title: Boosting Gene Mapping Power and Efficiency with Efficient Exact Variance Component Tests of Single Nucleotide Polymorphism Sets.
abstract: Single nucleotide polymorphism (SNP) set tests have been a powerful method in analyzing next-generation sequencing (NGS) data. The popular sequence kernel association test (SKAT) method tests a set of variants as random effects in the linear mixed model setting. Its P-value is calculated based on asymptotic theory that requires a large sample size. Therefore, it is known that SKAT is conservative and can lose power at small or moderate sample sizes. Given the current cost of sequencing technology, scales of NGS are still limited. In this report, we derive and implement computationally efficient, exact (nonasymptotic) score (eScore), likelihood ratio (eLRT), and restricted likelihood ratio (eRLRT) tests, ExactVCTest, that can achieve high power even when sample sizes are small. We perform simulation studies under various genetic scenarios. Our ExactVCTest (i.e., eScore, eLRT, eRLRT) exhibits well-controlled type I error. Under the alternative model, eScore P-values are universally smaller than those from SKAT. eLRT and eRLRT demonstrate significantly higher power than eScore, SKAT, and SKAT optimal (SKAT-o) across all scenarios and various samples sizes. We applied these tests to an exome sequencing study. Our findings replicate previous results and shed light on rare variant effects within genes. The software package is implemented in the open source, high-performance technical computing language Julia, and is freely available at https://github.com/Tao-Hu/VarianceComponentTest.jl Analysis of each trait in the exome sequencing data set with 399 individuals and 16,619 genes takes around 1 min on a desktop computer.
authors.affiliation: Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona 85724 jzhou@email.arizona.edu.
authors.forename: Jin J
authors.identifier: 0000-0001-7983-0274
authors.initials: JJ
authors.lastname: Zhou
authors.affiliation: Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695.|Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695.
authors.forename: Tao
authors.initials: T
authors.lastname: Hu
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts.
authors.forename: Dandi
authors.initials: D
authors.lastname: Qiao
authors.affiliation: Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.|Harvard Medical School, Boston, Massachusetts.|Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115.
authors.forename: Michael H
authors.initials: MH
authors.lastname: Cho
authors.affiliation: Department of Biostatistics, University of California, Los Angeles, California 90095.
authors.forename: Hua
authors.initials: H
authors.lastname: Zhou
mesh_terms: D020451:Contig Mapping / Q000379:methods*; D059472:Exome; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D012984:Software
keywords: SNP set tests; exact tests; linear mixed effect model; next-generation sequencing studies; small sample sizes