Polygenic Risk Score Models Research Articles

Developing and validating a comprehensive polygenic risk score to enhance keratoconus risk prediction.

This study aimed to develop and validate a comprehensive polygenic risk score (PRS) for keratoconus, enhancing the predictive accuracy for identifying individuals at increased risk, which is crucial for preventing keratoconus-associated visual impairment such as post-Laser-assisted in situ keratomileusis (LASIK) ectasia. We applied a multi-trait analysis approach (MTAG) to genome-wide association study data on keratoconus and quantitative keratoconus-related traits and used this to construct PRS models for keratoconus risk using several PRS methodologies. We evaluated the predictive performance of the PRSs in two biobanks: Estonian Biobank (EstBB; 375 keratoconus cases and 17 902 controls) and UK Biobank (UKB: 34 keratoconus cases and 1000 controls). Scores were compared using the area under the curve (AUC) and odds ratios (ORs) for various PRS models. The PRS models demonstrated significant predictive capabilities in EstBB, with the SBayesRC model achieving the highest OR of 2.28 per standard deviation increase in PRS, with a model containing age, sex and PRS showing good predictive accuracy (AUC = 0.72). In UKB, we found that adding the best-performing PRS to a model containing corneal measurements increased the AUC from 0.84 to 0.88 (P = 0.012 for difference), with an OR of 4.26 per standard deviation increase in the PRS. These models showed improved predictive capability compared to previous keratoconus PRS. The PRS models enhanced prediction of keratoconus risk, even with corneal measurements, showing potential for clinical use to identify individuals at high risk of keratoconus, and potentially help reduce the risk of post-LASIK ectasia.

A polygenic risk score model for psoriasis based on the protein interactions of psoriasis susceptibility loci

IntroductionPolygenic Risk Scores (PRS) are an emerging tool for predicting an individual’s genetic risk to a complex trait. Several methods have been proposed to construct and calculate these scores. Here, we develop a biologically driven PRS using the UK BioBank cohort through validated protein interactions (PPI) and network construction for psoriasis, incorporating variants mapped to the interacting genes of 14 psoriasis susceptibility (PSORS) loci, as identified from previous genetic linkage studies.MethodsWe constructed the PPI network via the implementation of two major meta-databases of protein interactions, and identified variants mapped to the identified PSORS-interacting genes. We selected only European unrelated participants including individuals with psoriasis and randomly selected healthy controls using an at least 1:4 ratio to maximize statistical power. We next compared our PPI-PRS model to (i) clinical risk models and (ii) conventional PRS calculations through p-value thresholding.ResultsOur PPI-PRS model provides comparable results to both clinical risk models and conventional approaches, despite the incorporation of a limited number of variants which have not necessarily reached genome-wide significance (GWS). Exclusion of variants mapped to the HLA-C locus, an established risk locus for psoriasis resulted in highly similar associations compared to our primary model, indicating the contribution of the genetic variability mapped to non-GWS variants in PRS computations.DiscussionOur findings support the implementation of biologically driven approaches in PRS calculations in psoriasis, highlighting their potential clinical utility in risk assessment and treatment management.

Comprehensive Evaluation of the Genetic Basis of Keratoconus: New Perspectives for Clinical Translation.

