Eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein.

ABSTRACT:

Background

Pseudo-phenotypes, such as 305-day yields, estimated breeding values or deregressed proofs, are usually used as response variables for genome-wide association studies (GWAS) of milk production traits in dairy cattle. Computational inefficiency challenges the direct use of test-day records for longitudinal GWAS with large datasets.

Results

We propose a rapid longitudinal GWAS method that is based on a random regression model. Our method uses Eigen decomposition of the phenotypic covariance matrix to rotate the data, thereby transforming the complex mixed linear model into weighted least squares analysis. We performed a simulation study that showed that our method can control type I errors well and has higher power than a longitudinal GWAS method that does not inclu

SUBMITTER: Ning C 

PROVIDER: S-EPMC5868076 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature