Assessing statistical significance in multivariable genome wide association analysis
- Publikationstyp:
- Zeitschriftenaufsatz
- Metadaten:
-
- Autoren
- Laura Buzdugan
- Markus Kalisch
- Arcadi Navarro
- Daniel Schunk
- Ernst Fehr
- Peter Buhlmann
- Autoren-URL
- https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000379761500010&DestLinkType=FullRecord&DestApp=WOS_CPL
- DOI
- 10.1093/bioinformatics/btw128
- eISSN
- 1460-2059
- Externe Identifier
- Clarivate Analytics Document Solution ID: DR2VH
- PubMed Identifier: 27153677
- ISSN
- 1367-4803
- Ausgabe der Veröffentlichung
- 13
- Zeitschrift
- BIOINFORMATICS
- Paginierung
- 1990 - 2000
- Datum der Veröffentlichung
- 2016
- Status
- Published
- Titel
- Assessing statistical significance in multivariable genome wide association analysis
- Sub types
- Article
- Ausgabe der Zeitschrift
- 32
Datenquelle: Web of Science (Lite)
- Andere Metadatenquellen:
-
- Abstract
- <jats:title>Abstract</jats:title> <jats:p>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.</jats:p> <jats:p>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.</jats:p> <jats:p>Availability and implementation: Reproducibility of our research is supported by the open-source Bioconductor package hierGWAS.</jats:p> <jats:p>Contact: peter.buehlmann@stat.math.ethz.ch</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p>
- Autoren
- Laura Buzdugan
- Markus Kalisch
- Arcadi Navarro
- Daniel Schunk
- Ernst Fehr
- Peter Bühlmann
- DOI
- 10.1093/bioinformatics/btw128
- eISSN
- 1367-4811
- ISSN
- 1367-4803
- Ausgabe der Veröffentlichung
- 13
- Zeitschrift
- Bioinformatics
- Sprache
- en
- Online publication date
- 2016
- Paginierung
- 1990 - 2000
- Datum der Veröffentlichung
- 2016
- Status
- Published
- Herausgeber
- Oxford University Press (OUP)
- Herausgeber URL
- http://dx.doi.org/10.1093/bioinformatics/btw128
- Datum der Datenerfassung
- 2023
- Titel
- Assessing statistical significance in multivariable genome wide association analysis
- Ausgabe der Zeitschrift
- 32
Datenquelle: Crossref
- Abstract
- <h4>Motivation</h4>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.<h4>Results</h4>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.<h4>Availability and implementation</h4>Reproducibility of our research is supported by the open-source Bioconductor package hierGWAS.<h4>Contact</h4>peter.buehlmann@stat.math.ethz.ch<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics online.
- Addresses
- Seminar for Statistics, Department of Mathematics, ETH Zürich, Zürich 8092, Switzerland Department of Economics, University of Zürich, Zürich 8006, Switzerland.
- Autoren
- Laura Buzdugan
- Markus Kalisch
- Arcadi Navarro
- Daniel Schunk
- Ernst Fehr
- Peter Bühlmann
- DOI
- 10.1093/bioinformatics/btw128
- eISSN
- 1367-4811
- Externe Identifier
- PubMed Identifier: 27153677
- PubMed Central ID: PMC4920127
- Funding acknowledgements
- European Research Council: 295642
- Wellcome Trust: 076113
- Open access
- true
- ISSN
- 1367-4803
- Ausgabe der Veröffentlichung
- 13
- Zeitschrift
- Bioinformatics (Oxford, England)
- Schlüsselwörter
- Humans
- Cluster Analysis
- Linear Models
- Reproducibility of Results
- Computational Biology
- Genotype
- Phenotype
- Polymorphism, Single Nucleotide
- Computer Simulation
- Genome-Wide Association Study
- Sprache
- eng
- Medium
- Print-Electronic
- Online publication date
- 2016
- Open access status
- Open Access
- Paginierung
- 1990 - 2000
- Datum der Veröffentlichung
- 2016
- Status
- Published
- Publisher licence
- CC BY-NC
- Datum der Datenerfassung
- 2016
- Titel
- Assessing statistical significance in multivariable genome wide association analysis.
- Sub types
- research-article
- Journal Article
- Ausgabe der Zeitschrift
- 32
Files
https://academic.oup.com/bioinformatics/article-pdf/32/13/1990/49020121/bioinformatics_32_13_1990.pdf https://europepmc.org/articles/PMC4920127?pdf=render
Datenquelle: Europe PubMed Central
- 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.
- Date of acceptance
- 2016
- Autoren
- Laura Buzdugan
- Markus Kalisch
- Arcadi Navarro
- Daniel Schunk
- Ernst Fehr
- Peter Bühlmann
- Autoren-URL
- https://www.ncbi.nlm.nih.gov/pubmed/27153677
- DOI
- 10.1093/bioinformatics/btw128
- eISSN
- 1367-4811
- Externe Identifier
- PubMed Central ID: PMC4920127
- Funding acknowledgements
- European Research Council: 295642
- Wellcome Trust: 076113
- Ausgabe der Veröffentlichung
- 13
- Zeitschrift
- Bioinformatics
- Schlüsselwörter
- Cluster Analysis
- Computational Biology
- Computer Simulation
- Genome-Wide Association Study
- Genotype
- Humans
- Linear Models
- Phenotype
- Polymorphism, Single Nucleotide
- Reproducibility of Results
- Sprache
- eng
- Country
- England
- Paginierung
- 1990 - 2000
- PII
- btw128
- Datum der Veröffentlichung
- 2016
- Status
- Published
- Datum, an dem der Datensatz öffentlich gemacht wurde
- 2017
- Titel
- Assessing statistical significance in multivariable genome wide association analysis.
- Sub types
- Journal Article
- Ausgabe der Zeitschrift
- 32
Datenquelle: PubMed
- Autoren
- Laura Buzdugan
- Markus Kalisch
- Arcadi Navarro
- Daniel Schunk
- Ernst Fehr
- Peter Bühlmann
- DOI
- 10.1093/bioinformatics/btw128
- Zeitschrift
- Bioinform.
- Artikelnummer
- 13
- Paginierung
- 1990 - 2000
- Datum der Veröffentlichung
- 2016
- Titel
- Assessing statistical significance in multivariable genome wide association analysis.
- Ausgabe der Zeitschrift
- 32
Datenquelle: DBLP
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