1e:I[23489,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js","/_next/static/immutable/chunks/2bmdwv49ba1qx.js","/_next/static/immutable/chunks/3ld61_1ghpsxn.js"],"ShareMenu"] 1f:I[55078,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js","/_next/static/immutable/chunks/2bmdwv49ba1qx.js","/_next/static/immutable/chunks/3ld61_1ghpsxn.js"],"AuthorMetricsPanel"] 20:I[39060,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js","/_next/static/immutable/chunks/2bmdwv49ba1qx.js","/_next/static/immutable/chunks/3ld61_1ghpsxn.js"],"AuthorPapers"] 39:I[49310,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js"],"IconMark"] 21:T8c2,The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies. Results for the final phase of the 1000 Genomes Project are presented including whole-genome sequencing, targeted exome sequencing, and genotyping on high-density SNP arrays for 2,504 individuals across 26 populations, providing a global reference data set to support biomedical genetics. The 1000 Genomes Project has sought to comprehensively catalogue human genetic variation across populations, providing a valuable public genomic resource. The data obtained so far have found applications ranging from association studies and fine mapping studies to the filtering of likely neutral variants in rare-disease cohorts. The authors now report on the final phase of the project, phase 3, which covers previously uncharacterized areas of human genetic diversity in terms of the populations sampled and categories of characterized variation. The sample now includes more than 2,500 individuals from 26 global populations, with low coverage whole-genome and deep exome sequencing, as well as dense microarray genotyping. They find that while most common variants are shared across populations, rarer variants are often restricted to closely related populations. The authors also demonstrate the use of the phase 3 dataset as a reference panel for imputation to improve the resolution in genetic association studies.22:T783,Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human ‘knockout’ variants in protein-coding genes. Exome sequencing data from 60,706 people of diverse geographic ancestry is presented, providing insight into genetic variation across populations, and illuminating the relationship between DNA variants and human disease. As part of the Exome Aggregation Consortium (ExAC) project, Daniel MacArthur and colleagues report on the generation and analysis of high-quality exome sequencing data from 60,706 individuals of diverse ancestry. This provides the most comprehensive catalogue of human protein-coding genetic variation to date, yielding unprecedented resolution for the analysis of very rare variants across multiple human populations. The catalogue is freely accessible and provides a critical reference panel for the clinical interpretation of genetic variants and the discovery of disease-related genes.23:T4aa,Abstract Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.24:T8c7,BACKGROUND: Clonal hematopoiesis of indeterminate potential (CHIP), which is defined as the presence of an expanded somatic blood-cell clone in persons without other hematologic abnormalities, is common among older persons and is associated with an increased risk of hematologic cancer. We previously found preliminary evidence for an association between CHIP and atherosclerotic cardiovascular disease, but the nature of this association was unclear. METHODS: We used whole-exome sequencing to detect the presence of CHIP in peripheral-blood cells and associated such presence with coronary heart disease using samples from four case-control studies that together enrolled 4726 participants with coronary heart disease and 3529 controls. To assess causality, we perturbed the function of Tet2, the second most commonly mutated gene linked to clonal hematopoiesis, in the hematopoietic cells of atherosclerosis-prone mice. RESULTS: In nested case-control analyses from two prospective cohorts, carriers of CHIP had a risk of coronary heart disease that was 1.9 times as great as in noncarriers (95% confidence interval [CI], 1.4 to 2.7). In two retrospective case-control cohorts for the evaluation of early-onset myocardial infarction, participants with CHIP had a risk of myocardial infarction that was 4.0 times as great as in noncarriers (95% CI, 2.4 to 6.7). Mutations in DNMT3A, TET2, ASXL1, and JAK2 were each individually associated with coronary heart disease. CHIP carriers with these mutations also had increased coronary-artery calcification, a marker of coronary atherosclerosis burden. Hypercholesterolemia-prone mice that were engrafted with bone marrow obtained from homozygous or heterozygous Tet2 knockout mice had larger atherosclerotic lesions in the aortic root and aorta than did mice that had received control bone marrow. Analyses of macrophages from Tet2 knockout mice showed elevated expression of several chemokine and cytokine genes that contribute to atherosclerosis. CONCLUSIONS: The presence of CHIP in peripheral-blood cells was associated with nearly a doubling in the risk of coronary heart disease in humans and with accelerated atherosclerosis in mice. (Funded by the National Institutes of Health and others.).25:T699,Abstract The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes) 1 . In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.26:T697,Abstract Structural variants (SVs) rearrange large segments of DNA 1 and can have profound consequences in evolution and human disease 2,3 . As national biobanks, disease-association studies, and clinical genetic testing have grown increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD) 4 have become integral in the interpretation of single-nucleotide variants (SNVs) 5 . However, there are no reference maps of SVs from high-coverage genome sequencing comparable to those for SNVs. Here we present a reference of sequence-resolved SVs constructed from 14,891 genomes across diverse global populations (54% non-European) in gnomAD. We discovered a rich and complex landscape of 433,371 SVs, from which we estimate that SVs are responsible for 25–29% of all rare protein-truncating events per genome. We found strong correlations between natural selection against damaging SNVs and rare SVs that disrupt or duplicate protein-coding sequence, which suggests that genes that are highly intolerant to loss-of-function are also sensitive to increased dosage 6 . We also uncovered modest selection against noncoding SVs in cis -regulatory elements, although selection against protein-truncating SVs was stronger than all noncoding effects. Finally, we identified very large (over one megabase), rare SVs in 3.9% of samples, and estimate that 0.13% of individuals may carry an SV that meets the existing criteria for clinically important incidental findings 7 . This SV resource is freely distributed via the gnomAD browser 8 and will have broad utility in population genetics, disease-association studies, and diagnostic screening.27:T544,Sekar Kathiresan and colleagues examine 185 common variants using a modified mendelian randomization approach and provide evidence supporting a causal role of triglyceride-rich lipoproteins in the development of coronary artery disease. Triglycerides are transported in plasma by specific triglyceride-rich lipoproteins; in epidemiological studies, increased triglyceride levels correlate with higher risk for coronary artery disease (CAD). However, it is unclear whether this association reflects causal processes. We used 185 common variants recently mapped for plasma lipids (P < 5 × 10−8 for each) to examine the role of triglycerides in risk for CAD. First, we highlight loci associated with both low-density lipoprotein cholesterol (LDL-C) and triglyceride levels, and we show that the direction and magnitude of the associations with both traits are factors in determining CAD risk. Second, we consider loci with only a strong association with triglycerides and show that these loci are also associated with CAD. Finally, in a model accounting for effects on LDL-C and/or high-density lipoprotein cholesterol (HDL-C) levels, the strength of a polymorphism's effect on triglyceride levels is correlated with the magnitude of its effect on CAD risk. These results suggest that triglyceride-rich lipoproteins causally influence risk for CAD.28:T6b4,Abstract Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes 1 . Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel 2 ) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.29:Ta4c,Importance: Human genetic studies have indicated that plasma lipoprotein(a) (Lp[a]) is causally associated with the risk of coronary heart disease (CHD), but randomized trials of several therapies that reduce Lp(a) levels by 25% to 35% have not provided any evidence that lowering Lp(a) level reduces CHD risk. Objective: To estimate the magnitude of the change in plasma Lp(a) levels needed to have the same evidence of an association with CHD risk as a 38.67-mg/dL (ie, 1-mmol/L) change in low-density lipoprotein cholesterol (LDL-C) level, a change that has been shown to produce a clinically meaningful reduction in the risk of CHD. Design, Setting, and Participants: A mendelian randomization analysis was conducted using individual participant data from 5 studies and with external validation using summarized data from 48 studies. Population-based prospective cohort and case-control studies featured 20 793 individuals with CHD and 27 540 controls with individual participant data, whereas summarized data included 62 240 patients with CHD and 127 299 controls. Data were analyzed from November 2016 to March 2018. Exposures: Genetic LPA score and plasma Lp(a) mass concentration. Main Outcomes and Measures: Coronary heart disease. Results: Of the included study participants, 53% were men, all were of white European ancestry, and the mean age was 57.5 years. The association of genetically predicted Lp(a) with CHD risk was linearly proportional to the absolute change in Lp(a) concentration. A 10-mg/dL lower genetically predicted Lp(a) concentration was associated with a 5.8% lower CHD risk (odds ratio [OR], 0.942; 95% CI, 0.933-0.951; P = 3 × 10-37), whereas a 10-mg/dL lower genetically predicted LDL-C level estimated using an LDL-C genetic score was associated with a 14.5% lower CHD risk (OR, 0.855; 95% CI, 0.818-0.893; P = 2 × 10-12). Thus, a 101.5-mg/dL change (95% CI, 71.0-137.0) in Lp(a) concentration had the same association with CHD risk as a 38.67-mg/dL change in LDL-C level. The association of genetically predicted Lp(a) concentration with CHD risk appeared to be independent of changes in LDL-C level owing to genetic variants that mimic the relationship of statins, PCSK9 inhibitors, and ezetimibe with CHD risk. Conclusions and Relevance: The clinical benefit of lowering Lp(a) is likely to be proportional to the absolute reduction in Lp(a) concentration. Large absolute reductions in Lp(a) of approximately 100 mg/dL may be required to produce a clinically meaningful reduction in the risk of CHD similar in magnitude to what can be achieved by lowering LDL-C level by 38.67 mg/dL (ie, 1 mmol/L).2a:T765,Abstract Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry 1,2 . Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated ( P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis 3 , and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN ) and variants (such as at GRK5 and NOS3 ). Using a three-pronged approach 4 , we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry 5 . Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.2b:T6af,Abstract Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes 1,2 and molecular mechanisms that are often specific to cell type 3,4 . Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance ( P < 5 × 10 −8 ) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores 5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.2c:T4c7,Mark Caulfield, Paul Elliott and colleagues use data from the UK Biobank to perform genome-wide association analysis for blood pressure traits. They identify and validate 107 novel loci and highlight new biological pathways for potential therapeutic intervention for hypertension. Elevated blood pressure is the leading heritable risk factor for cardiovascular disease worldwide. We report genetic association of blood pressure (systolic, diastolic, pulse pressure) among UK Biobank participants of European ancestry with independent replication in other cohorts, and robust validation of 107 independent loci. We also identify new independent variants at 11 previously reported blood pressure loci. In combination with results from a range of in silico functional analyses and wet bench experiments, our findings highlight new biological pathways for blood pressure regulation enriched for genes expressed in vascular tissues and identify potential therapeutic targets for hypertension. Results from genetic risk score models raise the possibility of a precision medicine approach through early lifestyle intervention to offset the impact of blood pressure–raising genetic variants on future cardiovascular disease risk.2d:T4dc,Scavenger receptor BI (SR-BI) is the major receptor for high-density lipoprotein (HDL) cholesterol (HDL-C). In humans, high amounts of HDL-C in plasma are associated with a lower risk of coronary heart disease (CHD). Mice that have depleted Scarb1 (SR-BI knockout mice) have markedly elevated HDL-C levels but, paradoxically, increased atherosclerosis. The impact of SR-BI on HDL metabolism and CHD risk in humans remains unclear. Through targeted sequencing of coding regions of lipid-modifying genes in 328 individuals with extremely high plasma HDL-C levels, we identified a homozygote for a loss-of-function variant, in which leucine replaces proline 376 (P376L), in SCARB1, the gene encoding SR-BI. The P376L variant impairs posttranslational processing of SR-BI and abrogates selective HDL cholesterol uptake in transfected cells, in hepatocyte-like cells derived from induced pluripotent stem cells from the homozygous subject, and in mice. Large population-based studies revealed that subjects who are heterozygous carriers of the P376L variant have significantly increased levels of plasma HDL-C. P376L carriers have a profound HDL-related phenotype and an increased risk of CHD (odds ratio = 1.79, which is statistically significant).2e:T44b,Reduced glomerular filtration rate defines chronic kidney disease and is associated with cardiovascular and all-cause mortality. We conducted a meta-analysis of genome-wide association studies for estimated glomerular filtration rate (eGFR), combining data across 133,413 individuals with replication in up to 42,166 individuals. We identify 24 new and confirm 29 previously identified loci. Of these 53 loci, 19 associate with eGFR among individuals with diabetes. Using bioinformatics, we show that identified genes at eGFR loci are enriched for expression in kidney tissues and in pathways relevant for kidney development and transmembrane transporter activity, kidney structure, and regulation of glucose metabolism. Chromatin state mapping and DNase I hypersensitivity analyses across adult tissues demonstrate preferential mapping of associated variants to regulatory regions in kidney but not extra-renal tissues. These findings suggest that genetic determinants of eGFR are mediated largely through direct effects within the kidney and highlight important cell types and biological pathways.2f:T93c,BACKGROUND: Various genome-wide association studies (GWAS) have been done in ischaemic stroke, identifying a few loci associated with the disease, but sample sizes have been 3500 cases or less. We established the METASTROKE collaboration with the aim of validating associations from previous GWAS and identifying novel genetic associations through meta-analysis of GWAS datasets for ischaemic stroke and its subtypes. METHODS: We meta-analysed data from 15 ischaemic stroke cohorts with a total of 12 389 individuals with ischaemic stroke and 62 004 controls, all of European ancestry. For the associations reaching genome-wide significance in METASTROKE, we did a further analysis, conditioning on the lead single nucleotide polymorphism in every associated region. Replication of novel suggestive signals was done in 13 347 cases and 29 083 controls. FINDINGS: We verified previous associations for cardioembolic stroke near PITX2 (p=2·8×10(-16)) and ZFHX3 (p=2·28×10(-8)), and for large-vessel stroke at a 9p21 locus (p=3·32×10(-5)) and HDAC9 (p=2·03×10(-12)). Additionally, we verified that all associations were subtype specific. Conditional analysis in the three regions for which the associations reached genome-wide significance (PITX2, ZFHX3, and HDAC9) indicated that all the signal in each region could be attributed to one risk haplotype. We also identified 12 potentially novel loci at p<5×10(-6). However, we were unable to replicate any of these novel associations in the replication cohort. INTERPRETATION: Our results show that, although genetic variants can be detected in patients with ischaemic stroke when compared with controls, all associations we were able to confirm are specific to a stroke subtype. This finding has two implications. First, to maximise success of genetic studies in ischaemic stroke, detailed stroke subtyping is required. Second, different genetic pathophysiological mechanisms seem to be associated with different stroke subtypes. FUNDING: Wellcome Trust, UK Medical Research Council (MRC), Australian National and Medical Health Research Council, National Institutes of Health (NIH) including National Heart, Lung and Blood Institute (NHLBI), the National Institute on Aging (NIA), the National Human Genome Research Institute (NHGRI), and the National Institute of Neurological Disorders and Stroke (NINDS).30:T8d2,BACKGROUND: Although HDL cholesterol concentrations are strongly and inversely associated with risk of coronary heart disease, interventions that raise HDL cholesterol do not reduce risk of coronary heart disease. HDL cholesterol efflux capacity-a prototypical measure of HDL function-has been associated with coronary heart disease after adjusting for HDL cholesterol, but its effect on incident coronary heart disease risk is uncertain. METHODS: We measured cholesterol efflux capacity and assessed its relation with vascular risk factors and incident coronary heart disease events in a nested case-control sample from the prospective EPIC-Norfolk study of 25 639 individuals aged 40-79 years, assessed in 1993-97 and followed up to 2009. We quantified cholesterol efflux capacity in 1745 patients with incident coronary heart disease and 1749 control participants free of any cardiovascular disorders by use of a validated ex-vivo radiotracer assay that involved incubation of cholesterol-labelled J774 macrophages with apoB-depleted serum from study participants. FINDINGS: Cholesterol efflux capacity was positively correlated with HDL cholesterol concentration (r=0·40; p<0·0001) and apoA-I concentration (r=0·22; p<0·0001). It was also inversely correlated with type 2 diabetes (r=-0·18; p<0·0001) and positively correlated with alcohol consumption (r=0·12; p<0·0001). In analyses comparing the top and bottom tertiles, cholesterol efflux capacity was significantly and inversely associated with incident coronary heart disease events, independent of age, sex, diabetes, hypertension, smoking and alcohol use, waist:hip ratio, BMI, LDL cholesterol concentration, log-triglycerides, and HDL cholesterol or apoA-I concentrations (odds ratio 0·64, 95% CI 0·51-0·80). After a similar multivariable adjustment the risk of incident coronary heart disease was 0·80 (95% CI 0·70-0·90) for a per-SD change in cholesterol efflux capacity. INTERPRETATION: HDL cholesterol efflux capacity might provide an alternative mechanism for therapeutic modulation of the HDL pathway beyond HDL cholesterol concentration to help reduce risk of coronary heart disease. FUNDING: US National Institutes of Health, UK Medical Research Council, Cancer Research UK.31:T874,BACKGROUND: Familial combined hypolipidemia, a Mendelian condition characterized by substantial reductions in all 3 major lipid fractions, is caused by mutations that inactivate the gene angiopoietin-like 3 (ANGPTL3). Whether ANGPTL3 deficiency reduces risk of coronary artery disease (CAD) is unknown. OBJECTIVES: The study goal was to leverage 3 distinct lines of evidence-a family that included individuals with complete (compound heterozygote) ANGPTL3 deficiency, a population based-study of humans with partial (heterozygote) ANGPTL3 deficiency, and biomarker levels in patients with myocardial infarction (MI)-to test whether ANGPTL3 deficiency is associated with lower risk for CAD. METHODS: We assessed coronary atherosclerotic burden in 3 individuals with complete ANGPTL3 deficiency and 3 wild-type first-degree relatives using computed tomography angiography. In the population, ANGPTL3 loss-of-function (LOF) mutations were ascertained in up to 21,980 people with CAD and 158,200 control subjects. LOF mutations were defined as nonsense, frameshift, and splice-site variants, along with missense variants resulting in <25% of wild-type ANGPTL3 activity in a mouse model. In a biomarker study, circulating ANGPTL3 concentration was measured in 1,493 people who presented with MI and 3,232 control subjects. RESULTS: The 3 individuals with complete ANGPTL3 deficiency showed no evidence of coronary atherosclerotic plaque. ANGPTL3 gene sequencing demonstrated that approximately 1 in 309 people was a heterozygous carrier for an LOF mutation. Compared with those without mutation, heterozygous carriers of ANGPTL3 LOF mutations demonstrated a 17% reduction in circulating triglycerides and a 12% reduction in low-density lipoprotein cholesterol. Carrier status was associated with a 34% reduction in odds of CAD (odds ratio: 0.66; 95% confidence interval: 0.44 to 0.98; p = 0.04). Individuals in the lowest tertile of circulating ANGPTL3 concentrations, compared with the highest, had reduced odds of MI (adjusted odds ratio: 0.65; 95% confidence interval: 0.55 to 0.77; p < 0.001). CONCLUSIONS: ANGPTL3 deficiency is associated with protection from CAD.32:T73a,Identification of coding variant associations for complex diseases offers a direct route to biological insight, but is dependent on appropriate inference concerning the causal impact of those variants on disease risk. We aggregated coding variant data for 81,412 type 2 diabetes (T2D) cases and 370,832 controls of diverse ancestry, identifying 40 distinct coding variant association signals (at 38 loci) reaching significance ( p <2.2×10 −7 ). Of these, 16 represent novel associations mapping outside known genome-wide association study (GWAS) signals. We make two important observations. First, despite a threefold increase in sample size over previous efforts, only five of the 40 signals are driven by variants with minor allele frequency <5%, and we find no evidence for low-frequency variants with allelic odds ratio >1.29. Second, we used GWAS data from 50,160 T2D cases and 465,272 controls of European ancestry to fine-map these associated coding variants in their regional context, with and without additional weighting to account for the global enrichment of complex trait association signals in coding exons. At the 37 signals for which we attempted fine-mapping, we demonstrate convincing support (posterior probability >80% under the “annotation-weighted” model) that coding variants are causal for the association at 16 (including novel signals involving POC5 p.His36Arg, ANKH p.Arg187Gln, WSCD2 p.Thr113Ile, PLCB3 p.Ser778Leu, and PNPLA3 p.Ile148Met). However, at 13 of the 37 loci, the associated coding variants represent “false leads” and naïve analysis could have led to an erroneous inference regarding the effector transcript mediating the signal. Accurate identification of validated targets is dependent on correct specification of the contribution of coding and non-coding mediated mechanisms at associated loci.33:T713,Background: Clonal hematopoiesis of indeterminate potential (CHIP) refers to clonal expansion of hematopoietic stem cells attributable to acquired leukemic mutations in genes such as DNMT3A or TET2 . In humans, CHIP associates with prevalent myocardial infarction. In mice, CHIP accelerates atherosclerosis and increases IL-6/IL-1β expression, raising the hypothesis that IL-6 pathway antagonism in CHIP carriers would decrease cardiovascular disease (CVD) risk. Methods: We analyzed exome sequences from 35 416 individuals in the UK Biobank without prevalent CVD, to identify participants with DNMT3A or TET2 CHIP. We used the IL6R p.Asp358Ala coding mutation as a genetic proxy for IL-6 inhibition. We tested the association of CHIP status with incident CVD events (myocardial infarction, coronary revascularization, stroke, or death), and whether it was modified by IL6R p.Asp358Ala. Results: We identified 1079 (3.0%) individuals with CHIP, including 432 (1.2%) with large clones (allele fraction >10%). During 6.9-year median follow-up, CHIP associated with increased incident CVD event risk (hazard ratio, 1.27 [95% CI, 1.04–1.56], P =0.019), with greater risk from large CHIP clones (hazard ratio, 1.59 [95% CI, 1.21–2.09], P <0.001). IL6R p.Asp358Ala attenuated CVD event risk among participants with large CHIP clones (hazard ratio, 0.46 [95% CI, 0.29–0.73], P <0.001) but not in individuals without CHIP (hazard ratio, 0.95 [95% CI, 0.89–1.01], P =0.08; P interaction =0.003). In 9951 independent participants, the association of CHIP status with myocardial infarction similarly varied by IL6R p.Asp358Ala ( P interaction =0.036). Conclusions: CHIP is associated with increased risk of incident CVD. Among carriers of large CHIP clones, genetically reduced IL-6 signaling abrogated this risk.34:T41b,To dissect the genetic architecture of blood pressure and assess effects on target organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry, and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure-associated loci, of which 17 were new; 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target organ damage in multiple tissues but with minor effects in the kidney. Our findings expand current knowledge of blood pressure-related pathways and highlight tissues beyond the classical renal system in blood pressure regulation.35:T5b2,Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity. Exome-wide analysis identifies rare and low-frequency coding variants associated with body mass index. Gene-based meta-analysis and functional studies implicate 13 genes, eight of which are novel, and neuronal pathways as factors in human obesity.36:Tc8e,BACKGROUND: Glycated hemoglobin (HbA1c) is used to diagnose type 2 diabetes (T2D) and assess glycemic control in patients with diabetes. Previous genome-wide association studies (GWAS) have identified 18 HbA1c-associated genetic variants. These variants proved to be classifiable by their likely biological action as erythrocytic (also associated with erythrocyte traits) or glycemic (associated with other glucose-related traits). In this study, we tested the hypotheses that, in a very large scale GWAS, we would identify more genetic variants associated with HbA1c and that HbA1c variants implicated in erythrocytic biology would affect the diagnostic accuracy of HbA1c. We therefore expanded the number of HbA1c-associated loci and tested the effect of genetic risk-scores comprised of erythrocytic or glycemic variants on incident diabetes prediction and on prevalent diabetes screening performance. Throughout this multiancestry study, we kept a focus on interancestry differences in HbA1c genetics performance that might influence race-ancestry differences in health outcomes. METHODS & FINDINGS: Using genome-wide association meta-analyses in up to 159,940 individuals from 82 cohorts of European, African, East Asian, and South Asian ancestry, we identified 60 common genetic variants associated with HbA1c. We classified variants as implicated in glycemic, erythrocytic, or unclassified biology and tested whether additive genetic scores of erythrocytic variants (GS-E) or glycemic variants (GS-G) were associated with higher T2D incidence in multiethnic longitudinal cohorts (N = 33,241). Nineteen glycemic and 22 erythrocytic variants were associated with HbA1c at genome-wide significance. GS-G was associated with higher T2D risk (incidence OR = 1.05, 95% CI 1.04-1.06, per HbA1c-raising allele, p = 3 × 10-29); whereas GS-E was not (OR = 1.00, 95% CI 0.99-1.01, p = 0.60). In Europeans and Asians, erythrocytic variants in aggregate had only modest effects on the diagnostic accuracy of HbA1c. Yet, in African Americans, the X-linked G6PD G202A variant (T-allele frequency 11%) was associated with an absolute decrease in HbA1c of 0.81%-units (95% CI 0.66-0.96) per allele in hemizygous men, and 0.68%-units (95% CI 0.38-0.97) in homozygous women. The G6PD variant may cause approximately 2% (N = 0.65 million, 95% CI 0.55-0.74) of African American adults with T2D to remain undiagnosed when screened with HbA1c. Limitations include the smaller sample sizes for non-European ancestries and the inability to classify approximately one-third of the variants. Further studies in large multiethnic cohorts with HbA1c, glycemic, and erythrocytic traits are required to better determine the biological action of the unclassified variants. CONCLUSIONS: As G6PD deficiency can be clinically silent until illness strikes, we recommend investigation of the possible benefits of screening for the G6PD genotype along with using HbA1c to diagnose T2D in populations of African ancestry or groups where G6PD deficiency is common. Screening with direct glucose measurements, or genetically-informed HbA1c diagnostic thresholds in people with G6PD deficiency, may be required to avoid missed or delayed diagnoses.37:T74c,BACKGROUND: Measurement of B-type natriuretic peptide (BNP) concentration or its precursor (N-terminal fragment [NT-proBNP]) is recommended in patients with symptoms of left ventricular dysfunction and in other settings, but the relevance of these peptides to cardiovascular disease (CVD) in general populations or in patients with stable vascular disease is uncertain. METHODS AND RESULTS: Data were collated from 40 long-term prospective studies involving a total of 87 474 participants and 10 625 incident CVD outcomes. In a comparison of individuals in the top third with those in the bottom third of baseline values of natriuretic peptides, the combined risk ratio (RR), adjusted for several conventional risk factors, was 2.82 (95% confidence interval [CI], 2.40 to 3.33) for CVD. Analysis of the 6 studies with at least 250 CVD outcomes (which should be less prone to selective reporting than are smaller studies) yielded an adjusted RR of 1.94 (95% CI, 1.57 to 2.39). RRs were broadly similar with BNP or NT-proBNP (RR, 2.89 [95% CI, 1.91 to 4.38] and 2.82 [95% CI, 2.35 to 3.38], respectively) and by different baseline vascular risk (RR, 2.68 [95% CI, 2.07 to 3.47] in approximately general populations; RR, 3.35 [95% CI, 2.38 to 4.72] in people with elevated vascular risk factors; RR, 2.60 [95% CI, 1.99 to 3.38] in patients with stable CVD). Assay of BNP or NT-proBNP in addition to measurement of conventional CVD risk factors yielded generally modest improvements in risk discrimination. CONCLUSIONS: Available prospective studies indicate strong associations between circulating concentration of natriuretic peptides and CVD risk under a range of different circumstances. 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Munroe","id":"openalex:A5019782579","orcid":"https://orcid.org/0000-0002-4176-2947","affiliation":"Queen Mary University of London","institutionId":"I166337079","institutionUrl":"https://ror.org/026zzn846","affiliations":["Clinical Pharmacology, NIHR Cardiovascular Biomedical Research Unit, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry Queen Mary University of London, London, UK","Genome Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK","Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, OX3 7LJ, United Kingdom"]},{"name":"Marika Kaakinen","id":"openalex:A5065042677","orcid":"https://orcid.org/0000-0002-9228-0462","affiliation":"Umeå University","institutionId":"I90267481","institutionUrl":"https://ror.org/05kb8h459","affiliations":["Biocenter Oulu, University of Oulu, Oulu, Finland","Institute of Health Sciences, University of Oulu, Finland","Department of Public Health and Clinical Medicine, Nutritional research, Umeå University, Umeå, Sweden"]},{"name":"Marjo‐Riitta Järvelin","id":"openalex:A5068508022","orcid":"https://orcid.org/0000-0002-2149-0630","affiliation":"Oulu University Hospital","institutionId":"I2799272722","institutionUrl":"https://ror.org/045ney286","affiliations":["Biocenter Oulu, University of Oulu, Oulu, Finland","Department of Epidemiology and Biostatistics, MRC Health Protection Agency (HPA) Centre for Environment and Health, School of Public Health, Imperial College London, UK","Institute of Health Sciences, University of Oulu, Finland","National Institute for Health and Welfare, Oulu, Finland","Unit of Primary Care, Oulu University Hospital, Oulu, Finland","Department of Clinical Sciences/Obstetrics and Gynecology, Oulu University Hospital, Oulu, Finland"]},{"name":"Paul Elliott","id":"openalex:A5034710571","orcid":"https://orcid.org/0000-0002-7511-5684","affiliation":"Western General Hospital","institutionId":"I2800150677","institutionUrl":"https://ror.org/009kr6r15","affiliations":["Department of Epidemiology and Biostatistics, MRC Health Protection Agency (HPA) Centre for Environment and Health, School of Public Health, Imperial College London, UK","Institute of Health Sciences, University of Oulu, Finland","MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, Scotland, United Kingdom"]},{"name":"Johannes Kettunen","id":"openalex:A5022876944","orcid":"https://orcid.org/0000-0002-3345-491X","affiliation":"National Institutes of Health","institutionId":"I1299303238","institutionUrl":"https://ror.org/01cwqze88","affiliations":["Institute for Molecular Medicine Finland FIMM, University of Helsinki, Finland","Public Health Genomics Unit, National Institute for Health and Welfare, Helsinki, Finland","Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD 20892, USA"]},{"name":"Ida L. Surakka","id":"openalex:A5024138963","orcid":"https://orcid.org/0000-0002-6669-5541","affiliation":"Eurac Research","institutionId":"I1319360392","institutionUrl":"https://ror.org/01xt1w755","affiliations":["Institute for Molecular Medicine Finland FIMM, University of Helsinki, Finland","Public Health Genomics Unit, National Institute for Health and Welfare, Helsinki, Finland","Center for Biomedicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy - Affiliated Institute of the University of Lübeck, Lübeck, Germany"]},{"name":"Aarno V. Palotie","id":"openalex:A5085708490","orcid":"https://orcid.org/0000-0002-2527-5874","affiliation":"deCODE Genetics (Iceland)","institutionId":"I1327191685","institutionUrl":"https://ror.org/04dzdm737","affiliations":["Department of Medical Genetics, Haartman Institute, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland","Genetic Epidemiology Group, Wellcome Trust Sanger Institute, Hinxton, Cambridge, United ingdom","Institute for Molecular Medicine Finland FIMM, University of Helsinki, Finland","deCODE Genetics/Amgen, 101 Reykjavik, Iceland"]},{"name":"Kaisa Silander","id":"openalex:A5110085238","affiliation":"Tri-Service General Hospital","institutionId":"I1293718874","institutionUrl":"https://ror.org/007h4qe29","affiliations":["Institute for Molecular Medicine Finland FIMM, University of Helsinki, Finland","Public Health Genomics Unit, National Institute for Health and Welfare, Helsinki, Finland","Division of Endocrinology & Metabolism, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan"]},{"name":"Marcus Edi Kleber","id":"openalex:A5060221179","orcid":"https://orcid.org/0000-0003-0663-7275","affiliation":"Universität Ulm","institutionId":"I196349391","institutionUrl":"https://ror.org/032000t02","affiliations":["Department of Internal Medicine II - Cardiology, University of Ulm Medical Centre, Ulm, Germany","Mannheim Institute of Public Health, Social and Preventive Medicine, Medical Faculty of Mannheim, University of Heidelberg, Ludolf-Krehl-Strasse 7-11, 68167 Mannheim, Germany","Hannover Unified Biobank, Hannover Medical School, Hannover 30625, Germany","Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg 85764, Germany"]},{"name":"Winfried März","id":"openalex:A5114253426","affiliation":"Cedars-Sinai Medical Center","institutionId":"I1282927834","institutionUrl":"https://ror.org/02pammg90","affiliations":["Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Austria","Mannheim Institute of Public Health, Social and Preventive Medicine, Medical Faculty of Mannheim, University of Heidelberg, Ludolf-Krehl-Strasse 7-11, 68167 Mannheim, Germany","Synlab Academy, Synlab Services GmbH,Gottlieb-Daimler-Straße 25, 68165 Mannheim, Germany","Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA"]},{"name":"Xiaohui Li","id":"openalex:A5100338538","orcid":"https://orcid.org/0000-0002-5037-3572","affiliation":"Cedars-Sinai Medical Center","institutionId":"I1282927834","institutionUrl":"https://ror.org/02pammg90","affiliations":["Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA","MRC/UVRI Uganda Research Unit on AIDS, Entebbe, Uganda"]},{"name":"Eric H. 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the Department of Cardiology, University Hospital, Parma, Italy (D","Department of Medicine, Division of Hematology, Brigham and Women's Hospital","Department of Pathology"]},{"name":"Pradeep Natarajan","id":"openalex:A5047884631","orcid":"https://orcid.org/0000-0001-8402-7435","affiliation":"Broad Institute","institutionId":"I107606265","institutionUrl":"https://ror.org/05a0ya142","affiliations":["From the Department of Medicine, Division of Hematology, Brigham and Women's Hospital (S.J., A.J.S., M.M.) and Harvard Medical School (B.L.E.), the Department of Medicine, Division of Cardiovascular Medicine, Brigham and Women's Hospital (E.S.) and Harvard Medical School (G.K.S., P.L.), the Department of Pathology (S.J.) and the Center for Genomic Medicine (P.N., S.K.), Massachusetts General Hospital, the Department of Medicine, Division of Cardiology, and Cardiovascular Research Center (P.N., S.K.), and the Department of Medicine (A.G.B.), Massachusetts General Hospital and Harvard Medical School, and the Departments of Medical Oncology (C.J.G.) and Biostatistics and Computational Biology (D.N.), Dana-Farber Cancer Institute, Boston, and the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge (P.N., A.G.B., N.G., S.G., S.K.) - all in Massachusetts; the Department of Cardiology, University Hospital, Parma, Italy (D","Center for Genomic Medicine","Massachusetts General Hospital, the Department of Medicine, Division of Cardiology, and Cardiovascular Research Center","Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge"]},{"name":"Alexander James Silver","id":"openalex:A5025545410","orcid":"https://orcid.org/0000-0001-8255-3140","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["From the Department of Medicine, Division of Hematology, Brigham and Women's Hospital (S.J., A.J.S., M.M.) and Harvard Medical School (B.L.E.), the Department of Medicine, Division of Cardiovascular Medicine, Brigham and Women's Hospital (E.S.) and Harvard Medical School (G.K.S., P.L.), the Department of Pathology (S.J.) and the Center for Genomic Medicine (P.N., S.K.), Massachusetts General Hospital, the Department of Medicine, Division of Cardiology, and Cardiovascular Research Center (P.N., S.K.), and the Department of Medicine (A.G.B.), Massachusetts General Hospital and Harvard Medical School, and the Departments of Medical Oncology (C.J.G.) and Biostatistics and Computational Biology (D.N.), Dana-Farber Cancer Institute, Boston, and the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge (P.N., A.G.B., N.G., S.G., S.K.) - all in Massachusetts; 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the Department of Cardiology, University Hospital, Parma, Italy (D","Department of Cardiology, University Hospital, Parma, Italy, Medical Research Council–British Heart Foundation Cardiovascular Epidemiology Unit and National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care"]},{"name":"Usman Baber","id":"openalex:A5083495465","orcid":"https://orcid.org/0000-0003-1614-5987","affiliation":"Brigham and Women's Hospital","institutionId":"I1283280774","institutionUrl":"https://ror.org/04b6nzv94","affiliations":["From the Department of Medicine, Division of Hematology, Brigham and Women's Hospital (S.J., A.J.S., M.M.) and Harvard Medical School (B.L.E.), the Department of Medicine, Division of Cardiovascular Medicine, Brigham and Women's Hospital (E.S.) and Harvard Medical School (G.K.S., P.L.), the Department of Pathology (S.J.) and the Center for Genomic Medicine (P.N., S.K.), Massachusetts General Hospital, the Department of Medicine, Division of Cardiology, and Cardiovascular Research Center (P.N., S.K.), and the Department of Medicine (A.G.B.), Massachusetts General Hospital and Harvard Medical School, and the Departments of Medical Oncology (C.J.G.) and Biostatistics and Computational Biology (D.N.), Dana-Farber Cancer Institute, Boston, and the Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge (P.N., A.G.B., N.G., S.G., S.K.) - all in Massachusetts; 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Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.","authors":[{"name":"Anubha Mahajan","id":"openalex:A5030469052","orcid":"https://orcid.org/0000-0001-5585-3420","affiliation":"Centre for Human Genetics","institutionId":"I1336263701","institutionUrl":"https://ror.org/01rjnta51","affiliations":["Genentech, South San Francisco, CA, USA. mahajan.anubha@gene.com","Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK. mahajan.anubha@gene.com","Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK. mahajan.anubha@gene.com","Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK","Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK"],"corresponding":true},{"name":"Cassandra Nichole Spracklen","id":"openalex:A5052590220","orcid":"https://orcid.org/0000-0003-3590-7182","affiliation":"University of North Carolina at Chapel Hill","institutionId":"I114027177","institutionUrl":"https://ror.org/0130frc33","affiliations":["Department of Epidemiology and Biostatistics, University of Massachusetts Amherst, Amherst, MA, USA","Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"]},{"name":"Weihua Zhang","id":"openalex:A5108049965","orcid":"https://orcid.org/0000-0001-8279-3611","affiliation":"Ealing Hospital","institutionId":"I2799657458","institutionUrl":"https://ror.org/05a90fj07","affiliations":["Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK","Department of Epidemiology and Biostatistics, Imperial College London, London, UK"]},{"name":"Maggie C.Y. Ng","id":"openalex:A5034303175","orcid":"https://orcid.org/0000-0002-4133-2007","affiliation":"Vanderbilt University","institutionId":"I200719446","institutionUrl":"https://ror.org/02vm5rt34","affiliations":["Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA","Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA","Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA"]},{"name":"Lauren E. Petty","id":"openalex:A5022981309","orcid":"https://orcid.org/0000-0003-1619-6303","affiliation":"Vanderbilt University","institutionId":"I200719446","institutionUrl":"https://ror.org/02vm5rt34","affiliations":["Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA"]},{"name":"Hidetoshi Kitajima","id":"openalex:A5022853833","orcid":"https://orcid.org/0000-0001-6545-1177","affiliation":"Centre for Human Genetics","institutionId":"I1336263701","institutionUrl":"https://ror.org/01rjnta51","affiliations":["Cancer Center, Tohoku University Hospital, Tohoku University, Sendai, Japan","Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan","The Advanced Research Center for Innovations in Next-Generation Medicine (INGEM), Tohoku University, Sendai, Japan","Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK"]},{"name":"Grace Zhuo Yu","id":"openalex:A5037800360","orcid":"https://orcid.org/0000-0002-4159-7398","affiliation":"Centre for Human Genetics","institutionId":"I1336263701","institutionUrl":"https://ror.org/01rjnta51","affiliations":["Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK","Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK"]},{"name":"Sina Rüeger","id":"openalex:A5054695994","orcid":"https://orcid.org/0000-0003-2848-9242","affiliation":"University of Helsinki","institutionId":"I133731052","institutionUrl":"https://ror.org/040af2s02","affiliations":["Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland"]},{"name":"Leo Speidel","id":"openalex:A5017944564","orcid":"https://orcid.org/0000-0002-4644-8033","affiliation":"The Francis Crick Institute","institutionId":"I2801081054","institutionUrl":"https://ror.org/04tnbqb63","affiliations":["Francis Crick Institute, London, UK","Genetics Institute, University College London, London, UK"]},{"name":"Young Jin Kim","id":"openalex:A5100387373","orcid":"https://orcid.org/0000-0002-4132-4437","affiliation":"Korea National Institute of Health","institutionId":"I4210097010","institutionUrl":"https://ror.org/00qdsfq65","affiliations":["Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea"]},{"name":"Momoko Horikoshi","id":"openalex:A5000163164","orcid":"https://orcid.org/0000-0002-7103-2267","affiliation":"RIKEN Center for Integrative Medical Sciences","institutionId":"I4210150921","institutionUrl":"https://ror.org/04mb6s476","affiliations":["Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan"]},{"name":"Josep Maria Mercader","id":"openalex:A5101966760","orcid":"https://orcid.org/0000-0001-8494-3660","affiliation":"Broad Institute","institutionId":"I107606265","institutionUrl":"https://ror.org/05a0ya142","affiliations":["Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA","Harvard Medical School, Boston, MA, USA","Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA"]},{"name":"Daniel Taliun","id":"openalex:A5075177958","orcid":"https://orcid.org/0000-0002-8874-5044","affiliation":"University of Michigan","institutionId":"I27837315","institutionUrl":"https://ror.org/00jmfr291","affiliations":["Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA"]},{"name":"Sanghoon Moon","id":"openalex:A5087673453","orcid":"https://orcid.org/0000-0001-5987-9505","affiliation":"Centre for Human Genetics","institutionId":"I1336263701","institutionUrl":"https://ror.org/01rjnta51","affiliations":["Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea","Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK"]},{"name":"Soo‐Heon Kwak","id":"openalex:A5014265519","orcid":"https://orcid.org/0000-0003-1230-0919","affiliation":"Centre for Human Genetics","institutionId":"I1336263701","institutionUrl":"https://ror.org/01rjnta51","affiliations":["Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea","Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK"]},{"name":"Neil R. 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[2] Program in Medical and Population Genetics, Broad Institute, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA","Program in Medical and Population Genetics, Broad Institute, 7 Cambridge Center, Cambridge, USA"]},{"name":"Stacey Gabriel","id":"openalex:A5061030489","orcid":"https://orcid.org/0000-0001-6939-2215","affiliation":"Broad Institute","institutionId":"I107606265","institutionUrl":"https://ror.org/05a0ya142","affiliations":["Program in Medical and Population Genetics, Broad Institute, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA","Cardiovascular Research Center, Massachusetts General Hospital, Boston, USA","Center for Human Genetic Research, Massachusetts General Hospital, Boston, USA","Department of Medicine, Harvard Medical School, Boston, USA","Program in Medical and Population Genetics, Broad Institute, 7 Cambridge Center, Cambridge, USA"]},{"name":"Sekar Kathiresan","id":"openalex:A5043655881","orcid":"https://orcid.org/0000-0002-6724-032X","affiliation":"Broad Institute","institutionId":"I107606265","institutionUrl":"https://ror.org/05a0ya142","affiliations":["1] Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA. 