Keratoconus (KC) is a corneal disorder with complex etiology, apparently involving both genetic and environmental factors, characterized by progressive thinning and protrusion of the cornea. We aimed to identify novel genetic regions associated with KC susceptibility, elucidate relevant genes for disease development, and explore the translational implications for therapeutic intervention and risk assessment. We conducted a genome-wide association study (GWAS) that integrated previously published data with newly generated genotyping data from an independent European cohort. To evaluate the clinical translation of our results, we performed functional annotation, gene prioritization, polygenic risk score (PRS), and drug repositioning analyses. We identified two novel genetic loci associated with KC, with rs2806689 and rs807037 emerging as lead variants (P = 1.71E-08, odds ratio [OR] = 0.88; P = 1.93E-08, OR = 1.16, respectively). Most importantly, we identified 315 candidate genes influenced by confirmed KC-associated variants. Among these, MINK1 was found to play a pivotal role in KC pathogenesis through the WNT signaling pathway. Moreover, we developed a PRS model that successfully differentiated KC patients from controls (P = 7.61E-16; area under the curve = 0.713). This model has the potential to identify individuals at high risk for developing KC, which could be instrumental in early diagnosis and management. Additionally, our drug repositioning analysis identified acetylcysteine as a potential treatment option for KC, opening up new avenues for therapeutic intervention. Our study provides valuable insights into the genetic and molecular basis of KC, offering new targets for therapy and highlighting the clinical utility of PRS models in predicting disease risk.

Abstract A021: Development of cross-ancestry polygenic risk. scores for breast cancer in women of African ancestry

Abstract Introduction: Polygenic risk scores (PRSs) predict breast cancer (BC) risk and have shown utility for screening in European ancestry women (EUR). However, PRSs developed for non-European ancestry, particularly African American (AA) women, have limited predictive accuracy. Methods: We analyzed data from 17,454 BC cases and 19,093 controls in the African American Breast Cancer Genetics (AABCG) consortium, split into training (70%), tuning (10%), and testing (20%) sets. Genome-wide association analyses for overall BC, ER-positive BC, ER- negative BC and triple-negative BC (TNBC) were performed in the training dataset, controlling for age, genotyping platform, and the top 10 principal components. We built single-ancestry PRS models using forward stepwise selection (FSS), PRSice-2, PLINK C+T, Lassosum2, and LDpred2-auto. These were combined with published EUR 313-SNP models (330-SNP for TNBC) in the tuning set to obtain cross-ancestry models. Genome-wide cross-ancestry PRS methods, including CT-SLEB and PRScsx, were also used. We created an ensemble model by combining different PRS models across subtypes using ensemble-based approaches. All developed PRS models were then evaluated in the independent testing set. Additional validation was performed using the All of Us (AoU) study with 834 BC cases and 29,122 controls from the AA population. Results: The observed-scale SNP-based heritability was estimated at 0.29 (SE: 0.12) for overall BC risk, 0.43 (SE: 0.18) for ER-positive BC, 0.44 (SE: 0.24) for ER-negative BC, and 1.13 (SE: 0.40) for TNBC. The best models showed good discriminating capacity: ensemble-based models for overall (AUC=0.626) and ER-positive BC (AUC=0.626), and combined cross-subtype and cross-ancestry FSS models for ER-negative (AUC=0.647) and TNBC (AUC=0.654). The odds ratio per standard deviation was 1.63 (95% CI: 1.55, 1.71) for the ensemble-based PRS of overall BC risk, with an estimated life risk of 28% for the top 1%. The cross-ancestry model using CT-SLEB combined with the EUR 313-SNP model was validated (AUC=0.61) among AA women in the AoU study. Conclusion: We developed PRSs with improved discriminating ability for BC risk in women with African ancestry. Cross- ancestry and subtype learning enhanced model performance. These findings indicate common variation has a larger role in BC in AA women than previously reported, warranting further validation in external AA populations and clinical evaluation. Citation Format: James L. Li, Haoyu Zhang, Julian C McClellan, Guimin Gao, Kevin Wang, Julie R. Palmer, Olufunmilayo I. Olopade, Christopher A. Haiman, Dezheng Huo. Development of cross-ancestry polygenic risk. scores for breast cancer in women of African ancestry [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A021.

GWAS and polygenic risk score of severe COVID-19 in Eastern Europe.

