Effect of genomic selection and genotyping strategy on estimation of variance components in animal models using different relationship matrices

Genetics, Selection, Evolution : GSE
Lei WangJust Jensen

Abstract

The traditional way to estimate variance components (VC) is based on the animal model using a pedigree-based relationship matrix (A) (A-AM). After genomic selection was introduced into breeding programs, it was anticipated that VC estimates from A-AM would be biased because the effect of selection based on genomic information is not captured. The single-step method (H-AM), which uses an H matrix as (co)variance matrix, can be used as an alternative to estimate VC. Here, we compared VC estimates from A-AM and H-AM and investigated the effect of genomic selection, genotyping strategy and genotyping proportion on the estimation of VC from the two methods, by analyzing a dataset from a commercial broiler line and a simulated dataset that mimicked the broiler population. VC estimates from H-AM were severely overestimated with a high proportion of selective genotyping, and overestimation increased as proportion of genotyping increased in the analysis of both commercial and simulated data. This bias in H-AM estimates arises when selective genotyping is used to construct the H-matrix, regardless of whether selective genotyping is applied or not in the selection process. For simulated populations under genomic selection, estimates of ge...Continue Reading

References

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Citations

Mar 29, 2021·Journal of Animal Breeding and Genetics = Zeitschrift Für Tierzüchtung Und Züchtungsbiologie·Hélène RoméJust Jensen

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Methods Mentioned

BETA
genotyping

Software Mentioned

ADAM
REML
AI
DMU
AM

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