Publications by authors named "Anuj Goel"

112 Publications

The trans-ancestral genomic architecture of glycemic traits.

Nat Genet 2021 06 31;53(6):840-860. Epub 2021 May 31.

Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.
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http://dx.doi.org/10.1038/s41588-021-00852-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610958PMC
June 2021

Common genetic variants and modifiable risk factors underpin hypertrophic cardiomyopathy susceptibility and expressivity.

Nat Genet 2021 02 25;53(2):135-142. Epub 2021 Jan 25.

Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford, Oxford, UK.

Hypertrophic cardiomyopathy (HCM) is a common, serious, genetic heart disorder. Rare pathogenic variants in sarcomere genes cause HCM, but with unexplained phenotypic heterogeneity. Moreover, most patients do not carry such variants. We report a genome-wide association study of 2,780 cases and 47,486 controls that identified 12 genome-wide-significant susceptibility loci for HCM. Single-nucleotide polymorphism heritability indicated a strong polygenic influence, especially for sarcomere-negative HCM (64% of cases; h = 0.34 ± 0.02). A genetic risk score showed substantial influence on the odds of HCM in a validation study, halving the odds in the lowest quintile and doubling them in the highest quintile, and also influenced phenotypic severity in sarcomere variant carriers. Mendelian randomization identified diastolic blood pressure (DBP) as a key modifiable risk factor for sarcomere-negative HCM, with a one standard deviation increase in DBP increasing the HCM risk fourfold. Common variants and modifiable risk factors have important roles in HCM that we suggest will be clinically actionable.
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http://dx.doi.org/10.1038/s41588-020-00764-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240954PMC
February 2021

Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability.

Nat Commun 2021 01 5;12(1):24. Epub 2021 Jan 5.

Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA.

Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.
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http://dx.doi.org/10.1038/s41467-020-19366-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785747PMC
January 2021

Genetic Predisposition to Coronary Artery Disease in Type 2 Diabetes Mellitus.

Circ Genom Precis Med 2020 12 13;13(6):e002769. Epub 2020 Aug 13.

The Usher Institute of Population Health Sciences & Informatics (A.D.M.), University of Edinburgh, Edinburgh, U.K.

Background: Coronary artery disease (CAD) is accelerated in subjects with type 2 diabetes mellitus (T2D).

Methods: To test whether this reflects differential genetic influences on CAD risk in subjects with T2D, we performed a systematic assessment of genetic overlap between CAD and T2D in 66 643 subjects (27 708 with CAD and 24 259 with T2D). Variants showing apparent association with CAD in stratified analyses or evidence of interaction were evaluated in a further 117 787 subjects (16 694 with CAD and 11 537 with T2D).

Results: None of the previously characterized CAD loci was found to have specific effects on CAD in T2D individuals, and a genome-wide interaction analysis found no new variants for CAD that could be considered T2D specific. When we considered the overall genetic correlations between CAD and its risk factors, we found no substantial differences in these relationships by T2D background.

Conclusions: This study found no evidence that the genetic architecture of CAD differs in those with T2D compared with those without T2D.
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http://dx.doi.org/10.1161/CIRCGEN.119.002769DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7748049PMC
December 2020

Rare Ocular Involvement in a Newly Diagnosed AIDS Patient With Diffuse Kaposi's Sarcoma.

Cureus 2020 Jun 8;12(6):e8502. Epub 2020 Jun 8.

Internal Medicine, Methodist Health System, Dallas, USA.

Kaposi's sarcoma (KS) is a cancer that often affects individuals with human immunodeficiency virus, acquired immunodeficiency syndrome (AIDS), those who have received an organ transplant, or others who are immunocompromised. KS is a vascular tumor that most often presents in cutaneous sites, including the lower extremities, oral mucosa, and genitalia. Literature regarding KS with ocular involvement is scarce. We present a rare case in which a patient diagnosed with AIDS-associated KS exhibited ocular and diffuse manifestations. The lesions of cutaneous KS are frequently mistaken for an alternative diagnoses; therefore, the clinician should have a high index of suspicion for this vascular tumor in AIDS patients.
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http://dx.doi.org/10.7759/cureus.8502DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7346319PMC
June 2020

Primary Gastrointestinal Stromal Tumor Presenting as an Isolated Lung Mass.

Cureus 2020 May 28;12(5):e8343. Epub 2020 May 28.

Internal Medicine, Methodist Health System, Dallas, USA.

Gastrointestinal stromal tumors (GISTs) are rare gastrointestinal (GI) tumors, representing a small portion of soft tissue tumors of the abdominal cavity. Extraintestinal gastrointestinal stromal tumors (EGISTs) are uncommon forms of GISTs that present outside the GI tract. There have only been a rare number of reported cases of EGIST presenting above the diaphragm. We present the case of a 50-year-old female with shortness of breath, and found to have bilateral pleural effusions and left-sided lung mass. The initial lung mass aspiration was negative for malignancy; yet, pleural fluid was suggestive of malignancy, and repeat biopsy and immunohistochemical stain were diagnostic for GIST. Ultimately, the patient underwent video-assisted thoracoscopic surgery, pleurodesis with doxycycline, and adjuvant therapy with imatinib. This is a report of primary EGIST presenting as an isolated lung lesion with no involvement of the GI tract. In patients with suspected malignancy, it is of paramount importance to obtain a detailed history, including remote signs and symptoms, while performing a thorough work-up. Especially in the lung where initial biopsies can be skewed due to inflammation and atelectasis, repeat biopsies may be necessary to obtain an accurate diagnosis.
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http://dx.doi.org/10.7759/cureus.8343DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325361PMC
May 2020

Reevaluation of the South Asian Intronic Deletion in Hypertrophic Cardiomyopathy.

Circ Genom Precis Med 2020 06 12;13(3):e002783. Epub 2020 Mar 12.

Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom (A.R.H., A.G., E.O., S.N., M.F., H.W., K.L.T.).

Background: The common intronic deletion, , detected in 4% to 8% of South Asian populations, is reported to be associated with cardiomyopathy, with ≈7-fold increased risk of disease in variant carriers. Here, we examine the contribution of to hypertrophic cardiomyopathy (HCM) in a large patient cohort.

Methods: Sequence data from 2 HCM cohorts (n=5393) was analyzed to determine frequency and co-occurrence of pathogenic variants in HCM genes. Case-control and haplotype analyses were performed to compare variant frequencies and assess disease association. Analyses were also undertaken to investigate the pathogenicity of a candidate variant c.1224-52G>A.

Results: Our data suggest that the risk of HCM, previously attributed to , can be explained by enrichment of a derived haplotype, , whereby a small subset of individuals bear both and a rare pathogenic variant, c.1224-52G>A. The intronic c.1224-52G>A variant, which is not routinely evaluated by gene panel or exome sequencing, was detected in ≈1% of our HCM cohort.

Conclusions: The c.1224-52G>A variant explains the disease risk previously associated with in the South Asian population and is one of the most frequent pathogenic variants in HCM in all populations; genotyping services should ensure coverage of this deep intronic mutation. Individuals carrying alone are not at increased risk of HCM, and this variant should not be tested in isolation; this is important for the large majority of the 100 million individuals of South Asian ancestry who carry and would previously have been declared at increased risk of HCM.
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http://dx.doi.org/10.1161/CIRCGEN.119.002783DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7299222PMC
June 2020

Manhattan++: displaying genome-wide association summary statistics with multiple annotation layers.

BMC Bioinformatics 2019 Nov 27;20(1):610. Epub 2019 Nov 27.

Division of Cardiovascular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DU, UK.

Background: Over the last 10 years, there have been over 3300 genome-wide association studies (GWAS). Almost every GWAS study provides a Manhattan plot either as a main figure or in the supplement. Several software packages can generate a Manhattan plot, but they are all limited in the extent to which they can annotate gene-names, allele frequencies, and variants having high impact on gene function or provide any other added information or flexibility. Furthermore, in a conventional Manhattan plot, there is no way of distinguishing a locus identified due to a single variant with very significant p-value from a locus with multiple variants which appear to be in a haplotype block having very similar p-values.

Results: Here we present a software tool written in R, which generates a transposed Manhattan plot along with additional features like variant consequence and minor allele frequency to annotate the plot and addresses these limitations. The software also gives flexibility on how and where the user wants to display the annotations. The software can be downloaded from CRAN repository and also from the GitHub project page.

Conclusions: We present a major step up to the existing conventional Manhattan plot generation tools. We hope this form of display along with the added annotations will bring more insight to the reader from this new Manhattan++ plot.
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http://dx.doi.org/10.1186/s12859-019-3201-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6882345PMC
November 2019

Associations of autozygosity with a broad range of human phenotypes.

Nat Commun 2019 10 31;10(1):4957. Epub 2019 Oct 31.

Department of Neurology, Brain Centre Rudolf Magnus, University Medical Centre Utrecht, Utrecht University, Utrecht, 3584 CX, The Netherlands.

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (F) for >1.4 million individuals, we show that F is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: F equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of F are confirmed within full-sibling pairs, where the variation in F is independent of all environmental confounding.
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http://dx.doi.org/10.1038/s41467-019-12283-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6823371PMC
October 2019

A key role for the novel coronary artery disease gene JCAD in atherosclerosis via shear stress mechanotransduction.

Cardiovasc Res 2020 09;116(11):1863-1874

Division of Cardiovascular Medicine, Radcliffe Department of Medicine, BHF Centre of Research Excellence, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.

Aims: Genome-wide association studies (GWAS) have consistently identified an association between coronary artery disease (CAD) and a locus on chromosome 10 containing a single gene, JCAD (formerly KIAA1462). However, little is known about the mechanism by which JCAD could influence the development of atherosclerosis.

Methods And Results: Vascular function was quantified in subjects with CAD by flow-mediated dilatation (FMD) and vasorelaxation responses in isolated blood vessel segments. The JCAD risk allele identified by GWAS was associated with reduced FMD and reduced endothelial-dependent relaxations. To study the impact of loss of Jcad on atherosclerosis, Jcad-/- mice were crossed to an ApoE-/- background and fed a high-fat diet from 6 to16 weeks of age. Loss of Jcad did not affect blood pressure or heart rate. However, Jcad-/-ApoE-/- mice developed significantly less atherosclerosis in the aortic root and the inner curvature of the aortic arch. En face analysis revealed a striking reduction in pro-inflammatory adhesion molecules at sites of disturbed flow on the endothelial cell layer of Jcad-/- mice. Loss of Jcad lead to a reduced recovery perfusion in response to hind limb ischaemia, a model of altered in vivo flow. Knock down of JCAD using siRNA in primary human aortic endothelial cells significantly reduced the response to acute onset of flow, as evidenced by reduced phosphorylation of NF-КB, eNOS, and Akt.

Conclusion: The novel CAD gene JCAD promotes atherosclerotic plaque formation via a role in the endothelial cell shear stress mechanotransduction pathway.
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http://dx.doi.org/10.1093/cvr/cvz263DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7449560PMC
September 2020

Identifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery disease.

Bioinformatics 2020 01;36(2):552-557

Wellcome Centre for Human Genetics.

Motivation: Common small-effect genetic variants that contribute to human complex traits and disease are typically identified using traditional fixed-effect (FE) meta-analysis methods. However, the power to detect genetic associations under FE models deteriorates with increasing heterogeneity, so that some small-effect heterogeneous loci might go undetected. A modified random-effects meta-analysis approach (RE2) was previously developed that is more powerful than traditional fixed and random-effects methods at detecting small-effect heterogeneous genetic associations, the method was updated (RE2C) to identify small-effect heterogeneous variants overlooked by traditional fixed-effect meta-analysis. Here, we re-appraise a large-scale meta-analysis of coronary disease with RE2C to search for small-effect genetic signals potentially masked by heterogeneity in a FE meta-analysis.

Results: Our application of RE2C suggests a high sensitivity but low specificity of this approach for discovering small-effect heterogeneous genetic associations. We recommend that reports of small-effect heterogeneous loci discovered with RE2C are accompanied by forest plots and standardized predicted random-effects statistics to reveal the distribution of genetic effect estimates across component studies of meta-analyses, highlighting overly influential outlier studies with the potential to inflate genetic signals.

Availability And Implementation: Scripts to calculate standardized predicted random-effects statistics and generate forest plots are available in the getspres R package entitled from https://magosil86.github.io/getspres/.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz590DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7223261PMC
January 2020

A multi-ancestry genome-wide study incorporating gene-smoking interactions identifies multiple new loci for pulse pressure and mean arterial pressure.

Hum Mol Genet 2019 08;28(15):2615-2633

Icelandic Heart Association, Kopavogur, Iceland.

Elevated blood pressure (BP), a leading cause of global morbidity and mortality, is influenced by both genetic and lifestyle factors. Cigarette smoking is one such lifestyle factor. Across five ancestries, we performed a genome-wide gene-smoking interaction study of mean arterial pressure (MAP) and pulse pressure (PP) in 129 913 individuals in stage 1 and follow-up analysis in 480 178 additional individuals in stage 2. We report here 136 loci significantly associated with MAP and/or PP. Of these, 61 were previously published through main-effect analysis of BP traits, 37 were recently reported by us for systolic BP and/or diastolic BP through gene-smoking interaction analysis and 38 were newly identified (P < 5 × 10-8, false discovery rate < 0.05). We also identified nine new signals near known loci. Of the 136 loci, 8 showed significant interaction with smoking status. They include CSMD1 previously reported for insulin resistance and BP in the spontaneously hypertensive rats. Many of the 38 new loci show biologic plausibility for a role in BP regulation. SLC26A7 encodes a chloride/bicarbonate exchanger expressed in the renal outer medullary collecting duct. AVPR1A is widely expressed, including in vascular smooth muscle cells, kidney, myocardium and brain. FHAD1 is a long non-coding RNA overexpressed in heart failure. TMEM51 was associated with contractile function in cardiomyocytes. CASP9 plays a central role in cardiomyocyte apoptosis. Identified only in African ancestry were 30 novel loci. Our findings highlight the value of multi-ancestry investigations, particularly in studies of interaction with lifestyle factors, where genomic and lifestyle differences may contribute to novel findings.
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http://dx.doi.org/10.1093/hmg/ddz070DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6644157PMC
August 2019

Genetic variation in CADM2 as a link between psychological traits and obesity.

Sci Rep 2019 05 14;9(1):7339. Epub 2019 May 14.

Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK.

CADM2 has been associated with a range of behavioural and metabolic traits, including physical activity, risk-taking, educational attainment, alcohol and cannabis use and obesity. Here, we set out to determine whether CADM2 contributes to mechanisms shared between mental and physical health disorders. We assessed genetic variants in the CADM2 locus for association with phenotypes in the UK Biobank, IMPROVE, PROCARDIS and SCARFSHEEP studies, before performing meta-analyses. A wide range of metabolic phenotypes were meta-analysed. Psychological phenotypes analysed in UK Biobank only were major depressive disorder, generalised anxiety disorder, bipolar disorder, neuroticism, mood instability and risk-taking behaviour. In UK Biobank, four, 88 and 172 genetic variants were significantly (p < 1 × 10) associated with neuroticism, mood instability and risk-taking respectively. In meta-analyses of 4 cohorts, we identified 362, 63 and 11 genetic variants significantly (p < 1 × 10) associated with BMI, SBP and CRP respectively. Genetic effects on BMI, CRP and risk-taking were all positively correlated, and were consistently inversely correlated with genetic effects on SBP, mood instability and neuroticism. Conditional analyses suggested an overlap in the signals for physical and psychological traits. Many significant variants had genotype-specific effects on CADM2 expression levels in adult brain and adipose tissues. CADM2 variants influence a wide range of both psychological and metabolic traits, suggesting common biological mechanisms across phenotypes via regulation of CADM2 expression levels in adipose tissue. Functional studies of CADM2 are required to fully understand mechanisms connecting mental and physical health conditions.
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http://dx.doi.org/10.1038/s41598-019-43861-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6517397PMC
May 2019

Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids.

Nat Genet 2019 04 29;51(4):636-648. Epub 2019 Mar 29.

Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA.

The concentrations of high- and low-density-lipoprotein cholesterol and triglycerides are influenced by smoking, but it is unknown whether genetic associations with lipids may be modified by smoking. We conducted a multi-ancestry genome-wide gene-smoking interaction study in 133,805 individuals with follow-up in an additional 253,467 individuals. Combined meta-analyses identified 13 new loci associated with lipids, some of which were detected only because association differed by smoking status. Additionally, we demonstrate the importance of including diverse populations, particularly in studies of interactions with lifestyle factors, where genomic and lifestyle differences by ancestry may contribute to novel findings.
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http://dx.doi.org/10.1038/s41588-019-0378-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6467258PMC
April 2019

Multiancestry Genome-Wide Association Study of Lipid Levels Incorporating Gene-Alcohol Interactions.

Am J Epidemiol 2019 06;188(6):1033-1054

Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom.

A person's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-alcohol interactions into a multiancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in stage 1 (genome-wide discovery) and 66 studies in stage 2 (focused follow-up), for a total of 394,584 individuals from 5 ancestry groups. Analyses covered the period July 2014-November 2017. Genetic main effects and interaction effects were jointly assessed by means of a 2-degrees-of-freedom (df) test, and a 1-df test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P < 1 × 10-6) with lipid levels in stage 1 and were evaluated in stage 2, followed by combined analyses of stage 1 and stage 2. In the combined analysis of stages 1 and 2, a total of 147 independent loci were associated with lipid levels at P < 5 × 10-8 using 2-df tests, of which 18 were novel. No genome-wide-significant associations were found testing the interaction effect alone. The novel loci included several genes (proprotein convertase subtilisin/kexin type 5 (PCSK5), vascular endothelial growth factor B (VEGFB), and apolipoprotein B mRNA editing enzyme, catalytic polypeptide 1 (APOBEC1) complementation factor (A1CF)) that have a putative role in lipid metabolism on the basis of existing evidence from cellular and experimental models.
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http://dx.doi.org/10.1093/aje/kwz005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6545280PMC
June 2019

Differential Gene Expression in Macrophages From Human Atherosclerotic Plaques Shows Convergence on Pathways Implicated by Genome-Wide Association Study Risk Variants.

Arterioscler Thromb Vasc Biol 2018 11;38(11):2718-2730

From the Division of Cardiovascular Medicine, Radcliffe Department of Medicine (J.T.C., N.R., A.G., T.K., L.B., L.E., M.F., H.W., R.P.C.), University of Oxford, United Kingdom.

