Evidence map›Paper›PMID 42461975›Full record

ArticleGenetics2026

Hiding in plain sight: uncovering the genetic basis of complex phenotypes through low-risk groups.

Ana I Vazquez, Yifei Li, Guanqi Lu, Harish Neelam, Molly S Bray, Sadeep Shrestha, Richard J Reynolds, Hongsheng Gui, Davorka Gulisija

Abstract read
In one paragraph

Article in Genetics, 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

9 authors.

Ana I VazquezDepartment of Epidemiology and Biostatistics, Michigan State University (MSU), East Lansing, MI 48824, United States.ORCID 0000-0001-6618-7486
Yifei LiDepartment of Epidemiology and Biostatistics, Michigan State University (MSU), East Lansing, MI 48824, United States.
Guanqi LuDepartment of Epidemiology and Biostatistics, Michigan State University (MSU), East Lansing, MI 48824, United States.ORCID 0009-0003-8393-5150
Harish NeelamDepartment of Epidemiology and Biostatistics, Michigan State University (MSU), East Lansing, MI 48824, United States.
Molly S BrayDepartment of Nutritional Sciences, University of Texas at Austin, Austin, TX 78712, United States.
Sadeep ShresthaDepartment of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35233, United States.
Richard J ReynoldsDivision of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at  Birmingham, AL 35294, United States.ORCID 0000-0001-6419-7779
Hongsheng GuiPsychiatry Research and Behavioral Health Services, Henry Ford Health, Detroit, MI 48202, United States.
Davorka GulisijaDepartment of Biology, University of New Mexico, Albuquerque, NM 87131, United States.ORCID 0000-0003-0363-3981

Funding

Using Metabolomics to Define the Behavioral Phenomics of Energy Balance and Exercise ResponseR01DK119836 · NIDDK · UNIVERSITY OF TEXAS AT AUSTIN · PI BRAY, MOLLY, VAZQUEZ, ANA I · 2019 to 2021
$2.0M
NHG R01 DK119836NIDDKNIDDK NIH HHS R01 DK119836NIHNIH HHS R01 00691244University of New Mexico
6 · The paper itself

Abstract

Medical genetic studies focus on high-risk groups to enrich for targeted phenotypes. Although genetic effects may vary across the lifespan, age-related enrichment of target phenotypes can also arise from cumulative environmental exposures, without requiring changes in genetic architecture. Accumulating environmental effects over the lifetime can inflate nongenetic variance, creating the illusion of greater genetic signal while in fact masking underlying genetic effects. We propose investigating the genetic architecture of complex phenotypes in lower-risk, younger individuals, which may reduce residual variance and improve power to isolate the genetic underpinnings of complex disease-related traits. To illustrate this, we analyzed 9 complex traits across age-stratified White European cohorts in the UK Biobank. Genome-wide association analyses reveal a greater number of genome-wide significant SNPs in younger cohorts. Complementing these results, heritability analyses show a higher relative genetic contribution in younger cohorts. Together, these patterns demonstrate that genetic signals are more readily detected in lower-risk, younger individuals. This perspective suggests that temporal dynamics in genetic architecture should guide participant selection, challenging the traditional focus on high-risk older groups and highlighting the value of targeting higher-heritability groups for genetic discovery.

Indexed as

Genome-Wide Association StudyMultifactorial InheritancePhenotypeGenetic Predisposition to DiseaseHumansPolymorphism, Single NucleotideUK Biobankage effectscomplex traitsgenetic architecturehuman phenotypesPEQG2026phenotypic plasticitystatistical geneticsstudy design

Identifiers

PMID42461975
PMCPMC13535256

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.