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Results for “Genetic Risk Score”

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PubMed2026

Rare variant effect estimation and polygenic risk prediction.

Due to their low frequency, estimating the effects of rare variants is challenging. Here we propose RareEffect, a method that first estimates gene-based or region-based heritability and then each variant effect size using an empirical Bayes approach. Our method uses a variance component model, which is popular in rare variant tests, and is designed to provide two levels of effect sizes-gene/region level and variant level-that can provide better interpretation. To adjust for the case-control imbalance in phenotypes, our approach uses a fast implementation of the Firth bias correction. We demonstrate the accuracy and computational efficiency of our method through extensive simulations and analysis of UK Biobank whole-exome sequencing data for 100 traits. Additionally, we show that the effect sizes obtained from our model can be leveraged to improve polygenic score performance, thereby outperforming recently developed methods for rare variant polygenic scoring.

Humans↗

PubMed2026

Context-specific genetic effects inform endotypes and treatment in asthma.

BACKGROUND: Asthma has heterogeneous risk factors, subtypes, and treatments. It is often unclear how to stratify this heterogeneity in scientific studies and clinical care. Genetics could explain root causes of this clinical heterogeneity, called endotypes, but prior studies have used models that are not designed for complex diseases like asthma. OBJECTIVE: We aimed to find genetic effects that partly explain different asthma endotypes. METHODS: We used recent powerful and robust statistical models of context-specific genetic effects in complex traits. We identified genetic subtypes by clustering clinical asthma features in a case-control cohort, GALA II. We replicated the genetic endotypes in the UK Biobank with gene-context interaction tests. RESULTS: Asthma-associated single nucleotide polymorphisms, polygenic scores, and genome-wide heritability revealed subtype-specific genetic endotypes correlated with type 2 inflammation, allergy, and neuroticism. We validated the type 2 associations with molecular data including nasal RNA sequencing. In the UK Biobank, we replicated these endotypes and found they interact with several polygenic scores and drug-relevant genes. CONCLUSION: Our results show how context-specific genetic effects can unravel biomedically meaningful endotypes of complex disease and suggest novel precision treatment strategies.

Humans↗

PubMed2024

Genetic Analysis of Asymptomatic Antinuclear Antibody Production.

OBJECTIVE: Antinuclear antibodies (ANA) are detected in up to 14% of the population, and many individuals with ANA are asymptomatic. The literature on the genetic contribution to asymptomatic ANA positivity is limited. In this study, we aimed to perform a genome-wide association study of asymptomatic ANA positivity in multiple populations. METHODS: Asymptomatic individuals who were either ANA positive or ANA negative from the All of Us Research Program were included in this study, selecting those with an ANA test performed by immunofluorescence and no evidence of autoimmune disease. Imputation was performed, and a multipopulation meta-analysis including approximately 6 million single-nucleotide polymorphisms (SNPs) was conducted. Genome-wide SNP-based heritability was estimated using the Genome-wide Complex Trait Analysis software. A cumulative genetic risk score for lupus was constructed using previously reported genome-wide significant loci. RESULTS: A total of 1,955 asymptomatic ANA positive and 3,634 asymptomatic ANA negative individuals across three populations were included. The multipopulation meta-analysis revealed SNPs with a suggestive association (P <1 × 10-5) across 8 different loci, but no genome-wide significant loci were identified. A gene variant upstream of HLA-DQB1, (rs17211748, P = 1.4 × 10-6, odds ratio 0.82, 95% confidence interval 0.76-0.89), showed the most significant association. The heritability of asymptomatic ANA positivity was estimated to be 24.9%. Individuals who were asymptomatic and ANA positive did not exhibit increased cumulative genetic risk for lupus compared with individuals who were ANA negative. CONCLUSION: ANA production is not associated with significant genetic risk and is primarily determined by environmental factors.

Humans↗

PubMed2026

Gene-Temperature Interactions and Risk of Childhood Acute Lymphoblastic Leukemia.

BACKGROUND: High ambient temperature in early pregnancy has been linked to an increased risk of childhood acute lymphoblastic leukemia (ALL). To better understand biological mechanisms, the current study evaluated potential interaction between temperature and genetic characteristics. METHODS: We used data from California birth records (1982-2008) and California Cancer Registry (1988-2011) to identify ALL cases (n=3,353) diagnosed ≤14 years of age and non-cancer controls (n=3,530) matched 1:1 on sex, race, ethnicity, and birth year and month. Weekly ambient temperatures throughout pregnancy were assessed on a 1-km grid around the birth address, while genetic data were available from a genome-wide association study using neonatal blood spots. We evaluated the association between ambient temperature and ALL risk by quartiles of established genetic risk score for ALL. Next, we formally tested gene-temperature interactions in the association with ALL, correcting for multiple testing, for genes previously identified with epigenetic changes due to both temperature and ALL. All analyses were adjusted for potential confounders. RESULTS: The elevated risk of ALL per 5 °C increase of weekly mean ambient temperature, confined to early pregnancy, was more pronounced among children with the lowest genetic susceptibility to ALL, especially among Latino children (first quartile: odds ratio [OR] = 1.50, 95% confidence interval [CI]: 1.14-1.97); fourth quartile: OR=1.03, 95% CI: 0.83-1.28). There were significant interactions (p<0.002) between ambient temperature and polymorphisms in BNC1 among non-Latino White children, and suggestive interactions (p<0.05) with TBPL2 and NRXN1 in the full population. CONCLUSIONS: Our findings suggest that there may be interactions between ambient temperature in early pregnancy and offspring genotype in the risk of childhood ALL. IMPACT: If replicated, these findings could help elucidate the biological mechanisms linking high ambient temperature in early pregnancy and the risk of childhood ALL.

Journal Article↗

PubMed2026

Assessing the de novo paradigm in sporadic early-onset Alzheimer disease trios.

The genetic architecture of sporadic Early-Onset Alzheimer Disease (sEOAD, onset ≤65 years) remains largely unknown. To assess the de novo mutation (DNM) hypothesis, we performed a nationwide recruitment of 37 novel sEOAD patients-unaffected parents trios. After assessing known monogenic genes, we performed trio-based exome sequencing and jointly analyzed novel trios with 12 previously reported ones. Of these, we selected 16 trios for genome sequencing. We identified three patients with a pathogenic DNM in APP or PSEN1. Then, from the 46 remaining trios, we identified 38 non-synonymous coding DNM and 4 de novo copy number variants (CNVs) in exome data. Four DNM (2 novel, in SPHK2 and DDR1) and bi-allelic inherited variants in two genes affected Alzheimer disease-related genes. No significant burden of rare coding variants in exome/genome data from 5643 EOAD cases and 16097 controls was identified using nested windows centered on each DNM position, at the transcript level. From genome data, one non-coding DNM was predicted to affect splicing in an AD-associated gene, PINX1. Overall, 48% probands carried ≥1 inherited risk factor with odds ratio (OR) > 1.5 and GWAS-defined Genetic Risk Scores (GRS) distribution was more consistent with random distribution than enrichment in higher scores in probands. We confirm that DNMs in known monogenic genes explain sEOAD in a minority of cases, while candidate DNMs in other genes might account for a small proportion of additional cases. The majority of sEOAD patients may have a complex etiology including multiple inherited variants, however, GRS might not explain most of its genetic component.

Humans↗

PubMed2025

Germline Variants Influence Chronic Liver Disease Progression through Distinct Pathways.

Cirrhosis and hepatocellular carcinoma (HCC) are long-term complications of chronic liver disease (CLD). In this large multi-ancestry genome-wide association study of all-cause cirrhosis (35,481 cases, 2.36M controls) and HCC (6,680 cases, 1.76M controls), we identified 27 loci associated with cirrhosis (10 novel) and 11 with HCC (three novel). Three novel cirrhosis loci were replicated in independent cohorts (e.g. FGF21, RPTOR, and IFNL3/4). Fifteen cirrhosis loci exhibited differential effects on cirrhosis risk via underlying etiologies, and six HCC loci influenced HCC risk indirectly via cirrhosis. In a gene-burden analysis of rare variants from whole-genome sequencing data in the VA Million Veteran Program (n=102,677), we identified GSTA5 as a novel cirrhosis-associated gene, while APOB and ATP9B were associated with and replicated for HCC. A high genetic risk score for cirrhosis was associated with a nearly doubled risk of CLD progressing to cirrhosis (HR=1.94, P=2×10-68) and of cirrhosis progressing to HCC (HR=1.65, P=7×10-08). Finally, among individuals with chronic hepatitis C who underwent antiviral therapy, cirrhosis risk was modified by variants in PNPLA3, IFNL3/4, and CD81 following pegylated interferon-α therapy, and by APOE lead variant following direct-acting antiviral therapy. These findings provide new insights into the complex genetic architecture of CLD progression with potential clinical and therapeutic implications.

