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PubMed2026

Shared genetic architecture between ADHD and intelligence varies across ADHD subtypes.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental condition frequently accompanied by cognitive difficulties. Although previous genetic studies have demonstrated substantial overlap between ADHD and intelligence, most have treated ADHD as a single phenotype. However, whether this shared genetic architecture differs across ADHD subtypes remains unclear. METHODS: We conducted a genome-wide cross-trait analysis integrating large-scale genome-wide association study (GWAS) datasets of overall ADHD, its subtypes-childhood ADHD, persistent ADHD, and late-diagnosed ADHD-and intelligence (total N > 300,000). Genome-wide genetic correlations, polygenic overlap, local genetic correlations, and variant-level associations between ADHD phenotypes and intelligence were evaluated to characterize their shared genetic architecture. Shared variants were identified through cross-trait enrichment analyses and subsequently mapped to genes for functional annotation and gene-set enrichment. Bidirectional associations were evaluated using two-sample Mendelian randomization with sensitivity analyses. Additional GWAS datasets were used to validate the robustness of shared loci by assessing the consistency of effect directions. RESULTS: All ADHD phenotypes showed significant negative genetic correlations with intelligence (rg ranging from -0.3442 to -0.4205). Despite these modest genome-wide correlations, cross-trait analyses revealed substantial genetic overlap, including polygenic overlap, local genetic correlations, and variant-level associations. We identified 184 loci jointly associated with ADHD traits and intelligence, including 64 novel loci, whereas no shared loci were detected for persistent ADHD under the current analysis. Functional annotation revealed biologically distinct enrichment patterns across subtypes: childhood ADHD loci were linked to early neurodevelopmental processes, while late-diagnosed ADHD loci were enriched in synapse-related and neuronal signaling pathways. Mendelian randomization analyses suggested bidirectional associations, with stronger evidence supporting a directional association from intelligence to ADHD risk. Furthermore, these shared loci showed largely consistent effect directions across additional GWAS datasets, providing support for the robustness of the findings. CONCLUSIONS: The shared genetic architecture between ADHD and intelligence varies across ADHD subtypes, highlighting distinct biological pathways underlying cognitive heterogeneity in ADHD. These findings suggest that the relationship between ADHD liability and general cognitive ability is not uniform across ADHD subtypes and may inform future research on risk stratification and early identification in child and adolescent psychiatry.

Humans↗

PubMed2025

Development and evaluation of patient-centred polygenic risk score reports for glaucoma screening.

BACKGROUND: Polygenic risk scores (PRS), which provide an individual probabilistic estimate of genetic susceptibility to develop a disease, have shown effective risk stratification for glaucoma onset. However, there is limited best practice evidence for reporting PRS and patient-friendly reports for communicating PRS effectively are lacking. Here we developed patient-centred PRS reports for glaucoma screening based on the literature, and evaluated them with participants using a qualitative research approach. METHODS: We first reviewed existing PRS reports and literature on probabilistic risk communication. This informed the development of a draft glaucoma screening PRS report for a hypothetical high risk individual from the general population. We designed three versions of the report to illustrate risk using a pictograph, a pie chart and a bell curve. We then conducted semi-structured interviews to assess preference of visual risk communication aids, understanding of risk, content, format and structure of the reports. Participants were invited from an existing study, which aims to evaluate the clinical validity of glaucoma PRS among individuals > 50 years from the general population. Numeracy and literacy levels were assessed. RESULTS: We interviewed 12 individuals. The cohort was highly educated (42% university education), all were European and 50% were female. Numeracy (mean 2.1 ± 0.9, range 0 to 3), graph literacy (mean 2.8 ± 0.8, range 0 to 4) and genetic literacy (mean 24.2 ± 6.2, range - 20 to + 46) showed a range of levels. We analysed the reports under three main themes: visual preferences, understanding risk and reports formatting. The visual component was deemed important to understanding risk, with the pictograph being the preferred visual risk representation, followed by the pie chart and the bell curve. Participants expressed preference for absolute risk in understanding risk, along with the written content explaining the results. The importance of follow-up recommendations and time to glaucoma onset were deemed important. Participants expressed varied opinions in the level of information and the colours used, which informed revisions of the report. CONCLUSIONS: Our study revealed preferences for reporting PRS information in the context of glaucoma screening, to support the development of clinical PRS reporting. Further research is needed to assess PRS communication in other groups representative of target populations and with other target audiences (e.g. referring clinicians), and its potential psychosocial impact in the wider community.

