Scalable analysis of large multi-ancestry biobanks by leveraging sparse ancestry-adjusted sample-relatedness.

ABSTRACT: Linear mixed-effects models (LMMs) and ridge regression are commonly applied in genetic association studies to control for population structure and sample-relatedness. To control for sample-relatedness, the existing methods use empirical genetic relatedness matrices (GRM) either explicitly or conceptually. This works well with mostly homogeneous populations, however, in multi-ancestry heterogeneous populations, GRMs are confounded with population structure which leads to inflated type I error rates, massively increased computation, and reduced power. Here, we propose FastSparseGRM, a scalable pipeline for multi-ancestry Genome-Wide Association studies (GWAS) and Whole Genome Sequencing (WGS) studies. It utilizes a block-diagonal sparse ancestry-adjusted (BDSA) GRM to model sample-relatedne

SUBMITTER: Lin X 

PROVIDER: S-EPMC11601839 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature