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Download Robust Rare Variant Association Testing in Samples with Related Individuals
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Session IV Invited Talks, Graduate Student Talks DUO JIANG The University of Chicago Robust Rare Variant Association Testing in Samples with Related Individuals Authors: Duo Jiang and Mary Sara McPeek SATURDAY, March 22, 2014, at 12:00 PM 133 Eckhart Hall, 5734 S. University Avenue, Chicago, IL 60637 ABSTRACT One fundamental problem of interest is to identify genetic variants that contribute to observed variation in complex human traits. Recent advances in DNA sequencing technologies call for effective statistical tests to detect rare genetic variants that influence a trait. While statistical power may be low to identify association with any individual rare variant, power can be increased by combining information across a group of rare variants. Moreover, many genetic studies involve data on related individuals, for which familial correlation needs to be accounted for to ensure correct control over type I error and to improve power. Recognizing the limitations of existing rare-variant association tests for related individuals, we propose MONSTER, a robust approach to detecting associations, which adaptively adjusts to the unknown configuration of effects of rare-variant sites. MONSTER also offers an analytical way of assessing p-values, which is desirable because permutation is not straightforward to conduct in related samples. We further propose a pathway-based association test in sequencing studies that exploits the hierarchical structure of gene pathways. We show, through simulation with a wide range of trait models, that MONSTER effectively accounts for family structure, is computationally efficient and compares very favorably, in terms of power, to previously-proposed tests that allow related individuals. We further illustrate the proposed approach using a candidate gene study for high-density lipoprotein cholesterol, where we are able to replicate association with three genes previously linked to the trait. For further information and about building access for persons with disabilities, please contact Kirsten Wellman at 773.702.8333 or send email ([email protected]).
