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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.

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.

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.

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.