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Activity Number: 237 - SPEED:Statistical Methods for GWAs, Genetics, Genomics, and Other Omics Studies, Part 1
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #307156
Title: Intergrated Quantile Rank Test (IQRAT) for Heterogeneous Joint Effect of Rare and Common Variants in Sequencing Studies
Author(s): Tianying Wang* and Iuliana Ionita-Laza and Ying Wei
Companies: Columbia University, Biostatistics Department and Columbia University, Biostatistics Department and Columbia University, Biostatistics Department
Keywords: quantile regression; genetic association; joint group-wise effects

Genetic association studies often evaluate the combined group-wise effects of rare and common genetic variants on phenotype at gene levels. Many approaches have been proposed for group-wise association tests, such as the widely used burden tests and sequence kernel association tests. Most of these approaches focus on identifying mean effects. As the genetic associations are complex, we propose an efficient integrated rank test to investigate the genetic effect across the entire distribution/quantile function of a phenotype. The resulting test complements the mean-based analysis and improve efficiency and robustness. The proposed test integrates the rank score test statistics over quantile levels while incorporating Cauchy combination test scheme and Fisher's method to maximize the power. It generalized the classical quantile-specific rank-score test. Using simulations studies and real Metabochip data on lipid traits, we investigated the performance of the new test in comparison with the burden tests and sequence kernel association tests in multiple scenarios.

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

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