Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Set-based variance component tests have been identified as a way to increase por in association studies by aggregating ak individual effects. Hover, the choice of test statistic has been largely ignored even though it may play an important role in obtaining optimal por.  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  develop new computationally efficient methods.#R##N##R##N#Results: After reviewing theoretical differences in performance beten the score and LR tests,  find empirically on real data that the LR test generally has more por. 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,  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.#R##N##R##N#Availability: Software available at http://research.microsoft.com/en-us/um/redmond/projects/MSCompBio/Fastlmm/.#R##N##R##N#Contact: moc.tfosorcim@amrekceh#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online.
['Christoph Lippert', 'Jing Xiang', 'Danilo Horta', 'Christian Widmer', 'Carl M. Kadie', 'David Heckerman', 'Jennifer Listgarten']
I am writing a research paper on a idea Greater power and computational efficiency for kernel-based association testing of sets of genetic variants
[{"from": "human", "value": "I am writing a research paper on a idea Greater power and computational efficiency for kernel-based association testing of sets of genetic variants"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Set-based variance component tests have been identified as a way to increase por in association studies by aggregating ak individual effects. Hover, the choice of test statistic has been largely ignored even though it may play an important role in obtaining optimal por.  compared a standard statistical test\u2014a score test\u2014with a recently developed likelihood ratio (LR) test. Further, when correction for hidden structure is needed, or gene\u2013gene interactions are sought, state-of-the art algorithms for both the score and LR tests can be computationally impractical. Thus  develop new computationally efficient methods.#R##N##R##N#Results: After reviewing theoretical differences in performance beten the score and LR tests,  find empirically on real data that the LR test generally has more por. In particular, on 15 of 17 real datasets, the LR test yielded at least as many associations as the score test\u2014up to 23 more associations\u2014whereas the score test yielded at most one more association than the LR test in the two remaining datasets. On synthetic data,  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\u2013gene interaction tests. The latter yielded a factor of 2000 speedup on a cohort of size 13 500.#R##N##R##N#Availability: Software available at http://research.microsoft.com/en-us/um/redmond/projects/MSCompBio/Fastlmm/.#R##N##R##N#Contact: moc.tfosorcim@amrekceh#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online."}]