Application of a Bayesian dominance model improves power in quantitative trait genome-wide association analysis.

Jörn BennewitzChristian EdelRuedi FriesTheo H E MeuwissenRobin Wellmann

Published in: Genetics, selection, evolution : GSE (2017)

BayesD improved power, but precision only slightly. Application of BayesD needs large datasets with genotypes and own performance records as phenotypes. Given the current efforts to establish cow reference populations in dairy cattle genomic selection schemes, such datasets are expected to be soon available, which will enable the application of BayesD for association mapping and genomic prediction purposes.

Keyphrases

genome wide association
high resolution
copy number
rna seq
genome wide
dna methylation
gene expression
data analysis