COVID-19 disease has infected more than 772 million people, leading to 7 million deaths. Although the severe course of COVID-19 can be prevented using appropriate treatments, effective interventions require a thorough research of the genetic factors involved in its pathogenesis. We conducted a genome-wide association study (GWAS) on 7,124 individuals (comprising 6,400 controls who had mild to moderate COVID-19 and 724 cases with severe COVID-19). The inclusion criteria were acute respiratory distress syndrome (ARDS), acute respiratory failure (ARF) requiring respiratory support, or CT scans indicative of severe COVID-19 infection without any competing diseases. We also developed a polygenic risk score (PRS) model to identify individuals at high risk. We identified two genome-wide significant loci (P-value <5 × 10-8) and one locus with approximately genome-wide significance (P-value = 5.92 × 10-8-6.15 × 10-8). The most genome-wide significant variants were located in the leucine zipper transcription factor like 1 (LZTFL1) gene, which has been highlighted in several previous GWAS studies. Our PRS model results indicated that individuals in the top 10% group of the PRS had twice the risk of severe course of the disease compared to those at median risk [odds ratio = 2.18 (1.66, 2.86), P-value = 8.9 × 10-9]. We conducted one of the largest studies to date on the genetics of severe COVID-19 in an Eastern European cohort. Our results are consistent with previous research and will guide further epidemiologic studies on host genetics, as well as for the development of targeted treatments.

Improving genetic risk modeling of dementia from real-world data in underrepresented populations

Genetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited. We employ an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compare this model with APOE and polygenic risk score models across genetic ancestry groups (Hispanic Latino American sample: 610 patients with 126 cases; African American sample: 440 patients with 84 cases; East Asian American sample: 673 patients with 75 cases), using electronic health records from UCLA Health for discovery and the All of Us cohort for validation. Our model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 31–84% (Wilcoxon signed-rank test p-value <0.05) and the area-under-the-receiver-operating characteristic by 11–17% (DeLong test p-value <0.05) compared to the APOE and the polygenic risk score models. We identify shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge. Our study highlights the benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.

Enhancing portability of trans-ancestral polygenic risk scores through tissue-specific functional genomic data integration.

Portability of trans-ancestral polygenic risk scores is often confounded by differences in linkage disequilibrium and genetic architecture between ancestries. Recent literature has shown that prioritizing GWAS SNPs with functional genomic evidence over strong association signals can improve model portability. We leveraged three RegulomeDB-derived functional regulatory annotations-SURF, TURF, and TLand-to construct polygenic risk models across a set of quantitative and binary traits highlighting functional mutations tagged by trait-associated tissue annotations. Tissue-specific prioritization by TURF and TLand provide a significant improvement in model accuracy over standard polygenic risk score (PRS) models across all traits. We developed the Trans-ancestral Iterative Tissue Refinement (TITR) algorithm to construct PRS models that prioritize functional mutations across multiple trait-implicated tissues. TITR-constructed PRS models show increased predictive accuracy over single tissue prioritization. This indicates our TITR approach captures a more comprehensive view of regulatory systems across implicated tissues that contribute to variance in trait expression.

Genome-wide association and polygenic risk score estimation of type 2 diabetes mellitus in Kinh Vietnamese-A pilot study.

A genome-wide association study (GWAS) is a powerful tool in investigating genetic contribution, which is a crucial factor in the development of complex multifactorial diseases, such as type 2 diabetes mellitus. Type 2 diabetes mellitus is a major healthcare burden in the Western Pacific region; however, there is limited availability of genetic-associated data for type 2 diabetes in Southeast Asia, especially among the Kinh Vietnamese population. This lack of information exacerbates global healthcare disparities. In this study, 997 Kinh Vietnamese individuals (503 with type 2 diabetes and 494 controls) were prospectively recruited and their clinical and paraclinical information was recorded. DNA samples were collected and whole genome genotyping was performed. Standard quality control and genetic imputation using the 1000 Genomes database were executed. A polygenic risk score for type 2 diabetes was generated in different models using East Asian, European, and mix ancestry GWAS summary statistics as training datasets. After quality control and genetic imputation, 107 polymorphisms reached suggestive statistical significance for GWAS (≤5 × 10-6) and rs11079784 was one of the potential markers strongly associated with type 2 diabetes in the studied population. The best polygenic risk score model predicting type 2 diabetes mellitus had AUC = 0.70 (95% confidence interval = 0.62-0.77) based on a mix of ancestral GWAS summary statistics. These data show promising results for genetic association with a polygenic risk score estimation in the Kinh Vietnamese population; the results also highlight the essential role of population diversity in a GWAS of type 2 diabetes mellitus.

