The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation

Polygenic risk for triglyceride levels in the presence of a high impact rare variant

BACKGROUND

Elevated triglyceride (TG) levels are a heritable and modifiable risk factor for cardiovascular disease and have well-established associations with common genetic variation captured in a polygenic risk score (PRS). In young adulthood, the 22q11.2 microdeletion conveys a 2-fold increased risk for mild-moderate hypertriglyceridemia. This study aimed to assess the role of the TG-PRS in individuals with this elevated baseline risk for mild-moderate hypertriglyceridemia.

METHODS

We studied a deeply phenotyped cohort of adults (n = 157, median age 34 years) with a 22q11.2 microdeletion and available genome sequencing, lipid level, and other clinical data. The association between a previously developed TG-PRS and TG levels was assessed using a multivariable regression model adjusting for effects of sex, BMI, and other covariates. We also constructed receiver operating characteristic (ROC) curves using logistic regression models to assess the ability of TG-PRS and significant clinical variables to predict mild-moderate hypertriglyceridemia status.

RESULTS

The TG-PRS was a significant predictor of TG-levels (p = 1.52E-04), along with male sex and BMI, in a multivariable model (pmodel = 7.26E-05). The effect of TG-PRS appeared to be slightly stronger in individuals with obesity (BMI ≥ 30) (beta = 0.4617) than without (beta = 0.1778), in a model unadjusted for other covariates (p-interaction = 0.045). Among ROC curves constructed, the inclusion of TG-PRS, sex, and BMI as predictor variables produced the greatest area under the curve (0.749) for classifying those with mild-moderate hypertriglyceridemia, achieving an optimal sensitivity and specificity of 0.746 and 0.707, respectively.

CONCLUSIONS

These results demonstrate that in addition to significant effects of sex and BMI, genome-wide common variation captured in a PRS also contributes to the variable expression of the 22q11.2 microdeletion with respect to elevated TG levels.

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.

Power of inclusion: Enhancing polygenic prediction with admixed individuals

Admixed individuals offer unique opportunities for addressing limited transferability in polygenic scores (PGSs), given the substantial trans-ancestry genetic correlation in many complex traits. However, they are rarely considered in PGS training, given the challenges in representing ancestry-matched linkage-disequilibrium reference panels for admixed individuals. Here we present inclusive PGS (iPGS), which captures ancestry-shared genetic effects by finding the exact solution for penalized regression on individual-level data and is thus naturally applicable to admixed individuals. We validate our approach in a simulation study across 33 configurations with varying heritability, polygenicity, and ancestry composition in the training set. When iPGS is applied to n = 237,055 ancestry-diverse individuals in the UK Biobank, it shows the greatest improvements in Africans by 48.9% on average across 60 quantitative traits and up to 50-fold improvements for some traits (neutrophil count, R2 = 0.058) over the baseline model trained on the same number of European individuals. When we allowed iPGS to use n = 284,661 individuals, we observed an average improvement of 60.8% for African, 11.6% for South Asian, 7.3% for non-British White, 4.8% for White British, and 17.8% for the other individuals. We further developed iPGS+refit to jointly model the ancestry-shared and -dependent genetic effects when heterogeneous genetic associations were present. For neutrophil count, for example, iPGS+refit showed the highest predictive performance in the African group (R2 = 0.115), which exceeds the best predictive performance for the White British group (R2 = 0.090 in the iPGS model), even though only 1.49% of individuals used in the iPGS training are of African ancestry. Our results indicate the power of including diverse individuals for developing more equitable PGS models.

Secondary Prevention and Extreme Cardiovascular Risk Evaluation (SEVERE-1), Focus on Prevalence and Associated Risk Factors: The Study Protocol

INTRODUCTION

Despite significant improvement in secondary CardioVascular (CV) preventive strategies, some acute and chronic coronary syndrome (ACS and CCS) patients will suffer recurrent events (also called "extreme CV risk"). Recently new biochemical markers, such as uric acid (UA), lipoprotein A [Lp(a)] and several markers of inflammation, have been described to be associated with CV events recurrence. The SEcondary preVention and Extreme cardiovascular Risk Evaluation (SEVERE-1) study will accurately characterize extreme CV risk patients enrolled in cardiac rehabilitation (CR) programs.

AIM

 Our aims will be to describe the prevalence of extreme CV risk and its association with newly described biochemical CV risk factors.

AIM

Our aims will be to describe the prevalence of extreme CV risk and its association with newly described biochemical CV risk factors.

METHODS

We will prospectively enrol 730 ACS/CCS patients at the beginning of a CR program. Extreme CV risk will be retrospectively defined as the presence of a previous (within 2 years) CV events in the patients' clinical history. UA, Lp(a) and inflammatory markers (interleukin-6 and -18, tumor necrosis factor alpha, C-reactive protein, calprotectin and osteoprotegerin) will be assessed in ACS/CCS patients with extreme CV risk and compared with those without extreme CV risk but also with two control groups: 1180 hypertensives and 765 healthy subjects. The association between these biomarkers and extreme CV risk will be assessed with a multivariable model and two scoring systems will be created for an accurate identification of extreme CV risk patients. The first one will use only clinical variables while the second one will introduce the biochemical markers. Finally, by exome sequencing we will both evaluate polygenic risk score ability to predict recurrent events and perform mendellian randomization analysis on CV biomarkers.

CONCLUSIONS

Our study proposal was granted by the European Union PNRR M6/C2 call. With this study we will give definitive data on extreme CV risk prevalence rising attention on this condition and leading cardiologist to do a better diagnosis and to carry out a more intensive treatment optimization that will finally leads to a reduction of future ACS recurrence. This will be even more important for cardiologists working in CR that is a very important place for CV risk definition and therapies refinement.

Genetic distance informs polygenic score predictive accuracy

Polygenic scores (PGSs) aggregate the effects of variants across the genome to estimate genetic liability, but have lower performance in external study populations. A new study by Ding et al. has applied a novel framework to estimate the individual-level predictive accuracy of PGSs, and demonstrates that performance reduction occurs linearly with genetic distance.

Interplay Of Serum Bilirubin and Tobacco Smoking with Lung and Head and Neck Cancers in a Diverse, EHR-linked Los Angeles Biobank

Background

Bilirubin is a potent antioxidant with a protective role in many diseases. We examined the relationships between serum bilirubin (SB) levels, tobacco smoking (a known cause of low SB), and aerodigestive cancers, grouped as lung cancers (LC) and head and neck cancers (HNC).

Methods

We examined the associations between SB, LC, and HNC using data from 393,210 participants from a real-world, diverse, de-identified data repository and biobank linked to the UCLA Health system. We employed regression models, propensity score matching, and polygenic scores to investigate the associations and interactions between SB, tobacco smoking, LC, and HNC.

Results

Current tobacco smokers showed lower SB (-0.04mg/dL, 95% CI: [-0.04, -0.03]), compared to never-smokers. Lower SB levels were observed in HNC and LC cases (-0.10 mg/dL, [-0.13, -0.09] and - 0.09 mg/dL, CI [-0.1, -0.07] respectively) compared to cancer-free controls with the effect persisting after adjusting for smoking. SB levels were inversely associated with HNC and LC risk (ORs per SD change in SB: 0.64, CI [0.59,0.69] and 0.57, CI [0.43,0.75], respectively). Lastly, a polygenic score (PGS) for SB was associated with LC (OR per SD change of SB-PGS: 0.71, CI [0.67, 0.76]).

Conclusions

Low SB levels are associated with an increased risk of both HNC and LC, independent of the effect of tobacco smoking. Additionally, tobacco smoking demonstrated a strong interaction with SB on LC risk. Lastly, genetically predicted low SB (using a polygenic score) is negatively associated with LC. These findings suggest that SB could serve as a potential early and low-cost biomarker for LC and HNC. The interaction with tobacco smoking suggests that smokers with lower bilirubin could likely be at higher risk for LC compared to never smokers, suggesting the utility of SB in risk stratification for patients at risk for LC. Lastly, the results of the polygenic score analyses suggest potential shared biological pathways between the genetic control of SB and the risk of LC development.

Risk assessment of venous thromboembolism in inflammatory bowel disease by inherited risk in a population-based incident cohort

BACKGROUND

Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is a chronic inflammatory disease of the digestive tract with increasing prevalence globally. Although venous thromboembolism (VTE) is a major complication in IBD patients, it is often underappreciated with limited tools for risk stratification.

AIM

To estimate the proportion of VTE among IBD patients and assess genetic risk factors (monogenic and polygenic) for VTE.

METHODS

Incident VTE was followed for 8465 IBD patients in the UK Biobank (UKB). The associations of VTE with F5 factor V leiden (FVL) mutation, F2 G20210A prothrombin gene mutation (PGM), and polygenic score (PGS003332) were tested using Cox hazards regression analysis, adjusting for age at IBD diagnosis, gender, and genetic background (top 10 principal components). The performance of genetic risk factors for discriminating VTE diagnosis was estimated using the area under the receiver operating characteristic curve (AUC).

RESULTS

The overall proportion of incident VTE was 4.70% in IBD patients and was similar for CD (4.46%), UC (4.49%), and unclassified (6.42%), and comparable to that of cancer patients (4.66%) who are well-known at increased risk for VTE. Mutation carriers of F5/F2 had a significantly increased risk for VTE compared to non-mutation carriers, hazard ratio (HR) was 1.94, 95% confidence interval (CI): 1.42-2.65. In contrast, patients with the top PGS decile had a considerably higher risk for VTE compared to those with intermediate scores (middle 8 deciles), HR was 2.06 (95%CI: 1.57-2.71). The AUC for differentiating VTE diagnosis was 0.64 (95%CI: 0.61-0.67), 0.68 (95%CI: 0.66-0.71), and 0.69 (95%CI: 0.66-0.71), respectively, for F5/F2 mutation carriers, PGS, and combined.

CONCLUSION

Similar to cancer patients, VTE complications are common in IBD patients. PGS provides more informative risk information than F5/F2 mutations (FVL and PGM) for personalized thromboprophylaxis.

Minimal improvement in coronary artery disease risk prediction in Chinese population using polygenic risk scores: evidence from the China Kadoorie Biobank

BACKGROUND

Several studies have reported that polygenic risk scores (PRSs) can enhance risk prediction of coronary artery disease (CAD) in European populations. However, research on this topic is far from sufficient in non-European countries, including China. We aimed to evaluate the potential of PRS for predicting CAD for primary prevention in the Chinese population.

METHODS

Participants with genome-wide genotypic data from the China Kadoorie Biobank were divided into training ( n = 28,490) and testing sets ( n = 72,150). Ten previously developed PRSs were evaluated, and new ones were developed using clumping and thresholding or LDpred method. The PRS showing the strongest association with CAD in the training set was selected to further evaluate its effects on improving the traditional CAD risk-prediction model in the testing set. Genetic risk was computed by summing the product of the weights and allele dosages across genome-wide single-nucleotide polymorphisms. Prediction of the 10-year first CAD events was assessed using hazard ratios (HRs) and measures of model discrimination, calibration, and net reclassification improvement (NRI). Hard CAD (nonfatal I21-I23 and fatal I20-I25) and soft CAD (all fatal or nonfatal I20-I25) were analyzed separately.

RESULTS

In the testing set, 1214 hard and 7201 soft CAD cases were documented during a mean follow-up of 11.2 years. The HR per standard deviation of the optimal PRS was 1.26 (95% CI:1.19-1.33) for hard CAD. Based on a traditional CAD risk prediction model containing only non-laboratory-based information, the addition of PRS for hard CAD increased Harrell's C index by 0.001 (-0.001 to 0.003) in women and 0.003 (0.001 to 0.005) in men. Among the different high-risk thresholds ranging from 1% to 10%, the highest categorical NRI was 3.2% (95% CI: 0.4-6.0%) at a high-risk threshold of 10.0% in women. The association of the PRS with soft CAD was much weaker than with hard CAD, leading to minimal or no improvement in the soft CAD model.

CONCLUSIONS

In this Chinese population sample, the current PRSs minimally changed risk discrimination and offered little improvement in risk stratification for soft CAD. Therefore, this may not be suitable for promoting genetic screening in the general Chinese population to improve CAD risk prediction.

Clinical utility of polygenic scores for cardiometabolic disease in Arabs

Arabs account for 5% of the world population and have a high burden of cardiometabolic disease, yet clinical utility of polygenic risk prediction in Arabs remains understudied. Among 5399 Arab patients, we optimize polygenic scores for 10 cardiometabolic traits, achieving a performance that is better than published scores and on par with performance in European-ancestry individuals. Odds ratio per standard deviation (OR per SD) for a type 2 diabetes score was 1.83 (95% CI 1.74-1.92), and each SD of body mass index (BMI) score was associated with 1.18 kg/m2 difference in BMI. Polygenic scores associated with disease independent of conventional risk factors, and also associated with disease severity-OR per SD for coronary artery disease (CAD) was 1.78 (95% CI 1.66-1.90) for three-vessel CAD and 1.41 (95% CI 1.29-1.53) for one-vessel CAD. We propose a pragmatic framework leveraging public data as one way to advance equitable clinical implementation of polygenic scores in non-European populations.

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

Objective

To clarify the performance of polygenic risk scores in population screening, individual risk prediction, and population risk stratification.

Design

Secondary analysis of data in the Polygenic Score Catalog.

Setting

Polygenic Score Catalog, April 2022. Secondary analysis of 3915 performance metric estimates for 926 polygenic risk scores for 310 diseases to generate estimates of performance in population screening, individual risk, and population risk stratification.

Participants

Individuals contributing to the published studies in the Polygenic Score Catalog.

Main outcome measures

Detection rate for a 5% false positive rate (DR5) and the population odds of becoming affected given a positive result; individual odds of becoming affected for a person with a particular polygenic score; and odds of becoming affected for groups of individuals in different portions of a polygenic risk score distribution. Coronary artery disease and breast cancer were used as illustrative examples.

Results

For performance in population screening, median DR5 for all polygenic risk scores and all diseases studied was 11% (interquartile range 8-18%). Median DR5 was 12% (9-19%) for polygenic risk scores for coronary artery disease and 10% (9-12%) for breast cancer. The population odds of becoming affected given a positive results were 1:8 for coronary artery disease and 1:21 for breast cancer, with background 10 year odds of 1:19 and 1:41, respectively, which are typical for these diseases at age 50. For individual risk prediction, the corresponding 10 year odds of becoming affected for individuals aged 50 with a polygenic risk score at the 2.5th, 25th, 75th, and 97.5th centiles were 1:54, 1:29, 1:15, and 1:8 for coronary artery disease and 1:91, 1:56, 1:34, and 1:21 for breast cancer. In terms of population risk stratification, at age 50, the risk of coronary artery disease was divided into five groups, with 10 year odds of 1:41 and 1:11 for the lowest and highest quintile groups, respectively. The 10 year odds was 1:7 for the upper 2.5% of the polygenic risk score distribution for coronary artery disease, a group that contributed 7% of cases. The corresponding estimates for breast cancer were 1:72 and 1:26 for the lowest and highest quintile groups, and 1:19 for the upper 2.5% of the distribution, which contributed 6% of cases.

Conclusion

Polygenic risk scores performed poorly in population screening, individual risk prediction, and population risk stratification. Strong claims about the effect of polygenic risk scores on healthcare seem to be disproportionate to their performance.

The Role of Genetics in the Management of Heart Failure Patients

Over the last decades, the relevance of genetics in cardiovascular diseases has expanded, especially in the context of cardiomyopathies. Its relevance extends to the management of patients diagnosed with heart failure (HF), given its capacity to provide invaluable insights into the etiology of cardiomyopathies and identify individuals at a heightened risk of poor outcomes. Notably, the identification of an etiological genetic variant necessitates a comprehensive evaluation of the family lineage of the affected patients. In the future, these genetic variants hold potential as therapeutic targets with the capability to modify gene expression. In this complex setting, collaboration among cardiologists, specifically those specializing in cardiomyopathies and HF, and geneticists becomes paramount to improving individual and family health outcomes, as well as therapeutic clinical results. This review is intended to offer geneticists and cardiologists an updated perspective on the value of genetic research in HF and its implications in clinical practice.

Revealing polygenic pleiotropy using genetic risk scores for asthma

In this study we examined how genetic risk for asthma associates with different features of the disease and with other medical conditions and traits. Using summary statistics from two multi-ancestry genome-wide association studies of asthma, we modeled polygenic risk scores (PRSs) and validated their predictive performance in the UK Biobank. We then performed phenome-wide association studies of the asthma PRSs with 371 heritable traits in the UK Biobank. We identified 228 total significant associations across a variety of organ systems, including associations that varied by PRS model, sex, age of asthma onset, ancestry, and human leukocyte antigen region alleles. Our results highlight pervasive pleiotropy between asthma and numerous other traits and conditions and elucidate pathways that contribute to asthma and its comorbidities.

PRScalc, a privacy-preserving calculation of raw polygenic risk scores from direct-to-consumer genomics data

Motivation

Currently, the Polygenic Score (PGS) Catalog curates over 400 publications on over 500 traits corresponding to over 3000 polygenic risk scores (PRSs). To assess the feasibility of privately calculating the underlying multivariate relative risk for individuals with consumer genomics data, we developed an in-browserPRS calculator for genomic data that does not circulate any data or engage in any computation outside of the user's personal device.

Results

A prototype personal risk score calculator, created for research purposes, was developed to demonstrate how the PGS Catalog can be privately and readily applied to readily available direct-to-consumer genetic testing services, such as 23andMe. No software download, installation, or configuration is needed. The PRS web calculator matches individual PGS catalog entries with an individual's 23andMe genome data composed of 600k to 1.4 M single-nucleotide polymorphisms (SNPs). Beta coefficients provide researchers with a convenient assessment of risk associated with matched SNPs. This in-browser application was tested in a variety of personal devices, including smartphones, establishing the feasibility of privately calculating personal risk scores with up to a few thousand reference genetic variations and from the full 23andMe SNP data file (compressed or not).

Availability and implementation

The PRScalc web application is developed in JavaScript, HTML, and CSS and is available at GitHub repository (https://episphere.github.io/prs) under an MIT license. The datasets were derived from sources in the public domain: [PGS Catalog, Personal Genome Project].

Risk Prediction of Chronic Rhinosinusitis with or without Nasal Polyps in Taiwanese Population Using Polygenic Risk Score for Nasal Polyps

The association between single nucleotide polymorphisms and chronic rhinosinusitis (CRS) has been determined. However, it was not known whether the polygenic risk score (PRS) for nasal polyps (NP) could predict CRS with NP (CRSwNP) or without NP (CRSsNP). The aim of this study was to investigate the association between PRSs for NP and the risk of CRS with or without NP. Data from 535 individuals with CRS and 5350 control subjects in the Taiwan Precision Medicine Initiative project were collected. Four PRSs for NP, including PGS000933, PGS000934, PGS001848, and PGS002060 from UK Biobank, were tested in these participants. They were divided into four groups according to quartiles of PRSs. The logistic regression model was performed to evaluate CRSwNP and CRSsNP risk according to PRSs for NP. The PGS002060 had the highest area under the curve at 0.534 for CRSsNP prediction and at 0.588 for CRSwNP prediction. Compared to subjects in the lowest PRS category, the PGS002060 significantly increased the odds for CRSsNP by 1.48 at the highest quintile (p = 0.003) and by 2.32 at the highest quintile for CRSwNP (p = 0.002). In addition, the odds for CRSwNP increased by 3.01 times in female CRSwNP patients (p = 0.009) at the highest quintile compared with those in the lowest PRS category. The PRSs for NP developed from European populations could be applied to the Taiwanese population to predict CRS risk, especially for female CRSwNP.

Large sample size and nonlinear sparse models outline epistatic effects in inflammatory bowel disease

BACKGROUND

Despite clear evidence of nonlinear interactions in the molecular architecture of polygenic diseases, linear models have so far appeared optimal in genotype-to-phenotype modeling. A key bottleneck for such modeling is that genetic data intrinsically suffers from underdetermination ([Formula: see text]). Millions of variants are present in each individual while the collection of large, homogeneous cohorts is hindered by phenotype incidence, sequencing cost, and batch effects.

RESULTS

We demonstrate that when we provide enough training data and control the complexity of nonlinear models, a neural network outperforms additive approaches in whole exome sequencing-based inflammatory bowel disease case-control prediction. To do so, we propose a biologically meaningful sparsified neural network architecture, providing empirical evidence for positive and negative epistatic effects present in the inflammatory bowel disease pathogenesis.

CONCLUSIONS

In this paper, we show that underdetermination is likely a major driver for the apparent optimality of additive modeling in clinical genetics today.

Interplay Of Serum Bilirubin and Tobacco Smoking with Lung and Head and Neck Cancers in a Diverse, EHR-linked Los Angeles Biobank

Background

Bilirubin is a potent antioxidant with a protective role in many diseases. We examined the relationships between serum bilirubin (SB) levels, tobacco smoking (a known cause of low SB), and aerodigestive cancers, grouped as lung (LC) and head and neck (HNC).

Methods

We examined the associations between SB, LC and HNC using data from 393,210 participants from UCLA Health, employing regression models, propensity score matching, and polygenic scores.

Results

Current tobacco smokers showed lower SB (-0.04mg/dL, 95% CI: [-0.04, -0.03]), compared to never-smokers. Lower SB levels were observed in HNC and LC cases (-0.10 mg/dL, [-0.13, -0.09] and -0.09 mg/dL, CI [-0.1, -0.07] respectively) compared to cancer-free controls with the effect persisting after adjusting for smoking. SB levels were inversely associated with HNC and LC risk (ORs per SD change in SB: 0.64, CI [0.59,0.69] and 0.57, CI [0.43,0.75], respectively). Lastly, a polygenic score (PGS) for SB was associated with LC (OR per SD change of SB-PGS: 0.71, CI [0.67, 0.76]).

Conclusions

Low SB levels are associated with an increased risk of both HNC and LC, independent of the effect of tobacco smoking with tobacco smoking demonstrating a strong interaction with SB on LC risk. Additionally, genetically predicted low SB (from polygenic scores) is negatively associated with LC.

Impact

These findings suggest that SB could serve as a potential early biomarker for LC and HNC.

Polygenic Risk Score-Based Association Analysis of Speech-in-Noise and Hearing Threshold Measures in Healthy Young Adults with Self-reported Normal Hearing

PURPOSE

Speech-in-noise (SIN) traits exhibit high inter-subject variability, even for healthy young adults reporting normal hearing. Emerging evidence suggests that genetic variability could influence inter-subject variability in SIN traits. Genome-wide association studies (GWAS) have uncovered the polygenic architecture of various adult-onset complex human conditions. Polygenic risk scores (PRS) summarize complex genetic susceptibility to quantify the degree of genetic risk for health conditions. The present study conducted PRS-based association analyses to identify PRS risk factors for SIN and hearing threshold measures in 255 healthy young adults (18-40 years) with self-reported normal hearing.

METHODS

Self-reported SIN perception abilities were assessed by the Speech, Spatial, and Qualities of Hearing Scale (SSQ12). QuickSIN and audiometry (0.25-16 kHz) were performed on 218 participants. Saliva-derived DNA was used for low-pass whole genome sequencing, and 2620 PRS variables for various traits were calculated using the models derived from the polygenic risk score (PGS) catalog. The regression analysis was conducted to identify predictors for SSQ12, QuickSIN, and better ear puretone averages at conventional (PTA0.5-2), high (PTA4-8), and extended-high (PTA12.5-16) frequency ranges.

RESULTS

Participants with a higher genetic predisposition to HDL cholesterol reported better SSQ12. Participants with high PRS to dementia revealed significantly elevated PTA4-8, and those with high PRS to atrial fibrillation and flutter revealed significantly elevated PTA12.5-16.

CONCLUSION

These results indicate that healthy individuals with polygenic risk of certain health conditions could exhibit a subclinical decline in hearing health measures at young ages, decades before clinically meaningful SIN deficits and hearing loss could be observed. PRS could be used to identify high-risk individuals to prevent hearing health conditions by promoting a healthy lifestyle.

Clinical utility of polygenic risk scores: a critical 2023 appraisal

Since their first appearance in the context of schizophrenia and bipolar disorder in 2009, polygenic risk scores (PRSs) have been described for a large number of common complex diseases. However, the clinical utility of PRSs in disease risk assessment or therapeutic decision making is likely limited because PRSs usually only account for the heritable component of a trait and ignore the etiological role of environment and lifestyle. We surveyed the current state of PRSs for various diseases, including breast cancer, diabetes, prostate cancer, coronary artery disease, and Parkinson disease, with an extra focus upon the potential improvement of clinical scores by their combination with PRSs. We observed that the diagnostic and prognostic performance of PRSs alone is consistently low, as expected. Moreover, combining a PRS with a clinical score at best led to moderate improvement of the power of either risk marker. Despite the large number of PRSs reported in the scientific literature, prospective studies of their clinical utility, particularly of the PRS-associated improvement of standard screening or therapeutic procedures, are still rare. In conclusion, the benefit to individual patients or the health care system in general of PRS-based extensions of existing diagnostic or treatment regimens is still difficult to judge.

Genetic determinants of Vitamin D deficiency in the Middle Eastern Qatari population: a genome-wide association study

Introduction

Epidemiological studies have consistently revealed that Vitamin D deficiency is most prevalent in Middle Eastern countries. However, research on the impact of genetic loci and polygenic models related to Vitamin D has primarily focused on European populations.

Methods

We conducted the first genome-wide association study to identify genetic determinants of Vitamin D levels in Middle Easterners using a whole genome sequencing approach in 6,047 subjects from the Qatar Biobank (QBB) project. We performed a GWAS meta-analysis, combining the QBB cohort with recent European GWAS data from the UK Biobank (involving 345,923 individuals). Additionally, we evaluated the performance of European-derived polygenic risk scores using UK Biobank data in the QBB cohort.

Results

Our study identified an association between a variant in a known locus for the group-specific component gene (GC), specifically rs2298850 (p-value = 1.71 × 10-08, Beta = -0.1285), and Vitamin D levels. Furthermore, our GWAS meta-analysis identified two novel variants at a known locus on chromosome 11, rs67609747 and rs1945603, that reached the GWAS significance threshold. Notably, we observed a moderately high heritability of Vitamin D, estimated at 18%, compared to Europeans. Despite the lower predictive performance of Vitamin D levels in Qataris compared to Europeans, the European-derived polygenic risk scores exhibited significant links to Vitamin D deficiency risk within the QBB cohort.

Conclusion

This novel study reveals the genetic architecture contributing to Vitamin D deficiency in the Qatari population, emphasizing the genetic heterogeneity across different populations.

Evaluating and Improving Health Equity and Fairness of Polygenic Scores

Polygenic scores (PGS) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWAS, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum. In the simulation settings we explore, Joint-Lassosum provides more accurate PGS compared with other methods, especially when measured in terms of fairness. Like all PGS methods, Joint-Lassosum requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how Joint-Lassosum can help mitigate fairness-related harms that might result from the use of PGS scores in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWAS for different ancestries, Joint-Lassosum is an effective approach for enhancing portability and reducing predictive bias.

Association of a Polygenic Risk Score With Osteoporosis in People Living With HIV: The Swiss HIV Cohort Study

BACKGROUND

Bone mineral density (BMD) loss may be accelerated in people with HIV (PLWH). It is unknown whether a polygenic risk score (PRS) is associated with low BMD in PLWH.

METHODS

Swiss HIV Cohort Study participants of self-reported European descent underwent ≥2 per-protocol dual x-ray absorptiometry (DXA) measurements ≥2 years apart (2011-2020). Univariable and multivariable odds ratios (ORs) for DXA-defined osteoporosis were based on traditional and HIV-related risk factors and a genome-wide PRS built from 9413 single-nucleotide polymorphisms associated with low BMD in the general population. Controls were free from osteoporosis/osteopenia on all DXA measurements.

RESULTS

We included 438 participants: 149 with osteoporosis and 289 controls (median age, 53 years; 82% male, 95% with suppressed HIV RNA). Participants with unfavorable osteoporosis PRS (top vs bottom quintile) had univariable and multivariable-adjusted osteoporosis ORs of 4.76 (95% CI, 2.34-9.67) and 4.13 (1.86-9.18), respectively. For comparison, hepatitis C seropositivity, 5-year tenofovir disoproxil fumarate exposure, and parent history of hip fracture yielded univariable osteoporosis ORs of 2.26 (1.37-3.74), 1.84 (1.40-2.43), and 1.54 (0.82-2.9).

CONCLUSIONS

In PLWH in Switzerland, osteoporosis was independently associated with a BMD-associated PRS after adjustment for established risk factors, including exposure to tenofovir disoproxil fumarate.

Overestimated prediction using polygenic prediction derived from summary statistics

BACKGROUND

When polygenic risk score (PRS) is derived from summary statistics, independence between discovery and test sets cannot be monitored. We compared two types of PRS studies derived from raw genetic data (denoted as rPRS) and the summary statistics for IGAP (sPRS).

RESULTS

Two variables with the high heritability in UK Biobank, hypertension, and height, are used to derive an exemplary scale effect of PRS. sPRS without APOE is derived from International Genomics of Alzheimer's Project (IGAP), which records ΔAUC and ΔR2 of 0.051 ± 0.013 and 0.063 ± 0.015 for Alzheimer's Disease Sequencing Project (ADSP) and 0.060 and 0.086 for Accelerating Medicine Partnership - Alzheimer's Disease (AMP-AD). On UK Biobank, rPRS performances for hypertension assuming a similar size of discovery and test sets are 0.0036 ± 0.0027 (ΔAUC) and 0.0032 ± 0.0028 (ΔR2). For height, ΔR2 is 0.029 ± 0.0037.

CONCLUSION

Considering the high heritability of hypertension and height of UK Biobank and sample size of UK Biobank, sPRS results from AD databases are inflated. Independence between discovery and test sets is a well-known basic requirement for PRS studies. However, a lot of PRS studies cannot follow such requirements because of impossible direct comparisons when using summary statistics. Thus, for sPRS, potential duplications should be carefully considered within the same ethnic group.

Polygenic scores in cancer

Since the publication of the first genome-wide association study for cancer in 2007, thousands of common alleles that are associated with the risk of cancer have been identified. The relative risk associated with individual variants is small and of limited clinical significance. However, the combined effect of multiple risk variants as captured by polygenic scores (PGSs) may be much greater and therefore provide risk discrimination that is clinically useful. We review the considerable research efforts over the past 15 years for developing statistical methods for PGSs and their application in large-scale genome-wide association studies to develop PGSs for various cancers. We review the predictive performance of these PGSs and the multiple challenges currently limiting the clinical application of PGSs. Despite this, PGSs are beginning to be incorporated into clinical multifactorial risk prediction models to stratify risk in both clinical trials and clinical implementation studies.

An epidemiological introduction to human metabolomic investigations

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Multi-PGS enhances polygenic prediction by combining 937 polygenic scores

The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.

Genotype error due to low-coverage sequencing induces uncertainty in polygenic scoring

Polygenic scores (PGSs) have emerged as a standard approach to predict phenotypes from genotype data in a wide array of applications from socio-genomics to personalized medicine. Traditional PGSs assume genotype data to be error-free, ignoring possible errors and uncertainties introduced from genotyping, sequencing, and/or imputation. In this work, we investigate the effects of genotyping error due to low coverage sequencing on PGS estimation. We leverage SNP array and low-coverage whole-genome sequencing data (lcWGS, median coverage 0.04×) of 802 individuals from the Dana-Farber PROFILE cohort to show that PGS error correlates with sequencing depth (p = 1.2 × 10-7). We develop a probabilistic approach that incorporates genotype error in PGS estimation to produce well-calibrated PGS credible intervals and show that the probabilistic approach increases classification accuracy by up to 6% as compared to traditional PGSs that ignore genotyping error. Finally, we use simulations to explore the combined effect of genotyping and effect size errors and their implication on PGS-based risk-stratification. Our results illustrate the importance of considering genotyping error as a source of PGS error especially for cohorts with varying genotyping technologies and/or low-coverage sequencing.

Ancestral diversity in lipoprotein(a) studies helps address evidence gaps

INTRODUCTION

The independent and causal cardiovascular disease risk factor lipoprotein(a) (Lp(a)) is elevated in >1.5 billion individuals worldwide, but studies have prioritised European populations.

METHODS

Here, we examined how ancestrally diverse studies could clarify Lp(a)'s genetic architecture, inform efforts examining application of Lp(a) polygenic risk scores (PRS), enable causal inference and identify unexpected Lp(a) phenotypic effects using data from African (n=25 208), East Asian (n=2895), European (n=362 558), South Asian (n=8192) and Hispanic/Latino (n=8946) populations.

RESULTS

Fourteen genome-wide significant loci with numerous population specific signals of large effect were identified that enabled construction of Lp(a) PRS of moderate (R2=15% in East Asians) to high (R2=50% in Europeans) accuracy. For all populations, PRS showed promise as a 'rule out' for elevated Lp(a) because certainty of assignment to the low-risk threshold was high (88.0%-99.9%) across PRS thresholds (80th-99th percentile). Causal effects of increased Lp(a) with increased glycated haemoglobin were estimated for Europeans (p value =1.4×10-6), although inverse effects in Africans and East Asians suggested the potential for heterogeneous causal effects. Finally, Hispanic/Latinos were the only population in which known associations with coronary atherosclerosis and ischaemic heart disease were identified in external testing of Lp(a) PRS phenotypic effects.

CONCLUSIONS

Our results emphasise the merits of prioritising ancestral diversity when addressing Lp(a) evidence gaps.

Generalisation of genomic findings and applications of polygenic risk scores

Polygenic Risk Scores (PRS) (also known as polygenic scores, genetic risk scores or polygenic indexes) capture genetic contributions of a multitude of markers that characterise complex traits. Although their likely application to precision medicine remains to be established, promising advances have included their ability to stratify high risk individuals and targeted screening interventions. Current PRS have been mostly optimised for individuals of Northern European ancestries. If PRS are to become widespread as a tool for healthcare applications, more diverse populations and greater capacity for derived interventions need to be accomplished. In this editorial we aim to attract submissions from the research community that highlight current challenges in development of PRS applications at scale. We also welcome manuscripts that delve into the ethical, social and legal implications that the implementation of PRS may generate.

Calibrated prediction intervals for polygenic scores across diverse contexts

Polygenic scores (PGS) have emerged as the tool of choice for genomic prediction in a wide range of fields from agriculture to personalized medicine. We analyze data from two large biobanks in the US (All of Us) and the UK (UK Biobank) to find widespread variability in PGS performance across contexts. Many contexts, including age, sex, and income, impact PGS accuracies with similar magnitudes as genetic ancestry. PGSs trained in single versus multi-ancestry cohorts show similar context-specificity in their accuracies. We introduce trait prediction intervals that are allowed to vary across contexts as a principled approach to account for context-specific PGS accuracy in genomic prediction. We model the impact of all contexts in a joint framework to enable PGS-based trait predictions that are well-calibrated (contain the trait value with 90% probability in all contexts), whereas methods that ignore context are mis-calibrated. We show that prediction intervals need to be adjusted for all considered traits ranging from 10% for diastolic blood pressure to 80% for waist circumference. Adjustment of prediction intervals depends on the dataset; for example, prediction intervals for education years need to be adjusted by 90% in All of Us versus 8% in UK Biobank. Our results provide a path forward towards utilization of PGS as a prediction tool across all individuals regardless of their contexts while highlighting the importance of comprehensive profile of context information in study design and data collection.

The Genetic Architecture of Vitamin D Deficiency among an Elderly Lebanese Middle Eastern Population: An Exome-Wide Association Study

The Middle East region experiences a high prevalence of vitamin D deficiency, yet most genetic studies on vitamin D have focused on European populations. Furthermore, there is a lack of research on the genomic risk factors affecting elderly people, who are more susceptible to health burdens. We investigated the genetic determinants of 25-hydroxyvitamin D concentrations in elderly Lebanese individuals (n = 199) through a whole-exome-based genome-wide association study. Novel genomic loci displaying suggestive evidence of association with 25-hydroxyvitamin D levels were identified in our study, including rs141064014 in the MGAM (p-value of 4.40 × 10^-6) and rs7036592 in PHF2 (p-value of 8.43 × 10^-6). A meta-analysis of the Lebanese data and the largest European genome-wide association study confirmed consistency replication of numerous variants, including rs2725405 in SLC38A10 (p-value of 3.73 × 10^-8). Although the polygenic risk score model derived from European populations exhibited lower performance than European estimations, it still effectively predicted vitamin D deficiency among our cohort. Our discoveries offer novel perspectives on the genetic mechanisms underlying vitamin D deficiency among elderly Middle Eastern populations, facilitating the development of personalized approaches for more effective management of vitamin D deficiency. Additionally, we demonstrated that whole-exome-based genome-wide association study is an effective method for identifying genetic components associated with phenotypes.

Assessing the performance of European-derived cardiometabolic polygenic risk scores in South-Asians and their interplay with family history

BACKGROUND & AIMS

We aimed to assess the performance of European-derived polygenic risk scores (PRSs) for common metabolic diseases such as coronary artery disease (CAD), obesity, and type 2 diabetes (T2D) in the South Asian (SAS) individuals in the UK Biobank. Additionally, we studied the interaction between PRS and family history (FH) in the same population.

METHODS

To calculate the PRS, we used a previously published model derived from the EUR population and applied it to the individuals of SAS ancestry from the UKB study. Each PRS was adjusted according to an individual's genotype location in the principal components (PC) space to derive an ancestry adjusted PRS (aPRS). We calculated the percentiles based on aPRS and stratified individuals into three aPRS categories: low, intermediate, and high. Considering the intermediate-aPRS percentile as a reference, we compared the low and high aPRS categories and generated the odds ratio (OR) estimates. Further, we measured the combined role of aPRS and first-degree family history (FH) in the SAS population.

RESULTS

The risk of developing severe obesity for SAS individuals was almost twofold higher for individuals with high aPRS than for those with intermediate aPRS, with an OR of 1.95 (95% CI = 1.71-2.23, P < 0.01). At the same time, the risk of severe obesity was lower in the low-aPRS group (OR = 0.60, CI = 0.53-0.67, P < 0.01). Results in the same direction were found in the EUR data, where the low-PRS group had an OR of 0.53 (95% CI = 0.51-0.56, P < 0.01) and the high-PRS group had an OR of 2.06 (95% CI = 2.00-2.12, P < 0.01). We observed similar results for CAD and T2D. Further, we show that SAS individuals with a familial history of CAD and T2D with high-aPRS are associated with a higher risk of these diseases, implying a greater genetic predisposition.

CONCLUSION

Our findings suggest that CAD, obesity, and T2D GWAS summary statistics generated predominantly from the EUR population can be potentially used to derive aPRS in SAS individuals for risk stratification. With future GWAS recruiting more SAS participants and tailoring the PRSs towards SAS ancestry, the predictive power of PRS is likely to improve further.

A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease

Identification of individuals at highest risk of coronary artery disease (CAD)-ideally before onset-remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPSMult, that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPSMult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10-2.19, P < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPSMult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70-1.76, P < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPSMult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPSMult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction.

Improve the model of disease subtype heterogeneity by leveraging external summary data

Researchers are often interested in understanding the disease subtype heterogeneity by testing whether a risk exposure has the same level of effect on different disease subtypes. The polytomous logistic regression (PLR) model provides a flexible tool for such an evaluation. Disease subtype heterogeneity can also be investigated with a case-only study that uses a case-case comparison procedure to directly assess the difference between risk effects on two disease subtypes. Motivated by a large consortium project on the genetic basis of non-Hodgkin lymphoma (NHL) subtypes, we develop PolyGIM, a procedure to fit the PLR model by integrating individual-level data with summary data extracted from multiple studies under different designs. The summary data consist of coefficient estimates from working logistic regression models established by external studies. Examples of the working model include the case-case comparison model and the case-control comparison model, which compares the control group with a subtype group or a broad disease group formed by merging several subtypes. PolyGIM efficiently evaluates risk effects and provides a powerful test for disease subtype heterogeneity in situations when only summary data, instead of individual-level data, is available from external studies due to various informatics and privacy constraints. We investigate the theoretic properties of PolyGIM and use simulation studies to demonstrate its advantages. Using data from eight genome-wide association studies within the NHL consortium, we apply it to study the effect of the polygenic risk score defined by a lymphoid malignancy on the risks of four NHL subtypes. These results show that PolyGIM can be a valuable tool for pooling data from multiple sources for a more coherent evaluation of disease subtype heterogeneity.

Immunogenetics of lithium response and psychiatric phenotypes in patients with bipolar disorder

The link between bipolar disorder (BP) and immune dysfunction remains controversial. While epidemiological studies have long suggested an association, recent research has found only limited evidence of such a relationship. To clarify this, we investigated the contributions of immune-relevant genetic factors to the response to lithium (Li) treatment and the clinical presentation of BP. First, we assessed the association of a large collection of immune-related genes (4,925) with Li response, defined by the Retrospective Assessment of the Lithium Response Phenotype Scale (Alda scale), and clinical characteristics in patients with BP from the International Consortium on Lithium Genetics (ConLi+Gen, N = 2,374). Second, we calculated here previously published polygenic scores (PGSs) for immune-related traits and evaluated their associations with Li response and clinical features. We found several genes associated with Li response at p < 1×10- 4 values, including HAS3, CNTNAP5 and NFIB. Network and functional enrichment analyses uncovered an overrepresentation of pathways involved in cell adhesion and intercellular communication, which appear to converge on the well-known Li-induced inhibition of GSK-3β. We also found various genes associated with BP's age-at-onset, number of mood episodes, and presence of psychosis, substance abuse and/or suicidal ideation at the exploratory threshold. These included RTN4, XKR4, NRXN1, NRG1/3 and GRK5. Additionally, PGS analyses suggested serum FAS, ECP, TRANCE and cytokine ligands, amongst others, might represent potential circulating biomarkers of Li response and clinical presentation. Taken together, our results support the notion of a relatively weak association between immunity and clinically relevant features of BP at the genetic level.

Refining Risk for Alzheimer's Disease Among Heterozygous APOEɛ4 Carriers

In a large population-based cohort, we show not all heterozygous APOEɛ4 carriers are at increased risk for Alzheimer's disease (AD); a significantly higher AD proportion was only found for ɛ3/ɛ4, not ɛ2/ɛ4. Among ɛ3/ɛ4 carriers (24% in the cohort), the AD proportion differed considerably by polygenic risk score (PRS). In particular, the AD proportion was lower than the entire cohort for subjects in the bottom 20-percentile PRS and was higher than that of homozygous ɛ4 carriers for subjects at the top 5th-percentile PRS. Family history was no longer a significant predictor of AD risk after adjusting APOE and PRS.

Selection, optimization, and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse populations

Polygenic risk scores (PRS) have improved in predictive performance supporting their use in clinical practice. Reduced predictive performance of PRS in diverse populations can exacerbate existing health disparities. The NHGRI-funded eMERGE Network is returning a PRS-based genome-informed risk assessment to 25,000 diverse adults and children. We assessed PRS performance, medical actionability, and potential clinical utility for 23 conditions. Standardized metrics were considered in the selection process with additional consideration given to strength of evidence in African and Hispanic populations. Ten conditions were selected with a range of high-risk thresholds: atrial fibrillation, breast cancer, chronic kidney disease, coronary heart disease, hypercholesterolemia, prostate cancer, asthma, type 1 diabetes, obesity, and type 2 diabetes. We developed a pipeline for clinical PRS implementation, used genetic ancestry to calibrate PRS mean and variance, created a framework for regulatory compliance, and developed a PRS clinical report. eMERGE's experience informs the infrastructure needed to implement PRS-based implementation in diverse clinical settings.

Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study

BACKGROUND

Machine learning has been used to analyse heart failure subtypes, but not across large, distinct, population-based datasets, across the whole spectrum of causes and presentations, or with clinical and non-clinical validation by different machine learning methods. Using our published framework, we aimed to discover heart failure subtypes and validate them upon population representative data.

METHODS

In this external, prognostic, and genetic validation study we analysed individuals aged 30 years or older with incident heart failure from two population-based databases in the UK (Clinical Practice Research Datalink [CPRD] and The Health Improvement Network [THIN]) from 1998 to 2018. Pre-heart failure and post-heart failure factors (n=645) included demographic information, history, examination, blood laboratory values, and medications. We identified subtypes using four unsupervised machine learning methods (K-means, hierarchical, K-Medoids, and mixture model clustering) with 87 of 645 factors in each dataset. We evaluated subtypes for (1) external validity (across datasets); (2) prognostic validity (predictive accuracy for 1-year mortality); and (3) genetic validity (UK Biobank), association with polygenic risk score (PRS) for heart failure-related traits (n=11), and single nucleotide polymorphisms (n=12).

FINDINGS

We included 188 800, 124 262, and 9573 individuals with incident heart failure from CPRD, THIN, and UK Biobank, respectively, between Jan 1, 1998, and Jan 1, 2018. After identifying five clusters, we labelled heart failure subtypes as (1) early onset, (2) late onset, (3) atrial fibrillation related, (4) metabolic, and (5) cardiometabolic. In the external validity analysis, subtypes were similar across datasets (c-statistics: THIN model in CPRD ranged from 0·79 [subtype 3] to 0·94 [subtype 1], and CPRD model in THIN ranged from 0·79 [subtype 1] to 0·92 [subtypes 2 and 5]). In the prognostic validity analysis, 1-year all-cause mortality after heart failure diagnosis (subtype 1 0·20 [95% CI 0·14-0·25], subtype 2 0·46 [0·43-0·49], subtype 3 0·61 [0·57-0·64], subtype 4 0·11 [0·07-0·16], and subtype 5 0·37 [0·32-0·41]) differed across subtypes in CPRD and THIN data, as did risk of non-fatal cardiovascular diseases and all-cause hospitalisation. In the genetic validity analysis the atrial fibrillation-related subtype showed associations with the related PRS. Late onset and cardiometabolic subtypes were the most similar and strongly associated with PRS for hypertension, myocardial infarction, and obesity (p<0·0009). We developed a prototype app for routine clinical use, which could enable evaluation of effectiveness and cost-effectiveness.

INTERPRETATION

Across four methods and three datasets, including genetic data, in the largest study of incident heart failure to date, we identified five machine learning-informed subtypes, which might inform aetiological research, clinical risk prediction, and the design of heart failure trials.

FUNDING

European Union Innovative Medicines Initiative-2.

Implementing Reporting Standards for Polygenic Risk Scores for Atherosclerotic Cardiovascular Disease

PURPOSE OF REVIEW

There is considerable interest in using polygenic risk scores (PRSs) for assessing risk of atherosclerotic cardiovascular disease (ASCVD). A barrier to the clinical use of PRSs is heterogeneity in how PRS studies are reported. In this review, we summarize approaches to establish a uniform reporting framework for PRSs for coronary heart disease (CHD), the most common form of ASCVD.

