Accounting for overlapping annotations in genomic prediction models of complex traits.

ABSTRACT:

Background

It is now widespread in livestock and plant breeding to use genotyping data to predict phenotypes with genomic prediction models. In parallel, genomic annotations related to a variety of traits are increasing in number and granularity, providing valuable insight into potentially important positions in the genome. The BayesRC model integrates this prior biological information by factorizing the genome according to disjoint annotation categories, in some cases enabling improved prediction of heritable traits. However, BayesRC is not adapted to cases where markers may have multiple annotations.

Results

We propose two novel Bayesian approaches to account for multi-annotated markers through a cumulative (BayesRC+) or preferential (BayesRC[Formula: see text]) model of t

SUBMITTER: Mollandin F 

PROVIDER: S-EPMC9446854 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature