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Association Test Based on SNP Set: Logistic Kernel Machine Based Test vs. Principal Component Analysis

Author

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  • Yang Zhao
  • Feng Chen
  • Rihong Zhai
  • Xihong Lin
  • Nancy Diao
  • David C Christiani

Abstract

GWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary. Several methods have been proposed based on grouping SNPs into SNP sets using biological knowledge and/or genomic features. In this article, we compare the linear kernel machine based test (LKM) and principal components analysis based approach (PCA) using simulated datasets under the scenarios of 0 to 3 causal SNPs, as well as simple and complex linkage disequilibrium (LD) structures of the simulated regions. Our simulation study demonstrates that both LKM and PCA can control the type I error at the significance level of 0.05. If the causal SNP is in strong LD with the genotyped SNPs, both the PCA with a small number of principal components (PCs) and the LKM with kernel of linear or identical-by-state function are valid tests. However, if the LD structure is complex, such as several LD blocks in the SNP set, or when the causal SNP is not in the LD block in which most of the genotyped SNPs reside, more PCs should be included to capture the information of the causal SNP. Simulation studies also demonstrate the ability of LKM and PCA to combine information from multiple causal SNPs and to provide increased power over individual SNP analysis. We also apply LKM and PCA to analyze two SNP sets extracted from an actual GWAS dataset on non-small cell lung cancer.

Suggested Citation

  • Yang Zhao & Feng Chen & Rihong Zhai & Xihong Lin & Nancy Diao & David C Christiani, 2012. "Association Test Based on SNP Set: Logistic Kernel Machine Based Test vs. Principal Component Analysis," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-11, September.
  • Handle: RePEc:plo:pone00:0044978
    DOI: 10.1371/journal.pone.0044978
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    Citations

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    Cited by:

    1. Min Cai & Hui Dai & Yongyong Qiu & Yang Zhao & Ruyang Zhang & Minjie Chu & Juncheng Dai & Zhibin Hu & Hongbing Shen & Feng Chen, 2013. "SNP Set Association Analysis for Genome-Wide Association Studies," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-10, May.
    2. Qiuyi Zhang & Yang Zhao & Ruyang Zhang & Yongyue Wei & Honggang Yi & Fang Shao & Feng Chen, 2016. "A Comparative Study of Five Association Tests Based on CpG Set for Epigenome-Wide Association Studies," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-13, June.
    3. Hui Dai & Yang Zhao & Cheng Qian & Min Cai & Ruyang Zhang & Minjie Chu & Juncheng Dai & Zhibin Hu & Hongbing Shen & Feng Chen, 2013. "Weighted SNP Set Analysis in Genome-Wide Association Study," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-7, September.
    4. Clemontina A. Davenport & Arnab Maity & Patrick F. Sullivan & Jung-Ying Tzeng, 2018. "A Powerful Test for SNP Effects on Multivariate Binary Outcomes Using Kernel Machine Regression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 117-138, April.

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