RECENT FINDINGS

Reporting standards for PRSs need to be contextualized for disease specific applications. In addition to metrics of predictive performance, reporting standards for PRSs for CHD should include how cases/control were ascertained, degree of adjustment for conventional CHD risk factors, portability to diverse genetic ancestry groups and admixed individuals, and quality control measures for clinical deployment. Such a framework will enable PRSs to be optimized and benchmarked for clinical use.

eXclusionarY: 10 years later, where are the sex chromosomes in GWASs?

10 years ago, a detailed analysis showed that only 33% of genome-wide association study (GWAS) results included the X chromosome. Multiple recommendations were made to combat such exclusion. Here, we re-surveyed the research landscape to determine whether these earlier recommendations had been translated. Unfortunately, among the genome-wide summary statistics reported in 2021 in the NHGRI-EBI GWAS Catalog, only 25% provided results for the X chromosome and 3% for the Y chromosome, suggesting that the exclusion phenomenon not only persists but has also expanded into an exclusionary problem. Normalizing by physical length of the chromosome, the average number of studies published through November 2022 with genome-wide-significant findings on the X chromosome is ∼1 study/Mb. By contrast, it ranges from ∼6 to ∼16 studies/Mb for chromosomes 4 and 19, respectively. Compared with the autosomal growth rate of ∼0.086 studies/Mb/year over the last decade, studies of the X chromosome grew at less than one-seventh that rate, only ∼0.012 studies/Mb/year. Among the studies that reported significant associations on the X chromosome, we noted extreme heterogeneities in data analysis and reporting of results, suggesting the need for clear guidelines. Unsurprisingly, among the 430 scores sampled from the PolyGenic Score Catalog, 0% contained weights for sex chromosomal SNPs. To overcome the dearth of sex chromosome analyses, we provide five sets of recommendations and future directions. Finally, until the sex chromosomes are included in a whole-genome study, instead of GWASs, we propose such studies would more properly be referred to as "AWASs," meaning "autosome-wide scans."

Cardiovascular Disease Risk Assessment Using Traditional Risk Factors and Polygenic Risk Scores in the Million Veteran Program

Importance

Primary prevention of atherosclerotic cardiovascular disease (ASCVD) relies on risk stratification. Genome-wide polygenic risk scores (PRSs) are proposed to improve ASCVD risk estimation.

Objective

To determine whether genome-wide PRSs for coronary artery disease (CAD) and acute ischemic stroke improve ASCVD risk estimation with traditional clinical risk factors in an ancestrally diverse midlife population.

Design, Setting, and Participants

This was a prognostic analysis of incident events in a retrospectively defined longitudinal cohort conducted from January 1, 2011, to December 31, 2018. Included in the study were adults free of ASCVD and statin naive at baseline from the Million Veteran Program (MVP), a mega biobank with genetic, survey, and electronic health record data from a large US health care system. Data were analyzed from March 15, 2021, to January 5, 2023.

Exposures

PRSs for CAD and ischemic stroke derived from cohorts of largely European descent and risk factors, including age, sex, systolic blood pressure, total cholesterol, high-density lipoprotein (HDL) cholesterol, smoking, and diabetes status.

Main Outcomes and Measures

Incident nonfatal myocardial infarction (MI), ischemic stroke, ASCVD death, and composite ASCVD events.

Results

A total of 79 151 participants (mean [SD] age, 57.8 [13.7] years; 68 503 male [86.5%]) were included in the study. The cohort included participants from the following harmonized genetic ancestry and race and ethnicity categories: 18 505 non-Hispanic Black (23.4%), 6785 Hispanic (8.6%), and 53 861 non-Hispanic White (68.0%) with a median (5th-95th percentile) follow-up of 4.3 (0.7-6.9) years. From 2011 to 2018, 3186 MIs (4.0%), 1933 ischemic strokes (2.4%), 867 ASCVD deaths (1.1%), and 5485 composite ASCVD events (6.9%) were observed. CAD PRS was associated with incident MI in non-Hispanic Black (hazard ratio [HR], 1.10; 95% CI, 1.02-1.19), Hispanic (HR, 1.26; 95% CI, 1.09-1.46), and non-Hispanic White (HR, 1.23; 95% CI, 1.18-1.29) participants. Stroke PRS was associated with incident stroke in non-Hispanic White participants (HR, 1.15; 95% CI, 1.08-1.21). A combined CAD plus stroke PRS was associated with ASCVD deaths among non-Hispanic Black (HR, 1.19; 95% CI, 1.03-1.17) and non-Hispanic (HR, 1.11; 95% CI, 1.03-1.21) participants. The combined PRS was also associated with composite ASCVD across all ancestry groups but greater among non-Hispanic White (HR, 1.20; 95% CI, 1.16-1.24) than non-Hispanic Black (HR, 1.11; 95% CI, 1.05-1.17) and Hispanic (HR, 1.12; 95% CI, 1.00-1.25) participants. Net reclassification improvement from adding PRS to a traditional risk model was modest for the intermediate risk group for composite CVD among men (5-year risk >3.75%, 0.38%; 95% CI, 0.07%-0.68%), among women, (6.79%; 95% CI, 3.01%-10.58%), for age older than 55 years (0.25%; 95% CI, 0.03%-0.47%), and for ages 40 to 55 years (1.61%; 95% CI, -0.07% to 3.30%).

Conclusions and Relevance

Study results suggest that PRSs derived predominantly in European samples were statistically significantly associated with ASCVD in the multiancestry midlife and older-age MVP cohort. Overall, modest improvement in discrimination metrics were observed with addition of PRSs to traditional risk factors with greater magnitude in women and younger age groups.

A Mendelian Randomization Analysis of 55 Genetically Predicted Metabolic Traits with Breast Cancer Survival Outcomes in the Pathways Study

Previous studies suggest associations of metabolic syndromes with breast cancer prognosis, yet the evidence is mixed. In recent years, the maturation of genome-wide association study findings has led to the development of polygenic scores (PGS) for many common traits, making it feasible to use Mendelian randomization to examine associations between metabolic traits and breast cancer outcomes. In the Pathways Study of 3,902 patients and a median follow-up time of 10.5 years, we adapted a Mendelian randomization approach to calculate PGS for 55 metabolic traits and tested their associations with seven survival outcomes. Multivariable Cox proportional hazards models were used to derive HRs and 95% confidence intervals (CI) with adjustment for covariates. The highest tertile (T3) of PGS for cardiovascular disease was associated with shorter overall survival (HR = 1.34, 95% CI = 1.11-1.61) and second primary cancer-free survival (HR = 1.31, 95% CI = 1.12-1.53). PGS for hypertension (T3) was associated with shorter overall survival (HR = 1.20, 95% CI = 1.00-1.43), second primary cancer-free survival (HR = 1.24, 95% CI = 1.06-1.45), invasive disease-free survival (HR = 1.18, 95% CI = 1.01-1.38), and disease-free survival (HR = 1.21, 95% CI = 1.04-1.39). PGS for serum cystatin C levels (T3) was associated with longer disease-free survival (HR = 0.82, 95% CI = 0.71-0.95), breast event-free survival (HR = 0.74, 95% CI = 0.61-0.91), and breast cancer-specific survival (HR = 0.72, 95% CI = 0.54-0.95). The above associations were significant at a nominal P < 0.05 level but not after correcting for multiple testing (Bonferroni P < 0.0009). Our analyses revealed notable associations of PGS for cardiovascular disease, hypertension, and cystatin C levels with breast cancer survival outcomes. These findings implicate metabolic traits in breast cancer prognosis.

Significance

To our knowledge, this is the largest study of PGS for metabolic traits with breast cancer prognosis. The findings revealed significant associations of PGS for cardiovascular disease, hypertension, and cystatin C levels with several breast cancer survival outcomes. These findings implicate an underappreciated role of metabolic traits in breast cancer prognosis that would warrant further exploration.

