The study, published in BMC Genomics, took advantage of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be, known as nuMoM2b, a multi-center prospective cohort that collected biospecimens in early pregnancy, genome-wide genotype data, and detailed clinical outcomes across multiple ancestry groups. Led by researchers at Indiana University and collaborators across the United States, the team performed genome-wide association studies of first-trimester CRP levels using maternal genotypes from 4,326 participants and offspring genotypes from 2,140 children. The design allowed them to ask two distinct questions at once: which parts of the genome influence CRP levels during early pregnancy, and whether the inherited propensity toward higher or lower CRP translates into a measurable shift in gestational diabetes risk.

The first answer came cleanly. In the European-ancestry maternal sub-cohort, the researchers identified three genome-wide significant loci associated with early-pregnancy CRP levels: the CRP gene itself, LEPR, which encodes the leptin receptor, and HNF1A, a transcription factor with a well-documented role in regulating hepatic CRP production. These findings align closely with what large GWAS in non-pregnant populations have reported, indicating that the fundamental genetic architecture of CRP regulation persists into pregnancy. That consistency matters, because it suggests the pregnancy state does not wholesale rewrite the genetic control of this inflammatory marker, even though pregnancy itself dramatically reshapes inflammatory physiology.

When the analysis was widened to the full multi-ancestry maternal cohort, two additional loci emerged: ENSG00000257703 and APOC1. The appearance of ancestry-informative signals underscores a recurring theme in human genomics: cohorts that concentrate on a single ancestry group miss variants that are common or consequential elsewhere. APOC1, involved in lipid metabolism, sits in a genomic region long known to influence CRP, and its detection here hints at connections between inflammatory and metabolic pathways that pregnancy may bring into sharper focus. By contrast, the offspring GWAS of CRP levels produced no genome-wide significant associations at all, a null result the authors interpret in light of the smaller sample size and the distinct biology of fetal and neonatal CRP regulation.

The more provocative part of the study concerns gestational diabetes. Using linkage disequilibrium score regression, a technique that estimates the genetic correlation between traits from summary statistics alone, the team found no significant genetic correlation between CRP and gestational diabetes when using the nuMoM2b CRP GWAS as the input. That absence of correlation would seem to close the door on the idea that genetically influenced CRP levels meaningfully shape GDM risk. But the picture grew more complicated: when the researchers substituted an external, much larger population-based CRP GWAS, a significant genetic correlation with gestational diabetes did appear.

That discrepancy is not a contradiction so much as a lesson in how context shapes genomic inference. Genetic correlations estimated from summary statistics can vary depending on the population studied, the environment in which the phenotype was measured, and the specific covariates and ascertainment of each cohort. A CRP GWAS measured in early pregnancy among nulliparous women captures a different biological moment than one measured in the general adult population, where inflammation reflects age, adiposity, infection history, and chronic disease. The authors suggest that the genetic overlap between CRP and gestational diabetes may therefore vary across study contexts, a caution that extends well beyond this particular pair of traits.

To probe the relationship more directly, the team constructed polygenic risk scores for CRP, aggregating the small effects of thousands of variants into a single inherited score per individual, and tested whether those scores predicted gestational diabetes in the nuMoM2b cohort. They did not, regardless of whether the scores were derived from the pregnancy-specific GWAS or from the external population-based one. In other words, even where a statistical genetic correlation could be detected at the level of populations, the aggregate inherited influence on CRP was not a useful predictor of who would develop gestational diabetes within this cohort. The polygenic signal, the study concludes, is limited.

For clinicians and researchers, the result is a sobering check on a tempting hypothesis. Elevated CRP in early pregnancy is associated with later gestational diabetes in observational studies, and it has been natural to wonder whether inflammation is part of the causal chain. This work indicates that the genetic component of CRP variation, at least as it can currently be measured, contributes little to that association. If CRP and gestational diabetes are connected, the link is more likely mediated by environmental factors, adiposity, insulin resistance, or the hormonal shifts of pregnancy than by inherited differences in inflammatory set-point. Genetic risk prediction for gestational diabetes will need to look elsewhere, most plausibly toward the substantial polygenic architecture of glycemic traits themselves.

The study also carries a broader methodological message. Pregnancy-specific genomic resources remain scarce relative to their clinical importance, and this analysis demonstrates both their value and their limits. The nuMoM2b cohort, with thousands of well-phenotyped pregnancies and paired maternal-offspring genotypes, was large enough to replicate known CRP loci and to detect new ones in a multi-ancestry framework, yet the authors are explicit that larger pregnancy cohorts are needed to fully untangle the relationship between inflammation and metabolic complications of pregnancy. Gestational diabetes affects millions of pregnancies worldwide each year, and its long-term sequelae, including elevated lifetime risk of type 2 diabetes in mothers and altered metabolic programming in offspring, make it a priority target for precision approaches.

What emerges from this work is a carefully bounded conclusion rather than a dramatic one. The genetic architecture of CRP in early pregnancy mirrors what has been mapped in non-pregnant populations, with CRP, LEPR, and HNF1A as anchor points and APOC1 and one additional locus joining the map in a multi-ancestry analysis. Offspring CRP genetics yielded no significant signals. Genetic correlation with gestational diabetes appears only under some analytic conditions and not others, and polygenic risk scores for CRP fail to predict the disease. In an era when inflammatory biomarkers are frequently proposed as early warning signs of pregnancy complications, the study offers a useful corrective: a biomarker can travel with a disease without its genes being responsible for it, and disentangling the two requires cohorts, ancestries, and analytic frameworks designed specifically for the biology of pregnancy.

Subject of Research: Genome-wide association study of C-reactive protein levels in pregnancy and its polygenic relationship with gestational diabetes mellitus

Article Title: Maternal and offspring genome-wide association study of C-reactive protein reveals limited polygenic association with gestational diabetes mellitus

Article References: Zhang, Y., Moore, A., Ryckman, K. K., Yan, Q., Guerrero, R. F., Li, M., Silver, R. M., Luo, J., Yee, L. M., Reddy, U. M., Feghali, M. N., Chung, J., Haas, D. M., Kua, K. L., & Liu, N. (2026). Maternal and offspring genome-wide association study of C-reactive protein reveals limited polygenic association with gestational diabetes mellitus. BMC Genomics, 27(1), Article 782. https://doi.org/10.1186/s12864-026-12878-6

Image Credits: AI Generated

DOI: 10.1186/s12864-026-12878-6

Keywords: C-reactive protein, gestational diabetes, GWAS, polygenic risk score, nuMoM2b, pregnancy, inflammation, genetic correlation, LEPR, HNF1A, APOC1, maternal-fetal health

The research, led by a team at Peking University working with the Chinese National Twin Registry, took a two-pronged approach. On one side, the investigators analyzed twin data from 2,548 individuals, comparing identical twins, who share essentially all of their DNA, with fraternal twins, who share roughly half. On the other side, they mined GWAS summary statistics from more than 92,615 participants in Asian cohorts. By estimating heritability through both classical twin modeling and modern SNP-based methods, they could directly compare the two traditions and quantify exactly where the numbers diverge.

The traits under scrutiny were ten of the most clinically important cardiometabolic measures: body mass index (BMI), waist-to-hip ratio (WHR), hemoglobin A1c (HbA1c), fasting blood glucose (FBG), systolic and diastolic blood pressure (SBP and DBP), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Together, these markers capture the biological landscape of obesity, glucose metabolism, blood pressure regulation, and blood lipid profiles—the very factors that drive the world’s leading causes of death.

The headline result is striking. Twin-based analyses yielded moderate to high heritability estimates, ranging from 0.34 to 0.72 across the ten traits, meaning that between a third and nearly three-quarters of the variation in these measures could be attributed to genetic differences. GWAS-based estimates, by contrast, came in far lower, between 0.10 and 0.22. This confirms, in an Asian population, the same pattern long observed in European cohorts: twin studies see more heritability than GWAS can account for, leaving a substantial residue that must be explained by other mechanisms.

One leading candidate for the missing heritability has been non-additive genetic effects—particularly dominance effects, where the combined action of two alleles at a locus produces an effect that simple additive models cannot capture. To test this, the team built structural equation models that decompose trait variance into additive genetic, dominance genetic, shared environmental, and unique environmental components. The verdict was unambiguous: dominance genetic variance was statistically significant only for BMI, with an estimated dominance component of 0.42, and no evidence of non-additive effects appeared anywhere in the GWAS summary statistics. For most cardiometabolic traits, the dominant role of simple additive inheritance stands, and the missing heritability cannot be pinned on gene-gene interactions of this kind.

That leaves gene-environment interactions as a prime suspect, and here the study delivered some of its most intriguing findings. Using variance components models that allow genetic and environmental influences to vary across levels of environmental exposures, the researchers identified twelve small but statistically significant interaction effects. These involved age, smoking, alcohol consumption, education, and mental health status—factors that modulate how strongly genetic variants express themselves in measurable traits. In other words, the same set of genes may push blood pressure or blood sugar to different degrees depending on whether a person smokes, drinks, has reached a certain age, or carries psychological stress. The effect sizes were modest, but the pattern is consistent with the idea that genes and environment do not operate in separate silos; they collaborate, and that collaboration quietly shapes the heritability we observe.

The study also mapped the genetic correlations between pairs of cardiometabolic traits, asking whether the same genes influence more than one measure. The answers ranged from low to high, spanning 0.09 to 0.90, and—crucially—the patterns estimated from twin data broadly matched those derived from GWAS. This convergence matters. It suggests that the discrepancy between twin and GWAS heritability estimates does not reflect fundamentally different genetic pictures, but rather differences in what each method can detect. Twin models capture all inherited variation, including rare variants and effects in genomic regions poorly tagged by genotyping arrays, while GWAS-based estimates reflect only the additive effects of common SNPs that are well captured in the data.

There is also a broader significance to the population in which this work was conducted. The vast majority of large-scale genetic studies have been performed in populations of European ancestry, and researchers have warned repeatedly that findings from these studies do not always translate to other groups. Differences in allele frequencies, linkage disequilibrium patterns, and environmental contexts can all alter how genetic risk manifests. By grounding both the twin analyses and the GWAS heritability estimates in Asian populations, this study helps close a critical gap in the global picture of cardiometabolic genetics, providing evidence that the fundamental genetic architecture—high twin heritability, modest SNP heritability, minimal dominance, and pervasive gene-environment interplay—holds across ancestries.

Methodologically, the work is a showcase of integration. The twin component relied on classical structural equation modeling of monozygotic and dizygotic pairs, with zygosity confirmed through questionnaire-based assessment validated by methylation array data. The GWAS component drew on publicly available summary statistics from Asian cohorts, applying SNP-based heritability methods that estimate the variance explained by common variants through their correlations with the trait. By running both approaches on the same ten traits and comparing their outputs side by side, the team created a natural experiment in which each method could serve as a check and complement to the other.

For clinicians and public health researchers, the implications are tangible. The confirmation that BMI carries a substantial dominance component suggests that some obesity risk is inherited in ways that current additive polygenic models may underrepresent, potentially affecting the accuracy of genetic risk scores. The dozen identified gene-environment interactions point to modifiable levers—smoking cessation, alcohol moderation, mental health support, and attention to age-related changes—that could blunt genetic susceptibility. And the finding that genetic correlations between cardiometabolic traits are broadly consistent across methods strengthens confidence that pleiotropy, the phenomenon of one gene influencing multiple traits, is real and quantifiable. As the era of precision medicine accelerates, studies like this one provide the calibrated foundation on which genetic prediction, prevention, and treatment strategies must ultimately rest. The mystery of missing heritability is not solved in a single paper, but by systematically ruling out dominance effects, spotlighting gene-environment interactions, and demonstrating cross-method consistency in an understudied population, this work takes a substantial and welcome step toward that goal.

Subject of Research: Estimation of heritability and gene-environment interactions for cardiometabolic traits by integrating twin and GWAS data in Asian populations

Article Title: Revealing genetic foundations underlying cardiometabolic traits: integrating twin and GWAS data in Asian populations

Article References: Hong, X., Li, M., Cao, W., Lv, J., Yu, C., Huang, T., Sun, D., Liao, C., Pang, Y., Hu, R., Gao, R., Yu, M., Zhou, J., Wu, X., Liu, Y., Yin, S., Gao, W., & Li, L. (2026). Revealing genetic foundations underlying cardiometabolic traits: integrating twin and GWAS data in Asian populations. International Journal of Obesity. https://doi.org/10.1038/s41366-026-02209-w

