Assessing statistical significance in multivariable genome wide association analysis

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  • dc.contributor.author Buzdugan, Lauraca
  • dc.contributor.author Kalisch, Markusca
  • dc.contributor.author Navarro i Cuartiellas, Arcadi, 1969-ca
  • dc.contributor.author Schunk, Danielca
  • dc.contributor.author Fehr, Ernstca
  • dc.contributor.author Bühlmann, Peterca
  • dc.date.accessioned 2017-02-08T08:17:42Z
  • dc.date.available 2017-02-08T08:17:42Z
  • dc.date.issued 2016ca
  • dc.description.abstract Motivation: Although Genome Wide Association Studies (GWAS) genotype a very large number of single nucleotide polymorphisms (SNPs), the data are often analyzed one SNP at a time. The low predictive power of single SNPs, coupled with the high significance threshold needed to correct for multiple testing, greatly decreases the power of GWAS. Results: We propose a procedure in which all the SNPs are analyzed in a multiple generalized linear model, and we show its use for extremely high-dimensional datasets. Our method yields P -values for assessing significance of single SNPs or groups of SNPs while controlling for all other SNPs and the family wise error rate (FWER). Thus, our method tests whether or not a SNP carries any additional information about the phenotype beyond that available by all the other SNPs. This rules out spurious correlations between phenotypes and SNPs that can arise from marginal methods because the ‘spuriously correlated’ SNP merely happens to be correlated with the ‘truly causal’ SNP. In addition, the method offers a data driven approach to identifying and refining groups of SNPs that jointly contain informative signals about the phenotype. We demonstrate the value of our method by applying it to the seven diseases analyzed by the Wellcome Trust Case Control Consortium (WTCCC). We show, in particular, that our method is also capable of finding significant SNPs that were not identified in the original WTCCC study, but were replicated in other independent studies. Availability and implementation: Reproducibility of our research is supported by the open-source Bioconductor package hierGWAS. Contact:peter.buehlmann@stat.math.ethz.ch Supplementary information:Supplementary data are available at Bioinformatics online.
  • dc.description.sponsorship E.F. and L.B. gratefully acknowledge financial support from the European Research Council (grant 295642, The Foundations of Economic Preferences, FEP). D.S. gratefully acknowledges financial support from the German National Science Foundation (DFG, grant SCHU 2828/2-1, Inference statistical methods for behavioral genetics and neuroeconomics). A.N. gratefully acknowledges support from the Instituto de Salud Carlos III (grants RD12/0032/0011 and PT13/0001/0026) and the Spanish Government Grant (BFU2012-38236) and from FEDER.
  • dc.format.mimetype application/pdfca
  • dc.identifier.citation Buzdugan L, Kalisch M, Navarro i Cuartiellas A, Schunk D, Fehr E, Bühlmann P. Assessing statistical significance in multivariable genome wide association analysis. Bioinformatics. 2016; 32(13): 1990-2000. DOI: 10.1093/bioinformatics/btw128ca
  • dc.identifier.doi http://dx.doi.org/10.1093/bioinformatics/btw128
  • dc.identifier.issn 1367-4803ca
  • dc.identifier.uri http://hdl.handle.net/10230/28077
  • dc.language.iso engca
  • dc.publisher Oxford University Press
  • dc.relation.ispartof Bioinformatics. 2016; 32(13): 1990-2000
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/BFU2012-38236
  • dc.rights © The Author 2016. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/ ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.comca
  • dc.rights.accessRights info:eu-repo/semantics/openAccessca
  • dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/
  • dc.title Assessing statistical significance in multivariable genome wide association analysisca
  • dc.type info:eu-repo/semantics/articleca
  • dc.type.version info:eu-repo/semantics/publishedVersionca

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