Performance Of Polygenic Risk Scores Research Articles

Abstract PO5-08-10: Breast cancer risk prediction performance of polygenic risk score in Taiwanese female with dense breast: A nested case-control study

Abstract Introduction: Breast cancer is the most common cancer among women in Taiwan and the incidence rate of breast cancer has been increasing steadily over the past 30 years. To address this, a nationwide screening program with biennial mammography for women aged 40-69 was implemented in 2004, which has contributed to a significant reduction in breast cancer mortality. Asian women are more likely to have dense breast tissue, and studies have shown a correlation between breast density and the risk of developing breast cancer, with higher breast density values associated with increased risk. Polygenic risk scores (PRS) are calculated based on an individual's genetic information and can estimate their risk of developing a particular disease. Although PRS can identify low-penetrance risk variants, its use in breast cancer risk estimation is typically limited to gene-only models, and integration with breast cancer screening databases is rare. Hence, the aim of this specific study was to evaluate the predictive capacity of PRS in women with dense breast tissue by exploring potential genetic markers and constructing a PRS to investigate the genetic factors associated with clinical characteristics and the risk of developing breast cancer. Methods: The PRS was developed using genetic information from the Taiwan Precision Medicine Initiative genotyping data. A total of 6335 patients were enrolled, and 101 single nucleotide polymorphisms (SNPs) were selected as candidate markers based on the PGS Catalog (PGS000001). Clinical characteristics of the study cohort were summarised using median and range, or frequency and percentage. The distribution of characteristics between breast cancer and controls was estimated using an independent two-sample t-test, chi-square, and Fisher’s exact test. The association of the PRS and related characteristics with breast cancer risk in the study cohort was estimated using univariate and multivariate binomial logistic regression. Harrel’s C-index was reported to demonstrate the predictive performance of PRS in both univariate and multivariate models. All p-values were two-sided, and a p less than .05 is considered statistically significant. All analyses were performed using R 4.1.2 (R core team, 2023) Results: The results showed that breast cancer patients constituted a higher proportion of individuals in PRS Q4 (37.8% vs 24.8% in controls). The model's predictive performance for breast cancer risk increased from 0.565 (95% CI = 0.520-0.611) to 0.699 (95% CI = 0.644-0.755) by adding related clinical characteristics compared to the PRS-only model. Patients characterized with benign breast disease (OR = 0.50, 95% CI = 0.31-0.79, P = 0.004) showed a decreased breast cancer risk, and patients with mastalgia or palpable breast lesion (OR = 6.87, 95% CI = 4.29-10.8, P < 0.001) predicted significant greater breast cancer risk. Subgroup analysis of patients without breast symptoms was conducted as they may be overlooked or underestimated for breast cancer screening. For dense breast patients without 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 = 2.22, 95% CI = 1.41-3.46, P < 0.001). Conclusion: Breast cancer screening has undergone a shift from a general approach to a personalized, risk-based approach. Besides breast cancer screening using family history as the only risk factor, our proposed model identifies both genetic and clinical risk factors using big data analyses including the EHR, cancer screening, and registry from a single institute. The study suggests that integrating PRS into personalized screening strategies could improve risk prediction for Taiwanese females with dense breasts without prominent symptoms. Table 1. Clinical characteristics of study cohort (n=6335). Table 2. Association of the PRS quartiles in breast cancer risk. Table 3. Predictive performance of PRS for breast cancer risk. Citation Format: Chih Yean Lum, Chih Chiang Hung, Chi-Cheng Huang, Tzu-Hung Hsiao, Sin-Hua Moi. Breast cancer risk prediction performance of polygenic risk score in Taiwanese female with dense breast: A nested case-control study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-08-10.

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.

Risk prediction and interaction analysis using polygenic risk score of type 2 diabetes in a Korean population

Joint modelling of genetic and environmental risk factors can provide important information to predict the risk of type 2 diabetes (T2D). Therefore, to predict the genetic risk of T2D, we constructed a polygenic risk score (PRS) using genotype data of one Korean cohort, KARE (745 cases and 2549 controls), and the genome-wide association study summary statistics of Biobank Japan. We evaluated the performance of PRS in an independent Korean cohort, HEXA (5684 cases and 35,703 controls). Individuals with T2D had a significantly higher mean PRS than controls (0.492 vs. − 0.078, p≈0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx 0$$\\end{document}). PRS predicted the risk of T2D with an AUC of 0.658 (95% CI 0.651–0.666). We also evaluated interaction between PRS and waist circumference (WC) in the HEXA cohort. PRS exhibited a significant sub-multiplicative interaction with WC (ORinteraction 0.991, 95% CI 0.987–0.995, pinteraction = 4.93 × 10–6) in T2D. The effect of WC on T2D decreased as PRS increased. The sex-specific analyses produced similar interaction results, revealing a decreased WC effect on T2D as the PRS increased. In conclusion, the risk of WC for T2D may differ depending on PRS and those with a high PRS might develop T2D with a lower WC threshold. Our findings are expected to improve risk prediction for T2D and facilitate the identification of individuals at an increased risk of T2D.

Abstract PR001: Highlighting the combined effects of BMI and polygenic risk score on endometrial cancer risk

Abstract Obesity is more strongly associated with endometrial cancer risk than any other cancer type. However, the extent to which genetic susceptibility influences the risk of endometrial cancer independent of obesity remains unknown. Thus, this study aimed to evaluate the predictive performance of polygenic risk score (PRS), body mass index (BMI) and other factors in determining endometrial cancer risk, as well as to explore joint effects on endometrial cancer. PRS was generated using 1.1 million HapMap3 variants from the largest published endometrial cancer genome-wide association study. An endometrial cancer prediction model was constructed using established endometrial cancer risk factors (age, BMI, number of live births, ever taken oral contraceptive pill, age of menarche, and predicted age of menopause). Additionally, an integrated prediction model was constructed, combining the endometrial cancer PRS with established risk factors. The ability of models to predict endometrial cancer status was assessed in unrelated participants of European ancestry from the UK Biobank cohort, encompassing 1,867 participants with endometrial cancer (860 incident cases) and 133,322 cancer-free controls. The integrated model including PRS and risk factors had a modest yet statistically significant improvement in prediction of endometrial cancer status, compared with the model using risk factors alone (AUC 0.752 versus 0.741; P = 1.28 × 10−7). When examining incident cases, participants in the middle and top tertiles of the PRS distribution had a 1.96- (95% CI 1.39-2.77; P = 1.28 × 10−4) and 3.03-fold (95% CI 2.01-4.58; P = 1.60 × 10−8) elevated risk of developing endometrial cancer, respectively, in contrast to those in the lowest tertile. As expected, elevated BMI was associated with increased endometrial cancer risk. Compared to participants with a normal BMI, overweight participants (25kg/m2 ≤ BMI < 30kg/m2) had a 1.58-fold increased hazard ratio (HR; 95% CI 1.32-1.90; P = 8.43 × 10−7), while obese participants (BMI ≥ 30kg/m2) a 3.23-fold increased HR (95% CI 2.71-3.85; P < 2.00 × 10−16). Notably, we observed multiplying effects of BMI and PRS on endometrial cancer risk, with obese participants in the top tertile of the PRS distribution having a 7.53-fold increased HR (95% CI 3.78-14.99; P = 9.41 × 10−9). In summary, these findings indicate that higher PRS was associated with endometrial cancer risk, irrespective of BMI. Moreover, the integration of PRS with established risk factors may aid in better stratifying the general population for their susceptibility to endometrial cancer. Citation Format: Tracy A. O'Mara, Xuemin Wang, Laure Dossus, Marc J. Gunter, Emma J. Crosbie, Dylan M. Glubb. Highlighting the combined effects of BMI and polygenic risk score on endometrial cancer risk [abstract]. In: Proceedings of the AACR Special Conference on Endometrial Cancer: Transforming Care through Science; 2023 Nov 16-18; Boston, Massachusetts. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(5_Suppl):Abstract nr PR001.

Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization.

Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (CHD; PRSCHD) for 5 genetic ancestry groups. We derived ancestry-specific and multi-ancestry PRSCHD based on pruning and thresholding (PRSPT) and ancestry-based continuous shrinkage priors (PRSCSx) applied to summary statistics from the largest multi-ancestry genome-wide association study meta-analysis for CHD to date, including 1.1 million participants from 5 major genetic ancestry groups. Following training and optimization in the Million Veteran Program, we evaluated the best-performing PRSCHD in 176,988 individuals across 9 diverse cohorts. Multi-ancestry PRSPT and PRSCSx outperformed ancestry-specific PRSPT and PRSCSx across a range of tuning values. Two best-performing multi-ancestry PRSCHD (ie, PRSPTmult and PRSCSxmult) and 1 ancestry-specific (PRSCSxEUR) were taken forward for validation. PRSPTmult demonstrated the strongest association with CHD in individuals of South Asian ancestry and European ancestry (odds ratio per 1 SD [95% CI, 2.75 [2.41-3.14], 1.65 [1.59-1.72]), followed by East Asian ancestry (1.56 [1.50-1.61]), Hispanic/Latino ancestry (1.38 [1.24-1.54]), and African ancestry (1.16 [1.11-1.21]). PRSCSxmult showed the strongest associations in South Asian ancestry (2.67 [2.38-3.00]) and European ancestry (1.65 [1.59-1.71]), lower in East Asian ancestry (1.59 [1.54-1.64]), Hispanic/Latino ancestry (1.51 [1.35-1.69]), and the lowest in African ancestry (1.20 [1.15-1.26]). The use of summary statistics from a large multi-ancestry genome-wide meta-analysis improved the performance of PRSCHD in most ancestry groups compared with single-ancestry methods. Despite the use of one of the largest and most diverse sets of training and validation cohorts to date, improvement of predictive performance was limited in African ancestry. This highlights the need for larger genome-wide association study datasets of underrepresented populations to enhance the performance of PRSCHD.

Incorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction

BackgroundGenetic variants can contribute differently to trait heritability by their functional categories, and recent studies have shown that incorporating functional annotation can improve the predictive performance of polygenic risk scores (PRSs). In addition, when only a small proportion of variants are causal variants, PRS methods that employ a Bayesian framework with shrinkage can account for such sparsity. It is possible that the annotation group level effect is also sparse. However, the number of PRS methods that incorporate both annotation information and shrinkage on effect sizes is limited. We propose a PRS method, PRSbils, which utilizes the functional annotation information with a bilevel continuous shrinkage prior to accommodate the varying genetic architectures both on the variant-specific level and on the functional annotation level.ResultsWe conducted simulation studies and investigated the predictive performance in settings with different genetic architectures. Results indicated that when there was a relatively large variability of group-wise heritability contribution, the gain in prediction performance from the proposed method was on average 8.0% higher AUC compared to the benchmark method PRS-CS. The proposed method also yielded higher predictive performance compared to PRS-CS in settings with different overlapping patterns of annotation groups and obtained on average 6.4% higher AUC. We applied PRSbils to binary and quantitative traits in three real world data sources (the UK Biobank, the Michigan Genomics Initiative (MGI), and the Korean Genome and Epidemiology Study (KoGES)), and two sources of annotations: ANNOVAR, and pathway information from the Kyoto Encyclopedia of Genes and Genomes (KEGG), and demonstrated that the proposed method holds the potential for improving predictive performance by incorporating functional annotations.ConclusionsBy utilizing a bilevel shrinkage framework, PRSbils enables the incorporation of both overlapping and non-overlapping annotations into PRS construction to improve the performance of genetic risk prediction. The software is available at https://github.com/styvon/PRSbils.

Transcriptional risk score in Alzheimer’s disease: from pathology to cognition

AbstractBackgroundTranscriptional risk scores (TRS), a summation of polarized expression levels of functional genes, have been proposed for disease risk assessment using transcriptome profiles. Our previously developed blood‐based TRS showed associations with diagnosis and neuroimaging biomarkers for Alzheimer’s disease (AD). Here, we developed brain‐based TRS models and calculated TRS using brain tissue‐based transcriptome profiles.MethodWe integrated AD genome‐wide association study summary and expression quantitative trait locus data to prioritize target genes using Mendelian randomization and then calculated individual TRS by summing the polarized z‐scores over the target genes. We used brain transcriptome data from the Religious Orders Study and Rush Memory and Aging Project cohort (ROSMAP; N = 634) as a discovery set and the Mount Sinai Brain Bank cohort (MSBB; N = 244) as a replication set. We performed association analysis of TRS with AD diagnosis, amyloidopathy, tauopathy, and cross‐sectional and longitudinal cognitive function. We compared AD classification performance of TRS with polygenic risk scores (PRS) using a machine learning approach (Fig. 1).ResultHigher TRS values were significantly associated with AD compared to cognitively normal controls (Table 1). Higher TRS values were also related with amyloidopathy, tauopathy, poor cognitive performance, and faster cognitive decline. The AD classification performance of PRS was increased with the inclusion of TRS up to 12% with the area under the curve value of 0.842 (Fig. 2). These findings were replicated in the replication dataset.ConclusionOur results showed that brain‐based TRS is associated with AD pathology and clinical course. Furthermore, TRS improves the AD classification performance of PRS. Along with static PRS, dynamic TRS based on transcriptional expression levels at a given time could be a potential disease‐stage specific AD biomarker.

Latin American Trans-ancestry INitiative for OCD genomics (LATINO): Study protocol.

Obsessive-compulsive disorder (OCD) is a debilitating psychiatric disorder. Worldwide, its prevalence is ~2% and its etiology is mostly unknown. Identifying biological factors contributing to OCD will elucidate underlying mechanisms and might contribute to improved treatment outcomes. Genomic studies of OCD are beginning to reveal long-sought risk loci, but >95% of the cases currently in analysis are of homogenous European ancestry. If not addressed, this Eurocentric bias will result in OCD genomic findings being more accurate for individuals of European ancestry than other ancestries, thereby contributing to health disparities in potential future applications of genomics. In this study protocol paper, we describe the Latin American Trans-ancestry INitiative for OCD genomics (LATINO, https://www.latinostudy.org). LATINO is a new network of investigators from across Latin America, the United States, and Canada who have begun to collect DNA and clinical data from 5000 richly phenotyped OCD cases of Latin American ancestry in a culturally sensitive and ethical manner. In this project, we will utilize trans-ancestry genomic analyses to accelerate the identification of OCD risk loci, fine-map putative causal variants, and improve the performance of polygenic risk scores in diverse populations. We will also capitalize on rich clinical data to examine the genetics of treatment response, biologically plausible OCD subtypes, and symptom dimensions. Additionally, LATINO will help elucidate the diversity of the clinical presentations of OCD across cultures through various trainings developed and offered in collaboration with Latin American investigators. We believe this study will advance the important goal of global mental health discovery and equity.

Evaluation of optimal methods and ancestries for calculating polygenic risk scores in East Asian population

Polygenic risk scores (PRSs) have been studied for predicting human diseases, and various methods for PRS calculation have been developed. Most PRS studies to date have focused on European ancestry, and the performance of PRS has not been sufficiently assessed in East Asia. Herein, we evaluated the predictive performance of PRSs for East Asian populations under various conditions. Simulation studies using data from the Korean cohort, Health Examinees (HEXA), demonstrated that SBayesRC and PRS-CS outperformed other PRS methods (lassosum, LDpred-funct, and PRSice) in high fixed heritability (0.3 and 0.7). In addition, we generated PRSs using real-world data from HEXA for ten diseases: asthma, breast cancer, cataract, coronary artery disease, gastric cancer, glaucoma, hyperthyroidism, hypothyroidism, osteoporosis, and type 2 diabetes (T2D). We utilized the five previous PRS methods and genome-wide association study (GWAS) data from two biobank-scale datasets [European (UK Biobank) and East Asian (BioBank Japan) ancestry]. Additionally, we employed PRS-CSx, a PRS method that combines GWAS data from both ancestries, to generate a total of 110 PRS for ten diseases. Similar to the simulation results, SBayesRC showed better predictive performance for disease risk than the other methods. Furthermore, the East Asian GWAS data outperformed those from European ancestry for breast cancer, cataract, gastric cancer, and T2D, but neither of the two GWAS ancestries showed a significant advantage on PRS performance for the remaining six diseases. Based on simulation data and real data studies, it is expected that SBayesRC will offer superior performance for East Asian populations, and PRS generated using GWAS from non-East Asian may also yield good results.

Performance of polygenic risk scores in screening, prediction, and risk stratification: secondary analysis of data in the Polygenic Score Catalog

ObjectiveTo clarify the performance of polygenic risk scores in population screening, individual risk prediction, and population risk stratification.DesignSecondary analysis of data in the Polygenic Score Catalog.SettingPolygenic Score Catalog, April 2022....

Founder population-specific weights yield improvements in performance of polygenic risk scores for Alzheimer disease in the Midwestern Amish

Alzheimer disease (AD) is the most common type of dementia and is estimated to affect 6 million Americans. Risk for AD is multifactorial, including both genetic and environmental risk factors. AD genomic research has generally focused on identification of risk variants. Using this information, polygenic risk scores (PRSs) can be calculated to quantify an individual's relative disease risk due to genetic factors. The Amish are a founder population descended from German and Swiss Anabaptist immigrants. They experienced a genetic bottleneck after arrival in the United States, making their genetic architecture different from the broader European ancestry population. Prior work has demonstrated the lack of transferability of PRSs across populations. Here, we compared the performance of PRSs derived from genome-wide association studies (GWASs) of Amish individuals to those derived from a large European ancestry GWAS. Participants were screened for cognitive impairment with further evaluation for AD. Genotype data were imputed after collection via Illumina genotyping arrays. The Amish individuals were split into two groups based on the primary site of recruitment. For each group, GWAS was conducted with account for relatedness and adjustment for covariates. PRSs were then calculated using weights from the other Amish group. PRS models were evaluated with and without covariates. The Amish-derived PRSs distinguished between dementia status better than the European-derived PRS in our Amish populations and demonstrated performance improvements despite a smaller training sample size. This work highlighted considerations for AD PRS usage in populations that cannot be adequately described by basic race/ethnicity or ancestry classifications.

Transcriptional risk scores in Alzheimer's disease: From pathology to cognition.

Our previously developed blood-based transcriptional risk scores (TRS) showed associations with diagnosis and neuroimaging biomarkers for Alzheimer's disease (AD). Here, we developed brain-based TRS. We integrated AD genome-wide association study summary and expression quantitative trait locus data to prioritize target genes using Mendelian randomization. We calculated TRS using brain transcriptome data of two independent cohorts (N=878) and performed association analysis of TRS with diagnosis, amyloidopathy, tauopathy, and cognition. We compared AD classification performance of TRS with polygenic risk scores (PRS). Higher TRS values were significantly associated with AD, amyloidopathy, tauopathy, worse cognition, and faster cognitive decline, which were replicated in an independent cohort. The AD classification performance of PRS was increased with the inclusion of TRS up to 16% with the area under the curve value of 0.850. Our results suggest brain-based TRS improves the AD classification of PRS and may be a potential AD biomarker. Transcriptional risk score (TRS) is developed using brain RNA-Seq data. Higher TRS values are shown in Alzheimer's disease (AD). TRS improves the AD classification power of PRS up to 16%. TRS is associated with AD pathology presence. TRS is associated with worse cognitive performance and faster cognitive decline.

