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PubMed2026

Conventional and Shared Genetic Association Analysis Between Diabetes Mellitus and Sensorineural Hearing Loss.

PURPOSE: This study aims to investigate the epidemiological and genetic associations between diabetes mellitus (DM) and sensorineural hearing loss (SNHL) across different subtypes. METHODS: We analyzed 502,490 participants from the UK Biobank using multivariate logistic regression to examine the association between DM and SNHL, considering gender, age, and HbA1c levels. Genetic correlations and causality were examined by linkage disequilibrium score regression and bidirectional Mendelian randomization. Cross-trait meta-analyses identified shared loci between DM and SNHL, followed by gene annotation, functional analysis, and drug candidate exploration for the shared traits. RESULTS: Observational analysis revealed significant associations between DM and SNHL, consistent in subgroups based on age, sex, and certain HbA1c levels. A positive genetic correlation was found between type 2 diabetes mellitus (T2D) and SNHL (Rg = 0.0982, p = 0.0095) between T2D and SNHL, and four loci were identified, with ARHGEF28 and TCF7L2 prioritized as credible pleiotropic genes. Enrichment was indicated in glucose metabolism and organogenesis, with shared heritability in metabolic tissues and outer hair cells. Metformin was identified as potential drug candidates for the T2D-SNHL comorbidity. CONCLUSION: These findings progress our understanding of the epidemiological association, shared genetic basis, and potential therapeutic targets between T2D and SNHL, which might contribute to the management of their comorbidity.

Humans↗

PubMed2026

Multi-Ancestry Survival GWAS of Substance Use Initiation in the ABCD Study.

BACKGROUND: Substance use initiation in adolescence is influenced by both genetic and environmental factors; however, large-scale genetic studies often treat initiation as a binary outcome and underuse longitudinal timing information. METHODS: We conducted time-to-event (survival) genome-wide association analyses (GWAS) of initiation for four outcomes-alcohol, nicotine, cannabis, and any substance use-using longitudinal follow-up data from the Adolescent Brain Cognitive Development (ABCD) Study. We performed ancestry-stratified GWAS within European (EUR), African (AFR), and Hispanic (HISP) groups, applying consistent quality control and covariate adjustment. Summary statistics were harmonized across ancestries and meta-analyzed using inverse-variance weighted fixed-effects and DerSimonian-Laird random-effects models. We evaluated genomic inflation and heterogeneity (Cochran's Q and I 2), identified independent lead variants at genome-wide and suggestive significance thresholds, and assessed cross-trait overlap of associated loci. RESULTS: In the multi-ancestry meta-analysis, we observed suggestive association signals across traits (minimum p-values: alcohol ~ 1 × 10-7, any ~ 1 × 10-7, cannabis ~ 5 × 10-8, nicotine ~ 1 × 10-8). Nicotine initiation showed one genome-wide significant variant in both fixed- and random-effects meta-analyses (p < 5 × 10-8). Across traits, suggestive loci demonstrated limited overlap, with the strongest concordance between alcohol and any substance use, consistent with shared liability. Heterogeneity statistics indicated that some loci exhibited cross-ancestry variation in effect estimates. CONCLUSIONS: Survival GWAS leveraging initiation timing can identify genetic signals that may be missed by binary designs and enables principled multi-ancestry synthesis. Our results highlight both shared and trait-specific genetic contributions to early substance initiation and provide a foundation for downstream functional annotation and integrative modeling with environmental risk factors. These findings demonstrate the value of incorporating developmental timing into genetic discovery and provide a framework for integrating longitudinal risk modeling with genomic analyses.

ABCD↗

PubMed2002

Pleiotropic relationships between cortisol levels and adiposity: The HERITAGE Family Study.

OBJECTIVE: To investigate familial basis for the relationship between cortisol adiposity at baseline and their training responses. RESEARCH METHODS AND PROCEDURES: Bivariate correlation and segregation analyses were employed between cortisol and several adiposity measures [body mass index, fat mass (FM), fat-free mass, percentage of body fat (% BF), abdominal visceral fat (AVF), abdominal subcutaneous fat (ASF), and abdominal total fat (ATF)] from 99 white families and 105 black families. RESULTS: In both races, significant inverse phenotypic correlations were generally observed between cortisol and adiposity measures at baseline but not for training responses. Significant cross-trait familial correlations were found for cortisol with abdominal fat (ASF, AVF, ATF) and overall body adiposity (FM, % BF) measures at baseline, which accounted for 14% to 20% of the phenotypic variance in whites. The cross-trait correlations were not significant for baseline phenotypes in blacks, perhaps because of the small sample size. A bivariate segregation analysis showed evidence of polygenic pleiotropy for cortisol with both abdominal fat and overall adiposity measures that accounted for 14% to 17% of the phenotypic covariance, but major gene pleiotropy was not suggested in whites. However, when ASF, AVF, and ATF were additionally adjusted for FM, no familial cross-trait correlations or polygenic pleiotropy between cortisol and the abdominal fat measures remained. DISCUSSION: Evidence was found for polygenic pleiotropy but not for pleiotropic major gene effects between cortisol and overall adiposity in whites. However, the covariation of cortisol with abdominal fat phenotypes is dependent on concomitant polygenic factors for total-body fat.

Abdomen↗

PubMed2026

Genetic Correlation Between Brain Imaging Phenotypes and Externalizing Behavior: A Large-Scale LDSC Analysis of UK Biobank IDPs.

Externalizing has been associated with differences in brain structure and function; however, it remains unclear whether these associations reflect shared common-variant genetic influences. Cross-trait linkage disequilibrium score regression was used to estimate genome-wide genetic correlations between externalizing genome-wide association study (GWAS) results and 3,935 brain imaging-derived phenotypes from the UK Biobank BIG40 resource. The imaging phenotypes covered structural magnetic resonance imaging (MRI), diffusion MRI, susceptibility-weighted imaging, resting-state functional MRI, and task-based functional MRI. Results were included in the primary analysis when the imaging phenotype had positive single-nucleotide polymorphism (SNP) heritability, a heritability Z statistic of at least 1.96, a mean GWAS chi-square statistic of at least 1.02, at least 200,000 regression SNPs, and a complete LDSC result without a fatal error. Technical imaging quality-control phenotypes were excluded from biological inference. Individual results were corrected using the Benjamini-Hochberg false discovery rate procedure. Aggregated Cauchy association tests (ACATs) were used to evaluate evidence across all imaging phenotypes and within predefined imaging categories. Statistical power, simultaneous confidence bounds, and alternative quality-control definitions were examined in sensitivity analyses. Of the 3,935 imaging phenotypes, 3,716 produced estimable genetic correlations, 2,980 met the primary LDSC quality-control criteria, and 2,967 were classified as biological imaging phenotypes. No individual phenotype survived false discovery rate correction. The smallest unadjusted P value was 0.0005, and the minimum adjusted q value was 0.486. The distribution of genetic correlations was centered near zero, with a median genetic correlation of 0.0014 and a median absolute genetic correlation of 0.0338. ACAT provided no evidence of an aggregate association across all biological imaging phenotypes (P = 0.302), and no predefined imaging category survived multiple-testing correction. The median minimum detectable genetic correlation at 80% power was 0.216. Bonferroni-adjusted simultaneous confidence intervals were fully contained within the interval [-0.30, 0.30] for 80.0% of phenotypes in the primary analysis and 88.0% under the stringent heritability quality-control definition. Broad and stringent sensitivity analyses produced the same overall conclusions. In this study, no statistically robust evidence of genome-wide genetic correlations between externalizing and individual UK Biobank brain imaging phenotypes was found. Nevertheless, small, localized, mixed-direction, or developmentally specific genetic effects remain possible.

Journal Article↗

PubMed2004

The contribution of risk factors to blood pressure heritability estimates in young adults: the East flanders prospective twin study.

The heritability of blood pressure estimated in previous studies may be confounded by the influence of potential blood pressure risk factors. We applied the classical twin design to estimate the contribution of these covariates to blood pressure heritability. The study consisted of 173 dizygotic and 251 monozygotic twin pairs aged 18-34 years, randomly selected from the East Flanders Prospective Twin Survey. In a standardized examination, blood pressure and anthropometry was measured, a questionnaire was completed, and a fasting blood sample was taken. In univariate and bivariate modeling, diastolic and systolic heritability were estimated both unadjusted and adjusted for potential risk factors. Also, covariate interaction was modeled. Bivariate analysis gave heritability estimates of 0.63 (95%CI 0.55-0.59), 0.74 (95%CI: 0.68-0.79), and 0.78 (95%CI: 0.70-0.84) for diastolic, systolic, and cross-trait heritability, respectively. The remaining variances could be attributed to unique environmental influences. These heritability estimates did not change substantially in univariate analyses or after adjustment for risk factors. A sex-limitation model showed that the heritability estimates for women were significantly higher than for men, but the same genetic factors were operating across sexes. Sex and cigarette smoking appeared to be statistically significant interaction terms. The heritability of blood pressure is relatively high in young adults. Potential risk factors of blood pressure do not appear to confound the heritability estimates. However, gene by sex by smoking interaction is indicated.

