Shoa L. Clarke, MD, PhD

All Publications

• Guideline recommended statin eligibility and use among U.S. adults ages 20 to 39 years. American journal of preventive cardiology Clarke, S. L., Thomson, B. 2024; 20: 100890

  Abstract

  Guidelines for statin therapy emphasize treatment of adults ages 40-75 years, with less guidance for the treatment of younger adults, ages 20-39 years. Only two class 1 recommendations for statin apply to younger adults: 1) secondary prevention and 2) severe hypercholesterolemia (LDL-C ≥ 190 mg/dL). The implementation of guidelines within this age group has not been well studied.Here, we use data from the National Health and Nutrition Examination Survey (2013-2020) to estimate statin eligibility and use among US younger adults. Based on this nationally representative sample, we extrapolate that approximately 923,000 younger adults had a history of atherosclerotic cardiovascular disease, but only ∼24 % were on statin. Among younger adults in the primary prevention group, we extrapolate that at least 1.09 million had severe hypercholesterolemia. To expand on this analysis, we calculated untreated LDL-C values for individuals on statin using two methods, and we estimate that only ∼11-20 % of younger adults with severe hypercholesterolemia were on statin. Lastly, among untreated younger adults with a class 1 indication for statin, fewer than 25 % reported that a doctor or healthcare provider had recommended cholesterol medication.The implementation of class 1 recommendations for statin treatment in younger adults is poor. While efforts to improve risk prediction in the young have recently received significant attention, our results indicate that identifying high risk younger adults is insufficient. We must also improve guideline-recommended treatment in this age group.

  View details for DOI 10.1016/j.ajpc.2024.100890

  View details for PubMedID 39618938

  View details for PubMedCentralID PMC11607643

• Rare variant contribution to the heritability of coronary artery disease. Nature communications Rocheleau, G., Clarke, S. L., Auguste, G., Hasbani, N. R., Morrison, A. C., Heath, A. S., Bielak, L. F., Iyer, K. R., Young, E. P., Stitziel, N. O., Jun, G., Laurie, C., Broome, J. G., Khan, A. T., Arnett, D. K., Becker, L. C., Bis, J. C., Boerwinkle, E., Bowden, D. W., Carson, A. P., Ellinor, P. T., Fornage, M., Franceschini, N., Freedman, B. I., Heard-Costa, N. L., Hou, L., Chen, Y. I., Kenny, E. E., Kooperberg, C., Kral, B. G., Loos, R. J., Lutz, S. M., Manson, J. E., Martin, L. W., Mitchell, B. D., Nassir, R., Palmer, N. D., Post, W. S., Preuss, M. H., Psaty, B. M., Raffield, L. M., Regan, E. A., Rich, S. S., Smith, J. A., Taylor, K. D., Yanek, L. R., Young, K. A., Hilliard, A. T., Tcheandjieu, C., Peyser, P. A., Vasan, R. S., Rotter, J. I., Miller, C. L., Assimes, T. L., de Vries, P. S., Do, R. 2024; 15 (1): 8741

  Abstract

  Whole genome sequences (WGS) enable discovery of rare variants which may contribute to missing heritability of coronary artery disease (CAD). To measure their contribution, we apply the GREML-LDMS-I approach to WGS of 4949 cases and 17,494 controls of European ancestry from the NHLBI TOPMed program. We estimate CAD heritability at 34.3% assuming a prevalence of 8.2%. Ultra-rare (minor allele frequency ≤ 0.1%) variants with low linkage disequilibrium (LD) score contribute ~50% of the heritability. We also investigate CAD heritability enrichment using a diverse set of functional annotations: i) constraint; ii) predicted protein-altering impact; iii) cis-regulatory elements from a cell-specific chromatin atlas of the human coronary; and iv) annotation principal components representing a wide range of functional processes. We observe marked enrichment of CAD heritability for most functional annotations. These results reveal the predominant role of ultra-rare variants in low LD on the heritability of CAD. Moreover, they highlight several functional processes including cell type-specific regulatory mechanisms as key drivers of CAD genetic risk.

  View details for DOI 10.1038/s41467-024-52939-6

  View details for PubMedID 39384761

  View details for PubMedCentralID 7755038

• Exome wide association study for blood lipids in 1,158,017 individuals from diverse populations. medRxiv : the preprint server for health sciences Koyama, S., Yu, Z., Choi, S. H., Jurgens, S. J., Selvaraj, M. S., Klarin, D., Huffman, J. E., Clarke, S. L., Trinh, M. N., Ravi, A., Dron, J. S., Spinks, C., Surakka, I., Bhatnagar, A., Lannery, K., Hornsby, W., Damrauer, S. M., Chang, K. M., Lynch, J. A., Assimes, T. L., Tsao, P. S., Rader, D. J., Cho, K., Peloso, G. M., Ellinor, P. T., Sun, Y. V., Wilson, P. W., Program, M. V., Natarajan, P. 2024

  Abstract

  Rare coding alleles play crucial roles in the molecular diagnosis of genetic diseases. However, the systemic identification of these alleles has been challenging due to their scarcity in the general population. Here, we discovered and characterized rare coding alleles contributing to genetic dyslipidemia, a principal risk for coronary artery disease, among over a million individuals combining three large contemporary genetic datasets (the Million Veteran Program, n = 634,535, UK Biobank, n = 431,178, and the All of Us Research Program, n = 92,304) totaling 1,158,017 multi-ancestral individuals. Unlike previous rare variant studies in lipids, this study included 238,243 individuals (20.6%) from non-European-like populations. Testing 2,997,401 rare coding variants from diverse backgrounds, we identified 800 exome-wide significant associations across 209 genes including 176 predicted loss of function and 624 missense variants. Among these exome-wide associations, 130 associations were driven by non-European-like populations. Associated alleles are highly enriched in functional variant classes, showed significant additive and recessive associations, exhibited similar effects across populations, and resolved pathogenicity for variants enriched in African or South-Asian populations. Furthermore, we identified 5 lipid-related genes associated with coronary artery disease (RORC, CFAP65, GTF2E2, PLCB3, and ZNF117). Among them, RORC is a potentially novel therapeutic target through the down regulation of LDLC by its silencing. This study provides resources and insights for understanding causal mechanisms, quantifying the expressivity of rare coding alleles, and identifying novel drug targets across diverse populations.

  View details for DOI 10.1101/2024.09.17.24313718

  View details for PubMedID 39371182

  View details for PubMedCentralID PMC11451673

• A plasma proteomic signature for atherosclerotic cardiovascular disease risk prediction in the UK Biobank cohort. medRxiv : the preprint server for health sciences Gupte, T. P., Azizi, Z., Kho, P. F., Zhou, J., Chen, M., Panyard, D. J., Guarischi-Sousa, R., Hilliard, A. T., Sharma, D., Watson, K., Abbasi, F., Tsao, P. S., Clarke, S. L., Assimes, T. L. 2024

  Abstract

  Background: While risk stratification for atherosclerotic cardiovascular disease (ASCVD) is essential for primary prevention, current clinical risk algorithms demonstrate variability and leave room for further improvement. The plasma proteome holds promise as a future diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict ASCVD.Method: Clinical, genetic, and high-throughput plasma proteomic data were analyzed for association with ASCVD in a cohort of 41,650 UK Biobank participants. Selected features for analysis included clinical variables such as a UK-based cardiovascular clinical risk score (QRISK3) and lipid levels, 36 polygenic risk scores (PRSs), and Olink protein expression data of 2,920 proteins. We used least absolute shrinkage and selection operator (LASSO) regression to select features and compared area under the curve (AUC) statistics between data types. Randomized LASSO regression with a stability selection algorithm identified a smaller set of more robustly associated proteins. The benefit of plasma proteins over standard clinical variables, the QRISK3 score, and PRSs was evaluated through the derivation of Delta AUC values. We also assessed the incremental gain in model performance using proteomic datasets with varying numbers of proteins. To identify potential causal proteins for ASCVD, we conducted a two-sample Mendelian randomization (MR) analysis.Result: The mean age of our cohort was 56.0 years, 60.3% were female, and 9.8% developed incident ASCVD over a median follow-up of 6.9 years. A protein-only LASSO model selected 294 proteins and returned an AUC of 0.723 (95% CI 0.708-0.737). A clinical variable and PRS-only LASSO model selected 4 clinical variables and 20 PRSs and achieved an AUC of 0.726 (95% CI 0.712-0.741). The addition of the full proteomic dataset to clinical variables and PRSs resulted in a Delta AUC of 0.010 (95% CI 0.003-0.018). Fifteen proteins selected by a stability selection algorithm offered improvement in ASCVD prediction over the QRISK3 risk score [Delta AUC: 0.013 (95% CI 0.005-0.021)]. Filtered and clustered versions of the full proteomic dataset (consisting of 600-1,500 proteins) performed comparably to the full dataset for ASCVD prediction. Using MR, we identified 11 proteins as potentially causal for ASCVD.Conclusion: A plasma proteomic signature performs well for incident ASCVD prediction but only modestly improves prediction over clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of this signature in predicting the risk of ASCVD over the standard practice of using the QRISK3 score.

  View details for DOI 10.1101/2024.09.13.24313652

  View details for PubMedID 39314942

• Plasma proteomic signatures for type 2 diabetes mellitus and related traits in the UK Biobank cohort. medRxiv : the preprint server for health sciences Gupte, T. P., Azizi, Z., Kho, P. F., Zhou, J., Nzenkue, K., Chen, M., Panyard, D. J., Guarischi-Sousa, R., Hilliard, A. T., Sharma, D., Watson, K., Abbasi, F., Tsao, P. S., Clarke, S. L., Assimes, T. L. 2024

  Abstract

  Aims/hypothesis: The plasma proteome holds promise as a diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict type 2 diabetes mellitus (T2DM) and related traits.Methods: Clinical, genetic, and high-throughput proteomic data from three subcohorts of UK Biobank participants were analyzed for association with dual-energy x-ray absorptiometry (DXA) derived truncal fat (in the adiposity subcohort), estimated maximum oxygen consumption (VO 2 max) (in the fitness subcohort), and incident T2DM (in the T2DM subcohort). We used least absolute shrinkage and selection operator (LASSO) regression to assess the relative ability of non-proteomic and proteomic variables to associate with each trait by comparing variance explained (R 2 ) and area under the curve (AUC) statistics between data types. Stability selection with randomized LASSO regression identified the most robustly associated proteins for each trait. The benefit of proteomic signatures (PSs) over QDiabetes, a T2DM clinical risk score, was evaluated through the derivation of delta (Delta) AUC values. We also assessed the incremental gain in model performance metrics using proteomic datasets with varying numbers of proteins. A series of two-sample Mendelian randomization (MR) analyses were conducted to identify potentially causal proteins for adiposity, fitness, and T2DM.Results: Across all three subcohorts, the mean age was 56.7 years and 54.9% were female. In the T2DM subcohort, 5.8% developed incident T2DM over a median follow-up of 7.6 years. LASSO-derived PSs increased the R 2 of truncal fat and VO 2 max over clinical and genetic factors by 0.074 and 0.057, respectively. We observed a similar improvement in T2DM prediction over the QDiabetes score [Delta AUC: 0.016 (95% CI 0.008, 0.024)] when using a robust PS derived strictly from the T2DM outcome versus a model further augmented with non-overlapping proteins associated with adiposity and fitness. A small number of proteins (29 for truncal adiposity, 18 for VO2max, and 26 for T2DM) identified by stability selection algorithms offered most of the improvement in prediction of each outcome. Filtered and clustered versions of the full proteomic dataset supplied by the UK Biobank (ranging between 600-1,500 proteins) performed comparably to the full dataset for T2DM prediction. Using MR, we identified 4 proteins as potentially causal for adiposity, 1 as potentially causal for fitness, and 4 as potentially causal for T2DM.Conclusions/Interpretation: Plasma PSs modestly improve the prediction of incident T2DM over that possible with clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of these signatures in predicting the risk of T2DM over the standard practice of using the QDiabetes score. Candidate causally associated proteins identified through MR deserve further study as potential novel therapeutic targets for T2DM.

  View details for DOI 10.1101/2024.09.13.24313501

  View details for PubMedID 39314935

• Genetically predicted lipoprotein(a) associates with coronary artery plaque severity independent of low-density lipoprotein cholesterol. European journal of preventive cardiology Clarke, S. L., Huang, R. D., Hilliard, A. T., Levin, M. G., Sharma, D., Thomson, B., Lynch, J., Tsao, P. S., Gaziano, J. M., Assimes, T. L. 2024

  Abstract

  Elevated Lipoprotein(a) [Lp(a)] is a causal risk factor for atherosclerotic cardiovascular disease, but the mechanisms of risk are debated. Studies have found inconsistent associations between Lp(a) and measurements of atherosclerosis. We aimed to assess the relationship between Lp(a), low-density lipoprotein cholesterol (LDL-C) and coronary artery plaque severity.The study population consisted of participants of the Million Veteran Program who have undergone an invasive angiogram. The primary exposure was genetically predicted Lp(a), estimated by a polygenic score. Genetically predicted LDL-C was also assessed for comparison. The primary outcome was coronary artery plaque severity, categorized as normal, non-obstructive disease, 1-vessel disease, 2-vessel disease, and 3-vessel or left main disease.Among 18,927 adults of genetically inferred European ancestry and 4,039 adults of genetically inferred African ancestry, we observed consistent associations between genetically predicted Lp(a) and obstructive coronary plaque, with effect sizes trending upward for increasingly severe categories of disease. Associations were independent of risk factors, clinically measured LDL-C and genetically predicted LDL-C. However, we did not find strong or consistent evidence for an association between genetically predicted Lp(a) and risk for non-obstructive plaque.Genetically predicted Lp(a) is positively associated with coronary plaque severity independent of LDL-C, consistent with Lp(a) promoting atherogenesis. However, the effects of Lp(a) may be greater for progression of plaque to obstructive disease than for the initial development of non-obstructive plaque. A limitation of this study is that Lp(a) was estimated using genetic markers and could not be directly assayed, nor could apo(a) isoform size.

  View details for DOI 10.1093/eurjpc/zwae271

  View details for PubMedID 39158116

• PLASMA PROTEOMICS AND VISCERAL ADIPOSE TISSUE VOLUME: A MACHINE LEARNING ANALYSIS OF INTERACTION BETWEEN BIOMARKERS, SOCIO-BEHAVIORAL, AND FITNESS FACTORS IN UK BIOBANK Azizi, Z., Gupte, T., Kho, P., Nzenkue, K., Zhou, J., Guarischi-Sousa, R., Panyard, D., Chen, M., Abbasi, F., Clarke, S., Tsao, P., Assimes, T. L. ELSEVIER SCIENCE INC. 2024: 1699

  View details for Web of Science ID 001324901501731

• Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization. Circulation. Genomic and precision medicine Smith, J. L., Tcheandjieu, C., Dikilitas, O., Iyer, K., Miyazawa, K., Hilliard, A., Lynch, J., Rotter, J. I., Chen, Y. I., Sheu, W. H., Chang, K. M., Kanoni, S., Tsao, P., Ito, K., Kosel, M., Clarke, S. L., Schaid, D. J., Assimes, T. L., Kullo, I. J. 2024: e004272

  Abstract

  Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (CHD; PRSCHD) for 5 genetic ancestry groups.We derived ancestry-specific and multi-ancestry PRSCHD based on pruning and thresholding 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.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]).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.

  View details for DOI 10.1161/CIRCGEN.123.004272

  View details for PubMedID 38380516

• Impact of Measurement Noise on Genetic Association Studies of Cardiac Function Vukadinovic, M., Renjith, G., Yuan, V., Kwan, A., Cheng, S. C., Li, D., Clarke, S. L., Ouyang, D., Hunter, L., Altman, R. B., Ritchie, M. D., Murray, T., Klein, T. E. WORLD SCIENTIFIC PUBL CO PTE LTD. 2024: 134-147

  Abstract

  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.

  View details for Web of Science ID 001258333100011

  View details for PubMedID 38160275

• Whole-genome sequencing uncovers two loci for coronary artery calcification and identifies ARSE as a regulator of vascular calcification. Nature cardiovascular research de Vries, P. S., Conomos, M. P., Singh, K., Nicholson, C. J., Jain, D., Hasbani, N. R., Jiang, W., Lee, S., Cardenas, C. L., Lutz, S. M., Wong, D., Guo, X., Yao, J., Young, E. P., Tcheandjieu, C., Hilliard, A. T., Bis, J. C., Bielak, L. F., Brown, M. R., Musharoff, S., Clarke, S. L., Terry, J. G., Palmer, N. D., Yanek, L. R., Xu, H., Heard-Costa, N., Wessel, J., Selvaraj, M. S., Li, R. H., Sun, X., Turner, A. W., Stilp, A. M., Khan, A., Newman, A. B., Rasheed, A., Freedman, B. I., Kral, B. G., McHugh, C. P., Hodonsky, C., Saleheen, D., Herrington, D. M., Jacobs, D. R., Nickerson, D. A., Boerwinkle, E., Wang, F. F., Heiss, G., Jun, G., Kinney, G. L., Sigurslid, H. H., Doddapaneni, H., Hall, I. M., Bensenor, I. M., Broome, J., Crapo, J. D., Wilson, J. G., Smith, J. A., Blangero, J., Vargas, J. D., Mosquera, J. V., Smith, J. D., Viaud-Martinez, K. A., Ryan, K. A., Young, K. A., Taylor, K. D., Lange, L. A., Emery, L. S., Bittencourt, M. S., Budoff, M. J., Montasser, M. E., Yu, M., Mahaney, M. C., Mahamdeh, M. S., Fornage, M., Franceschini, N., Lotufo, P. A., Natarajan, P., Wong, Q., Mathias, R. A., Gibbs, R. A., Do, R., Mehran, R., Tracy, R. P., Kim, R. W., Nelson, S. C., Damrauer, S. M., Kardia, S. L., Rich, S. S., Fuster, V., Napolioni, V., Zhao, W., Tian, W., Yin, X., Min, Y. I., Manning, A. K., Peloso, G., Kelly, T. N., O'Donnell, C. J., Morrison, A. C., Curran, J. E., Zapol, W. M., Bowden, D. W., Becker, L. C., Correa, A., Mitchell, B. D., Psaty, B. M., Carr, J. J., Pereira, A. C., Assimes, T. L., Stitziel, N. O., Hokanson, J. E., Laurie, C. A., Rotter, J. I., Vasan, R. S., Post, W. S., Peyser, P. A., Miller, C. L., Malhotra, R. 2023; 2 (12): 1159-1172

  Abstract

  Coronary artery calcification (CAC) is a measure of atherosclerosis and a well-established predictor of coronary artery disease (CAD) events. Here we describe a genome-wide association study (GWAS) of CAC in 22,400 participants from multiple ancestral groups. We confirmed associations with four known loci and identified two additional loci associated with CAC (ARSE and MMP16), with evidence of significant associations in replication analyses for both novel loci. Functional assays of ARSE and MMP16 in human vascular smooth muscle cells (VSMCs) demonstrate that ARSE is a promoter of VSMC calcification and VSMC phenotype switching from a contractile to a calcifying or osteogenic phenotype. Furthermore, we show that the association of variants near ARSE with reduced CAC is likely explained by reduced ARSE expression with the G allele of enhancer variant rs5982944. Our study highlights ARSE as an important contributor to atherosclerotic vascular calcification, and a potential drug target for vascular calcific disease.

  View details for DOI 10.1038/s44161-023-00375-y

  View details for PubMedID 38817323

  View details for PubMedCentralID PMC11138106

• A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease. Nature medicine Patel, A. P., Wang, M., Ruan, Y., Koyama, S., Clarke, S. L., Yang, X., Tcheandjieu, C., Agrawal, S., Fahed, A. C., Ellinor, P. T., Genes & Health Research Team; the Million Veteran Program, Tsao, P. S., Sun, Y. V., Cho, K., Wilson, P. W., Assimes, T. L., van Heel, D. A., Butterworth, A. S., Aragam, K. G., Natarajan, P., Khera, A. V. 2023

  Abstract

  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.

  View details for DOI 10.1038/s41591-023-02429-x

  View details for PubMedID 37414900

• Contemporary Polygenic Scores of Low-Density Lipoprotein Cholesterol and Coronary Artery Disease Predict Coronary Atherosclerosis in Adolescents and Young Adults. Circulation. Genomic and precision medicine Guarischi-Sousa, R., Salfati, E., Kho, P. F., Iyer, K. R., Hilliard, A. T., Herrington, D. M., Tsao, P. S., Clarke, S. L., Assimes, T. L. 2023: e004047

  View details for DOI 10.1161/CIRCGEN.122.004047

  View details for PubMedID 37409455

• A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records. EBioMedicine Shuey, M. M., Lee, K. M., Keaton, J., Khankari, N. K., Breeyear, J. H., Walker, V. M., Miller, D. R., Heberer, K. R., Reaven, P. D., Clarke, S. L., Lee, J., Lynch, J. A., Vujkovic, M., Edwards, T. L. 2023; 94: 104674

  Abstract

  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.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).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).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.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.

  View details for DOI 10.1016/j.ebiom.2023.104674

  View details for PubMedID 37399599

• Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes. Med (New York, N.Y.) Vukadinovic, M., Kwan, A. C., Yuan, V., Salerno, M., Lee, D. C., Albert, C. M., Cheng, S., Li, D., Ouyang, D., Clarke, S. L. 2023

  Abstract

  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.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.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.Variation in LV sphericity in otherwise normal hearts predicts risk for cardiomyopathy and related outcomes and is caused by non-ischemic cardiomyopathy.This study was supported by grants K99-HL157421 (D.O.) and KL2TR003143 (S.L.C.) from the National Institutes of Health.

  View details for DOI 10.1016/j.medj.2023.02.009

  View details for PubMedID 36996817

• Does low-density lipoprotein fully explain atherosclerotic risk in familial hypercholesterolemia? Current opinion in lipidology Clarke, S. L. 2023

  Abstract

  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.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.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.

  View details for DOI 10.1097/MOL.0000000000000868

  View details for PubMedID 36853849

• The Value of Measuring Lipoprotein(a) in Children. Circulation Khoury, M., Clarke, S. L. 2023; 147 (1): 32-34

  View details for DOI 10.1161/CIRCULATIONAHA.122.062592

  View details for PubMedID 36576957

• Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis. Genome biology Kanoni, S., Graham, S. E., Wang, Y., Surakka, I., Ramdas, S., Zhu, X., Clarke, S. L., Bhatti, K. F., Vedantam, S., Winkler, T. W., Locke, A. E., Marouli, E., Zajac, G. J., Wu, K. H., Ntalla, I., Hui, Q., Klarin, D., Hilliard, A. T., Wang, Z., Xue, C., Thorleifsson, G., Helgadottir, A., Gudbjartsson, D. F., Holm, H., Olafsson, I., Hwang, M. Y., Han, S., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W., Brumpton, B. M., Rasheed, H., Havulinna, A. S., Veturi, Y., Pacheco, J. A., Rosenthal, E. A., Lingren, T., Feng, Q., Kullo, I. J., Narita, A., Takayama, J., Martin, H. C., Hunt, K. A., Trivedi, B., Haessler, J., Giulianini, F., Bradford, Y., Miller, J. E., Campbell, A., Lin, K., Millwood, I. Y., Rasheed, A., Hindy, G., Faul, J. D., Zhao, W., Weir, D. R., Turman, C., Huang, H., Graff, M., Choudhury, A., Sengupta, D., Mahajan, A., Brown, M. R., Zhang, W., Yu, K., Schmidt, E. M., Pandit, A., Gustafsson, S., Yin, X., Luan, J., Zhao, J. H., Matsuda, F., Jang, H. M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J. J., Wood, A. R., Ji, Y., Gao, Z., Haworth, S., Yousri, N. A., Mitchell, R. E., Chai, J. F., Aadahl, M., Bjerregaard, A. A., Yao, J., Manichaikul, A., Hwu, C. M., Hung, Y. J., Warren, H. R., Ramirez, J., Bork-Jensen, J., Kårhus, L. L., Goel, A., Sabater-Lleal, M., Noordam, R., Mauro, P., Matteo, F., McDaid, A. F., Marques-Vidal, P., Wielscher, M., Trompet, S., Sattar, N., Møllehave, L. T., Munz, M., Zeng, L., Huang, J., Yang, B., Poveda, A., Kurbasic, A., Lamina, C., Forer, L., Scholz, M., Galesloot, T. E., Bradfield, J. P., Ruotsalainen, S. E., Daw, E., Zmuda, J. M., Mitchell, J. S., Fuchsberger, C., Christensen, H., Brody, J. A., Vazquez-Moreno, M., Feitosa, M. F., Wojczynski, M. K., Wang, Z., Preuss, M. H., Mangino, M., Christofidou, P., Verweij, N., Benjamins, J. W., Engmann, J., Tsao, N. L., Verma, A., Slieker, R. C., Lo, K. S., Zilhao, N. R., Le, P., Kleber, M. E., Delgado, G. E., Huo, S., Ikeda, D. D., Iha, H., Yang, J., Liu, J., Demirkan, A., Leonard, H. L., Marten, J., Frank, M., Schmidt, B., Smyth, L. J., Cañadas-Garre, M., Wang, C., Nakatochi, M., Wong, A., Hutri-Kähönen, N., Sim, X., Xia, R., Huerta-Chagoya, A., Fernandez-Lopez, J. C., Lyssenko, V., Nongmaithem, S. S., Bayyana, S., Stringham, H. M., Irvin, M. R., Oldmeadow, C., Kim, H. N., Ryu, S., Timmers, P. R., Arbeeva, L., Dorajoo, R., Lange, L. A., Prasad, G., Lorés-Motta, L., Pauper, M., Long, J., Li, X., Theusch, E., Takeuchi, F., Spracklen, C. N., Loukola, A., Bollepalli, S., Warner, S. C., Wang, Y. X., Wei, W. B., Nutile, T., Ruggiero, D., Sung, Y. J., Chen, S., Liu, F., Yang, J., Kentistou, K. A., Banas, B., Nardone, G. G., Meidtner, K., Bielak, L. F., Smith, J. A., Hebbar, P., Farmaki, A. E., Hofer, E., Lin, M., Concas, M. P., Vaccargiu, S., van der Most, P. J., Pitkänen, N., Cade, B. E., van der Laan, S. W., Chitrala, K. N., Weiss, S., Bentley, A. R., Doumatey, A. P., Adeyemo, A. A., Lee, J. Y., Petersen, E. R., Nielsen, A. A., Choi, H. S., Nethander, M., Freitag-Wolf, S., Southam, L., Rayner, N. W., Wang, C. A., Lin, S. Y., Wang, J. S., Couture, C., Lyytikäinen, L. P., Nikus, K., Cuellar-Partida, G., Vestergaard, H., Hidalgo, B., Giannakopoulou, O., Cai, Q., Obura, M. O., van Setten, J., Li, X., Liang, J., Tang, H., Terzikhan, N., Shin, J. H., Jackson, R. D., Reiner, A. P., Martin, L. W., Chen, Z., Li, L., Kawaguchi, T., Thiery, J., Bis, J. C., Launer, L. J., Li, H., Nalls, M. A., Raitakari, O. T., Ichihara, S., Wild, S. H., Nelson, C. P., Campbell, H., Jäger, S., Nabika, T., Al-Mulla, F., Niinikoski, H., Braund, P. S., Kolcic, I., Kovacs, P., Giardoglou, T., Katsuya, T., de Kleijn, D., de Borst, G. J., Kim, E. K., Adams, H. H., Ikram, M. A., Zhu, X., Asselbergs, F. W., Kraaijeveld, A. O., Beulens, J. W., Shu, X. O., Rallidis, L. S., Pedersen, O., Hansen, T., Mitchell, P., Hewitt, A. W., Kähönen, M., Pérusse, L., Bouchard, C., Tönjes, A., Chen, Y. I., Pennell, C. E., Mori, T. A., Lieb, W., Franke, A., Ohlsson, C., Mellström, D., Cho, Y. S., Lee, H., Yuan, J. M., Koh, W. P., Rhee, S. Y., Woo, J. T., Heid, I. M., Stark, K. J., Zimmermann, M. E., Völzke, H., Homuth, G., Evans, M. K., Zonderman, A. B., Polasek, O., Pasterkamp, G., Hoefer, I. E., Redline, S., Pahkala, K., Oldehinkel, A. J., Snieder, H., Biino, G., Schmidt, R., Schmidt, H., Bandinelli, S., Dedoussis, G., Thanaraj, T. A., Kardia, S. L., Peyser, P. A., Kato, N., Schulze, M. B., Girotto, G., Böger, C. A., Jung, B., Joshi, P. K., Bennett, D. A., De Jager, P. L., Lu, X., Mamakou, V., Brown, M., Caulfield, M. J., Munroe, P. B., Guo, X., Ciullo, M., Jonas, J. B., Samani, N. J., Kaprio, J., Pajukanta, P., Tusié-Luna, T., Aguilar-Salinas, C. A., Adair, L. S., Bechayda, S. A., de Silva, H. J., Wickremasinghe, A. R., Krauss, R. M., Wu, J. Y., Zheng, W., Hollander, A. I., Bharadwaj, D., Correa, A., Wilson, J. G., Lind, L., Heng, C. K., Nelson, A. E., Golightly, Y. M., Wilson, J. F., Penninx, B., Kim, H. L., Attia, J., Scott, R. J., Rao, D. C., Arnett, D. K., Hunt, S. C., Walker, M., Koistinen, H. A., Chandak, G. R., Mercader, J. M., Costanzo, M. C., Jang, D., Burtt, N. P., Villalpando, C. G., Orozco, L., Fornage, M., Tai, E., van Dam, R. M., Lehtimäki, T., Chaturvedi, N., Yokota, M., Liu, J., Reilly, D. F., McKnight, A. J., Kee, F., Jöckel, K. H., McCarthy, M. I., Palmer, C. N., Vitart, V., Hayward, C., Simonsick, E., van Duijn, C. M., Jin, Z. B., Qu, J., Hishigaki, H., Lin, X., März, W., Gudnason, V., Tardif, J. C., Lettre, G., Hart, L. M., Elders, P. J., Damrauer, S. M., Kumari, M., Kivimaki, M., van der Harst, P., Spector, T. D., Loos, R. J., Province, M. A., Parra, E. J., Cruz, M., Psaty, B. M., Brandslund, I., Pramstaller, P. P., Rotimi, C. N., Christensen, K., Ripatti, S., Widén, E., Hakonarson, H., Grant, S. F., Kiemeney, L. A., de Graaf, J., Loeffler, M., Kronenberg, F., Gu, D., Erdmann, J., Schunkert, H., Franks, P. W., Linneberg, A., Jukema, J. W., Khera, A. V., Männikkö, M., Jarvelin, M. R., Kutalik, Z., Francesco, C., Mook-Kanamori, D. O., van Dijk, K. W., Watkins, H., Strachan, D. P., Grarup, N., Sever, P., Poulter, N., Chuang, L. M., Rotter, J. I., Dantoft, T. M., Karpe, F., Neville, M. J., Timpson, N. J., Cheng, C. Y., Wong, T. Y., Khor, C. C., Li, H., Sabanayagam, C., Peters, A., Gieger, C., Hattersley, A. T., Pedersen, N. L., Magnusson, P. K., Boomsma, D. I., Willemsen, A. H., Cupples, L., van Meurs, J. B., Ghanbari, M., Gordon-Larsen, P., Huang, W., Kim, Y. J., Tabara, Y., Wareham, N. J., Langenberg, C., Zeggini, E., Kuusisto, J., Laakso, M., Ingelsson, E., Abecasis, G., Chambers, J. C., Kooner, J. S., de Vries, P. S., Morrison, A. C., Hazelhurst, S., Ramsay, M., North, K. E., Daviglus, M., Kraft, P., Martin, N. G., Whitfield, J. B., Abbas, S., Saleheen, D., Walters, R. G., Holmes, M. V., Black, C., Smith, B. H., Baras, A., Justice, A. E., Buring, J. E., Ridker, P. M., Chasman, D. I., Kooperberg, C., Tamiya, G., Yamamoto, M., van Heel, D. A., Trembath, R. C., Wei, W. Q., Jarvik, G. P., Namjou, B., Hayes, M. G., Ritchie, M. D., Jousilahti, P., Salomaa, V., Hveem, K., Åsvold, B. O., Kubo, M., Kamatani, Y., Okada, Y., Murakami, Y., Kim, B. J., Thorsteinsdottir, U., Stefansson, K., Zhang, J., Chen, Y., Ho, Y. L., Lynch, J. A., Rader, D. J., Tsao, P. S., Chang, K. M., Cho, K., O'Donnell, C. J., Gaziano, J. M., Wilson, P. W., Frayling, T. M., Hirschhorn, J. N., Kathiresan, S., Mohlke, K. L., Sun, Y. V., Morris, A. P., Boehnke, M., Brown, C. D., Natarajan, P., Deloukas, P., Willer, C. J., Assimes, T. L., Peloso, G. M. 2022; 23 (1): 268

  Abstract

  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.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.Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

  View details for DOI 10.1186/s13059-022-02837-1

  View details for PubMedID 36575460

  View details for PubMedCentralID PMC9793579

• Confounders mediate AI prediction of demographics in medical imaging. NPJ digital medicine Duffy, G., Clarke, S. L., Christensen, M., He, B., Yuan, N., Cheng, S., Ouyang, D. 2022; 5 (1): 188

  Abstract

  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.

  View details for DOI 10.1038/s41746-022-00720-8

  View details for PubMedID 36550271

• Genetic evidence for causal relationships between age at natural menopause and the risk of ageing-associated adverse health outcomes. International journal of epidemiology Lankester, J., Li, J., Salfati, E. L., Stefanick, M. L., Chan, K. H., Liu, S., Crandall, C. J., Clarke, S. L., Assimes, T. L. 2022

  Abstract

  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.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.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.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.

  View details for DOI 10.1093/ije/dyac215

  View details for PubMedID 36409989

• The Contribution of Rare Variants to the Heritability of Coronary Artery Disease Based on 38,544 Whole Genome Sequences from the NHLBI TOPMed Program Rocheleau, G., Clarke, S. L., Hasbani, N. R., Peyser, P. A., Vasan, R. S., Rotter, J. I., Saleheen, D., Assimes, T. L., De Vries, P. S., Do, R., Natl Heart Lung Blood Inst NHLBI WILEY. 2022: 527

