Stacked neural network for predicting polygenic risk score.

In recent years, the utility of polygenic risk scores (PRS) in forecasting disease susceptibility from genome-wide association studies (GWAS) results has been widely recognised. Yet, these models face limitations due to overfitting and the potential overestimation of effect sizes in correlated variants. To surmount these obstacles, we devised the Stacked Neural Network Polygenic Risk Score (SNPRS). This novel approach synthesises outputs from multiple neural network models, each calibrated using genetic variants chosen based on diverse p-value thresholds. By doing so, SNPRS captures a broader array of genetic variants, enabling a more nuanced interpretation of the combined effects of these variants. We assessed the efficacy of SNPRS using the UK Biobank data, focusing on the genetic risks associated with breast and prostate cancers, as well as quantitative traits like height and BMI. We also extended our analysis to the Korea Genome and Epidemiology Study (KoGES) dataset. Impressively, our results indicate that SNPRS surpasses traditional PRS models and an isolated deep neural network in terms of accuracy, highlighting its promise in refining the efficacy and relevance of PRS in genetic studies.

The brain insulin receptor gene network and associations with frailty index.

OBJECTIVE: To investigate longitudinal associations between variations in the co-expression-based brain insulin receptor polygenic risk score and frailty, as well as change in frailty across follow-up. METHODS: This longitudinal study included 1605 participants from the Helsinki Birth Cohort Study. Biologically informed expression-based polygenic risk scores for the insulin receptor gene network, which measure genetic variation in the function of the insulin receptor, were calculated for the hippocampal (hePRS-IR) and the mesocorticolimbic (mePRS-IR) regions. Frailty was assessed in at baseline in 2001-2004, 2011-2013 and 2017-2018 by applying a deficit accumulation-based frailty index. Analyses were carried out by applying linear mixed models and logistical regression models adjusted for adult socioeconomic status, birthweight, smoking and their interactions with age. RESULTS: The FI levels of women were 1.19%-points (95% CI 0.12-2.26, P = 0.029) higher than in men. Both categorical and continuous hePRS-IR in women were associated with higher FI levels than in men at baseline (P < 0.05). In women with high hePRS-IR, the rate of change was steeper with increasing age compared to those with low or moderate hePRS-IR (P < 0.05). No associations were detected between mePRS-IR and frailty at baseline, nor between mePRS-IR and the increase in mean FI levels per year in either sex (P > 0.43). CONCLUSIONS: Higher variation in the function of the insulin receptor gene network in the hippocampus is associated with increasing frailty in women. This could potentially offer novel targets for future drug development aimed at frailty and ageing.

Genome-wide association analyses of breast cancer in women of African ancestry identify new susceptibility loci and improve risk prediction.

We performed genome-wide association studies of breast cancer including 18,034 cases and 22,104 controls of African ancestry. Genetic variants at 12 loci were associated with breast cancer risk (P < 5 × 10-8), including associations of a low-frequency missense variant rs61751053 in ARHGEF38 with overall breast cancer (odds ratio (OR) = 1.48) and a common variant rs76664032 at chromosome 2q14.2 with triple-negative breast cancer (TNBC) (OR = 1.30). Approximately 15.4% of cases with TNBC carried six risk alleles in three genome-wide association study-identified TNBC risk variants, with an OR of 4.21 (95% confidence interval = 2.66-7.03) compared with those carrying fewer than two risk alleles. A polygenic risk score (PRS) showed an area under the receiver operating characteristic curve of 0.60 for the prediction of breast cancer risk, which outperformed PRS derived using data from females of European ancestry. Our study markedly increases the population diversity in genetic studies for breast cancer and demonstrates the utility of PRS for risk prediction in females of African ancestry.

A polygenetic risk score combined with environmental factors better predict susceptibility to hepatocellular carcinoma in Chinese population.

AIMS: This study aimed to investigate environmental factors and genetic variant loci associated with hepatocellular carcinoma (HCC) in Chinese population and construct a weighted genetic risk score (wGRS) and polygenic risk score (PRS). METHODS: A case-control study was applied to confirm the single nucleotide polymorphisms (SNPs) and environmental variables linked to HCC in the Chinese population, which had been screened by meta-analyses. wGRS and PRS were built in training sets and validation sets. Area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were applied to evaluate the performance of the models. RESULTS: A total of 13 SNPs were included in both risk prediction models. Compared with wGRS, PRS had better accuracy and discrimination ability in predicting HCC risk. The AUC for PRS in combination with drinking history, cirrhosis, HBV infection, and family history of HCC in training sets and validation sets (AUC: 0.86, 95% CI: 0.84-0.89; AUC: 0.85, 95% CI: 0.81-0.89) increased at least 20% than the AUC for PRS alone (AUC: 0.63, 95% CI: 0.60-0.67; AUC: 0.65, 95% CI: 0.60-0.71). CONCLUSIONS: A novel model combining PRS with alcohol history, HBV infection, cirrhosis, and family history of HCC could be applied as an effective tool for risk prediction of HCC, which could discriminate at-risk individuals for precise prevention.

Polygenic risk-stratified screening for nasopharyngeal carcinoma in high-risk endemic areas of China: a cost-effectiveness study.

Background: Nasopharyngeal carcinoma (NPC) has an extremely high incidence rate in Southern China, resulting in a severe disease burden for the local population. Current EBV serologic screening is limited by false positives, and there is opportunity to integrate polygenic risk scores for personalized screening which may enhance cost-effectiveness and resource utilization. Methods: A Markov model was developed based on epidemiological and genetic data specific to endemic areas of China, and further compared polygenic risk-stratified screening [subjects with a 10-year absolute risk (AR) greater than a threshold risk underwent EBV serological screening] to age-based screening (EBV serological screening for all subjects). For each initial screening age (30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, and 65-69 years), a modeled cohort of 100,000 participants was screened until age 69, and then followed until age 79. Results: Among subjects aged 30 to 54 years, polygenic risk-stratified screening strategies were more cost-effective than age-based screening strategies, and almost comprised the cost-effectiveness efficiency frontier. For men, screening strategies with a 1-year frequency and a 10-year absolute risk (AR) threshold of 0.7% or higher were cost-effective, with an incremental cost-effectiveness ratio (ICER) below the willingness to pay (¥203,810, twice the local per capita GDP). Specifically, the strategies with a 10-year AR threshold of 0.7% or 0.8% are the most cost-effective strategies, with an ICER ranging from ¥159,752 to ¥201,738 compared to lower-cost non-dominated strategies on the cost-effectiveness frontiers. The optimal strategies have a higher probability (29.4-35.8%) of being cost-effective compared to other strategies on the frontier. Additionally, they reduce the need for nasopharyngoscopies by 5.1-27.7% compared to optimal age-based strategies. Likewise, for women aged 30-54 years, the optimal strategy with a 0.3% threshold showed similar results. Among subjects aged 55 to 69 years, age-based screening strategies were more cost-effective for men, while no screening may be preferred for women. Conclusion: Our economic evaluation found that the polygenic risk-stratified screening could improve the cost-effectiveness among individuals aged 30-54, providing valuable guidance for NPC prevention and control policies in endemic areas of China.

Genome­wide association study and polygenic risk scores predict psoriasis and its shared phenotypes in Taiwan.

Psoriasis is a chronic inflammatory dermatological disease, and there is a lack of understanding of the genetic factors involved in psoriasis in Taiwan. To establish associations between genetic variations and psoriasis, a genome­wide association study was performed in a cohort of 2,248 individuals with psoriasis and 67,440 individuals without psoriasis. Using the ingenuity pathway analysis software, biological networks were constructed. Human leukocyte antigen (HLA) diplotypes and haplotypes were analyzed using Attribute Bagging (HIBAG)­R software and chi­square analysis. The present study aimed to assess the potential risks associated with psoriasis using a polygenic risk score (PRS) analysis. The genetic association between single nucleotide polymorphisms (SNPs) in psoriasis and various human diseases was assessed by phenome­wide association study. METAL software was used to analyze datasets from China Medical University Hospital (CMUH) and BioBank Japan (BBJ). The results of the present study revealed 8,585 SNPs with a significance threshold of P<5x10­8, located within 153 genes strongly associated with the psoriasis phenotype, particularly on chromosomes 5 and 6. This specific genomic region has been identified by analyzing the biological networks associated with numerous pathways, including immune responses and inflammatory signaling. HLA genotype analysis indicated a strong association between HLA­A*02:07 and HLA­C*06:02 in a Taiwanese population. Based on our PRS analysis, the risk of psoriasis associated with the SNPs identified in the present study was quantified. These SNPs are associated with various dermatological, circulatory, endocrine, metabolic, musculoskeletal, hematopoietic and infectious diseases. The meta­analysis results indicated successful replication of a study conducted on psoriasis in the BBJ. Several genetic loci are significantly associated with susceptibility to psoriasis in Taiwanese individuals. The present study contributes to our understanding of the genetic determinants that play a role in susceptibility to psoriasis. Furthermore, it provides valuable insights into the underlying etiology of psoriasis in the Taiwanese community.