Humans↗

PubMed2025

The relationship between vitamin D levels and depression: a genetically informed study.

BACKGROUND: Low vitamin D (vitD) levels are consistently associated with an increased risk of depression. However, the biological mechanisms underlying this relationship and potential shared genetic overlap remain elusive. METHODS: We investigated the genetic overlap and causal relationships between depression (N = 589,356) and vitD levels (N = 417,580) using genome-wide association study (GWAS) summary statistics. We performed genome-wide and local genetic correlation analyses, followed by quantification of polygenic overlap variants. Shared genetic loci were identified and mapped to genes, which were further analyzed through gene expression and lifespan brain expression trajectory analyses. Bidirectional causal relationships were examined using multiple Mendelian randomization approaches. RESULTS: We observed significant negative genetic correlations (rg = -0.079) and identified genetic overlap (N = 410 variants). Genes mapped to the 13 shared loci showed opposing expression patterns. Tissue- and cell-specific functional enrichment analyses revealed significant signals related to brain development, with distinct patterns emerging between fetal development and adulthood. Shared genes (TRMT61A, ITIH4, RASGRP1, CTNND1, HERC1, IP6K1, FURIN ESR1, ZMYND and GRM5) exhibited notable expression variation in the brian throughout the lifespan, aligning with functional enrichment findings. CONCLUSIONS: Our findings elucidate the shared biological mechanisms underlying the relationship between vitD and depression, suggesting that vitD play an important role in the development of depression through altered early neurodevelopmental processes.

Humans↗

PubMed2025

Genomic structural equation modeling elucidates the shared genetic architecture of allergic disorders.

BACKGROUND: The intricate shared genetic architecture underlying allergic disorders-including allergic asthma, atopic dermatitis, contact dermatitis, allergic rhinitis, allergic conjunctivitis, allergic urticaria, anaphylaxis, and eosinophilic esophagitis-remains incompletely characterized. METHODS: Our study employed genomic structural equation modeling (Genomic SEM) to define the common factor representing the shared genetic architecture of allergic disorders. Coupled with diverse post-GWAS analytical methods, we aimed to discover susceptible loci and investigate genetic associations with external traits. Furthermore, we explored enriched genetic pathways, cellular layers, and genomic elements, and investigated putative plasma protein biomarkers. Polygenic risk score (PRS) analyses, leveraging our integrated GWAS data, were conducted to assess chromosomal-level risk associations for allergic disorders. RESULTS: A well-fitted genomic SEM integrated GWAS data, revealing the shared genetic architecture of allergic disorders. We identified a total of 2038 genome-wide significant SNP loci (p < 5e-8), including 31 previously unreported loci. Fine-mapping of variants and gene sets pinpointed 2 causal variants and 31 candidate susceptible genes. Genetic correlation analyses further illuminated the shared genetic architecture underlying multiple traits, notably psychiatric disorders. Preliminary findings identified four putative causal plasma protein biomarkers. CONCLUSION: Notably, this study presents the first comprehensive genetic characterization of allergic disorders through a GWAS analysis of an unmeasured composite phenotype, providing novel insights into shared etiological pathways across these conditions.

Humans↗

PubMed2026

B cell pathways implicate shared genetic architecture between schizophrenia and immune-mediated diseases.

