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PubMed2025

Using Large Genomic Biobanks to Generate Insights into Genetic Kidney Disease.

Chronic kidney disease (CKD) affects approximately 9% of the global population, leading to increased risks of end-stage kidney disease (ESKD), cardiovascular disease (CVD), and mortality. Patients with CKD are a huge burden on health care resources globally. CKD is a complex condition influenced by a combination of genetic, environmental, and traditional risk factors. Family studies have suggested heritability rates for CKD ranging from 30% to 75%, and large genomic biobank studies have proven essential in identifying genes with substantial effects on CKD risk and in capturing cumulative genetic risk through polygenic risk scores. These biobanks are crucial for discovering new genes associated with kidney health and disease, and their growing size enhances the power to detect novel genetic associations. Integrating multi-omics technologies such as transcriptomics, metabolomics, and proteomics further enriches our understanding of CKD, while advanced computational tools continue to expand our insights into genetic data. Polygenic risk scores, derived from hundreds of genetic variants with small effect sizes, can help identify individuals at high risk of CKD. Genomic biobanks offer valuable opportunities for early identification and personalized treatment of monogenic kidney disorders, such as autosomal dominant polycystic kidney disease and Alport syndrome. These biobanks help fill knowledge gaps, particularly in individuals with milder or asymptomatic presentations who are often underrepresented in traditional studies. Expanding genomic biobank efforts globally, especially in diverse populations, is vital to enhancing our understanding of the genetic underpinnings of kidney disease. This review highlights the significant contributions of genomic biobanks to advancing our comprehension of the genetics of CKD.

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↗
PubMed2026

Hereditary cancer: Germline testing practices across ERN GENTURIS member countries.

Germline genetic testing practices for hereditary cancer vary across the European Reference Network on Genetic Tumour Risk Syndromes (ERN GENTURIS) member countries. We surveyed experts in genetic testing from 20 EU member countries and Norway to assess multi-gene panel usage, availability of genome-wide sequencing, first-tier testing approaches, implementation of polygenic risk scores, and the roles of non-genetic healthcare professionals. National experts and members of the ERN GENTURIS completed a structured questionnaire covering founder germline pathogenic variants (gPV) testing, panel testing for common genetic tumour risk syndromes, use of whole-exome sequencing (WES) and whole-genome sequencing, polygenic risk score implementation, use of formalin-fixed paraffin-embedded tumour samples, laboratory accreditation, and the clinical roles of physicians, genetic counselors and nurses. Significant inter-country heterogeneity was observed. Most countries rely on next-generation sequencing (NGS) multi-gene panels. Founder gPV testing is first-line in a few high-prevalence populations (e.g., BRCA1/2 founders). All 21 countries offer NGS panel tests for hereditary breast and ovarian cancer, and ≥19 countries do so for colorectal and prostate cancers. However, NGS panel size and gene composition exhibit substantial variability. WES is available in 12 countries on a routine basis. Most countries implemented genetic testing on stored tumour tissue from deceased patients. In all countries, clinical geneticists can order germline genetic tests, and in 9 countries, any physician can do so. These findings show differences in accessibility to germline genetic testing of hereditary cancer in Europe. We propose EU-wide guidance via pathways, standards of care, and sharing of best practices to further optimize access to hereditary cancer genetic molecular diagnostics.

Humans↗
PubMed2026

The Continuity Trap in Data Science Health Research.

Secondary use is now the ordinary condition of data science health research rather than an exception to it. Electronic health records collected for clinical care become prediction tools and inputs for generative AI; imaging archives become foundation-model corpora; genomic datasets become resources for polygenic risk scores; and legacy biospecimens become renewable, indefinitely distributable cell lines. Governance has responded by emphasizing verifiable instruments such as provenance logs, repository approvals, broad-consent forms, data-use agreements, model cards, records of processing, and locality-preserving architectures. These instruments are necessary, and they answer real questions about lineage, privacy, institutional responsibility, and accountability, but they are not sufficient to establish that a present use remains ethically justified. We define ethical continuity as the persistence of normatively relevant relationships between the original conditions of data generation or material collection and subsequent downstream uses, such that current uses remain justifiable in light of the expectations, permissions, meanings, and relational obligations present at entrustment. We then define the Continuity Trap as a review-stage governance error in which a salient signal of continuity in one domain is treated as sufficient evidence of ethical continuity overall, causing inquiry into the remaining domains to close prematurely. The trap is not ordinary noncompliance, ethics creep, or a demand for universal rereview; it is a cross-domain inference error that can arise even in careful, good-faith review. We distinguish it from proxy closure, of which it is a continuity-specific subtype, and from Goodhart's and Campbell's laws, which describe how measures degrade once they become targets. We operationalize ethical continuity across 4 domains: provenance, semantics, authorization, and relational standing, developed in our Representational Veracity framework, and we show that these domains can diverge as data are linked, transformed, modeled, and redeployed. We identify the institutional mechanisms-provenance privilege, descriptor sedimentation, authorization fossilization, and community effacement-that cause auditable signals to be overread, and we examine how the US Health Insurance Portability and Accountability Act (HIPAA) of 1996, the General Data Protection Regulation, the European Health Data Space, US Food and Drug Administration guidance, the US National Institute of Standards and Technology (NIST) AI Risk Management Framework, and federated-learning governance can reduce risk while still inducing continuity traps. We apply the framework to consent and nonconsent settings, including public health, immunization, syndromic, and wastewater surveillance, polygenic risk scores, induced pluripotent stem cells, federated learning, and health-related large language models. The policy implication is trigger-based continuity review: rather than rereviewing every reuse, investigators and reviewers should identify the weakest continuity domain at the present data stage and impose a domain-matched safeguard, recorded in a short continuity statement. This reframing is intended for the committees, repositories, funders, and governance bodies that decide whether reuse may proceed, and it matters most in cross-border and low-resource settings. Provenance should begin ethical review; it should not end it.

Data Science↗
PubMed2026

Exploring the shared genetic architecture of sarcopenia using genomic structural equation modeling.

Sarcopenia is a common age-associated condition characterized by the progressive loss of skeletal muscle mass, strength, and physical functionality. While large-scale genome-wide association studies (GWAS) have previously addressed isolated traits of sarcopenia, the multifactorial genetic architecture underlying this condition remains largely undefined. To characterize the common genetic basis of sarcopenia-related traits, genomic structural equation modeling (Genomic-SEM) was implemented. Multiple post-GWAS analytic approaches were integrated to pinpoint susceptibility loci. These analyses encompassed identifying enriched genetic pathways and relevant genomic elements, as well as cell-type-specific enrichment in skeletal muscle satellite stem cells, mesenchymal stem cells, and skeletal muscle satellite cells in limb muscle. Furthermore, based on the integrated GWAS data of sarcopenia-related traits, polygenic risk score (PRS) analysis was conducted to evaluate risk associations at the chromosomal level. A well-fitted Genomic-SEM successfully integrated the GWAS data, revealing the shared genetic architecture of sarcopenia-related traits. We identified 110 single nucleotide polymorphisms (SNPs) reaching genome-wide significance (p&#x2009;<&#x2009;5&#x2009;&#xd7;&#x2009;10-8), of which 9 represent novel discoveries. Subsequent fine-mapping procedures and gene-set analyses identified 15 causal variants alongside 77 candidate susceptibility genes. This study provides a comprehensive genetic characterization of sarcopenia via Genomic-SEM, offering new insights into the etiological pathways underlying sarcopenia.

Sarcopenia↗
PubMed2025

The role of candidate genetic polymorphisms in covid-19 susceptibility and outcomes.

BACKGROUND: This study aims to investigate the association between candidate host genetic polymorphisms and COVID-19 susceptibility, severity, hospitalization, hypoxia, and their combined effect, measured by the polygenic risk score (PRS). METHODS: Three hundred and seventy-six Lebanese participants, comprising 151 controls and 225 cases, were included. Clinical data were obtained from questionnaires and medical records. DNA isolated from peripheral blood was genotyped for ACE1 rs1799752, ACE2 rs2074192, TMPRSS2 rs75603675 and OAS1 rs107746771 using TaqMan assays, and for TMPRSS2 rs35074065 using Sanger Sequencing. Candidate genetic variants were analyzed in association with COVID-19 susceptibility, severity, hospitalization and hypoxia, using univariate and multivariate models. PRS constructed from the weighted sum of variants was evaluated in association with COVID-19 outcomes. RESULTS: In this study, there were no statistically significant differences in the frequencies of candidate variant alleles between cases, controls and within disease outcomes subgroups, after adjustment for confounders. PRS was not associated with COVID-19 susceptibility and hospitalization, it however significantly predicted COVID-19 severity (P&#x2009;=&#x2009;0.01). CONCLUSION: This study highlights the importance of genetic testing for key host genes involved in COVID-19 life cycle and eventually measuring the PRS which proves to be an important tool for prognosis assessment in vulnerable individuals, potentially enhancing patient care.

Polymorphism, Genetic↗
PubMed2026

Development and pilot testing of a prostate cancer polygenic risk report.

BACKGROUND: Polygenic risk scores (PRS) are increasingly being incorporated into clinical care, yet optimal strategies for communicating PRS results to patients and clinicians remain undefined. Effective report design is critical to ensure comprehension and appropriate use, particularly for complex conditions such as prostate cancer where screening decisions are nuanced. We developed and pilot tested patient-facing materials to communicate integrated polygenic and monogenic risk for prostate cancer in the context of a randomized clinical trial. METHODS: We designed a summary report and accompanying Frequently Asked Questions (FAQ) page to communicate prostate cancer genetic risk within the Prostate Cancer, Genetic Risk, and Equitable Screening Study (ProGRESS). Materials were developed through an iterative, multidisciplinary process informed by existing literature on genomic risk communication. We conducted semi-structured interviews with a national sample of eight men eligible for prostate cancer screening to evaluate comprehension, interpretation of visual elements, perceived usefulness, and preferences for improvement. Interviews were transcribed and analyzed using reflexive thematic analysis. RESULTS: Participants generally found the summary report and FAQ page understandable and visually engaging. Graphical displays of absolute risk, particularly pictograph arrays, facilitated comprehension and helped contextualize risk. Visual cues such as color and bold formatting effectively directed attention to key information, with red coloring perceived as particularly salient for high-risk results. In contrast, more complex visualizations, including bell curves and incidence curves, were frequently misunderstood or not interpreted as intended. Participants expressed a desire for clearer guidance regarding next steps and additional accessible information, suggesting supplementary resources such as hyperlinks or QR codes. Concerns about readability included small font size and high text density. CONCLUSIONS: In this qualitative pilot study, patient-facing materials for communicating prostate cancer PRS were generally well received, with specific design features such as simple visualizations and clear formatting enhancing understanding. Findings highlight the importance of intuitive risk displays and actionable guidance in PRS reporting. These results provide practical insights to inform the design of genomic risk reports as PRS-based prostate cancer screening approaches move toward clinical implementation. TRIAL REGISTRATION: ClinicalTrials.gov NCT05926102; date of registry: July 3, 2023.

Aged↗
PubMed2026

Polygenic Risk Based Detection and Treatment of Subclinical Coronary Atherosclerosis in the PROACT Clinical Trials.

BACKGROUND: Coronary artery disease (CAD) polygenic risk scores (PRS) may identify individuals at elevated genetic risk "flying under the radar" in contemporary practice. The aims of the PROACT (Polygenic Risk Based Detection and Treatment of Subclinical Coronary Atherosclerosis) trials are to prospectively identify these individuals, quantify subclinical coronary plaque, and slow its progression with pharmacologic interventions. OBJECTIVES: The aim of this study is to report interim feasibility and implementation findings from PROACT, a genotype-first, biobank-enabled trial, characterizing eligibility yield, callback engagement, and subclinical coronary atherosclerosis on coronary computed tomographic angiography among individuals with high CAD PRS. METHODS: Within a hospital-based biobank, adults 40 to 75 years of age with high CAD PRS, without cardiovascular disease, and not on lipid-lowering therapy were invited. The authors characterize 2,495 eligible individuals with high CAD PRS, report on the feasibility and early operational outcomes of a genotype-first callback strategy for a clinical trial in the first 1,314 invited, and describe plaque prevalence by age and sex in the first 204 participants using coronary computed tomographic angiography. RESULTS: Among 64,092 genotyped participants, 2,495 (3.9%) were eligible and had high CAD PRS despite low clinical risk (median 10-year pooled cohort equations risk for atherosclerotic cardiovascular disease 3%; Q1-Q3: 1%-8%). Recruitment showed high engagement: among 1,314 invited individuals, 283 (21.5%) opted in, and 204 (15.5%) completed baseline imaging. Compared with participants who did not opt in, those who opted in had higher specialty care engagement and lived closer to the study site. Analysis of the first 204 participants enrolled by January 31, 2025 (mean age 56.3 &#xb1; 8.5 years, 69% women), showed that despite the low clinical risk and favorable cardiovascular health (mean Life's Essential 8 score 73.3 &#xb1; 11.5 vs the U.S. average of &#x223c;65), one-half the participants (102 of 204) had subclinical plaque. Subclinical plaque prevalence was 76.2% in men and 38.3% in women and was high across age groups. CONCLUSIONS: These exploratory findings highlight the feasibility of implementing genotype-first recruitment for prevention trials and reveal a large proportion of "silent" high-genetic risk individuals with subclinical plaque for whom pharmacotherapy could be beneficial but who remain undetected by standard clinical assessments. (Polygenic Risk Based Detection of Subclinical Coronary Atherosclerosis and Change in Cardiovascular Health [PROACT 1], NCT05819814; Polygenic Risk Based Detection of Subclinical Coronary Atherosclerosis and Intervention With Statin and Colchicine [PROACT 2], NCT05850091).

Adult↗
PubMed2026

Genetic evidence for causality of late chronotype on metabolic syndrome in East Asians and Europeans.

CONTEXT: The impact of chronotype-defined as an individuals' inherent preference of sleep timing-and its genetic determinants on metabolic syndrome (MetS) has been less studied. OBJECTIVE: This study investigated the causal relationship between late chronotype and MetS using Mendelian randomization (MR) analysis, based on data from the Taiwan Biobank (TWB) and parallel analyses in the UK Biobank (UKB). METHODS: A total of 36,845 participants from TWB served as the discovery cohort, and 235,639 participants from UKB served as the replication cohort. Late chronotype was defined in TWB as a preference for bedtime after midnight, and in UKB as self-report as being an 'evening' person. The association between late chronotype and MetS, along with its components, was evaluated in TWB, and validated in UKB. Genome-wide association analyses for late chronotype were first conducted in TWB and then meta-analyzed with UKB. Polygenic risk scores (PRS) for late chronotype were constructed and tested for association with MetS. Causality between late chronotype and MetS was examined using one-sample MR analysis in TWB and validated in UKB. RESULTS: Late chronotype was significantly associated with MetS, as well as with central obesity, hyperglycemia, and hypertriglyceridemia, in both TWB and UKB (all P&#xa0;<&#xa0;0.0083, considering Bonferroni correction). The constructed PRS of late chronotype also showed significant associations with MetS and several of its components (several P&#xa0;<&#xa0;0.0083, considering Bonferroni correction). Findings from the one-sample MR analysis indicated a potential causal effect of late chronotype on MetS. CONCLUSIONS: This study provides evidence of a robust association between late chronotype and MetS across populations of diverse ancestry, including Taiwanese and European.

Humans↗
PubMed2026

Deep learning and statistical methods identify novel asthma risk variants in Europeans.

BACKGROUND: Asthma is a common heritable respiratory disorder with a complex genetic basis. Although large-scale genome-wide association studies have identified many risk loci, the full spectrum of its polygenic architecture remains to be defined. OBJECTIVE: We refined the genetic landscape of asthma in individuals of European ancestry and improve polygenic risk prediction through statistical and deep learning-based methods. METHODS: We conducted the largest genome-wide association study meta-analysis of asthma in individuals of European ancestry, combining data from the Global Biobank Meta-analysis Initiative (121,940 cases, 1,254,131 controls) and the Million Veteran Program (36,823 cases, 398,278 controls). To enhance discovery, we applied pleiotropy-informed multitrait analysis and conditional false discovery rate approaches, each incorporating eosinophil counts as a secondary trait. In parallel, we used a Transformer-based deep learning framework to further prioritize variants and improve polygenic risk prediction. RESULTS: The meta-analysis identified 69 independent genome-wide significant loci (P&#x2009;<&#x2009;5 &#xd7; 10-8) not previously reported in asthma. Multitrait analysis of genome-wide association studies, conditional false discovery rate, and deep learning approaches uncovered additional candidate loci. Functional annotation and expression quantitative trait locus mapping implicated novel genes in immune regulation, airway remodeling, and metabolic processes. Polygenic risk score models derived from deep learning-prioritized variants outperformed those based on conventional genome-wide association study and standard statistical approaches. CONCLUSIONS: Our study yields a comprehensive map of asthma-associated loci in European ancestry populations, improves genetic risk prediction, and informs future mechanistic studies.

Humans↗
PubMed2026

Pharmacogenomics of antipsychotic-induced weight gain: A systematic review.

BACKGROUND: Antipsychotic-induced weight gain (AIWG) is a major clinical concern, affecting approximately 30% of patients. Clinical predictors explain only part of AIWG risk. Genetic and molecular variations are hypothesized to contribute to susceptibility. The purpose of this review is to summarize recent results to identify replicated and novel findings. STUDY DESIGN: Applying PRISMA guidelines, we searched MEDLINE, Embase, and PsycINFO (May 2018-May 2026) for studies on genetic and molecular associations with AIWG, extending our prior review. Reviews, editorials, and conference abstracts were excluded. We extracted study characteristics (design, diagnosis, antipsychotic exposure, sample size, ancestry, genetic variants, and AIWG outcomes) (e.g., &#x2265;7% weight gain, BMI change). RESULTS: Fifty-three studies met inclusion criteria. In candidate gene studies, the most consistently replicated genes associated with AIWG were observed for DRD2, HTR2C, and MC4R. Multiple novel associations were identified by genome-wide association studies (GWAS) (e.g., MAP2K1, ZDBF2, PEPD), polygenic risk scores (PRS) (e.g., body mass index PRS), gene expression (e.g., CYP3A4, EP300), and epigenetic analyses (e.g., cg12034943 at CRTC1). CONCLUSIONS: Polymorphisms in candidate genes related to neurotransmission and appetite regulation continue to be investigated for associations with AIWG, while novel findings have emerged from GWAS, gene expression, and epigenetic studies. Evidence remains inconsistent due to limited replication, methodological variability, sparse ancestry data, and geographical underrepresentation. No single genetic variant is ready for clinical use, and multi-omic and multi-ancestry models are needed to improve prediction and clinical utility.

Humans↗
PubMed2026

Impacts of host genetics on gut microbiome composition in Alzheimer's disease.

BACKGROUND: Host-microbiome interactions play essential roles in the development of Alzheimer's disease (AD), yet&#xa0;the host genetic impacts on gut microbial alterations in AD remain poorly understood. RESULTS: Here, we simultaneously profiled host genotype and gut microbiome in 252 Chinese individuals with varying degrees of cognitive disability. Using the latent Dirichlet allocation topic model, we identified the Anaerostipes-enriched enterosignature (ES-Ana) at the microbial subgroup level as significantly negatively associated with cognitive disability, which could be recapitulated in external cohorts. With the whole-genome sequencing data, we performed microbiome genome-wide association studies for the ES-Ana relative abundance. We prioritized 41 lead genetic variants and confirmed that the high ES-Ana relative abundance showed a negative correlation with the polygenic risk score of AD, indicating&#xa0;its protective effect against AD. Furthermore, we identified 174 ES-Ana-associated genes, which&#xa0;are&#xa0;enriched in AD-related biological functions and phenotypes, and exhibite pervasive underexpression in glial cells during brain aging. CONCLUSIONS: In summary, our study reveals the complex genetic effects on the gut microbiota in AD, and provides novel evidence for the roles of the gut-brain axis in AD. Video Abstract.

Alzheimer Disease↗
PubMed2025

Validation of a genome-wide polygenic score for body mass index in South Asians.

Obesity is a complex disorder, manifested by the interaction of inherited and environmental factors and modulated by a person's lifestyle habits. India has witnessed more than a two-fold increase in the number of overweight adults in the last 30 years. The polygenic risk score (PRS) quantitatively measures an individual's risk for common diseases. The PRS for obesity have been validated in the Caucasian population but not in the South Asian (SAS) population. In this study, we benchmarked and validated the existing genome-wide PRS model of obesity with 2.1 million variants in the SAS population. We analyzed a total of 14,263 individuals from three different South Asian cohorts. We compared the risk score with the body mass index (BMI) categories (underweight, normal weight, overweight, and obese) in all three cohorts. High PRS was associated with increased BMI in all the three cohorts. This study also compared validation results from another population-specific PRS model for the BMI. We conclude that high PRS is associated with high BMI in South Asians. Our study suggests that the PRS score can perhaps be an early predictor of overweight and obesity in the South Asian population.

South Asian↗
PubMed2026

Divergent autoimmune genetic landscapes in diabetic and non-diabetic individuals.

BACKGROUND: Type 2 diabetes (T2D) and autoimmune diseases are complex disorders shaped by genetic, immunological, and environmental factors. However, differences in genetic susceptibility to autoimmune disorders between diabetic and non-diabetic individuals remain poorly understood, particularly within the Indian population. This study aimed to compare polygenic susceptibility to multiple autoimmune diseases between individuals with and without T2D from Gujarat, India. RESULTS: Polygenic risk scores were evaluated for eleven autoimmune diseases in 474 individuals, including 99 diabetics and 375 non-diabetics. Significant differences were observed for systemic lupus erythematosus and rheumatoid arthritis. Individuals with diabetes showed a higher genetic risk for systemic lupus erythematosus, whereas non-diabetic individuals exhibited a higher genetic risk for rheumatoid arthritis. Pathway enrichment analysis of disease-associated genetic variants revealed significant involvement of immune-related pathways, including antigen processing and presentation, T-helper cell differentiation, and immune responses to viral infections. CONCLUSIONS: The findings reveal disease-specific differences in genetic susceptibility to autoimmune disorders between diabetic and non-diabetic individuals. These results highlight the complex immunogenetic relationship between type 2 diabetes and autoimmune diseases and provide insight into shared and distinct immune pathways within an Indian population. Such information may support future efforts in risk stratification and precision medicine approaches for immune-related comorbidities in diabetes.

Humans↗
PubMed2026

Additive value of polygenic risk and family history for coronary heart disease risk stratification in two diverse US cohorts.

Whether polygenic risk, monogenic familial hypercholesterolemia (FH), and family history (FamHx) are additively informative for coronary heart disease (CHD) risk prediction across self-identified race/ethnicity (SIRE) groups has not been established. In two diverse cohorts-Electronic Medical Records and Genomics (eMERGE) phase IV (eIV; n = 19,348) and All of Us (AoU; n = 239,645)-we quantified the associations of a polygenic risk score (PRSCHD), pathogenic/likely pathogenic variants in genes associated with FH, and FamHx with CHD and evaluated their incremental value when added to the pooled cohort equations (PCEs). CHD was defined as myocardial infarction, unstable angina, or coronary revascularization. We modeled associations with multivariable logistic regression (prevalent CHD in eIV) and Cox proportional hazards (incident CHD in AoU) and characterized predictive performance with the c-statistic and reclassification and decision-curve net benefits across actionable 10-year risk thresholds. The effects of PRSCHD and FamHx were independent and additive in both cohorts and consistent across White, Black, and Latino SIRE groups. In eIV, adding PRSCHD and FamHx to the PCE increased the c-statistic for prevalent CHD from 0.719 to 0.753 (p-diff = 9.1 &#xd7; 10-3) and reclassified 18.8% of participants at the 7.5% 10-year threshold, yielding approximately 4 additional true-positive CHD identifications per 1,000 screened. Net benefit gains were observed between the 7.5% and 10% thresholds across all three SIRE groups. In conclusion, PRSCHD and FamHx were independently and additively associated with CHD across major SIRE groups in two diverse cohorts in the United States (US), motivating the addition of these factors to clinical risk algorithms.

Humans↗
PubMed2025

Prematurity and Genetic Liability for Autism Spectrum Disorder.

BACKGROUND: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by diverse presentations and a strong genetic component. Environmental factors, such as prematurity, have also been linked to increased liability for ASD, though the interaction between genetic predisposition and prematurity remains unclear. This study aims to investigate the impact of genetic liability and preterm birth on ASD conditions. METHODS: We analyzed phenotype and genetic data from two large ASD cohorts, the Simons Foundation Powering Autism Research for Knowledge (SPARK) and Simons Simplex Collection (SSC), encompassing 78,559 individuals for phenotype analysis, 12,519 individuals with genome sequencing data, and 8,104 individuals with exome sequencing data. Statistical significance of differences in clinical measures was evaluated between individuals with different ASD and preterm status. We assessed the rare variants burden using generalized estimating equations (GEE) models and polygenic load using ASD-associated polygenic risk score (PRS). Furthermore, we developed a machine learning model to predict ASD in preterm children using phenotype and genetic features available at birth. RESULTS: Individuals with both preterm birth and ASD exhibit more severe phenotypic outcomes despite similar levels of genetic liability for ASD across the term and preterm groups. Notably, preterm ASD individuals showed an elevated rate of de novo variants identified in exome sequencing (GEE model, p=0.005) in comparison to the non-ASD preterm group. Additionally, a GEE model showed that a higher ASD PRS, preterm birth, and male sex were positively associated with a higher predicted probability for ASD, reaching a probability close to 90% in SPARK. Lastly, we developed a machine learning model using phenotype and genetic features available at birth with limited predictive power (AUROC = 0.65). CONCLUSIONS: Preterm birth may exacerbate the multimorbidity present in ASD, which was not due to the ASD genetic factors. However, increased genetic factors may elevate the likelihood of a preterm child being diagnosed with ASD. Additionally, a polygenic load of ASD-associated variants had an additive role with preterm birth in the predicted probability for ASD, especially for boys. We propose that incorporating genetic assessment into neonatal care could benefit early ASD identification and intervention for preterm infants.

Autism Spectrum Disorder↗