The personal and clinical utility of polygenic risk scores

AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare

The convergence of artificial intelligence (AI) and biomedical data is transforming precision medicine by enabling the use of genetic risk factors (GRFs) for customized healthcare services based on individual needs. Although GRFs play an essential role in disease susceptibility, progression, and therapeutic outcomes, a gap exists in exploring their contribution to AI-powered precision medicine. This paper addresses this need by investigating the significance and potential of utilizing GRFs with AI in the medical field. We examine their applications, particularly emphasizing their impact on disease prediction, treatment personalization, and overall healthcare improvement. This review explores the application of AI algorithms to optimize the use of GRFs, aiming to advance precision medicine in disease screening, patient stratification, drug discovery, and understanding disease mechanisms. Through a variety of case studies and examples, we demonstrate the potential of incorporating GRFs facilitated by AI into medical practice, resulting in more precise diagnoses, targeted therapies, and improved patient outcomes. This review underscores the potential of GRFs, empowered by AI, to enhance precision medicine by improving diagnostic accuracy, treatment precision, and individualized healthcare solutions.

Recurrent Stroke Prediction by Applying a Stroke Polygenic Risk Score in the Japanese Population

BACKGROUND

Recently, various polygenic risk score (PRS)-based methods were developed to improve stroke prediction. However, current PRSs (including cross-ancestry PRS) poorly predict recurrent stroke. Here, we aimed to determine whether the best PRS for Japanese individuals can also predict stroke recurrence in this population by extensively comparing the methods and maximizing the predictive performance for stroke onset.

METHODS

We used data from the disease-oriented BBJ1 (BioBank Japan first cohort; recruited between 2003 and 2007, n=179 938) to derive and optimize the PRSs using a 10-fold cross-validation. We integrated the optimized PRSs for multiple traits, such as vascular risk factors and stroke subtypes to generate a single PRS using the meta-scoring approach (metaGRS). We used an independent BBJ2 (BBJ second cohort; recruited between 2012 and 2017, n=41 929) as a test sample to evaluate the association of the metaGRS with stroke and recurrent stroke. In addition, we analyzed its association stratified by risk factors. We administered 3 distinct tests to consider the potential index event bias.

RESULTS

We analyzed recurrent stroke cases (n=174) and nonrecurrent stroke controls (n=1153) among subjects within the BBJ2. After adjusting for known risk factors, metaGRS was associated with stroke recurrence (adjusted odds ratio per SD, 1.18 [95% CI, 1.00-1.39]; P=0.044), although no significant correlation was observed with the published PRSs. The outcomes derived from these examinations did not provide any significant indication of the influence of index event bias. The high metaGRS group without a history of hypertension had a higher risk of stroke recurrence than that of the low metaGRS group (adjusted odds ratio, 2.24 [95% CI, 1.07-4.66]; P=0.032). There was no association at all in the hypertension group (adjusted odds ratio, 1.21 [95% CI, 0.69-2.13]; P=0.50).

CONCLUSIONS

The metaGRS developed in a Japanese cohort predicted stroke recurrence in an independent cohort of patients. In particular, it predicted an increased risk of recurrence among stroke patients without hypertension. These findings provide clues for additional genetic risk stratification and help in developing personalized strategies for stroke recurrence prevention.

Polyconnectomic Scoring of Functional Connectivity Patterns Across Eight Neuropsychiatric and Three Neurodegenerative Disorders

BACKGROUND

Neuropsychiatric and neurodegenerative disorders involve diverse changes in brain functional connectivity. As an alternative to approaches that search for specific mosaic patterns of affected connections and networks, we used polyconnectomic scoring to quantify disorder-related whole-brain connectivity signatures into interpretable, personalized scores.

METHODS

The polyconnectomic score (PCS) measures the extent to which an individual's functional connectivity mirrors the whole-brain circuitry characteristics of a trait. We computed PCSs for 8 neuropsychiatric conditions (attention-deficit/hyperactivity disorder, anxiety-related disorders, autism spectrum disorder, obsessive-compulsive disorder, bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and 3 neurodegenerative conditions (Alzheimer's disease, frontotemporal dementia, and Parkinson's disease) across 22 datasets with resting-state functional magnetic resonance imaging data from 10,667 individuals (5325 patients, 5342 control participants). We also examined PCSs in 26,673 individuals from the population-based UK Biobank cohort.

RESULTS

PCSs were consistently higher in out-of-sample patients across 6 of the 8 neuropsychiatric and across all 3 investigated neurodegenerative disorders ([minimum, maximum]: area under the receiver operating characteristic curve = [0.55, 0.73], false discovery rate-corrected p [pFDR] = [1.8 × 10-16, 4.5 × 10-2]). Individuals with elevated PCS levels for neuropsychiatric conditions exhibited higher neuroticism (pFDR < 9.7 × 10-5), lower cognitive performance (pFDR < 5.3 × 10-5), and lower general well-being (pFDR < 9.7 × 10-4).

CONCLUSIONS

Our findings reveal generalizable whole-brain connectivity alterations in brain disorders. Polyconnectomic scoring effectively aggregates disorder-related signatures across the entire brain into an interpretable, participant-specific metric. A toolbox is provided for PCS computation.

Association between genetic liability to physical health conditions and comorbidities in individuals with severe mental illness: an analysis of two cross-sectional observational studies in the UK

BACKGROUND

Individuals with severe mental illness, including schizophrenia and bipolar disorder, have elevated rates of physical health conditions, contributing to increased morbidity and mortality. While environmental factors such as adverse effects from medication and lifestyle changes play a role, the contribution of genetic liability to physical health comorbidities remains underexplored. We investigated whether genetic risk for physical health conditions influences comorbidities in people with severe mental illness and compared these effects with those in the general population. Additionally, we explored the effects of psychiatric genetic liabilities and the occurrence of physical health problems in those with severe mental illness.

METHODS

We analysed two UK cross-sectional cohorts of people with severe mental illness-the Cardiff Cognition in Schizophrenia study (CardiffCOGS) cohort and the National Centre for Mental Health (NCMH) cohort. Individuals were selected for analyses if they responded to a validated self-report questionnaire of physical health problems and if their genetic data passed quality control. These subsets of individuals were used to test associations between polygenic risk scores for six physical health conditions (high cholesterol, type 2 diabetes, hypertension, asthma, heart disease, and rheumatoid arthritis) and corresponding physical health conditions in this population. Models were further adjusted for demographic and clinical covariates (sex, age, smoking, and clozapine use). Effect sizes from these analyses were compared in magnitude to those reported in studies conducted in the general population. We also evaluated associations between psychiatric polygenic risk scores (schizophrenia, bipolar disorder, major depressive disorder, and ADHD) and physical comorbidities. People with lived experience were involved in the analysis planning and guided the choices of outcomes analysed.

FINDINGS

Following exclusions due to missing phenotypic or genetic data (403 individuals in CardiffCOGS; 1704 individuals in NCMH), our analyses included 721 individuals from the CardiffCOGS cohort (mean age 43·7 years [SD 12·1], 267 [37·0%] females, 454 [63·0%] males, and 703 [97·5%] with self-reported White ethnicity) and 1011 from the NCMH cohort (mean age 47·6 years [SD 13·7], 553 [54·7%] females, 458 (45·3%) males, and 928 [91·8%] with self-reported White ethnicity). Polygenic risk scores for physical health conditions were associated with corresponding conditions in one or both of these cohorts, explaining between 1·4% and 6·5% of the variability in these comorbidities. Polygenic risk score effect sizes for at least one of the cohorts overlapped with the reported effects (within 84% CIs) in the general population. Adjustments for clinical and demographic factors had minimal impact on these associations. Psychiatric polygenic risk scores showed weaker and less consistent associations with physical comorbidities.

INTERPRETATION

Our findings support the role of genetic risk in physical health comorbidities among individuals with severe mental illness. Genetic liability to physical health conditions was more strongly associated with comorbidities than psychiatric genetic liability, highlighting its additive contribution alongside environmental and clinical factors. These findings indicate that there would be value in incorporating genetic risk information into predictive algorithms for physical health comorbidities in those with severe mental illness, and that polygenic risk scores should be included in research studies developing and validating such algorithms.

FUNDING

EU Horizon 2020 programme.

Genomics of Attention Deficit Hyperactivity Disorder: What the Clinician Needs to Know

This report provides an update on current knowledge and applications of genomic research in attention deficit hyperactivity disorder (ADHD). The history, principles, and underlying assumptions for genetic studies on psychiatric disorders are reviewed. Recent DNA sequencing and genome-wide association studies have revealed common and rare genetic variants associated with ADHD. Communication of genetic knowledge in meetings with patients and their relatives and common misconceptions are addressed. The importance of recognizing genetic syndromes masquerading as ADHD or other common psychiatric disorders is emphasized and how genetic information can be used to improve diagnosis and therapy are discussed.

Diversifying Psychiatric Genomics: Globally Inclusive Strategies Toward Health Equity

The underrepresentation of non-European researchers, participants, and datasets in psychiatric genetics hinders the understanding of mental health conditions and perpetuates health inequities. Ancestral diversity in research is crucial for advancing insights into disease etiology and achieving equity in precision medicine. Key strategies include optimizing data use, fostering global collaboration for capacity building, and adopting best practices in research methods. Ensuring clinical impact, accountability, and multi-agency commitment is vital. A more inclusive approach will enhance understanding of genetic and environmental factors in mental health, leading to equitable and accessible health care outcomes for all populations.

Foretelling the Future: Preimplantation Genetic Testing and the Coming of Polygenic Embryo Screening

Preimplantation genetic testing (PGT) has been used in various forms over the last two decades. PGT involves testing early embryos following in vitro fertilization and has now become an accepted part of genetic testing. Nowadays, PGT serves as a resource for couples who have a family history of monogenic disorders, wherein the fetus is at high risk of inheriting the condition. PGT is also used to improve pregnancy outcomes in IVF patients in cases of recurrent IVF implantation failure, recurrent miscarriages, as well as male factor. It is also used in screening for sex-linked disorders and sourcing stem cells for therapy. The latest PGT direction is polygenic embryo screening (PES, PGT-P), which allows the identification of embryos that are at elevated risk for significant diseases in adulthood, such as coronary artery disease (CAD), diabetes, hypertension, and breast cancer. As the prevalence and the potential for the use of PES grow, fundamental ethical issues have been underlined, raising concerns about the broader implications of genetic testing. This narrative review summarizes indications, methods, applications, and limitations for PGT, with a particular focus on PES.

Polygenic Risk Score for Metabolic Dysfunction-Associated Steatotic Liver Disease and Steatohepatitis: A Narrative Review

Metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH) are spreading worldwide as the most critical causes of cirrhosis and hepatocellular carcinoma (HCC). Thus, improving the screening and managing strategies for patients with MASLD or MASH is necessary. A traditional non-systemic review provided this narrative. Genetic variations associated with the development of MASLD and MASH, such as PNPLA3, TM6SF2, GCKR, MBOAT7, MERTK, and HSD17B13, were initially reviewed. PNPLA3 genetic variants appeared to be strongly associated with the increased pathogenesis of MASLD, MASH, cirrhosis, and HCC. We also reviewed the useful polygenic risk score (PRS) for the development of MASLD, MASH, their related cirrhosis, and the occurrence of HCC. PRSs appeared to be better predictors of MASLD, MASH, the development of cirrhosis, and the occurrence of HCC in patients with MASLD or MASH than any single-nucleotide polymorphisms. RNA interference and antisense nucleotides against the genetic variations of PNPLA3 and HSD17B13 are also being developed. Multidisciplinary collaboration and cooperation involving hepatologists, geneticists, pharmacologists, and pathologists should resolve complicated problems in MASLD and MASH. This narrative review highlights the importance of the genetic susceptibility and PRS as predictive markers and personalized medicine for patients with MASLD or MASH in the future.

The Eating Disorders Genetics Initiative 2 (EDGI2): study protocol

BACKGROUND

The Eating Disorders Genetics Initiative 2 (EDGI2) is designed to explore the role of genes and environment in anorexia nervosa, bulimia nervosa, binge-eating disorder, and avoidant/restrictive food intake disorder (ARFID) with a focus on broad population representation and severe and/or longstanding illness.

METHODS

A total of 20,000 new participants (18,700 cases and 1,300 controls) will be ascertained from the United States (US), Mexico (MX), Australia (AU), Aotearoa New Zealand (NZ), Sweden (SE), and Denmark (DK). Comprehensive phenotyping and genotyping will be performed for participants in US, MX, AU, NZ, and SE using the EDGI2 questionnaire battery and participant saliva samples. In DK, case identification and genotyping will be through the National Patient Register and bloodspots archived near birth. Case-control and case-case genome-wide association studies will be conducted within EDGI2 and enhanced via meta-analysis with external data from the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED). Additional analyses will explore genetic correlations between eating disorders (EDs) and other psychiatric and metabolic traits, calculate polygenic risk scores (PRS), and leverage functional biology to evaluate clinical outcomes. Moreover, analyzing PRS for patient stratification and linking identified risk loci to clinically relevant phenotypes highlight the potential of EDGI2 for clinical translation.

DISCUSSION

EDGI2 is a global expansion of the EDGI study to increase sample size, increase participant representation across multiple ancestral backgrounds, and to include ARFID. ED genetics research has historically lagged behind other psychiatric disorders, and EDGI2 is designed to rapidly advance the study of the genetics of the major EDs. Exploring EDs at both the diagnostic level and the symptom level will provide an unprecedented look at the genetic architecture underlying EDs.

TRIAL REGISTRATION

EDGI2 is a registered clinical trial: clinicaltrials.gov NCT06594913. https://clinicaltrials.gov/study/NCT06594913 (posted September 19, 2024).

PIGEON: a statistical framework for estimating gene-environment interaction for polygenic traits

Understanding gene-environment interaction (GxE) is crucial for deciphering the genetic architecture of human complex traits. However, current statistical methods for GxE inference face challenges in both scalability and interpretability. Here we introduce PIGEON-a unified statistical framework for quantifying polygenic GxE using a variance component analytical approach. Based on this framework, we outline the main objectives in GxE studies and introduce an estimation procedure that requires only summary statistics data as input. We demonstrate the effectiveness of PIGEON through theoretical and empirical analyses, including a quasi-experimental gene-by-education study of health outcomes and gene-by-sex interaction for 530 traits using UK Biobank. We also identify genetic interactors that explain the treatment effect heterogeneity in a clinical trial on smoking cessation. PIGEON suggests a path towards polygenic, summary statistics-based inference in future GxE studies.

AoUPRS: A cost-effective and versatile PRS calculator for the All of Us Program

BACKGROUND

The All of Us (AoU) Research Program provides a comprehensive genomic dataset to accelerate health research and medical breakthroughs. Despite its potential, researchers face significant challenges, including high costs and inefficiencies associated with data extraction and analysis. AoUPRS addresses these challenges by offering a versatile and cost-effective tool for calculating polygenic risk scores (PRS), enabling both experienced and novice researchers to leverage the AoU dataset for large-scale genomic discoveries.

METHODS

We evaluated three PRS models from the PGS Catalog (coronary artery disease, atrial fibrillation, and type 2 diabetes) using two distinct approaches in the Hail framework: MatrixTable (MT), a dense representation, and Variant Dataset (VDS), a sparse representation optimized for large-scale genomic data. Computational cost, resource usage, and processing time were compared. To assess the similarity of PRS performance between these two approaches, we compared odds ratios (ORs) and area under the curve (AUC). Lin's concordance correlation coefficient (CCC) was also computed to quantify agreement between PRS scores generated by MT and VDS.

RESULTS

The VDS approach reduced computational costs by up to 99.51% (e.g., from $32 to $0.036 for a 51-SNP score) while maintaining PRS estimates that were highly similar to those obtained using the MT approach. Across all three PRS models, AUC comparisons showed minimal differences between MT and VDS, indicating that both approaches yield consistent PRS performance. Agreement between PRS scores calculated by both approaches was further supported by Lin's CCC values ranging from 0.9199 to 0.9944, confirming strong concordance. Empirical cumulative distribution function (ECDF) plots further illustrated the near-identical distribution of PRS values across methods.

CONCLUSIONS

AoUPRS enables efficient and cost-effective PRS computation within AoU, providing substantial cost savings while maintaining highly consistent PRS estimates. These findings support the use of AoUPRS for large-scale genomic risk assessment, making the AoU dataset more accessible and practical for diverse research applications. The tool's open-source availability on GitHub, coupled with detailed documentation and tutorials, ensures accessibility and ease of use for the scientific community.

Integrating brain imaging features and genomic profiles for the subtyping of major depression

BACKGROUND

Precise stratification of patients into homogeneous disease subgroups could address the heterogeneity of phenotypes and enhance understanding of the pathophysiology underlying specific subtypes. Existing literature on subtyping patients with major depressive disorder (MDD) mainly utilized clinical features only. Genomic and imaging data may improve subtyping, but advanced methods are required due to the high dimensionality of features.

METHODS

We propose a novel disease subtyping framework for MDD by integrating brain structural features, genotype-predicted expression levels in brain tissues, and clinical features. Using a multi-view biclustering approach, we classify patients into clinically and biologically homogeneous subgroups. Additionally, we propose approaches to identify causally relevant genes for clustering.

RESULTS

We verified the reliability of the subtyping model by internal and external validation. High prediction strengths (PS) (average PS: 0.896, minimum: 0.854), a measure of generalizability of the derived clusters in independent datasets, support the validity of our approach. External validation using patient outcome variables (treatment response and hospitalization risks) confirmed the clinical relevance of the identified subgroups. Furthermore, subtype-defining genes overlapped with known susceptibility genes for MDD and were involved in relevant biological pathways. In addition, drug repositioning analysis based on these genes prioritized promising candidates for subtype-specific treatments.

CONCLUSIONS

Our approach successfully stratified MDD patients into subgroups with distinct clinical prognoses. The identification of biologically and clinically meaningful subtypes may enable more personalized treatment strategies. This study also provides a framework for disease subtyping that can be extended to other complex disorders.

Genetic Risk Scores in Stroke Research and Care

Stroke remains a leading cause of death and disability worldwide. While well-established risk factors play a major role, genetic predisposition is a crucial determinant of stroke susceptibility, with heritability estimates up to 39% for ischemic stroke and 29% for intracerebral hemorrhage. Advances in next-generation sequencing and genome-wide association studies have identified numerous genetic loci associated with stroke risk, paving the way for the development of genetic risk scores. These scores aggregate information from multiple genetic variants to estimate an individual's stroke risk, offering a promising tool for personalized risk stratification that complements traditional clinical models. While GRSs have demonstrated strong predictive potential for primary stroke events in population-based settings, their integration into clinical practice remains limited. Emerging evidence suggests that GRSs could add value in clinical decision-making, for instance, for stratifying ischemic stroke risk in patients with atrial fibrillation, assessing intracerebral hemorrhage risk in anticoagulant users, and predicting vascular risk factor control in stroke survivors. The incorporation of GRSs with multiomics data and machine learning may further refine risk assessment, driving personalized prevention strategies for both primary and secondary stroke preventions. A major challenge is the limited applicability of GRS across diverse populations, as most genome-wide association studies have been conducted in individuals of European ancestry. Addressing this limitation is critical for ensuring equitable and effective implementation of GRSs in clinical settings. As methodologies continue to evolve, integrating GRS into stroke research could significantly enhance risk assessment and support precision medicine approaches tailored to individual patients.

Optimization of multi-ancestry polygenic risk score disease prediction models

Polygenic risk scores (PRS) have ushered in a new era in genetic epidemiology, offering insights into individual predispositions to a wide range of diseases. However, despite recent marked enhancements in predictive power, PRS-based models still need to overcome several hurdles before they can be broadly applied in the clinic. Chiefly, they need to achieve sufficient accuracy, easy interpretability and portability across diverse populations. Leveraging trans-ancestry genome-wide association study (GWAS) meta-analysis, we generated novel, diverse summary statistics for 30 medically-related traits and benchmarked the performance of six existing PRS algorithms using UK Biobank. We built an ensemble model using logistic regression to combine outputs of top-performing algorithms and validated it on the diverse eMERGE and PAGE MEC cohorts. It surpassed current state-of-the-art PRS models, with minimal performance drops in external cohorts, indicating good calibration. To enhance predictive accuracy for clinical application, we incorporated easily-accessible clinical characteristics such as age, gender, ancestry and risk factors, creating disease prediction models intended as prospective diagnostic tests, with easily interpretable positive or negative outcomes. After adding clinical characteristics, 12 out of 30 models surpassed 80% AUC. Further, 25 traits exceeded the diagnostic odds ratio (DOR) of five, and 19 traits exceeded DOR of 10 for all ancestry groups, indicating high predictive value. Our PRS model for coronary artery disease identified 55-80 times more true coronary events than rare pathogenic variant models, reinforcing its clinical potential. The polygenic component modulated the effect of high-risk rare variants, stressing the need to consider all genetic components in clinical settings. These findings show that newly developed PRS-based disease prediction models have sufficient accuracy and portability to warrant consideration of being used in the clinic.

Cognitive Dysfunction in the Addictions (CDiA): protocol for a neuron-to-neighbourhood collaborative research program

Substance use disorders (SUDs), including Alcohol Use Disorder, are pressing global public health problems. Executive functions (EFs) are prominently featured in mechanistic models of addiction. However, significant gaps remain in our understanding of EFs in SUDs, including the dimensional relationships of EFs to underlying neural circuits, molecular biomarkers, disorder heterogeneity, and functional ability. Transforming health outcomes for people with SUDs requires an integration of clinical, biomedical, preclinical, and health services research. Through such interdisciplinary research, we can develop policies and interventions that align with biopsychosocial models of addiction, addressing the complex cognitive concerns of people with SUDs in a more holistic and effective way. Here, we introduce the design and procedures underlying Cognitive Dysfunction in the Addictions (CDiA), an integrative research program, which aims to fill these knowledge gaps and facilitate research discoveries to enhance treatments for people living with SUDs. The CDiA Program comprises seven interdisciplinary projects that aim to evaluate the central thesis that EF has a crucial role in functional outcomes in SUDs. The projects draw on a diverse sample of adults aged 18-60 (target N=400) seeking treatment for SUD, who are followed over one year to identify specific EF domains most associated with improved functioning. Projects 1-3 investigate SUD symptoms, brain circuits, and blood biomarkers and their associations with key EF domains (inhibition, working memory, and set-shifting) and functional outcomes (disability, quality of life). Projects 4 and 5 evaluate interventions for SUDs and their impacts on EF: a clinical trial of repetitive transcranial magnetic stimulation and a preclinical study of potential new pharmacological treatments in rodents. Project 6 links EF to healthcare utilization and is supplemented with a qualitative investigation of EF-related barriers to treatment engagement. Project 7 uses whole-person modeling to integrate the multi-modal data generated across projects, applying clustering and deep learning methods to identify patient subtypes and drive future cross-disciplinary initiatives. The CDiA Program will bring scientific domains together to uncover novel ways in which EFs are linked to SUD severity and functional recovery, and facilitate future discoveries to improve health outcomes in individuals living with SUDs.

A comprehensive exploration of the impact and contribution of polygenic risk score on age at onset of 30 complex diseases

OBJECTIVES

Polygenic risk score (PRS) has increasingly shown promise in predicting disease risk; however, studies examining the influence of PRS on age at onset remain limited. This study aimed to systematically assess the impact of PRS on age at onset across multiple diseases.

STUDY DESIGN

Prospective cohort study METHODS: We calculated PRS with two methods (C+T and PRS-CS) and compared their predictive capability in age at onset of 30 diseases in the UK Biobank. We next evaluated the effect of PRS on age at onset and quantified the influence of PRS on disease risk across early and late onset cases.

RESULTS

PRS-CS behaved better in predicting age at onset of most diseases (except for Alzheimer's disease) compared to C+T. Higher PRS was associated with earlier age at onset for 23 diseases, with the average age at onset advanced by 1.94 years. Compared to women, men faced an advanced onset for 5 diseases. Compared to average PRS (20-80 %), individuals in the top 2.5 % of the PRS distribution displayed a significantly earlier age at onset for 19 diseases, ranging from 2.85 (1.68-4.03) years advancement for gout to 13.70 (9.88-17.52) years advancement for Crohn's disease. Compared to the late-onset group, the early-onset group exhibited a greater onset risk in 21 diseases, with the early-onset risk of colon cancer being 2.78-fold higher than the late-onset risk (OR = 11.42 [9.77-12.45] vs. 3.95 [3.85-4.06], P < 0.001).

CONCLUSIONS

Higher PRS generally leads to earlier age at onset, which supports the potential role of PRS in screening high early-onset risk individuals susceptible to chronic diseases.

Preimplantation genetic testing for type 2 diabetes based on family-specific polygenic risk score: A proof-of-concept study

AIMS

This study aims to evaluate the feasibility of family-specific polygenic risk prediction in reducing the risk of type 2 diabetes (T2D) in the offspring from an infertile couple with a family history of early-onset T2D.

METHODS

We innovatively established a family-specific polygenic risk prediction model for this T2D family and the embryo with the lowest risk of T2D were selected for implantation.

RESULTS

Initially, whole exome sequencing analysis in the family failed to identify monogenic-level pathogenic or likely pathogenic variants responsible for T2D. Thus, preimplantation genetic testing for monogenic disease (PGT-M) may be not applicable. Subsequently, we innovatively developed a family-specific polygenic-level T2D risk prediction model including 114 T2D risk SNPs and weighted by the genotype-phenotype correlation of asymptomatic individuals and T2D patients in the pedigree. Using this model, the euploid embryo P_5977_1C exhibited the lowest T2D risk and was selected for implantation. The newborn displayed the same lowest T2D polygenic risk and normal growth and development after a 16-month follow-up.

CONCLUSION

Our study provided preliminary evidence for the feasibility of developing a more accurate polygenic risk prediction model using pedigree information and its application in embryo selection.

Psychological and behavioural considerations for integrating polygenic risk scores for disease into clinical practice

A polygenic risk score (PRS) summarizes in one number an individual's estimated genetic association with a specific trait or disease based on the common DNA variants included in the score. Disease PRSs have the potential to positively affect population health by improving disease risk prediction, thereby also potentially improving disease prevention, early intervention and treatment. However, given the potential psychological, behavioural and other harms, there are also concerns about integrating PRSs into clinical tools and healthcare systems. Here we assess five arguments against implementing PRSs for physical disease in clinical practice that revolve around psychological and behavioural considerations. For each argument, we consider a counterargument, the evidence and underlying theory, any gaps in the evidence base and possible future directions and research priorities. We conclude that, although there may be other barriers to implementation, there is currently little evidence of psychological or behavioural harms from integrating PRSs into practice.

Team Science in Precision Medicine Research:The Case for Inclusion of Adults With Intellectual Disability

The inclusion of adults with intellectual disability (ID) in precision medicine research has scientific, public health, and social justice justifications. Yet there is an indication that this population is excluded from general (i.e., nondisability specific) health research, including precision medicine research. Adults with ID are thus unlikely to reap the benefits emerging from such scientific endeavors-today and in the future. In this commentary, we explore key issues in research ethics, including cohort diversity, the principle of justice, and consent, and discuss their ramifications for adults with ID and precision medicine researchers. We call for endorsing team science collaboration and community engagement to promote health equity for adults with ID and disability justice in precision medicine research.

Genetic burden of lupus increases the risk of transition from normal to preclinical autoimmune conditions via antinuclear antibody development

OBJECTIVES

This study aimed to investigate the association between the genetic burden of systemic lupus erythematosus (SLE) and the loss of tolerance to self-nuclear antigens in the preclinical stage.

METHODS

We analysed genetic data from 349 Korean individuals who tested positive for autoantibodies in the preclinical stage, along with 33,596 healthy controls and 2057 patients with SLE. Genome-wide and pathway-specific polygenic risk scores (PRSs) of SLE were calculated based on 180 known non-human leukocyte antigen (non-HLA) SLE loci, HLA-DRB1 classical alleles, and predefined immune-related pathways to subsequently correlate with clinical phenotypes, particularly the presence of antinuclear antibodies (ANAs) at various titre thresholds.

RESULTS

Individuals with preclinical autoimmune conditions exhibited significantly higher SLE PRSs than healthy controls (P = 2.99 × 10-5), with a significantly upward trend between ANA titres and PRS (P = 1.12 × 10-3). Stratification analysis revealed that preclinical-stage individuals with PRSs exceeding the means of age- and sex-matched PRSs among patients with SLE were at a significantly higher risk of ANA development (odds ratio = 2.25; P = 8.12 × 10-3 at a dilution factor of 1:80). Pathway-specific PRS analysis identified the significant enrichment of SLE-risk effects in nine pathways, such as signalling related to reactive oxygen species production, T cell receptor, B cell receptor, and cytokines, in ANA-positive preclinical individuals (Padjusted < 0.05).

CONCLUSIONS

Our findings illustrate that the genetic burden of SLE may lead to a crucial transition from normal to preclinical autoimmune conditions prior to the pathogenic stage by increasing the susceptibility to and levels of ANAs.

Polygenic Risk Score Is Associated with Developing and Dying from Lung Cancer in the National Lung Screening Trial

Background: Epidemiological studies suggest lung cancer results from the combined effects of smoking and genetic susceptibility. The clinical application of polygenic risk scores (PRSs), derived from combining the results from multiple germline genetic variants, have not yet been explored in a lung cancer screening cohort. Methods: This was a post hoc analysis of 9191 non-Hispanic white subjects from the National Lung Screening Trial (NLST), a sub-study of high-risk smokers randomised to annual computed tomography (CT) or chest X-ray (CXR) and followed for 6.4 years (mean). This study's primary aim was to examine the relationship between a composite polygenic risk score (PRS) calculated from 12 validated risk genotypes and developing or dying from lung cancer during screening. Validation was undertaken in the UK Biobank of unscreened ever-smokers (N = 167,796) followed for 10 years (median). Results: In this prospective study, we found our PRS correlated with lung cancer incidence (p < 0.0001) and mortality (p = 0.004). In an adjusted multivariable logistic regression analysis, PRS was independently associated with lung cancer death (p = 0.0027). Screening participants with intermediate and high PRS scores had a higher lung cancer mortality, relative to those with a low PRS score (rate ratios = 1.73 (95%CI 1.14-2.64, p = 0.010) and 1.89 (95%CI 1.28-2.78, p = 0.009), respectively). This was despite comparable baseline demographics (including lung function) and comparable lung cancer characteristics. The PRS's association with lung cancer mortality was validated in an unscreened cohort from the UK Biobank (p = 0.002). Conclusions: In this biomarker-based cohort study, an elevated PRS was independently associated with dying from lung cancer in both screening and non-screening cohorts.

The Spanish Polygenic Score reference distribution: a resource for personalized medicine

Here we present the Polygenic Score (PGS) distributions for 3124 common diseases and quantitative traits observed in the Spanish population. To achieve so, the genomes and exomes of 2190 unrelated individuals of Spanish ancestry were used. The analysis covered a wide range of diseases and traits, including both complex disorders, such as various types of cancer, and disorders associated with the digestive, cardiovascular, neuronal, and immune systems, as well as quantitative traits like hematological and anthropometric measurements. The resulting PGS distributions provide valuable insights into the genetic architecture of the Spanish population, offering a comprehensive framework for investigating disease susceptibility and potential risk factors in this specific population. The study has also explored potential relationships between diseases and traits based on PGS pairwise correlations, revealing significant correlations that warrant further investigation. These findings have contributed to increase our understanding of the genetic basis of human traits and have implications for personalized medicine and public health interventions in the Spanish population. In addition, for the sake of reproducibility, we provide a data processing pipeline, enabling the computation of PGS for external genomes and exomes. The pipeline, accessible on GitHub, supports parallel tasks on various computing platforms and contributes to the standardization of PGS comparisons globally. Lastly, a user-friendly web interface facilitates the exploration of PGS reference distributions, featuring a detailed table, distribution plots, and filtering options. This interface enhances accessibility for researchers and clinicians, fostering informed decision-making based on population-specific PGS distributions. The PGS reference distributions can be explored at the SpPGS Atlas repository through the web interface: https://csvs.clinbioinfosspa.es/?tab=pgs .

The accuracy of polygenic score models for BMI and Type II diabetes in the Native Hawaiian population

Polygenic scores (PGS) are promising in stratifying individuals based on the genetic susceptibility to complex diseases or traits. However, the accuracy of PGS models, typically trained in European- or East Asian-ancestry populations, tend to perform poorly in other ethnic minority populations and their accuracies have not been evaluated for Native Hawaiians. In particular, for body mass index (BMI) and type-2 diabetes (T2D), Polynesian-ancestry individuals such as Native Hawaiians or Samoans exhibit varied distribution from other continental populations, but are understudied, particularly in the context of PGS. Using BMI and T2D as examples of metabolic traits of importance to Polynesian populations (along with height as a comparison of a similarly highly polygenic trait), here we examine the prediction accuracies of PGS models in a large Native Hawaiian sample from the Multiethnic Cohort with up to 5300 individuals. We find evidence of lowered prediction accuracies for the PGS models in some cases, particularly for height. We also find that using the Native Hawaiian samples as an optimization cohort during training does not consistently improve PGS performance. Moreover, even the best-performing PGS models among Native Hawaiians have lowered prediction accuracy among the subset of individuals most enriched with Polynesian ancestry. Our findings indicate that factors such as admixture histories, sample size, and diversity in GWAS can influence PGS performance for complex traits among Native Hawaiian samples. This study provides an initial survey of PGS performance among Native Hawaiians and exposes the current gaps and challenges associated with improving polygenic prediction models for underrepresented minority populations.

Associations between serum lipids and glaucoma: a cohort study of 400 229 UK Biobank participants

PURPOSE

To examine the associations of commonly-used serum lipid measures (high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC) and triglycerides (TG)) with glaucoma.

METHODS

This prospective cohort study included 400 229 participants from the UK Biobank. Cox regression and restricted cubic spline models and polygenic risk scores were employed to investigate the associations between serum lipids and glaucoma.

RESULTS

Over a mean follow-up of 14.44 years, 6868 (1.72%) participants developed glaucoma. Multivariate Cox regression revealed that higher levels of HDL-C were associated with an increased risk of glaucoma (HR for 1-SD increase in HDL-C 1.05, 95% CI 1.02 to 1.08, p=0.001), while elevated levels of LDL-C (HR 0.96, 95% CI 0.94 to 0.99, p=0.005), TC (HR 0.97, 95% CI 0.94 to 1.00, p=0.037) and TG (HR 0.96, 95% CI 0.94 to 0.99, p=0.008) were all associated with reduced risk. The analysis examining the associations between polygenic risk score of serum lipids and glaucoma showed per 1-SD increment of HDL-C genetic risk was associated with a 5% greater hazard of glaucoma (HR 1.05, 95% CI 1.00 to 1.11, p=0.031). However, the polygenic risk score of LDL-C, TC, and TG did not show a significant association with glaucoma.

CONCLUSIONS

Elevated HDL-C is associated with an increased risk of glaucoma, while elevated LDL-C, TC, and TG levels are associated with a lower risk of glaucoma. This study enhances our understanding of the association between lipid profile and glaucoma and warrants further investigation of lipid-focused treatments in glaucoma management.

Trans-ancestry GWAS identifies 59 loci and improves risk prediction and fine-mapping for kidney stone disease

Kidney stone disease is a multifactorial disease with increasing incidence worldwide. Trans-ancestry GWAS has become a popular strategy to dissect genetic structure of complex traits. Here, we conduct a large trans-ancestry GWAS meta-analysis on kidney stone disease with 31,715 cases and 943,655 controls in European and East Asian populations. We identify 59 kidney stone disease susceptibility loci, including 13 novel loci and show similar effects across populations. Using fine-mapping, we detect 1612 variants at these loci, and pinpoint 25 causal signals with a posterior inclusion probability >0.5 among them. At a novel locus, we pinpoint TRIOBP gene and discuss its potential link to kidney stone disease. We show that a cross-population polygenic risk score, PRS-CSxEAS&EUR, exhibits superior predictive performance for kidney stone disease than other polygenic risk scores constructed in our study. Relative to individuals in the third quintile of PRS-CSxEAS&EUR, those in the lowest and highest quintiles exhibit distinct kidney stone disease risks with odds ratios of 0.57 (0.51-0.63) and 1.83 (1.68-1.98), respectively. Our results suggest that kidney stone disease patients with higher polygenic risk scores are younger at onset. In summary, our study advances the understanding of kidney stone disease genetic architecture and improves its genetic predictability.

Parents facing polygenic embryo scores: the 'best choice of a best life' and psychological counselling

Advances in reproductive medicine and genetic technologies now offer prospective parents the option to test IVF embryos for genetic predispositions to complex diseases, such as coronary heart disease and psychiatric disorders, through polygenic embryo screening (PES). However, limited clinical data on its real-world use leaves parents facing complex decisions based on probabilistic risk scores, requiring them to weigh uncertain benefits against potential harms. While clinical, ethical, and societal concerns regarding PES have been extensively discussed, the psychological considerations have received less attention. This paper highlights the importance of decision aids as part of psychological interventions, which are crucial for helping parents navigate these choices and make informed decisions based on individual perceptions and experiences. Additionally, determining how and when to disclose genetic risk information to children presents significant challenges for families. Early disclosure may lead to anxiety, while withholding information could undermine trust later in life. Psychological counseling is therefore an essential component in supporting families through these sensitive decisions. While PES offers opportunities to reduce genetic risks, it also introduces significant challenges that require thoughtful consideration and comprehensive support for both parents and children.

Genomic prediction with kinship-based multiple kernel learning produces hypothesis on the underlying inheritance mechanisms of phenotypic traits

BACKGROUND

Genomic prediction encompasses the techniques used in agricultural technology to predict the genetic merit of individuals towards valuable phenotypic traits. It is related to Genome Interpretation in humans, which models the individual risk of developing disease traits. Genomic prediction is dominated by linear mixed models, such as the Genomic Best Linear Unbiased Prediction (GBLUP), which computes kinship matrices from SNP array data, while Genome Interpretation applications to clinical genetics rely mainly on Polygenic Risk Scores.

RESULTS

In this article, we exploit the positive semidefinite characteristics of the kinship matrices that are conventionally used in GBLUP to propose a novel Genomic Multiple Kernel Learning method (GMKL), in which the multiple kinship matrices corresponding to Additive, Dominant, and Epistatic Inheritance Mechanisms are used as kernels in support vector machines, and we apply it to both worlds. We benchmark GMKL on simulated cattle phenotypes, showing that it outperforms the classical GBLUP predictors for genomic prediction. Moreover, we show that GMKL ranks the kinship kernels representing different inheritance mechanisms according to their compatibility with the observed data, allowing it to produce hypotheses on the normally unknown inheritance mechanisms generating the target phenotypes. We then apply GMKL to the prediction of two inflammatory bowel disease cohorts with more than 6500 samples in total, consistently obtaining results suggesting that epistasis might have a relevant, although underestimated role in inflammatory bowel disease (IBD).

CONCLUSIONS

We show that GMKL performs similarly to GBLUP, but it can formulate biological hypotheses about inheritance mechanisms, such as suggesting that epistasis influences IBD.

The development and evaluation of polygenic risk score reports: A systematized review of the literature

PURPOSE

The return of polygenic risk scores (PGS) is currently being assessed in research settings for clinical utility and validity, and it is anticipated that PGS will soon be implemented in a clinical setting. There are limited guidelines regarding PGS communication and reporting; thus, there is a need to identify and analyze the current research to determine the most acceptable means of presenting PGS results through reports. The aim of this review is to examine the literature regarding the development and evaluation of PGS communication tools, including risk reports, visual aids, and online tools.

METHODS

Research studies that evaluated preferences, understanding or interpretation of PGS through a report, visual aid, or tool were included. The search strategy was applied to MEDLINE (via Ovid) and American Psychological Association PsychInfo.

RESULTS

Thirteen studies met the inclusion criteria. The presentation of PGS differed across studies, including icon arrays and bell curves for visual presentation and absolute risk, relative risk, and genetic risk score for numerical presentation. Participants' understanding of PGS differed between studies. Studies supported using absolute risk and avoiding stigmatizing colors to communicate results.

CONCLUSION

To support PGS clinical implementation, the development of an evidence-based PGS report evaluated by consumers and various health care professionals is needed.

Relatively independent and complementary roles of family history and polygenic risk score in age at onset and incident cases of 12 common diseases

Few studies have systematically compared the overlap and complementarity of family history (FH) and polygenic risk score (PRS) in terms of disease risk. We here investigated the impacts of FH and PRS on the risk of incident diseases or age at disease onset, as well as their clinical value in risk prediction. We analyzed 12 diseases in the prospective cohort study of UK Biobank (N = 461,220). First, restricted mean survival time analysis was performed to evaluate the influences of FH and PRS on age at onset. Then, Cox proportional hazards model was employed to estimate the effects of FH and PRS on the incident risk. Finally, prediction models were constructed to examine the clinical value of FH and PRS in the incident disease risk. Compared to negative FH, positive FH led to an earlier onset, with an average of 2.29 years earlier between the top and bottom 2.5% PRSs and high blood pressure showing the greatest difference of 6.01 years earlier. Both FH and PRS were related to higher incident risk; but they only exhibited weak interactions on high blood pressure and Alzheimer's disease/dementia, and provided relatively independent and partially complementary information on disease susceptibility, with PRS explaining 7.0% of the FH effect but FH accounting for only 1.1% of the PRS effect for incident cases. Further, FH and PRS showed additional predictive value in risk evaluation, with breast cancer showing the greatest improvement (31.3%). FH and PRS significantly affect a variety of diseases, and they are not interchangeable measures of genetic susceptibility, but instead offer largely independent and partially complementary information. Incorporating FH, PRS, and clinical risk factors simultaneously leads to the greatest predictive value for disease risk assessment.

Unleashing the Power of Multiomics: Unraveling the Molecular Landscape of Peripheral Neuropathy

Peripheral neuropathies (PNs) affect over 20 million individuals in the United States, manifesting as a wide range of sensory, motor, and autonomic nerve symptoms. While various conditions such as diabetes, metabolic disorders, trauma, autoimmune disease, and chemotherapy-induced neurotoxicity have been linked to PN, approximately one-third of PN cases remain idiopathic, underscoring a critical gap in our understanding of these disorders. Over the years, considerable efforts have focused on unraveling the complex molecular pathways underlying PN to advance diagnosis and treatment. Traditional methods such as linkage analysis, fluorescence in situ hybridization, polymerase chain reaction, and Sanger sequencing identified initial genetic variants associated with PN. However, the establishment and application of next-generation sequencing (NGS) and, more recently, long-read/single-cell sequencing have revolutionized the field, accelerating the discovery of novel disease-causing variants and challenging previous assumptions about pathogenicity. This review traces the evolution of genomic technologies in PN research, emphasizing the pivotal role of NGS in uncovering genetic complexities. We provide a comprehensive analysis of established genomic approaches such as genome-wide association studies, targeted gene panel sequencing, and whole-exome/genome sequencing, alongside emerging multiomic technologies including RNA sequencing and proteomics. Integrating these approaches promises holistic insights into PN pathophysiology, potentially revealing new biomarkers and therapeutic targets. Furthermore, we discuss the clinical implications of genomic and multiomic integration, highlighting their potential to enhance diagnostic accuracy, prognostic assessment, and personalized treatment strategies for PN. Challenges and questions in standardizing these technologies for clinical use are raised, underscoring the need for robust guidelines to maximize their clinical utility.

Personalization of primary prevention: Exploring the role of coronary artery calcium and polygenic risk score in cardiovascular diseases

Personalized healthcare is becoming increasingly popular given the vast heterogeneity in disease manifestation between individuals. Many commonly encountered diseases within cardiology are multifactorial in nature and disease progression and response is often variable due to environmental and genetic factors influencing disease states. This makes accurate early identification and primary prevention difficult in certain populations, especially young patients with limited Atherosclerotic Cardiovascular Disease (ASCVD) risk factors. Newer strategies, such as coronary artery calcium (CAC) scans and polygenic risk scores (PRS), are being implemented to aid in the detection of subclinical disease and heritable risk, respectively. Data surrounding CAC scans have shown promising results in their ability to detect subclinical atherosclerosis and predict the risk of future coronary events, especially at the extremes; however, predictive variability exists among different patient populations, limiting the test's specificity. Furthermore, relying only on CAC scores and ASCVD risk scores may fail to identify a large group of patients needing primary prevention who lack subclinical disease and traditional risk factors, but harbor genetic variabilities strongly associated with certain cardiovascular diseases. PRS can overcome these limitations. These scores can be measured in individuals as early as birth to identify genetic variants placing them at elevated risk for developing cardiovascular disease, irrespective of their current cardiovascular health status. By applying PRS alongside CAC scores, previously overlooked patient populations can be identified and begin primary prevention strategies early to achieve optimal outcomes. In this review, we expand on the current knowledge surrounding CAC scores and PRS and highlight the future possibilities of these technologies for preventive cardiology.

PCOS - the many faces of a disorder in women and men

PURPOSE

Polycystic ovary syndrome (PCOS) is a very common endocrine, metabolic and reproductive disorder. The underlying pathophysiology is not yet fully understood and both genetic and environmental factors contribute to its development. We aimed to explore clinical and genetic aspects of familial clustering in PCOS, shedding light on its reproductive and metabolic consequences in both male and female first-degree relatives of the affected women.

METHODS

Searching the electronic database of PubMed up to October 2023, we synthesized findings from available prospective and retrospective studies and review articles, investigating the familial clustering of PCOS and incorporating data on its metabolic consequences and genetic associations.

RESULTS

There is a significant clustering of reproductive and metabolic abnormalities in first-degree relatives of women with PCOS. Genetic studies, including genome-wide association studies (GWAS), reveal a complex molecular etiology, emphasizing polygenic architecture. This is supported by the identification of two distinct PCOS subtypes, termed "reproductive" and "metabolic" which exhibit differential genetic underpinnings.

CONCLUSION

Clinicians should be aware of increased reproductive and metabolic dysfunction both in female and male first-degree relatives of PCOS probands. Current challenges include refining genetic risk scores and understanding the impact of PCOS genetic factors on diverse outcomes, necessitating a sex-specific approach in research and clinical practice. Future directions should address causality, improve diagnostic capability, and unravel the long-term consequences in both genders, emphasizing the importance of proactive clinical assessment in PCOS probands and their families.

Knowledge, attitudes and demand toward cardiovascular polygenic risk testing in clinical practice: cross-sectional survey of patients

The goal of this study was to assess patients' prior exposure and current level of knowledge of polygenic risk scores (PRSs). We also explored reactions to receiving a high-risk or low-risk score, and gauged the overall attitudes and demand patients have with regards to PRSs. We developed an online investigator-designed survey based on existing validated tools in genetic testing. There were two versions of the survey, one including a hypothetical high-risk PRS and one with a low-risk PRS. This survey was distributed to patients attending a cardiovascular clinic for primary or secondary prevention. A total of 226 participants responded to the survey. 177 patients (79%) had not read nor heard about polygenic testing. 209 patients (93%) had never discussed polygenic testing with their health care professional (HCP). 208 patients (93%) had never received polygenic testing. The average score on the knowledge quiz was 2.47/10 [95% C.I. (2.17, 2.78)]. Participants that received a high-risk survey scored 20.52/35 [95% C.I. (16.14, 24.9)] with regards to negative emotions while low-risk survey participants scored 17.96/35 [95% C.I. (13.98, 21.94)] (p < 0.001). Participants that received a high-risk survey scored 12.42/15 [95% C.I. (10.43, 14.41)] for demand and low-risk survey participants scored 12.22/15 [95% C.I. (9.66, 14.78)] (p = 0.549). Patients have limited prior exposure and knowledge of PRSs. Compared to receiving a low-risk score, participants receiving a high-risk score have more negative emotions and feelings of uncertainty. Despite the lack of knowledge, and the high rate of negative emotions and uncertainty, demand for PRSs in cardiology practice is high.

Interaction between polygenic risk score and reproductive factors in relation to lung cancer risk among Singaporean Chinese women

OBJECTIVES

Studies have shown that reproductive factors can influence hormone levels in females, potentially affecting the risk of developing lung cancer. However, it remains unclear whether this association is modified by genetic variants.

STUDY DESIGN

Age-matched case-control study.

METHODS

Reproductive factors included menopausal status, age at menopause, hormone use, hysterectomy and oophorectomy. Odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between reproductive factors and lung cancer risk were estimated using a multivariable conditional logistic regression model. A polygenic risk score (PRS) was calculated using a clumping plus thresholding approach. Gene-environment interactions between reproductive factors and PRS on lung cancer risk were evaluated.

RESULTS

Our analysis included a total of 2910 female participants (1455 cases and 1455 controls). Compared to women with no surgical history, those who had undergone hysterectomy (OR = 1.41, 95 % CI = 1.10-1.82) or oophorectomy (OR = 1.52, 95% CI = 1.15-2.02) were associated with an increased risk of lung cancer. A PRS for lung cancer derived from 7 genetic variants showed a linear association with lung cancer risk (Ptrend < 0.001). After adjusting for false discovery rate (FDR), we found a borderline non-significant interaction between hormone use and PRS on lung cancer risk (Pinteraction-FDR = 0.05).

CONCLUSIONS

Women with a history of hysterectomy or oophorectomy had a higher risk of lung cancer compared to those without such surgical history, highlighting the need for targeted prevention strategies in this high-risk population. No significant effect modification by the lung cancer PRS was observed in the associations between reproductive factors and lung cancer risk. Larger prospective studies are warranted to validate these findings.

Perceived risk of type 2 diabetes: Using linked genomic, clinical and questionnaire data to understand the potential use of genetic risk tools in British South Asians

Despite growing interest surrounding the integration of genetic risk tools such as polygenic risk scores (PRSs) into routine care for early disease identification and management, major questions remain about whether and how these tools are to be implemented at-scale. Many interventions have explored their use in encouraging the adoption of preventative health behaviours-yet existing evidence remains undetermined, limited by the focus on White European populations. The present study used structural equation modelling to explore genetic risk perceptions surrounding type 2 diabetes (T2D) in a sample of British Bangladeshi and British Pakistani volunteers-combining questionnaire data alongside genomic and clinical information to identify the characteristics of individuals who are likely to act on genetic risk information. We conducted this study with volunteers enrolled in Genes & Health-a large-scale (n > 60,000) study in the UK recruiting British Bangladeshi and British Pakistani volunteers from community and NHS settings. Eligible participants between the ages of 16 to 59 years were invited to complete a 15-minute questionnaire containing measures of genetic risk perceptions surrounding T2D, as well as intention to adopt health behaviours and that can prevent or delay T2D. Questionnaire responses were then integrated with participants' genomic and clinical data available at Genes & Health to construct a model-characterising their mediating relationships in informing participants' intention. A total of 626 participants responded to the questionnaire (response rate = 17%, 37.70% aged 46 to 59 years, 62.62% female). Being between the ages of 46 to 59 years (β = 0.52, 95% CI [0.26, 0.79], p < 0.05), having greater self-reported perceived control over health (β = 0.41, 95% CI [0.26, 0.56], p < 0.05) and interest in genetic testing (β = 0.62, 95% CI [0.46, 0.78], p < 0.05) all had direct positive effects on participants' intention. Household income showed an indirect effect on intention, mediated by interest in genetic testing, β = 0.24, 95% CI [0.12, 0.37]. Self-identified ethnicity also demonstrated indirect effects on intention via two mediating pathways-both involving participants' T2D PRSs and self-reported family history of T2D (β = 0.03, 95% CI [0.02, 0.05] and β = 0.002, 95% CI [0.001, 0.01]). Our results showed that older age, greater perceived control over health and interest in genetic testing are all predictive of participants' likelihood of adopting preventative heath behaviours in response to genetic risk information about T2D. We also found evidence pointing to the roles that wider socio-demographic, clinical and familial variables can play in informing and mediating genetic risk perceptions. These findings should raise awareness about potential challenges to the equitable delivery and management of genetic risk tools-and strengthen calls for wider family- and system-level approaches that can help address potential health inequalities, as efforts surrounding the large-scale implementation of genomics into existing health systems continue to grow.

Multi-trait Polygenic Probability Risk Score Enhances Glaucoma Prediction Across Ancestries

Primary open-angle glaucoma (POAG) remains the leading cause of irreversible blindness worldwide, with early detection crucial for preventing vision loss. However, current risk assessment methods have limited predictive power. Here, we present a multi-trait polygenic probability risk score (PPRS) approach that integrates multiple glaucoma-related traits and leverages functional genomic annotations to enhance POAG prediction across diverse ancestries. We constructed PRSs for POAG, intraocular pressure (IOP), vertical cup-to-disc ratio (VCDR), and retinal nerve fiber layer (RNFL) thickness using extensive genomic coverage (>7 million variants) and 96 functional annotations through the SBayesRC method. Validation in the UK Biobank (n=324,713, European ancestry) and Mexican American Glaucoma Genetic Study (MAGGS, n=4,549, Latino ancestry) demonstrated significant improvements in predictive accuracy over conventional approaches. Our multi-trait PPRS achieved area under the curve (AUC) values of 0.814 in Europeans and 0.801 in Latinos, compared to AUC ≤0.79 for single-trait models. We identified ancestry-specific differences in genetic contributions, with IOP demonstrating the strongest association in Europeans (OR=1.63, P = 5.37 × 10-89), while VCDR was predominant in Latinos (OR=1.64, P = 2.04 × 10-11). The model achieved remarkable risk stratification, with the highest PPRS decile showing 80.2-fold and 51.1-fold increased POAG risk in Europeans and Latinos, respectively, compared to the lowest decile. Importantly, the top PPRS quintile captured 65.9% and 62.2% of POAG cases in Europeans and Latinos, substantially improving upon previous approaches. Our findings demonstrate that integrating multiple disease-relevant traits and functional annotations significantly enhances polygenic prediction of POAG across diverse populations, with significant implications for targeted screening, early intervention, and reduction of disease burden.

Sensing the Future of Thrombosis Management: Integrating Vessel-on-a-Chip Models, Advanced Biosensors, and AI-Driven Digital Twins

Thrombotic events, such as strokes and deep vein thrombosis, remain a significant global health burden, with traditional diagnostic methods often failing to capture the complex, patient-specific nuances of thrombosis risk. This Perspective explores the revolutionary potential of microengineered vessel-on-chip platforms in thrombosis research and personalized medicine. We discuss the evolution from basic microfluidic channels to advanced 3D-printed, patient-specific models that accurately replicate complex vascular geometries, incorporating all elements of Virchow's triad. Integrating these platforms with cutting-edge sensing technologies, including wearable ultrasonic devices and electrochemical biosensors, enables real-time monitoring of thrombosis-related parameters. Crucially, we highlight the transformative role of artificial intelligence and digital twin technology in leveraging vast patient-specific data collected from these models. This integration allows for the development of predictive algorithms and personalized digital twins, offering unprecedented thrombosis risk assessment, treatment optimization, and drug screening capabilities. The clinical relevance and validation of these models are examined, showcasing their potential to predict thrombotic events and guide personalized treatment strategies. While challenges in scalability, standardization, and regulatory approval persist, the convergence of vessel-on-chip platforms, advanced sensing, and AI-driven digital twins promises to revolutionize thrombosis management. This approach paves the way for a new era of precision cardiovascular care, offering noninvasive, predictive, and personalized strategies for thrombosis prevention and treatment, ultimately improving patient outcomes and reducing the global burden of cardiovascular diseases.

Heritability and polygenic load for comorbid anxiety and depression

Anxiety and depression commonly occur together resulting in worse health outcomes than when they occur in isolation. We aimed to determine whether the genetic liability for comorbid anxiety and depression was greater than when anxiety or depression occurred alone. Data from 12,792 genotyped twins (ages 38-85) were analysed, including 1,986 complete monozygotic and 1,594 complete dizygotic pairs. Outcomes were prescription of antidepressant and anxiolytic drugs, as defined by the World Health Organization Anatomical Therapeutic Chemical Classification System (ATC) convention, for comorbid anxiety and depression (n = 1028), anxiety only (n = 718), and depression only (n = 484). Heritability of each outcome was estimated using twin modelling, and the influence of common genetic variation was assessed from polygenic scores (PGS) for depressive symptoms, anxiety, and 40 other traits. Heritability of comorbid anxiety and depression was 79% compared with 41% for anxiety and 50% for depression alone. The PGS for depressive symptoms likewise predicted more variation in comorbid anxiety and depression (adjusted odds ratio per SD PGS = 1.53, 95% CI = 1.43-1.63; ΔR2 = 0.031, ΔAUC = 0.044) than the other outcomes, with nearly identical results when comorbid anxiety and depression was defined by International Classification of Diseases (ICD) diagnoses (adjusted odds ratio per SD PGS = 1.70, 95% CI = 1.53-1.90; ΔR2 = 0.036, ΔAUC = 0.051). Individuals in the highest decile of PGS for depressive symptoms had over 5 times higher odds of being prescribed medication for comorbid anxiety and depression compared to those in the lowest decile. While results on a predominant role of depressive symptoms may have been biased by the size and heterogeneity of available data bases, they are consistent with the conclusion that genetic factors explain substantially more variation in comorbid anxiety and depression than anxiety or depression alone.

