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

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.

BackgroundRecent studies have leveraged quantitative traits from imaging to amplify the power of genome-wide association studies (GWAS) to gain further insights into the biology of diseases and traits. However, measurement imprecision is intrinsic to phenotyping and can impact downstream genetic analyses.MethodsLeft ventricular ejection fraction (LVEF), an important but imprecise quantitative imaging measurement, was examined to assess the impact of precision of phenotype measurement on genetic studies. Multiple approaches to obtain LVEF, as well as simulated measurement noise, were evaluated with their impact on downstream genetic analyses.ResultsEven within the same population, small changes in the measurement of LVEF drastically impacted downstream genetic analyses. Introducing measurement noise as little as 7.9% can eliminate all significant genetic associations in an GWAS with almost forty thousand individuals. An increase of 1% in mean absolute error (MAE) in LVEF had an equivalent impact on GWAS power as a decrease of 10% in the cohort sample size, suggesting optimizing phenotyping precision is a cost-effective way to improve power of genetic studies.ConclusionsImproving the precision of phenotyping is important for maximizing the yield of genome-wide association studies.Clinical perspectiveWhat Is New?Measurement imprecision in cardiac imaging phenotypes can substantially impact downstream genetic association studies, explaining much of the difference in identified genetic variants between echocardiography and cardiac magnetic resonance imaging.Using the example of left ventricular ejection fraction as an important but imprecise clinical measurement, the analysis suggests that the measurement variation within the range of clinician interpretation reduced genome-wide association studies’ power to detect genetic risk factors as much as decreasing the study population size by 20%.What Are the Clinical Implications?More precise measurements can result in a better understanding of the genetics of cardiac phenotypes and accelerate the development of precision medicine.Rather than simply increasing population size, improving measurement precision allows for cost-effective discovery of genetic associations.

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 to 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 to 14.4]). NRI confidence intervals were wider and included zero for ethnicities with smaller sample sizes, including Hispanic (NRI = 7.5% [-1.4 to 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 analyzed by genetically inferred ancestry. Together, these results validate the performance of ASCVD-IRT in multiple ethnicities and ancestries, and favor their generalization to all ethnicities and ancestries.

Purpose of review 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. Recent findings 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. Summary 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.

Quantification of chamber size and systolic function is a fundamental component of cardiac imaging, as these measurements provide a basis for establishing both diagnosis and appropriate treatment for a spectrum of cardiomyopathies. However, the human heart is a complex structure with significant uncharacterized phenotypic variation beyond traditional metrics of size and function. Characterizing variation in cardiac shape and morphology can add to our ability to understand and classify cardiovascular risk and pathophysiology. We describe deep learning enabled measurement of left ventricle (LV) sphericity using cardiac magnetic resonance imaging data from the UK Biobank and show that among adults with normal LV volumes and systolic function, increased sphericity is associated with increased risk for incident atrial fibrillation (HR 1.31 per SD, 95% CI 1.23-1.38), cardiomyopathy (HR 1.62 per SD, 95% CI 1.29-2.02), and heart failure (HR 1.24, 95% CI 1.11-1.39), independent of traditional risk factors including age, sex, hypertension, and body mass index. Using genome-wide association studies, we identify four loci associated with sphericity at genome-wide significance. These loci harbor known and suspected cardiomyopathy genes. Through genetic correlation and Mendelian randomization, we provide evidence that sphericity may represent a subclinical manifestation of non-ischemic cardiomyopathy.

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.

Background: 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. Methods: 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 (Proprotein convertase subtilisin/kexin type 9) were assessed by custom genotype array. Results: 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. Conclusions: 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.

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 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. Employing in vitro CRISPR-Cas 9 genome and epigenome editing we show that complex genetic signals within a gene desert at 2q22.3 lie within novel smooth muscle long-distance enhancers for ZEB2 , a TF extensively studied in the context of epithelial mesenchymal transition (EMT) in development and cancer. Single-cell epigenetic and transcriptomic profiling demonstrated that Zeb2 regulates SMC phenotypic transition through chromatin remodeling that obviates accessibility and disrupts both Notch and TGFβ signaling, altering the epigenetic trajectory of SMC transitions. These changes resulted in an inability for transition SMC to turn off contractile programing and take on a fibroblast-like phenotype, and accelerated chondromyocyte differentiation, mirroring features of high-risk atherosclerotic plaques in human coronary arteries. Parallel epigenetic alterations are observed in human coronary artery smooth muscle cells, with ZEB binding motifs preferentially enriched for human genetic signals for coronary artery disease, affirming its role as a master epigenetic regulator of atherosclerosis pathology.

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.

Background: Lp(a) (lipoprotein [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 subtypes: coronary heart disease, cerebrovascular disease, peripheral artery disease, and abdominal aortic aneurysm and (2) nonatherosclerotic cardiovascular disease phenotypes, stratified by ancestry. Methods: We performed (1) Mendelian randomization analyses for previously reported cardiovascular associations and (2) Mendelian randomization–phenome-wide association 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 Mendelian randomization analyses using the combined cohort, a 1-SD genetic increase in Lp(a) level was associated with atherosclerotic cardiovascular disease subtypes in EA—odds ratio and 95% CI for coronary heart disease 1.28 (1.16–1.41); cerebrovascular disease 1.14 (1.07–1.21); peripheral artery disease 1.22 (1.11–1.34); abdominal aortic aneurysm 1.28 (1.17–1.40); in AA, the effect estimate was lower than in EA and nonsignificant for coronary heart disease 1.11 (0.99–1.24) and cerebrovascular disease 1.06 (0.99–1.14) but similar for peripheral artery disease 1.16 (1.01–1.33) and abdominal aortic aneurysm 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). Mendelian randomization–phenome-wide association analyses identified novel associations in EA with arterial thromboembolic disease, nonaortic 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 atherosclerotic cardiovascular disease in four major arterial beds in EA but only with peripheral artery disease and abdominal aortic aneurysm in AA. Additionally, novel cardiovascular associations were detected in EA.

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 (CHA 2 DS 2 -VASc) do not 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 genome-wide association study 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 (CHA 2 DS 2 -VASc), the integrated tool significantly improved Net Reclassification Index (2.3% [95% CI, 1.3%–3.0%]) and fit (χ 2 P =0.002). Using this improved tool, >115 000 people with AF would have improved risk classification in the United States. Independently, PRS was a significant predictor of ischemic stroke in patients with AF prospectively (hazard ratio, 1.13 per 1 SD [95% CI, 1.06–1.23]). Lastly, polygenic risk scores were uncorrelated with clinical risk factors (Pearson 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.

Coronary artery disease (CAD) is a leading cause of death, yet its genetic determinants are not fully elucidated. We report a multi-ethnic genome-wide association study of CAD involving nearly a quarter of a million cases, incorporating the largest cohorts to date of Whites, Blacks, and Hispanics from the Million Veteran Program with existing studies including CARDIoGRAMplusC4D, UK Biobank, and Biobank Japan. We verify substantial and nearly equivalent heritability of CAD across multiple ancestral groups, discover 107 novel loci including the first nine on the X-chromosome, identify the first eight genome-wide significant loci among Blacks and Hispanics, and demonstrate that two common haplotypes are largely responsible for the risk stratification at the well-known 9p21 locus in most populations except those of African origin where both haplotypes are virtually absent. We identify 15 loci for angiographically derived burden of coronary atherosclerosis, which robustly overlap with the strongest and earliest loci reported to date for clinical CAD. Phenome-wide association analyses of novel loci and externally validated polygenic risk scores (PRS) augment signals from the insulin resistance cluster of risk factors and consequences, extend previously established pleiotropic associations of loci with traditional risk factors to include smoking and family history, and confirm a substantially reduced transferability of existing PRS to Blacks. Downstream integrative genomic analyses reinforce the critical role of endothelial, fibroblast, and smooth muscle cells within the coronary vessel wall in CAD susceptibility. Our study highlights the value of a multi-ethnic design in efficiently characterizing the genetic architecture of CAD across all human populations.

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 Trans-Omics for Precision Medicine (TOPMed) program (N=63,302) and UK Biobank (N=48,658). Bidirectional Mendelian randomization studies were consistent with LTL lengthening increasing propensity to develop CHIP, but CHIP then in turn hastening LTL shortening. We also demonstrated evidence of modest mediation between CHIP and CAD by LTL. Our data promote an understanding of potential causal relationships across CHIP and LTL toward prevention of CAD.

Background: The pathophysiology of lipoprotein (a) [Lp(a)] remains obscure. We conducted a phenome wide analysis study (PheWAS) to identify pleiotropic effects of LPA variants. Methods: Among 51,843 European-ancestry participants (age 58±16 years, 54% female) of the electronic MEdical Records and Genomics (eMERGE) network we assessed whether LPA variants exhibited pleiotropic affects through an electronic health record-based PheWAS. We adjusted significant associations by presence of atherosclerotic cardiovascular disease (ASCVD; defined as a composite of coronary heart disease, cerebrovascular disease, and peripheral artery disease) to determine whether those associations were independent of ASCVD. Results: In PheWAS analysis, we tested 864 phecodes with 36 independent LPA variants. We identified 21 significant associations with non-ASCVD phenotypes including heart failure, aortic valve stenosis, and chronic kidney disease of which 14 were replicated across independent cohorts. The strength of associations between the LPA variant rs10455872 and both heart failure and aortic valve stenosis related phecodes were significantly attenuated after adjustment for ASCVD. However, the association with chronic kidney disease phecode remained independent following adjustment for ASCVD without any attenuation in the estimated odds ratio (Table). Conclusions: LPA genetic variants were associated with aortic valve stenosis, heart failure and chronic kidney disease. The observed association of LPA variant rs10455872 with aortic stenosis and heart failure appear to be partially mediated by ASCVD, while the association with chronic kidney disease appears to be independent of ASCVD. Additional studies are needed to elucidate potential mechanisms by which Lp(a) may contribute to the pathogenesis of non-ASCVD disease phenotypes.

Introduction: 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 (CHA 2 DS 2 -VASc) don’t include family history or genetic risk. Hypothesis: A polygenic risk scores (PRS) is both independently, and in integrated with clinical risk factors, predictive of ischemic stroke in patients with Atrial Fibrillation. 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 (UK Biobank), both independently and integrated with clinical risk factors. Results: The integrated PRS and clinical risk factors risk tool had the greatest predictive ability. Compared with the currently recommended risk tool (CHA 2 DS 2 -VASc), the integrated tool significantly improved net reclassification (NRI: 2.3% (95%CI: 1.3% to 3.0%)), and fit (χ2 P =0.002). Independently, PRS was a significant predictor of ischemic stroke in patients with AF prospectively (Hazard Ratio: 1.13 per 1 SD (95%CI: 1.04 to 1.21)). 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.

Introduction: Familial Hypercholesterolemia (FH) variants confer risk for coronary artery disease (CAD) even after adjusting for a single baseline LDL measure. Hypothesis: We hypothesized that a model incorporating serial LDL measures over many years may account for the risk associated with FH variants. Methods: We analyzed 418,790 participants in the Million Veteran Program with electronic health records (EHR) spanning up to 15 years prior to and 7 years after enrollment. FH variants were defined using ClinVar and standard bioinformatic approaches. Carriers were identified by custom genotype array. CAD cases were identified by chart codes for acute MI and coronary revascularization. We used a nested case-control design conditional on survival to enrollment. Controls were matched on year of first LDL, time between first LDL and the case index date, age, sex, and ancestry. Logistic regression was used to estimate CAD risk among FH carriers while adjusting for CAD risk factors and the first, maximum (max), or mean LDL prior to the index date. Results: We found 53 LDLR , 2 APOB , and 2 PCSK9 variants, and FH prevalence was 1:303. We identified 23,091 cases with ≥1 LDL measure prior to the diagnosis of CAD and matched 10 controls per case. Cases had a median of 6 LDL measures over a median of 49 months prior to the index date. Carriers had an increased risk for CAD compared to non-carriers. Adjusting for the first, max, and mean LDL progressively attenuated risk of CAD associated with FH, but residual FH risk remained substantial ( Figure ). This pattern did not change in the subset of subjects with the most extensive LDL data nor in the subset of incident cases. We additionally found evidence of modifying effects of sex and ancestry, with higher within-group risk for females and subjects of African ancestry ( Figure ). Conclusion: The risk associated with FH variants cannot be fully captured by the LDL data available in the EHR, even when considering multiple LDL measurements spanning several years.

AimsFamilial hypercholesterolemia (FH) genetic variants confer risk for coronary artery disease (CAD) even after adjusting for low-density lipoprotein cholesterol (LDL-C) levels, using a single measurement. This study evaluated whether multiple historical measures of LDL-C observed in the electronic health record (EHR) can account for the risk associated with FH variants.Methods and ResultsWe analyzed 418,790 participants in the Million Veteran Program with EHR data spanning up to 15 years prior to and 7 years after enrollment, including ∼6.3 million LDL-C measurements. FH variants in LDLR, APOB, and PCSK9 were identified using a custom genotype array. We implemented a nested case-control design, using incidence density sampling to match etiologic exposure windows and measure CAD risk while adjusting for LDL-C exposure. In a cohort of 23,091 primarily prevalent cases and 230,910 matched controls, FH variants conferred increased risk for CAD (odds ratio: 1.53; 95% confidence interval: 1.24 to 1.89; p: 7.8×10−5). Adjusting for mean LDL-C exposure prior to the index date attenuated this risk more than adjusting for a single measurement, but significant risk remained (odds ratio: 1.33; 95% confidence interval: 1.08 to 1.64; p = 8.4×10−3). The pattern was also apparent in stratified analyses by sex and ancestry, and we found evidence of an interaction between sex and FH carrier status.ConclusionThe risk associated with FH variants cannot be fully captured by the LDL-C data available in the EHR, even when considering multiple LDL-C measurements spanning more than a decade.