Image Credits: AI Generated

DOI: 10.1038/s41366-026-02209-w

Keywords: heritability, twin study, GWAS, cardiometabolic traits, missing heritability, gene-environment interaction, BMI, blood pressure, lipids, genetic correlation, Chinese National Twin Registry, Asian populations

The research, conducted by Chang S. Han of Kyung Hee University and LMU Munich, Cristina Tuni of LMU Munich and the University of Turin, and Niels J. Dingemanse of the University of Turin, focused on male two-spotted field crickets, Gryllus bimaculatus, drawn from a pedigreed laboratory population. The pedigree is crucial. Because the relatedness of every individual in the population is known, the researchers could apply quantitative genetic techniques, including animal model analyses, to separate the genetic contribution to behaviour from environmental effects. This allowed them to ask not merely whether the correlation between aggression and exploration declines with age, but whether the genetic underpinning of that correlation does too, and if so, why.

The behaviours in question are staples of personality research. Aggression was measured through staged contests in which males fought one another, with researchers scoring the intensity and outcome of each interaction. Exploration was assessed by observing how readily individuals moved through and investigated novel environments. Both are labile traits, meaning they can change from moment to moment, yet individuals differ consistently from one another in their typical expression. In this population, more aggressive males also tended to be more exploratory, producing a positive among-individual correlation of the kind documented across fishes, birds, mammals and insects, and central to the pace-of-life syndrome framework, which links behavioural types to differences in growth, reproduction and lifespan.

The study’s central finding is that this positive correlation, robust across the nymphal and young adult stages, steadily eroded as males aged through adulthood. At first glance, this pattern might suggest a simple developmental story: perhaps the developmental processes that synchronise aggression and exploration early in life simply dissolve over time. But the quantitative genetic analysis revealed something more intricate. The genetic correlation between the two behaviours, an estimate of the extent to which the same genes influence both traits, followed a parallel trajectory, remaining strong in early stages and weakening significantly in older adults. Crucially, the researchers found no evidence that short-term permanent environmental correlations were responsible for the observed age-related change. The similarity between the among-individual and genetic patterns pointed instead to causes operating at the level of genes and selection.

The first such cause is survival selection, a form of natural selection in which an individual’s phenotype determines whether it lives long enough to appear in the older age classes. In this population, selection at the young adult stage tended to favour less explorative males, meaning that highly exploratory individuals were disproportionately likely to die before reaching later ages. Because exploration was genetically linked to aggression, the selective removal of certain exploratory genotypes dragged the aggression-exploration correlation along with it. As the composition of surviving genotypes shifted with age, the tight coupling between the two behaviours weakened. This is a mechanism familiar from evolutionary genetics: selection on one trait can reshape the genetic architecture of correlated traits, and when selection is age-specific, that reshaping unfolds along the lifespan.

The second mechanism is arguably more surprising: genetic variation in age-related behavioural plasticity, sometimes described through genotype-by-age interactions. Different genotypes, the researchers found, do not all change their exploratory behaviour at the same rate as they age. Some genotypes maintain high exploration into old age, while others decline earlier or follow entirely different trajectories. This heritable variation in the age-specific expression of exploration meant that the genetic relationships among individuals were not fixed across the lifespan. As genotypes diverged in their ageing patterns, the genetic correlation between aggression and exploration diminished, independently of whether any individual survived or died. In other words, the genetic architecture of behaviour is itself dynamic, and genes that bind two traits together at one age may loosen their grip at another.

The significance of these findings extends well beyond crickets. Behavioural syndromes are widely regarded as evolutionary constraints: when the same genes influence multiple traits, selection cannot freely optimise one trait without dragging the other along. This idea has been formalised in models showing that behavioural correlations can slow or redirect adaptive evolution, and empirical work in wild birds, marmots, fishes and insects has repeatedly documented heritable correlations among personality traits. Yet most such studies capture a snapshot, typically of adult animals of unspecified or unremarked age. The new results warn that such snapshots may be misleading. A genetic correlation measured in young adults may overstate the constraint operating in older individuals, and predictions of evolutionary response that ignore age structure may therefore be systematically wrong.

