A Fast and Noise-Resilient Approach to Detect Rare-Variant Associations with Deep Sequencing Data for Complex Disorders

• 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
• 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

Abstract

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.