Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ CardiffĀ 
WelshClear Cookie - decide language by browser settings

On multiple-testing correction in genome-wide association studies

Escott-Price, Valentina ORCID: and Schmidt, Karl Michael ORCID: 2008. On multiple-testing correction in genome-wide association studies. Genetic Epidemiology 32 (6) , pp. 567-573. 10.1002/gepi.20331

Full text not available from this repository.


The interpretation of the results of large association studies encompassing much or all of the human genome faces the fundamental statistical problem that a correspondingly large number of single nucleotide polymorphisms markers will be spuriously flagged as significant. A common method of dealing with these false positives is to raise the significance level for the individual tests for association of each marker. Any such adjustment for multiple testing is ultimately based on a more or less precise estimate for the actual overall type I error probability. We estimate this probability for association tests for correlated markers and show that it depends in a nonlinear way on the significance level for the individual tests. This dependence of the effective number of tests is not taken into account by existing multiple-testing corrections, leading to widely overestimated results. We demonstrate a simple correction for multiple testing, which can easily be calculated from the pairwise correlation and gives far more realistic estimates for the effective number of tests than previous formulae. The calculation is considerably faster than with other methods and hence applicable on a genome-wide scale. The efficacy of our method is shown on a constructed example with highly correlated markers as well as on real data sets, including a full genome scan where a conservative estimate only 8% above the permutation estimate is obtained in about 1% of computation time. As the calculation is based on pairwise correlations between markers, it can be performed at the stage of study design using public databases.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Subjects: Q Science > QA Mathematics
Q Science > QH Natural history > QH426 Genetics
Uncontrolled Keywords: multiple testing; genome-wide significance; association studies
Publisher: Wiley-Blackwell
ISSN: 0741-0395
Last Modified: 03 Dec 2022 11:24

Citation Data

Cited 180 times in Scopus. View in Scopus. Powered By ScopusĀ® Data

Actions (repository staff only)

Edit Item Edit Item