Evidence map›Paper›PMID 41646310›Full record

ArticleResearch square2026

A resource of "bottom-line" variant associations for 1,281 complex traits by integrating data across published genome-wide association studies.

Trang Nguyen, Furkan Büyükgöl, Patrick Smadbeck, Jeffrey Massung, Maria C Costanzo, Monica Ruiz, Peter Dornbos, Satoshi Yoshiji, Ryan Koesterer, Thanh Long Nguyen and 14 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors.

Trang NguyenPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0009-0004-9413-5965
Furkan BüyükgölPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Patrick SmadbeckPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Jeffrey MassungPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Maria C CostanzoPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0001-9043-693X
Monica RuizPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Peter DornbosPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Satoshi YoshijiPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Ryan KoestererPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Thanh Long NguyenPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0009-0000-5940-8906
Dongkeun JangPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Quy HoangPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0002-7140-6097
Yue JiEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK.ORCID 0000-0001-9844-7297
Aoife McMahonEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK.ORCID 0000-0003-0978-0309
Sebanti SenguptaDepartment of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
Xianyong YinDepartment of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
Brady RyanDepartment of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.ORCID 0009-0009-4164-2871
Ryan P WelchDepartment of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
Jorien TreurGenetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, The Netherlands.
Connie R BezzinaDepartment of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, Cardiomyopathy and Arrhythmia, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0002-0633-3514
Goncalo AbecasisRegeneron Genetics Center, Tarrytown, New York, USA.ORCID 0000-0003-1509-1825
Michael BoehnkeDepartment of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0002-6442-7754
Noël P BurttPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Jason FlannickPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0002-3618-795X

Funding

The next iteration of the AMP-T2D Knowledge PortalUM1DK105554 · NIDDK · BROAD INSTITUTE, INC. · PI BOEHNKE, MICHAEL L, BURTT, NOEL P · 2020 to 2025
$18.5M
University of Michigan Training Program in Genomic ScienceT32HG000040 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Sebastian Zoellner · 1995 to 2026
$16.4M
Identifying Genes for Type 2 Diabetes:FUSIONR01DK062370 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L, SCOTT, LAURA J. · 2003 to 2025
$6.7M
In vivo and in vitro rare coding variant analyses to identify modulations of the adipocyte differentiation pathway that affect T2D riskR01DK125490 · NIDDK · BOSTON CHILDREN'S HOSPITAL · PI Jason Flannick · 2020 to 2026
$3.9M
The Association to Function Knowledge Portal: a genomic data resource for translating GWAS associations to biological effectsU24HG011453 · NHGRI · BROAD INSTITUTE, INC. · PI BURTT, NOEL P, FLANNICK, JASON · 2021 to 2025
$3.4M
NHGRI NIH HHS T32 HG000040NHGRI NIH HHS U24 HG011453NIDDK NIH HHS R01 DK062370NIDDK NIH HHS R01 DK125490NIDDK NIH HHS UM1 DK105554
6 · The paper itself

Abstract

Through an analysis of 2,602 genome-wide association studies (GWAS) across 830 human traits, we find that most (56% of) well-studied traits have at least two published GWAS, and many (29%) have at least five. We show that the lack of an established approach for adjudicating variant association estimates across multiple published studies can lead to uncertainty and invalid inferences: using all associations ever published for a trait increases true positives (by 12%) but also false positives (by 55%) relative to using associations from the largest published GWAS for the trait. We employ a "bottom-line" procedure for meta-analyzing published GWAS while inferring and accounting for sample overlap, which identifies a more accurate and comprehensive list of associations relative to existing approaches. Five commonly used bioinformatic methods for post-GWAS analyses produce reliable results when applied to the bottom-line associations. We present these results for 1,281 human complex traits, including 1,839 single-ancestry and 576 trans-ancestry analyses, for browsing or download via the NHGRI Association to Function Knowledge Portal. This resource of "consensus" GWAS results is intended to increase replicability, reuse, and interpretation of GWAS and downstream analyses.

Identifiers

PMID41646310
PMCPMC12869677

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.