BACKGROUND: Schizophrenia and immune-mediated diseases are globally prevalent and highly heritable conditions that frequently co-occur, posing major public health burdens. However, their shared genetic architecture remains poorly understood. METHODS: We applied the bivariate causal mixture model (MiXeR) to investigate the polygenic overlap between schizophrenia and eight common immune-mediated diseases, using genome-wide association study summary statistics comprising 2,489 to 67,323 cases and 9,066 to 497,622 controls. Shared loci were identified through conditional/conjunctional false discovery rate (cond/conjFDR), local genetic correlation (LAVA), and colocalization analyses. Subsequently, gene mapping, functional annotation, expression-trait association, and drug-gene interaction analyses were performed to explore shared genes and enriched pathways, and genetic risk scores (GRS) from the UK Biobank were used to validate the findings. RESULTS: MiXeR estimated substantial polygenic overlap between schizophrenia and immune-mediated diseases, and conjFDR identified 133 shared loci, with eight prioritized through local genetic correlation and colocalization signals. These eight loci were mapped to 85 protein-coding genes enriched in pathways essential for B cell function. Among them, S-PrediXcan analyses identified 14 genes whose expression in brain tissues or blood was associated with both diseases. These genes also interact with immunomodulatory or antihypertensive drugs. Additionally, 11 of the 14 genes were linked to innate immunity and/or cognitive traits. Using UK Biobank data, we further confirmed that overall, shared gene, and B cell activation and receptor signaling pathway–specific genetic risk for schizophrenia is associated with immune-mediated disease susceptibility. CONCLUSIONS: These findings underscore the shared genetic architecture of schizophrenia and immune-mediated diseases, advancing insights at the interface of psychiatric genetics and immunology.

Schizophrenia↗

PubMed2026

A multi-ancestry polygenic risk score for body mass index predicts longitudinal weight change.

BACKGROUND: Identifying individuals at risk for future weight gain is challenging, partly because associations with traditional clinical risk factors may be biased by confounding and reverse causation. Polygenic risk scores (PRS) provide a stable, lifelong measure of genetic predisposition to obesity. However, existing PRS have not been evaluated for their association with longitudinal weight change in adulthood and often lack generalizability across diverse genetic ancestry groups. METHODS: We conducted ancestry-specific genome-wide association study meta-analyses of body mass index (BMI) in populations of European, African or African American, Admixed American, East Asian, and South Asian ancestries and developed ancestry-specific PRS. A multi-ancestry polygenic risk score (MAPRS) was trained using ancestry-specific PRS in a model selection dataset (N = 39,685) from the All of Us Research Program (AoU). We evaluated the MAPRS in an independent AoU model evaluation dataset (N = 158,743) for BMI prediction and in a separate AoU test dataset (N = 78,219) with repeated measurements over 1.5-2.5 years for weight change prediction. The outcomes included change in BMI and ≥ 10% or ≥ 5% total body weight (TBW) gain. We further examined the relationship between MAPRS and 12 clinical risk factors commonly comorbid with obesity in relation to weight change. RESULTS: The MAPRS captured 7.05% of the variance in measured BMI in the AoU model evaluation dataset and demonstrated improved generalizability across all non-European genetic ancestry groups. In the AoU test dataset, conditioned on baseline BMI at the second-to-last measurement, a one SD increase in MAPRS was associated with a 0.16 kg/m2 increase in future BMI (standard error = 0.012 kg/m2; p-value = 2.2 × 10-39), 1.27-fold increased odds of experiencing ≥ 10% TBW gain (95% CI: 1.24-1.31; p-value = 1.4 × 10-55), and 1.15-fold increased odds of experiencing ≥ 5% TBW gain (95% CI: 1.13-1.18; p-value = 2.8 × 10-39). These associations were observed across all genetic ancestry groups and remained highly consistent after adjustment for any clinical risk factor. In contrast, most clinical risk factors demonstrated inconsistent or weaker associations with weight change outcomes. CONCLUSIONS: We developed an MAPRS for BMI that represents a robust and generalizable risk factor for longitudinal weight gain in adulthood, providing a foundation for genetically informed risk stratification and earlier, more targeted obesity prevention strategies.

Humans↗

PubMed2026

Cross-Ancestry and Phenome-Wide Associations of Cancer-Specific Polygenic Risk Scores.

