Genetic association models are robust to common population kinship estimation biases.

ABSTRACT: Common genetic association models for structured populations, including principal component analysis (PCA) and linear mixed-effects models (LMMs), model the correlation structure between individuals using population kinship matrices, also known as genetic relatedness matrices. However, the most common kinship estimators can have severe biases that were only recently determined. Here we characterize the effect of these kinship biases on genetic association. We employ a large simulated admixed family and genotypes from the 1000 Genomes Project, both with simulated traits, to evaluate key kinship estimators. Remarkably, we find practically invariant association statistics for kinship matrices of different bias types (matching all other features). We then prove using statistical theory and lin

SUBMITTER: Hou Z 

PROVIDER: S-EPMC10474929 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature