A Variational Bayes Discrete Mixture Test for Rare Variant Association

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.

Genetic variants of VWF gene in type 2 von Willebrand disease

“…Ser1486Leu was predicted as potentially pathogenic in MCAP (PP3). Ser1486Leu has been associated with VWF lower plasma levels …”

“…Ser1486Leu has been associated with VWF lower plasma levels. 34,36 Tyr1584Cys was reclassified as "likely pathogenic" because it is present in a functional domain (PM1), has been reported in families with VWD, 37,38 presents low frequency in GRCh37 or ExAC (PM2) and affects an evolutionary conserved position in almost all species investigated. Tyr1584Cys was detected as "possibly damaging" by PolyPhen2, "possibly pathogenic" by MCAP and "not tolerated" by SIFT in VEP (PP3).…”

A large-scale genome-wide enrichment analysis identifies new trait-associated genes, pathways and tissues across 31 human phenotypes*

“…Although these methods are computationally convenient in large datasets, and often produce reliable results (e.g. [8,10,[107][108][109][110][111][112][113][114][115]), they also have features to be aware of. One feature is that when multiple SNPs in strong LD are associated with a trait, the variational approximation tends to select one of them and ignore the others.…”

The effect of phenotypic outliers and non-normality on rare-variant association testing

“…[2][3][4][5][6][7][8][9] This has motivated development of statistical methods for testing rare-variant associations at the gene level. [10][11][12][13][14][15][16] Although these methods are useful for increasing statistical power to detect associations relative to single-variant analyses, valid well-powered statistical analyses are contingent on careful examination of phenotypes and underlying assumptions. Here we explore some commonly encountered issues with how phenotypes are distributed and how these issues affect inferences from rare-variant association tests.…”