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Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: impacts of the genetic architecture

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Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: impacts of the genetic architecture

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Mehrban, H.; Lee, D.; Moradi, M.; Ilcho, C.; Naserkheil, M.; Ibañez Escriche, N. (2017). Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: impacts of the genetic architecture. Genetics Selection Evolution. 49:1-13. https://doi.org/10.1186/s12711-016-0283-0

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Title: Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: impacts of the genetic architecture
Author: Mehrban, H. Lee, D.H. Moradi, M.H. IlCho, C. Naserkheil, M. Ibañez Escriche, Noelia
UPV Unit: Universitat Politècnica de València. Departamento de Ciencia Animal - Departament de Ciència Animal
Issued date:
[EN] Background: Hanwoo beef is known for its marbled fat, tenderness, juiciness and characteristic flavor, as well as for its low cholesterol and high omega 3 fatty acid contents. As yet, there has been no comprehensive ...[+]
Subjects: Effective population-size , Estimated breeding values , Residual feed-intake , Meat quality traits , Wide association , Linkage disequilibrium , Complex traits , Sequence data , Bos-Indicus , Accuracy
Copyrigths: Reconocimiento (by)
Genetics Selection Evolution. (issn: 0999-193X )
DOI: 10.1186/s12711-016-0283-0
Springer (Biomed Central Ltd.)
Publisher version: https://doi.org/10.1186/s12711-016-0283-0
Project ID:
This work was supported by a Grant from the IPET Program (No. 20093068), Ministry of Agriculture, Food and Rural Affairs, Republic of Korea. We are also grateful to all the staff of the Korean Hanwoo Improvement Center of ...[+]
Type: Artículo


Choi JW, Choi BH, Lee SH, Lee SS, Kim HC, Yu D, et al. Whole-genome resequencing analysis of Hanwoo and Yanbian cattle to identify genome-wide SNPs and signatures of selection. Mol Cells. 2015;38:466–73.

Lee SH, Choi BH, Cho SH, Lim D, Choi TJ, Park BH, et al. Genome-wide association study identifies three loci for intramuscular fat in Hanwoo (Korean cattle). Livest Sci. 2014;165:27–32.

Choi Y, Davis ME, Chung H. Effects of genetic variants in the promoter region of the bovine adiponectin (ADIPOQ) gene on marbling of Hanwoo beef cattle. Meat Sci. 2015;105:57–62. [+]
Choi JW, Choi BH, Lee SH, Lee SS, Kim HC, Yu D, et al. Whole-genome resequencing analysis of Hanwoo and Yanbian cattle to identify genome-wide SNPs and signatures of selection. Mol Cells. 2015;38:466–73.

Lee SH, Choi BH, Cho SH, Lim D, Choi TJ, Park BH, et al. Genome-wide association study identifies three loci for intramuscular fat in Hanwoo (Korean cattle). Livest Sci. 2014;165:27–32.

Choi Y, Davis ME, Chung H. Effects of genetic variants in the promoter region of the bovine adiponectin (ADIPOQ) gene on marbling of Hanwoo beef cattle. Meat Sci. 2015;105:57–62.

Jo C, Cho SH, Chang J, Nam KC. Keys to production and processing of Hanwoo beef: a perspective of tradition and science. Anim Front. 2012;2:32–8.

Korea Rural Economic Institute (KREI). Outlook and Agricultural Statistics Information System(OASIS). http://oasis.krei.re.kr (2015). Accessed 20 Oct 2015.

Lee SH, Park BH, Sharma A, Dang CG, Lee SS, Choi TJ, et al. Hanwoo cattle: origin, domestication, breeding strategies and genomic selection. J Anim Sci Technol. 2014;56:2.

Lee SH, Choi BH, Lim D, Gondro C, Cho YM, Dang CG, et al. Genome-wide association study identifies major loci for carcass weight on BTA14 in Hanwoo (Korean cattle). PLoS One. 2013;8:e74677.

Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157:1819–29.

Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME. Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci. 2009;92:433–43.

VanRaden PM, Sullivan PG. International genomic evaluation methods for dairy cattle. Genet Sel Evol. 2010;42:7.

Colombani C, Legarra A, Fritz S, Guillaume F, Croiseau P, Ducrocq V, et al. Application of Bayesian least absolute shrinkage and selection operator (LASSO) and BayesCpi methods for genomic selection in French Holstein and Montbeliarde breeds. J Dairy Sci. 2013;96:575–91.

Duchemin SI, Colombani C, Legarra A, Baloche G, Larroque H, Astruc JM, et al. Genomic selection in the French Lacaune dairy sheep breed. J Dairy Sci. 2012;95:2723–33.

Liu T, Qu H, Luo C, Shu D, Wang J, Lund MS, et al. Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens. BMC Genet. 2014;15:110.

Boerner V, Johnston DJ, Tier B. Accuracies of genomically estimated breeding values from pure-breed and across-breed predictions in Australian beef cattle. Genet Sel Evol. 2014;46:61.

Bolormaa S, Pryce JE, Kemper K, Savin K, Hayes BJ, Barendse W, et al. Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle. J Anim Sci. 2013;91:3088–104.

Rolf MM, Garrick DJ, Fountain T, Ramey HR, Weaber RL, Decker JE, et al. Comparison of Bayesian models to estimate direct genomic values in multi-breed commercial beef cattle. Genet Sel Evol. 2015;47:23.

Gianola D, de los Campos G, Hill WG, Manfredi E, Fernando R. Additive genetic variability and the Bayesian alphabet. Genetics. 2009;183:347–63.

Habier D, Fernando RL, Kizilkaya K, Garrick DJ. Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics. 2011;12:186.

Fernando RL, Garrick D. Bayesian methods applied to GWAS. Methods Mol Biol. 2013;1019:237–74.

van den Berg I, Fritz S, Boichard D. QTL fine mapping with Bayes C(pi): a simulation study. Genet Sel Evol. 2013;45:19.

de los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, et al. Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics. 2009;182:375–85.

Legarra A, Robert-Granie C, Croiseau P, Guillaume F, Fritz S. Improved Lasso for genomic selection. Genet Res. 2011;93:77–87.

Su G, Guldbrandtsen B, Gregersen VR, Lund MS. Preliminary investigation on reliability of genomic estimated breeding values in the Danish Holstein population. J Dairy Sci. 2010;93:1175–83.

Lund MS, Roos AP, Vries AG, Druet T, Ducrocq V, Fritz S, et al. A common reference population from four European Holstein populations increases reliability of genomic predictions. Genet Sel Evol. 2011;43:43.

Saatchi M, McClure MC, McKay SD, Rolf MM, Kim J, Decker JE, et al. Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genet Sel Evol. 2011;43:40.

Neves HH, Carvalheiro R, O’Brien AM, Utsunomiya YT, do Carmo AS, Schenkel FS, et al. Accuracy of genomic predictions in Bos indicus (Nellore) cattle. Genet Sel Evol. 2014;46:17.

Chen L, Vinsky M, Li C. Accuracy of predicting genomic breeding values for carcass merit traits in Angus and Charolais beef cattle. Anim Genet. 2015;46:55–9.

Tribout T, Larzul C, Phocas F. Efficiency of genomic selection in a purebred pig male line. J Anim Sci. 2012;90:4164–76.

Daetwyler HD, Swan AA, van der Werf JH, Hayes BJ. Accuracy of pedigree and genomic predictions of carcass and novel meat quality traits in multi-breed sheep data assessed by cross-validation. Genet Sel Evol. 2012;44:33.

Wolc A, Stricker C, Arango J, Settar P, Fulton JE, O’Sullivan NP, et al. Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model. Genet Sel Evol. 2011;43:5.

Hayes BJ, Pryce J, Chamberlain AJ, Bowman PJ, Goddard ME. Genetic architecture of complex traits and accuracy of genomic prediction: coat colour, milk-fat percentage, and type in Holstein cattle as contrasting model traits. PLoS Genet. 2010;6:e1001139.

Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA. The impact of genetic architecture on genome-wide evaluation methods. Genetics. 2010;185:1021–31.

Zhang Z, Liu J, Ding X, Bijma P, de Koning DJ, Zhang Q. Best linear unbiased prediction of genomic breeding values using a trait specific marker derived relationship matrix. PLoS One. 2010;5:e12648.

Clark SA, Hickey JM, van der Werf JH. Different models of genetic variation and their effect on genomic evaluation. Genet Sel Evol. 2011;43:18.

Moradi MH, Nejati-Javaremi A, Moradi-Shahrbabak M, Dodds KG, McEwan JC. Genomic scan of selective sweeps in thin and fat tail sheep breeds for identifying of candidate regions associated with fat deposition. BMC Genet. 2012;13:10.

Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing data inference for whole genome association studies by use of localized haplotype clustering. Am J Hum Genet. 2007;81:1084–97.

Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T, Lee DH. BLUPF90 and related programs (BGF90). In: Proceedings of the 7th world congress on genetics applied to livestock production: 19–23 August 2002; Montpellier. CD-ROM Communication No 28-27. 2002.

Legarra A RA, Filangi O. GS3 genomic Selection–Gibbs Sampling–Gauss Seidel (and BayesCπ) (2011). https://qgsp.jouy.inra.fr/index.php?option=com_content&view=article&id=60&Itemid=67 .

VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;91:4414–23.

Aguilar I, Misztal I, Tsuruta S, Legarra A, Wang H. PREGSF90–POSTGSF90: computational tools for the implementation of single-step genomic selection and genome-wide association with ungenotyped individuals in BLUPF90 programs. In: Proceedings of the 10th world congress on genetics applied to livestock production: 18–22 August 2014; Vancouver. 2014.

Meyer K, Tier B. SNP Snappy: a strategy for fast genome-wide association studies fitting a full mixed model. Genetics. 2012;190:275–7.

Khansefid M, Pryce JE, Bolormaa S, Miller SP, Wang Z, Li C, et al. Estimation of genomic breeding values for residual feed intake in a multibreed cattle population. J Anim Sci. 2014;92:3270–83.

Legarra A, Robert Granié C, Manfredi E, Elsen JM. Performance of genomic selection in mice. Genetics. 2008;180:611–8.

Abdollahi-Arpanahi R, Morota G, Valente BD, Kranis A, Rosa GJ, Gianola D. Assessment of bagging GBLUP for whole-genome prediction of broiler chicken traits. J Anim Breed Genet. 2015;132:218–28.

de los Campos G, Sorensen D, Gianola D. Genomic heritability: what is it? PLoS Genet. 2015;11:e1005048.

Corbin LJ, Liu AY, Bishop SC, Woolliams JA. Estimation of historical effective population size using linkage disequilibria with marker data. J Anim Breed Genet. 2012;129:257–70.

Tenesa A, Navarro P, Hayes BJ, Duffy DL, Clarke GM, Goddard ME, et al. Recent human effective population size estimated from linkage disequilibrium. Genome Res. 2007;17:520–6.

Rabier CE, Barre P, Asp T, Charmet G, Mangin B. On the accuracy of genomic selection. PLoS One. 2016;11:e0156086.

Hayes BJ, Visscher PM, Goddard ME. Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res. 2009;91:47–60.

Onogi A, Ogino A, Komatsu T, Shoji N, Simizu K, Kurogi K, et al. Genomic prediction in Japanese Black cattle: application of a single-step approach to beef cattle. J Anim Sci. 2014;92:1931–8.

Lee SH, Cho YM, Lim D, Kim HC, Choi BH, Park HS, et al. Linkage disequilibrium and effective population size in Hanwoo Korean cattle. Asian Aust J Anim Sci. 2011;24:1660–5.

Li Y, Kim JJ. Effective population size and signatures of selection using bovine 50 K SNP chips in Korean native cattle (Hanwoo). Evol Bioinform Online. 2015;11:143–53.

Marquez GC, Speidel SE, Enns RM, Garrick DJ. Genetic diversity and population structure of American Red Angus cattle. J Anim Sci. 2010;88:59–68.

Cleveland MA, Blackburn HD, Enns RM, Garrick DJ. Changes in inbreeding of US Herefords during the twentieth century. J Anim Sci. 2005;83:992–1001.