Polygenic risk score in comparison with C-reactive protein for predicting incident coronary heart disease

Background and aimsDespite interest in the use of polygenic risk scores (PRS) for predicting coronary heart disease (CHD) risk, the clinical utility of PRS compared to conventional risk factors has not been demonstrated. We compared the performance of PRS with that of high-sensitivity C-reactive protein (hsCRP) in two well-established cohorts. MethodsThe study population included individuals of European ancestry free of baseline CHD from ARIC (N = 13,113) and the Framingham Offspring Study (FHS) (N = 2,696). The primary predictors included a validated PRS consisting of >6.6 million single nucleotide polymorphisms and hsCRP. The outcome was incident CHD, defined as non-fatal or fatal myocardial infarction. We compared the performance of both predictors after adjusting for the Pooled Cohort Equations in multivariable-adjusted Cox regression models. We assessed discrimination and reclassification using c-statistics and net reclassification improvement. ResultsIncident CHD occurred in 565 ARIC and 153 FHS participants. In multivariable-adjusted models, both PRS and hsCRP were associated with incident CHD (p < 0.05 in both cohorts). In models incorporating both predictors, strengths of association were similar. For instance, in ARIC, the hazard ratio per SD increment was 1.38 (95% CI, 1.27–1.50, p = 2.94 × 10−14) for PRS and 1.41 (1.30–1.55, p = 3.10 × 10−15) for hsCRP. Neither predictor significantly increased model discrimination or net reclassification when compared with models containing the Pooled Cohort Equations alone. ConclusionsIn two independent cohorts, PRS performed similarly to hsCRP for the prediction of CHD risk. These findings suggest PRS does not have unique clinical utility beyond this widely-available, inexpensive measure of risk in unselected middle-aged populations.

A statistical boosting framework for polygenic risk scores based on large-scale genotype data.

Polygenic risk scores (PRS) evaluate the individual genetic liability to a certain trait and are expected to play an increasingly important role in clinical risk stratification. Most often, PRS are estimated based on summary statistics of univariate effects derived from genome-wide association studies. To improve the predictive performance of PRS, it is desirable to fit multivariable models directly on the genetic data. Due to the large and high-dimensional data, a direct application of existing methods is often not feasible and new efficient algorithms are required to overcome the computational burden regarding efficiency and memory demands. We develop an adapted component-wise L 2-boosting algorithm to fit genotype data from large cohort studies to continuous outcomes using linear base-learners for the genetic variants. Similar to the snpnet approach implementing lasso regression, the proposed snpboost approach iteratively works on smaller batches of variants. By restricting the set of possible base-learners in each boosting step to variants most correlated with the residuals from previous iterations, the computational efficiency can be substantially increased without losing prediction accuracy. Furthermore, for large-scale data based on various traits from the UK Biobank we show that our method yields competitive prediction accuracy and computational efficiency compared to the snpnet approach and further commonly used methods. Due to the modular structure of boosting, our framework can be further extended to construct PRS for different outcome data and effect types-we illustrate this for the prediction of binary traits.

Genetic Backgrounds Associated With Stent Thrombosis: A Pilot Study From a Percutaneous Coronary Intervention Registry