Genome-wide polygenic risk scores predict risk of glioma and molecular subtypes.

Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to efficiently capture genetic risk using available data. We applied a method based on continuous shrinkage priors (PRS-CS) to model the joint effects of over 1 million common variants on disease risk and compared this to an approach (PRS-CT) that only selects a limited set of independent variants that reach genome-wide significance (P < 5 × 10-8). PRS models were trained using GWAS stratified by histological (10 346 cases and 14 687 controls) and molecular subtype (2632 cases and 2445 controls), and validated in 2 independent cohorts. PRS-CS was generally more predictive than PRS-CT with a median increase in explained variance (R2) of 24% (interquartile range = 11-30%) across glioma subtypes. Improvements were pronounced for glioblastoma (GBM), with PRS-CS yielding larger odds ratios (OR) per standard deviation (SD) (OR = 1.93, P = 2.0 × 10-54 vs. OR = 1.83, P = 9.4 × 10-50) and higher explained variance (R2 = 2.82% vs. R2 = 2.56%). Individuals in the 80th percentile of the PRS-CS distribution had a significantly higher risk of GBM (0.107%) at age 60 compared to those with average PRS (0.046%, P = 2.4 × 10-12). Lifetime absolute risk reached 1.18% for glioma and 0.76% for IDH wildtype tumors for individuals in the 95th PRS percentile. PRS-CS augmented the classification of IDH mutation status in cases when added to demographic factors (AUC = 0.839 vs. AUC = 0.895, PΔAUC = 6.8 × 10-9). Genome-wide PRS has the potential to enhance the detection of high-risk individuals and help distinguish between prognostic glioma subtypes.

Genome-wide association study identifies novel susceptible loci and evaluation of polygenic risk score for chronic obstructive pulmonary disease in a Taiwanese population

BackgroundChronic Obstructive Pulmonary Disease (COPD) describes a group of progressive lung diseases causing breathing difficulties. While COPD development typically involves a complex interplay between genetic and environmental factors, genetics play a role in disease susceptibility. This study used genome-wide association studies (GWAS) and polygenic risk score (PRS) to elucidate the genetic basis for COPD in Taiwanese patients.ResultsGWAS was performed on a Taiwanese COPD case–control cohort with a sample size of 5,442 cases and 17,681 controls. Additionally, the PRS was calculated and assessed in our target groups. GWAS results indicate that although there were no single nucleotide polymorphisms (SNPs) of genome-wide significance, prominent COPD susceptibility loci on or nearby genes such as WWTR1, EXT1, INTU, MAP3K7CL, MAMDC2, BZW1/CLK1, LINC01197, LINC01894, and CFAP95 (C9orf135) were identified, which had not been reported in previous studies. Thirteen susceptibility loci, such as CHRNA4, AFAP1, and DTWD1, previously reported in other populations were replicated and confirmed to be associated with COPD in Taiwanese populations. The PRS was determined in the target groups using the summary statistics from our base group, yielding an effective association with COPD (odds ratio [OR] 1.09, 95% confidence interval [CI] 1.02–1.17, p = 0.011). Furthermore, replication a previous lung function trait PRS model in our target group, showed a significant association of COPD susceptibility with PRS of Forced Expiratory Volume in one second (FEV1)/Forced Vital Capacity (FCV) (OR 0.89, 95% CI 0.83–0.95, p = 0.001).ConclusionsNovel COPD-related genes were identified in the studied Taiwanese population. The PRS model, based on COPD or lung function traits, enables disease risk estimation and enhances prediction before suffering. These results offer new perspectives on the genetics of COPD and serve as a basis for future research.

Epistatic Features and Machine Learning Improve Alzheimer's Disease Risk Prediction Over Polygenic Risk Scores.

Polygenic risk scores (PRS) are linear combinations of genetic markers weighted by effect size that are commonly used to predict disease risk. For complex heritable diseases such as late-onset Alzheimer's disease (LOAD), PRS models fail to capture much of the heritability. Additionally, PRS models are highly dependent on the population structure of the data on which effect sizes are assessed and have poor generalizability to new data. The goal of this study is to construct a paragenic risk score that, in addition to single genetic marker data used in PRS, incorporates epistatic interaction features and machine learning methods to predict risk for LOAD. We construct a new state-of-the-art genetic model for risk of Alzheimer's disease. Our approach innovates over PRS models in two ways: First, by directly incorporating epistatic interactions between SNP loci using an evolutionary algorithm guided by shared pathway information; and second, by estimating risk via an ensemble of non-linear machine learning models rather than a single linear model. We compare the paragenic model to several PRS models from the literature trained on the same dataset. The paragenic model is significantly more accurate than the PRS models under 10-fold cross-validation, obtaining an AUC of 83% and near-clinically significant matched sensitivity/specificity of 75%. It remains significantly more accurate when evaluated on an independent holdout dataset and maintains accuracy within APOE genotype strata. Paragenic models show potential for improving disease risk prediction for complex heritable diseases such as LOAD over PRS models.

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.

A Polygenic Risk Analysis for Identifying Ulcerative Colitis Patients with European Ancestry.

The incidence of ulcerative colitis (UC) has increased globally. As a complex disease, the genetic predisposition for UC could be estimated by the polygenic risk score (PRS), which aggregates the effects of a large number of genetic variants in a single quantity and shows promise in identifying individuals at higher lifetime risk of UC. Here, based on a cohort of 2869 UC cases and 2900 controls with genotype array datasets, we used PRSice-2 to calculate PRS, and systematically analyzed factors that could affect the power of PRS, including GWAS summary statistics, population stratification, and impact of variants. After leveraging a stepwise condition analysis, we eventually established the best PRS model, achieving an AUC of 0.713. Meanwhile, samples in the top 20% of the PRS distribution had a risk of UC more than ten times higher than samples in the lowest 20% (OR = 10.435, 95% CI 8.571-12.703). Our analyses demonstrated that including population-enriched, more disease-associated SNPs and using GWAS summary statistics from similar ethnic background can improve the power of PRS. Strictly following the principle of focusing on one population in all aspects of generating PRS can be a cost-effective way to apply genotype-array-derived PRS to practical risk estimation.

Polygenic risk scores for autoimmune related diseases are significantly different in cancer exceptional responders

A small number of cancer patients respond exceptionally well to therapies and survive significantly longer than patients with similar diagnoses. Profiling the germline genetic backgrounds of exceptional responder (ER) patients, with extreme survival times, can yield insights into the germline polymorphisms that influence response to therapy. As ERs showed a high incidence in autoimmune diseases, we hypothesized the differences in autoimmune disease risk could reflect the immune background of ERs and contribute to better cancer treatment responses. We analyzed the germline variants of 51 ERs using polygenic risk score (PRS) analysis. Compared to typical cancer patients, the ERs had significantly elevated PRSs for several autoimmune-related diseases: type 1 diabetes, hypothyroidism, and psoriasis. This indicates that an increased genetic predisposition towards these autoimmune diseases is more prevalent among the ERs. In contrast, ERs had significantly lower PRSs for developing inflammatory bowel disease. The left-skew of type 1 diabetes score was significant for exceptional responders. Variants on genes involved in the T1D PRS model associated with cancer drug response are more likely to co-occur with other variants among ERs. In conclusion, ERs exhibited different risks for autoimmune diseases compared to typical cancer patients, which suggests that changes in a patient’s immune set point or immune surveillance specificity could be a potential mechanistic link to their exceptional response. These findings expand upon previous research on immune checkpoint inhibitor-treated patients to include those who received chemotherapy or radiotherapy.

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.

Polygenic Risk Score-Based Association Analysis Identifies Genetic Comorbidities Associated with Age-Related Hearing Difficulty in Two Independent Samples.

Age-related hearing loss is the most common form of permanent hearing loss that is associated with various health traits, including Alzheimer's disease, cognitive decline, and depression. The present study aims to identify genetic comorbidities of age-related hearing loss. Past genome-wide association studies identified multiple genomic loci involved in common adult-onset health traits. Polygenic risk scores (PRS) could summarize the polygenic inheritance and quantify the genetic susceptibility of complex traits independent of trait expression. The present study conducted a PRS-based association analysis of age-related hearing difficulty in the UK Biobank sample (N = 425,240), followed by a replication analysis using hearing thresholds (HTs) and distortion-product otoacoustic emissions (DPOAEs) in 242 young adults with self-reported normal hearing. We hypothesized that young adults with genetic comorbidities associated with age-related hearing difficulty would exhibit subclinical decline in HTs and DPOAEs in both ears. A total of 111,243 participants reported age-related hearing difficulty in the UK Biobank sample (> 40years). The PRS models were derived from the polygenic risk score catalog to obtain 2627 PRS predictors across the health spectrum. HTs (0.25-16kHz) and DPOAEs (1-16kHz, L1/L2 = 65/55dB SPL, F2/F1 = 1.22) were measured on 242 young adults. Saliva-derived DNA samples were subjected to low-pass whole genome sequencing, followed by genome-wide imputation and PRS calculation. The logistic regression analyses were performed to identify PRS predictors of age-related hearing difficulty in the UK Biobank cohort. The linear mixed model analyses were performed to identify PRS predictors of HTs and DPOAEs. The PRS-based association analysis identified 977 PRS predictors across the health spectrum associated with age-related hearing difficulty. Hearing difficulty and hearing aid use PRS predictors revealed the strongest association with the age-related hearing difficulty phenotype. Youth with a higher genetic predisposition to hearing difficulty revealed a subclinical elevation in HTs and a decline in DPOAEs in both ears. PRS predictors associated with age-related hearing difficulty were enriched for mental health, lifestyle, metabolic, sleep, reproductive, digestive, respiratory, hematopoietic, and immune traits. Fifty PRS predictors belonging to various trait categories were replicated for HTs and DPOAEs in both ears. The study identified genetic comorbidities associated with age-related hearing loss across the health spectrum. Youth with a high genetic predisposition to age-related hearing difficulty and other related complex traits could exhibit sub-clinical decline in HTs and DPOAEs decades before clinically meaningful age-related hearing loss is observed. We posit that effective communication of genetic risk, promoting a healthy lifestyle, and reducing exposure to environmental risk factors at younger ages could help prevent or delay the onset of age-related hearing difficulty at older ages.

Stacked neural network for predicting polygenic risk score

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

Generalizability of polygenic prediction models: how is theR2defined on test data?

