Characterizing substructure via mixture modeling in large-scale genetic summary statistics.

ABSTRACT: Genetic summary data are broadly accessible and highly useful including for risk prediction, causal inference, fine mapping, and incorporation of external controls. However, collapsing individual-level data into groups masks intra- and inter-sample heterogeneity, leading to confounding, reduced power, and bias. Ultimately, unaccounted substructure limits summary data usability, especially for understudied or admixed populations. Here, we present Summix2, a comprehensive set of methods and software based on a computationally efficient mixture model to estimate and adjust for substructure in genetic summary data. In extensive simulations and application to public data, Summix2 characterizes finer-scale population structure, identifies ascertainment bias, and identifies potentia

SUBMITTER: Stoneman HR 

PROVIDER: S-EPMC11100604 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature