Online Program Home
My Program

Abstract Details

Activity Number: 312 - Recent Methods Development for Sequence-Based Association Studies
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #327156 Presentation
Title: Identifying Individual Risk Rare Variants Using Structure-Guided Local Tests
Author(s): Rachel Marceau* and Wenbin Lu and Daniel Rotroff and Michael Wagner and John Buse and Jung-Ying Tzeng and Melaine Kuenemann and Denis Fourches and Alison Motsinger-Reif
Companies: North Carolina State University and North Carolina State University and North Carolina State University and UNC Chapel Hill and UNC Chapel Hill and North Carolina State University and North Carolina State University and North Carolina State University and North Carolina State University
Keywords: rare variant prioritization; kernel machine regression; protein structure; local kernel test
Abstract:

Rare variants are of increasing interest to genetic association studies largely due to their etiological contributions in human complex diseases. However, rare variants are difficult to detect individually due to the rarity of the mutant events. Collapsing analyses improve detection signal by aggregating information from multiple loci but are not able to pinpoint causal variants within a variant set. To perform inference at a localized level, additional information, e.g., on structure or predicted function, is needed to boost signal strength. We propose a rare variant association test which utilizes protein tertiary structure to increase signal and identify likely causal variants. Following the biological hypothesis that important variants are likely to cluster together in 3D protein space, we perform structure-guided collapsing, leading to local tests which borrow information from neighboring variants on a protein and provide association information on a variant-specific level. We use a kernel machine framework along with resampling to evaluate significance and show the utility of the proposed method using simulations and a real data application on the ACCORD clinical trials.


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

Back to the full JSM 2018 program