A2083
Title: An empirical Bayes algorithm for variable selection with applications in genetic fine-mapping
Authors: Xingbo Guan - Hong Kong Baptist University (Hong Kong) [presenting]
Abstract: Identifying causal variants within genome-wide association study loci is challenging due to linkage disequilibrium, multi-signal architectures, and the need for calibrated uncertainty at a large scale. A variational EM algorithm is proposed that formulates fine-mapping as Bayesian variable selection with binary inclusion indicators and learns feature-specific prior inclusion probabilities via empirical Bayes (EmpBVS). The E-step delivers closed-form updates for the variational Gaussian factor on effects and the noise precision, while the M-step updates per-SNP inclusion priors by maximizing the lower bound of marginal data likelihood. Irrelevant SNPs are shrunk out automatically as their weights contract to zero, yielding threshold-free selection. Computational sparsity results show geometric decay of null inclusion weights across iterations, and statistical consistency establishes that posterior mass concentrates on the true model and variational means consistently recover effect sizes. Simulations and comparative experiments demonstrate accurate posterior inclusion probabilities and credible sets with competitive runtime. The framework thus preserves automatic relevance learning while providing discrete selections and rigorous guarantees tailored to genetic fine-mapping.