Journal Article↗

PubMed2026

Diabetes related genetics and outcomes after total pancreatectomy with islet autotransplantation.

CONTEXT: Approximately 20% of patients with chronic pancreatitis achieve insulin independence one year after total pancreatectomy with islet autotransplantation (TPIAT), an outcome associated with younger age and lower pre-transplant HbA1c. The contribution of diabetes-related genetics to TPIAT outcomes is unknown. Genetic risk scores (GRS) are associated with C-peptide levels in type 1 (T1D) and type 2 (T2D) diabetes. OBJECTIVE: To evaluate whether T1D and T2D GRS and partitioned metabolic polygenic scores (PGS) are associated with diabetes before TPIAT and metabolic outcomes one year after transplant. METHODS: We genotyped 318 patients with chronic pancreatitis undergoing TPIAT in the multicenter "Advancing Treatment for Pancreatitis: A Prospective Observational Study of TPIAT" using a global screening array. Regression models assessed associations of T1D- and T2D-GRS and metabolic PGS with diabetes outcomes one year after TPIAT. RESULTS: Participants were 29±17 years old, 61% female, 84% non-Hispanic White, and 13% had diabetes prior to transplant. T2D-GRS was higher among those with pre-TPIAT diabetes (-0.6±0.3 versus -0.8±0.2, p=0.003) and was associated with higher pre-transplant HbA1c (β=0.19 % per 1-SD [95% CI:0.04, 0.33], p = 0.011). Adjusted for genotype-derived ancestry and islet equivalents/kg, the beta-cell PGS was associated with higher insulin dose (β=0.05 units/kg/day per 1-SD, [95%CI:0.01, 0.09], p=0.016) and higher insulin dose adjusted A1c (IDAA1c) (β=0.34[95%CI:0.03, 0.67], p=0.029) 1-year post-transplant. The lipodystrophy PGS was associated with higher IDAA1c (β=0.38 [95%CI:0.06, 0.69], p=0.019) and lower fasting C-peptide (β=-0.09 [95%CI:-0.18, 0], p=0.041) at 1 year. CONCLUSIONS: T2D genetic risk is associated with diabetes prior to TPIAT, and beta cell and lipodystrophy PGS are associated with post-TPIAT diabetes outcomes. Diabetes-related genetics may influence transplantation outcomes and inform metabolic heterogeneity.

C-peptide↗

PubMed2026

Delayed maturation of the milk microbiome in women with type 1 diabetes.

AIMS/HYPOTHESIS: The breastmilk microbiome plays a crucial role in gut microbial colonisation and immune development, but little is known about how it is influenced by type 1 diabetes. METHODS: We conducted a longitudinal 16S rRNA gene sequencing study of milk from women with type 1 diabetes (n=69 pregnancies; 174 samples) and women who did not have type 1 diabetes (n=49 pregnancies; 123 samples), collected at seven timepoints from birth to 15 months postpartum. Alpha diversity (richness, inverse Simpson evenness) was analysed by generalised linear mixed models, beta diversity was analysed by Bray-Curtis dissimilarities and PERMANOVA, and differential abundance was analysed by limma. Additionally, we examined associations with maternal genetic risk score (GRS), maternal HLA type, glycaemic management (HbA1c) and breastmilk secretory IgA (sIgA), and performed a parallel analysis for the infant stool microbiome. RESULTS: A significant interaction between type 1 diabetes status and timepoint was observed for alpha diversity, both richness (p=0.01) and inverse Simpson diversity (p=0.003), indicating distinct temporal trajectories between women with and without type 1 diabetes. In those without type 1 diabetes, richness increased significantly between birth and 1 week postpartum, but this early increase was delayed in women with type 1 diabetes to between 1 week and 3 months postpartum (p=0.002). Beta diversity analysis revealed earlier and more extensive compositional shifts in women without type 1 diabetes compared to those with type 1 diabetes. These differences persisted after adjusting for Caesarean delivery, BMI, parity and infant sex, and were not attributable to a delay in initiating breastfeeding. Taxa with delayed enrichment in women with type 1 diabetes included Streptococcus spp. and Rothia mucilaginosa, which metabolise human milk oligosaccharides to short-chain fatty acids to promote development of the infant's gut barrier and immune system. Maternal GRS, HLA, HbA1c or sIgA were not associated with milk microbiota diversity trajectories. In infant stool samples, alpha diversity did not differ between exposure groups, and showed no evidence of delayed maturation. Beta diversity revealed an early compositional shift between birth and 1 week postpartum only in infants born to women without type 1 diabetes. Similarly, significant taxonomic changes between birth and 1 week postpartum were detected only in infants born to women without type 1 diabetes, but with some taxa differing between exposure groups at 1 week. CONCLUSIONS/INTERPRETATION: Maternal type 1 diabetes is associated with delayed early maturation of the breastmilk microbiome. Early compositional differences in microbiota restructuring were also observed in the infant gut, partially mirroring the pattern in the milk microbiome; however, sustained differences in infant gut microbiota diversity were not detected. Further investigation could determine whether these changes affect development of the infant's gut and immune system.

