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

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

Impact of Measurement Noise on Genetic Association Studies of Cardiac Function.

Recent research has effectively used quantitative traits from imaging to boost the capabilities of genome-wide association studies (GWAS), providing further understanding of disease biology and various traits. However, it's important to note that phenotyping inherently carries measurement error and noise that could influence subsequent genetic analyses. The study focused on left ventricular ejection fraction (LVEF), a vital yet potentially inaccurate quantitative measurement, to investigate how imprecision in phenotype measurement affects genetic studies. Several methods of acquiring LVEF, along with simulating measurement noise, were assessed for their effects on ensuing genetic analyses. The results showed that by introducing just 7.9% of measurement noise, all genetic associations in an LVEF GWAS with almost forty thousand individuals could be eliminated. Moreover, a 1% increase in mean absolute error (MAE) in LVEF had an effect equivalent to a 10% reduction in the sample size of the cohort on the power of GWAS. Therefore, enhancing the accuracy of phenotyping is crucial to maximize the effectiveness of genome-wide association studies.

CXCL12 regulates coronary artery dominance in diverse populations and links development to disease.

Mammalian cardiac muscle is supplied with blood by right and left coronary arteries that form branches covering both ventricles of the heart. Whether branches of the right or left coronary arteries wrap around to the inferior side of the left ventricle is variable in humans and termed right or left dominance. Coronary dominance is likely a heritable trait, but its genetic architecture has never been explored. Here, we present the first large-scale multi-ancestry genome-wide association study of dominance in 61,043 participants of the VA Million Veteran Program, including over 10,300 Africans and 4,400 Admixed Americans. Dominance was moderately heritable with ten loci reaching genome wide significance. The most significant mapped to the chemokine CXCL12 in both Europeans and Africans. Whole-organ imaging of human fetal hearts revealed that dominance is established during development in locations where CXCL12 is expressed. In mice, dominance involved the septal coronary artery, and its patterning was altered with Cxcl12 deficiency. Finally, we linked human dominance patterns with coronary artery disease through colocalization, genome-wide genetic correlation and Mendelian Randomization analyses. Together, our data supports CXCL12 as a primary determinant of coronary artery dominance in humans of diverse backgrounds and suggests that developmental patterning of arteries may influence one's susceptibility to ischemic heart disease.

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

Background: Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (PRSCHD) for 5 genetic ancestry groups. Methods: We derived ancestry-specific and multi-ancestry PRSCHD based on pruning and thresholding (PRSP+T) and continuous shrinkage priors (PRSCSx) applied on summary statistics from the largest multi-ancestry genome-wide meta-analysis for CHD to date, including 1.1 million participants from 5 continental populations. Following training and optimization of PRSCHD in the Million Veteran Program, we evaluated predictive performance of the best performing PRSCHD in 176,988 individuals across 9 cohorts of diverse genetic ancestry. Results: Multi-ancestry PRSP+T outperformed ancestry specific PRSP+T across a range of tuning values. In training stage, for all ancestry groups, PRSCSx performed better than PRSP+T and multi-ancestry PRS outperformed ancestry-specific PRS. In independent validation cohorts, the selected multi-ancestry PRSP+T demonstrated the strongest association with CHD in individuals of South Asian (SAS) and European (EUR) ancestry (OR per 1SD[95% CI]; 2.75[2.41-3.14], 1.65[1.59-1.72]), followed by East Asian (EAS) (1.56[1.50-1.61]), Hispanic/Latino (HIS) (1.38[1.24-1.54]), and weakest in African (AFR) ancestry (1.16[1.11-1.21]). The selected multi-ancestry PRSCSx showed stronger associacion with CHD in comparison within each ancestry group where the association was strongest in SAS (2.67[2.38-3.00]) and EUR (1.65[1.59-1.71]), progressively decreasing in EAS (1.59[1.54-1.64]), HIS (1.51[1.35-1.69]), and lowest in AFR (1.20[1.15-1.26]). Conclusions: Utilizing diverse summary statistics from a large multi-ancestry genome-wide meta-analysis led to improved performance of PRSCHD in most ancestry groups compared to single-ancestry methods. Improvement of predictive performance was limited, specifically in AFR and HIS, despite use of one of the largest and most diverse set of training and validation cohorts to date. This highlights the need for larger GWAS datasets of AFR and HIS individuals to enhance performance of PRSCHD.

Carriers of rare damaging CCR2 genetic variants are at lower risk of atherosclerotic disease.

Background: The CCL2/CCR2 axis governs monocyte trafficking and recruitment to atherosclerotic lesions. Human genetic analyses and population-based studies support an association between circulating CCL2 levels and atherosclerosis. Still, it remains unknown whether pharmacological targeting of CCR2, the main CCL2 receptor, would provide protection against human atherosclerotic disease. Methods: In whole-exome sequencing data from 454,775 UK Biobank participants (40-69 years), we identified predicted loss-of-function (LoF) or damaging missense (REVEL score >0.5) variants within the CCR2 gene. We prioritized variants associated with lower monocyte count (p<0.05) and tested associations with vascular risk factors and risk of atherosclerotic disease over a mean follow-up of 14 years. The results were replicated in a pooled cohort of three independent datasets (TOPMed, deCODE and Penn Medicine BioBank; total n=441,445) and the effect of the most frequent damaging variant was experimentally validated. Results: A total of 45 predicted LoF or damaging missense variants were identified in the CCR2 gene, 4 of which were also significantly associated with lower monocyte count, but not with other white blood cell counts. Heterozygous carriers of these variants were at a lower risk of a combined atherosclerosis outcome, showed a lower burden of atherosclerosis across four vascular beds, and were at a lower lifetime risk of coronary artery disease and myocardial infarction. There was no evidence of association with vascular risk factors including LDL-cholesterol, blood pressure, glycemic status, or C-reactive protein. Using a cAMP assay, we found that cells transfected with the most frequent CCR2 damaging variant (3:46358273:T:A, M249K, 547 carriers, frequency: 0.14%) show a decrease in signaling in response to CCL2. The associations of the M249K variant with myocardial infarction were consistent across cohorts (ORUKB: 0.62 95%CI: 0.39-0.96; ORexternal: 0.64 95%CI: 0.34-1.19; ORpooled: 0.64 95%CI: 0.450.90). In a phenome-wide association study, we found no evidence for higher risk of common infections or mortality among carriers of damaging CCR2 variants. Conclusions: Heterozygous carriers of damaging CCR2 variants have a lower burden of atherosclerosis and lower lifetime risk of myocardial infarction. In conjunction with previous evidence from experimental and epidemiological studies, our findings highlight the translational potential of CCR2-targeting as an atheroprotective approach.

A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records.

BACKGROUND: The identification of new uses for existing drug therapies has the potential to identify treatments for comorbid conditions that have the added benefit of glycemic control while also providing a rapid, low-cost approach to drug (re)discovery. METHODS: We developed and tested a genetically-informed drug-repurposing pipeline for diabetes management. This approach mapped genetically-predicted gene expression signals from the largest genome-wide association study for type 2 diabetes mellitus to drug targets using publicly available databases to identify drug-gene pairs. These drug-gene pairs were then validated using a two-step approach: 1) a self-controlled case-series (SCCS) using electronic health records from a discovery and replication population, and 2) Mendelian randomization (MR). FINDINGS: After filtering on sample size, 20 candidate drug-gene pairs were validated and various medications demonstrated evidence of glycemic regulation including two anti-hypertensive classes: angiotensin-converting enzyme inhibitors as well as calcium channel blockers (CCBs). The CCBs demonstrated the strongest evidence of glycemic reduction in both validation approaches (SCCS HbA1c and glucose reduction: -0.11%, p = 0.01 and -0.85 mg/dL, p = 0.02, respectively; MR: OR = 0.84, 95% CI = 0.81, 0.87, p = 5.0 x 10-25). INTERPRETATION: Our results support CCBs as a strong candidate medication for blood glucose reduction in addition to cardiovascular disease reduction. Further, these results support the adaptation of this approach for use in future drug-repurposing efforts for other conditions. FUNDING: National Institutes of Health, Medical Research Council Integrative Epidemiology Unit at the University of Bristol, UK Medical Research Council, American Heart Association, and Department of Veterans Affairs (VA) Informatics and Computing Infrastructure and VA Cooperative Studies Program.

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.

Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes.

BACKGROUND: Quantification of chamber size and systolic function is a fundamental component of cardiac imaging. However, the human heart is a complex structure with significant uncharacterized phenotypic variation beyond traditional metrics of size and function. Examining variation in cardiac shape can add to our ability to understand cardiovascular risk and pathophysiology. METHODS: We measured the left ventricle (LV) sphericity index (short axis length/long axis length) using deep learning-enabled image segmentation of cardiac magnetic resonance imaging data from the UK Biobank. Subjects with abnormal LV size or systolic function were excluded. The relationship between LV sphericity and cardiomyopathy was assessed using Cox analyses, genome-wide association studies, and two-sample Mendelian randomization. FINDINGS: In a cohort of 38,897 subjects, we show that a one standard deviation increase in sphericity index is associated with a 47% increased incidence of cardiomyopathy (hazard ratio [HR]: 1.47, 95% confidence interval [CI]: 1.10-1.98, p = 0.01) and a 20% increased incidence of atrial fibrillation (HR: 1.20, 95% CI: 1.11-1.28, p < 0.001), independent of clinical factors and traditional magnetic resonance imaging (MRI) measurements. We identify four loci associated with sphericity at genome-wide significance, and Mendelian randomization supports non-ischemic cardiomyopathy as causal for LV sphericity. CONCLUSIONS: Variation in LV sphericity in otherwise normal hearts predicts risk for cardiomyopathy and related outcomes and is caused by non-ischemic cardiomyopathy. FUNDING: This study was supported by grants K99-HL157421 (D.O.) and KL2TR003143 (S.L.C.) from the National Institutes of Health.

Impact of Measurement Imprecision on Genetic Association Studies of Cardiac Function.

Background: Recent studies have leveraged quantitative traits from imaging to amplify the power of genome-wide association studies (GWAS) to gain further insights into the biology of diseases and traits. However, measurement imprecision is intrinsic to phenotyping and can impact downstream genetic analyses. Methods: Left ventricular ejection fraction (LVEF), an important but imprecise quantitative imaging measurement, was examined to assess the impact of precision of phenotype measurement on genetic studies. Multiple approaches to obtain LVEF, as well as simulated measurement noise, were evaluated with their impact on downstream genetic analyses. Results: Even within the same population, small changes in the measurement of LVEF drastically impacted downstream genetic analyses. Introducing measurement noise as little as 7.9% can eliminate all significant genetic associations in an GWAS with almost forty thousand individuals. An increase of 1% in mean absolute error (MAE) in LVEF had an equivalent impact on GWAS power as a decrease of 10% in the cohort sample size, suggesting optimizing phenotyping precision is a cost-effective way to improve power of genetic studies. Conclusions: Improving the precision of phenotyping is important for maximizing the yield of genome-wide association studies.

Does low-density lipoprotein fully explain atherosclerotic risk in familial hypercholesterolemia?

PURPOSE OF REVIEW: Familial hypercholesterolemia (FH) is a monogenic disorder of elevated low-density lipoprotein cholesterol (LDL-C) from birth leading to increased risk for atherosclerotic cardiovascular disease. However, not all carriers of FH variants display an FH phenotype. Despite this fact, FH variants confer increased risk for atherosclerotic disease in population cohorts. An important question to consider is whether measurements of LDL-C can fully account for this risk. RECENT FINDINGS: The atherosclerotic risk associated with FH variants is independent of observed adult LDL-C levels. Modeling adult longitudinal LDL-C accounts for more of this risk compared to using a single measurement. Still, even when adjusting for observed longitudinal LDL-C in adult cohorts, FH variant carriers are at increased risk for coronary artery disease. Genetic analyses, observational studies, and clinical trials all suggest that cumulative LDL-C is a critical driver of cardiovascular risk that may not be fully appreciated by routine LDL-C measurements in adulthood. As such, FH variants confer risk independent of adult LDL-C because these variants increase cumulative LDL-C exposure starting from birth. SUMMARY: Both research and clinical practice focus on LDL-C measurements in adults, but measurements during adulthood do not reflect lifelong cumulative exposure to LDL-C. Genetic assessments may compliment clinical assessments by better identifying patients who have experienced greater longitudinal LDL-C exposure.

Genetic evidence for causal relationships between age at natural menopause and the risk of ageing-associated adverse health outcomes.

BACKGROUND: A later age at natural menopause (ANM) has been linked to several ageing-associated traits including an increased risk of breast and endometrial cancer and a decreased risk of lung cancer, osteoporosis and Alzheimer disease. However, ANM is also related to several proxies for overall health that may confound these associations. METHODS: We investigated the causal association of ANM with these clinical outcomes using Mendelian randomization (MR). Participants and outcomes analysed were restricted to post-menopausal females. We conducted a one-sample MR analysis in both the Women's Health Initiative and UK Biobank. We further analysed and integrated several additional data sets of post-menopausal women using a two-sample MR design. We used ≤55 genetic variants previously discovered to be associated with ANM as our instrumental variable. RESULTS: A 5-year increase in ANM was causally associated with a decreased risk of osteoporosis [odds ratio (OR) = 0.80, 95% CI (0.70-0.92)] and fractures (OR = 0.76, 95% CI, 0.62-0.94) as well as an increased risk of lung cancer (OR = 1.35, 95% CI, 1.06-1.71). Other associations including atherosclerosis-related outcomes were null. CONCLUSIONS: Our study confirms that the decline in bone density with menopause causally translates into fractures and osteoporosis. Additionally, this is the first causal epidemiological analysis to our knowledge to find an increased risk of lung cancer with increasing ANM. This finding is consistent with molecular and epidemiological studies suggesting oestrogen-dependent growth of lung tumours.

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.

BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

Confounders mediate AI prediction of demographics in medical imaging.

Deep learning has been shown to accurately assess "hidden" phenotypes from medical imaging beyond traditional clinician interpretation. Using large echocardiography datasets from two healthcare systems, we test whether it is possible to predict age, race, and sex from cardiac ultrasound images using deep learning algorithms and assess the impact of varying confounding variables. Using a total of 433,469 videos from Cedars-Sinai Medical Center and 99,909 videos from Stanford Medical Center, we trained video-based convolutional neural networks to predict age, sex, and race. We found that deep learning models were able to identify age and sex, while unable to reliably predict race. Without considering confounding differences between categories, the AI model predicted sex with an AUC of 0.85 (95% CI 0.84-0.86), age with a mean absolute error of 9.12 years (95% CI 9.00-9.25), and race with AUCs ranging from 0.63 to 0.71. When predicting race, we show that tuning the proportion of confounding variables (age or sex) in the training data significantly impacts model AUC (ranging from 0.53 to 0.85), while sex and age prediction was not particularly impacted by adjusting race proportion in the training dataset AUC of 0.81-0.83 and 0.80-0.84, respectively. This suggests significant proportion of AI's performance on predicting race could come from confounding features being detected. Further work remains to identify the particular imaging features that associate with demographic information and to better understand the risks of demographic identification in medical AI as it pertains to potentially perpetuating bias and disparities.

Large-scale genome-wide association study of coronary artery disease in genetically diverse populations.

We report a genome-wide association study (GWAS) of coronary artery disease (CAD) incorporating nearly a quarter of a million cases, in which existing studies are integrated with data from cohorts of white, Black and Hispanic individuals from the Million Veteran Program. We document near equivalent heritability of CAD across multiple ancestral groups, identify 95 novel loci, including nine on the X chromosome, detect eight loci of genome-wide significance in Black and Hispanic individuals, and demonstrate that two common haplotypes at the 9p21 locus are responsible for risk stratification in all populations except those of African origin, in which these haplotypes are virtually absent. Moreover, in the largest GWAS for angiographically derived coronary atherosclerosis performed to date, we find 15 loci of genome-wide significance that robustly overlap with established loci for clinical CAD. Phenome-wide association analyses of novel loci and polygenic risk scores (PRSs) augment signals related to insulin resistance, extend pleiotropic associations of these loci to include smoking and family history, and precisely document the markedly reduced transferability of existing PRSs to Black individuals. Downstream integrative analyses reinforce the critical roles of vascular endothelial, fibroblast, and smooth muscle cells in CAD susceptibility, but also point to a shared biology between atherosclerosis and oncogenesis. This study highlights the value of diverse populations in further characterizing the genetic architecture of CAD.

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids.

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.

Broad clinical manifestations of polygenic risk for coronary artery disease in the Women's Health Initiative.

Background: The genetic basis for coronary artery disease (CAD) risk is highly complex. Genome-wide polygenic risk scores (PRS) can help to quantify that risk, but the broader impacts of polygenic risk for CAD are not well characterized. Methods: We measured polygenic risk for CAD using the meta genomic risk score, a previously validated genome-wide PRS, in a subset of genotyped participants from the Women's Health Initiative and applied a phenome-wide association study framework to assess associations between the PRS and a broad range of blood biomarkers, clinical measurements, and health outcomes. Results: Polygenic risk for CAD is associated with a variety of biomarkers, clinical measurements, behaviors, and diagnoses related to traditional risk factors, as well as risk-enhancing factors. Analysis of adjudicated outcomes shows a graded association between atherosclerosis related outcomes, with the highest odds ratios being observed for the most severe manifestations of CAD. We find associations between increased polygenic risk for CAD and decreased risk for incident breast and lung cancer, with replication of the breast cancer finding in an external cohort. Genetic correlation and two-sample Mendelian randomization suggest that breast cancer association is likely due to horizontal pleiotropy, while the association with lung cancer may be causal. Conclusion: Polygenic risk for CAD has broad clinical manifestations, reflected in biomarkers, clinical measurements, behaviors, and diagnoses. Some of these associations may represent direct pathways between genetic risk and CAD while others may reflect pleiotropic effects independent of CAD risk.

Use of Polygenic Risk Scores for Coronary Heart Disease in Ancestrally Diverse Populations.

PURPOSE OF REVIEW: A polygenic risk score (PRS) is a measure of genetic liability to a disease and is typically normally distributed in a population. Individuals in the upper tail of this distribution often have relative risk equivalent to that of monogenic form of the disease. The majority of currently available PRSs for coronary heart disease (CHD) have been generated from cohorts of European ancestry (EUR) and vary in their applicability to other ancestry groups. In this report, we review the performance of PRSs for CHD across different ancestries and efforts to reduce variability in performance including novel population and statistical genetics approaches. RECENT FINDINGS: PRSs for CHD perform robustly in EUR populations but lag in performance in non-EUR groups, particularly individuals of African ancestry. Several large consortia have been established to enable genomic studies in diverse ancestry groups and develop methods to improve PRS performance in multi-ancestry contexts as well as admixed individuals. These include fine-mapping to ascertain causal variants, trans ancestry meta-analyses, and ancestry deconvolution in admixed individuals. PRSs are being used in the clinical setting but enthusiasm has been tempered by the variable performance in non-EUR ancestry groups. Increasing diversity in genomic association studies and continued innovation in methodological approaches are needed to improve PRS performance in non-EUR individuals for equitable implementation of genomic medicine.