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Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of ∼185,000 CAD cases and controls, interrogating 6.7 million common (minor allele frequency (MAF) > 0.05) and 2.7 million low-frequency (0.005 < MAF < 0.05) variants. In addition to confirming most known CAD-associated loci, we identified ten new loci (eight additive and two recessive) that contain candidate causal genes newly implicating biological processes in vessel walls. We observed intralocus allelic heterogeneity but little evidence of low-frequency variants with larger effects and no evidence of synthetic association. Our analysis provides a comprehensive survey of the fine genetic architecture of CAD, showing that genetic susceptibility to this common disease is largely determined by common SNPs of small effect size.","authors":[{"name":"Majid Nikpay","id":"openalex:A5007653975","orcid":"https://orcid.org/0000-0003-0285-6454"},{"name":"Anuj Goel","id":"openalex:A5058389126","orcid":"https://orcid.org/0000-0003-2307-4021"},{"name":"Hong‐Hee Won","id":"openalex:A5039633463","orcid":"https://orcid.org/0000-0001-5719-0552"},{"name":"Leanne M. Hall","id":"openalex:A5107881981"},{"name":"Christina Willenborg","id":"openalex:A5110022752"},{"name":"Stavroula Kanoni","id":"openalex:A5011269649","orcid":"https://orcid.org/0000-0002-1691-9615"},{"name":"Danish Saleheen","id":"openalex:A5027044353","orcid":"https://orcid.org/0000-0001-6193-020X"},{"name":"Theodosios Kyriakou","id":"openalex:A5057224495","orcid":"https://orcid.org/0000-0001-7794-8789"},{"name":"Christopher P. 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Epstein","id":"openalex:A5107838634"},{"name":"Tõnu Esko","id":"openalex:A5007189110","orcid":"https://orcid.org/0000-0003-1982-6569"},{"name":"Mary Furlan Feitosa","id":"openalex:A5035488928","orcid":"https://orcid.org/0000-0002-0933-2410"}],"venue":"EUR Research Repository (Erasmus University Rotterdam)","venueId":"S4306401266","publisher":"Erasmus University Rotterdam","year":2015,"publishedDate":"2015-10-23","type":"review","citationCount":581,"referenceCount":0,"isOpenAccess":false,"sourceUrl":"https://pure.eur.nl/en/publications/9958178a-78db-444f-9e30-c730be5b0685","topics":[{"id":"T10261","name":"Genetic Associations and Epidemiology","score":0.9994999766349792,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T11468","name":"Genetic Mapping and Diversity in Plants and Animals","score":0.00009999999747378752,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T13354","name":"Genetics and Physical Performance","score":0,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"}],"sources":["openalex"],"id":"openalex:W2911336777"},{"ids":{"openalex":"W2581452190","doi":"10.1038/ng.3768","pmid":"28135244"},"title":"Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk","abstract":"$2c","authors":[{"name":"Helen R. Warren","id":"openalex:A5025847076","orcid":"https://orcid.org/0000-0003-0511-1386","affiliation":"Queen Mary University of London","institutionId":"I166337079","institutionUrl":"https://ror.org/026zzn846","affiliations":["National Institute for Health Research Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK","William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK"]},{"name":"Εvangelos Εvangelou","id":"openalex:A5057413062","orcid":"https://orcid.org/0000-0002-5488-2999","affiliation":"University of Ioannina","institutionId":"I194019607","institutionUrl":"https://ror.org/01qg3j183","affiliations":["Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK","Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece"]},{"name":"Claudia P. Cabrera","id":"openalex:A5107872071","affiliation":"Queen Mary University of London","institutionId":"I166337079","institutionUrl":"https://ror.org/026zzn846","affiliations":["National Institute for Health Research Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK","William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK"]},{"name":"He Gao","id":"openalex:A5107911632","orcid":"https://orcid.org/0009-0005-6950-6936","affiliation":"MRC Centre for Environment and Health","institutionId":"I4392738229","institutionUrl":"https://ror.org/01vw4c203","affiliations":["Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK","MRC-PHE Centre for Environment and Health, Imperial College London, London, UK"]},{"name":"Meixia Ren","id":"openalex:A5106731511","orcid":"https://orcid.org/0000-0002-6310-0792","affiliation":"Queen Mary University of London","institutionId":"I166337079","institutionUrl":"https://ror.org/026zzn846","affiliations":["National Institute for Health Research Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK","William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK"]},{"name":"Borbála Mifsud","id":"openalex:A5008938420","orcid":"https://orcid.org/0000-0003-3429-3094","affiliation":"Queen Mary University of London","institutionId":"I166337079","institutionUrl":"https://ror.org/026zzn846","affiliations":["William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK"]},{"name":"Ioanna Ntalla","id":"openalex:A5107882722","affiliation":"Queen Mary University of London","institutionId":"I166337079","institutionUrl":"https://ror.org/026zzn846","affiliations":["William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK"]},{"name":"Praveen Surendran","id":"openalex:A5101417034","orcid":"https://orcid.org/0000-0002-4911-6077","affiliation":"University of Cambridge","institutionId":"I241749","institutionUrl":"https://ror.org/013meh722","affiliations":["Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK","Department of Public Health and Primary Care, Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK"]},{"name":"Chunyu Liu","id":"openalex:A5100343311","orcid":"https://orcid.org/0000-0002-9160-0153","affiliation":"Boston University","institutionId":"I111088046","institutionUrl":"https://ror.org/05qwgg493","affiliations":["Boston University School of Public Health, Boston, Massachusetts, USA","National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA","Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA"]},{"name":"James P. 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Methylation","score":0.9907000064849854,"subfieldId":"1312","subfield":"Molecular Biology","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T10261","name":"Genetic Associations and Epidemiology","score":0.004999999888241291,"subfieldId":"1311","subfield":"Genetics","fieldId":"13","field":"Biochemistry, Genetics and Molecular Biology"},{"id":"T11629","name":"Birth, Development, and Health","score":0.0003000000142492354,"subfieldId":"2735","subfield":"Pediatrics, Perinatology and Child Health","fieldId":"27","field":"Medicine"}],"sources":["openalex"],"id":"openalex:W1808248494"},{"ids":{"openalex":"W2163331125","doi":"10.1016/s1474-4422(12)70234-x","pmid":"23041239"},"title":"Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE Collaboration): a meta-analysis of genome-wide association studies","abstract":"$2f","authors":[{"name":"Matthew Traylor","id":"openalex:A5011294322","orcid":"https://orcid.org/0000-0001-6624-8621","affiliation":"St George's, University of London","institutionId":"I165862685","institutionUrl":"https://ror.org/040f08y74","affiliations":["Stroke and Dementia Research Centre, St George's University of London, London, UK"]},{"name":"Martin Farrall","id":"openalex:A5045782628","orcid":"https://orcid.org/0000-0003-4564-2165","affiliation":"Centre for Human Genetics","institutionId":"I1336263701","institutionUrl":"https://ror.org/01rjnta51","affiliations":["Helgadottir MD )","Centrw for Clinical Epidemiology and Biostatistics","Department of Cardiovascular Medicine, University of Oxford, Oxford, UK","Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK"]},{"name":"Elizabeth G Holliday","affiliation":"Hunter Medical Research Institute","institutionId":"I4210087646","institutionUrl":"https://ror.org/0020x6414","affiliations":["Centrw for Clinical Epidemiology and Biostatistics, 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Sarwar, K.K.R., R.G., A.T., D.S., J.D.)","Section of Population Health Sciences, University of Aberdeen, Aberdeen, Scotland (N. Sarwar)","BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (N. Sattar); and Icelandic Heart Association, Kopavogur, and University of Iceland, Reykjavik (V.G.)","BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (N. Sattar)","Icelandic Heart Association, Kopavogur, and University of Iceland, Reykjavik (V.G.)"]