Whole-genome sequence-based genomic prediction in laying chickens with different genomic relationship matrices to account for genetic architecture

Genetics Selection Evolution

Research Article

Anna Fangmann¹  Guiyan Ni¹  Henner Simianer¹  Malena Erbe²  David Cavero³ 

[1] Animal Breeding and Genetics Group, Georg-August-Universität, Göttingen, Germany;Animal Breeding and Genetics Group, Georg-August-Universität, Göttingen, Germany;Institute for Animal Breeding, Bavarian State Research Centre for Agriculture, Grub, Germany;Lohmann Tierzucht GmbH, Cuxhaven, Germany;

关键词: Predictive Ability;    Genomic Prediction;    Imputation Accuracy;    Somatic Cell Score;    Genomic Good Linear Unbiased Prediction;   

DOI  :  10.1186/s12711-016-0277-y

 received in 2015-12-10, accepted in 2016-12-05,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundWith the availability of next-generation sequencing technologies, genomic prediction based on whole-genome sequencing (WGS) data is now feasible in animal breeding schemes and was expected to lead to higher predictive ability, since such data may contain all genomic variants including causal mutations. Our objective was to compare prediction ability with high-density (HD) array data and WGS data in a commercial brown layer line with genomic best linear unbiased prediction (GBLUP) models using various approaches to weight single nucleotide polymorphisms (SNPs).MethodsA total of 892 chickens from a commercial brown layer line were genotyped with 336 K segregating SNPs (array data) that included 157 K genic SNPs (i.e. SNPs in or around a gene). For these individuals, genome-wide sequence information was imputed based on data from re-sequencing runs of 25 individuals, leading to 5.2 million (M) imputed SNPs (WGS data), including 2.6 M genic SNPs. De-regressed proofs (DRP) for eggshell strength, feed intake and laying rate were used as quasi-phenotypic data in genomic prediction analyses. Four weighting factors for building a trait-specific genomic relationship matrix were investigated: identical weights, −(log10P) from genome-wide association study results, squares of SNP effects from random regression BLUP, and variable selection based weights (known as BLUP|GA). Predictive ability was measured as the correlation between DRP and direct genomic breeding values in five replications of a fivefold cross-validation.ResultsAveraged over the three traits, the highest predictive ability (0.366 ± 0.075) was obtained when only genic SNPs from WGS data were used. Predictive abilities with genic SNPs and all SNPs from HD array data were 0.361 ± 0.072 and 0.353 ± 0.074, respectively. Prediction with −(log10P) or squares of SNP effects as weighting factors for building a genomic relationship matrix or BLUP|GA did not increase accuracy, compared to that with identical weights, regardless of the SNP set used.ConclusionsOur results show that little or no benefit was gained when using all imputed WGS data to perform genomic prediction compared to using HD array data regardless of the weighting factors tested. However, using only genic SNPs from WGS data had a positive effect on prediction ability.

【 授权许可】

CC BY   
© The Author(s) 2017

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