Adolescent↗

PubMed2003

Lack of pleiotropic genetic effects between adiposity and sex hormone-binding globulin concentrations before and after 20 weeks of exercise training: the HERITAGE family study.

The relationship between sex hormone-binding globulin (SHBG) concentrations and body fat accumulation and distribution is governed by complex dynamic factors, which may involve common genetic and/or environmental factors. The current study investigated the genetic and environmental basis for the correlation between SHBG and body fat. Several measures of adiposity were investigated including body mass index (BMI) and a trunk to extremity skinfold thickness ratio (TER) assessed by anthropometry, body composition measured by hydrostatic weighing (total body fat mass [FM], fat-free mass [FFM], and percent body fat [%BF]), and abdominal fat measured by computerized tomography scanning (abdominal visceral fat [AVF]). The study comprised 501 white subjects from 99 families and 277 black subjects from 117 families participating in the HERITAGE Family Study. Familial correlations between traits and their cosegregation were investigated both at baseline and in response to endurance exercise training. Significant inverse phenotypic correlations were detected in both races between SHBG and adiposity measures at baseline and also in response to training. Significant cross-trait familial resemblance was found between SHBG and both BMI and FFM at baseline that accounted for 11% and 4% of maximal heritability, respectively, in white families. However, a joint segregation analysis of the traits failed to implicate shared genetic effects. Specifically, neither a pleiotropic major locus nor pleiotropic polygenic effects were detected between SHBG and BMI or FFM. A maximal cross-trait heritability of 45% was obtained for SHBG and TER at baseline in black families. However, no firm conclusions as to the etiology of this relationship could be drawn because of the limitations of small sample size. For the training response phenotypes, there was no significant cross-trait correlation between SHBG and any adiposity measures studied here, suggesting that their correlation may have an environmental basis. Therefore, this study fails to support the hypothesis of genetic pleiotropy between SHBG concentrations and body fat phenotypes, and suggests an environmental basis for the correlation, ie, SHBG concentrations are genetically independent of body composition and abdominal adiposity phenotypes.

Adipose Tissue↗

PubMed2001

Hypomanic personality, social anhedonia and impulsive nonconformity: evidence for familial aggregation?

Schizophrenic and affective spectrum disorders aggregate in the families of patients afflicted with such disorders. Possible vulnerability markers for these disorders should therefore also run in families. The Chapmans and their coworkers developed the Hypomanic Personality Scale (HYP) to identify people at risk for affective disorders, and the scales Social Anhedonia (SA) and Impulsive Nonconformity (IMP) to assess schizotypy (Chapman et al., 1976, 1984; Eckblad & Chapman, 1986). The present family study investigated the familial resemblance of the HYP, SA, and IMP Scale using a maximum-likelihood approach. Index participants and their relatives (n = 717) completed a questionnaire packet that included the above-mentioned scales. Stepwise several models of familial correlations were specified and tested dealing with the influence of sex of parents and offspring and of interindividual cross-trait resemblance. For all three measures, there was evidence of familial resemblance. For SA and IMP, we found hints for possible assortative mating; additionally for HYP and IMP, an interindividual cross-trait resemblance (with correlations of 0.14 and 0.18, respectively) between family members emerged. The results support the validity of the HYP, SA, and IMP Scale. It is discussed whether HYP and IMP represent different aspects of a shared latent liability.

Adolescent↗

PubMed2000

Major gene effect on body mass index: the role of energy intake and energy expenditure.

The evidence for a major gene for body mass index (BMI) was investigated using complex segregation analysis (POINTER) in 1691 individuals belonging to 432 nuclear families residing in the Chittoor district of Andhra Pradesh, India. Since the BMI is significantly correlated with energy intake (EI) and energy expenditure of activity (EEA), the effects of each were removed from the BMI using regression analysis, and the segregation analysis was repeated on the energy-adjusted BMI. For BMI, a putative major locus could not be ruled out, and the effect (q = 0.25, accounting for 37% of the phenotypic variance) was remarkably similar to that reported in Western populations. After adjusting the BMI for EI and EEA, however, no evidence in support of a major gene could be observed, suggesting either that EI and EEA mediate the expression of the major gene effect on BMI, or that the same major gene may influence both traits. The pleiotropy hypothesis was further explored using a simple bivariate familial correlation model, in which the significance of familial cross-trait correlations (e.g., BMI in parents with BMI as predicted from the energy variables in the offspring) was examined. The cross-trait resemblance between the two measures was significant for all biological relatives, verifying the presence of shared heritable determinants (i.e., the same gene[s] and/or familial environments) accounting for 58% of the covariation. The significant cross-trait spouse correlations further suggested that at least part of the cross-trait resemblance may be due to shared environmental factors. Therefore, we conclude that there is strong evidence for shared genetic effects between BMI and the energy variables.

Adolescent↗

PubMed1999

Genetic effects on weight change and food intake in Swedish adult twins.

BACKGROUND: Obesity is influenced by genetic and environmental factors. Additionally, synergistic effects of genes and environments may be important in the development of obesity. OBJECTIVE: The aim of this study was to test for genetic effects on food consumption frequency, food preferences, and their interaction with subsequent weight gain. DESIGN: Complete data on the frequencies of consumption of 11 foods typical of the Swedish diet were available for 98 monozygotic and 176 dizygotic twin pairs aged 25-59 y who are part of the Swedish Twin Registry. The data were collected in 1973 as part of a questionnaire study. Body mass index was measured in 1973 and again in 1984. RESULTS: There was some evidence that genetic effects influenced the frequency of intake of some foods. Similarity among monozygotic twins exceeded that among dizygotic twins for intake of flour and grain products and fruit in men and women, intake of milk in men, and intake of vegetables and rice in women, suggesting that genes influence preferences for these foods. Analyses conducted for twins reared together and apart also suggested greater monozygotic than dizygotic correlations, but cross-twin, cross-trait correlations were all insignificant, suggesting that the genes that affect consumption frequencies are not responsible for mediating the relation between the frequency of intake and weight change. CONCLUSIONS: Genetic effects and the frequency of intake are independently related to change in body mass index. However, there was no suggestion of differential genetic effects on weight gain that were dependent on the consumption frequency of the foods studied.

Adult↗

PubMed1995

Familial clustering of multiple measures of adiposity and fat distribution in the Québec Family Study: a trivariate analysis of percent body fat, body mass index, and trunk-to-extremity skinfold ratio.

OBJECTIVE: To assess whether independent or common (pleiotropic) familial factors (i.e., genetic and/or common environment) underlie the observed associations among measures of body mass, body fat, and its distribution. DESIGN: A familial correlation model involves both parents and offspring, and gives rise to three types of familial correlations (spouse, parent-offspring, and sibling). A pattern of significant familial correlations suggests that the trait is determined by familial factors (i.e., genetic and/or environmental heritability). Cross-trait familial correlations are also estimated, both within individuals (intraindividual) and between family members (interindividual). Interindividual cross-trait familial correlations (e.g., trait 1 in parents with trait 2 in offspring) lead to the same type of familial inferences regarding bivariate heritabilities. SUBJECTS AND MEASURES: Measures of total body fat (% body fat-%BF), fat distribution (trunk/extremity skinfold ratio-TER), and body mass index (BMI) were assessed in 1239 individuals from 309 nuclear families participating the Québec Family Study. RESULTS: All three adiposity measures are cross-correlated within individuals. However, interindividual cross-trait correlations, which alone are capable of suggesting common familial determinants, are significant only for BMI with each of %BF and TER (bivariate heritabilities of 10% and 18%, respectively), and not for %BF and TER. CONCLUSION: Although all three adiposity measures are correlated within individuals, there appear to be entirely different underlying genes and/or environmental factors influencing the adiposity phenotypes of total body fat and fat distribution. The BMI, however, apparently shares some familial determinants with both total body fat and fat distribution.

Adolescent↗

PubMed1996

Familial clustering of abdominal visceral fat and total fat mass: the Québec Family Study.

