A powerful and adaptive association test for rare variants.
This article focuses on conducting global testing for association between a binary trait and a set of rare variants (RVs), although its application can be much broader to other types of traits, common variants (CVs), and gene set or pathway analysis. We show that many of the existing tests have deteriorating performance in the presence of many nonassociated RVs: their power can dramatically drop as the proportion of nonassociated RVs in the group to be tested increases. We propose a class of so-called sum of powered score (SPU) tests, each of which is based on the score vector from a general regression model and hence can deal with different types of traits and adjust for covariates, e.g., principal components accounting for population stratification. The SPU tests generalize the sum test, a representative burden test based on pooling or collapsing genotypes of RVs, and a sum of squared score (SSU) test that is closely related to several other powerful variance component tests; a previous study (Basu and Pan 2011) has demonstrated good performance of one, but not both, of the Sum and SSU tests in many situations. The SPU tests are versatile in the sense that one of them is often powerful, although its identity varies with the unknown true association parameters. We propose an adaptive SPU (aSPU) test to approximate the most powerful SPU test for a given scenario, consequently maintaining high power and being highly adaptive across various scenarios. We conducted extensive simulations to show superior performance of the aSPU test over several state-of-the-art association tests in the presence of many nonassociated RVs. Finally we applied the SPU and aSPU tests to the GAW17 mini-exome sequence data to compare its practical performance with some existing tests, demonstrating their potential usefulness.
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455 weip@biostat.umn.edu peng.wei@uth.tmc.edu.
Wei
W
Pan
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455.
Junghi
J
Kim
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455.
Yiwei
Y
Zhang
School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455.
Xiaotong
X
Shen
Division of Biostatistics and Human Genetics Center, University of Texas School of Public Health, Houston, Texas 77030 weip@biostat.umn.edu peng.wei@uth.tmc.edu.
Peng
P
Wei
D003198:Computer Simulation; D030541:Databases, Genetic; D056726:Genetic Association Studies; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis; D012641:Selection, Genetic
adaptive SPU (aSPU) test; genome-wide association study (GWAS); score statistic; sequencing data; sum of powered score (SPU) test; sum of squared score (SSU) test; sum test