Journal article

On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL

Theo Meuwissen, Irene van den Berg, Mike Goddard

Genetics Selection Evolution | BMC | Published : 2021


BACKGROUND: Whole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision. METHODS: The Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unb..

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University of Melbourne Researchers


Awarded by Norwegian Research Council

Funding Acknowledgements

TM is grateful for funding from the Norwegian Research Council (project nr. 255297). The helpful comments from two reviewers are gratefully acknowledged.