},{"name":"Rajiv Chowdhury","id":"openalex:A5023059768","orcid":"https://orcid.org/0000-0003-4881-5690","affiliation":"University of Iceland","institutionId":"I165368041","institutionUrl":"https://ror.org/01db6h964","affiliations":["From the Department of Public Health and Primary Care, University of Cambridge, Cambridge, England (E.D.A., R.C., N. Sarwar, K.K.R., R.G., A.T., D.S., J.D.)","Section of Population Health Sciences, University of Aberdeen, Aberdeen, Scotland (N. Sarwar)","BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (N. Sattar); and Icelandic Heart Association, Kopavogur, and University of Iceland, Reykjavik (V.G.)","BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (N. Sattar)","Icelandic Heart Association, Kopavogur, and University of Iceland, Reykjavik (V.G.)"]},{"name":"Nadeem Sarwar","id":"openalex:A5002558255","orcid":"https://orcid.org/0000-0001-8681-6382","affiliation":"University of Iceland","institutionId":"I165368041","institutionUrl":"https://ror.org/01db6h964","affiliations":["From the Department of Public Health and Primary Care, University of Cambridge, Cambridge, England (E.D.A., R.C., N. Sarwar, K.K.R., R.G., A.T., D.S., J.D.)","Section of Population Health Sciences, University of Aberdeen, Aberdeen, Scotland (N. Sarwar)","BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (N. 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Sattar); and Icelandic Heart Association, Kopavogur, and University of Iceland, Reykjavik (V.G.)","BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (N. Sattar)","Icelandic Heart Association, Kopavogur, and University of Iceland, Reykjavik (V.G.)"]},{"name":"Danish Saleheen","id":"openalex:A5027044353","orcid":"https://orcid.org/0000-0001-6193-020X","affiliation":"University of Iceland","institutionId":"I165368041","institutionUrl":"https://ror.org/01db6h964","affiliations":["From the Department of Public Health and Primary Care, University of Cambridge, Cambridge, England (E.D.A., R.C., N. Sarwar, K.K.R., R.G., A.T., D.S., J.D.)","Section of Population Health Sciences, University of Aberdeen, Aberdeen, Scotland (N. Sarwar)","BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (N. Sattar); and Icelandic Heart Association, Kopavogur, and University of Iceland, Reykjavik (V.G.)","BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (N. Sattar)","Icelandic Heart Association, Kopavogur, and University of Iceland, Reykjavik (V.G.)"]},{"name":"Alexander Thompson","affiliation":"University of Iceland","institutionId":"I165368041","institutionUrl":"https://ror.org/01db6h964","affiliations":["From the Department of Public Health and Primary Care, University of Cambridge, Cambridge, England (E.D.A., R.C., N. Sarwar, K.K.R., R.G., A.T., D.S., J.D.)","Section of Population Health Sciences, University of Aberdeen, Aberdeen, Scotland (N. Sarwar)","BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (N. Sattar); and Icelandic Heart Association, Kopavogur, and University of Iceland, Reykjavik (V.G.)","BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (N. Sattar)","Icelandic Heart Association, Kopavogur, and University of Iceland, Reykjavik (V.G.)"]},{"name":"Vilmundur G. Gudnason","id":"openalex:A5000325789","orcid":"https://orcid.org/0000-0001-5696-0084","affiliation":"University of Iceland","institutionId":"I165368041","institutionUrl":"https://ror.org/01db6h964","affiliations":["From the Department of Public Health and Primary Care, University of Cambridge, Cambridge, England (E.D.A., R.C., N. Sarwar, K.K.R., R.G., A.T., D.S., J.D.)","Section of Population Health Sciences, University of Aberdeen, Aberdeen, Scotland (N. Sarwar)","BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (N. Sattar); and Icelandic Heart Association, Kopavogur, and University of Iceland, Reykjavik (V.G.)","BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (N. Sattar)","Icelandic Heart Association, Kopavogur, and University of Iceland, Reykjavik (V.G.)"]},{"name":"Naveed A. Sattar","id":"openalex:A5078498803","orcid":"https://orcid.org/0000-0002-1604-2593","affiliation":"University of Iceland","institutionId":"I165368041","institutionUrl":"https://ror.org/01db6h964","affiliations":["From the Department of Public Health and Primary Care, University of Cambridge, Cambridge, England (E.D.A., R.C., N. Sarwar, K.K.R., R.G., A.T., D.S., J.D.)","Section of Population Health Sciences, University of Aberdeen, Aberdeen, Scotland (N. Sarwar)","BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (N. Sattar); and Icelandic Heart Association, Kopavogur, and University of Iceland, Reykjavik (V.G.)","BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (N. Sattar)","Icelandic Heart Association, Kopavogur, and University of Iceland, Reykjavik (V.G.)"]},{"name":"John Danesh","id":"openalex:A5033502820","orcid":"https://orcid.org/0000-0003-1158-6791","affiliation":"University of Iceland","institutionId":"I165368041","institutionUrl":"https://ror.org/01db6h964","affiliations":["From the Department of Public Health and Primary Care, University of Cambridge, Cambridge, England (E.D.A., R.C., N. Sarwar, K.K.R., R.G., A.T., D.S., J.D.)","Section of Population Health Sciences, University of Aberdeen, Aberdeen, Scotland (N. Sarwar)","BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (N. Sattar); and Icelandic Heart Association, Kopavogur, and University of Iceland, Reykjavik (V.G.)","BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (N. Sattar)","Icelandic Heart Association, Kopavogur, and University of Iceland, Reykjavik (V.G.)"]}],"venue":"Circulation","venueId":"S116251202","publisher":"Lippincott Williams & Wilkins","publisherId":"P4310315671","year":2009,"publishedDate":"2009-11-17","type":"article","citationCount":378,"referenceCount":62,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.1161/circulationaha.109.884866","oaUrl":"https://www.ahajournals.org/doi/pdf/10.1161/CIRCULATIONAHA.109.884866","pdfUrl":"https://www.ahajournals.org/doi/pdf/10.1161/CIRCULATIONAHA.109.884866","topics":[{"id":"T10198","name":"Heart Failure Treatment and Management","score":0.9887999892234802,"subfieldId":"2705","subfield":"Cardiology and Cardiovascular Medicine","fieldId":"27","field":"Medicine"},{"id":"T10144","name":"Blood Pressure and Hypertension Studies","score":0.0015999999595806003,"subfieldId":"2705","subfield":"Cardiology and Cardiovascular Medicine","fieldId":"27","field":"Medicine"},{"id":"T10821","name":"Cardiovascular Function and Risk Factors","score":0.00139999995008111,"subfieldId":"2705","subfield":"Cardiology and Cardiovascular Medicine","fieldId":"27","field":"Medicine"}],"sources":["openalex"],"id":"openalex:W2164602128"}]}]]}],"$L38"]}]]}] 19:null 1d:[["$","title","0",{"children":"Danish Saleheen · SciRah"}],["$","meta","1",{"name":"description","content":"Danish Saleheen, Columbia University Irving Medical Center: research papers, citations, h-index and SciRah Score."}],["$","meta","2",{"name":"application-name","content":"SciRah"}],["$","meta","3",{"name":"keywords","content":"research papers,Pakistan research,academic search,Urdu summaries,researchers Pakistan,open access,SciRah"}],["$","link","4",{"rel":"canonical","href":"https://scirah.com/author/openalex%3AA50