🧬 biology

Disease–fertility coupling shapes disease prevalence

This study demonstrates that the widespread genetic coupling between disease and fertility, particularly through antagonistic pleiotropy where disease-risk variants increase reproductive success, is a key evolutionary driver shaping the varying prevalence of human diseases.

Original authors: Jianhai Chen, Bowei Kang, Manyuan Long, Lin Chen

Published 2026-09-01

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CC BY 4.0

Original authors: Jianhai Chen, Bowei Kang, Manyuan Long, Lin Chen

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ⚕️ This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer

Human health is often viewed through the lens of survival: why do some people live longer, and why do certain genetic weaknesses persist in a population when natural selection should have eliminated them? For decades, evolutionary biologists have puzzled over the fact that many complex diseases, such as heart conditions or mental health disorders, remain common despite the fact that they generally harm a person's ability to survive or reproduce. The standard assumption has been that genes causing illness should be weeded out over time. However, this view overlooks a second, equally powerful force in evolution: reproduction. A genetic variant that makes a person slightly more prone to a disease might simultaneously make them more likely to have children. If the benefit to reproduction outweighs the cost to health, that genetic variant can survive and spread, keeping the disease present in the population. This tension between staying healthy and having offspring is the central mystery that a new study from researchers at the University of Chicago and the University of California, Irvine, seeks to solve.

The researchers set out to map the invisible connections between the genes that influence disease and the genes that influence fertility. They focused on a concept called pleiotropy, which simply means that a single gene can affect multiple traits. In this case, they asked whether the same genetic instructions that increase the risk of a specific disease also change a person's reproductive success. To answer this, the team analyzed data from hundreds of thousands of people, combining genetic information on ninety-seven different complex diseases with data on how many children men and women had. They used a sophisticated statistical method to determine not just if a gene was linked to a trait, but the direction of that link: did the gene make the disease more likely or less likely, and did it make a person have more children or fewer?

What they found was a widespread and structured relationship between illness and reproduction. The study revealed that for many common diseases, the genetic factors that increase disease risk also tend to increase fertility. This creates a biological trade-off where the pressure to reproduce helps maintain the genetic variants that cause disease. The researchers examined nearly three thousand different diseases and discovered a clear pattern: the more strongly a disease was genetically linked to higher fertility, the more common that disease was in the population. For instance, conditions like heart failure, type 2 diabetes, and certain immune disorders showed a strong positive link to having more children, and these are also among the most prevalent diseases. Conversely, diseases where the genetic risk factors were linked to having fewer children tended to be much rarer. This suggests that the prevalence of a disease is not random; it is shaped by how much the disease-causing genes help or hinder a person's ability to pass on their DNA.

The study also uncovered a fascinating layer of complexity involving the sexes. Because men and women share most of their genes, a genetic variant can have opposite effects on reproduction depending on whether it is in a man or a woman. The researchers found that about twenty-nine percent of the genes associated with fertility in both sexes had opposite effects: a variant might help a man have more children but hurt a woman's chances, or vice versa. This creates a situation where a disease that primarily affects one sex can be maintained in the population because the same genetic factors provide a reproductive advantage to the other sex. For example, the study showed that gynecological diseases in women were more strongly linked to male fertility than to female fertility. This implies that genetic variations causing conditions like endometriosis or ovarian cysts may persist because they are tied to genes that boost reproductive success in men.

By connecting the dots between disease, fertility, and the sexes, this research offers a new explanation for why some diseases are common while others are rare. It suggests that the evolutionary fate of a disease is not determined solely by how sick it makes a person, but by how it interacts with the drive to reproduce. When a disease risk is coupled with a reproductive benefit, natural selection may allow that disease to thrive. This insight shifts the perspective on human genetics, showing that the persistence of illness is often a byproduct of the evolutionary strategies that ensure our species continues to grow. The findings do not suggest that disease is good, but rather that the genetic machinery of life is a complex balance sheet where the costs of health are sometimes offset by the gains of reproduction.

Technical Summary: Disease–fertility coupling shapes disease prevalence

Problem Statement
Complex genetic disorders constitute a major fraction of human morbidity and mortality, yet their population prevalence varies markedly (e.g., cardiovascular disease affects ~7% of the global population, while schizophrenia affects ~1%). From an evolutionary perspective, this variation is difficult to explain because purifying selection should theoretically oppose genetic effects that increase disease risk by compromising survival or reproduction. However, many complex diseases remain both common and heritable. The authors posit that the persistence of disease-associated variation and the differences in prevalence may be driven by disease–fertility coupling. Specifically, if genetic variants that increase disease liability also increase reproductive success (antagonistic pleiotropy), selection through fertility may offset health costs, allowing these variants to persist. Conversely, if disease risk is coupled with reduced fertility (synergistic pleiotropy), selection should be stronger, potentially keeping prevalence low. Additionally, the authors investigate whether sexually-antagonistic selection—where a variant benefits one sex but harms the other—contributes to the maintenance of disease-associated variation, particularly for sex-limited diseases.

Methodology
The study employs a multi-step analytical framework integrating transcriptome-wide Mendelian randomization (TWMR) with large-scale genomic and epidemiological data:

Data Integration: The authors integrated GTEx v10 tissue-specific expression quantitative trait locus (eQTL) data with Genome-Wide Association Study (GWAS) summary statistics for 97 complex disease phenotypes (from UK Biobank and consortia) and two fertility traits (number of children fathered and mothered).
FusioMR Application: They utilized FusioMR, a robust TWMR framework designed for settings with complex pleiotropy and limited cis-eQTLs. This method jointly estimates the signed effects of genetically regulated gene expression on disease liability ( $\beta_D$ ) and fertility ( $\beta_F$ ) while accounting for horizontal pleiotropy.
Coupling Metrics:
- Expression-level coupling ( $\rho_{GE}$ ): Defined as the across-gene Pearson correlation between standardized FusioMR estimates of expression effects on disease and fertility.
- SNP-level coupling ( $r_g$ ): Genome-wide genetic correlations estimated via LD-score regression (LDSC) to serve as a complementary measure.
Pleiotropy Classification: Shared gene–disease pairs were classified into antagonistic (effects on disease and fertility have the same sign: $d+f+$ or $d-f-$ ) and synergistic (effects have opposite signs: $d+f-$ or $d-f+$ ) categories.
Prevalence Analysis: The authors tested the relationship between these coupling metrics and population prevalence using data from FinnGen R13 (2,754 diseases) and FinRegistry (period prevalence).
Sexual Antagonism Analysis: They examined genes significant for both male and female fertility to identify those with opposite effect signs between sexes. They further analyzed sex-limited diseases (gynaecological and male reproductive tract diseases) to test if disease liability couples more strongly to fertility in the opposite sex.

Key Contributions and Results

Widespread Disease–Fertility Pleiotropy: The study identified 182 and 169 genes significantly associated with male and female fertility, respectively. A substantial proportion of these fertility-associated genes were also disease-associated (86.3% for male, 84.0% for female), far exceeding chance expectations.
Structured Genetic Coupling: Disease–fertility coupling is not random but structured by disease category. Cardiometabolic diseases (e.g., heart failure, type 2 diabetes), neuropsychiatric disorders (e.g., ADHD, depression), and immune diseases (e.g., asthma, psoriasis) showed strong positive correlations ( $r_g > 0, \rho_{GE} > 0$ ), indicating that genetic effects aligning toward higher disease liability also align with higher fertility. Negative correlations were less frequent, primarily observed in neurodegenerative diseases and some cancers.
Coupling Predicts Prevalence: Across 2,754 FinnGen diseases, population prevalence was positively associated with disease–fertility coupling. Diseases with positive coupling (antagonistic pleiotropy) were significantly more prevalent than those with negative coupling. For example, the median prevalence for diseases positively coupled to male fertility was 0.22% versus 0.08% for negatively coupled diseases.
Antagonistic vs. Synergistic Pleiotropy: The study decomposed coupling into directional components. Prevalence increased with the proportion of antagonistic effects (where increased expression raises both disease liability and fertility) and decreased with synergistic effects (where increased expression raises disease liability but reduces fertility). The $d+f-$ class (increased disease, reduced fertility) showed the strongest negative association with prevalence.
Sexually-Antagonistic Selection: Among 133 genes significant for both male and female fertility, 29.3% (39 genes) exhibited opposite expression effects between sexes. All 39 genes were also disease-associated. These genes were located in regions of significantly reduced recombination.
Opposite-Sex Coupling in Sex-Limited Diseases: Consistent with the hypothesis of sexually-antagonistic selection, the prevalence of gynaecological diseases was more strongly associated with coupling to male fertility than to female fertility. Conversely, male reproductive-tract diseases showed a tendency to couple more strongly with female fertility.

Significance and Claims
The paper claims to provide a complementary evolutionary explanation for the persistence of complex disease-associated variation and the variation in disease prevalence. The authors argue that:

Antagonistic pleiotropy between disease and fertility acts as a mechanism maintaining disease-associated genetic variation, explaining why some diseases are common despite their health costs.
Synergistic pleiotropy (where disease risk reduces fertility) contributes to the rarity of certain diseases by subjecting risk alleles to stronger purifying selection.
Sexually-antagonistic selection extends beyond individual genes to the level of sex-limited disease dynamics, where liability in one sex may persist due to reproductive benefits in the other.
These findings suggest that the reproductive effects of disease-associated variants are a fundamental, yet often overlooked, factor shaping the landscape of human complex disease prevalence.

The study does not propose new therapeutic interventions or future experimental proposals but emphasizes the need to interpret disease-risk effects within a broader fitness trade-off context, potentially aiding in the prioritization of therapeutic targets by distinguishing genes involved in general fitness trade-offs from those with disease-specific effects.

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