title: Greater power and computational efficiency for kernel-based association testing of sets of genetic variants.
abstract: BACKGROUND
  Set-based variance component tests have been identified as a way to increase power in association studies by aggregating weak individual effects. However, the choice of test statistic has been largely ignored even though it may play an important role in obtaining optimal power. We compared a standard statistical test-a score test-with a recently developed likelihood ratio (LR) test. Further, when correction for hidden structure is needed, or gene-gene interactions are sought, state-of-the art algorithms for both the score and LR tests can be computationally impractical. Thus we develop new computationally efficient methods.
  
  
  RESULTS
  After reviewing theoretical differences in performance between the score and LR tests, we find empirically on real data that the LR test generally has more power. In particular, on 15 of 17 real datasets, the LR test yielded at least as many associations as the score test-up to 23 more associations-whereas the score test yielded at most one more association than the LR test in the two remaining datasets. On synthetic data, we find that the LR test yielded up to 12% more associations, consistent with our results on real data, but also observe a regime of extremely small signal where the score test yielded up to 25% more associations than the LR test, consistent with theory. Finally, our computational speedups now enable (i) efficient LR testing when the background kernel is full rank, and (ii) efficient score testing when the background kernel changes with each test, as for gene-gene interaction tests. The latter yielded a factor of 2000 speedup on a cohort of size 13 500.
  
  
  BACKGROUND
  Software available at http://research.microsoft.com/en-us/um/redmond/projects/MSCompBio/Fastlmm/.
  
  
  BACKGROUND
  heckerma@microsoft.com
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: eScience Research Group, Microsoft Research, Los Angeles, CA, 90024 and eScience Research Group, Microsoft Research, Redmond, WA, 98052, USA.
authors: Christoph
authors: C
authors: Lippert
authors: eScience Research Group, Microsoft Research, Los Angeles, CA, 90024 and eScience Research Group, Microsoft Research, Redmond, WA, 98052, USA.
authors: Jing
authors: J
authors: Xiang
authors: eScience Research Group, Microsoft Research, Los Angeles, CA, 90024 and eScience Research Group, Microsoft Research, Redmond, WA, 98052, USA.
authors: Danilo
authors: D
authors: Horta
authors: eScience Research Group, Microsoft Research, Los Angeles, CA, 90024 and eScience Research Group, Microsoft Research, Redmond, WA, 98052, USA.
authors: Christian
authors: C
authors: Widmer
authors: eScience Research Group, Microsoft Research, Los Angeles, CA, 90024 and eScience Research Group, Microsoft Research, Redmond, WA, 98052, USA.
authors: Carl
authors: C
authors: Kadie
authors: eScience Research Group, Microsoft Research, Los Angeles, CA, 90024 and eScience Research Group, Microsoft Research, Redmond, WA, 98052, USA.
authors: David
authors: D
authors: Heckerman
authors: eScience Research Group, Microsoft Research, Los Angeles, CA, 90024 and eScience Research Group, Microsoft Research, Redmond, WA, 98052, USA.
authors: Jennifer
authors: J
authors: Listgarten
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D056726:Genetic Association Studies / Q000379:methods*; D014644:Genetic Variation; D006801:Humans; D016013:Likelihood Functions; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide