Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops.

ABSTRACT:

Background

We consider two key problems in genomics involving multiple traits: multi-trait genome wide association studies (GWAS), where the goal is to detect genetic variants associated with the traits; and multi-trait genomic selection (GS), where the emphasis is on accurately predicting trait values. Multi-trait linear mixed models build on the linear mixed model to jointly model multiple traits. Existing estimation methods, however, are limited to the joint analysis of a small number of genotypes; in fact, most approaches consider one SNP at a time. Estimating multi-dimensional genetic and environment effects also results in considerable computational burden. Efficient approaches that incorporate regularization into multi-trait linear models (no random effects) have been recent

SUBMITTER: Lozano AC 

PROVIDER: S-EPMC10604903 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature