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Fast implementation of a scan statistic for identifying chromosomal patterns of genome wide association studies

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  • Sun, Yan V.
  • Jacobsen, Douglas M.
  • Turner, Stephen T.
  • Boerwinkle, Eric
  • Kardia, Sharon L.R.

Abstract

In order to take into account the complex genomic distribution of SNP variations when identifying chromosomal regions with significant SNP effects, a single nucleotide polymorphism (SNP) association scan statistic was developed. To address the computational needs of genome wide association (GWA) studies, a fast Java application, which combines single-locus SNP tests and a scan statistic for identifying chromosomal regions with significant clusters of significant SNP effects, was developed and implemented. To illustrate this application, SNP associations were analyzed in a pharmacogenomic study of the blood pressure lowering effect of thiazide-diuretics (N=195) using the Affymetrix Human Mapping 100 K Set. 55,335 tagSNPs (pair-wise linkage disequilibrium R2

Suggested Citation

  • Sun, Yan V. & Jacobsen, Douglas M. & Turner, Stephen T. & Boerwinkle, Eric & Kardia, Sharon L.R., 2009. "Fast implementation of a scan statistic for identifying chromosomal patterns of genome wide association studies," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1794-1801, March.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:5:p:1794-1801
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    References listed on IDEAS

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    2. Robert Sladek & Ghislain Rocheleau & Johan Rung & Christian Dina & Lishuang Shen & David Serre & Philippe Boutin & Daniel Vincent & Alexandre Belisle & Samy Hadjadj & Beverley Balkau & Barbara Heude &, 2007. "A genome-wide association study identifies novel risk loci for type 2 diabetes," Nature, Nature, vol. 445(7130), pages 881-885, February.
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    1. Fuzhong Xue & Shengxu Li & Jian'an Luan & Zhongshang Yuan & Robert N Luben & Kay-Tee Khaw & Nicholas J Wareham & Ruth J F Loos & Jing Hua Zhao, 2012. "A Latent Variable Partial Least Squares Path Modeling Approach to Regional Association and Polygenic Effect with Applications to a Human Obesity Study," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-10, February.

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