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Activity Number:
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470
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2009 : 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 - #305305 |
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Title:
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On Combining Data from Genome-Wide Association Studies to Discover Disease-Associated SNPs
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Author(s):
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Ruth Pfeiffer*+ and Mitchell H. Gail
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Companies:
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National Cancer Institute and National Cancer Institute
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Address:
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6120 Executive Blvd, Rockville, MD, 20852,
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Keywords:
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Whole genome scans ; Hypothesis testing ; Multiple comparison
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Abstract:
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Combining data from several case-control genome-wide association studies (GWAS) can improve the efficiency for detecting associations of disease with genetic markers (alleles of single nucleotide polymorphisms or SNPs), across the genome, compared to separate analyses of the component studies. We compare several procedures to combine GWAS data both in terms of the power to detect a disease-associated SNP while controlling the experiment-wide (genome-wide) significance level, and in terms of the detection probability (DP). DP is the probability that a particular disease-associated SNP is among the T most promising SNPs selected on the basis of low-p-values. In settings of practical relevance, meta-analytic approaches had higher power and DP than summing chi-square test-statistics across studies, Fisher's combination of p-values and forming a combined list of the best SNPs within study.
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