title: An efficient gene-gene interaction test for genome-wide association studies in trio families.
abstract: Several efficient gene-gene interaction tests have been developed for unrelated case-control samples in genome-wide association studies (GWAS), making it possible to test tens of billions of interaction pairs of single-nucleotide polymorphisms (SNPs) in a reasonable timeframe. However, current family-based gene-gene interaction tests are computationally expensive and are not applicable to genome-wide interaction analysis.
  
  
  
  We developed an efficient family-based gene-gene interaction test, GCORE, for trios (i.e. two parents and one affected sib). The GCORE compares interlocus correlations at two SNPs between the transmitted and non-transmitted alleles. We used simulation studies to compare the statistical properties such as type I error rates and power for the GCORE with several other family-based interaction tests under various scenarios. We applied the GCORE to a family-based GWAS for autism consisting of approximately 2000 trios. Testing a total of 22 471 383 013 interaction pairs in the GWAS can be finished in 36 h by the GCORE without large-scale computing resources, demonstrating that the test is practical for genome-wide gene-gene interaction analysis in trios.
  
  
  
  GCORE is implemented with C ++ and is available at http://gscore.sourceforge.net
  
  
  
  rchung@nhri.org.tw
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute of Statistics, National Tsing Hua University, Hsin-Chu, Taiwan and.
authors.forename: Pei-Yuan
authors.initials: PY
authors.lastname: Sung
authors.affiliation: Institute of Statistics, National Tsing Hua University, Hsin-Chu, Taiwan and.
authors.forename: Yi-Ting
authors.initials: YT
authors.lastname: Wang
authors.affiliation: Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.
authors.forename: Ya-Wen
authors.initials: YW
authors.lastname: Yu
authors.affiliation: Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.
authors.forename: Ren-Hua
authors.initials: RH
authors.lastname: Chung
mesh_terms: D003627:Data Interpretation, Statistical; D004843:Epistasis, Genetic; D055106:Genome-Wide Association Study; D006801:Humans; D010290:Parents; D020641:Polymorphism, Single Nucleotide