Online Program Home
My Program

Abstract Details

Activity Number: 355 - Analysis of Complex Genetic Data
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #330903 Presentation
Title: SMMAT: a Powerful and Efficient Variant Set Mixed Model Association Test for Binary and Quantitative Traits in Whole Genome Sequencing Studies with Correlated Samples
Author(s): Han Chen*
Companies: The University of Texas Health Science Center at Houston
Keywords: generalized linear mixed model; rare variant test; correlated samples; statistical genetics; next-generation sequence data
Abstract:

With the advance in next-generation sequencing technology, massive genetic and genomic data have been produced. Statistical methods for testing genetic association with rare genetic variants have been well established and widely applied to unrelated samples. These methods are also known as gene-based or variant set association tests, since rare variants are often grouped by genes, functional units or genomic regions in the analysis. In recent years, large-scale sequencing projects have included correlated study samples from family studies, or with cryptic relatedness, and there is a pressing need of developing efficient statistical methods to analyze these data. Here we propose and implement SMMAT, a powerful and efficient variant set association test for correlated study samples in the generalized linear mixed model framework. SMMAT is a hybrid test that aggregates association evidence from the burden test and the sequence kernel association test. We show in simulation studies that SMMAT controls correct type I error rates and maintains good power in both single-cohort studies and meta-analysis. We also illustrate SMMAT in a real data example from a large-scale sequencing study.


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

Back to the full JSM 2018 program