PURPOSE: Genome-wide association studies have identified many common variants associated at low effect sizes with various cancers. Summing the effects of these variants into polygenic risk scores (PRS) can improve cancer risk prediction. However, cross-cancer and cross-phenotype pleiotropic associations of cancer-specific PRS are limited. METHODS: Using logistic regression models, we tested the association of 13 cancer-specific PRS with curated phenotypes representing the same 13 cancers and 340 cancer and cardiometabolic phecodes in 560,287 individuals (114,255 African ancestry [AFR] and 446,032 European ancestry [EUR]) from the Million Veteran Program. Models were stratified by ancestry and used age, principal components, and cancer-specific PRS per standard deviation as independent variables, and correction was applied for multiple comparisons. RESULTS: All 13 cancer-specific PRS were significantly associated with their respective cancers among EUR individuals with odds ratios per standard deviation of PRS (odds ratio [OR]) 1.05-1.70. Among AFR individuals, the effect sizes of the cancer PRS were lower, with OR 1.01-1.48, and cancer-specific PRS were significantly associated with their respective cancers for five of 13 cancers (bladder, breast in female patients, colorectal, prostate, and thyroid). In cancer-cancer pleiotropy studies, only the renal cancer-specific PRS was significantly associated with skin cancer (OR = 1.04, P = 4.5 × 10-06) among EUR individuals. PheWAS demonstrated five positive pleotropic associations with cardiometabolic conditions (thyroid cancer PRS with thyroid goiter, oral cancer PRS with diabetes phenotypes, and hypothyroidism) and two negative associations (oral and lung cancer PRS separately with coronary artery disease). CONCLUSION: Cancer PRS have stronger associations per cancer among EUR versus AFR individuals. In contrast to PRS of other chronic diseases, the majority of cancer-related PRS are highly specific and pleiotropic associations with other cancers and cardiometabolic traits are uncommon.

Female↗

PubMed2005

Modifier genes and oligogenic disease.

It is now increasingly apparent that modifier genes have a considerable role to play in phenotypic variations of single-gene disorders. Intrafamilial variations, altered penetrance, and altered severity are now common features of single gene disorders because of the involvement of several genes in the expression of the disease phenotype. Oligogenic disorders occur because of a second gene modifying the action of a dominant gene. It is now certain that cancer occurs due to the action of the environment acting in combination with several genes. Although modifier genes make it impossible to predict phenotype from the genotype and cause considerable difficulties in genetic counseling, they have their uses. In the future, it is hoped that modifier genes will allow us to understand cell and protein interactions and thus allow us to understand the pathogenesis of disease.

Genetic Diseases, Inborn↗

PubMed2005

Genetic variation in bone growth patterns defines adult mouse bone fragility.

UNLABELLED: Femoral morphology and composition were determined for three inbred mouse strains between ages E18.5 and 1 year. Genotype-specific variation in postnatal, pubertal, and postpubertal growth patterns and mineral accrual explained differences in adult bone trait combinations and thus bone fragility. INTRODUCTION: Fracture risk is strongly regulated by genetic factors. However, this regulation is generally considered complex and polygenic. Therefore, the development of effective genetic-based diagnostic and treatment tools hinges on understanding how multiple genes and multiple cell types interact to create mechanically functional structures. The goal of this study was to connect variability in whole bone mechanical function, including measures of fragility, to variability in the biological processes underlying skeletal development. We accomplished this by testing for variation in bone morphology and composition among three inbred mouse strains from E18.5 to 1 year of age. MATERIALS AND METHODS: Mid-diaphyseal cross-sectional areas, diameters, moments of inertia, and ash content were determined for three strains of mice with widely differing adult whole bone femoral mechanical properties (A/J, C57BL/6J, and C3H/HeJ) at E18.5 and postnatal days 1, 7, 14, 28, 56, 112, 182, and 365 (n = 5-15 mice/strain/age). RESULTS: Significant differences in the magnitude and rate of change in morphological and compositional bone traits were observed among the three strains at each phase of growth, including prenatal, postnatal, pubertal, and adult ages. These genotype-specific variations in growth patterns mathematically determined how variation in adult bone trait combinations and mechanical properties arose. Furthermore, six bone traits were identified that characterize phenotypic variability in femoral growth. These include (1) bone size and shape at postnatal day 1, (2) periosteal and (3) endosteal expansion during early growth, (4) periosteal expansion and (5) endosteal contraction in later growth, and (6) ash content. These results show that genetic variability in adult bone traits arises from variation in biological processes at each phase of growth. CONCLUSIONS: Inbred mice achieve different combinations of adult bone traits through genotype-specific regulation of bone surface activity, growth patterns, and whole bone mineral accrual throughout femoral development. This study provides a systematic approach, which can be applied to the human skeleton, to uncover genetic control mechanisms influencing bone fragility.