Polygenic risk scores (PRS) quantify an individual's genetic predisposition for different traits and are expected to play an increasingly important role in personalized medicine. A crucial challenge in clinical practice is the generalizability and transferability of PRS models to populations with different ancestries. When assessing the generalizability of PRS models for continuous traits, the is a commonly used measure to evaluate prediction accuracy. While the is a well-defined goodness-of-fit measure for statistical linear models, there exist different definitions for its application on test data, which complicates interpretation and comparison of results. Based on large-scale genotype data from the UK Biobank, we compare three definitions of the on test data for evaluating the generalizability of PRS models to different populations. Polygenic models for several phenotypes, including height, BMI and lipoproteinA, are derived based on training data with European ancestry using state-of-the-art regression methods and are evaluated on various test populations with different ancestries. Our analysis shows that the choice of the definition can lead to considerably different results on test data, making the comparison of values from the literature problematic. While the definition as the squared correlation between predicted and observed phenotypes solely addresses the discriminative performance and always yields values between 0 and 1, definitions of the based on the mean squared prediction error(MSPE) with reference to intercept-only models assess both discrimination and calibration. These MSPE-based definitions can yield negative values indicating miscalibrated predictions for out-of-target populations. We argue that the choice of the most appropriate definition depends on the aim of PRS analysis - whether it primarily serves for risk stratification or also for individual phenotype prediction. Moreover, both correlation-based and MSPE-based definitions of can provide valuable complementary information. Awareness of the different definitions of the on test data is necessary to facilitate the reporting and interpretation of results on PRS generalizability. It is recommended to explicitly state which definition was used when reporting values on test data. Further research is warranted to develop and evaluate well-calibrated polygenic models for diverse populations.

Risk loci involved in giant cell arteritis susceptibility: a genome-wide association study

Giant cell arteritis is an age-related vasculitis that mainly affects the aorta and its branches in individuals aged 50 years and older. Current options for diagnosis and treatment are scarce, highlighting the need to better understand its underlying pathogenesis. Genome-wide association studies (GWAS) have emerged as a powerful tool for unravelling the pathogenic mechanisms involved in complex diseases. We aimed to characterise the genetic basis of giant cell arteritis by performing the largest GWAS of this vasculitis to date and to assess the functional consequences and clinical implications of identified risk loci. We collected and meta-analysed genomic data from patients with giant cell arteritis and healthy controls of European ancestry from ten cohorts across Europe and North America. Eligible patients required confirmation of giant cell arteritis diagnosis by positive temporal artery biopsy, positive temporal artery doppler ultrasonography, or imaging techniques confirming large-vessel vasculitis. We assessed the functional consequences of loci associated with giant cell arteritis using cell enrichment analysis, fine-mapping, and causal gene prioritisation. We also performed a drug repurposing analysis and developed a polygenic risk score to explore the clinical implications of our findings. We included a total of 3498 patients with giant cell arteritis and 15 550 controls. We identified three novel loci associated with risk of giant cell arteritis. Two loci, MFGE8 (rs8029053; p=4·96 × 10-8; OR 1·19 [95% CI 1·12-1·26]) and VTN (rs704; p=2·75 × 10-9; OR 0·84 [0·79-0·89]), were related to angiogenesis pathways and the third locus, CCDC25 (rs11782624; p=1·28 × 10-8; OR 1·18 [1·12-1·25]), was related to neutrophil extracellular traps (NETs). We also found an association between this vasculitis and HLA region and PLG. Variants associated with giant cell arteritis seemed to fulfil a specific regulatory role in crucial immune cell types. Furthermore, we identified several drugs that could represent promising candidates for treatment of this disease. The polygenic risk score model was able to identify individuals at increased risk of developing giant cell arteritis (90th percentile OR 2·87 [95% CI 2·15-3·82]; p=1·73 × 10-13). We have found several additional loci associated with giant cell arteritis, highlighting the crucial role of angiogenesis in disease susceptibility. Our study represents a step forward in the translation of genomic findings to clinical practice in giant cell arteritis, proposing new treatments and a method to measure genetic predisposition to this vasculitis. Institute of Health Carlos III, Spanish Ministry of Science and Innovation, UK Medical Research Council, and National Institute for Health and Care Research.

Multiple polygenic risk scores can improve the prediction of systemic lupus erythematosus in Taiwan

ObjectiveTo identify new genetic variants associated with SLE in Taiwan and establish polygenic risk score (PRS) models to improve the early diagnostic accuracy of SLE.MethodsThe study enrolled 2429 patients with...