title: Assessing statistical significance in multivariable genome wide association analysis.
abstract: 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.
  
  
  
  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.
  
  
  
  Reproducibility of our research is supported by the open-source Bioconductor package hierGWAS.
  
  
  
  peter.buehlmann@stat.math.ethz.ch
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Seminar for Statistics, Department of Mathematics, ETH Zürich, Zürich 8092, Switzerland Department of Economics, University of Zürich, Zürich 8006, Switzerland.
authors.forename: Laura
authors.initials: L
authors.lastname: Buzdugan
authors.affiliation: Seminar for Statistics, Department of Mathematics, ETH Zürich, Zürich 8092, Switzerland.
authors.forename: Markus
authors.initials: M
authors.lastname: Kalisch
authors.affiliation: Institute of Evolutionary Biology (CSIC-UPF), Universitat Pompeu Fabra, Barcelona 08003, Spain Institució Catalana de Recerca i Estudis Avançats (ICREA) Center for Genomic Regulation (CRG), Barcelona Biomedical Research Park (PRBB), Barcelona 08003, Spain.
authors.forename: Arcadi
authors.initials: A
authors.lastname: Navarro
authors.affiliation: Department of Economics, University of Mainz, Mainz, Germany.
authors.forename: Daniel
authors.initials: D
authors.lastname: Schunk
authors.affiliation: Department of Economics, University of Zürich, Zürich 8006, Switzerland.
authors.forename: Ernst
authors.initials: E
authors.lastname: Fehr
authors.affiliation: Seminar for Statistics, Department of Mathematics, ETH Zürich, Zürich 8092, Switzerland.
authors.forename: Peter
authors.initials: P
authors.lastname: Bühlmann
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D016014:Linear Models; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results