title: Multiple testing correction in linear mixed models.
abstract: BACKGROUND
  Multiple hypothesis testing is a major issue in genome-wide association studies (GWAS), which often analyze millions of markers. The permutation test is considered to be the gold standard in multiple testing correction as it accurately takes into account the correlation structure of the genome. Recently, the linear mixed model (LMM) has become the standard practice in GWAS, addressing issues of population structure and insufficient power. However, none of the current multiple testing approaches are applicable to LMM.
  
  
  RESULTS
  We were able to estimate per-marker thresholds as accurately as the gold standard approach in real and simulated datasets, while reducing the time required from months to hours. We applied our approach to mouse, yeast, and human datasets to demonstrate the accuracy and efficiency of our approach.
  
  
  CONCLUSIONS
  We provide an efficient and accurate multiple testing correction approach for linear mixed models. We further provide an intuition about the relationships between per-marker threshold, genetic relatedness, and heritability, based on our observations in real data.
authors.affiliation: Bioinformatics IDP, University of California, Los Angeles, CA, USA.
authors.forename: Jong Wha J
authors.initials: JW
authors.lastname: Joo
authors.affiliation: Computer Science Department, University of California, Los Angeles, CA, USA.
authors.forename: Farhad
authors.initials: F
authors.lastname: Hormozdiari
authors.affiliation: Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul, 138-736, Republic of Korea. buhm.han@amc.seoul.kr.
authors.forename: Buhm
authors.initials: B
authors.lastname: Han
authors.affiliation: Computer Science Department, University of California, Los Angeles, CA, USA. eeskin@cs.ucla.edu.|Department of Human Genetics, University of California, Los Angeles, CA, USA. eeskin@cs.ucla.edu.
authors.forename: Eleazar
authors.initials: E
authors.lastname: Eskin
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D055106:Genome-Wide Association Study / Q000379:methods / Q000592:standards*; D006801:Humans; D016014:Linear Models; D051379:Mice; D012984:Software; D015003:Yeasts / Q000235:genetics*