Article

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

2023-03-25

Abstract excerpt

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...

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Literature Corpus work: 7e98a906-4456-5d30-9911-9b52c4222822
DOI: 10.1101/2023.03.24.534116

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