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Statistical Applications in Genetics and Molecular Biology

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Volume 16, Issue 3


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Genetic association test based on principal component analysis

Zhongxue Chen
  • Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, 1025 E. 7th Street, Bloomington, IN 47405, USA
  • Other articles by this author:
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/ Shizhong Han
  • Department of Psychiatry, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
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/ Kai Wang
  • Corresponding author
  • Department of Biostatistics, N322 CPHB College of Public Health, University of Iowa, 145 N. Riverside Drive, Iowa City, IA 52242, USA
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Published Online: 2017-06-26 | DOI: https://doi.org/10.1515/sagmb-2016-0061


Many gene- and pathway-based association tests have been proposed in the literature. Among them, the SKAT is widely used, especially for rare variants association studies. In this paper, we investigate the connection between SKAT and a principal component analysis. This investigation leads to a procedure that encompasses SKAT as a special case. Through simulation studies and real data applications, we compare the proposed method with some existing tests.

Keywords: gene-based association; pathway-based association; rare variants


  • Basu, S. and W. Pan (2011): “Comparison of statistical tests for disease association with rare variants,” Genet. Epidemiol. 35, 606–619.Web of ScienceCrossrefGoogle Scholar

  • Chen, Z. (2011a): “Is the weighted z−test the best method for combining probabilities from independent tests?” J. Evol. Biol. 24, 926–930.Web of ScienceCrossrefGoogle Scholar

  • Chen, Z. (2011b): “A new association test based on Chi−square partition for case−control GWA studies,” Genet. Epidemiol. 35, 658–663.Web of ScienceCrossrefGoogle Scholar

  • Chen, Z. (2013): “Association tests through combining p-values for case control genome-wide association studies,” Stat. Probab. Lett. 83, 1854–1862.Web of ScienceCrossrefGoogle Scholar

  • Chen, Z. (2014): “A new association test based on disease allele selection for case-control genome-wide association studies,” BMC Genomics 15, 358.Web of ScienceCrossrefGoogle Scholar

  • Chen, Z. and Q. Liu (2011): “A new approach to account for the correlations among single nucleotide polymorphisms in genome-wide association studies,” Hum. Hered. 72, 1–9.Web of ScienceCrossrefGoogle Scholar

  • Chen, Z. and H. K. T. Ng (2012): “A robust method for testing association in genome-wide association studies,” Hum. Hered. 73, 26–34.Web of ScienceCrossrefGoogle Scholar

  • Chen, Z. and S. Nadarajah (2014): “On the optimally weighted z-test for combining probabilities from independent studies,” Comput. Stat. Data Anal. 70, 387–394.CrossrefWeb of ScienceGoogle Scholar

  • Chen, L. S., L. Hsu, E. R. Gamazon, N. J. Cox and D. L. Nicolae (2012): “An exponential combination procedure for set-based association tests in sequencing studies,” Am. J. Hum. Genet. 91, 977–986.CrossrefWeb of ScienceGoogle Scholar

  • Chen, Z., H. K. T. Ng, J. Li, Q. Liu and H. Huang (2014a): “Detecting associated single-nucleotide polymorphisms on the X chromosome in case control genome-wide association studies,” Stat. Methods Med. Res. 26, 567–582.CrossrefWeb of ScienceGoogle Scholar

  • Chen, Z., W. Yang, Q. Liu, J. Y. Yang, J. Li and M. Q. Yang (2014b): “A new statistical approach to combining p-values using gamma distribution and its application to genome-wide association study,” BMC Bioinformatics 15 (Suppl 17), S3.Web of ScienceCrossrefGoogle Scholar

  • Chen, Z., H. Huang and H. K. T. Ng (2016): “Testing for association in case-control genome-wide association studies with shared controls,” Stat. Methods Med. Res. 25, 954–967.Web of ScienceCrossrefGoogle Scholar

