13: Functional Fine-Mapping using Variational Inference

Thomas M. Menino Convention & Exhibition Center 

Fine-mapping identifies causal variants from genome-wide association study signals, but is challenging due to high correlation between genetic variants. This is fundamentally a variable selection in regression problem with highly correlated predictors. Functional annotations, such as transcription factor binding predictions, provide external information that can improve selection when properly integrated through prior distributions in a Bayesian framework. However, existing functional fine-mapping methods either assume known relationships between annotations and causality (requiring pre-specification) or employ two-stage procedures: first computing annotation-based priors separately, then performing fine-mapping conditional on these fixed priors. We introduce a Bayesian fine-mapping method that jointly learns annotation-causality relationships and performs variable selection in a unified single-stage framework. Inference is performed via variational inference where both the posterior distribution over causal configurations and the parameters governing the annotation informed prior are optimised simultaneously. We demonstrate our model's power through comprehensive simulations.

Fine-Mapping

Variational Inference

Deep Learning

Variable Selection 

Biometrics Section