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A Variational Bayes Discrete Mixture Test for Rare Variant Association
Author(s) -
Logsdon Benjamin A.,
Dai James Y.,
Auer Paul L.,
Johnsen Jill M.,
Ganesh Santhi K.,
Smith Nicholas L.,
Wilson James G.,
Tracy Russell P.,
Lange Leslie A.,
Jiao Shuo,
Rich Stephen S.,
Lettre Guillaume,
Carlson Christopher S.,
Jackson Rebecca D.,
O'Donnell Christopher J.,
Wurfel Mark M.,
Nickerson Deborah A.,
Tang Hua,
Reiner Alexander P.,
Kooperberg Charles
Publication year - 2014
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21772
Subject(s) - bayes' theorem , exome , inference , missense mutation , genetic association , bayes factor , genome wide association study , genetics , exome sequencing , computational biology , biology , computer science , bayesian probability , gene , statistics , phenotype , mathematics , genotype , single nucleotide polymorphism , artificial intelligence
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.