Bayesian approach to assessing population differences in genetic risk of disease with application to prostate cancer.

Population differences in risk of disease are common, but the potential genetic basis for these differences is not well understood. A standard approach is to compare genetic risk across populations by testing for mean differences in polygenic scores, but existing studies that use this approach do not account for statistical noise in effect estimates (i.e., the GWAS betas) that arise due to the finite sample size of GWAS training data. Here, we show using Bayesian polygenic score methods that the level of uncertainty in estimates of genetic risk differences across populations is highly dependent on the GWAS training sample size, the polygenicity (number of causal variants), and genetic distance (FST) between the populations considered. We derive a Wald test for formally assessing the difference in genetic risk across populations, which we show to have calibrated type 1 error rates under a simplified assumption that all SNPs are independent, which we achieve in practise using linkage disequilibrium (LD) pruning. We further provide closed-form expressions for assessing the uncertainty in estimates of relative genetic risk across populations under the special case of an infinitesimal genetic architecture. We suggest that for many complex traits and diseases, particularly those with more polygenic architectures, current GWAS sample sizes are insufficient to detect moderate differences in genetic risk across populations, though more substantial differences in relative genetic risk (relative risk > 1.5) can be detected. We show that conventional approaches that do not account for sampling error from the training sample, such as using a simple t-test, have very high type 1 error rates. When applying our approach to prostate cancer, we demonstrate a higher genetic risk in African Ancestry men, with lower risk in men of European followed by East Asian ancestry.

Particulate matter pollution, polygenic risk score and mosaic loss of chromosome Y in middle-aged and older men from the Dongfeng-Tongji cohort study.

Mosaic loss of chromosome Y (mLOY) is the most common somatic alteration as men aging and may reflect genome instability. PM exposure is a major health concern worldwide, but its effects with genetic factors on mLOY has never been investigated. Here we explored the associations of PM2.5 and PM10 exposure with mLOY of 10,158 males measured via signal intensity of 2186 probes in male-specific chromosome-Y region from Illumina array data. The interactive and joint effects of PM2.5 and PM10 with genetic factors and smoking on mLOY were further evaluated. Compared with the lowest tertiles of PM2.5 levels in each exposure window, the highest tertiles in the same day, 7-, 14-, 21-, and 28-day showed a 0.005, 0.006, 0.007, 0.007, and 0.006 decrease in mLRR-Y, respectively (all P < 0.05), with adjustment for age, BMI, smoking pack-years, alcohol drinking status, physical activity, education levels, season of blood draw, and experimental batch. Such adverse effects were also observed in PM10-mLOY associations. Moreover, the unweighted and weighted PRS presented significant negative associations with mLRR-Y (both P < 0.001). Participants with high PRS and high PM2.5 or PM10 exposure in the 28-day separately showed a 0.018 or 0.019 lower mLRR-Y level [ß (95 %CI) = -0.018 (-0.023, -0.012) and - 0.019 (-0.025, -0.014), respectively, both P < 0.001], when compared to those with low PRS and low PM2.5 or PM10 exposure. We also observed joint effects of PM with smoking on exacerbated mLOY. This large study is the first to elucidate the impacts of PM2.5 exposure on mLOY, and provides key evidence regarding the interactive and joint effects of PM with genetic factors on mLOY, which may promote understanding of mLOY development, further modifying and increasing healthy aging in males.

Integrative common and rare variant analyses provide insights into the genetic architecture of liver cirrhosis.

We report a multi-ancestry genome-wide association study on liver cirrhosis and its associated endophenotypes, alanine aminotransferase (ALT) and γ-glutamyl transferase. Using data from 12 cohorts, including 18,265 cases with cirrhosis, 1,782,047 controls, up to 1 million individuals with liver function tests and a validation cohort of 21,689 cases and 617,729 controls, we identify and validate 14 risk associations for cirrhosis. Many variants are located near genes involved in hepatic lipid metabolism. One of these, PNPLA3 p.Ile148Met, interacts with alcohol intake, obesity and diabetes on the risk of cirrhosis and hepatocellular carcinoma (HCC). We develop a polygenic risk score that associates with the progression from cirrhosis to HCC. By focusing on prioritized genes from common variant analyses, we find that rare coding variants in GPAM associate with lower ALT, supporting GPAM as a potential target for therapeutic inhibition. In conclusion, this study provides insights into the genetic underpinnings of cirrhosis.

Analysis of blood methylation quantitative trait loci in East Asians reveals ancestry-specific impacts on complex traits.

Methylation quantitative trait loci (mQTLs) are essential for understanding the role of DNA methylation changes in genetic predisposition, yet they have not been fully characterized in East Asians (EAs). Here we identified mQTLs in whole blood from 3,523 Chinese individuals and replicated them in additional 1,858 Chinese individuals from two cohorts. Over 9% of mQTLs displayed specificity to EAs, facilitating the fine-mapping of EA-specific genetic associations, as shown for variants associated with height. Trans-mQTL hotspots revealed biological pathways contributing to EA-specific genetic associations, including an ERG-mediated 233 trans-mCpG network, implicated in hematopoietic cell differentiation, which likely reflects binding efficiency modulation of the ERG protein complex. More than 90% of mQTLs were shared between different blood cell lineages, with a smaller fraction of lineage-specific mQTLs displaying preferential hypomethylation in the respective lineages. Our study provides new insights into the mQTL landscape across genetic ancestries and their downstream effects on cellular processes and diseases/traits.

Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries.

We develop a method, SBayesRC, that integrates genome-wide association study (GWAS) summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyze 50 complex traits and diseases using ∼7 million common single-nucleotide polymorphisms (SNPs) and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and up to 34% in cross-ancestry prediction compared to the baseline method SBayesR, which does not use annotations, and outperforms other methods, including LDpred2, LDpred-funct, MegaPRS, PolyPred-S and PRS-CSx. Investigation of factors affecting prediction accuracy identifies a significant interaction between SNP density and annotation information, suggesting whole-genome sequence variants with annotations may further improve prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from nonsynonymous SNPs.

Benchmarking multi-ancestry prostate cancer polygenic risk scores in a real-world cohort.

Prostate cancer is a heritable disease with ancestry-biased incidence and mortality. Polygenic risk scores (PRSs) offer promising advancements in predicting disease risk, including prostate cancer. While their accuracy continues to improve, research aimed at enhancing their effectiveness within African and Asian populations remains key for equitable use. Recent algorithmic developments for PRS derivation have resulted in improved pan-ancestral risk prediction for several diseases. In this study, we benchmark the predictive power of six widely used PRS derivation algorithms, including four of which adjust for ancestry, against prostate cancer cases and controls from the UK Biobank and All of Us cohorts. We find modest improvement in discriminatory ability when compared with a simple method that prioritizes variants, clumping, and published polygenic risk scores. Our findings underscore the importance of improving upon risk prediction algorithms and the sampling of diverse cohorts.

Impact of 12-SNP and 6-SNP Polygenic Scores on Predisposition to High LDL-Cholesterol Levels in Patients with Familial Hypercholesterolemia.

BACKGROUND: Familial hypercholesterolemia (FH) comprises high LDL-cholesterol (LDL-c) levels and high cardiovascular disease risk. In the absence of pathogenic variants in causative genes, a polygenic basis was hypothesized. METHODS: In a population of 418 patients (excluding homozygotes) with clinical suspicion of FH, the FH-causative genes and the regions of single nucleotide polymorphisms (SNPs) included in 12-SNP and 6-SNP scores were sequenced by next-generation sequencing, allowing for the detection of pathogenic variants (V+) in 220 patients. To make a comparison, only patients without uncertain significance variants (V-/USV-) were considered (n = 162). RESULTS: Higher values of both scores were observed in V+ than in V-. Considering a cut-off leading to 80% of V-/USV- as score-positive, a lower prevalence of patients positive for both 12-SNP and 6-SNP scores was observed in V+ (p = 0.010 and 0.033, respectively). Mainly for the 12-SNP score, among V+ patients, higher LDL-c levels were observed in score-positive (223 mg/dL -IQR 187-279) than in negative patients (212 mg/dL -IQR 162-240; p = 0.006). Multivariate analysis confirmed the association of scores and LDL-c levels independently of age, sex, and presence of pathogenic variants and revealed a greater association in children. CONCLUSIONS: The 12-SNP and 6-SNP polygenic scores could explain hypercholesterolemia in patients without pathogenic variants as well as the variability of LDL-c levels among patients with FH-causative variants.

