Genomic prediction using imputed whole-genome sequence data in Holstein Friesian cattle

Genetics Selection Evolution

Research Article

Mario P. L. Calus¹  Roel F. Veerkamp¹  Rianne van Binsbergen²  Marco C. A. M. Bink³  Fred A. van Eeuwijk³  Chris Schrooten⁴ 

[1] Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 338, 6700 AH, Wageningen, The Netherlands;Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 338, 6700 AH, Wageningen, The Netherlands;Biometris, Wageningen University and Research Centre, PO Box 16, 6700 AA, Wageningen, The Netherlands;Biometris, Wageningen University and Research Centre, PO Box 16, 6700 AA, Wageningen, The Netherlands;CRV, Arnhem, The Netherlands;

关键词: Quantitative Trait Locus;    Genomic Prediction;    Single Nucleotide Polymorphism Genotype;    Imputation Accuracy;    Genomic Estimate Breeding Value;   

DOI  :  10.1186/s12711-015-0149-x

 received in 2014-12-16, accepted in 2015-08-21,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundIn contrast to currently used single nucleotide polymorphism (SNP) panels, the use of whole-genome sequence data is expected to enable the direct estimation of the effects of causal mutations on a given trait. This could lead to higher reliabilities of genomic predictions compared to those based on SNP genotypes. Also, at each generation of selection, recombination events between a SNP and a mutation can cause decay in reliability of genomic predictions based on markers rather than on the causal variants. Our objective was to investigate the use of imputed whole-genome sequence genotypes versus high-density SNP genotypes on (the persistency of) the reliability of genomic predictions using real cattle data.MethodsHighly accurate phenotypes based on daughter performance and Illumina BovineHD Beadchip genotypes were available for 5503 Holstein Friesian bulls. The BovineHD genotypes (631,428 SNPs) of each bull were used to impute whole-genome sequence genotypes (12,590,056 SNPs) using the Beagle software. Imputation was done using a multi-breed reference panel of 429 sequenced individuals. Genomic estimated breeding values for three traits were predicted using a Bayesian stochastic search variable selection (BSSVS) model and a genome-enabled best linear unbiased prediction model (GBLUP). Reliabilities of predictions were based on 2087 validation bulls, while the other 3416 bulls were used for training.ResultsPrediction reliabilities ranged from 0.37 to 0.52. BSSVS performed better than GBLUP in all cases. Reliabilities of genomic predictions were slightly lower with imputed sequence data than with BovineHD chip data. Also, the reliabilities tended to be lower for both sequence data and BovineHD chip data when relationships between training animals were low. No increase in persistency of prediction reliability using imputed sequence data was observed.ConclusionsCompared to BovineHD genotype data, using imputed sequence data for genomic prediction produced no advantage. To investigate the putative advantage of genomic prediction using (imputed) sequence data, a training set with a larger number of individuals that are distantly related to each other and genomic prediction models that incorporate biological information on the SNPs or that apply stricter SNP pre-selection should be considered.

【 授权许可】

CC BY   
© van Binsbergen et al. 2015

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