title: Rare variant association test with multiple phenotypes.
abstract: Although genome-wide association studies (GWAS) have now discovered thousands of genetic variants associated with common traits, such variants cannot explain the large degree of "missing heritability," likely due to rare variants. The advent of next generation sequencing technology has allowed rare variant detection and association with common traits, often by investigating specific genomic regions for rare variant effects on a trait. Although multiple correlated phenotypes are often concurrently observed in GWAS, most studies analyze only single phenotypes, which may lessen statistical power. To increase power, multivariate analyses, which consider correlations between multiple phenotypes, can be used. However, few existing multivariant analyses can identify rare variants for assessing multiple phenotypes. Here, we propose Multivariate Association Analysis using Score Statistics (MAAUSS), to identify rare variants associated with multiple phenotypes, based on the widely used sequence kernel association test (SKAT) for a single phenotype. We applied MAAUSS to whole exome sequencing (WES) data from a Korean population of 1,058 subjects to discover genes associated with multiple traits of liver function. We then assessed validation of those genes by a replication study, using an independent dataset of 3,445 individuals. Notably, we detected the gene ZNF620 among five significant genes. We then performed a simulation study to compare MAAUSS's performance with existing methods. Overall, MAAUSS successfully conserved type 1 error rates and in many cases had a higher power than the existing methods. This study illustrates a feasible and straightforward approach for identifying rare variants correlated with multiple phenotypes, with likely relevance to missing heritability.
authors.affiliation: Department of Statistics, Seoul National University, Seoul, Korea.
authors.forename: Selyeong
authors.initials: S
authors.lastname: Lee
authors.affiliation: Graduate School of Public Health, Seoul National University, Seoul, Korea.
authors.forename: Sungho
authors.initials: S
authors.lastname: Won
authors.affiliation: Division of Structural and Functional Genomics, Korean National Institute of Health, Osong, Chungchungbuk-do, Korea.
authors.forename: Young Jin
authors.initials: YJ
authors.lastname: Kim
authors.affiliation: Department of Statistics, Seoul National University, Seoul, Korea.
authors.forename: Yongkang
authors.initials: Y
authors.lastname: Kim
authors.affiliation: Division of Structural and Functional Genomics, Korean National Institute of Health, Osong, Chungchungbuk-do, Korea.
authors.forename: Bong-Jo
authors.initials: BJ
authors.lastname: Kim
authors.affiliation: Department of Statistics, Seoul National University, Seoul, Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.
authors.forename: Taesung
authors.initials: T
authors.lastname: Park
mesh_terms: D020022:Genetic Predisposition to Disease; D014644:Genetic Variation / Q000235:genetics*; D055106:Genome-Wide Association Study; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008107:Liver Diseases / Q000453:epidemiology / Q000235:genetics*; D008957:Models, Genetic; D015999:Multivariate Analysis; D010641:Phenotype; D056910:Republic of Korea / Q000453:epidemiology
keywords: SKAT; association test; exome sequencing data; multivariate analysis; rare variants