  View details for Web of Science ID 000850044000144

• A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids. American journal of human genetics Ramdas, S., Judd, J., Graham, S. E., Kanoni, S., Wang, Y., Surakka, I., Wenz, B., Clarke, S. L., Chesi, A., Wells, A., Bhatti, K. F., Vedantam, S., Winkler, T. W., Locke, A. E., Marouli, E., Zajac, G. J., Wu, K. H., Ntalla, I., Hui, Q., Klarin, D., Hilliard, A. T., Wang, Z., Xue, C., Thorleifsson, G., Helgadottir, A., Gudbjartsson, D. F., Holm, H., Olafsson, I., Hwang, M. Y., Han, S., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W., Brumpton, B. M., Rasheed, H., Havulinna, A. S., Veturi, Y., Pacheco, J. A., Rosenthal, E. A., Lingren, T., Feng, Q., Kullo, I. J., Narita, A., Takayama, J., Martin, H. C., Hunt, K. A., Trivedi, B., Haessler, J., Giulianini, F., Bradford, Y., Miller, J. E., Campbell, A., Lin, K., Millwood, I. Y., Rasheed, A., Hindy, G., Faul, J. D., Zhao, W., Weir, D. R., Turman, C., Huang, H., Graff, M., Choudhury, A., Sengupta, D., Mahajan, A., Brown, M. R., Zhang, W., Yu, K., Schmidt, E. M., Pandit, A., Gustafsson, S., Yin, X., Luan, J., Zhao, J. H., Matsuda, F., Jang, H. M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J. J., Wood, A. R., Ji, Y., Gao, Z., Haworth, S., Mitchell, R. E., Chai, J. F., Aadahl, M., Bjerregaard, A. A., Yao, J., Manichaikul, A., Lee, W. J., Hsiung, C. A., Warren, H. R., Ramirez, J., Bork-Jensen, J., Kårhus, L. L., Goel, A., Sabater-Lleal, M., Noordam, R., Mauro, P., Matteo, F., McDaid, A. F., Marques-Vidal, P., Wielscher, M., Trompet, S., Sattar, N., Møllehave, L. T., Munz, M., Zeng, L., Huang, J., Yang, B., Poveda, A., Kurbasic, A., Schönherr, S., Forer, L., Scholz, M., Galesloot, T. E., Bradfield, J. P., Ruotsalainen, S. E., Daw, E. W., Zmuda, J. M., Mitchell, J. S., Fuchsberger, C., Christensen, H., Brody, J. A., Le, P., Feitosa, M. F., Wojczynski, M. K., Hemerich, D., Preuss, M., Mangino, M., Christofidou, P., Verweij, N., Benjamins, J. W., Engmann, J., Noah, T. L., Verma, A., Slieker, R. C., Lo, K. S., Zilhao, N. R., Kleber, M. E., Delgado, G. E., Huo, S., Ikeda, D. D., Iha, H., Yang, J., Liu, J., Demirkan, A., Leonard, H. L., Marten, J., Emmel, C., Schmidt, B., Smyth, L. J., Cañadas-Garre, M., Wang, C., Nakatochi, M., Wong, A., Hutri-Kähönen, N., Sim, X., Xia, R., Huerta-Chagoya, A., Fernandez-Lopez, J. C., Lyssenko, V., Nongmaithem, S. S., Sankareswaran, A., Irvin, M. R., Oldmeadow, C., Kim, H. N., Ryu, S., Timmers, P. R., Arbeeva, L., Dorajoo, R., Lange, L. A., Prasad, G., Lorés-Motta, L., Pauper, M., Long, J., Li, X., Theusch, E., Takeuchi, F., Spracklen, C. N., Loukola, A., Bollepalli, S., Warner, S. C., Wang, Y. X., Wei, W. B., Nutile, T., Ruggiero, D., Sung, Y. J., Chen, S., Liu, F., Yang, J., Kentistou, K. A., Banas, B., Morgan, A., Meidtner, K., Bielak, L. F., Smith, J. A., Hebbar, P., Farmaki, A. E., Hofer, E., Lin, M., Concas, M. P., Vaccargiu, S., van der Most, P. J., Pitkänen, N., Cade, B. E., van der Laan, S. W., Chitrala, K. N., Weiss, S., Bentley, A. R., Doumatey, A. P., Adeyemo, A. A., Lee, J. Y., Petersen, E. R., Nielsen, A. A., Choi, H. S., Nethander, M., Freitag-Wolf, S., Southam, L., Rayner, N. W., Wang, C. A., Lin, S. Y., Wang, J. S., Couture, C., Lyytikäinen, L. P., Nikus, K., Cuellar-Partida, G., Vestergaard, H., Hidalgo, B., Giannakopoulou, O., Cai, Q., Obura, M. O., van Setten, J., He, K. Y., Tang, H., Terzikhan, N., Shin, J. H., Jackson, R. D., Reiner, A. P., Martin, L. W., Chen, Z., Li, L., Kawaguchi, T., Thiery, J., Bis, J. C., Launer, L. J., Li, H., Nalls, M. A., Raitakari, O. T., Ichihara, S., Wild, S. H., Nelson, C. P., Campbell, H., Jäger, S., Nabika, T., Al-Mulla, F., Niinikoski, H., Braund, P. S., Kolcic, I., Kovacs, P., Giardoglou, T., Katsuya, T., de Kleijn, D., de Borst, G. J., Kim, E. K., Adams, H. H., Ikram, M. A., Zhu, X., Asselbergs, F. W., Kraaijeveld, A. O., Beulens, J. W., Shu, X. O., Rallidis, L. S., Pedersen, O., Hansen, T., Mitchell, P., Hewitt, A. W., Kähönen, M., Pérusse, L., Bouchard, C., Tönjes, A., Ida Chen, Y. D., Pennell, C. E., Mori, T. A., Lieb, W., Franke, A., Ohlsson, C., Mellström, D., Cho, Y. S., Lee, H., Yuan, J. M., Koh, W. P., Rhee, S. Y., Woo, J. T., Heid, I. M., Stark, K. J., Zimmermann, M. E., Völzke, H., Homuth, G., Evans, M. K., Zonderman, A. B., Polasek, O., Pasterkamp, G., Hoefer, I. E., Redline, S., Pahkala, K., Oldehinkel, A. J., Snieder, H., Biino, G., Schmidt, R., Schmidt, H., Bandinelli, S., Dedoussis, G., Thanaraj, T. A., Peyser, P. A., Kato, N., Schulze, M. B., Girotto, G., Böger, C. A., Jung, B., Joshi, P. K., Bennett, D. A., De Jager, P. L., Lu, X., Mamakou, V., Brown, M., Caulfield, M. J., Munroe, P. B., Guo, X., Ciullo, M., Jonas, J. B., Samani, N. J., Kaprio, J., Pajukanta, P., Tusié-Luna, T., Aguilar-Salinas, C. A., Adair, L. S., Bechayda, S. A., de Silva, H. J., Wickremasinghe, A. R., Krauss, R. M., Wu, J. Y., Zheng, W., den Hollander, A. I., Bharadwaj, D., Correa, A., Wilson, J. G., Lind, L., Heng, C. K., Nelson, A. E., Golightly, Y. M., Wilson, J. F., Penninx, B., Kim, H. L., Attia, J., Scott, R. J., Rao, D. C., Arnett, D. K., Walker, M., Scott, L. J., Koistinen, H. A., Chandak, G. R., Mercader, J. M., Villalpando, C. G., Orozco, L., Fornage, M., Tai, E. S., van Dam, R. M., Lehtimäki, T., Chaturvedi, N., Yokota, M., Liu, J., Reilly, D. F., McKnight, A. J., Kee, F., Jöckel, K. H., McCarthy, M. I., Palmer, C. N., Vitart, V., Hayward, C., Simonsick, E., van Duijn, C. M., Jin, Z. B., Lu, F., Hishigaki, H., Lin, X., März, W., Gudnason, V., Tardif, J. C., Lettre, G., T Hart, L. M., Elders, P. J., Rader, D. J., Damrauer, S. M., Kumari, M., Kivimaki, M., van der Harst, P., Spector, T. D., Loos, R. J., Province, M. A., Parra, E. J., Cruz, M., Psaty, B. M., Brandslund, I., Pramstaller, P. P., Rotimi, C. N., Christensen, K., Ripatti, S., Widén, E., Hakonarson, H., Grant, S. F., Kiemeney, L., de Graaf, J., Loeffler, M., Kronenberg, F., Gu, D., Erdmann, J., Schunkert, H., Franks, P. W., Linneberg, A., Jukema, J. W., Khera, A. V., Männikkö, M., Jarvelin, M. R., Kutalik, Z., Francesco, C., Mook-Kanamori, D. O., Willems van Dijk, K., Watkins, H., Strachan, D. P., Grarup, N., Sever, P., Poulter, N., Huey-Herng Sheu, W., Rotter, J. I., Dantoft, T. M., Karpe, F., Neville, M. J., Timpson, N. J., Cheng, C. Y., Wong, T. Y., Khor, C. C., Li, H., Sabanayagam, C., Peters, A., Gieger, C., Hattersley, A. T., Pedersen, N. L., Magnusson, P. K., Boomsma, D. I., de Geus, E. J., Cupples, L. A., van Meurs, J. B., Ikram, A., Ghanbari, M., Gordon-Larsen, P., Huang, W., Kim, Y. J., Tabara, Y., Wareham, N. J., Langenberg, C., Zeggini, E., Tuomilehto, J., Kuusisto, J., Laakso, M., Ingelsson, E., Abecasis, G., Chambers, J. C., Kooner, J. S., de Vries, P. S., Morrison, A. C., Hazelhurst, S., Ramsay, M., North, K. E., Daviglus, M., Kraft, P., Martin, N. G., Whitfield, J. B., Abbas, S., Saleheen, D., Walters, R. G., Holmes, M. V., Black, C., Smith, B. H., Baras, A., Justice, A. E., Buring, J. E., Ridker, P. M., Chasman, D. I., Kooperberg, C., Tamiya, G., Yamamoto, M., van Heel, D. A., Trembath, R. C., Wei, W. Q., Jarvik, G. P., Namjou, B., Hayes, M. G., Ritchie, M. D., Jousilahti, P., Salomaa, V., Hveem, K., Åsvold, B. O., Kubo, M., Kamatani, Y., Okada, Y., Murakami, Y., Kim, B. J., Thorsteinsdottir, U., Stefansson, K., Zhang, J., Chen, Y. E., Ho, Y. L., Lynch, J. A., Tsao, P. S., Chang, K. M., Cho, K., O'Donnell, C. J., Gaziano, J. M., Wilson, P., Mohlke, K. L., Frayling, T. M., Hirschhorn, J. N., Kathiresan, S., Boehnke, M., Natarajan, P., Sun, Y. V., Morris, A. P., Deloukas, P., Peloso, G., Assimes, T. L., Willer, C. J., Zhu, X., Brown, C. D. 2022; 109 (8): 1366-1387

  Abstract

  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.

  View details for DOI 10.1016/j.ajhg.2022.06.012

  View details for PubMedID 35931049

• Large-scale genome-wide association study of coronary artery disease in genetically diverse populations. Nature medicine Tcheandjieu, C., Zhu, X., Hilliard, A. T., Clarke, S. L., Napolioni, V., Ma, S., Lee, K. M., Fang, H., Chen, F., Lu, Y., Tsao, N. L., Raghavan, S., Koyama, S., Gorman, B. R., Vujkovic, M., Klarin, D., Levin, M. G., Sinnott-Armstrong, N., Wojcik, G. L., Plomondon, M. E., Maddox, T. M., Waldo, S. W., Bick, A. G., Pyarajan, S., Huang, J., Song, R., Ho, Y. L., Buyske, S., Kooperberg, C., Haessler, J., Loos, R. J., Do, R., Verbanck, M., Chaudhary, K., North, K. E., Avery, C. L., Graff, M., Haiman, C. A., Le Marchand, L., Wilkens, L. R., Bis, J. C., Leonard, H., Shen, B., Lange, L. A., Giri, A., Dikilitas, O., Kullo, I. J., Stanaway, I. B., Jarvik, G. P., Gordon, A. S., Hebbring, S., Namjou, B., Kaufman, K. M., Ito, K., Ishigaki, K., Kamatani, Y., Verma, S. S., Ritchie, M. D., Kember, R. L., Baras, A., Lotta, L. A., Kathiresan, S., Hauser, E. R., Miller, D. R., Lee, J. S., Saleheen, D., Reaven, P. D., Cho, K., Gaziano, J. M., Natarajan, P., Huffman, J. E., Voight, B. F., Rader, D. J., Chang, K. M., Lynch, J. A., Damrauer, S. M., Wilson, P. W., Tang, H., Sun, Y. V., Tsao, P. S., O'Donnell, C. J., Assimes, T. L. 2022

  Abstract

  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.

  View details for DOI 10.1038/s41591-022-01891-3

  View details for PubMedID 35915156

• Race and Ethnicity Stratification for Polygenic Risk Score Analyses May Mask Disparities in Hispanics CIRCULATION Clarke, S. L., Huang, R. L., Hilliard, A. T., Tcheandjieu, C., Lynch, J., Damrauer, S. M., Chang, K., Tsao, P. S., Assimes, T. L. 2022; 146 (3): 265-267

  View details for DOI 10.1161/CIRCULATIONAHA.122.059162

  View details for Web of Science ID 000823965300016

• Use of Polygenic Risk Scores for Coronary Heart Disease in Ancestrally Diverse Populations. Current cardiology reports Dikilitas, O., Schaid, D. J., Tcheandjieu, C., Clarke, S. L., Assimes, T. L., Kullo, I. J. 2022

  Abstract

  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 thisdistribution 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.

  View details for DOI 10.1007/s11886-022-01734-0

  View details for PubMedID 35796859

• Genetic interactions drive heterogeneity in causal variant effect sizes for gene expression and complex traits. American journal of human genetics Patel, R. A., Musharoff, S. A., Spence, J. P., Pimentel, H., Tcheandjieu, C., Mostafavi, H., Sinnott-Armstrong, N., Clarke, S. L., Smith, C. J., V.A. Million Veteran Program,,, Durda, P. P., Taylor, K. D., Tracy, R., Liu, Y., Johnson, W. C., Aguet, F., Ardlie, K. G., Gabriel, S., Smith, J., Nickerson, D. A., Rich, S. S., Rotter, J. I., Tsao, P. S., Assimes, T. L., Pritchard, J. K. 2022

  Abstract

  Despite the growing number of genome-wide association studies (GWASs), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWASs are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate, although this is not significant, most likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

  View details for DOI 10.1016/j.ajhg.2022.05.014

  View details for PubMedID 35716666

• Using Mendelian randomisation to identify opportunities for type 2 diabetes prevention by repurposing medications used for lipid management. EBioMedicine Khankari, N. K., Keaton, J. M., Walker, V. M., Lee, K. M., Shuey, M. M., Clarke, S. L., Heberer, K. R., Miller, D. R., Reaven, P. D., Lynch, J. A., Vujkovic, M., Edwards, T. L. 2022; 80: 104038

  Abstract

  Maintaining a healthy lifestyle to reduce type 2 diabetes (T2D) risk is challenging and additional strategies for T2D prevention are needed. We evaluated several lipid control medications as potential therapeutic options for T2D prevention using tissue-specific predicted gene expression summary statistics in a two-sample Mendelian randomisation (MR) design.Large-scale European genome-wide summary statistics for lipids and T2D were leveraged in our multi-stage analysis to estimate changes in either lipid levels or T2D risk driven by tissue-specific predicted gene expression. We incorporated tissue-specific predicted gene expression summary statistics to proxy therapeutic effects of three lipid control medications [i.e., statins, icosapent ethyl (IPE), and proprotein convertase subtilisin/kexin type-9 inhibitors (PCSK-9i)] on T2D susceptibility using two-sample Mendelian randomisation (MR).IPE, as proxied via increased FADS1 expression, was predicted to lower triglycerides and was associated with a 53% reduced risk of T2D. Statins and PCSK-9i, as proxied by reduced HMGCR and PCSK9 expression, respectively, were predicted to lower LDL-C levels but were not associated with T2D susceptibility.Triglyceride lowering via IPE may reduce the risk of developing T2D in populations of European ancestry. However, experimental validation using animal models is needed to substantiate our results and to motivate randomized control trials (RCTs) for IPE as putative treatment for T2D prevention.Only summary statistics were used in this analysis. Funding information is detailed under Acknowledgments.

  View details for DOI 10.1016/j.ebiom.2022.104038

  View details for PubMedID 35500537

• Mendelian randomization supports bidirectional causality between telomere length and clonal hematopoiesis of indeterminate potential. Science advances Nakao, T., Bick, A. G., Taub, M. A., Zekavat, S. M., Uddin, M. M., Niroula, A., Carty, C. L., Lane, J., Honigberg, M. C., Weinstock, J. S., Pampana, A., Gibson, C. J., Griffin, G. K., Clarke, S. L., Bhattacharya, R., Assimes, T. L., Emery, L. S., Stilp, A. M., Wong, Q., Broome, J., Laurie, C. A., Khan, A. T., Smith, A. V., Blackwell, T. W., Codd, V., Nelson, C. P., Yoneda, Z. T., Peralta, J. M., Bowden, D. W., Irvin, M. R., Boorgula, M., Zhao, W., Yanek, L. R., Wiggins, K. L., Hixson, J. E., Gu, C. C., Peloso, G. M., Roden, D. M., Reupena, M. S., Hwu, C., DeMeo, D. L., North, K. E., Kelly, S., Musani, S. K., Bis, J. C., Lloyd-Jones, D. M., Johnsen, J. M., Preuss, M., Tracy, R. P., Peyser, P. A., Qiao, D., Desai, P., Curran, J. E., Freedman, B. I., Tiwari, H. K., Chavan, S., Smith, J. A., Smith, N. L., Kelly, T. N., Hidalgo, B., Cupples, L. A., Weeks, D. E., Hawley, N. L., Minster, R. L., Samoan Obesity, L. a., Deka, R., Naseri, T. T., de Las Fuentes, L., Raffield, L. M., Morrison, A. C., Vries, P. S., Ballantyne, C. M., Kenny, E. E., Rich, S. S., Whitsel, E. A., Cho, M. H., Shoemaker, M. B., Pace, B. S., Blangero, J., Palmer, N. D., Mitchell, B. D., Shuldiner, A. R., Barnes, K. C., Redline, S., Kardia, S. L., Abecasis, G. R., Becker, L. C., Heckbert, S. R., He, J., Post, W., Arnett, D. K., Vasan, R. S., Darbar, D., Weiss, S. T., McGarvey, S. T., de Andrade, M., Chen, Y. I., Kaplan, R. C., Meyers, D. A., Custer, B. S., Correa, A., Psaty, B. M., Fornage, M., Manson, J. E., Boerwinkle, E., Konkle, B. A., Loos, R. J., Rotter, J. I., Silverman, E. K., Kooperberg, C., Danesh, J., Samani, N. J., Jaiswal, S., Libby, P., Ellinor, P. T., Pankratz, N., Ebert, B. L., Reiner, A. P., Mathias, R. A., Do, R., NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Natarajan, P. 2022; 8 (14): eabl6579

  Abstract

  Human genetic studies support an inverse causal relationship between leukocyte telomere length (LTL) and coronary artery disease (CAD), but directionally mixed effects for LTL and diverse malignancies. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by expansion of hematopoietic cells bearing leukemogenic mutations, predisposes both hematologic malignancy and CAD. TERT (which encodes telomerase reverse transcriptase) is the most significantly associated germline locus for CHIP in genome-wide association studies. Here, we investigated the relationship between CHIP, LTL, and CAD in the Trans-Omics for Precision Medicine (TOPMed) program (n = 63,302) and UK Biobank (n = 47,080). Bidirectional Mendelian randomization studies were consistent with longer genetically imputed LTL increasing propensity to develop CHIP, but CHIP then, in turn, hastens to shorten measured LTL (mLTL). We also demonstrated evidence of modest mediation between CHIP and CAD by mLTL. Our data promote an understanding of potential causal relationships across CHIP and LTL toward prevention of CAD.

  View details for DOI 10.1126/sciadv.abl6579

  View details for PubMedID 35385311

• ZEB2 Shapes the Epigenetic Landscape of Atherosclerosis. Circulation Cheng, P., Wirka, R. C., Clarke, L. S., Zhao, Q., Kundu, R., Nguyen, T., Nair, S., Sharma, D., Kim, H. J., Shi, H., Assimes, T., Kim, J. B., Kundaje, A., Quertermous, T. 2022

  Abstract

  Background: Smooth muscle cells (SMC) transition into a number of different phenotypes during atherosclerosis, including those that resemble fibroblasts and chondrocytes, and make up the majority of cells in the atherosclerotic plaque. To better understand the epigenetic and transcriptional mechanisms that mediate these cell state changes, and how they relate to risk for coronary artery disease (CAD), we have investigated the causality and function of transcription factors (TFs) at genome wide associated loci. Methods: We employed CRISPR-Cas 9 genome and epigenome editing to identify the causal gene and cell(s) for a complex CAD GWAS signal at 2q22.3. Subsequently, single-cell epigenetic and transcriptomic profiling in murine models and human coronary artery smooth muscle cells were employed to understand the cellular and molecular mechanism by which this CAD risk gene exerts its function. Results: CRISPR-Cas 9 genome and epigenome editing showed that the complex CAD genetic signals within a genomic region at 2q22.3 lie within smooth muscle long-distance enhancers for ZEB2, a TF extensively studied in the context of epithelial mesenchymal transition (EMT) in development and cancer. ZEB2 regulates SMC phenotypic transition through chromatin remodeling that obviates accessibility and disrupts both Notch and TGFβ signaling, thus altering the epigenetic trajectory of SMC transitions. SMC specific loss of ZEB2 resulted in an inability of transitioning SMCs to turn off contractile programing and take on a fibroblast-like phenotype, but accelerated the formation of chondromyocytes, mirroring features of high-risk atherosclerotic plaques in human coronary arteries. Conclusions: These studies identify ZEB2 as a new CAD GWAS gene that affects features of plaque vulnerability through direct effects on the epigenome, providing a new thereapeutic approach to target vascular disease.

  View details for DOI 10.1161/CIRCULATIONAHA.121.057789

  View details for PubMedID 34990206

• Broad clinical manifestations of polygenic risk for coronary artery disease in the Women's Health Initiative. Communications medicine Clarke, S. L., Parham, M., Lankester, J., Shadyab, A. H., Liu, S., Kooperberg, C., Manson, J. E., Tcheandjieu, C., Assimes, T. L. 2022; 2: 108

  Abstract

  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.

  View details for DOI 10.1038/s43856-022-00171-y

  View details for PubMedID 36034645

• Coronary Artery Disease Risk of Familial Hypercholesterolemia Genetic Variants Independent of Clinically Observed Longitudinal Cholesterol Exposure. Circulation. Genomic and precision medicine Clarke, S. L., Tcheandjieu, C., Hilliard, A. T., Lee, M., Lynch, J., Chang, K. M., Miller, D., Knowles, J. W., O'Donnell, C., Tsao, P., Rader, D. J., Wilson, P. W., Sun, Y. V., Gaziano, M., Assimes, T. L. 2022: CIRCGEN121003501

  Abstract

  Familial hypercholesterolemia (FH) genetic variants confer risk for coronary artery disease independent of LDL-C (low-density lipoprotein cholesterol) when considering a single measurement. In real clinical settings, longitudinal LDL-C data are often available through the electronic health record. It is unknown whether genetic testing for FH variants provides additional risk-stratifying information once longitudinal LDL-C is considered.We used the extensive electronic health record data available through the Million Veteran Program to conduct a nested case-control study. The primary outcome was coronary artery disease, derived from electronic health record codes for acute myocardial infarction and coronary revascularization. Incidence density sampling was used to match case/control exposure windows, defined by the date of the first LDL-C measurement to the date of the first coronary artery disease code of the index case. Adjustments for the first, maximum, or mean LDL-C were analyzed. FH variants in LDLR, APOB, and PCSK9 were assessed by custom genotype array.In a cohort of 23 091 predominantly prevalent cases at enrollment and 230 910 matched controls, FH variant carriers had an increased risk for coronary artery disease (odds ratio [OR], 1.53 [95% CI, 1.24-1.89]). Adjusting for mean LDL-C led to the greatest attenuation of the risk estimate, but significant risk remained (odds ratio, 1.33 [95% CI, 1.08-1.64]). The degree of attenuation was not affected by the number and the spread of LDL-C measures available.The risk associated with carrying an FH variant cannot be fully captured by the LDL-C data available in the electronic health record, even when considering multiple LDL-C measurements spanning more than a decade.

  View details for DOI 10.1161/CIRCGEN.121.003501

  View details for PubMedID 35143253

• The power of genetic diversity in genome-wide association studies of lipids. Nature Graham, S. E., Clarke, S. L., Wu, K. H., Kanoni, S., Zajac, G. J., Ramdas, S., Surakka, I., Ntalla, I., Vedantam, S., Winkler, T. W., Locke, A. E., Marouli, E., Hwang, M. Y., Han, S., Narita, A., Choudhury, A., Bentley, A. R., Ekoru, K., Verma, A., Trivedi, B., Martin, H. C., Hunt, K. A., Hui, Q., Klarin, D., Zhu, X., Thorleifsson, G., Helgadottir, A., Gudbjartsson, D. F., Holm, H., Olafsson, I., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W., Brumpton, B. M., Rasheed, H., Ruotsalainen, S. E., Havulinna, A. S., Veturi, Y., Feng, Q., Rosenthal, E. A., Lingren, T., Pacheco, J. A., Pendergrass, S. A., Haessler, J., Giulianini, F., Bradford, Y., Miller, J. E., Campbell, A., Lin, K., Millwood, I. Y., Hindy, G., Rasheed, A., Faul, J. D., Zhao, W., Weir, D. R., Turman, C., Huang, H., Graff, M., Mahajan, A., Brown, M. R., Zhang, W., Yu, K., Schmidt, E. M., Pandit, A., Gustafsson, S., Yin, X., Luan, J., Zhao, J., Matsuda, F., Jang, H., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J. J., Willemsen, G., Wood, A. R., Ji, Y., Gao, Z., Haworth, S., Mitchell, R. E., Chai, J. F., Aadahl, M., Yao, J., Manichaikul, A., Warren, H. R., Ramirez, J., Bork-Jensen, J., Karhus, L. L., Goel, A., Sabater-Lleal, M., Noordam, R., Sidore, C., Fiorillo, E., McDaid, A. F., Marques-Vidal, P., Wielscher, M., Trompet, S., Sattar, N., Mollehave, L. T., Thuesen, B. H., Munz, M., Zeng, L., Huang, J., Yang, B., Poveda, A., Kurbasic, A., Lamina, C., Forer, L., Scholz, M., Galesloot, T. E., Bradfield, J. P., Daw, E. W., Zmuda, J. M., Mitchell, J. S., Fuchsberger, C., Christensen, H., Brody, J. A., Feitosa, M. F., Wojczynski, M. K., Preuss, M., Mangino, M., Christofidou, P., Verweij, N., Benjamins, J. W., Engmann, J., Kember, R. L., Slieker, R. C., Lo, K. S., Zilhao, N. R., Le, P., Kleber, M. E., Delgado, G. E., Huo, S., Ikeda, D. D., Iha, H., Yang, J., Liu, J., Leonard, H. L., Marten, J., Schmidt, B., Arendt, M., Smyth, L. J., Canadas-Garre, M., Wang, C., Nakatochi, M., Wong, A., Hutri-Kahonen, N., Sim, X., Xia, R., Huerta-Chagoya, A., Fernandez-Lopez, J. C., Lyssenko, V., Ahmed, M., Jackson, A. U., Irvin, M. R., Oldmeadow, C., Kim, H., Ryu, S., Timmers, P. R., Arbeeva, L., Dorajoo, R., Lange, L. A., Chai, X., Prasad, G., Lores-Motta, L., Pauper, M., Long, J., Li, X., Theusch, E., Takeuchi, F., Spracklen, C. N., Loukola, A., Bollepalli, S., Warner, S. C., Wang, Y. X., Wei, W. B., Nutile, T., Ruggiero, D., Sung, Y. J., Hung, Y., Chen, S., Liu, F., Yang, J., Kentistou, K. A., Gorski, M., Brumat, M., Meidtner, K., Bielak, L. F., Smith, J. A., Hebbar, P., Farmaki, A., Hofer, E., Lin, M., Xue, C., Zhang, J., Concas, M. P., Vaccargiu, S., van der Most, P. J., Pitkanen, N., Cade, B. E., Lee, J., van der Laan, S. W., Chitrala, K. N., Weiss, S., Zimmermann, M. E., Lee, J. Y., Choi, H. S., Nethander, M., Freitag-Wolf, S., Southam, L., Rayner, N. W., Wang, C. A., Lin, S., Wang, J., Couture, C., Lyytikainen, L., Nikus, K., Cuellar-Partida, G., Vestergaard, H., Hildalgo, B., Giannakopoulou, O., Cai, Q., Obura, M. O., van Setten, J., Li, X., Schwander, K., Terzikhan, N., Shin, J. H., Jackson, R. D., Reiner, A. P., Martin, L. W., Chen, Z., Li, L., Highland, H. M., Young, K. L., Kawaguchi, T., Thiery, J., Bis, J. C., Nadkarni, G. N., Launer, L. J., Li, H., Nalls, M. A., Raitakari, O. T., Ichihara, S., Wild, S. H., Nelson, C. P., Campbell, H., Jager, S., Nabika, T., Al-Mulla, F., Niinikoski, H., Braund, P. S., Kolcic, I., Kovacs, P., Giardoglou, T., Katsuya, T., Bhatti, K. F., de Kleijn, D., de Borst, G. J., Kim, E. K., Adams, H. H., Ikram, M. A., Zhu, X., Asselbergs, F. W., Kraaijeveld, A. O., Beulens, J. W., Shu, X., Rallidis, L. S., Pedersen, O., Hansen, T., Mitchell, P., Hewitt, A. W., Kahonen, M., Perusse, L., Bouchard, C., Tonjes, A., Chen, Y. I., Pennell, C. E., Mori, T. A., Lieb, W., Franke, A., Ohlsson, C., Mellstrom, D., Cho, Y. S., Lee, H., Yuan, J., Koh, W., Rhee, S. Y., Woo, J., Heid, I. M., Stark, K. J., Volzke, H., Homuth, G., Evans, M. K., Zonderman, A. B., Polasek, O., Pasterkamp, G., Hoefer, I. E., Redline, S., Pahkala, K., Oldehinkel, A. J., Snieder, H., Biino, G., Schmidt, R., Schmidt, H., Chen, Y. E., Bandinelli, S., Dedoussis, G., Thanaraj, T. A., Kardia, S. L., Kato, N., Schulze, M. B., Girotto, G., Jung, B., Boger, C. A., Joshi, P. K., Bennett, D. A., De Jager, P. L., Lu, X., Mamakou, V., Brown, M., Caulfield, M. J., Munroe, P. B., Guo, X., Ciullo, M., Jonas, J. B., Samani, N. J., Kaprio, J., Pajukanta, P., Adair, L. S., Bechayda, S. A., de Silva, H. J., Wickremasinghe, A. R., Krauss, R. M., Wu, J., Zheng, W., den Hollander, A. I., Bharadwaj, D., Correa, A., Wilson, J. G., Lind, L., Heng, C., Nelson, A. E., Golightly, Y. M., Wilson, J. F., Penninx, B., Kim, H., Attia, J., Scott, R. J., Rao, D. C., Arnett, D. K., Walker, M., Koistinen, H. A., Chandak, G. R., Yajnik, C. S., Mercader, J. M., Tusie-Luna, T., Aguilar-Salinas, C. A., Villalpando, C. G., Orozco, L., Fornage, M., Tai, E. S., van Dam, R. M., Lehtimaki, T., Chaturvedi, N., Yokota, M., Liu, J., Reilly, D. F., McKnight, A. J., Kee, F., Jockel, K., McCarthy, M. I., Palmer, C. N., Vitart, V., Hayward, C., Simonsick, E., van Duijn, C. M., Lu, F., Qu, J., Hishigaki, H., Lin, X., Marz, W., Parra, E. J., Cruz, M., Gudnason, V., Tardif, J., Lettre, G., 't Hart, L. M., Elders, P. J., Damrauer, S. M., Kumari, M., Kivimaki, M., van der Harst, P., Spector, T. D., Loos, R. J., Province, M. A., Psaty, B. M., Brandslund, I., Pramstaller, P. P., Christensen, K., Ripatti, S., Widen, E., Hakonarson, H., Grant, S. F., Kiemeney, L. A., de Graaf, J., Loeffler, M., Kronenberg, F., Gu, D., Erdmann, J., Schunkert, H., Franks, P. W., Linneberg, A., Jukema, J. W., Khera, A. V., Mannikko, M., Jarvelin, M., Kutalik, Z., Cucca, F., Mook-Kanamori, D. O., van Dijk, K. W., Watkins, H., Strachan, D. P., Grarup, N., Sever, P., Poulter, N., Rotter, J. I., Dantoft, T. M., Karpe, F., Neville, M. J., Timpson, N. J., Cheng, C., Wong, T., Khor, C. C., Sabanayagam, C., Peters, A., Gieger, C., Hattersley, A. T., Pedersen, N. L., Magnusson, P. K., Boomsma, D. I., de Geus, E. J., Cupples, L. A., van Meurs, J. B., Ghanbari, M., Gordon-Larsen, P., Huang, W., Kim, Y. J., Tabara, Y., Wareham, N. J., Langenberg, C., Zeggini, E., Kuusisto, J., Laakso, M., Ingelsson, E., Abecasis, G., Chambers, J. C., Kooner, J. S., de Vries, P. S., Morrison, A. C., North, K. E., Daviglus, M., Kraft, P., Martin, N. G., Whitfield, J. B., Abbas, S., Saleheen, D., Walters, R. G., Holmes, M. V., Black, C., Smith, B. H., Justice, A. E., Baras, A., Buring, J. E., Ridker, P. M., Chasman, D. I., Kooperberg, C., Wei, W., Jarvik, G. P., Namjou, B., Hayes, M. G., Ritchie, M. D., Jousilahti, P., Salomaa, V., Hveem, K., Asvold, B. O., Kubo, M., Kamatani, Y., Okada, Y., Murakami, Y., Thorsteinsdottir, U., Stefansson, K., Ho, Y., Lynch, J. A., Rader, D. J., Tsao, P. S., Chang, K., Cho, K., O'Donnell, C. J., Gaziano, J. M., Wilson, P., Rotimi, C. N., Hazelhurst, S., Ramsay, M., Trembath, R. C., van Heel, D. A., Tamiya, G., Yamamoto, M., Kim, B., Mohlke, K. L., Frayling, T. M., Hirschhorn, J. N., Kathiresan, S., VA Million Veteran Program, Global Lipids Genetics Consortium*, Boehnke, M., Natarajan, P., Peloso, G. M., Brown, C. D., Morris, A. P., Assimes, T. L., Deloukas, P., Sun, Y. V., Willer, C. J. 2021

  Abstract

  Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4-23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately295,000 individuals from 7ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice.

  View details for DOI 10.1038/s41586-021-04064-3

  View details for PubMedID 34887591

• Time to Relax the 40-Year Age Threshold for Pharmacologic Cholesterol Lowering. Journal of the American College of Cardiology Heidenreich, P. A., Clarke, S. L., Maron, D. J. 2021; 78 (20): 1965-1967

  View details for DOI 10.1016/j.jacc.2021.08.072

  View details for PubMedID 34763773

• The Propagation of Racial Disparities in Cardiovascular Genomics Research. Circulation. Genomic and precision medicine Clarke, S. L., Assimes, T. L., Tcheandjieu, C. 2021: CIRCGEN121003178

  Abstract

  Genomics research has improved our understanding of the genetic basis for human traits and diseases. This progress is now being translated into clinical care as we move toward a future of precision medicine. Many hope that expanded use of genomic testing will improve disease screening, diagnosis, risk stratification, and treatment. In many respects, cardiovascular medicine is leading this charge. However, most cardiovascular genomics research has been conducted in populations of primarily European ancestry. This bias has critical downstream effects. Here, we review the current disparities in cardiovascular genomics research, and we outline how these disparities propagate forward through all phases of the translational pipeline. If not adequately addressed, biases in genomics research will further compound the existing health disparities that face underrepresented and marginalized populations.

  View details for DOI 10.1161/CIRCGEN.121.003178

  View details for PubMedID 34461749

• Associations of Genetically Predicted Lipoprotein (a) Levels with Cardiovascular Traits in Individuals of European and African Ancestry. Circulation. Genomic and precision medicine Satterfield, B. A., Dikilitas, O., Safarova, M. S., Clarke, S. L., Tcheandjieu, C., Zhu, X., Bastarache, L., Larson, E. B., Justice, A. E., Shang, N., Rosenthal, E. A., Shah, A., Namjou-Khales, B., Urbina, E. M., Wei, W., Feng, Q., Jarvik, G. P., Hebbring, S. J., de Andrade, M., Manolio, T. A., Assimes, T. L., Kullo, I. J. 2021

  Abstract

  Background - Lipoprotein (a) [Lp(a)] levels are higher in individuals of African ancestry (AA) than in individuals of European ancestry (EA). We examined associations of genetically predicted Lp(a) levels with 1) atherosclerotic cardiovascular disease (ASCVD) subtypes: coronary heart disease (CHD), cerebrovascular disease (CVD), peripheral artery disease (PAD), and abdominal aortic aneurysm (AAA); and 2) non-ASCVD phenotypes, stratified by ancestry. Methods - We performed 1) Mendelian randomization (MR) analyses for previously reported cardiovascular associations, and 2) phenome-wide MR (MR-PheWAS) analyses for novel associations. Analyses were stratified by ancestry in electronic MEdical Records and GEnomics, United Kingdom Biobank, and Million Veteran Program cohorts separately and in a combined cohort of 804,507 EA and 103,580 AA participants. Results - In MR analyses using the combined cohort, a 1-standard deviation (SD) genetic increase in Lp(a) level was associated with ASCVD subtypes in EA - odds ratio and 95% confidence interval for CHD 1.28(1.16-1.41); CVD 1.14(1.07-1.21); PAD 1.22(1.11-1.34); AAA 1.28(1.17-1.40); in AA the effect estimate was lower than in EA and nonsignificant for CHD 1.11(0.99-1.24) and CVD 1.06(0.99-1.14) but similar for PAD 1.16(1.01-1.33) and AAA 1.34(1.11-1.62). In EA, a 1-SD genetic increase in Lp(a) level was associated with aortic valve disorders 1.34(1.10-1.62), mitral valve disorders 1.18(1.09-1.27), congestive heart failure 1.12(1.05-1.19), and chronic kidney disease 1.07(1.01-1.14). In AA no significant associations were noted for aortic valve disorders 1.08(0.94-1.25), mitral valve disorders 1.02(0.89-1.16), congestive heart failure 1.02(0.95-1.10), or chronic kidney disease 1.05(0.99-1.12). MR-PheWAS identified novel associations in EA with arterial thromboembolic disease, non-aortic aneurysmal disease, atrial fibrillation, cardiac conduction disorders, and hypertension. Conclusions - Many cardiovascular associations of genetically increased Lp(a) that were significant in EA were not significant in AA. Lp(a) was associated with ASCVD in four major arterial beds in EA but only with PAD and AAA in AA. Additional, novel cardiovascular associations were detected in EA.

  View details for DOI 10.1161/CIRCGEN.120.003354

  View details for PubMedID 34282949

• BROAD CLINICAL MANIFESTATIONS OF POLYGENIC RISK FOR CORONARY ARTERY DISEASE IN THE WOMEN'S HEALTH INITIATIVE Parham, M., Clarke, S., Tcheandjieu, C., Hilliard, A., Assimes, T. ELSEVIER SCIENCE INC. 2021: 1511

  View details for Web of Science ID 000647487501519

• Validation of an Integrated Risk Tool, Including Polygenic Risk Score, for Atherosclerotic Cardiovascular Disease in Multiple Ethnicities and Ancestries. The American journal of cardiology Weale, M. E., Riveros-Mckay, F., Selzam, S., Seth, P., Moore, R., Tarran, W. A., Gradovich, E., Giner-Delgado, C., Palmer, D., Wells, D., Saffari, A., Sivley, R. M., Lachapelle, A. S., Wand, H., Clarke, S. L., Knowles, J. W., O'Sullivan, J. W., Ashley, E. A., McVean, G., Plagnol, V., Donnelly, P. 2021

  Abstract

  The American College of Cardiology / American Heart Association pooled cohort equations tool (ASCVD-PCE) is currently recommended to assess 10-year risk for atherosclerotic cardiovascular disease (ASCVD). ASCVD-PCE does not currently include genetic risk factors. Polygenic risk scores (PRSs) have been shown to offer a powerful new approach to measuring genetic risk for common diseases, including ASCVD, and to enhance risk prediction when combined with ASCVD-PCE. Most work to date, including the assessment of tools, has focused on performance in individuals of European ancestries. Here we present evidence for the clinical validation of a new integrated risk tool (IRT), ASCVD-IRT, which combines ASCVD-PCE with PRS to predict 10-year risk of ASCVD across diverse ethnicity and ancestry groups. We demonstrate improved predictive performance of ASCVD-IRT over ASCVD-PCE, not only in individuals of self-reported White ethnicities (net reclassification improvement (NRI) (with 95% confidence interval) = 2.7% (1.1 - 4.2)) but also Black / African American / Black Caribbean / Black African (NRI = 2.5% (0.6 - 4.3)) and South Asian (Indian, Bangladeshi or Pakistani) ethnicities (NRI = 8.7% (3.1 - 14.4)). NRI confidence intervals were wider and included zero for ethnicities with smaller sample sizes, including Hispanic (NRI = 7.5% (-1.4 - 16.5)), but PRS effect sizes in these ethnicities were significant and of comparable size to those seen in individuals of White ethnicities. Comparable results were obtained when individuals were analysed by genetically inferred ancestry. Together, these results validate the performance of ASCVD-IRT in multiple ethnicities and ancestries, and favour their generalisation to all ethnicities and ancestries.

  View details for DOI 10.1016/j.amjcard.2021.02.032

  View details for PubMedID 33675770

• The need for polygenic score reporting standards in evidence-based practice: lipid genetics use case. Current opinion in lipidology Wand, H. n., Knowles, J. W., Clarke, S. L. 2021

  Abstract

  Polygenic scores (PGS) are used to quantify the genetic predisposition for heritable traits, with hypothesized utility for personalized risk assessments. Lipid PGS are primed for clinical translation, but evidence-based practice changes will require rigorous PGS standards to ensure reproducibility and generalizability. Here we review applicable reporting and technical standards for dyslipidemia PGS translation along phases of the ACCE (Analytical validity, Clinical validity, Clinical utility, Ethical considerations) framework for evaluating genetic tests.New guidance suggests existing standards for study designs incorporating the ACCE framework are applicable to PGS and should be adopted. One recent example is the Clinical Genomics Resource (ClinGen) and Polygenic Score Catalog's PRS reporting standards, which define minimal requirements for describing rationale for score development, study population definitions and data parameters, risk model development and application, risk model evaluation, and translational considerations, such as generalizability beyond the target population studied.Lipid PGS are likely to be integrated into clinical practice in the future. Clinicians will need to be prepared to determine if and when lipid PGS is useful and valid. This decision-making will depend on the quality of evidence for the clinical use of PGS. Establishing reporting standards for PGS will help facilitate data sharing and transparency for critical evaluation, ultimately benefiting the efficiency of evidence-based practice.

  View details for DOI 10.1097/MOL.0000000000000733

  View details for PubMedID 33538426

• Combining Clinical and Polygenic Risk Improves Stroke Prediction Among Individuals with Atrial Fibrillation. Circulation. Genomic and precision medicine O'Sullivan, J. W., Shcherbina, A., Justesen, J. M., Turakhia, M., Perez, M., Wand, H., Tcheandjieu, C., Clarke, S. L., Rivas, M. A., Ashley, E. A. 2021

  Abstract

  Background - Atrial fibrillation (AF) is associated with a five-fold increased risk of ischemic stroke. A portion of this risk is heritable, however current risk stratification tools (CHA2DS2-VASc) don't include family history or genetic risk. We hypothesized that we could improve ischemic stroke prediction in patients with AF by incorporating polygenic risk scores (PRS). Methods - Using data from the largest available GWAS in Europeans, we combined over half a million genetic variants to construct a PRS to predict ischemic stroke in patients with AF. We externally validated this PRS in independent data from the UK Biobank, both independently and integrated with clinical risk factors. The integrated PRS and clinical risk factors risk tool had the greatest predictive ability. Results - Compared with the currently recommended risk tool (CHA2DS2-VASc), the integrated tool significantly improved net reclassification (NRI: 2.3% (95%CI: 1.3% to 3.0%)), and fit (χ2 P =0.002). Using this improved tool, >115,000 people with AF would have improved risk classification in the US. Independently, PRS was a significant predictor of ischemic stroke in patients with AF prospectively (Hazard Ratio: 1.13 per 1 SD (95%CI: 1.06 to 1.23)). Lastly, polygenic risk scores were uncorrelated with clinical risk factors (Pearson's correlation coefficient: -0.018). Conclusions - In patients with AF, there appears to be a significant association between PRS and risk of ischemic stroke. The greatest predictive ability was found with the integration of PRS and clinical risk factors, however the prediction of stroke remains challenging.

  View details for DOI 10.1161/CIRCGEN.120.003168

  View details for PubMedID 34029116

• A New Era for Preventive Cardiology. Trends in cardiovascular medicine Clarke, S. L. 2021

  View details for DOI 10.1016/j.tcm.2021.04.007

  View details for PubMedID 33932569

• Combining Clinical and Polygenic Risk Improves Stroke Prediction Among Individuals With Atrial Fibrillation Osullivan, J. W., Shcherbina, A., Justesen, J. M., Turakhia, M., Perez, M. V., Wand, H., Tcheandjieu, C., Clarke, S. L., Harrington, R. A., Rivas, M. A., Ashley, E. A. LIPPINCOTT WILLIAMS & WILKINS. 2020

  View details for Web of Science ID 000607190401150

• Risk of Coronary Artery Disease Associated With Familial Hypercholesterolemia Genetic Variants is Independent of Historical Low-density Lipoprotein Cholesterol Exposure Clarke, S. L., Tcheandjieu, C., Hilliard, A., Lee, K., Lynch, J., Chang, K., Miller, D., O'Donnell, C. J., Tsao, P. S., Rader, D. J., Wilson, P., Sun, Y. V., Gaziano, M., Assimes, T. L., VA Million Veteran Program LIPPINCOTT WILLIAMS & WILKINS. 2020

  View details for DOI 10.1161/circ.142.suppl_3.13601

  View details for Web of Science ID 000607190401052

• LPA Variants Are Associated With Aortic Valve Stenosis, Heart Failure and Chronic Kidney Disease Dikilitas, O., Satterfield, B. A., Safarova, M., Clarke, S. L., Tcheandjieu, C., Zhu, X., Bastarache, L., Larson, E. B., Justice, A. E., Shang, N., Rosenthal, E., Shah, A. S., Namjou-Khales, B., Urbina, E. M., Wei, W., Feng, Q., Hebbring, S. J., Jarvik, G. P., de Andrade, M., Manolio, T. A., Assimes, T. L., Kullo, I. J. LIPPINCOTT WILLIAMS & WILKINS. 2020

  View details for Web of Science ID 000607190403040

• Cardiorespiratory Fitness, Body-Mass Index, and Markers of Insulin Resistance in Apparently Healthy Women and Men. The American journal of medicine Clarke, S. L., Reaven, G. M., Leonard, D., Barlow, C. E., Haskell, W. L., Willis, B. L., DeFina, L., Knowles, J. W., Maron, D. J. 2020

  Abstract

  BACKGROUND: Insulin resistance may be present in healthy adults and is associated poor health outcomes. Obesity is a risk factor for insulin resistance, but most obese adults do not have insulin resistance. Fitness may be protective, but the association between fitness, weight, and insulin resistance has not been studied in a large population of healthy adults.METHODS: A cross-sectional analysis of cardiorespiratory fitness, body-mass index, and markers of insulin resistance was performed. Study participants were enrolled at the Cooper Clinic (Dallas, Texas). The analysis included 19,263 women and 48,433 men with no history of diabetes or cardiovascular disease. Cardiorespiratory fitness was measured using exercise treadmill testing. Impaired fasting glucose (100-125 mg/dL) and elevated fasting triglycerides (≥150 mg/dL) were used as a markers of insulin resistance.RESULTS: Among normal weight individuals, poor fitness was associated with a 2.2 (1.4-3.6; p=0.001) fold higher odds of insulin resistance in women and a 2.8 (2.1-3.6; p<0.001) fold higher odds in men. The impact of fitness remained significant for overweight and obese individuals, with the highest risk group being the unfit obese. Among obese women, the odds ratio for insulin resistance was 11.0 (8.7-13.9; p<0.001) for fit and 20.3 (15.5-26.5; p<0.001) for unfit women. Among obese men, the odds ratio for insulin resistance was 7.4 (6.7-8.2; p<0.001) for fit and 12.9 (11.4-14.6; p<0.001) for unfit men.CONCLUSION: Independent of weight, poor fitness is associated with risk of insulin resistance. Obese individuals, particularly women, may benefit from the greatest absolute risk reduction by achieving moderate fitness.

  View details for DOI 10.1016/j.amjmed.2019.11.031

  View details for PubMedID 31926863

• Performance of Polygenic Risk Scores for Coronary Artery Disease in the Million Veteran Program Tcheandjieu, C., Zhu, X., Ma, S., Hilliard, A., Clarke, S. L., Lynch, J. A., Damrauer, S. M., Khera, A. V., Kathiresan, S., Tsao, P. S., Gaziano, J., Wilson, P. W., O'Donnell, C., Assimes, T. L., VA Million Vet Program LIPPINCOTT WILLIAMS & WILKINS. 2019

  View details for DOI 10.1161/circ.139.suppl_1.012

  View details for Web of Science ID 000478079000013

• Genome-Wide Association Studies of Coronary Artery Disease: Recent Progress and Challenges Ahead. Current atherosclerosis reports Clarke, S. L., Assimes, T. L. 2018; 20 (9): 47

  Abstract

  Genome-wide association studies (GWAS) have been the primary tool for unbiased assessment of the genetic basis of coronary artery disease (CAD) for more than a decade. We summarize successes as well as shortcomings of recent studies in this context.The number of CAD-associated loci has more than doubled in the past year to 161. This rapid progress has been in large part due to the release of genome-wide genotyping data for the largely European participants of the UK Biobank study which has been combined with existing GWAS from the CARDIoGRAMplusC4D consortium. Additional discoveries have been achieved through large-scale genotyping of participants using custom high-yield genotyping arrays including the Metabochip and the Exome chip. As a consequence, the ability of genetic risk scores in predicting incident CAD events has improved but that improvement has only been shown in European populations. GWAS have proven to be a fruitful approach for uncovering the genetic drivers of CAD. However, determining the mechanisms of association of GWAS findings remains a challenging endeavor requiring long-term investment. Genetic risk scores offer an opportunity for recent findings to have an immediate clinical impact. Going forward, CAD genetics will benefit greatly from the release of more genetic data produced by mega-biobanks. These new data will allow for the more comprehensive examination of underrepresented populations.

  View details for DOI 10.1007/s11883-018-0748-4

  View details for PubMedID 30022313

• Erosion of Conserved Binding Sites in Personal Genomes Points to Medical Histories. PLoS computational biology Guturu, H., Chinchali, S., Clarke, S. L., Bejerano, G. 2016; 12 (2)

  Abstract

  Although many human diseases have a genetic component involving many loci, the majority of studies are statistically underpowered to isolate the many contributing variants, raising the question of the existence of alternate processes to identify disease mutations. To address this question, we collect ancestral transcription factor binding sites disrupted by an individual's variants and then look for their most significant congregation next to a group of functionally related genes. Strikingly, when the method is applied to five different full human genomes, the top enriched function for each is invariably reflective of their very different medical histories. For example, our method implicates "abnormal cardiac output" for a patient with a longstanding family history of heart disease, "decreased circulating sodium level" for an individual with hypertension, and other biologically appealing links for medical histories spanning narcolepsy to axonal neuropathy. Our results suggest that erosion of gene regulation by mutation load significantly contributes to observed heritable phenotypes that manifest in the medical history. The test we developed exposes a hitherto hidden layer of personal variants that promise to shed new light on human disease penetrance, expressivity and the sensitivity with which we can detect them.

  View details for DOI 10.1371/journal.pcbi.1004711

  View details for PubMedID 26845687

  View details for PubMedCentralID PMC4742230

• The enhancer landscape during early neocortical development reveals patterns of dense regulation and co-option. PLoS genetics Wenger, A. M., Clarke, S. L., Notwell, J. H., Chung, T., Tuteja, G., Guturu, H., Schaar, B. T., Bejerano, G. 2013; 9 (8)

  Abstract

  Genetic studies have identified a core set of transcription factors and target genes that control the development of the neocortex, the region of the human brain responsible for higher cognition. The specific regulatory interactions between these factors, many key upstream and downstream genes, and the enhancers that mediate all these interactions remain mostly uncharacterized. We perform p300 ChIP-seq to identify over 6,600 candidate enhancers active in the dorsal cerebral wall of embryonic day 14.5 (E14.5) mice. Over 95% of the peaks we measure are conserved to human. Eight of ten (80%) candidates tested using mouse transgenesis drive activity in restricted laminar patterns within the neocortex. GREAT based computational analysis reveals highly significant correlation with genes expressed at E14.5 in key areas for neocortex development, and allows the grouping of enhancers by known biological functions and pathways for further studies. We find that multiple genes are flanked by dozens of candidate enhancers each, including well-known key neocortical genes as well as suspected and novel genes. Nearly a quarter of our candidate enhancers are conserved well beyond mammals. Human and zebrafish regions orthologous to our candidate enhancers are shown to most often function in other aspects of central nervous system development. Finally, we find strong evidence that specific interspersed repeat families have contributed potentially key developmental enhancers via co-option. Our analysis expands the methodologies available for extracting the richness of information found in genome-wide functional maps.

  View details for DOI 10.1371/journal.pgen.1003728

  View details for PubMedID 24009522

  View details for PubMedCentralID PMC3757057

• PRISM offers a comprehensive genomic approach to transcription factor function prediction. Genome research Wenger, A. M., Clarke, S. L., Guturu, H., Chen, J., Schaar, B. T., McLean, C. Y., Bejerano, G. 2013; 23 (5): 889-904

  Abstract

  The human genome encodes 1500-2000 different transcription factors (TFs). ChIP-seq is revealing the global binding profiles of a fraction of TFs in a fraction of their biological contexts. These data show that the majority of TFs bind directly next to a large number of context-relevant target genes, that most binding is distal, and that binding is context specific. Because of the effort and cost involved, ChIP-seq is seldom used in search of novel TF function. Such exploration is instead done using expression perturbation and genetic screens. Here we propose a comprehensive computational framework for transcription factor function prediction. We curate 332 high-quality nonredundant TF binding motifs that represent all major DNA binding domains, and improve cross-species conserved binding site prediction to obtain 3.3 million conserved, mostly distal, binding site predictions. We combine these with 2.4 million facts about all human and mouse gene functions, in a novel statistical framework, in search of enrichments of particular motifs next to groups of target genes of particular functions. Rigorous parameter tuning and a harsh null are used to minimize false positives. Our novel PRISM (predicting regulatory information from single motifs) approach obtains 2543 TF function predictions in a large variety of contexts, at a false discovery rate of 16%. The predictions are highly enriched for validated TF roles, and 45 of 67 (67%) tested binding site regions in five different contexts act as enhancers in functionally matched cells.

  View details for DOI 10.1101/gr.139071.112

  View details for PubMedID 23382538

  View details for PubMedCentralID PMC3638144

• Human Developmental Enhancers Conserved between Deuterostomes and Protostomes PLOS GENETICS Clarke, S. L., VanderMeer, J. E., Wenger, A. M., Schaar, B. T., Ahituv, N., Bejerano, G. 2012; 8 (8)

  Abstract

  The identification of homologies, whether morphological, molecular, or genetic, is fundamental to our understanding of common biological principles. Homologies bridging the great divide between deuterostomes and protostomes have served as the basis for current models of animal evolution and development. It is now appreciated that these two clades share a common developmental toolkit consisting of conserved transcription factors and signaling pathways. These patterning genes sometimes show common expression patterns and genetic interactions, suggesting the existence of similar or even conserved regulatory apparatus. However, previous studies have found no regulatory sequence conserved between deuterostomes and protostomes. Here we describe the first such enhancers, which we call bilaterian conserved regulatory elements (Bicores). Bicores show conservation of sequence and gene synteny. Sequence conservation of Bicores reflects conserved patterns of transcription factor binding sites. We predict that Bicores act as response elements to signaling pathways, and we show that Bicores are developmental enhancers that drive expression of transcriptional repressors in the vertebrate central nervous system. Although the small number of identified Bicores suggests extensive rewiring of cis-regulation between the protostome and deuterostome clades, additional Bicores may be revealed as our understanding of cis-regulatory logic and sample of bilaterian genomes continue to grow.

  View details for DOI 10.1371/journal.pgen.1002852

  View details for Web of Science ID 000308529300014

  View details for PubMedID 22876195

  View details for PubMedCentralID PMC3410860

• Coding exons function as tissue-specific enhancers of nearby genes GENOME RESEARCH Birnbaum, R. Y., Clowney, E. J., Agamy, O., Kim, M. J., Zhao, J., Yamanaka, T., Pappalardo, Z., Clarke, S. L., Wenger, A. M., Loan Nguyen, L., Gurrieri, F., Everman, D. B., Schwartz, C. E., Birk, O. S., Bejerano, G., Lomvardas, S., Ahituv, N. 2012; 22 (6): 1059-1068

  Abstract

  Enhancers are essential gene regulatory elements whose alteration can lead to morphological differences between species, developmental abnormalities, and human disease. Current strategies to identify enhancers focus primarily on noncoding sequences and tend to exclude protein coding sequences. Here, we analyzed 25 available ChIP-seq data sets that identify enhancers in an unbiased manner (H3K4me1, H3K27ac, and EP300) for peaks that overlap exons. We find that, on average, 7% of all ChIP-seq peaks overlap coding exons (after excluding for peaks that overlap with first exons). By using mouse and zebrafish enhancer assays, we demonstrate that several of these exonic enhancer (eExons) candidates can function as enhancers of their neighboring genes and that the exonic sequence is necessary for enhancer activity. Using ChIP, 3C, and DNA FISH, we further show that one of these exonic limb enhancers, Dync1i1 exon 15, has active enhancer marks and physically interacts with Dlx5/6 promoter regions 900 kb away. In addition, its removal by chromosomal abnormalities in humans could cause split hand and foot malformation 1 (SHFM1), a disorder associated with DLX5/6. These results demonstrate that DNA sequences can have a dual function, operating as coding exons in one tissue and enhancers of nearby gene(s) in another tissue, suggesting that phenotypes resulting from coding mutations could be caused not only by protein alteration but also by disrupting the regulation of another gene.

  View details for DOI 10.1101/gr.133546.111

  View details for Web of Science ID 000304728100007

  View details for PubMedID 22442009

  View details for PubMedCentralID PMC3371700

• Control of Pelvic Girdle Development by Genes of the Pbx Family and Emx2 DEVELOPMENTAL DYNAMICS Capellini, T. D., Handschuh, K., Quintana, L., Ferretti, E., Di Giacomo, G., Fantini, S., Vaccari, G., Clarke, S. L., Wenger, A. M., Bejerano, G., Sharpe, J., Zappavigna, V., Selleri, L. 2011; 240 (5): 1173-1189

  Abstract

  Genes expressed in the somatopleuric mesoderm, the embryonic domain giving rise to the vertebrate pelvis, appear important for pelvic girdle formation. Among such genes, Pbx family members and Emx2 were found to genetically interact in hindlimb and pectoral girdle formation. Here, we generated compound mutant embryos carrying combinations of mutated alleles for Pbx1, Pbx2, and Pbx3, as well as Pbx1 and Emx2, to examine potential genetic interactions during pelvic development. Indeed, Pbx genes share overlapping functions and Pbx1 and Emx2 genetically interact in pelvic formation. We show that, in compound Pbx1;Pbx2 and Pbx1;Emx2 mutants, pelvic mesenchymal condensation is markedly perturbed, indicative of an upstream control by these homeoproteins. We establish that expression of Tbx15, Prrx1, and Pax1, among other genes involved in the specification and development of select pelvic structures, is altered in our compound mutants. Lastly, we identify potential Pbx1-Emx2-regulated enhancers for Tbx15, Prrx1, and Pax1, using bioinformatics analyses.

  View details for DOI 10.1002/dvdy.22617

  View details for Web of Science ID 000289942300023

  View details for PubMedID 21455939

  View details for PubMedCentralID PMC3081414

• GREAT improves functional interpretation of cis-regulatory regions NATURE BIOTECHNOLOGY McLean, C. Y., Bristor, D., Hiller, M., Clarke, S. L., Schaar, B. T., Lowe, C. B., Wenger, A. M., Bejerano, G. 2010; 28 (5): 495-U155

  Abstract

  We developed the Genomic Regions Enrichment of Annotations Tool (GREAT) to analyze the functional significance of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. Applying GREAT to data sets from chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) of multiple transcription-associated factors, including SRF, NRSF, GABP, Stat3 and p300 in different developmental contexts, we recover many functions of these factors that are missed by existing gene-based tools, and we generate testable hypotheses. The utility of GREAT is not limited to ChIP-seq, as it could also be applied to open chromatin, localized epigenomic markers and similar functional data sets, as well as comparative genomics sets.

  View details for DOI 10.1038/nbt.1630

  View details for Web of Science ID 000277452700030

  View details for PubMedID 20436461