BEATRICE: Bayesian Fine-mapping from Summary Data using Deep Variational Inference.

ABSTRACT: We introduce a novel framework BEATRICE to identify putative causal variants from GWAS statistics. Identifying causal variants is challenging due to their sparsity and high correlation in the nearby regions. To account for these challenges, we rely on a hierarchical Bayesian model that imposes a binary concrete prior on the set of causal variants. We derive a variational algorithm for this fine-mapping problem by minimizing the KL divergence between an approximate density and the posterior probability distribution of the causal configurations. Correspondingly, we use a deep neural network as an inference machine to estimate the parameters of our proposal distribution. Our stochastic optimization procedure allows us to simultaneously sample from the space of causal configurations. We use th

SUBMITTER: Ghosal S 

PROVIDER: S-EPMC10055416 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature