title: Gene-set association tests for next-generation sequencing data.
abstract: Recently, many methods have been developed for conducting rare-variant association studies for sequencing data. These methods have primarily been based on gene-level associations but have not been proven to be as effective as expected. Gene-set-level tests have shown great advantages over gene-level tests in terms of power and robustness, because complex diseases are often caused by multiple genes that comprise of biological gene sets.
  
  
  
  Here, we propose several novel gene-set tests that employ rapid and efficient dimensionality reduction. The performance of these tests was investigated using extensive simulations and application to 1058 whole-exome sequences from a Korean population. We identified some known pathways and novel pathways whose rare or common variants are associated with elevated liver enzymes and replicated the results in an independent cohort.
  
  
  
  Source R code for our algorithm is freely available at http://statgen.snu.ac.kr/software/QTest
  
  
  
  tspark@stats.snu.ac.kr
  
  
  
  Supplementary data are available at Bioinformatics online.
authors: Department of Statistics, Seoul National University, Seoul 151-742, Korea.
authors: Jaehoon
authors: J
authors: Lee
authors: Division of Structural and Functional Genomics, Korean National Institute of Health, Osong, Chungchungbuk-Do 363-951, Korea.
authors: Young Jin
authors: YJ
authors: Kim
authors: Division of Structural and Functional Genomics, Korean National Institute of Health, Osong, Chungchungbuk-Do 363-951, Korea.
authors: Juyoung
authors: J
authors: Lee
authors: Division of Structural and Functional Genomics, Korean National Institute of Health, Osong, Chungchungbuk-Do 363-951, Korea.
authors: Bong-Jo
authors: BJ
authors: Kim
authors: Department of Mathematics and Statistics, Sejong University, Seoul 143-747, Korea.
authors: Seungyeoun
authors: S
authors: Lee
authors: Department of Statistics, Seoul National University, Seoul 151-742, Korea.
authors: Taesung
authors: T
authors: Park
mesh_terms: D000465:Algorithms; D066264:Datasets as Topic; D059472:Exome; D056726:Genetic Association Studies; D005820:Genetic Testing; D014644:Genetic Variation; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D007723:Korea; D053858:Metabolic Networks and Pathways