Genomic predictions based on animal models using genotype imputation on a national scale in Norwegian Red cattle

Genetics Selection Evolution

Research Article

Jørgen Ødegård¹  Morten Svendsen²  Trygve Solberg²  Theo H. E. Meuwissen³ 

[1] Aqua Aqua Gen AS, P.O. Box 1240, Sluppen, 7462, Trondheim, Norway;GENO SA, Holsegata 22, 2317, Hamar, Norway;Institute of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Ås, Norway;

关键词: Relationship Matrix;    Genomic Prediction;    Sire Model;    Estimate Breeding Value;    Relationship Matrice;   

DOI  :  10.1186/s12711-015-0159-8

 received in 2014-11-18, accepted in 2015-09-29,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundIn dairy cattle, current genomic predictions are largely based on sire models that analyze daughter yield deviations of bulls, which are derived from pedigree-based animal model evaluations (in a two-step approach). Extension to animal model genomic predictions (AMGP) is not straightforward, because most of the animals that are involved in the genetic evaluation are not genotyped. In single-step genomic best linear unbiased prediction (SSGBLUP), the pedigree-based relationship matrix A and the genomic relationship matrix G are combined in a matrix H, which allows for AMGP. However, as the number of genotyped animals increases, imputation of the genotypes for all animals in the pedigree may be considered. Our aim was to impute genotypes for all animals in the pedigree, construct alternative relationship matrices based on the imputation results, and evaluate the accuracy of the resulting AMGP by cross-validation in the national Norwegian Red dairy cattle population.ResultsA large-scale national dataset was effectively handled by splitting it into two sets: (1) genotyped animals and their ancestors (i.e. GA set with 20,918 animals) and (2) the descendants of the genotyped animals (i.e. D set with 4,022,179 animals). This allowed restricting genomic computations to a relatively small set of animals (GA set), whereas the majority of the animals (D set) were added to the animal model equations using Henderson’s rules, in order to make optimal use of the D set information. Genotypes were imputed by segregation analysis of a large pedigree with relatively few genotyped animals (3285 out of 20,918). Among the AMGP models, the linkage and linkage disequilibrium based G matrix (GLDLA0) yielded the highest accuracy, which on average was 0.06 higher than with SSGBLUP and 0.07 higher than with two-step sire genomic evaluations.ConclusionsAMGP methods based on genotype imputation on a national scale were developed, and the most accurate method, GLDLA0BLUP, combined linkage and linkage disequilibrium information. The advantage of AMGP over a sire model based on two-step genomic predictions is expected to increase as the number of genotyped cows increases and for species, with smaller sire families and more dam relationships.

【 授权许可】

CC BY   
© Meuwissen et al. 2015

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