The study also connects to a broader literature on the evolutionary genetics of ageing. Research on wild passerine birds, swans, houbara bustards and other organisms has shown that genetic variances and covariances of traits can change with age, consistent with theoretical predictions from mutation-accumulation and antagonistic pleiotropy theories of senescence. Previous work, including studies reporting that strong genetic correlations underlying behavioural syndromes disappear during development through genotype-age interactions, hinted at the kind of dynamics now documented in crickets. What distinguishes the new research is its explicit attempt to weigh competing mechanisms against one another within a single pedigreed population. By jointly estimating genetic correlations, age-related plasticity and survival selection, the authors demonstrated that age-related change in behavioural architecture is a multi-causal phenomenon, produced by both the selective sorting of genotypes and the age-dependent expression of behaviour within genotypes.

For evolutionary biologists, the practical message is that age must enter the models. Quantitative geneticists have developed powerful tools, notably the animal model, to partition phenotypic variance into additive genetic and environmental components, and these tools can now be extended to ask how the entire genetic covariance matrix, often abbreviated as the G-matrix, transforms across the lifespan. The cricket results suggest that the G-matrix is not a static property of a population but a moving target, reshaped continuously by mortality and by the plastic, genotype-specific unfolding of behaviour over time. Studies of morphological integration and developmental modularity have made similar arguments for structural traits; this work brings labile behavioural traits squarely into that conversation.

For anyone who has watched a young animal grow calmer, slower or more predictable with age, the findings offer a mechanistic explanation grounded in genetics and selection. The personalities we observe are not engraved once at birth and fixed forever; they are the output of genes whose effects shift as organisms age, filtered by the unforgiving arithmetic of survival. As highly exploratory crickets are weeded out and as different genotypes age along different behavioural paths, the once-tight bonds between boldness and aggression loosen. What looks like the mellowing of old age is, at the genetic level, a population’s architecture being rewritten. Understanding that rewriting, the authors argue, is essential if we hope to predict how animal populations will respond to selection in a changing world, one behavioural correlation at a time.

Subject of Research: Age-related changes in genetic correlations between aggression and exploration in male field crickets

Article Title: Selection and genetic variation in age-related plasticity drive the erosion of among-individual behavioural correlations in later life

Article References: Han, C. S., Tuni, C., & Dingemanse, N. J. (2026). Selection and genetic variation in age-related plasticity drive the erosion of among-individual behavioural correlations in later life. Heredity. https://doi.org/10.1038/s41437-026-00884-z

Image Credits: AI Generated

DOI: 10.1038/s41437-026-00884-z

Keywords: behavioural syndromes, animal personality, genetic correlation, age-related plasticity, survival selection, genotype-by-age interactions, quantitative genetics, field crickets, Gryllus bimaculatus, pace-of-life syndrome, aggression, exploration

The team’s starting point was a clinical observation that has puzzled gastroenterologists and psychiatrists alike. Patients diagnosed with gallstones, reflux disease, irritable bowel syndrome, or constipation are diagnosed with major depression far more often than population averages would predict. Stress and inflammation have been proposed as bridges between the two domains, but such mechanisms cannot fully explain why comorbidity persists across care settings and age groups. Because both depression and digestive disorders are known to be moderately heritable, the researchers asked whether the same inherited variants might be quietly raising risk for both sets of conditions simultaneously.

To answer that question, the investigators adopted a hierarchical analytical strategy that layered several complementary statistical methods, each probing the genome at a different resolution. At the broadest level, they used linkage disequilibrium score regression, alongside genetic covariance analysis with GNOVA and high-definition likelihood modeling with HDL, to estimate genome-wide genetic correlations between depression and each digestive disease. These techniques treat the tiny effect sizes scattered across millions of variants as a genome-wide signal, allowing researchers to ask not whether any single gene links two conditions, but whether their overall polygenic architectures lean on the same variants.

The results at this first tier were unambiguous. All four digestive diseases showed statistically significant, positive genetic correlations with major depressive disorder in the European-ancestry data. In other words, populations of genetic variants that elevate depression risk also tend, on average, to elevate risk of gallstones, reflux, irritable bowel syndrome, and constipation. The direction of the correlations is as informative as their significance: shared variants predispose individuals to both conditions rather than protecting against one while promoting the other, a pattern consistent with genuine biological overlap rather than statistical artifact.