Uncovering the genetic architecture and evolutionary roots of androgenetic alopecia in African men

Androgenetic alopecia is a highly heritable trait. However, much of our understanding about the genetics of male-pattern baldness comes from individuals of European descent. Here, we examined a dataset comprising 2,136 men from Ghana, Nigeria, Senegal, and South Africa that were genotyped using the Men of African Descent and Carcinoma of the Prostate Array. We first tested how genetic predictions of baldness generalize from Europe to Africa and found that polygenic scores from European genome-wide association studies (GWASs) yielded area under the curve statistics that ranged from 0.513 to 0.546, indicating that genetic predictions of baldness generalized poorly from European to African populations. Subsequently, we conducted an African GWAS of androgenetic alopecia, focusing on self-reported baldness patterns at age 45. After correcting for age at recruitment, population structure, and study site, we identified 266 moderately significant associations, 51 of which were independent (p < 10-5, r2 < 0.2). Most baldness associations were autosomal, and the X chromosome does not seem to have a large impact on baldness in African men. Although Neanderthal alleles have previously been associated with skin and hair phenotypes, within the limits of statistical power, we did not find evidence that continental differences in the genetic architecture of baldness are due to Neanderthal introgression. While most loci that are associated with androgenetic alopecia do not have large integrative haplotype scores or fixation index statistics, multiple baldness-associated SNPs near the EDA2R and AR genes have large allele frequency differences between continents. Collectively, our findings illustrate how population genetic differences contribute to the limited portability of polygenic predictions across ancestries.

Defining next steps in the clinical implementation of polygenic scores: A landscape analysis of professional groups' perspectives

PURPOSE

Professional perspectives on polygenic scores (PGS) have surged in-line with significant research investment. It is unclear whether these perspectives are leading the health care sector toward a comprehensive implementation approach. This scoping review addresses this knowledge gap, analyzing available publications for concurring and discordant perspectives.

METHODS

Methodology followed the Arksey and O'Malley framework. Six databases were systematically searched alongside screening of professional websites. Descriptive and deductive content analyses were completed using the Consolidated Framework for Implementation Research and the Expert Recommendations for Implementing Change compilation.

RESULTS

A total of 28 perspectives were analyzed. Implementation was supportable if evidentiary thresholds for clinical utility could be met, with exceptions being in vitro fertilization and prenatal settings. Evidence base and relative advantage of PGS were the strongest determinants of implementation success, with resourcing also being emphasized. Key strategies included ongoing research, developing education materials, and facilitating relay of information. Attention was not paid to leadership nor to stakeholder interrelationships. There was no recommended framework to facilitate the clinical implementation of PGS.

CONCLUSION

The steps toward executing implementation remain vague. Commonalities in perspectives suggest value in a transferable approach. If PGS are to be successful, policy makers and leaders must consider effective resource allocation by addressing priority barriers and utilizing implementation methodologies. Continuing efforts to establish PGS clinical utility and value, guidelines and policies, and educational materials are needed.

Low Stability and Specificity of Polygenic Risk Scores for Major Psychiatric Disorders Limit Their Clinical Utility

BACKGROUND

There has been little examination of the stability and validity of polygenic risk scores (PRSs), i.e., whether individuals identified as high risk for a disorder with one PRS are identified as high risk with another PRS and whether high-risk individuals have the disorder.

METHODS

The UK Biobank recruited 502,534 individuals ages 37 to 73 years in the United Kingdom between 2006 and 2010. PRSs were calculated for 408,853 White British individuals. PRS continuous shrinkage (CS), which uses single nucleotide polymorphism effect sizes under CS, was used to calculate 3 different PRSs for major depressive disorder (MDD), alcohol use disorder (AUD), and type 2 diabetes (T2D) and 2 different PRSs for schizophrenia (SCZ). PRS stability was measured using correlations between different PRSs for the same disorder and the percentage of individuals consistently identified as high risk (top 5% PRS). Sensitivity and specificity were used to measure PRS validity.

RESULTS

Correlations between PRSs ranged from low to high (SCZ: r = 0.78; MDD: rs = 0.16-0.78; AUD: rs = 0.13-0.90; T2D rs = 0.29-0.77). The percentage of individuals consistently identified as high risk (top 5% PRS) for SCZ with a different SCZ PRS was 47.7%, i.e., less than half of individuals identified as high risk with one PRS were identified as high risk with another PRS. Percentages of individuals consistently identified as high risk were 9.5% to 47.0% for MDD, 8.3% to 63.5% for AUD, and 14.1% to 45.2% for T2D. PRS sensitivity was moderate for MDD (66.1%-74.4%) and AUD (72.3%-74.2%), moderate/good for T2D (77.3%-96.3%), and good for SCZ (90.2%-93.3%). Specificity was low for all PRSs (50.7%-56.4%).

CONCLUSIONS

Limited stability and specificity of PRSs highlight their current lack of clinical utility in psychiatry.

Association between single nucleotide polymorphisms in PNPLA3, TM6SF2 and MBOAT7 genes and markers of cancer aggressiveness in a Sri Lankan NASH-related HCC cohort

BACKGROUND

Single nucleotide polymorphisms (SNPs) in patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily member 2 (TM6SF2) and membrane bound O-acyltransferase domain containing 7 (MBOAT7) genes were reported to be strongly associated with non-alcoholic fatty liver disease (NAFLD) pathogenicity among different populations. We investigated whether these SNPs are associated with prognostic factors and genetic biomarkers of non-alcoholic steatohepatitis (NASH)-related hepatocellular carcinoma (HCC) in the Sri Lankan context.

METHODS

We conducted an exploratory study to evaluate the prevalence of five SNPs (PNPLA3 rs738409, PNPLA3 rs2281135, PNPLA3 rs2294918, TM6SF2 rs58542926 and MBOAT7 rs641738) as genetic risk factors for NASH-HCC pathogenicity. We genotyped 48 NASH-HCC patient samples collected at a clinical setting using a minisequencing method. Impact of each SNP with tumor prognostic factors such as nodularity, tumor size and AFP (alpha-feto protein) level was analyzed using chi square test. We also analyzed the expression of micro RNA-122 (miR-122) in serum and leukocyte telomere length via quantitative real-time PCR. Associations between each SNP with micro RNA-122 (miR-122) expression level and leukocyte telomere length of NASH-HCC patients were analyzed using one-way analysis of variance (ANOVA) test and independent t test. Relationships among tested SNPs and some well-established HCC risk factors such as age, BMI, gender, diabetes status and the cirrhotic stage were also analyzed using chi square test, independent t-test and One-way ANOVA test.

RESULTS

Our analyses demonstrated significant associations between PNPLA3 rs2281135 variant and tumor nodularity. Also, PNPLA3 rs2281135 and PNPLA3 rs2294918 variants were significantly associated with miR-122 expression levels of NASH-HCC patients. Further, age and body mass index (BMI) were significantly associated with PNPLA3 rs2281135 variant in our study cohort.

CONCLUSION

We found that in the Sri Lankan NASH-related HCC cohort, some PNPLA3 variants (rs2281135 and rs2294918) correlate with tumor nodularity, higher miR-122 expression, and distinct demographic features such as age and BMI. Our work highlights the role of specific SNPs in tumor aggressiveness, contributing to the precision screening for HCC in NASH patients.

Influence and role of polygenic risk score in the development of 32 complex diseases

Background

The polygenic risk score (PRS) has been perceived as advantageous in predicting the risk of complex diseases compared to other measures. We aimed to systematically evaluate the influence of PRS on disease outcome and to explore its predictive value.

Methods

We comprehensively assessed the relationship between PRS and 32 complex diseases in the UK Biobank. We used Cox models to estimate the effects of PRS on the incidence risk. Then, we constructed prediction models to assess the clinical utility of PRS in risk prediction. For 16 diseases, we further compared the disease risk and prediction capability of PRS across early and late-onset cases.

Results

Higher PRS led to greater incident risk, with hazard ratio (HR) ranging from 1.07 (95% confidence interval (CI) = 1.06-1.08) for panic/anxiety disorder to 4.17 (95% CI = 4.03-4.31) for acute pancreatitis. This effect was more pronounced in early-onset cases for 12 diseases, increasing by 52.8% on average. Particularly, the early-onset risk of heart failure associated with PRS (HR = 3.02; 95% CI = 2.53-3.59) was roughly twice compared to the late-onset risk (HR = 1.48; 95% CI = 1.46-1.51). Compared to average PRS (20-80%), individuals positioned within the top 2.5% of the PRS distribution exhibited varying degrees of elevated risk, corresponding to a more than five times greater risk on average. PRS showed additional value in clinical risk prediction, causing an average improvement of 6.1% in prediction accuracy. Further, PRS demonstrated higher predictive accuracy for early-onset cases of 11 diseases, with heart failure displaying the most significant (37.5%) improvement when incorporating PRS into the prediction model (concordance index (C-index) = 0.546; standard error (SE) = 0.011 vs. C-index = 0.751; SE = 0.010, P = 2.47 × 10-12).

Conclusions

As a valuable complement to traditional clinical risk tools, PRS is closely related to disease risk and can further enhance prediction accuracy, especially for early-onset cases, underscoring its potential role in targeted prevention for high-risk groups.

Genome-wide association study of idiopathic epilepsy in the Italian Spinone dog breed

Idiopathic epilepsy (IE) has a high prevalence and a severe clinical course in the Italian Spinone breed of dog. A genome-wide association study meta-analysis of 52 cases and 51 controls was conducted to identify genomic regions that may be involved with the development of IE. Subsequent to the meta-analysis, a set of 175 controls and an independent validation set of 23 cases and 23 controls were genotyped for SNPs showing suggestive association with IE to find variants exhibiting evidence of replicable association and to test the predictiveness of SNPs for IE status when combined in a weighted risk score. Although two regions showed statistically significant association with IE in the GWAS meta-analysis, and additional regions with suggestive association were identified, the findings were not emulated in the validation set. This is the first GWAS of IE in the Italian Spinone, and the findings suggest that IE in the breed is not monogenic and demonstrates the challenges when investigating a multigenic or complex inherited disease in a numerically small domesticated animal population.

Patient perspectives after receiving simulated preconception polygenic risk scores (PRS) for family planning

PURPOSE

The study investigates patient perspectives on the use of Preimplantation Genetic Testing for Polygenic disease (PGT-P) to select embryos with lower risks for common polygenic diseases. Participant responses and attitudes were evaluated after receiving simulated embryo PRS generated from their personal genetic profile.