Sorensen AC, Sorensen MK, Berg P. Inbreeding in Danish dairy cattle breeds. J Dairy Sci. 2005;88:1865–72.

Kim ES, Kirkpatrick BW. Linkage disequilibrium in the North American Holstein population. Anim Genet. 2009;40:279–88.

de Roos AP, Hayes BJ, Spelman RJ, Goddard ME. Linkage disequilibrium and persistence of phase in Holstein–Friesian, Jersey and Angus cattle. Genetics. 2008;179:1503–12.

Ni GY, Zhang Z, Jiang L, Ma PP, Zhang Q, Ding XD. Chinese Holstein cattle effective population size estimated from whole genome linkage disequilibrium. Yi Chuan. 2012;34:50–8.

Goddard ME, Hayes BJ. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat Rev Genet. 2009;10:381–91.

González-Recio O, Rosa GJ, Gianola D. Machine learning methods and predictive ability metrics for genome-wide prediction of complex traits. Livest Sci. 2014;166:217–31.

Gao N, Li J, He J, Xiao G, Luo Y, Zhang H, et al. Improving accuracy of genomic prediction by genetic architecture based priors in a Bayesian model. BMC Genet. 2015;16:20.

Wolc A, Arango J, Settar P, Fulton JE, O’Sullivan NP, Dekkers JC, et al. Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions. J Anim Sci Biotechnol. 2016;7:7.

Nishimura S, Watanabe T, Mizoshita K, Tatsuda K, Fujita T, Watanabe N, et al. Genome-wide association study identified three major QTL for carcass weight including the PLAG1-CHCHD7 QTN for stature in Japanese Black cattle. BMC Genet. 2012;13:40.

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. 2014;15:15.

Fernandez Júnior GA, Rosa GJ, Valente BD, Carvalheiro R, Baldi F, Garcia DA, et al. Genomic prediction of breeding values for carcass traits in Nellore cattle. Genet Sel Evol. 2016;48:7.

Saatchi M, Schnabel RD, Taylor JF, Garrick DJ. Large-effect pleiotropic or closely linked QTL segregate within and across ten US cattle breeds. BMC Genomics. 2014;15:442.

Kizilkaya K, Fernando RL, Garrick DJ. Reduction in accuracy of genomic prediction for ordered categorical data compared to continuous observations. Genet Sel Evol. 2014;46:37.

Jensen J, Su G, Madsen P. Partitioning additive genetic variance into genomic and remaining polygenic components for complex traits in dairy cattle. BMC Genet. 2012;13:44.

Khatkar MS, Moser G, Hayes BJ, Raadsma HW. Strategies and utility of imputed SNP genotypes for genomic analysis in dairy cattle. BMC Genomics. 2012;13:538.

Druet T, Macleod IM, Hayes BJ. Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions. Heredity. 2014;112:39–47.

Heidaritabar M, Calus MP, Megens HJ, Vereijken A, Groenen MA, Bastiaansen JW. Accuracy of genomic prediction using imputed whole genome sequence data in white layers. J Anim Breed Genet. 2016;133:167–79.

Habier D, Fernando RL, Dekkers JC. The impact of genetic relationship information on genome-assisted breeding values. Genetics. 2007;177:2389–97.

Legarra A, Aguilar I, Misztal I. A relationship matrix including full pedigree and genomic information. J Dairy Sci. 2009;92:4656–63.

Misztal I, Aggrey SE, Muir WM. Experiences with a single-step genome evaluation. Poult Sci. 2013;92:2530–4.

Wang H, Misztal I, Aguilar I, Legarra A, Muir WM. Genome-wide association mapping including phenotypes from relatives without genotypes. Genet Res. 2012;94:73–83.

Tiezzi F, Maltecca C. Accounting for trait architecture in genomic predictions of US Holstein cattle using a weighted realized relationship matrix. Genet Sel Evol. 2015;47:24.

Neves HH, Carvalheiro R, Queiroz SA. A comparison of statistical methods for genomic selection in a mice population. BMC Genet. 2012;13:100.


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