Humans↗

PubMed2024

Fine-mapping the CYP2A6 regional association with nicotine metabolism among African American smokers.

The nicotine metabolite ratio (NMR; 3'hydroxycotinine/cotinine) is a stable biomarker for CYP2A6 enzyme activity and nicotine clearance, with demonstrated clinical utility in personalizing smoking cessation treatment. Common genetic variation in the CYP2A6 region is strongly associated with NMR in smokers. Here, we investigated this regional association in more detail. We evaluated the association of CYP2A6 single-nucleotide polymorphisms (SNPs) and * alleles with NMR among African American smokers (N = 953) from two clinical trials of smoking cessation. Stepwise conditional analysis and Bayesian fine-mapping were undertaken. Putative causal variants were incorporated into an existing African ancestry-specific genetic risk score (GRS) for NMR, and the performance of the updated GRS was evaluated in both African American (n = 953) and European ancestry smokers (n = 933) from these clinical trials. Five independent associations with NMR in the CYP2A6 region were identified using stepwise conditional analysis, including the deletion variant CYP2A6*4 (beta = -0.90, p = 1.55 × 10-11). Six putative causal variants were identified using Bayesian fine-mapping (posterior probability, PP = 0.67), with the top causal configuration including CYP2A6*4, rs116670633, CYP2A6*9, rs28399451, rs8192720, and rs10853742 (PP = 0.09). Incorporating these putative causal variants into an existing ancestry-specific GRS resulted in comparable prediction of NMR within African American smokers, and improved trans-ancestry portability of the GRS to European smokers. Our findings suggest that both * alleles and SNPs underlie the association of the CYP2A6 region with NMR among African American smokers, identify a shortlist of variants that may causally influence nicotine clearance, and suggest that portability of GRSs across populations can be improved through inclusion of putative causal variants.

Adult↗

PubMed2024

Stroke genetics and how it Informs novel drug discovery.

INTRODUCTION: Stroke is one of the main causes of death and disability worldwide. Nevertheless, despite the global burden of this disease, our understanding is limited and there is still a lack of highly efficient etiopathology-based treatment. It is partly due to the complexity and heterogenicity of the disease. It is estimated that around one-third of ischemic stroke is heritable, emphasizing the importance of genetic factors identification and targeting for therapeutic purposes. AREAS COVERED: In this review, the authors provide an overview of the current knowledge of stroke genetics and its value in diagnostics, personalized treatment, and prognostication. EXPERT OPINION: As the scale of genetic testing increases and the cost decreases, integration of genetic data into clinical practice is inevitable, enabling assessing individual risk, providing personalized prognostic models and identifying new therapeutic targets and biomarkers. Although expanding stroke genetics data provides different diagnostics and treatment perspectives, there are some limitations and challenges to face. One of them is the threat of health disparities as non-European populations are underrepresented in genetic datasets. Finally, a deeper understanding of underlying mechanisms of potential targets is still lacking, delaying the application of novel therapies into routine clinical practice.

Humans↗

PubMed1979

The prediction of the course of minor psychiatric disorders.

Recent studies have suggested that psychosocial factors play an important part in the prediction of the course of minor psychiatric disorders. Fifty-nine new psychiatric out-patients suffering from minor disorders were assessed, using standardized clinical and social interviews, and 52 were followed up after one year and the clinical assessment repeated. Social and clinical factors were equally important predictors of the number of months ill in the survey year, but social and constitutional variables were superior in the prediction of percentage change in symptoms over the year. The results of correlation, factor and multiple regression analyses suggest that the course of minor psychiatric disorder is best predicted by three sets of variables which are, in order of importance, the patient's material social circumstances, his clinical symptoms and his 'genetic risk' scores.