The evidence for common familial factors underlying total fat mass (estimated from underwater weighing) and abdominal visceral fat (assessed from CT scan) was examined in families participating in phase 2 of the Québec Family Study (QFS) using a bivariate familial correlation model. Previous QFS investigations suggest that both genetic (major and polygenic) and familial environmental factors influence each phenotype, accounting for between 55% to 71% of the phenotypic variance in fat mass, and between 55% to 72% for abdominal visceral fat. The current study suggests that the bivariate familial effect ranges from 29% to 50%. This pattern suggests that there may be common familial determinants for abdominal visceral fat and total fat mass, as well as additional familial factors which are specific to each. The relatively high spouse cross-trait correlations usually suggest that a large percent of the bivariate familial effect may be environmental in origin. However, if mating is not random, then the spouse resemblance may reflect either genetic or environmental causes, depending on the source [i.e., through similar genes or cohabitation (environmental) effects]. Finally, there are significant sex differences in the magnitude of the familial cross-trait correlations involving parents, but not offspring, suggesting complex generation (i.e., age) and sex effects. For example, genes may turn on or off as a function of age and sex, and/or there may be an accumulation over time of effects due to the environment which may vary by sex. Whether the common familial factors are genetic (major and/or polygenic), environmental, or some combination of both, and whether the familial expression depends on sex and/or age warrants further investigation using more complex models.

Abdomen↗

PubMed2026

Shared genetic architecture between major depression and intrinsic brain functional connectome organization.

BACKGROUND: Major depression (MD) is increasingly understood as a disorder characterized by widespread abnormalities in intrinsic brain functional network organization. Although both MD and brain functional connectome architecture are highly heritable, the genetic architecture underlying their relationship remains poorly characterized. METHODS: We integrated genome-wide association studies of MD with 191 ICA-based resting-state functional connectome traits to investigate their shared genetic architecture. These traits captured intrinsic connectome organization across amplitude, functional connectivity, and global connectivity domains. Cross-trait genetic analyses were used to assess pleiotropic overlap between traits. Locus-level and gene-based analyses integrating multi-omics evidence were performed to characterize the biological relevance of shared genetic signals. RESULTS: We identified significant genetic overlap between MD and 148 of 191 brain functional connectome traits. Cross-trait analyses revealed widespread shared genetic signals organized into 627 genomic loci across amplitude, functional connectivity, and global connectivity measures. Among these, 193 loci showed evidence consistent with shared causal variants based on colocalization analyses. Gene-level integration mapped these loci to 1459 protein-coding genes (390 unique genes). Multi-layer prioritization identified 17 high-confidence genes supported by convergent genomic, transcriptomic, and proteomic evidence, with enrichment in neurodevelopmental and lipid-related metabolism pathways. CONCLUSIONS: This study provides a multi-scale characterization of the shared genetic architecture between MD and intrinsic brain functional connectome organization, revealing that shared genetic signals between MD and brain functional systems are distributed across multiple functional levels and converge at the molecular level.

Connectome↗

PubMed1999

Bivariate familial correlation analysis of quantitative traits by use of estimating equations: application to a familial analysis of the insulin resistance syndrome.

Familial correlation analysis involving two traits may give a better insight into the etiology of multifactorial syndromes than familial analysis focused on single traits. Significant cross-trait correlations between biological relatives but not between spouses suggest that the two traits share common transmissible factors whereas correlations between spouses additionally suggest the influence of shared lifestyle factors. We apply the Estimating Equations (EE) technique to the estimation of intra-trait and cross-trait familial correlations on two quantitative traits. Unlike maximum likelihood methods, the EE method does not require one to specify the joint distribution of the traits. Estimation of correlations and of their variance involves an iterative three-stage algorithm which converges rapidly. The generalized Wald test can be used to test any specific hypothesis of familial resemblance. This method has great flexibility for handling covariates and incomplete family data. A simulation study indicated that the EE technique performed well in large samples (100 families), both in terms of type I error and coverage probability . However, in small samples (50 families), an increase of the type I error and a decrease of the coverage probability was observed. As an illustration, we applied this technique to a family study of metabolic factors involved in the Insulin Resistance Syndrome (body mass index, insulin, triglycerides, HDL-cholesterol, and diastolic blood pressure). The study was carried out in a sample of 216 healthy nuclear families with > or =2 offspring. The results suggested the existence of a common transmissible (genetic or cultural) factor influencing both body mass index and insulin, whereas the weak clustering of triglycerides and HDL-cholesterol would be more compatible with the influence of shared lifestyle factors.

