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Activity Number:
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536
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #309181 |
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Title:
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Regression-Based Association Approach Using Genetic Similarity for Genomewide Association Scans
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Author(s):
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Jung-Ying Tzeng*+ and Sheng-Mao Chang and Duncan C. Thomas and Marie Davidian
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Companies:
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North Carolina State University and North Carolina State University and University of Southern California and North Carolina State University
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Address:
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Department of Statistics, Raleigh, NC, 27695,
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Keywords:
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multi-marker analysis ; genetic association ; haplotype analysis ; genome-wide association studies
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Abstract:
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Genomewide association scans (GWAS) become a new tool to identify genetic variants underlying complex traits. While multimarker analyses have been frequently used in association studies, most statistical analyses of GWAS still focus on single-SNP analyses. One concern for multimarker analyses is its large degrees of freedom (df) for capturing genetic diversity. As the power of a single test is limited by the large df, the overall performance of a GWAS is further diminished by multiple testing. Here we consider a regression approach for testing association using genetic similarity. The method is inspired by the Haseman-Elston regression for linkage analysis and is connected with the ordinary regression that treats haplotypes as covariates under hierarchical modeling framework. Via simulation we assess its performance and demonstrate its validity and power in testing genetic association.
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