Adolescent↗

PubMed2026

Integrative genomic and transcriptomic analysis of hypertension in a Taiwanese population.

OBJECTIVES: Hypertension is highly prevalent in Asian populations and represents a major cardiovascular risk factor. However, most genome-wide association studies (GWASs) and transcriptome-wide association studies (TWASs) have focused primarily on Caucasian cohorts. This study aimed to identify genetic loci and gene expression signatures associated with hypertension in an Asian population. METHODS: We analyzed 10 739 hypertensive patients and 49 668 controls from the Taiwan Biobank, testing 4 512 191 genome-wide single nucleotide polymorphisms (SNPs). Integrated GWAS, TWAS, and expression quantitative trait locus (eQTL) analyses were conducted to characterize genetic risk. Additionally, a polygenic risk score (PRS) was constructed using a split-sample design to evaluate genetic risk stratification. RESULTS: We identified 14 loci significantly associated with hypertension, including a novel locus at 5p13.1. eQTL analysis linked this locus to DAB2 expression in whole blood. TWAS detected 55 hypertension-associated genes, with 20 (36%) overlapping GWAS loci. Several novel genes outside GWAS loci, including FBXL15, KCNIP2, and CRIP3, were highly significant and implicated in vascular biology and hypertension mechanisms. PRS analysis effectively differentiated hypertension risk, with individuals in the top 10% showing a > 3.5-fold increased risk compared to the bottom 10%. CONCLUSIONS: Our findings provide new insights into the genetic and transcriptomic landscape of hypertension in Asians. The identification of novel loci and genes advances understanding of disease biology and may guide precision medicine approaches for risk prediction and therapeutic development.

Female↗

PubMed2026

bioETH-PRS: confidential polygenic risk scoring with smart contracts on an FHE-enabled blockchain.

Polygenic risk scores (PRSs) aggregate genetic effect estimates to predict disease susceptibility, yet calculating one through an external service can require exposing raw genotype data. Homomorphic encryption hides those data during the calculation but, in prior work, still places a designated evaluator in a position of trust. We present bioETH-PRS, a protocol that replaces the evaluator with publicly auditable smart contracts on a blockchain supporting Fully Homomorphic Ethereum Virtual Machine (fhEVM). Using integer-exact encrypted arithmetic, bioETH-PRS computes the PRS dot product entirely in the encrypted domain, so genotype dosages and, at the model provider's discretion, the GWAS weights stay hidden from the parties performing the computation. A fixed-point encoding represents signed weights as nonnegative integers within a bound that rules out overflow, recovering the score to the precision of the published weights. A four-contract architecture separates data custody, model publication, computation, and output release, and supports both a classic path that stores encrypted inputs and an appreciably cheaper streaming path that discards them. A release oracle can return a randomized risk category instead of the raw score, limiting what a repeated querier learns. Prototype evaluation on real GWAS fixtures, including a run on a public testnet, shows cost growing linearly with variant count and suggests the approach may be practical where transaction fees are low. Trust is redistributed rather than removed: the system still depends on the contracts, the blockchain, and the fhEVM services. We evaluate additive models of moderate size, not genome-wide or clinical use.

Blockchain↗

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↗

PubMed2026

Meta-ERS: an exposome-based risk score using non-genetic factors to guide osteoporosis prevention.