METHODS

Couples seeking OB/GYN or Reproductive Endocrinology and Infertility (REI) care with an interest in genetic risks for common diseases in their prospective children participated. A tool provided PRS scores for 11 conditions, using parental DNA to simulate genetic risks for hypothetical embryos produced during IVF. Participants received counseling, reviewed results online, and completed a post-test survey. Feedback from 90 participants assessed understanding and attitudes toward PRS use in IVF.

RESULTS

Participants were overall more supportive of screening embryos for childhood-onset diseases (80%) compared to adult-onset conditions (63%); however, among specific diseases, participants expressed the greatest interest in screening for adult-onset cognitive disorders (Schizophrenia, 86%, Alzheimer's disease, 82%). Participant-free responses noted the importance of personalized counseling and participants not of European ancestry expressed frustration with limited PRS applicability. Negative reactions to testing (nervousness or anxiety 5%, regret 2%) were explored.

CONCLUSIONS

The findings examine the receipt of simulated embryo PRS in a patient population in which support for using PRS during embryo prioritization is high. Positive patient interest was consistent with other US studies; as prior studies identify significant clinician discomfort, these results highlight the need for comprehensive genetic counseling and inclusive stakeholder input in shaping guidelines for PRS during IVF.

Global research trends on the human exposome: a bibliometric analysis (2005-2024)

Exposome represents one of the most pressing issues in the environmental science research field. However, a comprehensive summary of worldwide human exposome research is lacking. We aimed to explore the bibliometric characteristics of scientific publications on the human exposome. A bibliometric analysis of human exposome publications from 2005 to December 2024 was conducted using the Web of Science in accordance with PRISMA guidelines. Trends/hotspots were investigated with keyword frequency, co-occurrence, and thematic map. Sex disparities in terms of publications and citations were examined. From 2005 to 2024, 931 publications were published in 363 journals and written by 4529 authors from 72 countries. The number of publications tripled during the last 5 years. Publications written by females (51% as first authors and 34% as last authors) were cited fewer times (13,674) than publications written by males (22,361). Human exposome studies mainly focused on air pollution, metabolomics, chemicals (e.g., per- and polyfluoroalkyl substances (PFAS), endocrine-disrupting chemicals, pesticides), early-life exposure, biomarkers, microbiome, omics, cancer, and reproductive disorders. Social and built environment factors, occupational exposure, multi-exposure, digital exposure (e.g., screen use), climate change, and late-life exposure received less attention. Our results uncovered high-impact countries, institutions, journals, references, authors, and key human exposome research trends/hotspots. The use of digital exposome technologies (e.g., sensors, and wearables) and data science (e.g., artificial intelligence) has blossomed to overcome challenges and could provide valuable knowledge toward precision prevention. Exposome risk scores represent a promising research avenue.

Noninvasive assessment of hepatic decompensation

Noninvasive tests (NITs) are used in all aspects of liver disease management. Their most prominent break-through since the millennium has been in advancing early detection of liver fibrosis, but their use is not limited to this. In contrast to the symptom-driven assessment of decompensation in patients with cirrhosis, NITs provide not only opportunities for earlier diagnoses but also accurate prognostication, targeted treatment decisions, and a means of monitoring disease. NITs can inform disease management and decision-making based on validated cutoffs and standardized interpretations as a valuable supplement to clinical acumen. The Baveno VI and VII consensus meetings resulted in tangible improvements to pathways of care for patients with compensated and decompensated advanced chronic liver disease, including the combination of platelet count and transient elastography to diagnose clinically significant portal hypertension. Furthermore, circulating NITs will play increasingly important roles in assessing the response to interventions against ascites, variceal bleeding, HE, acute kidney injury, and infections. However, due to NITs' wide availability, there is a risk of inaccurate use, leading to a waste of resources and flawed decisions. In this review, we describe the uses and pitfalls of NITs for hepatic decompensation, from risk stratification in primary care to treatment decisions in outpatient clinics, as well as for the in-hospital management of patients with acute-on-chronic liver failure. We summarize which NITs to use when, for what indications, and how to maximize the potential of NITs for improved patient management.

Genome-wide association study on chronic postsurgical pain in the UK Biobank

BACKGROUND

Chronic postsurgical pain (CPSP) persists beyond the expected healing period after surgery, imposing a substantial burden on overall patient well-being. Unfortunately, CPSP often remains underdiagnosed and undertreated. To better understand the mechanism of CPSP development, we aimed to identify genetic variants associated with CPSP.

METHODS

A genome-wide association study was conducted in a cohort of 95,931 individuals from the UK Biobank who had undergone different surgical procedures. Three analyses were performed: (1) case-control analysis (2923 cases with CPSP and 93,008 controls), (2) ordinal analysis in three groups based on time of analgesics use (n=95,931), and (3) a meta-analysis combining our dataset with a recent publication (n=97,281).

RESULTS

In the case-control analysis, one genetic locus within GLRA3 displayed a genome-wide significant (P<2.5×10-8) association with CPSP, and nine loci displayed suggestively significant associations (P<1×10-6). The ordinal analysis aligned with the case-control analysis, with an additional locus (rs140330443) reaching genome-wide significance. In the meta-analysis with the recently published dataset, the single nucleotide polymorphism (SNP) rs17298280 in the GLRA3 gene remained significant (P=2.19×10-9).

CONCLUSIONS

This study contributes new insights into the genetic factors associated with CPSP. The top hit GLRA3 is known for involvement in prostaglandin E2-induced pain processing pathways. Our study provides a foundation for future investigations into the function of these risk variants and the mechanisms underlying CPSP by offering summary statistics. However, further validation in other cohorts is required to confirm these findings.

A framework for detecting causal effects of risk factors at an individual level based on principles of Mendelian randomisation: applications to modelling individualised effects of lipids on coronary artery disease

BACKGROUND

Mendelian Randomisation (MR) has been widely used to study the causal effects of risk factors. However, almost all MR studies concentrate on the population's average causal effects. With the advent of precision medicine, the individualised treatment effect (ITE) is often of greater interest. For instance, certain risk factors may pose a higher risk to some individuals than others, and the benefits of treatments may vary across individuals. This study proposes a framework for estimating individualised causal effects in large-scale observational studies where unobserved confounding factors may be present.

METHODS

We propose a framework (MR-ITE) that expands the scope of MR from estimating average causal effects to individualised causal effects. We present several approaches for estimating ITEs within this MR framework, primarily grounded on the principles of the "R-learner". To evaluate the presence of causal effect heterogeneity, we also proposed two permutation testing methods. We employed polygenic risk score (PRS) as instruments and proposed methods to improve the accuracy of ITE estimates by removal of potentially pleiotropic single nucleotide polymorphisms (SNPs). The validity of our approach was substantiated through comprehensive simulations. The proposed framework also allows the identification of important effect modifiers contributing to individualised differences in treatment effects. We applied our framework to study the individualised causal effects of various lipid traits, including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and total cholesterol (TC), on the risk of coronary artery disease (CAD) based on the UK-Biobank (UKBB). We also studied the ITE of C-reactive protein (CRP) and insulin-like growth factor 1 (IGF-1) on CAD as secondary analyses.

FINDINGS

Simulation studies demonstrated that MR-ITE outperformed traditional causal forest approaches in identifying ITEs when unobserved confounders were present. The integration of the contamination mixture (ConMix) approach to remove invalid pleiotropic SNPs further enhanced MR-ITE's performance. In real-world applications, we identified positive causal associations between CAD and several factors (LDL-C, Total Cholesterol, and IGF-1 levels). Our permutation tests revealed significant heterogeneity in these causal associations across individuals. Using Shapley value analysis, we identified the top effect modifiers contributing to this heterogeneity.

INTERPRETATION

We introduced a new framework, MR-ITE, capable of inferring individualised causal effects in observational studies based on the MR approach, utilizing PRS as instruments. MR-ITE extends the application of MR from estimating the average treatment effect to individualised treatment effects. Our real-world application of MR-ITE underscores the importance of identifying ITEs in the context of precision medicine.

FUNDING

This work was supported partially by a National Natural Science Foundation of China grant (NSFC; grant number 81971706), the KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong, China, and the Lo Kwee Seong Biomedical Research Fund from The Chinese University of Hong Kong.

Risk Stratification for Endometrial Cancer Reveals Independent Contributions of Polygenic Risk and Body Mass Index

Background

Obesity is a major risk factor for endometrial cancer, but it is unknown whether it impacts the association between genetic risk and endometrial cancer. We incorporated polygenic risk score and epidemiological risk factors in the prediction of and investigated associations of BMI and polygenic risk score with endometrial cancer risk.

Methods

We generated polygenic risk score for endometrial cancer in 129,829 unrelated female participants of European ancestry (including 956 incident cases with endometrial cancer) in the UK Biobank and predicted endometrial cancer using endometrial cancer polygenic risk score and established epidemiological risk factors, including BMI. We evaluated the performance of endometrial cancer prediction models by odds ratios and area under the receiver operating characteristic curves (AUCs) to using logistic regression. Individual and joint associations of BMI and polygenic risk score with endometrial cancer were assessed using Cox proportional hazards models.

Results

An integrated model incorporating both polygenic risk score and epidemiological risk factors achieved a modest, but statistically significant, improvement in predicting endometrial cancer status compared with the model that included epidemiologic risk factors alone (AUC = 0.74 versus 0.73; P = 3.98 × 10 -5 ). Obese participants (BMI ≥ 30 kg/m 2 ) in the top polygenic risk tertile had the highest endometrial cancer risk. We observed independent effects of genetic risk and BMI on endometrial cancer risk.

Conclusion

Integrating polygenic risk score with epidemiological risk factors may offer insights into population stratification for endometrial cancer susceptibility. Higher endometrial cancer polygenic risk is associated with endometrial cancer, irrespective of BMI.