Journal article
Multi-breed genomic prediction using Bayes R with sequence data and dropping variants with a small effect
I Van Den Berg, PJ Bowman, IM MacLeod, BJ Hayes, T Wang, S Bolormaa, ME Goddard
Genetics Selection Evolution | BIOMED CENTRAL LTD | Published : 2017
Open access
Abstract
Background: The increasing availability of whole-genome sequence data is expected to increase the accuracy of genomic prediction. However, results from simulation studies and analysis of real data do not always show an increase in accuracy from sequence data compared to high-density (HD) single nucleotide polymorphism (SNP) chip genotypes. In addition, the sheer number of variants makes analysis of all variants and accurate estimation of all effects computationally challenging. Our objective was to find a strategy to approximate the analysis of whole-sequence data with a Bayesian variable selection model. Using a simulated dataset, we applied a Bayes R hybrid model to analyse whole-sequence ..
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Funding Acknowledgements
This research was supported by the Center for Genomic Selection in Animals and Plants (GenSAP) funded by The Danish Council for Strategic Research. We acknowledge DataGene and CRV Netherlands for providing access to data used in this study. We acknowledge our partners in the 1000 Bull Genomes Project for access to the reference genomes. We acknowledge Dr Paul Stothard and team at the University of Alberta for collating annotation information of sequence variants used in this study.