Abstract Details
Activity Number:
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514
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract #311143
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View Presentation
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Title:
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The Generalized Higher Criticism for Testing SNP-Sets in Genetic Association Testing
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Author(s):
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Ian Barnett*+ and Rajarshi Mukherjee and Xihong Lin
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Companies:
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Harvard and Harvard and Harvard School of Public Health
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Keywords:
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Higher criticism ;
Signal detection ;
Multiple hypothesis testing ;
Correlated test statistics ;
Genetic assocition testing
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Abstract:
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Genes, gene pathways, and network effects can contribute to the risk of complex genetic diseases. These genetic constructs each contain multiple SNPs, and only a sparse subset of these SNP-sets are generally related to the disease of interest. In this paper we adapt the higher criticism, a test traditionally used in high dimensional signal detection settings, to genetic association testing for SNP-sets. The higher criticism performs well when the signal is sparse, making it a potentially powerful tool for SNP-set association testing. Unlike past treatments of the higher criticism, we propose the generalized higher criticism (GHC) that does not require asymptotics in the number of SNPs in the SNP-set while simultaneously allowing for arbitrary correlation structures among the SNPs in the SNP-set. The power of this method is compared with existing SNP-set tests over simulated regions with varied correlation structures and signal sparsity. The relative performance of these methods is also compared in their analysis of the CGEM breast cancer genome-wide association study.
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Authors who are presenting talks have a * after their name.
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