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SNP Set Association Analysis for Genome-Wide Association Studies

Author

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

Abstract

Genome-wide association study (GWAS) is a promising approach for identifying common genetic variants of the diseases on the basis of millions of single nucleotide polymorphisms (SNPs). In order to avoid low power caused by overmuch correction for multiple comparisons in single locus association study, some methods have been proposed by grouping SNPs together into a SNP set based on genomic features, then testing the joint effect of the SNP set. We compare the performances of principal component analysis (PCA), supervised principal component analysis (SPCA), kernel principal component analysis (KPCA), and sliced inverse regression (SIR). Simulated SNP sets are generated under scenarios of 0, 1 and ≥2 causal SNPs model. Our simulation results show that all of these methods can control the type I error at the nominal significance level. SPCA is always more powerful than the other methods at different settings of linkage disequilibrium structures and minor allele frequency of the simulated datasets. We also apply these four methods to a real GWAS of non-small cell lung cancer (NSCLC) in Han Chinese population

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0062495
    DOI: 10.1371/journal.pone.0062495
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    References listed on IDEAS

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    3. 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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