How to analyze many contingency tables simultaneously in genetic association studies

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
  • Thorsten Dickhaus
  • Klaus Straßburger
  • Daniel Schunk
  • Carlos Morcillo-Suarez
  • Illig,Thomas
  • Arcadi Navarro
Sammlungen
  • metadata
ISSN
1544-6115
Ausgabe der Veröffentlichung
4
Zeitschrift
Statistical applications in genetics and molecular biology
Schlüsselwörter
  • 330 Wirtschaft
  • 330 Economics
Sprache
eng
Paginierung
Article 12
Datum der Veröffentlichung
2012
Herausgeber
De Gruyter
Herausgeber URL
http://dx.doi.org/10.1515/1544-6115.1776
Datum der Datenerfassung
2020
Datum, an dem der Datensatz öffentlich gemacht wurde
2020
Zugang
Public
Titel
How to analyze many contingency tables simultaneously in genetic association studies
Ausgabe der Zeitschrift
11

Datenquelle: METADATA.UB

Andere Metadatenquellen:
Autoren
  • Thorsten Dickhaus
  • Klaus Straßburger
  • Daniel Schunk
  • Carlos Morcillo-Suarez
  • Thomas Illig
  • Arcadi Navarro
DOI
10.1515/1544-6115.1776
eISSN
1544-6115
Ausgabe der Veröffentlichung
4
Zeitschrift
Statistical Applications in Genetics and Molecular Biology
Datum der Veröffentlichung
2012
Status
Published
Herausgeber
Walter de Gruyter GmbH
Herausgeber URL
http://dx.doi.org/10.1515/1544-6115.1776
Datum der Datenerfassung
2021
Titel
How to analyze many contingency tables simultaneously in genetic association studies
Ausgabe der Zeitschrift
11

Datenquelle: Crossref

Abstract
We study exact tests for (2 x 2) and (2 x 3) contingency tables, in particular exact chi-squared tests and exact tests of Fisher type. In practice, these tests are typically carried out without randomization, leading to reproducible results but not exhausting the significance level. We discuss that this can lead to methodological and practical issues in a multiple testing framework when many tables are simultaneously under consideration as in genetic association studies.Realized randomized p-values are proposed as a solution which is especially useful for data-adaptive (plug-in) procedures. These p-values allow to estimate the proportion of true null hypotheses much more accurately than their non-randomized counterparts. Moreover, we address the problem of positively correlated p-values for association by considering techniques to reduce multiplicity by estimating the "effective number of tests" from the correlation structure.An algorithm is provided that bundles all these aspects, efficient computer implementations are made available, a small-scale simulation study is presented and two real data examples are shown.
Addresses
  • Humboldt-University, Berlin.
Autoren
  • Thorsten Dickhaus
  • Klaus Straßburger
  • Daniel Schunk
  • Carlos Morcillo-Suarez
  • Thomas Illig
  • Arcadi Navarro
DOI
10.1515/1544-6115.1776
eISSN
1544-6115
Externe Identifier
  • PubMed Identifier: 22850061
Funding acknowledgements
  • Wellcome Trust: 076113
Open access
false
ISSN
2194-6302
Ausgabe der Veröffentlichung
4
Zeitschrift
Statistical applications in genetics and molecular biology
Schlüsselwörter
  • Humans
  • Genetic Markers
  • Chi-Square Distribution
  • Case-Control Studies
  • Random Allocation
  • Gene Expression Profiling
  • Computational Biology
  • Algorithms
  • Research Design
  • Computer Simulation
  • Genetic Association Studies
  • High-Throughput Screening Assays
Sprache
eng
Medium
Electronic
Online publication date
2012
Paginierung
/j/sagmb.2012.11.issue-4/1544-6115.1776/1544-6115.
Datum der Veröffentlichung
2012
Status
Published
Datum der Datenerfassung
2012
Titel
How to analyze many contingency tables simultaneously in genetic association studies.
Sub types
  • Research Support, Non-U.S. Gov't
  • Validation Study
  • Evaluation Study
  • Journal Article
Ausgabe der Zeitschrift
11

Datenquelle: Europe PubMed Central

Abstract
We study exact tests for (2 x 2) and (2 x 3) contingency tables, in particular exact chi-squared tests and exact tests of Fisher type. In practice, these tests are typically carried out without randomization, leading to reproducible results but not exhausting the significance level. We discuss that this can lead to methodological and practical issues in a multiple testing framework when many tables are simultaneously under consideration as in genetic association studies.Realized randomized p-values are proposed as a solution which is especially useful for data-adaptive (plug-in) procedures. These p-values allow to estimate the proportion of true null hypotheses much more accurately than their non-randomized counterparts. Moreover, we address the problem of positively correlated p-values for association by considering techniques to reduce multiplicity by estimating the "effective number of tests" from the correlation structure.An algorithm is provided that bundles all these aspects, efficient computer implementations are made available, a small-scale simulation study is presented and two real data examples are shown.
Autoren
  • Thorsten Dickhaus
  • Klaus Straßburger
  • Daniel Schunk
  • Carlos Morcillo-Suarez
  • Thomas Illig
  • Arcadi Navarro
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/22850061
DOI
10.1515/1544-6115.1776
eISSN
1544-6115
Funding acknowledgements
  • Wellcome Trust: 076113
Ausgabe der Veröffentlichung
4
Zeitschrift
Stat Appl Genet Mol Biol
Schlüsselwörter
  • Algorithms
  • Case-Control Studies
  • Chi-Square Distribution
  • Computational Biology
  • Computer Simulation
  • Gene Expression Profiling
  • Genetic Association Studies
  • Genetic Markers
  • High-Throughput Screening Assays
  • Humans
  • Random Allocation
  • Research Design
Sprache
eng
Country
Germany
PII
/j/sagmb.2012.11.issue-4/1544-6115.1776/1544-6115.1776.xml
Datum der Veröffentlichung
2012
Status
Published online
Datum, an dem der Datensatz öffentlich gemacht wurde
2013
Titel
How to analyze many contingency tables simultaneously in genetic association studies.
Sub types
  • Evaluation Study
  • Journal Article
  • Research Support, Non-U.S. Gov't
  • Validation Study
Ausgabe der Zeitschrift
11

Datenquelle: PubMed

Beziehungen:
Eigentum von

Werkzeuge