BackgroundAtrial 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).ObjectivesTo construct and test a PRS to predict ischemic stroke in patients with AF, both independently and integrated with clinical risk factors.MethodsUsing 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 (UK Biobank), both independently and integrated with clinical risk factors.ResultsThe integrated PRS and clinical risk factors risk tool had the greatest predictive ability. 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).ConclusionsIn 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.

Introduction: Recent studies suggest a substantial improvement in risk prediction of coronary artery disease (CAD) with polygenic risk scores (PRS). The degree to which this improvement is generalizable to other European (EUR) and non-EUR populations remains unclear. Hypothesis: We hypothesized that genome-wide PRSs trained and validated in the UK Biobank (UKBB) would perform best in external EUR populations and reduce the performance gap in prediction between EURs and African Americans (AA). Methods: We tested our hypothesis in ~91 000 EUR, AA, and Hispanic (HISP) Million Veteran Program (MVP) participants with CAD and ~225 000 non-cases as of July 2017. We created 5 separate weighted PRSs in all participants using: 1) 164 genome-wide significant SNPs for CAD (164SNPs), 2) 46 000 Metabochip SNPs (GRS46K), 3) 1.5 million (M) SNPs from standard pruning & thresholding of CARDIOGRAM+C4D (Khera_P&T), 4) 6.6M SNPs PRS created with LDpred (LDpred_Khera), and 5) 1.7M SNP PRS combining weights of 3 PRSs through meta-analysis (meta-GRS). We estimated age & sex adjusted Odds Ratios (OR) of CAD per standard deviation increase in each PRS using logistic regression and performed sensitivity analyses in subgroups of cases and non-cases. Results: LDpred and meta-GRS performed best among EURs (figure). However, the point estimate of the OR for these PRSs in MVP was notably lower than that observed in the UKBB (1.36 vs. ~1.7). ORs in MVP were higher among participants with early-onset disease (1.39), myocardial infarction (1.52), and revascularization (1.56) but lower for incident events after enrollment (1.27). Performance in HISP was mildly diluted but all PRS performed poorly among AA (figure). Conclusion: Genome-wide PRSs developed in the UKBB transfer best to external EUR populations but may over-estimate risk and do not close the performance gap in prediction between EURs and AAs. Our findings highlight a need for better calibrated PRSs specific to the cohort and race/ethnic group being tested prior to implementation in clinical practice.

BACKGROUND: Insulin resistance may be present in healthy adults and is associated with 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 in 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 individuals with normal weight, poor fitness was associated with 2.2-fold higher odds of insulin resistance in women (1.4–3.6; P = .001) and 2.8-fold higher odds in men (2.1–3.6; P

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.

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.

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., Author Summary A central goal of personal genomics is to interpret an individual’s genome to identify variants that confer disease risk, an aim that has far-reaching implications for personalized, precision medicine. Here, we leverage next generation sequencing, health records, and functional genome annotations to develop statistical methods that predict disease risk from a single genome. Motivated by the fact that about 90% of genome-wide association study disease-associated variants lie in the non-coding genome, we identify personal variants that mutate conserved transcription factor binding sites. To identify if such non-coding personal variants collectively dysregulate a key biological process, we employ the enrichment analysis tool GREAT to identify if a person’s noncoding mutations are over-represented in the regulatory domains of genes involved in a common biological pathway. Notably, in five unrelated genomes we analyzed, the most statistically significant, seemingly dysregulated pathway is indicative of that person’s medical history, ranging from neuropathy to heart disease. Statistical analysis confirms that associations from our predicted pathway to an individual’s medical record are rigorous and significant in the context of the un-phenotyped, race-matched 1,000 Genomes cohort. As such, we present a novel method that leverages the contribution of multifactorial non-coding variation to predict disease risk in individual genomes.

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.

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.

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., Author Summary Sequencing based technologies provide global snapshots of transcriptional regulation. These data promise insights into gene regulation, disease susceptibility and organismal evolution. They also provide a methodological challenge in distilling specific hypotheses from large masses of data. Most work to date has focused on deriving broad biochemical insights. Here we obtain the active enhancer landscape of the dorsal cerebral wall during early neocortical development. We show that our set likely contains enhancers from both the developing neocortex, the ventricular, subventricular and intermediate zones, and develop methods to separate this mass into subsets of interest in particular contexts. We discover novel enhancers next to key neocortex development genes. We show that some known key and novel genes are regulated by dozens of enhancers each, and find known and novel enriched binding sites for key transcription factors in our set. Nearly all newly discovered enhancers are conserved in human. A quarter of loci are shared with non-mammalian vertebrates. We show that the human and zebrafish orthologs of our enhancers mostly drive expression in related nervous system contexts. We also show that particular interspersed repeats were preferentially co-opted into potentially key neocortex development enhancers.

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. Developmental Dynamics 240:1173–1189, 2011. © 2011 Wiley-Liss, Inc.