Summary: A milestone statistical genetics study challenged a core tenet of human genetics by proving that individuals at the extreme high or low ends of common health traits are often driven by rare, high-impact genetic variants rather than thousands of minor ones.
The research analyzed 74 quantitative traits, including cholesterol, blood glucose, and body weight, across hundreds of thousands of individuals. By establishing that the “tails” of trait spectrums possess a completely distinct genetic architecture, the findings offer a predictive roadmap to identify individuals at acute risk for diseases like diabetes and stroke, paving the way for targeted preventive care.
Key Facts
- The Polygenic Assumption Challenged: Historically, complex health traits like height, blood glucose, and cholesterol have been classified as “polygenic,” meaning they are shaped by the combined, incremental influence of thousands of common genetic variants. This new study proves that for individuals at extreme values, this assumption fails.
- The Rare Variant Shift: Instead of thousands of tiny genetic modifications working in unison, individuals at the far edges of health spectrums are frequently propelled there by a much smaller number of rare genetic variants that possess disproportionately large biological effects.
- The Evolutionary Trap: The research team based their hypothesis on evolutionary biology. Because extremely high or low trait values can create survival disadvantages, natural selection actively suppresses the frequency of these high-impact driving variants, keeping them exceedingly rare within the general population.
- Dual-Method Verification: To confirm this structural anomaly without genetic bias, Mount Sinai researchers engineered two complementary statistical approaches. One framework interrogated broad population-level genetic data, while the second eliminated environmental noise by comparing trait levels directly between siblings.
- Massive Scale Datasets: The investigators stress-tested their models across 74 quantitative traits, mining massive health repositories including the UK Biobank and the All of Us Research Program in the United States, capturing diverse geographic backgrounds and ancestries.
- Validating Clinical Risk Profiles: Identifying individuals driven by these intense, rare mutations allows clinicians to transition from broad, generic health advice to hyper-targeted, proactive preventive care and custom medical treatments uniquely suited to an individual’s true genetic risk profile.
Source: Mount Sinai Hospital
Researchers at the Icahn School of Medicine at Mount Sinai have found evidence that people who fall at the extreme high or low ends of certain traits, such as cholesterol, blood glucose, height, and age at menopause, are more likely to have a simple genetic explanation than previously thought.
Their findings, reported in the May 27 issue of Nature, may lead to new insights into the causes of common diseases.
Many traits linked to human health are considered “polygenic,” meaning they are shaped by the combined influence of many common genetic variants, each contributing only a small effect. But the new study explored whether individuals with extreme trait values may instead be influenced by rarer genetic variants that have a much larger impact.
The researchers say this possibility could help explain why some individuals develop unusually high or low levels of traits associated with conditions such as diabetes, heart disease, and stroke.
“We typically think of these traits as being shaped by thousands of genetic changes, each having a very small effect,” says senior corresponding author Paul O’Reilly, PhD, Professor of Statistical Genetics in the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai in New York.
“But our findings suggest that some people are at the ends of the trait spectrum because of a much smaller number of rare genetic variants with far stronger effects. If we can identify who these people are, clinicians may be able to offer them preventive care or treatments better suited to their genetic risk profile.”
The team’s hypothesis was based in part on evolutionary biology. Because extremely high or low trait values can sometimes be disadvantageous, natural selection may reduce the frequency of genetic variants that strongly drive those extremes. As a result, such variants are expected to be relatively rare in the population.
“Our goal was to better understand whether extreme trait values might sometimes arise from a different kind of genetic architecture,” says Dr. O’Reilly. “If so, that could eventually help researchers pinpoint biological pathways that are especially important in disease.”
To conduct the study, the researchers analyzed genetic patterns linked to a range of biomarkers and physical measurements, including hemoglobin, heart rate, and body weight. The team first developed two complementary statistical approaches to test whether people with extremely high or low trait values have a different genetic architecture from the broader population. One method relied on population-level genetic data, while the other compared trait levels among siblings.
Using these approaches, the team analyzed 74 quantitative traits from large-scale health and genetic datasets, including the UK Biobank and the All of Us Research Program in the United States. Together, these databases include health and genetic information from hundreds of thousands of volunteer participants representing a wide range of geographic backgrounds and ancestries.
The researchers then looked for evidence that people at the extreme ends of the traits were more likely to carry rare genetic variants with larger biological effects.
“By focusing on individuals at the extremes, we may be able to uncover clearer biological signals that are harder to detect in the general population,” says Dr. O’Reilly.
The authors note that additional studies will be needed to determine how broadly these findings apply across populations and traits. They also acknowledged that their analysis focused on the genetic causes of these traits and did not fully capture the potential influence of environmental and lifestyle factors, which are also major causes of extreme trait values.
Future research will aim to further characterize the rare variants involved and better understand how they influence disease risk.
The paper is titled “Distinct genetic architecture in the tails of complex traits.”
The study’s authors, as listed in the journal, 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: Because the far edges of human biology operate on a different genetic blueprint. While an average person’s health traits are shaped by thousands of tiny genetic variations, Mount Sinai discovered that people at the extreme ends of the spectrum are often pushed there by just a few rare, hyper-powerful genetic variants that dial the trait all the way up or down.
A: It acts as a natural survival filter. Because living at the absolute extremes of traits like blood glucose or body weight can cause severe health disadvantages, natural selection continuously works to weed these aggressive variants out. As a result, they can never become common, remaining hidden at the edges of the population.
A: By showing doctors exactly where to look for the strongest biological warning signs. Instead of treating everyone with high blood pressure or diabetes risk the exact same way, clinicians can use these findings to identify the specific individuals carrying these high-impact rare variants, offering them precision preventive treatments tailored perfectly to their unique genetic reality.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- Journal paper reviewed in full.
- Additional context added by our 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 highly polygenic, with heritability explained by many hundreds of common variants of small effect together with rare variants of large effect. Yet how this genetic architecture varies along the trait continuum has been underexplored, as has the role of natural selection in shaping this variation.
Here we developed an approach based on polygenic risk scores that reveals widespread departures from common-variant architecture in one or both of the tails of 74 quantitative traits.
These observations were replicated across ancestries, cohorts and repeated measures and using an alternative family-based approach. Incorporating rare variants identified from sequence data resulted in marked reductions in these deviations, suggesting that rare alleles of large effect are key drivers of trait-tail architecture.
Forward simulations showed that stabilizing selection could generate the observed patterns, whereas modelling reproductive success provided empirical support for the role of selection.
These findings show that although complex traits are polygenic in the population at large, they have a distinct and less polygenic architecture in their tails due to selection. This has implications for rare-variant discovery and complex trait and disease prediction.