BackgroundStent thrombosis (ST) is a rare, yet devastating, complication following percutaneous coronary intervention (PCI), with poorly understood pathophysiologic characteristics and genetic backgrounds. ObjectivesThe authors performed a genome-wide association study to identify the common genetic loci associated with early stent thrombosis (EST) and late/very late ST (LST/VLST) in a contemporary Japanese multicenter PCI registry. MethodsAmong 8,642 PCI patients included in the registry, 42 who experienced stent thrombosis [EST (n = 15) and LST/VLST (n = 27)] were included (mean age, 67.6 ± 10.8 years; and 88.1% men). We conducted a genome-wide association study using the BioBank Japan patient population as the control (control #1: acute coronary syndrome [n = 29,542] and control #2: effort angina [n = 8,900]) to identify significant single nucleotide polymorphisms (SNPs) and evaluate the performance of polygenic risk scores (PRSs) for predicting these conditions. ResultsWe compared patients with EST with controls #1 and #2 and identified SNPs (rs565401593 and rs561634568) in NSD1, and patients with LST/VLST with controls #1 and #2 and identified SNPs (rs532623294 and rs199546342) in GRIN2A. PRS for LST/VLST showed high predictive performance (area under the curve 0.83 [95% CI: 0.76-0.89] and 0.83 [95% CI: 0.77-0.89]), whereas PRS for EST showed modest predictive performance (area under the curve 0.71 [95% CI: 0.58-0.85] and 0.72 [95% CI: 0.58-0.85]). ConclusionsWe identified different genetic predispositions between EST and LST/VLST and demonstrated that the incorporation of PRS may aid in risk prediction of this highly fatal event.

Association and performance of polygenic risk scores for breast cancer among French women presenting or not a familial predisposition to the disease

BackgroundThree partially overlapping breast cancer polygenic risk scores (PRS) comprising 77, 179 and 313 SNPs have been proposed for European-ancestry women by the Breast Cancer Association Consortium (BCAC) for improving risk prediction in the general population. However, the effect of these SNPs may vary from one country to another and within a country because of other factors. ObjectiveTo assess their associated risk and predictive performance in French women from (1) the CECILE population-based case-control study, (2) BRCA1 or BRCA2 (BRCA1/2) pathogenic variant (PV) carriers from the GEMO study, and (3) familial breast cancer cases with no BRCA1/2 PV and unrelated controls from the GENESIS study. ResultsAll three PRS were associated with breast cancer in all studies, with odds ratios per standard deviation varying from 1.7 to 2.0 in CECILE and GENESIS, and hazard ratios varying from 1.1 to 1.4 in GEMO. The predictive performance of PRS313 in CECILE was similar to that reported in BCAC but lower than that in GENESIS (area under the receiver operating characteristic curve (AUC) = 0.67 and 0.75, respectively). PRS were less performant in BRCA2 and BRCA1 PV carriers (AUC = 0.58 and 0.54 respectively). ConclusionOur results are in line with previous validation studies in the general population and in BRCA1/2 PV carriers. Additionally, we showed that PRS may be of clinical utility for women with a strong family history of breast cancer and no BRCA1/2 PV, and for those carrying a predicted PV in a moderate-risk gene like ATM, CHEK2 or PALB2.

Abstract 14905: A Combination of Polygenic Risk Scores for Coronary Artery Disease and for Circulating Blood Lipids Predicts the Incidence of Myocardial Infarction and Coronary Revascularization

Introduction: Estimating the genetic risk of developing complex diseases, such as coronary artery disease (CAD), is now possible by aggregating data from genome-wide association studies (GWAS) into polygenic risk scores (PRS). This study aims to evaluate the performance of PRS derived from CAD (PRS CAD ) and blood lipids GWAS to predict severe CAD defined as myocardial infarction or coronary revascularization (MI-REVASC). Methods: Using nuclear magnetic resonance data from UK Biobank (UKB, n=90300), we conducted 29 GWAS for blood lipids (9 for fatty acids and 20 for lipoproteins). Summary statistics of these GWAS were then used to derive PRS in the rest of UKB (n=318422, median follow-up = 12.6 years). A PRS CAD was also calculated using the publicly available GWAS from the CARDIoGRAMplusC4D consortium. All PRS included &gt;1.1 million of genetic variants and were calculated using LDpred2. Hazard Ratio (HR) for incident MI-REVASC (n=10935) are reported with their 95% CIs, for 1-SD increase of each PRS. Selected independent PRS were combined into multivariate Cox regression models, for which predictive performance was evaluated. All models were adjusted for age, sex and genetic principal components. Results: Out of 30 PRS, 28 were significantly associated with incident MI-REVASC; PRS CAD showed the strongest association (HR=1.60 [1.57-1.63], p&lt;10e-300), whereas PRS for small LDL particles (sLDL-P) was the most associated blood lipids PRS (HR=1.14 [1.12-1.16], p=7.63e-42). The optimal model included PRS for CAD, sLDL-P, HDL-C and the PUFA/MUFA (poly-/mono- unsaturated fatty acids) ratio. Discriminative capacities for incident MI-REVASC were significantly increased in the model including PRS CAD (C-statistic (C) = 0.744 [0.739-0.748]) compared to the model without PRS (C= 0.710 [0.705-0.714]). No significant difference was observed when independent blood lipids PRS were added to the model (C= 0.745 [0.741 to 0.749]). Conclusions: Although independently associated with the incidence of severe CAD, blood lipids PRS provide little improvement in the predictive performance when added to PRS CAD .

