Unbiased estimation of odds ratios: Combining genomewide association scans with replication studies

Jack Bowden*, Frank Dudbridge

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

48 Citations (Scopus)


Odds ratios or other effect sizes estimated from genome scans are upwardly biased, because only the top-ranking associations are reported, and moreover only if they reach a defined level of significance. No unbiased estimate exists based on data selected in this fashion, but replication studies are routinely performed that allow unbiased estimation of the effect sizes. Estimation based on replication data alone is inefficient in the sense that the initial scan could, in principle, contribute information on the effect size. We propose an unbiased estimator combining information from both the initial scan and the replication study, which is more efficient than that based just on the replication. Specifically, we adjust the standard combined estimate to allow for selection by rank and significance in the initial scan. Our approach explicitly allows for multiple associations arising from a scan, and is robust to mis-specification of a significance threshold. We require replication data to be available but argue that, in most applications, estimates of effect sizes are only useful when associations have been replicated. We illustrate our approach on some recently completed scans and explore its efficiency by simulation.

Original languageEnglish
Pages (from-to)406-418
Number of pages13
JournalGenetic Epidemiology
Issue number5
Publication statusPublished - 29 Sept 2009


  • Genomewide scans
  • Selection bias
  • Winner's curse


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