Objective- Plaque macrophages are intricately involved in atherogenesis and plaque destabilization. We sought to identify functional pathways in human plaque macrophages that are differentially regulated in respect of (1) plaque stability and (2) lipid content. We hypothesized that differentially regulated macrophage gene sets would relate to genome-wide association study variants associated with risk of acute complications of atherosclerosis. Approach and Results- Forty patients underwent carotid magnetic resonance imaging for lipid quantification before endarterectomy. Carotid plaque macrophages were procured by laser capture microdissection from (1) lipid core and (2) cap region, in 12 recently symptomatic and 12 asymptomatic carotid plaques. Applying gene set enrichment analysis, a number of gene sets were found to selectively upregulate in symptomatic plaque macrophages, which corresponded to 7 functional pathways: inflammation, lipid metabolism, hypoxic response, cell proliferation, apoptosis, antigen presentation, and cellular energetics. Predicted upstream regulators included IL-1β, TNF-α, and NF-κB. In vivo lipid quantification by magnetic resonance imaging correlated most strongly with the upregulation of genes of the IFN/ STAT1 pathways. Cross-interrogation of gene set enrichment analysis and meta-analysis gene set enrichment of variant associations showed lipid metabolism pathways, driven by genes coding for APOE and ABCA1/G1 coincided with known risk-associated SNPs (single nucleotide polymorphisms) from genome-wide association studies. Conclusions- Macrophages from recently symptomatic carotid plaques show differential regulation of functional gene pathways. There were additional quantitative relationships between plaque lipid content and key gene sets. The data show a plausible mechanism by which known genome-wide association study risk variants for atherosclerotic complications could be linked to (1) a relevant cellular process, in (2) the key cell type of atherosclerosis, in (3) a human disease-relevant setting.
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http://dx.doi.org/10.1161/ATVBAHA.118.311209DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6217969PMC
November 2018

Neonatal Micro-RNA Profile Determines Endothelial Function in Offspring of Hypertensive Pregnancies.

Hypertension 2018 10 20;72(4):937-945. Epub 2018 Aug 20.

Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.

Offspring of hypertensive pregnancies are at increased risk of developing hypertension in adulthood. In the neonatal period they display endothelial cell dysfunction and altered microvascular development. MicroRNAs, as important endothelial cellular regulators, may play a role in this early endothelial dysfunction. Therefore we identified differential microRNA patterns in endothelial cells from offspring of hypertensive pregnancies and determined their role in postnatal vascular cell function. Studies were performed on human umbilical vein endothelial cell (HUVECs) samples from 57 pregnancies. Unbiased RNA-sequencing identified 30 endothelial-related microRNAs differentially expressed in HUVECs from hypertensive compared to normotensive pregnancies. Quantitative reverse transcription PCR (RT-qPCR) confirmed a significant higher expression level of the top candidate, miR-146a. Combined miR-146a targeted gene expression and pathway analysis revealed significant alterations in genes involved in inflammation, angiogenesis and immune response in the same HUVECs. Elevated miR-146a expression level at birth identified cells with reduced ability for vascular tube formation, which was rescued by miR-146a inhibition. In contrast, miR-146a overexpression significantly reduced vascular tube formation in HUVECs from normotensive pregnancies. Finally, we confirmed that mir146a levels at birth predicted microvascular development during the first three postnatal months. Offspring of hypertensive pregnancy have a distinct endothelial regulatory microRNA profile at birth, which is related to altered endothelial cell behaviour, and predicts patterns of microvascular development during the first three months of life. Modification of this microRNA profile can restore impaired vascular cell function.
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http://dx.doi.org/10.1161/HYPERTENSIONAHA.118.11343DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6166786PMC
October 2018

Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits.

Nat Genet 2018 10 17;50(10):1412-1425. Epub 2018 Sep 17.

Laboratory of Genetics and Genomics, NIA/NIH, Baltimore, MD, USA.

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.
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http://dx.doi.org/10.1038/s41588-018-0205-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6284793PMC
October 2018

Mutant Muscle LIM Protein C58G causes cardiomyopathy through protein depletion.

J Mol Cell Cardiol 2018 08 23;121:287-296. Epub 2018 Jul 23.

Division of Cardiovascular Medicine, Radcliffe Department of Medicine, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK; Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK. Electronic address:

Cysteine and glycine rich protein 3 (CSRP3) encodes Muscle LIM Protein (MLP), a well-established disease gene for Hypertrophic Cardiomyopathy (HCM). MLP, in contrast to the proteins encoded by the other recognised HCM disease genes, is non-sarcomeric, and has important signalling functions in cardiomyocytes. To gain insight into the disease mechanisms involved, we generated a knock-in mouse (KI) model, carrying the well documented HCM-causing CSRP3 mutation C58G. In vivo phenotyping of homozygous KI/KI mice revealed a robust cardiomyopathy phenotype with diastolic and systolic left ventricular dysfunction, which was supported by increased heart weight measurements. Transcriptome analysis by RNA-seq identified activation of pro-fibrotic signalling, induction of the fetal gene programme and activation of markers of hypertrophic signalling in these hearts. Further ex vivo analyses validated the activation of these pathways at transcript and protein level. Intriguingly, the abundance of MLP decreased in KI/KI mice by 80% and in KI/+ mice by 50%. Protein depletion was also observed in cellular studies for two further HCM-causing CSRP3 mutations (L44P and S54R/E55G). We show that MLP depletion is caused by proteasome action. Moreover, MLP C58G interacts with Bag3 and results in a proteotoxic response in the homozygous knock-in mice, as shown by induction of Bag3 and associated heat shock proteins. In conclusion, the newly generated mouse model provides insights into the underlying disease mechanisms of cardiomyopathy caused by mutations in the non-sarcomeric protein MLP. Furthermore, our cellular experiments suggest that protein depletion and proteasomal overload also play a role in other HCM-causing CSPR3 mutations that we investigated, indicating that reduced levels of functional MLP may be a common mechanism for HCM-causing CSPR3 mutations.
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http://dx.doi.org/10.1016/j.yjmcc.2018.07.248DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6117453PMC
August 2018

Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570K individuals across multiple ancestries.

PLoS One 2018 18;13(6):e0198166. Epub 2018 Jun 18.

Icelandic Heart Association, Kopavogur, Iceland.

Heavy alcohol consumption is an established risk factor for hypertension; the mechanism by which alcohol consumption impact blood pressure (BP) regulation remains unknown. We hypothesized that a genome-wide association study accounting for gene-alcohol consumption interaction for BP might identify additional BP loci and contribute to the understanding of alcohol-related BP regulation. We conducted a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions. In Stage 1, genome-wide discovery meta-analyses in ≈131K individuals across several ancestry groups yielded 3,514 SNVs (245 loci) with suggestive evidence of association (P < 1.0 x 10-5). In Stage 2, these SNVs were tested for independent external replication in ≈440K individuals across multiple ancestries. We identified and replicated (at Bonferroni correction threshold) five novel BP loci (380 SNVs in 21 genes) and 49 previously reported BP loci (2,159 SNVs in 109 genes) in European ancestry, and in multi-ancestry meta-analyses (P < 5.0 x 10-8). For African ancestry samples, we detected 18 potentially novel BP loci (P < 5.0 x 10-8) in Stage 1 that warrant further replication. Additionally, correlated meta-analysis identified eight novel BP loci (11 genes). Several genes in these loci (e.g., PINX1, GATA4, BLK, FTO and GABBR2) have been previously reported to be associated with alcohol consumption. These findings provide insights into the role of alcohol consumption in the genetic architecture of hypertension.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0198166PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6005576PMC
January 2019

Lack of genetic support for shared aetiology of Coronary Artery Disease and Late-onset Alzheimer's disease.

Sci Rep 2018 05 8;8(1):7102. Epub 2018 May 8.

Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.

Epidemiological studies suggest a positive association between coronary artery disease (CAD) and late-onset Alzheimer's disease (LOAD). This large-scale genetic study brings together 'big data' resources to examine the causal impact of genetic determinants of CAD on risk of LOAD. A two-sample Mendelian randomization approach was adopted to estimate the causal effect of CAD on risk of LOAD using summary data from 60,801 CAD cases from CARDIoGRAMplusC4D and 17,008 LOAD cases from the IGAP Consortium. Additional analyses assessed the independent relevance of genetic associations at the APOE locus for both CAD and LOAD. Higher genetically determined risk of CAD was associated with a slightly higher risk of LOAD (Odds Ratio (OR) per log-odds unit of CAD [95% CI]: 1.07 [1.01-1.15]; p = 0.027). However, after exclusion of the APOE locus, the estimate of the causal effect of CAD for LOAD was attenuated and no longer significant (OR 0.94 [0.88-1.01]; p = 0.072). This Mendelian randomization study indicates that the APOE locus is the chief determinant of shared genetic architecture between CAD and LOAD, and suggests a lack of causal relevance of CAD for risk of LOAD after exclusion of APOE.
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http://dx.doi.org/10.1038/s41598-018-25460-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5940751PMC
May 2018

Network analysis of coronary artery disease risk genes elucidates disease mechanisms and druggable targets.

Sci Rep 2018 02 21;8(1):3434. Epub 2018 Feb 21.

Clinical Gene Networks AB, Stockholm, Sweden.

Genome-wide association studies (GWAS) have identified over two hundred chromosomal loci that modulate risk of coronary artery disease (CAD). The genes affected by variants at these loci are largely unknown and an untapped resource to improve our understanding of CAD pathophysiology and identify potential therapeutic targets. Here, we prioritized 68 genes as the most likely causal genes at genome-wide significant loci identified by GWAS of CAD and examined their regulatory roles in 286 metabolic and vascular tissue gene-protein sub-networks ("modules"). The modules and genes within were scored for CAD druggability potential. The scoring enriched for targets of cardiometabolic drugs currently in clinical use and in-depth analysis of the top-scoring modules validated established and revealed novel target tissues, biological processes, and druggable targets. This study provides an unprecedented resource of tissue-defined gene-protein interactions directly affected by genetic variance in CAD risk loci.
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http://dx.doi.org/10.1038/s41598-018-20721-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5821758PMC
February 2018

A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure.

