Summary: A landmark study in statistical genetics challenges a central assumption about complex human traits. The research demonstrates that people who fall at the extreme high or low ends of many common health measures are often driven not by thousands of tiny, common genetic changes but by a smaller number of rare variants with large biological effects.
Analyzing 74 quantitative traits — from cholesterol and blood glucose to body weight and age at menopause — across hundreds of thousands of individuals, the investigators show that the genetic architecture in the tails of trait distributions is fundamentally different from that of the general population. These findings create a practical roadmap for identifying individuals at unusually high genetic risk for conditions such as diabetes, heart disease, and stroke and point toward more targeted prevention and treatment strategies.
Key Facts
- Polygenic assumption re-evaluated: Many complex traits have been considered polygenic, shaped by the combined small effects of thousands of common variants. The study finds that this model does not always apply to people with extreme trait values.
- Role of rare, large-effect variants: Individuals at the far ends of trait spectra are frequently influenced by rare genetic variants that exert disproportionately large effects, rather than by many common, tiny-effect variants acting together.
- Evolutionary explanation: The authors used evolutionary principles to explain these findings. Strongly deleterious or advantageous extremes can reduce reproductive fitness, so natural selection tends to keep powerful, trait-shifting variants rare in the population.
- Two complementary methods: To avoid methodological bias, researchers developed and applied two independent statistical strategies. One analyzed population-level genetic data, while the other compared trait variation within sibling pairs to reduce environmental confounding.
- Large, diverse datasets: The models were validated across 74 traits using major health and genetic resources that together cover hundreds of thousands of participants from diverse ancestries and geographic backgrounds.
- Clinical implications: Identifying people whose extreme trait values are driven by rare, high-impact variants makes it possible to move from generic risk management to precision prevention and individualized treatment plans tailored to each person’s genetic profile.
Source: Mount Sinai Hospital
Overview of the study
Researchers at the Icahn School of Medicine at Mount Sinai report evidence that individuals with extreme values for certain traits — such as very high cholesterol, unusually low or high blood glucose, exceptional height, or atypical age at menopause — frequently have a relatively simple genetic explanation for their position on the trait spectrum. The findings were published in the May 27 issue of Nature and offer new perspectives on the genetic origins of common diseases.
While many health-related traits are commonly described as polygenic, meaning they arise from the additive impact of many common genetic variants each with small effect, this study specifically tested whether extreme trait values might instead be driven by rarer variants with much larger effects. The investigators reasoned that natural selection should reduce the population frequency of variants that push traits to biologically disadvantageous extremes, making those variants rare but highly influential when present.
Paul O’Reilly, PhD, Professor of Statistical Genetics at the Icahn School of Medicine, explains that although most people’s traits reflect thousands of small genetic influences, the extremes can arise from a different genetic architecture. Detecting these rare, large-effect variants could allow clinicians to identify individuals at unusually high risk and offer preventive care or therapies tailored to their genetic risks.
To test this idea, the team examined genetic patterns associated with a broad set of biomarkers and physical measurements — including hemoglobin, heart rate, and body weight — and developed two independent analytical frameworks. One approach examined population-level polygenic score patterns, while a family-based method compared siblings to control for shared environment and background genetics. These complementary analyses strengthen confidence that the results reflect genuine genetic differences rather than confounding factors.
Across the 74 quantitative traits analyzed, the researchers consistently observed departures from the common-variant, highly polygenic model in one or both tails of many trait distributions. Incorporating rare variants identified through sequence data substantially reduced these departures, supporting the view that rare alleles of large effect are major contributors to trait-tail architecture. Forward evolutionary simulations and analyses of reproductive success provided additional support that stabilizing selection shapes these patterns.
The authors emphasize that further work is needed to determine how generalizable these results are across additional traits and populations, and to more fully incorporate environmental and lifestyle contributors to extreme trait values. Future research will target identification and functional characterization of the implicated rare variants and will explore how this knowledge can improve disease prediction and personalized medical care.
The published paper is titled “Distinct genetic architecture in the tails of complex traits.” The listed authors are T. Souaiaia, H. M. Wu, A. P. S. Ori, S. W. Choi, C. J. Hoggart, and P. F. O’Reilly.
Key Questions Answered:
A: The study shows that the genetic blueprint operating at the extremes differs from that of the general population. Instead of many small-effect variants, a few rare, large-effect variants can push an individual’s trait value far from the average.
A: Traits at biological extremes can reduce fitness or survival, so selection tends to remove or limit the spread of variants that cause those extremes. As a result, such variants remain uncommon but have large effects when present.
A: By pinpointing individuals whose extreme risk is driven by rare, high-impact variants, clinicians could move beyond one-size-fits-all prevention and offer focused interventions or treatments tailored to the underlying genetic cause.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- The journal paper was reviewed in full.
- Additional context was added by staff.
About this genetics research news
Author: Karin Eskenazi
Source: Mount Sinai Hospital
Contact: Karin Eskenazi – Mount Sinai Hospital
Image: The image is credited to Neuroscience News
Original Research: Open access. “Distinct genetic architecture in the tails of complex traits” by T. Souaiaia, H. M. Wu, A. P. S. Ori, S. W. Choi, C. J. Hoggart & P. F. O’Reilly. Nature. DOI: 10.1038/s41586-026-10516-5
Abstract
Distinct genetic architecture in the tails of complex traits
Complex traits are typically highly polygenic, with heritability attributable to many hundreds of common variants of small effect together with rare variants of large effect. However, how genetic architecture varies along the trait continuum and the role of natural selection in shaping this variation have been underexplored.
The authors developed a polygenic-risk-score–based approach that reveals widespread departures from a common-variant architecture in one or both tails of 74 quantitative traits. These patterns were replicated across ancestries, cohorts, repeated measures, and a family-based method. Incorporating rare variants identified from sequence data notably reduced the observed deviations, indicating that rare alleles of large effect are key drivers of tail-specific architecture.
Forward simulations demonstrated that stabilizing selection can produce the observed signatures, and analysis of reproductive success provided empirical support for the role of selection. Overall, these findings indicate that while complex traits are broadly polygenic in the population at large, the extreme tails often exhibit a distinct and less polygenic architecture shaped by selection. This has important implications for rare-variant discovery and for improving prediction of complex traits and disease risk.