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Univariate/Multivariate Genome-Wide Association Scans Using Data from Families and Unrelated Samples

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  • Lei Zhang
  • Yu-Fang Pei
  • Jian Li
  • Christopher J Papasian
  • Hong-Wen Deng

Abstract

As genome-wide association studies (GWAS) are becoming more popular, two approaches, among others, could be considered in order to improve statistical power for identifying genes contributing subtle to moderate effects to human diseases. The first approach is to increase sample size, which could be achieved by combining both unrelated and familial subjects together. The second approach is to jointly analyze multiple correlated traits. In this study, by extending generalized estimating equations (GEEs), we propose a simple approach for performing univariate or multivariate association tests for the combined data of unrelated subjects and nuclear families. In particular, we correct for population stratification by integrating principal component analysis and transmission disequilibrium test strategies. The proposed method allows for multiple siblings as well as missing parental information. Simulation studies show that the proposed test has improved power compared to two popular methods, EIGENSTRAT and FBAT, by analyzing the combined data, while correcting for population stratification. In addition, joint analysis of bivariate traits has improved power over univariate analysis when pleiotropic effects are present. Application to the Genetic Analysis Workshop 16 (GAW16) data sets attests to the feasibility and applicability of the proposed method.

Suggested Citation

  • Lei Zhang & Yu-Fang Pei & Jian Li & Christopher J Papasian & Hong-Wen Deng, 2009. "Univariate/Multivariate Genome-Wide Association Scans Using Data from Families and Unrelated Samples," PLOS ONE, Public Library of Science, vol. 4(8), pages 1-12, August.
    • Handle: RePEc:plo:pone00:0006502
    DOI: 10.1371/journal.pone.0006502
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    References listed on IDEAS

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    1. B. Devlin & Kathryn Roeder, 1999. "Genomic Control for Association Studies," Biometrics, The International Biometric Society, vol. 55(4), pages 997-1004, December.
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    Cited by:

    1. Heejung Shim & Daniel I Chasman & Joshua D Smith & Samia Mora & Paul M Ridker & Deborah A Nickerson & Ronald M Krauss & Matthew Stephens, 2015. "A Multivariate Genome-Wide Association Analysis of 10 LDL Subfractions, and Their Response to Statin Treatment, in 1868 Caucasians," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-20, April.
    2. Matthew Stephens, 2013. "A Unified Framework for Association Analysis with Multiple Related Phenotypes," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-19, July.

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