imputed whole-genome sequence data in white layers. J. Anim. Breed. Genet. 133 (2016), 167–179.
Henderson C.R. Applications of linear models in animal breeding. 1984.
Howard, J.T., Rathje, T.A., Bruns, C.E., Wilson-Wells, D.F., Kachman, S.D., Spangler, M.L., The impact of truncating data on the predictive ability for single-step genomic best linear unbiased prediction. J. Anim. Breed. Genet., 2018, 10.1111/jbg.12334.
Kim, H., Grueneberg, A., Vazquez, A.I., Hsu, S., de Los Campos, G., Will big data close the missing heritability gap?. Genetics 207 (2017), 1135–1145, 10.1534/genetics.117.300271.
Kranis, A., Gheyas, A.A., Boschiero, C., Turner, F., Yu, L., Smith, S., et al. Development of a high density 600K SNP genotyping array for chicken. BMC Genom., 14, 2013, 59, 10.1186/1471-2164-14-59.
Legarra, A., Aguilar, I., Misztal, I., A relationship matrix including full pedigree and genomic information. J. Dairy Sci., 92(9), 2009, 4656, 10.3168/jds.2009-2061.
Legarra, A., Robert-Granié, C., Manfredi, E., Elsen, J.M., Performance of genomic selection in mice. Genetics, 180(1), 2008, 611, 10.1534/genetics.108.088575.
Li, B., Zhang, N., Wang, Y.G., George, A.W., Reverter, A., Li, Y., Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods. Front. Genet. 9 (2018), 1–20, 10.3389/fgene.2018.00237 9 JUL.
Ledur, M.C., Melo, C.M.R., Nones EL, K., Zanella, K.N., Bonassi, C.A., Jaenisch, F.R.F., Moura, A.S.A.M.T., Coutinho G, L.L., Schmidt, S., Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, MG, Brasil, 5, 2006, 8–9 August 13-18, 2006Genetics.
MacLeod, I.M., Bowman, P.J., Vander Jagt, C.J., Haile-Mariam, M., Kemper, K.E., Chamberlain, A.J., et al. Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits. BMC Genom., 17, 2016, 144, 10.1186/s12864-016-2443-6.
MacLeod, I.M., Hayes, B.J., Goddard, M.E., The effects of demography and long-term selection on the accuracy of genomic prediction with sequence data. Genetics, 198(4), 2014, 1671, 10.1534/genetics.114.
Marchesi, J.A.P., Buzanskas, M.E., Cantão, M.E., Ibelli, A.M.G., Peixoto, J.O., Joaquim, L.B., et al. Relationship of runs of homozygosity with adaptive and production traits in a paternal broiler line. Animal 12 (2018), 1126–1134.
McLaren, W., Gil, L., Hunt, S.E., Riat, H.S., Ritchie, G.R.S., Thormann, A., et al. The ensembl variant effect predictor. Genome Biol., 17, 2016, 122, 10.1186/s13059-016-0974-4.
Meuwissen, T., Goddard, M., Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics, 185(2), 2010, 623, 10.1534/genetics.110.116590.
Meuwissen, T., Hayes, B., Goddard, M., Genomic selection: a paradigm shift in animal breeding. Anim. Front., 6, 2016, 6, 10.2527/af.2016-0002 x.
Misztal, I., Aggrey, S.E., Muir, W.M., Experiences with a single-step genome evaluation. Poult. Sci., 92(9), 2013, 2530, 10.3382/ps.2012-02739.
Misztal I., Tsuruta S., Lourenco D., Aguilar I., Legarra A., Vitezica Z. Manual for BLUPF90 family of programs. 2018.
Moghadam, H.K., McMillan, I., Chambers, J.R., Julian, R.J., Estimation of genetic parameters for ascites syndrome in broiler chickens. Poult. Sci. 80 (2001), 844–848, 10.1093/ps/80.7.844.
Moreira, G.C.M., Boschiero, C., Cesar, A.S.M., Reecy, J.M., Godoy, T.F., Trevisoli, P.A., et al. A genome-wide association study reveals novel genomic regions and positional candidate genes for fat deposition in broiler chickens. BMC Genom., 19(1), 2018, 374, 10.1186/s12864-018-4779-6.
Ni, G., Cavero, D., Fangmann, A., Erbe, M., Simianer, H., Whole-genome sequence-based genomic prediction in laying chickens with different genomic relationship matrices to account for genetic architecture. Genet. Sel. Evol. 49 (2017), 1–14, 10.1186/s12711-016-0277-y.
