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Activity Number:
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373
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Type:
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Contributed
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Date/Time:
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Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #304851 |
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Title:
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Nonmetric Multidimensional Scaling to Correct for Population Stratification in Association Studies
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Author(s):
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Kelci J. Miclaus*+
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Companies:
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SAS Institute Inc./North Carolina State University
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Address:
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100 SAS Campus Dr. , Cary, NC, 27513,
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Keywords:
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Population Stratification ; GWAS ; Association Studies ; Multidimensional Scaling ; Principal Components Analysis
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Abstract:
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With the increasing size and scope of genetic association studies, bias due to population stratification is an ever-present concern. An unequal distribution of cases and controls with differing ethnic backgrounds can lead to spurious association due to allele frequency variance unrelated to case-control status. Eigen decomposition methods such as principal components analysis (PCA) and classical multidimensional scaling (CMDS) have shown promise in correcting for the bias. Alternatively, nonmetric MDS (NMDS) obtains individual coordinates via an optimization routine; which yields a more accurate representation. In simulation studies, the use of NMDS and nonparametric clustering is shown to accurately correct Type I error and to be significantly more powerful than PCA and CMDS when the set of loci used to infer population structure contains disease related genetic markers.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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