Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

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

Genetic epidemiology - 1 Jul 2009

Bowden Jack, Dudbridge Frank

Abstract excerpt

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...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Epidemiologic Research Design
Share to Genome, Human
Share to Likelihood Functions

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Models, Statistical

Share to Odds Ratio

Share to Regression Analysis

Share to Reproducibility of Results

Share to Stochastic Processes

Source

Open in PubMed Open full text

Identifiers

PMID: 19140132
DOI: 10.1002/gepi.20394
PMCID: PMC2726957

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Unbiased estimation of odds ratios: combining genomewide association scans with replication studies.PMID 19140132Read ↗

Novel rank-based approaches for discovery and replication in genome-wide association studies.Semantic proximity Read ↗

A flexible genome-wide bootstrap method that accounts for ranking and threshold-selection bias in GWAS interpretation and replication study design.Semantic proximity Read ↗

Statistical Correction of the Winner’s Curse Explains Replication Variability in Quantitative Trait Genome-Wide Association StudiesSemantic proximity Read ↗

Accurate modeling of replication rates in genome-wide association studies by accounting for Winner's Curse and study-specific heterogeneity.Semantic proximity Read ↗

Review and further developments in statistical corrections for Winner's Curse in genetic association studies.Semantic proximity Read ↗

What is the probability of replicating a statistically significant association in genome-wide association studies?Semantic proximity Read ↗

Accurate modeling of replication rates in genome-wide association studies by accounting for Winner’s Curse and study-specific heterogeneitySemantic proximity Read ↗

Statistical correction of the Winner's Curse explains replication variability in quantitative trait genome-wide association studies.Semantic proximity Read ↗

Jointly determining significance levels of primary and replication studies by controlling the false discovery rate in two-stage genome-wide association studies.Semantic proximity Read ↗

Power estimation and sample size determination for replication studies of genome-wide association studies.Semantic proximity Read ↗

Effectiveness of strategies to increase the validity of findings from association studies: size vs. replication.Semantic proximity Read ↗

repfdr: a tool for replicability analysis for genome-wide association studies.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.