Age Factors↗

PubMed2025

Simple scaling laws control the genetic architectures of human complex traits.

Genome-wide association studies have revealed that the genetic architectures of complex traits vary widely, including in terms of the numbers, effect sizes, and allele frequencies of significant hits. However, at present we lack a principled way of understanding the similarities and differences among traits. Here, we describe a probabilistic model that combines the effects of mutation, drift, and stabilizing selection at individual sites with a genome-scale model of phenotypic variation. In this model, the architecture of a trait arises from the distribution of selection coefficients of mutations and from two scaling parameters. We fit this model for 95 highly polygenic quantitative traits of different kinds from the UK Biobank. Notably, we infer that all these traits have fairly similar, though not identical, distributions of selection coefficients. This similarity suggests that differences in architectures of highly polygenic traits arise mainly from the two scaling parameters: the mutational target size and heritability per site, which vary by orders of magnitude among traits. When these two scale factors are accounted for, we find that the architectures of all 95 traits are very similar.

Humans↗

PubMed2025

Robust pleiotropy-decomposed polygenic scores identify distinct contributions to elevated coronary artery disease polygenic risk.

BACKGROUND: Polygenic risk score (PRS) have proved to offer robust risk prediction for coronary artery disease (CAD). However, the global CAD PRS summarizes the joint effects of all the markers in the genome, masking potential genetic heterogeneity that may be important for disease interpretation and targeted interventions. METHODS: Using summary-level data, we identified 43 significant CAD-related traits based on genetic correlations, and further classified them into eight pleiotropy clusters based on their biological functions. We then partitioned the genome into 2,353 near-independent regions. Variants in each region were assigned to the trait most genetically similar to CAD, and then were labeled with the corresponding pleiotropy cluster. We grouped variants without labels into a ninth, non-specific cluster. The Pleiotropy Decomposed (PD) PRSs for each of the nine clusters were calculated using variants assigned to each cluster for 407,903 samples of European ancestry from the UK Biobank (UKBB). RESULTS: We decomposed the CAD PRS into nine PD-PRSs and further stratified individuals with high CAD-PRS into nine subgroups. Each PD-PRS accounted for a higher proportion of the global CAD-PRS within its corresponding subgroup than in the remaining subjects with high CAD-PRS (e.g., 25.2% (0.07) vs. 10.06% (0.07) for lipids-PD-PRS). Additionally, these subgroups showed distinct clinical features. For example, in the lipids-related subgroup, lipoprotein(a) and LDL-cholesterol levels were 67.5% and 18.3% higher, respectively, compared to the remaining high-risk individuals. Furthermore, significant interactions were observed between blood pressure and BP PD-PRS, and between current smoking and respiratory system PD-PRS. CONCLUSION: Our findings suggest that PD-PRSs may reveal substantial genetic and phenotypic heterogeneity among individuals with high CAD-PRS. The unique PD-PRS compositions of each individual can highlight the relative importance of different pleiotropic regions.

Humans↗

PubMed2025

Tensor decomposition of multi-dimensional splicing events across multiple tissues to identify splicing-mediated risk genes associated with complex traits.

