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Greater power and computational efficiency for kernel-based association testing of sets of genetic variants

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Item Type:Article
Title:Greater power and computational efficiency for kernel-based association testing of sets of genetic variants
Creators Name:Lippert, C. and Xiang, J. and Horta, D. and Widmer, C. and Kadie, C. and Heckerman, D. and Listgarten, J.
Abstract:Motivation: 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. Availability: Software available at http://research.microsoft.com/en-us/um/redmond/projects/MSCompBio/Fastlmm/. Contact: heckerma@microsoft.com Supplementary Information: Supplementary data are available at Bioinformatics online.
Keywords:Algorithms, Genetic Association Studies, Genetic Variation, Likelihood Functions, Phenotype, Single Nucleotide Polymorphism, Statistical Data Interpretation
Page Range:3206-3214
Date:15 November 2014
Official Publication:https://doi.org/10.1093/bioinformatics/btu504
PubMed:View item in PubMed

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