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Ranking of genome-wide association scan signals by different measures.

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Author

Summary, in English

BACKGROUND: The P-value approach has been employed to prioritizing genome-wide association (GWA) scan signals, with a genome-wide significance defined by a prior P-value threshold, although this is not ideal. A rationale put forward is that the association signals rather should be expected to give less support for single nucleotide polymorphisms (SNPs) that are rare (with associated low-power tests) than for common SNPs with equivalent P-values, unless investigators believe, a priori, that rare causative variants contribute to the disease and have more pronounced effects. METHODS: Using data from a GWA scan for type 2 diabetes (1924 cases, 2938 controls, 393 453 SNPs), we compared P-values with four alternative signal measures: likelihood ratio (LR), Bayes factor (BF; with a specified prior distribution for true effects), 'frequentist factor' (FF; reflecting the ratio between estimated-post-data- 'power' and P-value) and probability of pronounced effect size (PrPES). RESULTS: The 19 common SNPs [minor allele frequency (MAF) among the controls >29%] yielding strong P-value signals (P < 5 x 10(-7)) were also top ranked by the other approaches. There was a strong similarity between the P-values, LR and BF signals, in terms of ranking SNPs. In contrast, FF and PrPES signals down-weighted rare SNPs (control MAF <10%) with low P-values. CONCLUSIONS: For prioritization of signals that do not achieve compelling levels of evidence for association, the main driving force behind observed differences between the various association signals appears to be SNP MAF. The statistical power afforded by follow-up samples for establishing replication should be taken into account when tailoring the signal selection strategy.

Publishing year

2009

Language

English

Pages

1364-1373

Publication/Series

International Journal of Epidemiology

Volume

38

Document type

Journal article

Publisher

Oxford University Press

Topic

  • Public Health, Global Health, Social Medicine and Epidemiology

Status

Published

ISBN/ISSN/Other

  • ISSN: 1464-3685