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

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.

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Publication Details

Journal
Genetics
Published
2026-07-16
DOI
https://doi.org/10.1093/genetics/iyag162
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Hongsheng Gui, Davorka Gulisija, Guanqi Lu, Ana I. Vázquez et al.
Genetics
Genetic Associations and Epidemiology
article

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

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

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.

Genetics
Henry Ford Health System (US), University of New Mexico (US), University of Alabama at Birmingham (US), Quantitative BioSciences (US), The University of Texas at Austin (US), Michigan State University (US)
University of New Mexico, National Healthcare Group, Norges Idrettshøgskole, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Pakistan
Responsible consumption and production
Openalex Percentile: Top 8%
Genetic Associations and Epidemiology
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