Genome-wide Polygenic Risk Score Research Articles

Modeling gene interactions in polygenic prediction via geometric deep learning.

Polygenic risk score (PRS) is a widely-used approach for predicting individuals' genetic risk of complex diseases, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall short in capturing the intricate relationships between genotype and phenotype. In this study, we present PRS-Net, an interpretable geometric deep learning-based framework that effectively models the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the genome-wide PRS at the single-gene resolution, and then explicitly encapsulates gene-gene interactions leveraging a graph neural network (GNN) for genetic risk prediction, enabling a systematic characterization of molecular interplay underpinning diseases. An attentive readout module is introduced to facilitate model interpretation. Extensive tests across multiple complex traits and diseases demonstrate the superior prediction performance of PRS-Net compared to conventional PRS methods. The interpretability of PRS-Net further enhances the identification of disease-relevant genes and gene programs. PRS-Net provides a potent tool for concurrent genetic risk prediction and biological discovery for complex diseases.

Polygenic Risk Score and Lung Adenocarcinoma Risk Among Never-Smokers by EGFR Mutation Status: A Brief Report

We assessed the association between a genome-wide polygenic risk score (PRS) developed for lung adenocarcinoma (LUAD) risk and mutation on the EGFR gene in 998 East Asian never-smoking female LUAD cases (518 EGFR-positive; 480 EGFR-negative) and 4544 never-smoking controls using case-case and multinomial regression analyses. We found that the PRS was more strongly associated with EGFR-positive LUAD compared with EGFR-negative LUAD, where the association between the fourth quartile of the PRS and EGFR-positive LUAD (odds ratio = 8.63, 95% confidence interval: 5.67–13.14) was significantly higher than the association between the fourth quartile of the PRS with EGFR-negative LUAD (odds ratio = 3.50, 95% confidence interval: 2.44–5.00) (p-heterogeneity = 3.66 × 10−3). Our findings suggest that germline genetic susceptibility may be differentially associated with LUAD in never-smoking female East Asian patients depending on the cancer's mutation status, which may have important public health and clinical implications.

The association between periodontal disease and coronary artery disease: insights from imaging, observational and genome-wide data

Abstract Background Periodontal disease is the sixth most common disease worldwide. It has been suggested that it may be a contributory risk factor for coronary artery disease (CAD). From a public health perspective, prevention of periodontal disease is relatively straightforward and cost-effective: daily brushing, flossing and, where applicable, smoking cessation. Purpose This study utilises imaging, longitudinal and genomic data to characterise the association between periodontal disease and CAD. Methods 481,915 individuals from the UK Biobank were included in this study, 18.9% (n = 91,022) of whom had self-reported periodontal disease. For the imaging analysis, a maximum of 59,019 had paired cardiovascular magnetic resonance (CMR) data. Multivariable linear regression models were constructed to examine the association of periodontal disease on CMR outcomes with LV end-diastolic volume, LV mass, LV mass:volume ratio (MVR), LV ejection fraction, LV global longitudinal strain and native T1 values considered as prognostically important phenotypes. The relationship between periodontal disease and incident CAD was assessed using Cox proportional hazards regression models. To examine the relationship of genetically-determined periodontal disease on CAD, a de novo GWAS for periodontal disease was performed and a genome-wide polygenic risk score (PRS) was constructed. Results Periodontal disease was associated with a significantly higher LV MVR (effect size = 0.00233, 95% confidence interval [CI] = 0.0006, 0.004) and significantly lower LV native global T1 value (effect size = -0.86 ms, 95% CI = -1.63, -0.09). The presence of periodontal disease was independently associated with a significantly increased hazard of incident CAD (HR = 1.09, 95% CI = 1.07, 1.13) at a median follow-up time of 13.8 years (Figure 1). Each standard deviation increase in the periodontal disease PRS was associated with increased odds of CAD (OR = 1.03, 95% CI = 1.02, 1.05) (Figure 2). Conclusion Using an integrated approach across imaging, observational and genomic data, periodontal disease is independently associated with biomarkers of subclinical remodelling as well as incident CAD. These findings highlight the potential importance of periodontal disease in the broader context of cardiovascular disease prevention.

Genetic and lifestyle risks for coronary artery disease and risk of incident dementia

Abstract Background Lifestyle risk factors for coronary artery disease (CAD) have been associated with neurodegeneration and dementia. Whether an underlying genetic risk for the development of CAD further increases this risk, however, remains unclear. Purpose To examine whether an increased genetic risk for CAD is associated with an increased risk of dementia, and to investigate whether lifestyle factors modify these associations. Methods A prospective cohort study of 457,353 participants of European ancestry without dementia at baseline (mean [SD], age 57 [8] years; 55% were female) was conducted within the UK Biobank study. Exposures were a genome-wide polygenic risk score (PRS) for CAD and a modified version of the American Heart Association’s Life’s Essential 8 (LE8) cardiovascular health score comprising four biological (BMI, BP, lipids, glucose) and four lifestyle (smoking status, diet, physical activity, and sleep duration) risk factors previously linked to both CAD and dementia. Dementia diagnoses were obtained from death register and inpatient records. Results 9,337 cases of all-cause dementia were observed over a median 13.9-year follow-up. There were no significant interactions between genetic risk for CAD and lifestyle factors for all-cause dementia, or any other dementia subtype. After adjusting for potential demographic confounders, a higher genetic risk for CAD was associated with elevated risk of developing all-cause dementia (hazard ratio (HR) per 1-SD increase in PRS = 1.08 [95% CI, 1.05-1.10]), Alzheimer’s disease (HR 1.08 [1.03-1.12]), and vascular dementia (HR 1.13 [1.06-1.19]). An unfavourable LE8 cardiovascular health score was also associated with an increased risk of developing all-cause dementia (HR 1.05 [1.02-1.08]), predominantly through its association with vascular dementia (HR 1.14 [1.06-1.22]), as we found no evidence for any associations between LE8 and Alzheimer’s disease (HR 1.00 [0.97-1.02]). Conclusion These findings indicate that a high genetic risk for CAD is associated with an increased risk of developing major dementia subtypes. Regardless of this underlying genetic risk, however, our results suggest that lifestyle interventions designed to reduce the risk of CAD may also reduce one’s risk of developing dementia, particularly in those with underlying vascular pathology.

Incremental Prognostic Value of a Coronary Heart Disease Polygenic Risk Score in Type 2 Diabetes.

The recent availability of cardiovascular risk-reducing type 2 diabetes (T2D) therapies makes it imperative to optimally identify individuals who could derive benefit. Current clinical risk prediction may misclassify individuals as low risk and could be improved. Our aim was to determine the incremental prognostic value of a coronary heart disease (CHD) genome-wide polygenic risk score (PRS) to a clinical risk score in prediction of major adverse cardiovascular events (MACE) in patients with T2D. We evaluated 10,556 individuals with T2D aged 40-79 without a prior cardiovascular hospitalization. We calculated 10-year clinical cardiovascular risk at the date of recruitment using the Pooled Cohort Equation (PCE Risk) and constructed a CHD PRS. The primary outcome was time to first MACE incidence, and we assessed the additional incremental predictive value of the CHD PRS to the PCE risk. At 10 years, there were 1,477 MACE. After adjustment for clinical risk, the CHD PRS was significantly associated with MACE (hazard ratio [HR] 1.69 per SD increase, 95% CI 1.60-1.79). Individuals with PCE Risk <7.5% but in the top quintile of CHD PRS had a significantly increased likelihood of MACE (HR 10.69, 95% CI 5.07-22.55) compared with those in the lowest. The addition of the PRS to the clinical risk score led to significant improvements in risk prediction, particularly in those at low clinical risk. The addition of a CHD PRS to clinical assessment improved MACE prediction in T2D individuals without prior cardiovascular disease, particularly in those at low clinical risk.

Prediction of metabolic syndrome using machine learning approaches based on genetic and nutritional factors: a 14-year prospective-based cohort study

IntroductionMetabolic syndrome is a chronic disease associated with multiple comorbidities. Over the last few years, machine learning techniques have been used to predict metabolic syndrome. However, studies incorporating demographic, clinical, laboratory, dietary, and genetic factors to predict the incidence of metabolic syndrome in Koreans are limited. In the present study, we propose a genome-wide polygenic risk score for the prediction of metabolic syndrome, along with other factors, to improve the prediction accuracy of metabolic syndrome.MethodsWe developed 7 machine learning-based models and used Cox multivariable regression, deep neural network (DNN), support vector machine (SVM), stochastic gradient descent (SGD), random forest (RAF), Naïve Bayes (NBA) classifier, and AdaBoost (ADB) to predict the incidence of metabolic syndrome at year 14 using the dataset from the Korean Genome and Epidemiology Study (KoGES) Ansan and Ansung.ResultsOf the 5440 patients, 2,120 were considered to have new-onset metabolic syndrome. The AUC values of model, which included sex, age, alcohol intake, energy intake, marital status, education status, income status, smoking status, dried laver intake, and genome-wide polygenic risk score (gPRS) Z-score based on 344,447 SNPs (p-value < 1.0), were the highest for RAF (0.994 [95% CI 0.985, 1.000]) and ADB (0.994 [95% CI 0.986, 1.000]).ConclusionsIncorporating both gPRS and demographic, clinical, laboratory, and seaweed data led to enhanced metabolic syndrome risk prediction by capturing the distinct etiologies of metabolic syndrome development. The RAF- and ADB-based models predicted metabolic syndrome more accurately than the NBA-based model for the Korean population.

The Association Between Periodontal Disease and Cardiovascular Disease: Insights From Imaging, Observational, and Genetic Data

BackgroundPeriodontal disease is the sixth most common disease worldwide and may be a contributory risk factor for cardiovascular disease (CVD). ObjectivesThis study utilizes noninvasive cardiac imaging and longitudinal and genetic data to characterize the association between periodontal disease and both cardiovascular magnetic resonance (CMR) imaging biomarkers of remodeling and incident coronary artery disease (CAD). MethodsFrom the UK Biobank, 481,915 individuals were included, 91,022 (18.9%) of whom had self-reported periodontal disease. For imaging analysis, 59,019 had paired CMR data. Multivariable linear regression models were constructed to examine the association of periodontal disease on CMR outcomes. The endpoints for the CMR analyses were left ventricle (LV) end-diastolic volume, LV ejection fraction, LV mass, LV mass:volume ratio, LV global longitudinal strain, and native T1 values. The relationship between periodontal disease and CVD was assessed using Cox proportional hazards regression models, with incident CAD as the endpoint. To examine the relationship of genetically determined periodontal disease on CAD, a genome-wide polygenic risk score was constructed. ResultsPeriodontal disease was associated with a significantly higher LV mass:volume ratio (effect size: 0.00233; 95% CI: 0.0006-0.004) and significantly lower T1 values (effect size: −0.86 ms; 95% CI: −1.63 to −0.09). Periodontal disease was independently associated with an increased hazard of incident CAD (HR: 1.09; 95% CI: 1.07-1.13) at a median follow-up time of 13.8 years. Each SD increase in the periodontal disease polygenic risk score was associated with increased odds of CAD (OR: 1.03; 95% CI: 1.02-1.05). ConclusionsUsing an integrated approach across imaging, observational, and genomic data, periodontal disease is associated with biomarkers of subclinical remodeling as well as incident CAD. These findings highlight the potential importance of periodontal disease in the broader context of CVD prevention.

Polygenic inheritance and its interplay with smoking history in predicting lung cancer diagnosis: a French-Canadian case-control cohort

The most near-term clinical application of genome-wide association studies in lung cancer is a polygenic risk score (PRS). A case-control dataset was generated consisting of 4002 lung cancer cases from the LORD project and 20,010 ethnically matched controls from CARTaGENE. A genome-wide PRS including >1.1 million genetic variants was derived and validated in UK Biobank (n=5419 lung cancer cases). The predictive ability and diagnostic discrimination performance of the PRS was tested in LORD/CARTaGENE and benchmarked against previous PRSs from the literature. Stratified analyses were performed by smoking status and genetic risk groups defined as low (<20th percentile), intermediate (20-80th percentile) and high (>80th percentile) PRS. The phenotypic variance explained and the effect size of the genome-wide PRS numerically outperformed previous PRSs. Individuals with high genetic risk had a 2-fold odds of lung cancer compared to low genetic risk. The PRS was an independent predictor of lung cancer beyond conventional clinical risk factors, but its diagnostic discrimination performance was incremental in an integrated risk model. Smoking increased the odds of lung cancer by 7.7-fold in low genetic risk and by 11.3-fold in high genetic risk. Smoking with high genetic risk was associated with a 17-fold increase in the odds of lung cancer compared to individuals who never smoked and with low genetic risk. Individuals at low genetic risk are not protected against the smoking-related risk of lung cancer. The joint multiplicative effect of PRS and smoking increases the odds of lung cancer by nearly 20-fold. This work was supported by the CQDM and the IUCPQ Foundation owing to a generous donation from Mr. Normand Lord.

The association of lumbar intervertebral disc degeneration with low back pain is modified by underlying genetic propensity to pain

BACKGROUND CONTEXTAssociations between magnetic resonance imaging (MRI)-detected lumbar intervertebral disc degeneration (LDD) and LBP are often of modest magnitude. This association may be larger in specific patient subgroups. PURPOSETo examine whether the association between LDD and LBP is modified by underlying genetic predispositions to pain. STUDY DESIGNCross-sectional study in UK Biobank (UKB) and Twins UK. PATIENT SAMPLESA genome-wide association study (GWAS) of the number of anatomical chronic pain locations was conducted in 347,538 UKB participants. The GWAS was used to develop a genome-wide polygenic risk score (PRS) in a holdout sample of 30,000 UKB participants. The PRS model was then used in analyses of 645 TwinsUK participants with standardized LDD MRI assessments. OUTCOME MEASURESEver having had LBP associated with disability lasting ≥1 month (LBP1). METHODSUsing the PRS as a proxy for “genetically-predicted propensity to pain”, we stratified TwinsUK participants into PRS quartiles. A “basic” model examined the association between an LDD summary score (LSUM) and LBP1, adjusting for covariates. A “fully-adjusted” model also adjusted for PRS quartile and LSUM x PRS quartile interaction terms. RESULTSIn the basic model, the odds ratio (OR) of LBP1 was 1.8 per standard deviation of LSUM (95% confidence interval [CI] 1.4–2.3). In the fully-adjusted model, there was a statistically significant LSUM-LBP1 association in quartile 4, the highest PRS quartile (OR=2.5 [95% CI 1.7–3.7], p=2.6×10−6), and in quartile 3 (OR=2.0, [95% CI 1.3–3.0]; p=.002), with small-magnitude and/or nonsignificant associations in the lowest 2 PRS quartiles. PRS quartile was a significant effect modifier of the LSUM-LBP1 association (interaction p≤.05). CONCLUSIONSGenetically-predicted propensity to pain modifies the LDD-LBP association, with the strongest association present in people with the highest genetic propensity to pain. Lumbar MRI findings may have stronger connections to LBP in specific subgroups of people.

Higher Genetic Risk for Type 2 Diabetes Is Associated With a Faster Decline of β-Cell Function in an East Asian Population.

While most genetic variants of type 2 diabetes (T2D) are suggested to be associated with β-cell dysfunction cross sectionally, their association with the longitudinal change of β-cell function remains largely unknown. We analyzed data from 6,311 participants without T2D at baseline (mean [SD] age 51.6 [8.7] years) from a community-based prospective cohort in Korea. Participants underwent biennial 2-h 75-g oral glucose tolerance tests (OGTTs) during 14 years of follow-up, and the OGTT-derived disposition index (DI) was used as a marker for β-cell function. Genetic risk was quantified using the genome-wide polygenic risk score (PRS) and was stratified into low (1st quintile), intermediate (2nd-4th quintiles), and high (5th quintile) genetic risk. Lifestyle was assessed according to Life's Essential 8. During a mean follow-up of 10.9 years, 374 (29.6%), 851 (22.5%), and 188 (14.9%) participants developed T2D in the high, intermediate, and low genetic risk groups, respectively. Compared with the low genetic risk group, participants in the high genetic risk group had a 25% lower DI at baseline. Furthermore, in longitudinal analysis, we observed a 1.83-fold faster decline in log2-transformed DI per year (-0.034 vs. -0.019, P = 2.1 × 10-3; per 1-SD increase in T2D PRS, P = 1.2 × 10-4). Healthy lifestyle attenuated the rate of decline in DI across all genetic risk groups. Individuals with a higher genetic risk for T2D exhibited not only a lower OGTT-derived β-cell function at baseline but also a notably more rapid decline during follow-up. This information could be used to enable a focused precision prevention with lifestyle intervention.

Predicting Risks of Dry Eye Disease Development Using a Genome-Wide Polygenic Risk Score Model.

The purpose of this study was to conduct a large-scale genome-wide association study (GWAS) and construct a polygenic risk score (PRS) for risk stratification in patients with dry eye disease (DED) using the Taiwan Biobank (TWB) databases. This retrospective case-control study involved 40,112 subjects of Han Chinese ancestry, sourced from the publicly available TWB. Cases were patients with DED (n = 14,185), and controls were individuals without DED (n = 25,927). The patients with DED were further divided into 8072 young (<60 years old) and 6113 old participants (≥60 years old). Using PLINK (version 1.9) software, quality control was carried out, followed by logistic regression analysis with adjustments for sex, age, body mass index, depression, and manic episodes as covariates. We also built PRS prediction models using the standard clumping and thresholding method and evaluated their performance (area under the curve [AUC]) through five-fold cross-validation. Eleven independent risk loci were identified for these patients with DED at the genome-wide significance levels, including DNAJB6, MAML3, LINC02267, DCHS1, SIRPB3P, HULC, MUC16, GAS2L3, and ZFPM2. Among these, MUC16 encodes mucin family protein. The PRS model incorporated 932 and 740 genetic loci for young and old populations, respectively. A higher PRS score indicated a greater DED risk, with the top 5% of PRS individuals having a 10-fold higher risk. After integrating these covariates into the PRS model, the area under the receiver operating curve (AUROC) increased from 0.509 and 0.537 to 0.600 and 0.648 for young and old populations, respectively, demonstrating the genetic-environmental interaction. Our study prompts potential candidates for the mechanism of DED and paves the way for more personalized medication in the future. Our study identified genes related to DED and constructed a PRS model to improve DED prediction.

#1450 Polygenic risk score of native IgA nephropathy predicts risk of post-transplant disease recurrence

Abstract Background and Aims IgA nephropathy (IgAN) is a polygenic immune-mediated progressive glomerular disease. Recent GWAS reported that high genome-wide polygenic risk score (IgAN GPS), including 1 249 077 variants mapping to systemic immune pathways, was associated with risk of end stage kidney disease (ESKD). Post transplant (KTx) IgAN recurrence occurs in 30% of cases and it is a major cause of graft loss. Pathogenesis and predictors of IgAN recurrence are still not well defined. Herein, we studied the role of native IgAN GPS and clinical factors in predicting post-KTx IgAN recurrence and graft failure in a large cohort of KTx recipients. Method Four thousand one hundred eighty six KTx recipients from 6 sites were genome wide genotyped and imputed against multi-ancestry reference panels, retrieving &amp;gt;10 million variants. In each recipient, native IgAN GPS was computed and standard-normalized (i.e. expressed in units of standard deviations from the mean distribution in matched controls). Native kidney disease diagnosis, clinical parameters, and post-KTx outcome data were retrieved retrospectively from clinical charts. The IgAN GPS was tested as predictor of native IgAN in the entire KTx cohort in a logistic regression model adjusted for sex, age, recruitment site, and first 5 PCs. IgAN GPS was tested as predictor of age at transplantation (proxy for age at ESKD) among KTx recipients with native IgAN in a linear regression model adjusted for sex, recruitment site, and first 5 PCs. In recipients with native IgAN, recipient and donor parameters were individually tested as predictors for time to IgAN recurrence and time to graft failure in Cox proportional hazards models adjusted for recruitment site and first 5 PCs. Predictors with P-value &amp;lt; .1 were included in the multivariable models. Recipient IgAN GPS was added to the clinical models and tested for prediction of time-to IgAN recurrence, and, among KTx recipients with IgAN recurrence, of time-to subsequent graft failure. Results Three hundred sixty nine KTx recipients were affected by native IgAN, 271 were of male sex, median age at transplantation was 45 years, and 149 received a living donor KTx. Over a median follow-up time of 46 months, 96 IgAN recurrence events occurred, and, among KTx recipients with recurrent IgAN, 27 subsequent graft failure events occurred after 100 months post KTx. IgAN GPS significantly predicted native IgAN cases among all KTx recipients (OR [95% CI]: 1.95 [1.76,2.16], p = 5.60E-07, AUROC 0.73), and lower age at transplantation among recipients with native IgAN (beta [s.e.]: −1.11[0.55], p = 0.04). Clinical model for IgAN recurrence included age, sex, year of transplant, HLA mismatch, recruitment site, and first 5 PCs; clinical model for graft failure included age, sex, year of transplant, HLA mismatch, type of donation, donor age, sex, rejection events, recruitment site, and first 5 PCs. At the multivariable analysis, IgAN GPS, lower age at transplantation, lower HLA mismatch, and higher year of transplant were independently associated with time to IgAN recurrence (Fig. 1a). Among KTx recipients with recurrent IgAN, higher IgAN GPS predicted time-to graft failure independently from clinical factors (Fig. 1b), but not in non-recurrent cases. Conclusion In the KTx setting, IgAN GPS was an independent predictor of IgAN recurrence, suggesting shared genetic susceptibility between native and recurrent IgAN. In addition, associations of IgAN GPS with lower age at transplantation, and with graft failure among recurrent IgAN cases, suggest that IgAN GPS may be predictive of a more aggressive native as well as recurrent disease. Associations with lower HLA mismatch advocate donor-recipient genetic similarities as potential additional predictors of IgAN recurrence. These data, which need external validation, may provide potential new insights into the pathobiology and prediction of recurrent IgAN.

Abstract 6151: Relationship between polygenic risk score, lifestyle factors, and colorectal cancer risk

Abstract Although heritable factors are estimated to explain up to 35% of colorectal cancers (CRC), most are diagnosed in people without a family history of CRC suggesting that inherited genetic variants, environmental factors, or a combination of both, contribute to CRC risk. We explored the relationship between a polygenic risk score (PRS) for CRC risk and lifestyle risk factors to understand whether individuals with higher genetic risk, who do not develop CRC, engage in healthier lifestyle behaviors, or conversely, whether those with lower genetic risk, who do develop CRC, engage in riskier behaviors in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Using summary statistics obtained from a large independent GWAS meta-analysis of CRC by the GECCO consortium, we developed a genome-wide PRS for CRC using SBayesR and applied it to participants of European ancestry genotyped in the PLCO Trial (n=74,396). For all subjects, we computed an environmental (E) score based on 19 lifestyle risk factors for CRC, a WCRF/AICR score that measured adherence to the 2018 Cancer Prevention recommendations, and a 2015 healthy eating index (HEI) with higher values indicating healthier lifestyle profiles. We used t-tests to evaluate differences in lifestyle scores between CRC-free participants in the highest quartile of PRS and incident CRC cases in the lowest quartile of PRS. To examine the correlations between the PRS and lifestyle scores, we performed multivariable linear regression models regressing PRS on each lifestyle score. We used multivariable Cox hazards regression models to compute hazard ratios and 95% confidence intervals for CRC risk; multiplicative interactions between PRS and lifestyle scores were evaluated using the Wald test for the interaction terms. During the 15 years follow-up period, a total of 1,052 CRC cases were diagnosed. Individuals in the highest quartile of PRS who remained cancer-free were more likely to engage in healthier behaviors than those in the lowest quartile of PRS who were diagnosed with incident CRC (E-score: -0.01 vs. -0.18, WCRF/AICR score: -0.05 vs. -0.32, HEI score: 0.06 vs. -0.13); however, differences were not statistically significant (P value: 0.1 to 0.4). PRS and lifestyle scores were significantly (all P &amp;lt;0.0001), albeit weakly (β: -0.02 to -0.03), associated among participants who remained CRC-free but not among CRC cases (P: 0.1 to 0.3). PRS-lifestyle score interactions were not statistically significant (P: 0.2 to 0.4). Although the differences and multiplicative interaction models were not statistically significant, we found that individuals with a higher PRS who remained CRC-free were slightly more likely to engage in healthier behaviors than those at a lower genetic score who developed CRC. Additional studies with larger sample size are warranted to further explore the interplay between genetic risk and lifestyle factors related to CRC risk. Citation Format: Wen-Yi Huang, Erikka Loftfield, Rebecca Landy, Difei Wang, Hormuzd A. Katki, Jianxin Shi, Lingxiao Wang, Mitchell J. Machiela, Neal D. Freedman, Sonja I. Berndt. Relationship between polygenic risk score, lifestyle factors, and colorectal cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6151.

Sex-specific and polygenic effects underlying resting heart rate and associated risk of cardiovascular disease.

Resting heart rate (RHR) is associated with cardiovascular disease (CVD) and mortality. This study aimed to identify genetic loci associated with RHR, develop a genome-wide polygenic risk score (PRS) for RHR, and assess associations between the RHR PRS and CVD outcomes, to better understand the biological mechanisms linking RHR to disease. Sex-specific analyses were conducted to potentially elucidate different pathways between the sexes. We performed a genome-wide meta-analysis of RHR (n = 550 467) using two independent study populations, The Trøndelag Health Study (HUNT) and the UK Biobank (UKB), comprising 69 155 and 481 312 participants, respectively. We also developed a genome-wide PRS for RHR using UKB and tested for association between the PRS and 13 disease outcomes in HUNT. We identified 403, 253, and 167 independent single nucleotide polymorphisms (SNPs) significantly associated with RHR in the total population, women, and men, respectively. The sex-specified analyses indicated differences in the genetic contribution to RHR and revealed loci significantly associated with RHR in only one of the sexes. The SNPs were mapped to genes enriched in heart tissue and cardiac conduction pathways, as well as disease-pathways, including dilated cardiomyopathy. The PRS for RHR was associated with increased risk of hypertension and dilated cardiomyopathy, and decreased risk of atrial fibrillation. Our findings provide insight into the pleiotropic effects of the RHR variants, contributing towards an improved understanding of mechanisms linking RHR and disease. In addition, the sex-specific results might contribute to a more refined understanding of RHR as a risk factor for the different diseases.

A Functional Single-Nucleotide Polymorphism Upstream of the Collagen Type III Gene Is Associated with Catastrophic Fracture Risk in Thoroughbred Horses.

Fractures caused by bone overloading are a leading cause of euthanasia in Thoroughbred racehorses. The risk of fatal fracture has been shown to be influenced by both environmental and genetic factors but, to date, no specific genetic mechanisms underpinning fractures have been identified. In this study, we utilised a genome-wide polygenic risk score to establish an in vitro cell system to study bone gene regulation in horses at high and low genetic risk of fracture. Candidate gene expression analysis revealed differential expression of COL3A1 and STAT1 genes in osteoblasts derived from high- and low-risk horses. Whole-genome sequencing of two fracture cases and two control horses revealed a single-nucleotide polymorphism (SNP) upstream of COL3A1 that was confirmed in a larger cohort to be significantly associated with fractures. Bioinformatics tools predicted that this SNP may impact the binding of the transcription factor SOX11. Gene modulation demonstrated SOX11 is upstream of COL3A1, and the region binds to nuclear proteins. Furthermore, luciferase assays demonstrated that the region containing the SNP has promoter activity. However, the specific effect of the SNP depends on the broader genetic background of the cells and suggests other factors may also be involved in regulating COL3A1 expression. In conclusion, we have identified a novel SNP that is significantly associated with fracture risk and provide new insights into the regulation of the COL3A1 gene.

Genome-Wide Polygenic Risk Score for CKD in Individuals with APOL1 High-Risk Genotypes.

Genome-Wide Polygenic Risk Score for CKD in Individuals with APOL1 High-Risk Genotypes.

Gene-based burden scores identify rare variant associations for 28 blood biomarkers

BackgroundA relevant part of the genetic architecture of complex traits is still unknown; despite the discovery of many disease-associated common variants. Polygenic risk score (PRS) models are based on the evaluation of the additive effects attributable to common variants and have been successfully implemented to assess the genetic susceptibility for many phenotypes. In contrast, burden tests are often used to identify an enrichment of rare deleterious variants in specific genes. Both kinds of genetic contributions are typically analyzed independently. Many studies suggest that complex phenotypes are influenced by both low effect common variants and high effect rare deleterious variants. The aim of this paper is to integrate the effect of both common and rare functional variants for a more comprehensive genetic risk modeling.MethodsWe developed a framework combining gene-based scores based on the enrichment of rare functionally relevant variants with genome-wide PRS based on common variants for association analysis and prediction models. We applied our framework on UK Biobank dataset with genotyping and exome data and considered 28 blood biomarkers levels as target phenotypes. For each biomarker, an association analysis was performed on full cohort using gene-based scores (GBS). The cohort was then split into 3 subsets for PRS construction and feature selection, predictive model training, and independent evaluation, respectively. Prediction models were generated including either PRS, GBS or both (combined).ResultsAssociation analyses of the cohort were able to detect significant genes that were previously known to be associated with different biomarkers. Interestingly, the analyses also revealed heterogeneous effect sizes and directionality highlighting the complexity of the blood biomarkers regulation. However, the combined models for many biomarkers show little or no improvement in prediction accuracy compared to the PRS models.ConclusionThis study shows that rare variants play an important role in the genetic architecture of complex multifactorial traits such as blood biomarkers. However, while rare deleterious variants play a strong role at an individual level, our results indicate that classical common variant based PRS might be more informative to predict the genetic susceptibility at the population level.

Genome-wide polygenic risk score, cardiometabolic risk factors, and type 2 diabetes mellitus in the Chinese population.

Type 2 diabetes (T2D) is caused by both genetic and cardiometabolic risk factors. However, the magnitude of the genetic predisposition of T2D in the Chinese population remains largely unknown. This study included93,488 participants from the China Kadoorie Biobank, and multiple polygenic risk scores (PRS) were calculated. A common cardiometabolic risk score (CRS) using smoking, alcohol consumption, physical activity, diet, obesity, blood pressure, and blood lipids was constructed to investigate the effects of cardiometabolic risk factors on T2D. Furthermore, an equation based on ideal PRS, CRS, and their interaction was established to explore the combined effects on T2D. An ideally fitting PRS model (variance explained, R2 = 7.6%) was reached based on multiple PRS calculation methods. An additive interaction between PRS and CRS (coefficient = 28%, 95% CI: 0.20-0.36, p < 0.001) was found. The R2 of the T2D predictive model could increase to 8.3% when CRS and the interaction terms of PRS × CRS were considered. In the etiological composition of T2D, the ratio of genetic risk effect, cardiometabolic risk effect, and interaction between genetic and cardiometabolic factors was 67:16:17. This study identified an ideally fitting PRS model for identifying and predicting the risk of T2D suitable for the Chinese population. The quantified proportional structure of genetic risk factors, cardiometabolic risk factors, and their interaction was detected, which elucidated the critical effect of genetic factors.

Genome-wide polygenic risk score for type 2 diabetes in Indian population

Genome-wide polygenic risk scores (PRS) for lifestyle disorders, like Type 2 Diabetes (T2D), are useful in identifying at-risk individuals early on in life, and to guide them towards healthier lifestyles. The current study was aimed at developing PRS for the Indian population using imputed genotype data from UK Biobank and testing the developed PRS on data from GenomegaDB of Indians living in India. 959 T2D cases and 2,818 controls were selected from Indian participants of UK Biobank to develop the PRS. Summary statistics available for South Asians, from the DIAMANTE consortium, were used to weigh genetic variants. LDpred2 algorithm was used to adjust the effect of linkage disequilibrium among the variants. The association of PRS with T2D, after adjusting for age, sex and top ten genetic principal components, was found to be very significant (AUC = 0.7953, OR = 2.9856 [95% CI: 2.7044–3.2961]). When participants were divided into four PRS quartile groups, the odds of developing T2D increased sequentially with the higher PRS groups. The highest PRS group (top 25%) showed 5.79 fold increased risk compared to the rest of the participants (75%). The PRS derived using the same set of variants was found to be significantly associated with T2D in the test dataset of 445 Indians (AUC = 0.7781, OR = 1.6656 [95%CI = 0.6127–4.5278]). Our study demonstrates a framework to derive Indian-specific PRS for T2D. The accuracy of the derived PRS shows it’s potential to be used as a prognostic metric to stratify individuals, and to recommend personalized preventive strategies.

Development and Replication of a Genome-Wide Polygenic Risk Score for Chronic Back Pain.

Chronic back pain (CBP) is a complex heritable trait and a major cause of disability worldwide. We developed and validated a genome-wide polygenic risk score (PRS) for CBP using a large-scale GWAS based on UK Biobank participants of European ancestry (N = 265,000). The PRS showed poor overall predictive ability (AUC = 0.56 and OR = 1.24 per SD, 95% CI: 1.22-1.26), but individuals from the 99th percentile of PRS distribution had a nearly two-fold increased risk of CBP (OR = 1.82, 95% CI: 1.60-2.06). We validated the PRS on an independent TwinsUK sample, obtaining a similar magnitude of effect. The PRS was significantly associated with various ICD-10 and OPCS-4 diagnostic codes, including chronic ischemic heart disease (OR = 1.1, p-value = 4.8 × 10-15), obesity, metabolism-related traits, spine disorders, disc degeneration, and arthritis-related disorders. PRS and environment interaction analysis with twelve known CBP risk factors revealed no significant results, suggesting that the magnitude of G × E interactions with studied factors is small. The limited predictive ability of the PRS that we developed is likely explained by the complexity, heterogeneity, and polygenicity of CBP, for which sample sizes of a few hundred thousand are insufficient to estimate small genetic effects robustly.