Identifying risk genes associated with complex traits remains challenging. Integrating gene expression data with Genome-Wide Association Study (GWAS) through Transcriptome-Wide Association Study (TWAS) methods has discovered candidate risk genes for various complex traits. Splicing, which explains a comparable heritability of complex traits as gene expression, is under-explored due to its multidimensionality. To leverage multiple splicing events in a gene and shared splicing across tissues, we develop Multi-tissue Splicing Gene (MTSG), which employs tensor decomposition and sparse Canonical Correlation Analysis (sCCA) to extract meaningful information from high-dimensional multiple splicing events across multiple tissues. We build MTSG models using GTEx data and apply them to GWAS summary statistics of Alzheimer's disease (AD) (111,326 cases and 677,663 controls) and schizophrenia (SCZ) (36,989 cases and 113,075 controls). We identify 174 and 497 significant splicing-mediated risk genes for AD and SCZ, respectively, at Bonferroni correction. For AD, our results demonstrate significant enrichment of AD related pathways and identify additional AD risk genes not detected in the single-tissue analysis, while preserving most top genes identified in the brain frontal cortex. Consistently, for SCZ, genes identified by our brain-wide MTSG model, built from a cluster of 13 brain tissues, exhibit stronger enrichment in SCZ-relevant genes and MTSG identifies unique SCZ risk genes compared to single-tissue models. These results showcase that our MTSG models capture distinctive splicing events across tissues, which might be overlooked when using single tissue alone. Our MTSG models can be applied to other complex traits to help identify splicing-mediated disease risk genes.

Humans↗

PubMed2026

IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits.

Genetic contributions to complex traits are often mediated through coordinated gene-gene interaction networks, yet most existing association frameworks focus on marginal single-gene effects and overlook higher-order dependency structures. Direct modeling of interactions remains challenging due to combinatorial complexity and statistical instability. We introduce Interaction-Bridged Association Study (IBAS), a general framework that incorporates pathway-level interaction patterns into genotype-phenotype association analysis without explicitly enumerating interactions. IBAS leverages transcriptomic reference data to construct low-dimensional representations of pathway activity, which guide SNP-weighting and gene-level association testing within a kernel-based framework. In perturbation-based simulations, IBAS demonstrates improved stability and reproducibility compared to conventional TWAS and gene-based methods, while maintaining well-calibrated Type I error under phenotype permutation. Application to the WTCCC datasets identifies both known and novel genes across multiple complex diseases, including candidates with modest marginal effects missed by standard approaches. These findings are supported by replication in an independent cohort, and analyses across multiple reference tissues revealing both shared and tissue-specific signals. Overall, IBAS provides a statistically robust and computationally tractable framework for incorporating interaction effects into association mapping, extending beyond the single-gene paradigm and enabling more comprehensive characterization of complex trait. IBAS is available on GitHub at: https://github.com/QingrunZhangLab/IBAS.

Polymorphism, Single Nucleotide↗

PubMed2026

Improving the reliability of polygenic risk score-based prediction for cardiovascular and renal complications across ancestries in type 2 diabetes using Mondrian Cross-Conformal Prediction.

Polygenic risk scores (PRS) developed in European populations often show reduced predictive performance in non-European populations, limiting their clinical utility. This lack of transferability across ancestries remains a major challenge in genomic medicine and raises concerns about health equity. We aimed to evaluate whether uncertainty-aware prediction, implemented through Mondrian Cross-Conformal Prediction, improves the performance and reliability of polygenic risk score-based predictions across ancestries for nephropathy, stroke, and myocardial infarction in individuals with type 2 diabetes in a multi-ethnic cohort. We leveraged Mondrian Cross-Conformal Prediction (MCCP), an uncertainty quantification framework, combined with logistic regression applied to a multi-polygenic risk score (multiPRS) to predict the risk of nephropathy, stroke, and myocardial infarction in individuals with type 2 diabetes. Two training frameworks were evaluated: one using 4,098 individuals with type 2 diabetes of European ancestry from the ADVANCE trial for training and 17,574 White British, 1,145 South Asian, and 749 African UK Biobank participants for testing; and another using the 17,574 White British UK Biobank participants for training and the South Asian and African participants for testing. Logistic regression provided robust baseline performance across populations. On top of this baseline, MCCP did not improve performance but added capabilities absent from probability-based stratification: for each individual, it issued a prediction together with an explicit confidence and credibility level; it allowed a tolerated error level to be set in advance and delivered prediction sets respecting it in the majority of settings; and it flagged individuals for whom no reliable prediction could be made. Applying MCCP to PRS-based prediction thus enables uncertainty-aware risk stratification and improves the reliability of risk prediction across ancestries, providing a more equitable framework for clinical use.

