Evidence map›Paper›PMID 41785861›Full record

ArticleAmerican journal of human genetics2026

Higher eQTL power reveals signals that boost GWAS colocalization.

Jonathan D Rosen, K Alaine Broadaway, Sarah M Brotman, Karen L Mohlke, Michael I Love

Abstract read
In one paragraph

Article in American journal of human genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Jonathan D RosenDepartment of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.
K Alaine BroadawayDepartment of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.
Sarah M BrotmanDepartment of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.
Karen L MohlkeDepartment of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA. Electronic address: mohlke@med.unc.edu.
Michael I LoveDepartment of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA. Electronic address: milove@email.unc.edu.

Funding

Targeted Genetic Analysis of T2D and Quantitative TraitsR01DK072193 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN L. MOHLKE · 2005 to 2026
$11.3M
Systematic in vivo characterization of disease-associated regulatory variantsUM1HG012003 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Michael Isaiah Love, KAREN L. MOHLKE · 2021 to 2026
$9.9M
Bridging the gap between type 2 diabetes GWAS and therapeutic targetsUM1DK126185 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI CLAUSSNITZER, MELINA C, GLOYN, ANNA LOUISE · 2020 to 2024
$9.5M
Genetic epidemiology of rare and regulatory variants for metabolic traitsR01DK093757 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN L. MOHLKE · 2011 to 2026
$8.2M
The Genetic Epidemiology of Heart, Lung, and Blood TraitsTraining Grant (GenHLB)T32HL129982 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Christy Leigh Avery, KAREN L. MOHLKE · 2016 to 2026
$3.8M
Genetics of adipose cell-type expression and cardiometabolic traitsR01DK132775 · NIDDK · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI MOHLKE, KAREN L., PAJUKANTA, PAIVI · 2022 to 2025
$2.4M
Analyzing gene expression in adipose tissue to identify candidate genes at cardiometabolic trait GWAS lociF31HL154730 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ZWEIFEL, SARAH · 2021 to 2023
$86k
NHGRI NIH HHS UM1 HG012003NHLBI NIH HHS F31 HL154730NHLBI NIH HHS T32 HL129982NIDDK NIH HHS R01 DK072193NIDDK NIH HHS R01 DK093757NIDDK NIH HHS R01 DK132775NIDDK NIH HHS UM1 DK126185
6 · The paper itself

Abstract

Expression quantitative trait locus (eQTL) studies in human cohorts typically detect at least one regulatory signal per gene and have been proposed as a way to explain mechanisms of genetic liability for other traits, as discovered in genome-wide association studies (GWASs). In particular, eQTL signals may colocalize with GWAS signals, suggesting gene expression as a possible mediator. However, recent studies have noted that colocalization occurs infrequently, even when expression is measured in biologically relevant tissues. Most eQTL studies to date include only hundreds of individuals and are underpowered to discover distal regulatory signals explaining smaller fractions of gene expression variance. Using evidence from recent eQTL studies, we demonstrate that limited statistical power due to sample size skews the detection of eQTL signals identified at various signal strengths. We estimate that a sample size of 500 detects <0.1% to 60% of eQTLs for a range of signal strengths and that a sample size of 2,000 detects 36.8% of eQTLs. We show that eQTL signals only discoverable in larger studies exhibit characteristics more similar to those of GWAS signals, including greater distance to the regulated gene and a higher probability of loss-of-function intolerance in the associated gene. Finally, using results from recent eQTL studies and meta-analyses, we observe a large increase in detected colocalizations with GWAS signals compared to previous studies. These findings caution against overinterpreting the absence of colocalization in underpowered studies and provide guidance for designing future eQTL experiments to improve power and complement perturbation-based approaches in characterizing gene-trait mechanisms.

Indexed as

Genome-Wide Association StudyQuantitative Trait LociGene Expression RegulationHumansPolymorphism, Single NucleotideSample Sizecolocalizationcomplex traitseQTLgene regulationgenome-wide associationGWAS

Identifiers

PMID41785861
PMCPMC13059134

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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.