Sample overlap in genome-wide association meta-analysis: practical comparisons across significance thresholds

Meta-analysis is widely used in genome-wide association studies (GWAS) to combine evidence across studies without requiring individual-level data.

By Xu, Zhang, ElliottOct 18, 2026

Score████░░░░░░4.3

Caveats

Preprint; not yet peer reviewed.

VerdictWorth a reader's time today.

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Abstract

Meta-analysis is widely used in genome-wide association studies (GWAS) to combine evidence across studies without requiring individual-level data. However, as both the number of studies included in meta-analyses and their sample sizes continue to grow, the same participant may be included in more than one study. Such sample overlap induces dependence across studies and violates the independence assumptions underlying standard meta-analysis methods. HostSeq is a Canadian initiative in which 15 clinical and epidemiological studies were assembled with DNA samples from approximately 10,000 Canadians infected by the SARS-CoV-2 virus. We observed duplicate samples across different participating studies. This observation raises an important practical question: what is the impact of sample overlap on association results, and when should such overlap be a concern? Motivated by this real problem, we sought to compare five meta-analysis methods and evaluate their performance against a reference mega-analysis in which the true sample overlap is known. We show that when the degree of sample overlap is relatively small, its impact on association results is minimal, even at the genome-wide significance threshold of = 5*10 -8 . We demonstrate these findings through extensive simulation studies and an application to overlapping cohorts within HostSeq.

Z. Xu, L. Zhang, L. T. Elliott

The editor's rubric

Heuristic review

Leverage3 / 5: A method or resource many groups across the field will adopt within a year.; 24% of this desk’s rubric
Magnitude2 / 5: Solid incremental gain on a meaningful problem.; 16% of this desk’s rubric
Evidence3 / 5: Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.; 20% of this desk’s rubric
Novelty3 / 5: A genuinely new approach to an open problem.; 20% of this desk’s rubric
Trajectory2 / 5: Some room to improve with obvious engineering.; 10% of this desk’s rubric
Stakes2 / 5: Benefits a professional community (practitioners, clinicians, engineers).; 10% of this desk’s rubric

Dimension	Level	Weight	What that level means
Leverage	███░░ 3	24%	A method or resource many groups across the field will adopt within a year.
Magnitude	██░░░ 2	16%	Solid incremental gain on a meaningful problem.
Evidence	███░░ 3	20%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	███░░ 3	20%	A genuinely new approach to an open problem.
Trajectory	██░░░ 2	10%	Some room to improve with obvious engineering.
Stakes	██░░░ 2	10%	Benefits a professional community (practitioners, clinicians, engineers).

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we report); novelty (alternative to status quo); design (meta-analysis); stakes (major disease).

How the score was computed

rank-2026-10-18

Score████░░░░░░4.3

Score = 10 × (75% × adjusted merit / 10 + 15% × attention + 10% × freshness)

Desks place stronger verdicts first, then use this score to order work within the same verdict.

Merit: 5.3 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.5 / 10; Shrunk toward the desk prior by editor confidence (38%).
Attention: 0%; Citations, upvotes, points, mentions.
Freshness: 88%; Half-life decay since publication.

No attention signals recorded yet.

The record

Reviewed by heuristic-v7 on Oct 19, 2026, 05:48 UTC. Paper type: method.
Categories: genetics
BRIEF, No.2 in the Biology edition of October 19, 2026.