Genome-wide study on 72,298 individuals in Korean biobank data for 76 traits

Genome-wide association studies (GWAS) on diverse ancestry groups are lacking, resulting in deficits of genetic discoveries and polygenic scores. We conducted GWAS for 76 phenotypes in Korean biobank data, namely the Korean Genome and Epidemiology Study (KoGES) (n= 72,298). Our analysis discovered 2,242 associated loci, including 122 novel associations, many of which were replicated in Biobank Japan (BBJ) GWAS. We also applied several up-to-date methods for genetic association tests to increase the power, discovering additional associations that are not identified in simple case-control GWAS. We evaluated genetic pleiotropy to investigate genes associated with multiple traits. Following meta-analysis of 32 phenotypes between KoGES and BBJ, we further identified 379 novel associations and demonstrated the improved predictive performance of polygenic risk scores by using the meta-analysis results. The summary statistics of 76 KoGES GWAS phenotypes are publicly available, contributing to a better comprehension of the genetic architecture of the East Asian population.

Combining polygenic risk scores to predict myocardial infarction in the Canadian longitudinal study on aging

Background and Aims : Estimating the risk of developing complex pathologies, such as coronary artery disease (CAD), is now possible by aggregating data from genome-wide association studies (GWAS) into polygenic risk scores (PRS). This study aims to evaluate the performance of PRS derived from CAD and blood lipids GWAS to predict myocardial infarction (MI).

Gene-based polygenic risk scores analysis of alcohol use disorder in African Americans

Genome-wide association studies (GWAS) in admixed populations such as African Americans (AA) have limited sample sizes, resulting in poor performance of polygenic risk scores (PRS). Based on the observations that many disease-causing genes are shared between AA and European ancestry (EA) populations, and some disease-causing variants are located within the boundaries of these genes, we proposed a novel gene-based PRS framework (PRSgene) by using variants located within disease-associated genes. Using the AA GWAS of alcohol use disorder (AUD) from the Million Veteran Program and the EA GWAS of problematic alcohol use as the discovery GWAS, we identified 858 variants from 410 genes that were AUD-related in both AA and EA. PRSgene calculated using these variants were significantly associated with AUD in three AA target datasets (P-values ranged from 7.61E−05 to 6.27E−03; Betas ranged from 0.15 to 0.21) and outperformed PRS calculated using all variants (P-values ranged from 7.28E−03 to 0.16; Betas ranged from 0.06 to 0.18). PRSgene were also associated with AUD in an EA target dataset (P-value = 0.02, Beta = 0.11). In AA, individuals in the highest PRSgene decile had an odds ratio of 1.76 (95% CI: 1.32–2.34) to develop AUD compared to those in the lowest decile. The 410 genes were enriched in 54 Gene Ontology biological processes, including ethanol oxidation and processes involving the synaptic system, which are known to be AUD-related. In addition, 26 genes were targets of drugs used to treat AUD or other diseases that might be considered for repurposing to treat AUD. Our study demonstrated that the gene-based PRS had improved performance in evaluating AUD risk in AA and provided new insight into AUD genetics.