A multimetric approach to analysis of genome-wide association by single markers and composite likelihood

Jane Gibson, William Tapper, David Cox, Weihua Zhang, Arne Pfeufer, Christian Gieger, H. Erich Wichmann, Stefan Kääb, Andrew R. Collins, Thomas Meitinger, Newton Morton

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Two case/control studies with different phenotypes, marker densities, and microarrays were examined for the most significant single markers in defined regions. They show a pronounced bias toward exaggerated significance that increases with the number of observed markers and would increase further with imputed markers. This bias is eliminated by Bonferroni adjustment, thereby allowing combination by principal component analysis with a Malecot model composite likelihood evaluated by a permutation procedure to allow for multiple dependent markers. This intermediate value identifies the only demonstrated causal locus as most significant even in the preliminary analysis and clearly recognizes the strongest candidate in the other sample. Because the three metrics (most significant single marker, composite likelihood, and their principal component) are correlated, choice of the n smallest P values by each test gives <3n regions for follow-up in the next stage. In this way, methods with different response to marker selection and density are given approximately equal weight and economically compared, without expressing an untested prejudice or sacrificing the most significant results for any of them. Large numbers of cases, controls, and markers are by themselves insufficient to control type 1 and 2 errors, and so efficient use of multiple metrics with Bonferroni adjustment promises to be valuable in identifying causal variants and optimal design simultaneously.

Original languageEnglish
Pages (from-to)2592-2597
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number7
StatePublished - 19 Feb 2008


  • Bonferroni correction
  • Electrocardiographic QT interval
  • Empirical P values
  • Principal component analysis


Dive into the research topics of 'A multimetric approach to analysis of genome-wide association by single markers and composite likelihood'. Together they form a unique fingerprint.

Cite this