Polygenic scores for estimated glomerular filtration rate in a population of general adults and elderly - comparative results from the KORA and AugUR study

BACKGROUND

Polygenic scores (PGSs) combining genetic variants found to be associated with creatinine-based estimated glomerular filtration rate (eGFR_crea) have been applied in various study populations with different age ranges. This has shown that PGS explain less eGFR_crea variance in the elderly. Our aim was to understand how differences in eGFR variance and the percentage explained by PGS varies between population of general adults and elderly.

RESULTS

We derived a PGS for cystatin-based eGFR (eGFR_cys) from published genome-wide association studies. We used the 634 variants known for eGFR_crea and the 204 variants identified for eGFR_cys to calculate the PGS in two comparable studies capturing a general adult and an elderly population, KORA S4 (n = 2,900; age 24-69 years) and AugUR (n = 2,272, age ≥ 70 years). To identify potential factors determining age-dependent differences on the PGS-explained variance, we evaluated the PGS variance, the eGFR variance, and the beta estimates of PGS association on eGFR. Specifically, we compared frequencies of eGFR-lowering alleles between general adult and elderly individuals and analyzed the influence of comorbidities and medication intake. The PGS for eGFR_crea explained almost twice as much (R² = 9.6%) of age-/sex adjusted eGFR variance in the general adults compared to the elderly (4.6%). This difference was less pronounced for the PGS for eGFR_cys (4.7% or 3.6%, respectively). The beta-estimate of the PGS on eGFR_crea was higher in the general adults compared to the elderly, but similar for the PGS on eGFR_cys. The eGFR variance in the elderly was reduced by accounting for comorbidities and medication intake, but this did not explain the difference in R²-values. Allele frequencies between general adult and elderly individuals showed no significant differences except for one variant near APOE (rs429358). We found no enrichment of eGFR-protective alleles in the elderly compared to general adults.

CONCLUSIONS

We concluded that the difference in explained variance by PGS was due to the higher age- and sex-adjusted eGFR variance in the elderly and, for eGFR_crea, also by a lower PGS association beta-estimate. Our results provide little evidence for survival or selection bias.

Assessment of multi-population polygenic risk scores for lipid traits in African Americans

Polygenic risk scores (PRS) based on genome-wide discoveries are promising predictors or classifiers of disease development, severity, and/or progression for common clinical outcomes. A major limitation of most risk scores is the paucity of genome-wide discoveries in diverse populations, prompting an emphasis to generate these needed data for trans-population and population-specific PRS construction. Given diverse genome-wide discoveries are just now being completed, there has been little opportunity for PRS to be evaluated in diverse populations independent from the discovery efforts. To fill this gap, we leverage here summary data from a recent genome-wide discovery study of lipid traits (HDL-C, LDL-C, triglycerides, and total cholesterol) conducted in diverse populations represented by African Americans, Hispanics, Asians, Native Hawaiians, Native Americans, and others by the Population Architecture using Genomics and Epidemiology (PAGE) Study. We constructed lipid trait PRS using PAGE Study published genetic variants and weights in an independent African American adult patient population linked to de-identified electronic health records and genotypes from the Illumina Metabochip (n = 3,254). Using multi-population lipid trait PRS, we assessed levels of association for their respective lipid traits, clinical outcomes (cardiovascular disease and type 2 diabetes), and common clinical labs. While none of the multi-population PRS were strongly associated with the tested trait or outcome, PRS_LDL-Cwas nominally associated with cardiovascular disease. These data demonstrate the complexity in applying PRS to real-world clinical data even when data from multiple populations are available.

Investigation of heteroscedasticity in polygenic risk scores across 15 quantitative traits

The polygenic risk score (PRS) could be used to stratify individuals with high risk of diseases and predict complex trait of individual in a population. Previous studies developed a PRS-based prediction model using linear regression and evaluated the predictive performance of the model using the R ² value. One of the key assumptions of linear regression is that the variance of the residual should be constant at each level of the predictor variables, called homoscedasticity. However, some studies show that PRS models exhibit heteroscedasticity between PRS and traits. This study analyzes whether heteroscedasticity exists in PRS models of diverse disease-related traits and, if any, it affects the accuracy of PRS-based prediction in 354,761 Europeans from the UK Biobank. We constructed PRSs for 15 quantitative traits using LDpred2 and estimated the existence of heteroscedasticity between PRSs and 15 traits using three different tests of the Breusch-Pagan (BP) test, score test, and F test. Thirteen out of fifteen traits show significant heteroscedasticity. Further replication using new PRSs from the PGS catalog and independent samples (N = 23,620) from the UK Biobank confirmed the heteroscedasticity in ten traits. As a result, ten out of fifteen quantitative traits show statistically significant heteroscedasticity between the PRS and each trait. There was a greater variance of residuals as PRS increased, and the prediction accuracy at each level of PRS tended to decrease as the variance of residuals increased. In conclusion, heteroscedasticity was frequently observed in the PRS-based prediction models of quantitative traits, and the accuracy of the predictive model may differ according to PRS values. Therefore, prediction models using the PRS should be constructed by considering heteroscedasticity.

Non-invasive STEMI-related biomarkers based on meta-analysis and gene prioritization

BACKGROUND AND AIMS

Acute ST-Segment Myocardial infarction (STEMI) is a common cardiovascular issue with a considerable burden of the disease. The underlying genetic basis and non-invasive markers were not well-established.

METHODS

Here, we implemented a systematic literature review and meta-analyses integration methods on 217 STEMI patients and 72 normal individuals to prioritize and detect the STEMI-related non-invasive markers. Five high-scored genes were experimentally assessed on 10 STEMI patients and 9 healthy controls. Finally, the presence of co-expressed nodes of top-score genes was explored.

RESULTS

The differential expression of ARGL, CLEC4E, and EIF3D were significant for Iranian patients. The ROC curve for gene CLEC4E revealed an AUC (95% CI) of 0.786 (0.686-0.886) in the prediction of STEMI. The Cox-PH model was fitted to stratify high/low risk heart failure progression (CI-index = 0.83, Likelihood-Ratio-Test = 3e-10). The SI00AI2 was a common biomarker between STEMI and NSTEMI patients.

CONCLUSIONS

In conclusion, the high-scored genes and prognostic model could be applicable for Iranian patients.

Rank concordance of polygenic indices

Polygenic indices (PGIs) are increasingly used to identify individuals at risk of developing disease and are advocated as screening tools for personalized medicine and education. Here we empirically assess rank concordance between PGIs created with different construction methods and discovery samples, focusing on cardiovascular disease and educational attainment. We find Spearman rank correlations between 0.17 and 0.93 for cardiovascular disease, and 0.40 and 0.83 for educational attainment, indicating highly unstable rankings across different PGIs for the same trait. Potential consequences for personalized medicine and gene-environment (G × E) interplay are illustrated using data from the UK Biobank. Simulations show how rank discordance mainly derives from a limited discovery sample size and reveal a tight link between the explained variance of a PGI and its ranking precision. We conclude that PGI-based ranking is highly dependent on PGI choice, such that current PGIs do not have the desired precision to be used routinely for personalized intervention.

Potential utility of risk stratification for multicancer screening with liquid biopsy tests

Our proof-of-concept study reveals the potential of risk stratification by the combined effects of age, polygenic risk scores (PRS), and non-genetic risk factors in increasing the risk-benefit balance of rapidly emerging non-invasive multicancer early detection (MCED) liquid biopsy tests. We develop and validate sex-specific pan-cancer risk scores (PCRSs), defined by the combination of body mass index, smoking, family history of cancers, and cancer-specific polygenic risk scores (PRSs), to predict the absolute risk of developing at least one of the many common cancer types. We demonstrate the added value of PRSs in improving the predictive performance of the risk factors only model and project the positive and negative predictive values for two promising multicancer screening tests across risk strata defined by age and PCRS.

An atlas of genetic scores to predict multi-omic traits

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.