Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle

Genetics Selection Evolution

Research Article

Kyungdo Park¹  Deukhwan Lee²  Joonho Lee³  Jack Dekkers³  Rohan Fernando³  Hao Cheng⁴  Dorian Garrick⁵  Bruce Golden⁶ 

[1] Department of Animal Biotechnology, Chonbuk National University, Chonju, Jeollabuk-do, South Korea;Department of Animal Science, Hankyong National University, Anseong, Gyeonggi-do, South Korea;Department of Animal Science, Iowa State University, 50011, Ames, IA, USA;Department of Animal Science, Iowa State University, 50011, Ames, IA, USA;Department of Statistics, Iowa State University, 50011, Ames, IA, USA;Department of Animal Science, Iowa State University, 50011, Ames, IA, USA;Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand;ThetaSolutions, LLC, Atascadero, CA, USA;ThetaSolutions, LLC, Atascadero, CA, USA;

关键词: Quantitative Trait Locus;    Prediction Accuracy;    Genomic Prediction;    Single Nucleotide Polymorphism Genotype;    Genomic Relationship Matrix;   

DOI  :  10.1186/s12711-016-0279-9

 received in 2016-06-20, accepted in 2016-12-09,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundGenomic predictions from BayesA and BayesB use training data that include animals with both phenotypes and genotypes. Single-step methodologies allow additional information from non-genotyped relatives to be included in the analysis. The single-step genomic best linear unbiased prediction (SSGBLUP) method uses a relationship matrix computed from marker and pedigree information, in which missing genotypes are imputed implicitly. Single-step Bayesian regression (SSBR) extends SSGBLUP to BayesB-like models using explicitly imputed genotypes for non-genotyped individuals.MethodsCarcass records included 988 genotyped Hanwoo steers with 35,882 SNPs and 1438 non-genotyped steers that were measured for back-fat thickness (BFT), carcass weight (CWT), eye-muscle area, and marbling score (MAR). Single-trait pedigree-based BLUP, Bayesian methods using only genotyped individuals, SSGBLUP and SSBR methods were compared using cross-validation.ResultsMethods using genomic information always outperformed pedigree-based BLUP when the same phenotypic data were modeled from either genotyped individuals only or both genotyped and non-genotyped individuals. For BFT and MAR, accuracies were higher with single-step methods than with BayesB, BayesC and BayesCπ. Gains in accuracy with the single-step methods ranged from +0.06 to +0.09 for BFT and from +0.05 to +0.07 for MAR. For CWT, SSBR always outperformed the corresponding Bayesian methods that used only genotyped individuals. However, although SSGBLUP incorporated information from non-genotyped individuals, prediction accuracies were lower with SSGBLUP than with BayesC (π = 0.9999) and BayesB (π = 0.98) for CWT because, for this particular trait, there was a benefit from the mixture priors of the effects of the single nucleotide polymorphisms.ConclusionsSingle-step methods are the preferred approaches for prediction combining genotyped and non-genotyped animals. Alternative priors allow SSBR to outperform SSGBLUP in some cases.

【 授权许可】

CC BY   
© The Author(s) 2017

【 图 表 】

12864_2015_2296_Article_IEq159.gif

12864_2015_2296_Article_IEq100.gif

12864_2016_3440_Article_IEq32.gif

12864_2017_4258_Article_IEq3.gif

12864_2017_4025_Article_IEq10.gif

12864_2015_2055_Article_IEq70.gif

12864_2015_2198_Article_IEq41.gif

12864_2017_3500_Article_IEq12.gif

12864_2016_2443_Article_IEq15.gif

12864_2016_2443_Article_IEq14.gif

12864_2016_2443_Article_IEq13.gif

12864_2015_2118_Article_IEq5.gif

12864_2017_3785_Article_IEq2.gif

12864_2016_3098_Article_IEq40.gif

12864_2017_4309_Article_IEq10.gif