Faculty Collaboration Database

A variational Bayes discrete mixture test for rare variant association. Genet Epidemiol 2014 Jan;38(1):21-30

Date

02/01/2014

Pubmed ID

24482836

Pubmed Central ID

PMC4030763

DOI

10.1002/gepi.21772

Scopus ID

2-s2.0-84890185282 (requires institutional sign-in at Scopus site)   11 Citations

Abstract

Recently, many statistical methods have been proposed to test for associations between rare genetic variants and complex traits. Most of these methods test for association by aggregating genetic variations within a predefined region, such as a gene. Although there is evidence that "aggregate" tests are more powerful than the single marker test, these tests generally ignore neutral variants and therefore are unable to identify specific variants driving the association with phenotype. We propose a novel aggregate rare-variant test that explicitly models a fraction of variants as neutral, tests associations at the gene-level, and infers the rare-variants driving the association. Simulations show that in the practical scenario where there are many variants within a given region of the genome with only a fraction causal our approach has greater power compared to other popular tests such as the Sequence Kernel Association Test (SKAT), the Weighted Sum Statistic (WSS), and the collapsing method of Morris and Zeggini (MZ). Our algorithm leverages a fast variational Bayes approximate inference methodology to scale to exome-wide analyses, a significant computational advantage over exact inference model selection methodologies. To demonstrate the efficacy of our methodology we test for associations between von Willebrand Factor (VWF) levels and VWF missense rare-variants imputed from the National Heart, Lung, and Blood Institute's Exome Sequencing project into 2,487 African Americans within the VWF gene. Our method suggests that a relatively small fraction (~10%) of the imputed rare missense variants within VWF are strongly associated with lower VWF levels in African Americans.

Author List

Logsdon BA, Dai JY, Auer PL, Johnsen JM, Ganesh SK, Smith NL, Wilson JG, Tracy RP, Lange LA, Jiao S, Rich SS, Lettre G, Carlson CS, Jackson RD, O'Donnell CJ, Wurfel MM, Nickerson DA, Tang H, Reiner AP, Kooperberg C, NHLBI GO Exome Sequencing Project

Author

Paul L. Auer PhD Professor in the Institute for Health and Equity department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Algorithms
Bayes Theorem
Exome
Female
Genetic Association Studies
Genetic Variation
Humans
Male
Models, Genetic
Mutation, Missense
National Heart, Lung, and Blood Institute (U.S.)
Phenotype
Research Design
Sequence Analysis, DNA
Software
United States
von Willebrand Factor