Zooming from the whole genome down to individual chromosomes, the researchers deployed local genetic variation analysis using the LAVA framework to identify specific genomic regions where the signals for depression and digestive disease overlap. Multiple chromosomal regions carried significant shared genetic signals, providing the first fine-grained map of where in the genome the two disease families converge. To push the localization further, the team applied a conditional and conjunctional false discovery rate approach, a method designed to extract shared loci from noisy summary statistics. This analysis uncovered key overlapping genetic loci that had remained below the threshold of conventional genome-wide significance when each trait was analyzed alone.

Quantifying how much of the underlying genetic variation the conditions truly share, the researchers turned to MiXeR, a bivariate causal mixture model that estimates the number of variants influencing each trait and the proportion of those variants acting on both. The model indicated substantial genetic overlap between depression and each of the four digestive diseases, suggesting that a meaningful fraction of the polygenic components driving these conditions are common to both. This finding matters because it reframes comorbidity: rather than depression merely following digestive illness, or digestive dysfunction merely reflecting psychological distress, both appear to emerge in part from the same inherited susceptibility landscape.

The study then asked where in the body this shared genetic activity is expressed. Using linkage disequilibrium score regression for specifically expressed genes, the team conducted tissue-specific enrichment analyses to identify which tissues show concentrated expression of genes associated with the conditions. The results were striking: major depressive disorder, constipation, and irritable bowel syndrome all demonstrated significant tissue-specific enrichment in multiple brain regions, pointing to shared neurobiological pathways. Because the enteric nervous system and the brain share developmental origins and signaling machinery, enrichment in neural tissue for both a mood disorder and gut-motility disorders fits the emerging picture of gut-brain axis biology with a genetic foundation.

Taking the spatial analysis to its finest resolution, the researchers employed a genetics-informed cell-type spatial mapping approach, integrating summary statistics with single-cell reference data to generate maps of disease-associated cell populations at single-cell resolution. These maps revealed similar patterns of cell-type-specific distribution across related tissues for depression and the digestive conditions, suggesting that particular populations of cells—likely including neuronal and glial subtypes in the brain and corresponding neural elements of the gut—carry much of the shared genetic burden. Such cell-level resolution offers potential targets for future experimental work, as investigators can now prioritize specific cell populations when designing functional studies of the shared variants.

The authors emphasize that this investigation constitutes the first genomic-level evidence of genetic overlap between major depressive disorder and the four digestive diseases, and that the shared loci they identified may underlie the comorbidity mechanisms seen in clinical practice. The identification of specific overlapping loci, brain-region enrichments, and cell-type distributions opens the door to novel molecular pathways for integrated prevention and treatment. Clinically, the findings argue for a more integrated approach in which patients presenting with chronic digestive symptoms are considered for mental health screening, and vice versa, since genetic susceptibility to one condition signals elevated vulnerability to the other.

At the same time, the study’s scope and design frame its limitations. All analyses relied on summary-level genome-wide association data from European populations, meaning the results may not generalize directly to other ancestries, and genetic correlation, however strong, does not by itself establish causation or dictate that a shared variant acts through the same biological route in both conditions. Functional experiments will be needed to trace how the identified loci influence neural circuits, gut motility, and mood regulation. Even so, the convergence of evidence—from genome-wide correlations through shared loci to brain-region and cell-type enrichment—makes a compelling case that the gut-brain connection in depression is not metaphorical. It is written in the genome, and researchers now have a map of where to read it.

Subject of Research: Shared genetic architecture between major depressive disorder and four common digestive diseases in European populations

Article Title: Shared genetic architecture between major depressive disorder and four digestive diseases in European populations

Article References: Shared genetic architecture between major depressive disorder and four digestive diseases in European populations. (n.d.). https://doi.org/10.1186/s12991-026-00695-w

Image Credits: AI Generated

DOI: 10.1186/s12991-026-00695-w

Keywords: major depressive disorder, genetic correlation, cholelithiasis, gastroesophageal reflux disease, irritable bowel syndrome, constipation, genome-wide association study, gut-brain axis, shared loci, genetic overlap, single-cell mapping, comorbidity