Multivariate estimation of factor structures of complex traits using SNP-based genomic relationships.

ABSTRACT:

Background

Heritability and genetic correlation can be estimated from genome-wide single-nucleotide polymorphism (SNP) data using various methods. We recently developed multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) for statistically and computationally efficient estimation of SNP-based heritability ([Formula: see text]) and genetic correlation ([Formula: see text]) across many traits in large datasets. Here, we extend MGREML by allowing it to fit and perform tests on user-specified factor models, while preserving the low computational complexity.

Results

Using simulations, we show that MGREML yields consistent estimates and valid inferences for such factor models at low computational cost (e.g., for data on 50 traits and 20,000 individuals, a

SUBMITTER: De Vlaming R 

PROVIDER: S-EPMC9327374 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature