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Activity Number: 42 - Statistical Genetics I – New Approaches for Association Mapping
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #313444
Title: A Bayesian Method for Rare Variant Analysis Using Functional Annotations and Its Application to Autism
Author(s): Shengtong Han*
Companies: University of Wisconsin, Milwaukee
Keywords: Autism ; Bayesian method; rare variant
Abstract:

Rare genetic variants make significant contributions to human diseases. Compared to common variants, rare variants have larger effect sizes and are generally free of linkage disequilibrium (LD), which makes it easier to identify causal variants. Numerous methods have been developed to analyze rare variants in a gene or region in association studies, with the goal of finding risk genes by aggregating information of all variants of a gene. These methods, however, often make unrealistic assumptions, e.g. all rare variants in a risk gene would have non-zero effects. In this work, we develop a Bayesian method: MIxture model based Rare variant Analysis on GEnes (MIRAGE)(https://xinhe-lab.github.io/mirage). MIRAGE captures the heterogeneity of variant effects by treating all variants of a gene as a mixture of risk and non-risk variants, and models the prior probabilities of being risk variants as function of external information of variants, such as allele frequencies and predicted deleterious effects. We demonstrate in both simulations and analysis of an exome-sequencing dataset of Autism, that MIRAGE significantly outperforms current methods for rare variant analysis.


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