Increasing accuracy of genomic selection in presence of high density marker panels through the prioritization of relevant polymorphisms

BMC Genetics
Ling-Yun ChangRomdhane Rekaya

Abstract

It becomes clear that the increase in the density of marker panels and even the use of sequence data didn't result in any meaningful increase in the accuracy of genomic selection (GS) using either regression (RM) or variance component (VC) approaches. This is in part due to the limitations of current methods. Association model are well over-parameterized and suffer from severe co-linearity and lack of statistical power. Even when the variant effects are not directly estimated using VC based approaches, the genomic relationships didn't improve after the marker density exceeded a certain threshold. SNP prioritization-based fixation index (FST) scores were used to track the majority of significant QTL and to reduce the dimensionality of the association model. Two populations with average LD between adjacent markers of 0.3 (P1) and 0.7 (P2) were simulated. In both populations, the genomic data consisted of 400 K SNP markers distributed on 10 chromosomes. The density of simulated genomic data mimics roughly 1.2 million SNP markers in the bovine genome. The genomic relationship matrix (G) was calculated for each set of selected SNPs based on their FST score and similar numbers of SNPs were selected randomly for comparison. Using all ...Continue Reading

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Jan 7, 2018·BMC Genetics·Ling-Yun ChangRomdhane Rekaya

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Citations

Sep 3, 2020·TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·Dorcus C GemenetHugo Campos

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Methods Mentioned

BETA
genotyping

Software Mentioned

QMSim
AIREMLF90
BayesB
FImpute
GBLUP
BayesR
ssBLUP

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