Uncertainty quantification in variable selection for genetic fine-mapping using bayesian neural networks.

ABSTRACT: In this paper, we propose a new approach for variable selection using a collection of Bayesian neural networks with a focus on quantifying uncertainty over which variables are selected. Motivated by fine-mapping applications in statistical genetics, we refer to our framework as an "ensemble of single-effect neural networks" (ESNN) which generalizes the "sum of single effects" regression framework by both accounting for nonlinear structure in genotypic data (e.g., dominance effects) and having the capability to model discrete phenotypes (e.g., case-control studies). Through extensive simulations, we demonstrate our method's ability to produce calibrated posterior summaries such as credible sets and posterior inclusion probabilities, particularly for traits with genetic architectures that ha

SUBMITTER: Cheng W 

PROVIDER: S-EPMC9234235 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature