Online Program Home
My Program

Abstract Details

Activity Number: 233 - Innovative Approaches for High-Dimensional Omics and Neuroimaging Data
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: International Indian Statistical Association
Abstract #304919
Title: Nonparametric Bayes Multiresolution Testing for Detecting Rare Variants
Author(s): Jyotishka Datta* and David Dunson
Companies: University of Arkansas and Duke University
Keywords: Bayesian nonparametrics; Rare variants; multiscale methods

Rare variants play a critical role in explaining the genetic contribution to complex diseases by accounting for disease risk and trait variability. Building association tests of rare variants is challenging using the existing tools that merely compare their frequencies across different groups. In this talk, we will introduce a powerful new statistical method that incorporates spatial locations of variants, allows incorporation of previous gene ontology information, and appropriately characterizes uncertainty in inferences. We will build a multiresolution cluster detection algorithm using a binary-tree-based nonparametric Bayes test and show how it leads to robustness, adaptability to the underlying disease architecture, and biologically relevant segmentation of the genome. We will apply our method on 240 cases of patients with primary immune deficiency to identify patterns of genetic variation underlying the disease, compared to over 650,000 controls. We will end with extension of this model across a large range of disease models, while maintaining scalability, and incorporation of important covariates and adjustment for population stratification.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program