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

ABSTRACT:

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 unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described

SUBMITTER: Meuwissen T 

PROVIDER: S-EPMC7908738 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature