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Activity Number: 164
Type: Topic Contributed
Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #306260
Title: A Fast and Noise-Resilient Approach to Detect Rare-Variant Associations with Deep Sequencing Data for Complex Disorders
Author(s): Shuang Wang*+ and Yee Him Cheung and Gao Wang and Suzanne Leal
Companies: Columbia University and Columbia University and Baylor College of Medicine and Baylor College of Medicine
Address: 722 West 168th Street, New York, NY, , USA

Analyzing individual rare variants is known to be underpowered. Association methods developed aggregate variants across a genetic region. The foreseeable wide-spread use of whole genome sequencing calls for new rare variant association methods that are statistically powerful, robust against high levels of noise due to inclusion of non-causal variants, and yet computationally efficient. We propose a simple and powerful statistic that combines the disease-associated p-values of individual variants using a weight that is the inverse of the expected standard deviation of the allele frequencies under the null. This approach (dubbed as Sigma-P method) is extremely robust to the inclusion of a high proportion of non-causal variants and is also powerful when both detrimental and protective variants are present within a genetic region. We tested the performance of the Sigma-P method using simulations. The results demonstrate that this method generally outperforms other rare variant association methods over a wide range of models. Analysis on the sequence data on the ANGPTL family of genes from the Dallas Heart Study with nine metabolic traits uncovered both known and novel associations.

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