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Activity Number: 218 - Statistical Advances in the Design and Analysis of Sequence-Based Genetic Association Studies
Type: Invited
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #326943
Title: Analysis of Quantitative Traits in Sequencing Studies with Outcome-Dependent Sampling
Author(s): SAONLI BASU*
Companies: University of Minnesota
Keywords: outcome dependent sampling; extreme phenotype sampling; GWAS

Outcome-dependent sampling provides a cost-effective yet powerful strategy to perform sequencing studies of quantitative traits. When the number of individuals that can be genotyped is limited, such sampling design can generate good power to detect associations between genetic variants and the trait. However, failure to account for the biased nature of the sampling can produce inflated type I error and loss of power for both the analysis of primary and secondary traits, especially when using meta-analysis to combine results from multiple studies with different selection criteria. Commonly used methods designed for random sampling design are not equipped to properly account for the non-random sampling scheme. Here we review the problems with naive approaches and propose an alternative likelihood-based approach that accounts for the biased sampling design. We illustrate our approach through simulation studies and demonstrate through simulation and real data analysis that our proposed approach maintains correct type I error and provide a powerful alternative to perform association analysis under outcome dependent sampling design.

Authors who are presenting talks have a * after their name.

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