Am J Hum Genet 2018 03 15;102(3):375-400. Epub 2018 Feb 15.

Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH, Baltimore, MD 21224, USA.

Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactions in 610,091 individuals. Stage 1 analysis examined ∼18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-up analysis of promising variants in 480,178 additional individuals from five ancestries. We identified 15 loci that were genome-wide significant (p < 5 × 10) in stage 1 and formally replicated in stage 2. A combined stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci (13, 35, and 18 loci in European, African, and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (p < 5 × 10). Of the newly identified loci, ten showed significant interaction with smoking status, but none of them were replicated in stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies (SDCCAG8, RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling (MSRA, EBF2).
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http://dx.doi.org/10.1016/j.ajhg.2018.01.015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5985266PMC
March 2018

Identification of a novel proinsulin-associated SNP and demonstration that proinsulin is unlikely to be a causal factor in subclinical vascular remodelling using Mendelian randomisation.

Atherosclerosis 2017 Nov 28;266:196-204. Epub 2017 Sep 28.

Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.

Background And Aims: Increased proinsulin relative to insulin levels have been associated with subclinical atherosclerosis (measured by carotid intima-media thickness (cIMT)) and are predictive of future cardiovascular disease (CVD), independently of established risk factors. The mechanisms linking proinsulin to atherosclerosis and CVD are unclear. A genome-wide meta-analysis has identified nine loci associated with circulating proinsulin levels. Using proinsulin-associated SNPs, we set out to use a Mendelian randomisation approach to test the hypothesis that proinsulin plays a causal role in subclinical vascular remodelling.

Methods: We studied the high CVD-risk IMPROVE cohort (n = 3345), which has detailed biochemical phenotyping and repeated, state-of-the-art, high-resolution carotid ultrasound examinations. Genotyping was performed using Illumina Cardio-Metabo and Immuno arrays, which include reported proinsulin-associated loci. Participants with type 2 diabetes (n = 904) were omitted from the analysis. Linear regression was used to identify proinsulin-associated genetic variants.

Results: We identified a proinsulin locus on chromosome 15 (rs8029765) and replicated it in data from 20,003 additional individuals. An 11-SNP score, including the previously identified and the chromosome 15 proinsulin-associated loci, was significantly and negatively associated with baseline IMT and IMT (the primary cIMT phenotypes) but not with progression measures. However, MR-Eggers refuted any significant effect of the proinsulin-associated 11-SNP score, and a non-pleiotropic SNP score of three variants (including rs8029765) demonstrated no effect on baseline or progression cIMT measures.

Conclusions: We identified a novel proinsulin-associated locus and demonstrated that whilst proinsulin levels are associated with cIMT measures, proinsulin per se is unlikely to have a causative effect on cIMT.
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http://dx.doi.org/10.1016/j.atherosclerosis.2017.09.031DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5679136PMC
November 2017

A mouse-to-man candidate gene study identifies association of chronic otitis media with the loci TGIF1 and FBXO11.

Sci Rep 2017 10 2;7(1):12496. Epub 2017 Oct 2.

Nuffield Department of Surgical Sciences, University of Oxford, Headley Way, Oxford, OX3 9DU, UK.

Chronic otitis media with effusion (COME) is the most common cause of hearing loss in children, and known to have high heritability. Mutant mouse models have identified Fbxo11, Evi1, Tgif1, and Nisch as potential risk loci. We recruited children aged 10 and under undergoing surgical treatment for COME from 35 hospitals in the UK, and their nuclear family. We performed association testing with the loci FBXO11, EVI1, TGIF1 and NISCH and sought to replicate significant results in a case-control cohort from Finland. We tested 1296 families (3828 individuals), and found strength of association with the T allele at rs881835 (p = 0.006, OR 1.39) and the G allele at rs1962914 (p = 0.007, OR 1.58) at TGIF1, and the A allele at rs10490302 (p = 0.016, OR 1.17) and the G allele at rs2537742 (p = 0.038, OR 1.16) at FBXO11. Results were not replicated. This study supports smaller studies that have also suggested association of otitis media with polymorphism at FBX011, but this is the first study to report association with the locus TGIF1. Both FBX011 and TGIF1 are involved in TGF-β signalling, suggesting this pathway may be important in the transition from acute to chronic middle ear inflammation, and a potential molecular target.
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http://dx.doi.org/10.1038/s41598-017-12784-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5624881PMC
October 2017

Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis.

PLoS Med 2017 Sep 12;14(9):e1002383. Epub 2017 Sep 12.

William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.

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.
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http://dx.doi.org/10.1371/journal.pmed.1002383DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5595282PMC
September 2017

Novel Blood Pressure Locus and Gene Discovery Using Genome-Wide Association Study and Expression Data Sets From Blood and the Kidney.

