Large-scale genomic prediction using singular value decomposition of the genotype matrix.

ABSTRACT:

Background

For marker effect models and genomic animal models, computational requirements increase with the number of loci and the number of genotyped individuals, respectively. In the latter case, the inverse genomic relationship matrix (GRM) is typically needed, which is computationally demanding to compute for large datasets. Thus, there is a great need for dimensionality-reduction methods that can analyze massive genomic data. For this purpose, we developed reduced-dimension singular value decomposition (SVD) based models for genomic prediction.

Methods

Fast SVD is performed by analyzing different chromosomes/genome segments in parallel and/or by restricting SVD to a limited core of genotyped individuals, producing chromosome- or segment-specific principal components (PC

SUBMITTER: Odegard J 

PROVIDER: S-EPMC5831701 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature