Polygenic Risk Score Models Research Articles

Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection

Machine learning (ML) methods are increasingly becoming crucial in genome-wide association studies for identifying key genetic variants or SNPs that statistical methods might overlook. Statistical methods predominantly identify SNPs with notable effect sizes by conducting association tests on individual genetic variants, one at a time, to determine their relationship with the target phenotype. These genetic variants are then used to create polygenic risk scores (PRSs), estimating an individual's genetic risk for complex diseases like cancer or cardiovascular disorders. Unlike traditional methods, ML algorithms can identify groups of low-risk genetic variants that improve prediction accuracy when combined in a mathematical model. However, the application of ML strategies requires addressing the feature selection challenge to prevent overfitting. Moreover, ensuring the ML model depends on a concise set of genomic variants enhances its clinical applicability, where testing is feasible for only a limited number of SNPs. In this study, we introduce a robust pipeline that applies ML algorithms in combination with feature selection (ML-FS algorithms), aimed at identifying the most significant genomic variants associated with the coronary artery disease (CAD) phenotype. The proposed computational approach was tested on individuals from the UK Biobank, differentiating between CAD and non-CAD individuals within this extensive cohort, and benchmarked against standard PRS-based methodologies like LDpred2 and Lassosum. Our strategy incorporates cross-validation to ensure a more robust evaluation of genomic variant-based prediction models. This method is commonly applied in machine learning strategies but has often been neglected in previous studies assessing the predictive performance of polygenic risk scores. Our results demonstrate that the ML-FS algorithm can identify panels with as few as 50 genetic markers that can achieve approximately 80% accuracy when used in combination with known risk factors. The modest increase in accuracy over PRS performances is noteworthy, especially considering that PRS models incorporate a substantially larger number of genetic variants. This extensive variant selection can pose practical challenges in clinical settings. Additionally, the proposed approach revealed novel CAD-genetic variant associations.

Polygenic Risk Score Predicts Earlier-Onset Adult Systemic Lupus Erythematosus and First-Year Renal Diseases in a Taiwanese Cohort

ObjectivesThis study aimed to develop a predictive model using polygenic risk score (PRS) to forecast renal outcomes for adult systemic lupus erythematosus (SLE) in a Taiwanese population.MethodsPatients with SLE (n=2782)...

The interaction between sodium to potassium intake and genetics to inform dietary management in hypertension

Blood pressure (BP) is influenced by both genetics and diet. Dietary management of hypertension includes increasing potassium-rich foods while reducing sodium intakes. Dietary sodium and potassium intakes can be measured objectively using urinary sodium and urinary potassium, with lower urinary sodium to potassium ratios associated with lower BP(1). Understanding the interplay between diet and genetics may be useful in treating hypertension and determining which individuals may receive an outsized benefit from lowering their sodium potassium ratio. This study aims to investigate whether identifying genetic risk for hypertension could be utilised to identify individuals who may benefit most from lowering sodium intake and increasing potassium intake. UK Biobank cohort participants (n = 296,475) with data on genotype, BP and spot urinary sodium and potassium data were used. Diet quality was assessed using Oxford WebQ. Biologically directed genetic scores for BP were constructed for pathways related to sodium/potassium biology (pharmagenic enrichment scores [PES]), as well as traditional polygenic risk scores (PRS). A gene-by-environment effect between urinary electrolytes, diet quality and PRS on BP were tested. Genetic risk, diet quality and urinary electrolytes independently correlated with BP. Urinary sodium had larger BP increasing effects amongst individuals who had high genetic risk in sodium/potassium pathways than those with comparatively lower genetic risk. Higher diet quality had a small effect on reducing BP in baseline PRS models, but this did not remain significant in the full model. Polygenic scores for BP personalised to individual sodium/potassium biology (PES) could be used to identify individuals who may receive an outsized benefit from a personalised sodium/potassium dietary intervention. These findings may inform future precision and personalised dietary advice for the management of hypertension.

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.

Polygenic risk score-based prediction of breast cancer risk in Taiwanese women with dense breast using a retrospective cohort study

Mammographic screening has contributed to a significant reduction in breast cancer mortality. Several studies have highlighted the correlation between breast density, as detected through mammography, and a higher likelihood of developing breast cancer. A polygenic risk score (PRS) is a numerical score that is calculated based on an individual's genetic information. This study aims to explore the potential roles of PRS as candidate markers for breast cancer development and investigate the genetic profiles associated with clinical characteristics in Asian females with dense breasts. This is a retrospective cohort study integrated breast cancer screening, population genotyping, and cancer registry database. The PRSs of the study cohort were estimated using genotyping data of 77 single nucleotide polymorphisms based on the PGS000001 Catalog. A subgroup analysis was conducted for females without breast symptoms. Breast cancer patients constituted a higher proportion of individuals in PRS Q4 (37.8% vs. 24.8% in controls). Among dense breast patients with no symptoms, the high PRS group (Q4) consistently showed a significantly elevated breast cancer risk compared to the low PRS group (Q1–Q3) in both univariate (OR = 2.25, 95% CI 1.43–3.50, P < 0.001) and multivariate analyses (OR: 2.23; 95% CI 1.41–3.48, P < 0.001). The study was extended to predict breast cancer risk using common low-penetrance risk variants in a PRS model, which could be integrated into personalized screening strategies for Taiwanese females with dense breasts without prominent symptoms.

Obesity risk in young adults from the Jerusalem Perinatal Study (JPS): the contribution of polygenic risk and early life exposure

Background/ObjectivesThe effects of early life exposures on offspring life-course health are well established. This study assessed whether adding early socio-demographic and perinatal variables to a model based on polygenic risk score (PRS) improves prediction of obesity risk.MethodsWe used the Jerusalem Perinatal study (JPS) with data at birth and body mass index (BMI) and waist circumference (WC) measured at age 32. The PRS was constructed using over 2.1M common SNPs identified in genome-wide association study (GWAS) for BMI. Linear and logistic models were applied in a stepwise approach. We first examined the associations between genetic variables and obesity-related phenotypes (e.g., BMI and WC). Secondly, socio-demographic variables were added and finally perinatal exposures, such as maternal pre-pregnancy BMI (mppBMI) and gestational weight gain (GWG) were added to the model. Improvement in prediction of each step was assessed using measures of model discrimination (area under the curve, AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI).ResultsOne standard deviation (SD) change in PRS was associated with a significant increase in BMI (β = 1.40) and WC (β = 2.45). These associations were slightly attenuated (13.7–14.2%) with the addition of early life exposures to the model. Also, higher mppBMI was associated with increased offspring BMI (β = 0.39) and WC (β = 0.79) (p < 0.001). For obesity (BMI ≥ 30) prediction, the addition of early socio-demographic and perinatal exposures to the PRS model significantly increased AUC from 0.69 to 0.73. At an obesity risk threshold of 15%, the addition of early socio-demographic and perinatal exposures to the PRS model provided a significant improvement in reclassification of obesity (NRI, 0.147; 95% CI 0.068–0.225).ConclusionsInclusion of early life exposures, such as mppBMI and maternal smoking, to a model based on PRS improves obesity risk prediction in an Israeli population-sample.

Polygenic effects on the risk of Alzheimer’s disease in the Japanese population

BackgroundPolygenic effects have been proposed to account for some disease phenotypes; these effects are calculated as a polygenic risk score (PRS). This score is correlated with Alzheimer’s disease (AD)-related phenotypes, such as biomarker abnormalities and brain atrophy, and is associated with conversion from mild cognitive impairment (MCI) to AD. However, the AD PRS has been examined mainly in Europeans, and owing to differences in genetic structure and lifestyle, it is unclear whether the same relationships between the PRS and AD-related phenotypes exist in non-European populations. In this study, we calculated and evaluated the AD PRS in Japanese individuals using genome-wide association study (GWAS) statistics from Europeans.MethodsIn this study, we calculated the AD PRS in 504 Japanese participants (145 cognitively unimpaired (CU) participants, 220 participants with late mild cognitive impairment (MCI), and 139 patients with mild AD dementia) enrolled in the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) project. In order to evaluate the clinical value of this score, we (1) determined the polygenic effects on AD in the J-ADNI and validated it using two independent cohorts (a Japanese neuropathology (NP) cohort (n = 565) and the North American ADNI (NA-ADNI) cohort (n = 617)), (2) examined the AD-related phenotypes associated with the PRS, and (3) tested whether the PRS helps predict the conversion of MCI to AD.ResultsThe PRS using 131 SNPs had an effect independent of APOE. The PRS differentiated between CU participants and AD patients with an area under the curve (AUC) of 0.755 when combined with the APOE variants. Similar AUC was obtained when PRS calculated by the NP and NA-ADNI cohorts was applied. In MCI patients, the PRS was associated with cerebrospinal fluid phosphorylated-tau levels (β estimate = 0.235, p value = 0.026). MCI with a high PRS showed a significantly increased conversion to AD in APOE ε4 noncarriers with a hazard rate of 2.22. In addition, we also developed a PRS model adjusted for LD and observed similar results.ConclusionsWe showed that the AD PRS is useful in the Japanese population, whose genetic structure is different from that of the European population. These findings suggest that the polygenicity of AD is partially common across ethnic differences.

Genome-wide association study of cardiometabolic multimorbidity in the UK Biobank.

Considering the high prevalence and poor prognosis of cardiometabolic multimorbidity (CMM), identifying causal factors and actively implementing preventive measures is crucial. However, Mendelian randomization (MR), a key method for identifying the causal factors of CMM, requires knowledge of the effects of SNPs on CMM, which remain unknown. We first analyzed the genetic overlap of single cardiometabolic diseases (CMDs) using the latest genome-wide association study (GWAS) for evidential support and comparison. We observed strong positive genetic correlations and shared loci among all CMDs. Further, GWAS and post-GWAS analyses of CMM were performed in 407 949 European ancestry individuals from the UK Biobank. Eleven loci and 12 lead SNPs were identified. By comparison, we found these SNPs were a subset of SNPs associated with CMDs, including both shared and non-shared SNPs. Then, the polygenic risk score model predicted the risk of CMM (C-index = 0.62) and we identified candidate genes related to lipid metabolism and immune function. Finally, as an example, two-sample MR analysis based on the GWAS revealed potential causal effects of total cholesterol, serum urate, body mass index, and smoking on CMM. These results provide a basis for future MR research and inspire future studies on the mechanism and prevention of CMM.

Using clinical and genetic risk factors for risk prediction of 8 cancers in the UK Biobank.

Models with polygenic risk scores and clinical factors to predict risk of different cancers have been developed, but these models have been limited by the polygenic risk score-derivation methods and the incomplete selection of clinical variables. We used UK Biobank to train the best polygenic risk scores for 8 cancers (bladder, breast, colorectal, kidney, lung, ovarian, pancreatic, and prostate cancers) and select relevant clinical variables from 733 baseline traits through extreme gradient boosting (XGBoost). Combining polygenic risk scores and clinical variables, we developed Cox proportional hazards models for risk prediction in these cancers. Our models achieved high prediction accuracy for 8 cancers, with areas under the curve ranging from 0.618 (95% confidence interval = 0.581 to 0.655) for ovarian cancer to 0.831 (95% confidence interval = 0.817 to 0.845) for lung cancer. Additionally, our models could identify individuals at a high risk for developing cancer. For example, the risk of breast cancer for individuals in the top 5% score quantile was nearly 13 times greater than for individuals in the lowest 10%. Furthermore, we observed a higher proportion of individuals with high polygenic risk scores in the early-onset group but a higher proportion of individuals at high clinical risk in the late-onset group. Our models demonstrated the potential to predict cancer risk and identify high-risk individuals with great generalizability to different cancers. Our findings suggested that the polygenic risk score model is more predictive for the cancer risk of early-onset patients than for late-onset patients, while the clinical risk model is more predictive for late-onset patients. Meanwhile, combining polygenic risk scores and clinical risk factors has overall better predictive performance than using polygenic risk scores or clinical risk factors alone.

Polygenic risk score predicts all-cause death in East Asian patients with prior coronary artery disease.

Coronary artery disease (CAD) is a highly heritable and multifactorial disease. Numerous genome-wide association studies (GWAS) facilitated the construction of polygenic risk scores (PRS) for predicting future incidence of CAD, however, exclusively in European populations. Furthermore, identifying CAD patients with elevated risks of all-cause death presents a critical challenge in secondary prevention, which will contribute largely to reducing the burden for public healthcare. We recruited a cohort of 1,776 Chinese CAD patients and performed medical follow-up for up to 11 years. A pruning and thresholding method was used to calculate PRS of CAD and its 14 risk factors. Their correlations with all-cause death were computed via Cox regression. We found that the PRS for CAD and its seven risk factors, namely myocardial infarction, ischemic stroke, angina, heart failure, low-density lipoprotein cholesterol, total cholesterol and C-reaction protein, were significantly associated with death (P ≤ 0.05), whereas the PRS of body mass index displayed moderate association (P < 0.1). Elastic-net Cox regression with 5-fold cross-validation was used to integrate these nine PRS models into a meta score, metaPRS, which performed well in stratifying patients at different risks for death (P < 0.0001). Combining metaPRS with clinical risk factors further increased the discerning power and a 4% increase in sensitivity. The metaPRS generated from the genetic susceptibility to CAD and its risk factors can well stratify CAD patients by their risks of death. Integrating metaPRS and clinical risk factors may contribute to identifying patients at higher risk of poor prognosis.

Mitigating type 1 error inflation and power loss in GxE PRS: Genotype-environment interaction in polygenic risk score models.

The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an individual's genetic profile. However, the impact of genotype-environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing genotype-environment interaction polygenic risk score (GxE PRS) models are often inappropriately used, which can result in inflated type 1 error rates and compromised results. In this study, we propose novel GxE PRS models that jointly incorporate additive and interaction genetic effects although also including an additional quadratic term for nongenetic covariates, enhancing their robustness against model misspecification. Through extensive simulations, we demonstrate that our proposed models outperform existing models in terms of controlling type 1 error rates and enhancing statistical power. Furthermore, we apply the proposed models to real data, and report significant GxE effects. Specifically, we highlight the impact of our models on both quantitative and binary traits. For quantitative traits, we uncover the GxE modulation of genetic effects on body mass index by alcohol intake frequency. In the case of binary traits, we identify the GxE modulation of genetic effects on hypertension by waist-to-hip ratio. These findings underscore the importance of employing a robust model that effectively controls type 1 error rates, thus preventing the occurrence of spurious GxE signals. To facilitate the implementation of our approach, we have developed an innovative R software package called GxEprs, specifically designed to detect and estimate GxE effects. Overall, our study highlights the importance of accurate GxE modeling and its implications for genetic risk prediction, although providing a practical tool to support further research in this area.

Polygenic risk score model for renal cell carcinoma in the Korean population and relationship with lifestyle-associated factors

BackgroundThe polygenic risk score (PRS) is used to predict the risk of developing common complex diseases or cancers using genetic markers. Although PRS is used in clinical practice to predict breast cancer risk, it is more accurate for Europeans than for non-Europeans because of the sample size of training genome-wide association studies (GWAS). To address this disparity, we constructed a PRS model for predicting the risk of renal cell carcinoma (RCC) in the Korean population.ResultsUsing GWAS analysis, we identified 43 Korean-specific variants and calculated the PRS. Subsequent to plotting receiver operating characteristic (ROC) curves, we selected the 31 best-performing variants to construct an optimal PRS model. The resultant PRS model with 31 variants demonstrated a prediction rate of 77.4%. The pathway analysis indicated that the identified non-coding variants are involved in regulating the expression of genes related to cancer initiation and progression. Notably, favorable lifestyle habits, such as avoiding tobacco and alcohol, mitigated the risk of RCC across PRS strata expressing genetic risk.ConclusionA Korean-specific PRS model was established to predict the risk of RCC in the underrepresented Korean population. Our findings suggest that lifestyle-associated factors influencing RCC risk are associated with acquired risk factors indirectly through epigenetic modification, even among individuals in the higher PRS category.

Polygenic Risk Score Reveals Genetic Heterogeneity of Alzheimer's Disease between the Chinese and European Populations.

The polygenic risk score (PRS) aggregates the effects of numerous genetic variants associated with a condition across the human genome and may help to predict late-onset Alzheimer's disease (LOAD). Most of the current PRS studies on Alzheimer's disease (AD) have been conducted in Caucasian ancestry populations, while it is less studied in Chinese. To establish and examine the validity of Chinese PRS, and explore its racial heterogeneity. We constructed a PRS using both discovery (N = 2012) and independent validation samples (N = 1008) from Chinese population. The associations between PRS and age at onset of LOAD or cerebrospinal fluid (CSF) biomarkers were assessed. We also replicated the PRS in an independent replication cohort with CSF data and constructed an alternative PRS using European weights. Multi-center genetics study. A total of 3020 subjects were included in the study. PRS was calculated using genome-wide association studies data and evaluated the performance alone (PRSnoAPOE) and with other predictors (full model: LOAD ~ PRSnoAPOE + APOE+ sex + age) by measuring the area under the receiver operating curve (AUC). PRS of the full model achieved the highest AUC of 84.0% (95% CI = 81.4-86.5) with pT< 0.5, compared with the model containing APOE alone (61.0%). The AUC of PRS with pT<5e-8 was 77.8% in the PRSnoAPOE model, 81.5% in the full model, and only ranged from 67.5% to 75.1% in the PRS with the European weights model. A higher PRS was significantly associated with an earlier age at onset (P <0.001). The PRS also performed well in the replication cohort of the full model (AUC=83.1%, 95% CI = 74.3-92.0). The CSF biomarkers of Aβ42 and the ratio of Aβ42/Aβ40 were significantly inversely associated with the PRS, while p-Tau181 showed a positive association. This finding suggests that PRS reveal genetic heterogeneity and higher prediction accuracy of the PRS for AD can be achieved using a base dataset and validation within the same ethnicity. The effective PRS model has the clinical potential to predict individuals at risk of developing LOAD at a given age and with abnormal levels of CSF biomarkers in the Chinese population.

Improving dementia phenotype prediction with functional mapping of genetic data in non‐European ancestries

AbstractBackgroundMany common genetic variants have been associated with dementia, but their ability to predict dementia prospectively is unknown, especially in non‐European ancestral populations. We sought to optimize dementia risk prediction models with machine learning in individuals of diverse genetic ancestries.MethodSamples were selected from the University of California Los Angeles Health System with their electronic health records (EHRs) and linked genetic data. Dementia phenotype was defined using diagnosis codes and mapped phecodes. Polygenic risk scores (PRSs) were built using summary statistics of different population‐based genome‐wide association studies (GWASs). Independent significant and functionally mapped single nucleotide polymorphisms (SNPs) were identified using the FUMA tool. Logistic regression models were built with 1) APOE status, 2) a single PRS, or 3) multiple PRSs. The model with age and sex only was built as the baseline model. We trained LASSO models to determine dementia risk SNPs from weighted SNP‐level genetic data. We compared the predictive performance of these models after five‐fold cross‐validation with area under precision‐recall curve (AUPRC) and area under receiver operating characteristic (AUC). Finally, identified risk SNPs were mapped to genes for biological interpretation.ResultThere were 975 individuals included in the final Hispanic Latinx American ancestry sample, of which 9.44% (N = 92) were dementia cases. The model with LASSO‐selected functional mapped SNPs from multiple neurodegenerative diseases GWASs outperformed all other genetic risk score models (AUPRC: 0.39, AUROC: 0.87), resulting in a 19.6% increase in AUPRC from the best PRS model, and a 26.9% increase from the baseline model. The best‐performing LASSO model included nine SNPs, mostly located in the 19q13, 4p15, and 17q21 regions. Results of mapped genes not only revealed well‐known risk genes of dementia but identified risk genes from other neurodegenerative diseases.ConclusionWe demonstrated that building genetic risk scores with trans‐ancestry GWAS and selecting potential causal SNPs can be beneficial for dementia prediction in non‐European ancestry populations. Further training using machine learning methods with functionally mapped SNP‐level genetic data showed significant improvement in predictive power compared to PRS models. Genetic risks of multiple neurodegenerative diseases may contribute to the predisposition to dementia.

Multi‐Omics Insights Into The Heterogeneity Of Depression In Alzheimer’s Disease

AbstractBackgroundDepression is a common, though heterogenous, comorbidity in late‐onset Alzheimer’s Disease (LOAD) patients. Moreover, individuals with depression are at greater risk to develop LOAD. In prior work, we performed pleiotropy analysis and found shared genetic etiology between LOAD and Major Depressive Disorder (MDD), suggesting inter‐relationship between LOAD and depression symptoms. However, the underpinning genetic, epigenetic, and transcriptomic etiologies predisposing the comorbidity of depression in LOAD, are largely unknown.MethodThis work applied various bioinformatics methods including analyses of polygenic risk scores (PRS), differential gene expression and DNA‐methylation. The discovery analyses were performed using ROSMAP datasets including, genotypes, transcriptomic, DNA‐methylomic and depression diagnosis. When available the NACC dataset was used for validation. In addition, MDD‐GWAS summary statistics from the Psychiatric Genomics Consortium were used to create the PRS.ResultWe constructed PRS based on MDD‐GWAS data and assessed its performance in predicting depression onset in LOAD patients. The PRS showed marginal results in standalone models for predicting depression onset in both ROSMAP (AUC = 0.540) and NACC (AUC = 0.527). Full models, with baseline age, sex, education, and APOEε4 allele count, showed improved prediction of depression onset (ROSMAP AUC: 0.606, NACC AUC: 0.581). In time‐to‐event analysis, standalone PRS models showed significant effects in ROSMAP (P = 0.0051), but not in NACC cohort. Full models showed significant performance in predicting depression in LOAD for both datasets (P&lt;0.001). Next, we investigated differential gene expression in LOAD patients with and without comorbid depression and didn’t reveal significant association between differences in gene expression and the risk of depression in LOAD. Upon sex‐stratification, we identified 25 differential expressed genes (DEGs) in males, of which CHI3L2 showed the strongest upregulation, and only 3 DEGs in females. Finally, we tested differences in DNA‐methylation and found significant hypomethylation with only one CpG probe.ConclusionOur study is the first multi‐omics genome‐wide exploration of the genomic landscapes underlying the heterogeneity of depression in LOAD. Our developed PRS accurately predicted LOAD patients with depressive symptoms. Furthermore, we discovered sex‐dependent differences in gene expression associated with the risk of depression symptoms in LOAD. Collectively, our results have clinical implications for the diagnosis and treatment of depression in LOAD patients.

Identification of 13 Novel Loci in a Genome-Wide Association Study on Taiwanese with Hepatocellular Carcinoma.

Liver cancer is caused by complex interactions among genetic factors, viral infection, alcohol abuse, and metabolic diseases. We conducted a genome-wide association study and polygenic risk score (PRS) model in Taiwan, employing a nonspecific etiology approach, to identify genetic risk factors for hepatocellular carcinoma (HCC). Our analysis of 2836 HCC cases and 134,549 controls revealed 13 novel associated loci such as the FAM66C gene, noncoding genes, liver-fibrosis-related genes, metabolism-related genes, and HCC-related pathway genes. We incorporated the results from the UK Biobank and Japanese database into our study for meta-analysis to validate our findings. We also identified specific subtypes of the major histocompatibility complex that influence both viral infection and HCC progression. Using this data, we developed a PRS to predict HCC risk in the general population, patients with HCC, and HCC-affected families. The PRS demonstrated higher risk scores in families with multiple HCCs and other cancer cases. This study presents a novel approach to HCC risk analysis, identifies seven new genes associated with HCC development, and introduces a reproducible PRS model for risk assessment.

Comparison of clinical and genetic risk model based on ethnicity for ischemic stroke after atrial fibrillation catheter ablation

Abstract Introduction CHA₂DS₂-VASc score (CVS) is widely used to estimate the risk of stroke in atrial fibrillation (AF) patients. However, CVS does not include genetic risk, and the risk model for stroke dose not well developed after AF catheter ablation (AFCA). We explored the clinical and genetic risks for ischemic stroke (IS) and compared the difference in prediction power according to ethnicity. Methods We used available genome-wide association study (GWAS) data in European (34217 IS, 406111 controls) and Asian (22664 IS, 152022 controls) to develop polygenic risk score (PRS) for IS in 2897 independent Korean AF patients (Median 60 [IQR 52, 67] years, male 74.0%, paroxysmal AF 66.3%, anticoagulation rate 60% in 1367 patients over 2 points of CVS) who underwent the AFCA. The patients with IS within 2 weeks after the AFCA were excluded. In the independent AF cohort during the median 50 [IQR 28-94] months follow-up, 48 experienced the IS after the AFCA. We developed two different PRS derived from European and Asian GWAS data and then two different CVS-combined PRS models in the independent cohort. After including SNPs with p-value &amp;lt;5e-8 in the development cohort, rs635634 and rs671 were used in the CVS-combined European PRS (CVS-EPRS) and CVS-combined Asian PRS (CVS-APRS) model, respectively in the independent cohort. We compared the C index of risk models to investigate the prediction power for the IS. Results The CVS was significantly higher in the patients with IS than in their counterparts (3.0 IQR [1.0, 4.0 vs. 1.0 [1.0, 2.0], p&amp;lt;0.001). The overall C index was 0.716 in CVS alone model, 0.711 in the CVS-EPRS model, and 0.725 CVS-APRS model. For predicting 1-year IS after the AFCA, the CVS-APRS model improved the C index as compared to CVS alone model (0.834 vs. 0.813, p=0.037). For predicting 3-year IS after the AFCA, the C-index was higher in the CVS-APRS model as compared to the CVS-EPRS model (0.746 vs. 0.721, p=0.030) Conclusions In AF patients with the AFCA, genetic polymorphism with clinical risk factors contributed to predict the 1-year IS. However, ethnicity might be an important factor in adapting genetic contributions to predicting IS.Study flow chartTime dependent ROC curve

Abstract 16629: Comparison of Clinical and Genetic Risk Model for Ischemic Stroke After Atrial Fibrillation Catheter Ablation

Introduction: CHA 2 DS 2 -VASc score (CVS) is used to estimate risk of stroke in atrial fibrillation (AF) patients. But, CVS does not include genetic risk. We explored clinical and genetic risks for ischemic stroke (IS) after AF catheter ablation (AFCA). Methods: We used genome-wide association study (GWAS) data in European (34217 IS, 406111 controls) and Asian (22664 IS, 152022 controls) to develop polygenic risk score (PRS) for IS in 2897 independent Korean AF patients (Median 60 years, male 74.0%, paroxysmal AF 66.3%, anticoagulation rate 60% in 1367 patients with CVS ≥ 2 who underwent AFCA. Patients with IS within 2 weeks after AFCA were excluded. In the independent AF cohort during median 50 [IQR 28-94] months follow-up, 48 experienced IS after AFCA. We developed two different CVS-combined PRS models in the independent cohort using European and Asian GWAS data. Using p-value &lt;5e-8, rs635634 and rs671 were used in CVS-combined European PRS (CVS-EPRS) and CVS-combined Asian PRS (CVS-APRS) model, respectively in the independent cohort. We compared C index of risk models to investigate prediction power for IS. Results: CVS was higher in patients with IS than in their counterparts (3.0 IQR [1.0, 4.0] vs. 1.0 [1.0, 2.0], p&lt;0.001). Overall C index was 0.716 in CVS model, 0.711 in CVS-EPRS model, and 0.725 CVS-APRS model. For 1-year IS after AFCA, CVS-APRS model improved C index as compared to CVS model (0.834 vs. 0.813, p=0.037). For 3-year IS after AFCA, C index was higher in CVS-APRS model rather than CVS-EPRS model (0.746 vs. 0.721, p=0.030). In patients with CVS &lt; 2, both CVS-APRS (0.849, p=0.013) and CVS-EPRS (0.766, p=0.027) models showed better prediction for 1-year IS compared to CVS model (0.523). Conclusions: In AF patients with AFCA, genetic polymorphism with clinical risk factors contributed to predict 1-year IS. Genetic contribution was more obvious in AF patients with CVS &lt;2. However, ethnicity might be an important factor in adapting genetic contributions to predicting IS.

Cognitive impairment in long-living adults: a genome-wide association study, polygenic risk score model and molecular modeling of the APOE protein.

Cognitive impairment is an irreversible, aging-associated condition that robs people of their independence. The purpose of this study was to investigate possible causes of this condition and propose preventive options. We assessed cognitive status in long-living adults aged 90+ (n = 2,559) and performed a genome wide association study using two sets of variables: Mini-Mental State Examination scores as a continuous variable (linear regression) and cognitive status as a binary variable (> 24, no cognitive impairment; <10, impairment) (logistic regression). Both variations yielded the same polymorphisms, including a well-known marker of dementia, rs429358in the APOE gene. Molecular dynamics simulations showed that this polymorphism leads to changes in the structure of alpha helices and the mobility of the lipid-binding domain in the APOE protein. These changes, along with higher LDL and total cholesterol levels, could be the mechanism underlying the development of cognitive impairment in older adults. However, this polymorphism is not the only determining factor in cognitive impairment. The polygenic risk score model included 45 polymorphisms (ROC AUC 69%), further confirming the multifactorial nature of this condition. Our findings, particularly the results of PRS modeling, could contribute to the development of early detection strategies for predisposition to cognitive impairment in older adults.

The largest genome-wide association study for breast cancer in Taiwanese Han population.

Breast cancer is a molecularly heterogeneous disease, and multiple genetic variants contribute to its development and prognosis. Most of previous genome-wide association studies (GWASs) and polygenic risk scores (PRSs) analyses focused on studying breast cancers of Caucasian populations, which may not be applicable to other population. Therefore, we conducted the largest breast cancer cohort of Taiwanese population to fill in the knowledge gap. A total of 152,534 Participants recruited by China Medical University Hospital between 2003 and 2019 were filtered by several patient selection criteria and GWAS quality control steps, resulting in the inclusion of 2496 cases and 9984 controls for this study. We then conducted GWAS for all breast cancers and PRS analyses for all breast cancers and the four breast cancer subtypes, including luminal A, luminal B, basal-like, and HER2-enriched. The GWAS analyses identified 113 SNPs, 50 of which were novel. The PRS models for all breast cancers and the luminal A subtype showed positively correlated trends between the PRS and the risk of developing breast cancer. The odds ratios (95% confidence intervals) for the groups with the highest PRS in all breast cancers and the luminal A subtype were 5.33 (3.79-7.66) and 3.55 (2.13-6.14), respectively. In summary, we explored the association of genetic variants with breast cancer in the largest Taiwanese cohort and developed two PRS models that can predict the risk of developing any breast cancer and the luminal A subtype in Taiwanese women.