Nones, K., Ledur, M.C., Zanella, E.L., Klein, C., Pinto, L.F.B., Moura, A., et al. Quantitative trait loci associated with chemical composition of the chicken carcass. Anim. Genet., 43(5), 2012, 570, 10.1111/j.1365-2052.2012.02321.x.
Ober, U., Ayroles, J.F., Stone, E.A., Richards, S., Zhu, D., Gibbs, R.A., et al. Using whole-genome sequence data to predict quantitative trait phenotypes in Drosophila melanogaster. PLos Genet., 8(5), 2012, 10.1371/journal.pgen.1002685.
Ogawa, S., Matsuda, H., Taniguchi, Y., Watanabe, T., Nishimura, S., Sugimoto, Y., et al. Effects of single nucleotide polymorphism marker density on degree of genetic variance explained and genomic evaluation for carcass traits in Japanese Black beef cattle. BMC Genet. 15 (2014), 1–13.
Pakdel, A., van Arendonk, J.A.M., Vereijken, A.L.J., Bovenhuis, H., Genetic parameters of ascites-related traits in broilers: effect of cold and normal temperature conditions. Br. Poult. Sci. 46 (2005), 35–42, 10.1080/00071660400023938.
Purcell, S.M., Neale, B.M., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet., 81(3), 2007, 559, 10.1086/519795.
Rance, K.A., McEntee, G.M., McDevitt, R.M., Genetic and phenotypic relationships between and within support and demand tissues in a single line of broiler chicken. Br. Poult. Sci., 43(4), 2002, 10.1080/0007166022000004426.
Rolf, M.M., Taylor, J.F., Schnabel, R.D., McKay, S.D., McClure, M.C., Northcutt, S.L., et al. Impact of reduced marker set estimation of genomic relationship matrices on genomic selection for feed efficiency in Angus cattle. BMC Genet., 11(24), 2010, 10.1186/1471-2156-11-24.
Su, G., Brøndum, R.F., Ma, P., Guldbrandtsen, B., Aamand, G.P., Lund, M.S., Comparison of genomic predictions using medium-density (∼54,000) and high-density (∼777,000) single nucleotide polymorphism marker panels in Nordic Holstein and Red Dairy Cattle populations. J. Dairy Sci. 95 (2012), 4657–4665.
Tsuruta, S., Misztal, I., Aguilar, I., Lawlor, T.J., Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. J. Dairy Sci., 94(8), 2011, 10.3168/jds.2011-4256.
Van Binsbergen, R., Calus, M.P.L., Bink, M., Van Eeuwijk, F.A., Schrooten, C., Veerkamp, R.F., Genomic prediction using imputed whole-genome sequence data in Holstein Friesian cattle. Genet. Sel. Evol., 47, 2015, 10.1186/s12711-015-0149-x.
VanRaden, P.M., Van Tassell, C.P., Wiggans, G.R., Sonstegard, T.S., Schnabel, R.D., Taylor, J.F., et al. Invited review: reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci., 92(1), 2009, 10.3168/jds.2008-1514.
VanRaden, P.M., Efficient methods to compute genomic predictions. J. Dairy Sci., 91(11), 2008, 10.3168/jds.2007-0980.
Veerkamp, R.F., Bouwman, A.C., Schrooten, C., Calus, M.P.L., Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein-Friesian cattle. Genet. Sel. Evol. 48 (2016), 1–14, 10.1186/s12711-016-0274-1.
Venturini, G.C., Cruz, V.A.R., Rosa, J.O., Baldi, F., El Faro, L., Ledur, M.C., et al. Genetic and phenotypic parameters of carcass and organ traits of broiler chickens. Genet. Mol. Res., 13(4), 2014, 10294.
Vitezica, Z.G., Aguilar, I., Misztal, I., Legarra, A., Bias in genomic predictions for populations under selection. Genet. Res. (Camb.) 93 (2011), 357–366.
Wellmann, R., Preuß, S., Tholen, E., Heinkel, J., Wimmers, K., Bennewitz, J., Genomic selection using low density marker panels with application to a sire line in pigs. Genet. Sel. Evol. 45 (2013), 1–11.
Yan, G., Qiao, R., Zhang, F., Xin, W., Xiao, S., Huang, T., et al. Imputation-based whole-genome sequence association study rediscovered the missing QTL for lumbar number in sutai pigs. Sci. Rep., 7(1), 2017, 615, 10.1038/s41598-017-00729-0.
Zhang, X., Tsuruta, S., Andonov, S., Lourenco, D.A.L., Sapp, R.L., Wang, C., et al. Genetics and genomics: relationships among mortality, performance, and disorder traits in broiler chickens: a genetic and genomic approach. Poult. Sci. 97 (2018), 1511–1518.