  • Dering, C., C. Hemmelmann, E. Pugh and A. Ziegler (2011): “Statistical analysis of rare sequence variants: an overview of collapsing methods,” Genet. Epidemiol. 35, S12–S17.CrossrefWeb of ScienceGoogle Scholar

  • Dudbridge, F. and A. Gusnanto (2008): “Estimation of significance thresholds for genomewide association scans,” Genet. Epidemiol. 32, 227–234.Web of ScienceCrossrefGoogle Scholar

  • Fisher, R. A. (1932): Statistical methods for research workers. Edinburgh: Oliver and Boyd.Google Scholar

  • Gao, X., J. Starmer and E. R. Martin (2008): “A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms,” Genet. Epidemiol. 32, 361–369.CrossrefWeb of ScienceGoogle Scholar

  • Han, F. and W. Pan (2010): “A data-adaptive sum test for disease association with multiple common or rare variants,” Hum. Hered. 70, 42–54.Web of ScienceCrossrefGoogle Scholar

  • Lancaster, H. (1961): “The combination of probabilities: an application of orthonormal functions,” Aust. J. Stat. 3, 20–33.CrossrefGoogle Scholar

  • Lee, S., M. C. Wu and X. Lin (2012): “Optimal tests for rare variant effects in sequencing association studies,” Biostatistics 13, 762–775.CrossrefWeb of ScienceGoogle Scholar

  • Li, B. and S. M. Leal (2008): “Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data,” Am. J. Hum. Genet. 83, 311–321.CrossrefWeb of ScienceGoogle Scholar

  • Madsen, B. E. and S. R. Browning (2009): “A groupwise association test for rare mutations using a weighted sum statistic,” PLoS Genet. 5, e1000384.Web of ScienceCrossrefGoogle Scholar

  • Moskvina, V. and K. M. Schmidt (2008): “On multiple-testing correction in genome-wide association studies,” Genet. Epidemiol. 32, 567–573.CrossrefWeb of ScienceGoogle Scholar

  • Pan, W., J. Kim, Y. Zhang, X. Shen and P. Wei (2014): “A powerful and adaptive association test for rare variants,” Genetics 197, 1081–1095.Web of ScienceGoogle Scholar

  • Romeo, S., W. Yin, J. Kozlitina, L. A. Pennacchio, E. Boerwinkle, H. H. Hobbs and J. C. Cohen (2009): “Rare loss-of-function mutations in ANGPTL family members contribute to plasma triglyceride levels in humans,” J Clin. Invest. 119, 70–79.Web of ScienceGoogle Scholar

  • Wang, K. (2012): “Statistical tests of genetic association for case–control study designs,” Biostatistics 13, 724–733.CrossrefWeb of ScienceGoogle Scholar

  • Wang, K. (2016): “Boosting the power of the sequence Kernel Association test by properly estimating its null distribution,” Am. J. Hum. Genet. 99, 104–114.CrossrefWeb of ScienceGoogle Scholar

  • Wu, M. C., S. Lee, T. Cai, Y. Li, M. Boehnke and X. Lin (2011): “Rare-variant association testing for sequencing data with the sequence kernel association test,” Am. J. Hum. Genet. 89, 82–93.CrossrefWeb of ScienceGoogle Scholar

  • Wu, B., J. S. Pankow and W. Guan (2015): “Sequence kernel association analysis of rare variant set based on the marginal regression model for binary traits,” Genet. Epidemiol. 39, 399–405.Web of ScienceCrossrefGoogle Scholar

  • Zheng, G. and H. K. Ng (2008): “Genetic model selection in two-phase analysis for case-control association studies,” Biostatistics 9, 391–399.Web of ScienceCrossrefGoogle Scholar

About the article

Corresponding author: Kai Wang, PhD, Department of Biostatistics, N322 CPHB College of Public Health, University of Iowa, 145 N. Riverside Drive, Iowa City, IA 52242, USA

Published Online: 2017-06-26

Published in Print: 2017-07-26

Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 16, Issue 3, Pages 189–198, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.1515/sagmb-2016-0061.

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