An approach to identify gene-environment interactions and reveal new biological insight in complex traits.

There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identify and confirm 5 loci (6 independent signals) interacted with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrate that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated heritability is significant (P < 0.02) for low-density lipoprotein cholesterol and triglycerides in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.

Clinical associations with a polygenic predisposition to benign lower white blood cell counts.

Polygenic variation unrelated to disease contributes to interindividual variation in baseline white blood cell (WBC) counts, but its clinical significance is uncharacterized. We investigated the clinical consequences of a genetic predisposition toward lower WBC counts among 89,559 biobank participants from tertiary care centers using a polygenic score for WBC count (PGSWBC) comprising single nucleotide polymorphisms not associated with disease. A predisposition to lower WBC counts was associated with a decreased risk of identifying pathology on a bone marrow biopsy performed for a low WBC count (odds-ratio = 0.55 per standard deviation increase in PGSWBC [95%CI, 0.30-0.94], p = 0.04), an increased risk of leukopenia (a low WBC count) when treated with a chemotherapeutic (n = 1724, hazard ratio [HR] = 0.78 [0.69-0.88], p = 4.0 × 10-5) or immunosuppressant (n = 354, HR = 0.61 [0.38-0.99], p = 0.04). A predisposition to benign lower WBC counts was associated with an increased risk of discontinuing azathioprine treatment (n = 1,466, HR = 0.62 [0.44-0.87], p = 0.006). Collectively, these findings suggest that there are genetically predisposed individuals who are susceptible to escalations or alterations in clinical care that may be harmful or of little benefit.

Metabolites, Healthy Lifestyle, and Polygenic Risk Score Associated with Upper Gastrointestinal Cancer: Findings from the UK Biobank Study.

Previous metabolomics studies have highlighted the predictive value of metabolites on upper gastrointestinal (UGI) cancer, while most of them ignored the potential effects of lifestyle and genetic risk on plasma metabolites. This study aimed to evaluate the role of lifestyle and genetic risk in the metabolic mechanism of UGI cancer. Differential metabolites of UGI cancer were identified using partial least-squares discriminant analysis and the Wilcoxon test. Then, we calculated the healthy lifestyle index (HLI) score and polygenic risk score (PRS) and divided them into three groups, respectively. A total of 15 metabolites were identified as UGI-cancer-related differential metabolites. The metabolite model (AUC = 0.699) exhibited superior discrimination ability compared to those of the HLI model (AUC = 0.615) and the PRS model (AUC = 0.593). Moreover, subgroup analysis revealed that the metabolite model showed higher discrimination ability for individuals with unhealthy lifestyles compared to that with healthy individuals (AUC = 0.783 vs 0.684). Furthermore, in the genetic risk subgroup analysis, individuals with a genetic predisposition to UGI cancer exhibited the best discriminative performance in the metabolite model (AUC = 0.770). These findings demonstrated the clinical significance of metabolic biomarkers in UGI cancer discrimination, especially in individuals with unhealthy lifestyles and a high genetic risk.

Phenome-wide causal associations between osteoarthritis and other complex traits through the latent causal variable analysis.

