Interval-censored Bayesian Fine-mapping using Functional Annotations for Genetic Variants

Music City Center 

National and international genetic compendiums, such as the UK Biobank, have become invaluable resources for identifying genetic variants associated with complex diseases. These biobanks often collect data in interval-censored form; however, there is a lack of methodologies for performing genetic association testing with such outcomes. Specifically, the use of Bayesian variable selection methods to fine-map genetic variants linked to interval-censored outcomes remains an understudied area. Fine-mapping specific SNPs within causal gene sets can offer deeper insights into the genetic mechanisms underlying the condition. Additionally, incorporating functional annotation information into the variable selection framework can prioritize variants with biological relevance and offer more power in detection. In this work, we extend Bayesian fine-mapping methods to incorporate functional annotation information in the model to improve selection. Our selection algorithm includes a MCMC scheme that is computationally efficient and allows interpretable results. We apply this method in a study using data from the UK Biobank to identify causal variants associated with colorectal cancer.

GWAS

Interval-censored

Bayesian Fine-mapping

Functional Annotations 

Section on Statistics in Genomics and Genetics