title: A powerful and efficient set test for genetic markers that handles confounders.
abstract: BACKGROUND
  Approaches for testing sets of variants, such as a set of rare or common variants within a gene or pathway, for association with complex traits are important. In particular, set tests allow for aggregation of weak signal within a set, can capture interplay among variants and reduce the burden of multiple hypothesis testing. Until now, these approaches did not address confounding by family relatedness and population structure, a problem that is becoming more important as larger datasets are used to increase power.
  
  
  RESULTS
  We introduce a new approach for set tests that handles confounders. Our model is based on the linear mixed model and uses two random effects-one to capture the set association signal and one to capture confounders. We also introduce a computational speedup for two random-effects models that makes this approach feasible even for extremely large cohorts. Using this model with both the likelihood ratio test and score test, we find that the former yields more power while controlling type I error. Application of our approach to richly structured Genetic Analysis Workshop 14 data demonstrates that our method successfully corrects for population structure and family relatedness, whereas application of our method to a 15 000 individual Crohn's disease case-control cohort demonstrates that it additionally recovers genes not recoverable by univariate analysis.
  
  
  BACKGROUND
  A Python-based library implementing our approach is available at http://mscompbio.codeplex.com.
authors.affiliation: eScience Group, Microsoft Research, Los Angeles, CA 90024, USA. jennl@microsoft.com
authors.forename: Jennifer
authors.initials: J
authors.lastname: Listgarten
authors.forename: Christoph
authors.initials: C
authors.lastname: Lippert
authors.forename: Eun Yong
authors.initials: EY
authors.lastname: Kang
authors.forename: Jing
authors.initials: J
authors.lastname: Xiang
authors.forename: Carl M
authors.initials: CM
authors.lastname: Kadie
authors.forename: David
authors.initials: D
authors.lastname: Heckerman
mesh_terms: D000465:Algorithms; D016022:Case-Control Studies; D003424:Crohn Disease / Q000235:genetics; D005819:Genetic Markers; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D016014:Linear Models; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide
chemical_list: D005819:Genetic Markers