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Abstract Details
Activity Number:
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359
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract - #303332 |
Title:
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Use of Selective Phenotyping to Increase Power of Genetic Association Studies of Quantitative Biomarkers
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Author(s):
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Yunfei Wang*+ and Ethan M. Lange
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Companies:
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The University of North Carolina and The University of North Carolina
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Address:
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5000D,120 Manson Farm Road, Chapel Hill, NC, 27599,
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Keywords:
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biomarkers ;
simulated annealing ;
statistical power ;
genetic association ;
SNPs
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Abstract:
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Blood-based biomarkers are often used as intermediate outcomes for identifying genetic risk factors associated with cardiovascular disease (CVD). Measuring biomarkers, however, is typically expensive and time consuming. Genome-wide genetic data on single-nucleotide polymorphisms (SNPs) are now routinely available for tens of thousands of samples from large population-based cohorts. Given the expense of measuring biomarkers, it would be desirable to identify a subset of subjects that could be phenotyped for the biomarker of interest in order to optimize statistical power under fixed cost constraints. For any specific SNP and a fixed sample size, power is typically optimized when genotypes are partitioned equally between homozygotes for the major and minor allele. When trying to optimize power across multiple SNPs, using a selection strategy that optimizes power for one specific SNP does not benefit other SNPs of interest. We describe a simulated annealing-based algorithm that identifies an optimal selection of subjects to be phenotyped based on the weighted or unweighted average power across a group of SNPs.
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