BACKGROUND: Osteoporosis is influenced by both genetic and environmental factors, yet the relative contribution of the exposome remains unclear. This study aimed to systematically identify non-genetic exposures related to osteoporosis and develop an exposome risk score (ERS) to evaluate individual osteoporosis susceptibility. METHODS: We conducted an exposome-wide analysis of 477,792 UK Biobank participants to identify key exposures associated with osteoporosis. The selected exposures were combined into a weighted Meta-ERS and validated in the Scotland/Wales cohort. The Meta-ERS was further compared with polygenic risk scores (PRS) and linked to plasma proteomics to explore underlying biological pathways. RESULTS: We identified 41 independent non-genetic exposures spanning socioeconomic status, mental health, sleep, diet, smoking, physical activity, environment, and marital status, with socioeconomic status and mental health emerging as the most significant drivers. Based on the identified exposures, we constructed eight domain-specific exposure risk scores and integrated them into a weighted Meta-ERS. The Meta-ERS (R2 = 5.1%; Proportion of Chi-Square = 14.3%) demonstrated an ability to explain osteoporosis variation that was on par with polygenic risk scores (R2 = 4.8%; Proportion of Chi-Square = 12.0%). Importantly, modifying unfavorable exposures mitigated the negative effect of PRS on osteoporosis, particularly among high PRS individuals (1.5- to 1.8-fold greater absolute risk reduction than in those with low PRS). Proteomic analyses further revealed potential mechanisms through which the exposome influences osteoporosis, including hormonal regulation, inflammation, ossification, muscle development, lipid metabolism, and accelerated bone aging. Among these, growth/differentiation factor 15 was identified as a key mediator protein, with a mediation proportion of 13.13%-36.52%. CONCLUSIONS: The Meta-ERS facilitates the quantification of individual osteoporosis risk and identifies modifiable exposures for targeted prevention. Its application can enable personalized risk stratification and guide lifestyle or environmental interventions.

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 ± 8.5 years, 69% women), showed that despite the low clinical risk and favorable cardiovascular health (mean Life's Essential 8 score 73.3 ± 11.5 vs the U.S. average of ∼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

Polygenic risk score and its role in cancer susceptibility.

BACKGROUND: Polygenic risk score (PRS) has the ability to stratify inherited susceptibility to cancer and, as a complement to monogenic testing, can identify individuals at increased genetic risk even when no pathogenic variant is detected in high- or moderate-penetrance genes. It reflects the combined additive effects of a large number of low-penetrance variants across the genome, and represents a continuum of genetic susceptibility with an approximately normal distribution. Clinically relevant differences are typically observed in individuals in the highest and lowest percentiles of the PRS distribution, while relative risk gradients depend on the cancer type, the specific PRS model, and the reference population used. PRS is not a single test but rather a family of statistical models that differ in their design, predictive performance, and transferability across populations, underscoring the need for external validation and population-specific calibration of absolute risk. Broader implementation is thus still held back by differences between individual PRS models, limited transferability, and the lack of harmonized guidance on indication, reporting, and clinical decision-making. Consequently, clinical use in the European Union remains largely confined to pilot studies and local projects. Within these initiatives, PRS is most commonly applied in two main ways - either as a triage tool for intensified diagnostics or screening in higher-risk groups, or as a component of multifactorial absolute-risk models (e. g. BOADICEA/CanRisk) that integrate PRS with other risk factors such as pathogenic variants in moderate-penetrance genes (e. g. ATM or CHEK2), family history, or lifestyle factors. By refining absolute-risk estimates, PRS may shift individuals across clinical decision thresholds for more intensive surveillance and preventive strategies. AIM: This review summarizes the principles of PRS, the main sources of variability between models, and its potential applications in risk stratification and personalized cancer screening. It also addresses limitations in transferability, the need for calibration, and the currently limited evidence for improvements in hard clinical outcomes.

Humans↗

PubMed2026

PGS-GS: a framework integrating polygenic scores and genomic selection in animal breeding.

Genomic prediction has become a central paradigm in biology, enabling quantitative inference of genetic contributions to complex traits across humans, animals, and plants. Although genomic research in human genetics and animal breeding shares a highly homologous methodological foundation, significant barriers persist in their analytical paradigms and application scenarios. This study aims to promote cross-disciplinary integration by introducing human-derived polygenic scores (PGS) algorithms into animal genomic selection (GS) and proposing a PGS-GS framework with a preliminary weighting-based implementation. We systematically benchmarked the predictive performance and computational efficiency of 20 algorithms, including classical linear models, machine learning, PGS, and PGS-GS using both array and whole-genome sequencing (WGS) data across four major agricultural species: beef cattle, sheep, pigs, and chickens. Our results demonstrate that PGS and PGS-GS algorithms achieve predictive accuracy competitive with genomic best linear unbiased prediction (GBLUP) while offering markedly higher computational efficiency. Moreover, incorporating PGS-derived prior information into weighted linear and non-linear models outperformed conventional weighted GBLUP. The results provide empirical evidence to inform algorithm selection and highlight the potential of integrating human-derived PGS methodologies into animal genomic prediction frameworks.

Animals↗