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Weighted SNP Set Analysis in Genome-Wide Association Study

Author

Listed:
  • Hui Dai
  • Yang Zhao
  • Cheng Qian
  • Min Cai
  • Ruyang Zhang
  • Minjie Chu
  • Juncheng Dai
  • Zhibin Hu
  • Hongbing Shen
  • Feng Chen

Abstract

Genome-wide association studies (GWAS) are popular for identifying genetic variants which are associated with disease risk. Many approaches have been proposed to test multiple single nucleotide polymorphisms (SNPs) in a region simultaneously which considering disadvantages of methods in single locus association analysis. Kernel machine based SNP set analysis is more powerful than single locus analysis, which borrows information from SNPs correlated with causal or tag SNPs. Four types of kernel machine functions and principal component based approach (PCA) were also compared. However, given the loss of power caused by low minor allele frequencies (MAF), we conducted an extension work on PCA and used a new method called weighted PCA (wPCA). Comparative analysis was performed for weighted principal component analysis (wPCA), logistic kernel machine based test (LKM) and principal component analysis (PCA) based on SNP set in the case of different minor allele frequencies (MAF) and linkage disequilibrium (LD) structures. We also applied the three methods to analyze two SNP sets extracted from a real GWAS dataset of non-small cell lung cancer in Han Chinese population. Simulation results show that when the MAF of the causal SNP is low, weighted principal component and weighted IBS are more powerful than PCA and other kernel machine functions at different LD structures and different numbers of causal SNPs. Application of the three methods to a real GWAS dataset indicates that wPCA and wIBS have better performance than the linear kernel, IBS kernel and PCA.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0075897
    DOI: 10.1371/journal.pone.0075897
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    References listed on IDEAS

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    1. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
    2. 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.
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