Female↗

PubMed2006

Heritability of cardiovascular and personality traits in 6,148 Sardinians.

In family studies, phenotypic similarities between relatives yield information on the overall contribution of genes to trait variation. Large samples are important for these family studies, especially when comparing heritability between subgroups such as young and old, or males and females. We recruited a cohort of 6,148 participants, aged 14-102 y, from four clustered towns in Sardinia. The cohort includes 34,469 relative pairs. To extract genetic information, we implemented software for variance components heritability analysis, designed to handle large pedigrees, analyze multiple traits simultaneously, and model heterogeneity. Here, we report heritability analyses for 98 quantitative traits, focusing on facets of personality and cardiovascular function. We also summarize results of bivariate analyses for all pairs of traits and of heterogeneity analyses for each trait. We found a significant genetic component for every trait. On average, genetic effects explained 40% of the variance for 38 blood tests, 51% for five anthropometric measures, 25% for 20 measures of cardiovascular function, and 19% for 35 personality traits. Four traits showed significant evidence for an X-linked component. Bivariate analyses suggested overlapping genetic determinants for many traits, including multiple personality facets and several traits related to the metabolic syndrome; but we found no evidence for shared genetic determinants that might underlie the reported association of some personality traits and cardiovascular risk factors. Models allowing for heterogeneity suggested that, in this cohort, the genetic variance was typically larger in females and in younger individuals, but interesting exceptions were observed. For example, narrow heritability of blood pressure was approximately 26% in individuals more than 42 y old, but only approximately 8% in younger individuals. Despite the heterogeneity in effect sizes, the same loci appear to contribute to variance in young and old, and in males and females. In summary, we find significant evidence for heritability of many medically important traits, including cardiovascular function and personality. Evidence for heterogeneity by age and sex suggests that models allowing for these differences will be important in mapping quantitative traits.

Adolescent↗

PubMed2026

Pervasive context-dependent effects in the genetic architecture of complex and quantitative traits revealed by a powerful multiparent mapping population in yeast.

The genetic dissection of complex traits remains a major challenge in basic and biomedical research, but is essential for understanding the molecular pathways that shape phenotypic variation and for developing predictive models of trait and disease susceptibility. Here, we leverage a novel multiparent mapping population of budding yeast, CYClones, comprising 9,344 haploid strains derived from eight genetically diverse founders (~270,000 SNVs, ~ 1 per 44 bp, capturing 56% of common variants and 32% of all variants with a minor allele frequency greater than 0.005 in the global population), to identify quantitative trait loci (QTL) and systematically investigate the genetic architecture of growth rates across ten environmental conditions. In total, we identified 349 QTL (ranging from 18 to 49 QTL per growth condition) that explained between 60% and 100% of narrow sense heritability across traits. The high power and resolution of CYClones revealed that growth traits exhibited distinct, condition-specific genetic architectures with extensive allelic heterogeneity, where a QTL was the result of multiple tightly linked causal variants. We also observed pleiotropy among QTL with complex, trait-dependent allele effects that are also consistent with allelic heterogeneity. Genetic complexity varied widely, with some traits showing nearly Mendelian architectures, while others were highly polygenic. Introgressed loci played a prominent role in the landscape of growth rate QTL, including a QTL localized to a 2.4 kb interval in the PCA1 cadmium transporter that explains 72% of variation in cadmium resistance and is largely driven by an introgression, and a non-additive interaction between the GAL3 regulator and introgressed GAL1/7/10 alleles, extending a previously described three-locus GAL-pathway incompatibility to a four-locus interaction. In both cadmium and galactose conditions, we show that allelic variation at a small number of loci stratifies the population into regulatory or physiological subgroups, each with distinct genetic architectures, a specific manifestation of epistasis we term allele-dependent stratification. Collectively, our results provide novel insights into the genetics of growth rates in budding yeast, the architectural features of genetic complexity, and demonstrate that CYClones is a powerful platform for revealing the molecular basis of complex trait variation.