Algorithms↗

PubMed1996

Genetic pleiotropy for resting metabolic rate with fat-free mass and fat mass: the Québec Family Study.

Shared genetic and familial environmental causes for the associations among resting metabolic rate (RMR), fat-free mass (FFM), and fat mass (FM) were investigated in families participating in phase 2 of the Québec Family Study. A multivariate familial correlation model assessing the pattern of significant cross-trait correlations between family members (e.g., RMR in parents with FFM in offspring) was used to infer the etiology of the associations. For each of FM and FFM with RMR, significant sibling, parent-offspring, and intraindividual cross-trait correlations suggests the associations are familial. Furthermore, the lack of significant spouse cross-trait correlations suggests that the familial aggregation is primarily genetic. Bivariate heritability estimates suggest that as much as 45% to 50% of the shared variance between FFM and RMR may be genetic, and as much as 28% to 34% for FM and RMR. This study supports the notion that the gene(s) affecting each of FFM and FM also influence the RMR. Moreover, the lack of any familial associations between FFM and FM suggests that the effects of each body size component on RMR are independent, i.e., more than one genetic source on the RMR-body size association. The possibility that RMR is an oligogenic trait (i.e., more than one underlying genetic etiology) should be further investigated using more complex multivariate segregation methods until specific genes can be tested.

Adipose Tissue↗

PubMed2024

Multivariate genetic architecture reveals testosterone-driven sexual antagonism in contemporary humans.

Sex difference (SD) is ubiquitous in humans despite shared genetic architecture (SGA) between the sexes. A univariate approach, i.e., studying SD in single traits by estimating genetic correlation, does not provide a complete biological overview, because traits are not independent and are genetically correlated. The multivariate genetic architecture between the sexes can be summarized by estimating the additive genetic (co)variance across shared traits, which, apart from the cross-trait and cross-sex covariances, also includes the cross-sex-cross-trait covariances, e.g., between height in males and weight in females. Using such a multivariate approach, we investigated SD in the genetic architecture of 12 anthropometric, fat depositional, and sex-hormonal phenotypes. We uncovered sexual antagonism (SA) in the cross-sex-cross-trait covariances in humans, most prominently between testosterone and the anthropometric traits - a trend similar to phenotypic correlations. 27% of such cross-sex-cross-trait covariances were of opposite sign, contributing to asymmetry in the SGA. Intriguingly, using multivariate evolutionary simulations, we observed that the SGA acts as a genetic constraint to the evolution of SD in humans only when selection is sexually antagonistic and not concordant. Remarkably, we found that the lifetime reproductive success in both the sexes shows a positive genetic correlation with anthropometric traits, but not with testosterone. Moreover, we demonstrated that genetic variance is depleted along multivariate trait combinations in both the sexes but in different directions, suggesting absolute genetic constraint to evolution. Our results indicate that testosterone drives SA in contemporary humans and emphasize the necessity and significance of using a multivariate framework in studying SD.

Humans↗

PubMedDate not supplied

A twin study of alexithymia.

BACKGROUND: Factors contributing to the development of alexithymia and the nature of alexithymia's relation with trait negative and positive affectivity are unclear. In this study, a twin approach was used to examine the degree of genetic and environmental contributions to the different facets of alexithymia, and the nature of their relations to trait negative and positive affectivity. METHOD: Forty-five monozygotic and 32 same-sex dizygotic twin pairs completed the Toronto Alexithymia Scale-20, the Eysenck Personality Inventory, and a zygosity questionnaire. RESULTS: Model fitting analyses indicated that familial influences contributed significantly to all three facets of alexithymia. Parameter estimates and intraclass correlations suggested, though could not confirm, that it was shared environmental factors that contributed to difficulty identifying and communicating emotions (ID and COM), but shared genetic factors that contributed to externally oriented thinking (EOT). Between-twin cross-trait twin analyses revealed strong correlations between ID and neuroticism, and between COM and extraversion, and suggested that it is shared familial influences which account for these associations. CONCLUSIONS: The results of this study indicate that: (a) the different facets of alexithymia are influenced by familial factors; (b) the previously noted associations between ID and COM and trait affectivity are not merely methodological artifacts; and (c) the associations between ID and COM and trait affectivity are influenced by familial factors. The results also suggest that ID and COM are largely influenced by shared environmental factors, but that EOT is influenced by genetic factors.

Adolescent↗

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↗