Testing for an Unusual Distribution of Rare Variants

Technological advances make it possible to use high-throughput sequencing as a primary discovery tool of medical genetics, specifically for assaying rare variation. Still this approach faces the analytic challenge that the influence of very rare variants can only be evaluated effectively as a group. A further complication is that any given rare variant could have no effect, could increase risk, or could be protective. We propose here the C-alpha test statistic as a novel approach for testing for the presence of this mixture of effects across a set of rare variants. Unlike existing burden tests, C-alpha, by testing the variance rather than the mean, maintains consistent power when the target set contains both risk and protective variants. Through simulations and analysis of case/control data, we demonstrate good power relative to existing methods that assess the burden of rare variants in individuals.Author Summary: Developments in sequencing technology now enable us to assay all genetic variation, much of which is extremely rare. We propose to test the distribution of rare variants we observe in cases versus controls. To do so, we present a novel application of the C-alpha statistic to test these rare variants. C-alpha aims to determine whether the set of variants observed in cases and controls is a mixture, such that some of the variants confer risk or protection or are phenotypically neutral. Risk variants are expected to be more common in cases; protective variants more common in controls. C-alpha is sensitive to this imbalance, regardless of its origin—risk, protective, or both—but is ideally suited for a mixture of protective and risk variants. Variation in APOB nicely illustrates a mixture, in that certain rare variants increase triglyceride levels while others decrease it. The hallmark feature of C-alpha is that it uses the distribution of variation observed in cases and controls to detect the presence of a mixture, thus implicating genes or pathways as risk factors for disease.