BACKGROUND: Individuals with osteoarthritis present with comorbidities, and the potential causal associations remain incompletely elucidated. The present study undertook a large-scale investigation about the causality between osteoarthritis and variable traits, using the summary-level data of genome-wide association studies (GWAS). METHODS: The present study included the summary-level GWS data of knee osteoarthritis, hip osteoarthritis, hip or knee osteoarthritis, hand osteoarthritis, and other 1355 traits. Genetic correlation analysis was conducted between osteoarthritis and other traits through cross-trait bivariate linkage disequilibrium score regression. Subsequently, latent causal variable analysis was performed to explore the causal association when there was a significant genetic correlation. Genetic correlation and latent causal variable analysis were conducted on the Complex Traits Genomics Virtual Lab platform ( https://vl.genoma.io/ ). RESULTS: We found 133 unique phenotypes showing causal relationships with osteoarthritis. Our results confirmed several well-established risk factors of osteoarthritis, such as obesity, weight, BMI, and meniscus derangement. Additionally, our findings suggested putative causal links between osteoarthritis and multiple factors. Socioeconomic determinants such as occupational exposure to dust and diesel exhaust, extended work hours exceeding 40 per week, and unemployment status were implicated. Furthermore, our analysis revealed causal associations with cardiovascular and metabolic disorders, including heart failure, deep venous thrombosis, type 2 diabetes mellitus, and elevated cholesterol levels. Soft tissue and musculoskeletal disorders, such as hallux valgus, internal derangement of the knee, and spondylitis, were also identified to be causally related to osteoarthritis. The study also identified the putative causal associations of osteoarthritis with digestive and respiratory diseases, such as Barrett's esophagus, esophagitis, and asthma, as well as psychiatric conditions including panic attacks and manic or hyperactive episodes. Additionally, we observed osteoarthritis causally related to pharmacological treatments, such as the use of antihypertensive medications, anti-asthmatic drugs, and antidepressants. CONCLUSION: Our study uncovered a wide range of traits causally associated with osteoarthritis. Further studies are needed to validate and illustrate the detailed mechanism of those causal associations.

Convergence of coronary artery disease genes onto endothelial cell programs.

Linking variants from genome-wide association studies (GWAS) to underlying mechanisms of disease remains a challenge1-3. For some diseases, a successful strategy has been to look for cases in which multiple GWAS loci contain genes that act in the same biological pathway1-6. However, our knowledge of which genes act in which pathways is incomplete, particularly for cell-type-specific pathways or understudied genes. Here we introduce a method to connect GWAS variants to functions. This method links variants to genes using epigenomics data, links genes to pathways de novo using Perturb-seq and integrates these data to identify convergence of GWAS loci onto pathways. We apply this approach to study the role of endothelial cells in genetic risk for coronary artery disease (CAD), and discover 43 CAD GWAS signals that converge on the cerebral cavernous malformation (CCM) signalling pathway. Two regulators of this pathway, CCM2 and TLNRD1, are each linked to a CAD risk variant, regulate other CAD risk genes and affect atheroprotective processes in endothelial cells. These results suggest a model whereby CAD risk is driven in part by the convergence of causal genes onto a particular transcriptional pathway in endothelial cells. They highlight shared genes between common and rare vascular diseases (CAD and CCM), and identify TLNRD1 as a new, previously uncharacterized member of the CCM signalling pathway. This approach will be widely useful for linking variants to functions for other common polygenic diseases.

Interplay between polygenic variants related immune response and lifestyle factors mitigate the chances of stroke in a genome-wide association study.

We aimed to investigate the intricate interplay between genetic predisposition and lifestyle factors on stroke. We conducted a comprehensive genome-wide association study to identify the genetic variants linked to stroke in the participants who experienced a stroke event (cases; n 672) and those with no stroke history (non-stroke; n 58 029) in a large hospital-based cohort. Using generalised multifactor dimensionality reduction, we identified genetic variants with interactive effects and constructed polygenic risk scores (PRS) by summing up the risk alleles from the genetic variants. Food intake was measured with a validated semi-quantitative FFQ. No significant differences in stroke incidence were seen in demographic variables between the two groups. Among the metabolic indicators, only serum TAG levels were higher in males with stroke than those without stroke. The daily nutrient intake, dietary inflammation index, glycaemic index, dietary patterns, alcohol consumption, exercise and smoking did not display associations with the OR for stroke. The stroke-linked genetic variants were related to the IL-18 pathway. After accounting for covariates, the PRS derived from the 5-, 6- and 7-SNP models were positively associated with stroke chance with 2·5-, 2·9- and 2·8-fold. Furthermore, interactions between genetic predisposition and dietary components, including energy, carbohydrates, n-3 fatty acids and branched-chain amino acids (BCAA), that affected OR for stroke were observed. A high intake of energy, carbohydrates and BCAA and a low intake of n-3 fatty acids were positively associated with the chances of stroke occurrence. In conclusion, understanding the interaction between genetic variants and lifestyle factors can assist in developing stroke prevention and management strategies.