Quantitative Trait Loci↗

PubMed2025

Patient stratification by genetic risk in Alzheimer's disease is only effective in the presence of phenotypic heterogeneity.

Case-only designs in longitudinal cohorts are a valuable resource for identifying disease-relevant genes, pathways, and novel targets influencing disease progression. This is particularly relevant in Alzheimer's disease (AD), where longitudinal cohorts measure disease "progression," defined by rate of cognitive decline. Few of the identified drug targets for AD have been clinically tractable, and phenotypic heterogeneity is an obstacle to both clinical research and basic science. In four cohorts (n = 7241), we performed genome-wide association studies (GWAS) and Mendelian randomization (MR) to discover novel targets associated with progression and assess causal relationships. We tested opportunities for patient stratification by deriving polygenic risk scores (PRS) for AD risk and severity and tested the value of these scores in predicting progression. Genome-wide association studies identified no loci associated with progression at genome-wide significance (α = 5×10-8); MR analyses provided no significant evidence of an association between cognitive decline in AD patients and protein levels in brain, cerebrospinal fluid (CSF), and plasma. Polygenic risk scores for AD risk did not reliably stratify fast from slow progressors; however, a deeper investigation found that APOE ε4 status predicts amyloid-β and tau positive versus negative patients (odds ratio for an additional APOE ε4 allele = 5.78 [95% confidence interval: 3.76-8.89], P<0.001) when restricting to a subset of patients with available CSF biomarker data. These results provided no evidence for large-effect, common-variant loci involved in the rate of memory decline, suggesting that patient stratification based on common genetic risk factors for progression may have limited utility. Where clinically relevant biomarkers suggest diagnostic heterogeneity, there is evidence that a priori identified genetic risk factors may have value in patient stratification. Mendelian randomization was less tractable due to the lack of large-effect loci, and future analyses with increased samples sizes are needed to replicate and validate our results.

Alzheimer Disease↗

PubMed2026

Shared genetic basis and spatial cellular atlas of psoriasis and metabolic syndrome.

BACKGROUND: Psoriasis (PS) and metabolic syndrome (MetS) frequently co-occur. Characterizing their shared genetic architecture and spatially enriched cellular populations may clarify the context of their co-occurrence and generate hypotheses for functional validation. METHODS: We integrated genome-wide association study (GWAS) summary statistics for PS, MetS, and five related components with spatially resolved single-cell transcriptomic data. Global and local genetic correlations were assessed using linkage disequilibrium score regression, genetic covariance analysis, high-definition likelihood, and local analysis of variant association. A bivariate causal mixture model quantified polygenic overlap. Conditional/conjunctional false discovery rate and composite-null pleiotropy analyses identified shared susceptibility loci. Finally, gsMap evaluated trait-associated enrichment across annotated embryonic tissues at single-cell resolution. RESULTS: Genetic approaches identified significant genome-wide correlations and polygenic sharing between PS, MetS, and its components. Local and cross-trait analyses identified region-specific signals and cross-validated shared loci. gsMap revealed trait-specific tissue enrichment. PS showed the strongest enrichment in the epidermis (pCauchy = 1.0573 × 10  - ⁴), adipose tissue (pCauchy = 1.5366 × 10 - ⁴), and liver (pCauchy = 1.0167 × 10 - ³). Across MetS, FBG, HDL-C, hypertension, and TG, enriched regions mainly involved the liver, adipose tissue, and epidermis. WC enrichment was predominantly observed in adipose tissue (pCauchy = 1.7823 × 10 - ⁴), with no significant liver or epidermal enrichment. CONCLUSION: Integrating GWAS with single-cell transcriptomic and spatial information characterized shared genetic architecture between PS and MetS-related phenotypes and their spatial enrichment patterns. These findings provide a framework for generating testable hypotheses about comorbidity biology and guiding future functional and clinical validation.

Psoriasis↗