Hypertension 2017 Jul 24. Epub 2017 Jul 24.

From the Department of Health Sciences (L.V.W., A.M.E., N. Shrine, C.B., T.B., M.D.T.), and Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre (C.P.N., P.S.B., N.J.S.), University of Leicester, United Kingdom; Department of Epidemiology (A.V., P.J.v.d.M., I.M.N., H. Snieder), Division of Nephrology, Department of Internal Medicine (M.H.d.B., M.A.S.), Interdisciplinary Center Psychopathology and Emotion Regulation (IPCE) (A.J.O., H.R., C.A.H.), Department of Genetics, (M.S.), and Department of Cardiology (P.v.d.H.), University of Groningen, University Medical Center Groningen, The Netherlands; Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Iran (A.V.); Department of Psychiatry, VU University Medical Center, Neuroscience Campus Amsterdam, The Netherlands (R. Jansen); Hebrew SeniorLife, Harvard Medical School, Boston, MA (R. Joehanes); National Heart, Lung and Blood Institute's Framingham Heart Study, MA (R. Joehanes, A.D.J., M. Larson); Institute of Psychiatry, Psychology and Neuroscience (P.F.O.), and Department of Twin Research and Genetic Epidemiology (M.M., C. Menni, T.D.S.), King's College London, United Kingdom; Clinical Pharmacology, William Harvey Research Institute (C.P.C., H.R.W., M.R.B., M. Brown, B.M., M.R., P.B.M., M.J.C.) and NIHR Barts Cardiovascular Biomedical Research Unit (C.P.C., H.R.W., M.R.B., M. Brown, P.B.M., M.J.C.), Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom; Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA (L.M.R., F.G., P.M.R., D.I.C.); Department of Epidemiology (G.C.V., A. Hofman, A.G.U., O.H.F.), Genetic Epidemiology Unit, Department of Epidemiology (N.A., B.A.O., C.M.v.D.), and Department of Internal Medicine (A.G.U.), Erasmus MC, Rotterdam, The Netherlands; Department of Biological Psychology, Vrije Universiteit, Amsterdam, EMGO+ Institute, VU University Medical Center, The Netherlands (J.-J.H., E.J.d.G., G.W., D.I.B.); Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden (R.J.S., M. Frånberg, A. Hamsten); Centre for Molecular Medicine, Karolinska Universitetsjukhuset, Solna, Sweden (R.J.S., M. Frånberg, A. Hamsten); Estonian Genome Center (T.E., E.O., A. Metspalu), Institute of Biomedicine and Translational Medicine (S.S., M. Laan), and Estonian Genome Center (M.P.), University of Tartu, Estonia; Divisions of Endocrinology/Children's Hospital, Boston, MA (T.E.); Broad Institute of Harvard and MIT, Cambridge, MA (T.E., C.M.L., C.N.-C.); Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (D.E.A., P.N., A. Chakravarti, G.B.E.); The Population Science Branch, Division of Intramural Research, National Heart Lung and Blood Institute (S.-J.H., D.L.), Laboratory of Neurogenetics, National Institute on Aging (M.A.N.), Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute (F.C.), and Center for Information Technology (Y.D., P.J.M., Q.T.N.), National Institutes of Health, Bethesda, MD; The Framingham Heart Study, Framingham, MA (S.-J.H., D.L.); The Institute for Translational Genomics and Population Sciences, Department of Pediatrics (X.G., J.Y.), and The Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine (J.I.R.), LABioMed at Harbor-UCLA Medical Center, Torrance, CA; Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland (Z.K., M. Bochud); Swiss Institute of Bioinformatics, Lausanne, Switzerland (Z.K.); Department of Cardiology (S. Trompet, J.W.J.) Department of Gerontology and Geriatrics (S. Trompet), Department of Clinical Epidemiology (R.L.-G., R.d.M., D.O.M.-K.), Department of Molecular Epidemiology (J.D.), and Department of Public Health and Primary Care (D.O.M.-K.), Leiden University Medical Center, The Netherlands; Institute for Community Medicine (A.T.), Department of Internal Medicine B (M.D.), and Interfaculty Institute for Genetics and Functional Genomics (U.V.), University Medicine Greifswald, Germany; DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Germany (A.T., M.D., U.V.); Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany (J.S.R., A. Peters); Cardiovascular Health Research Unit, Department of Medicine (J.C.B., B.M.P.) and Departments of Biostatistics (K.R.), Epidemiology (B.M.P.), and Health Services (B.M.P.), University of Washington, Seattle; Icelandic Heart Association, Kopavogur, Iceland (A.V.S., V. Gudnason); Faculty of Medicine, University of Iceland, Reykjavik, Iceland (A.V.S., V. Gudnason); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (L.-P.L., T.L.); Department of Clinical Chemistry, Faculty of Medicine and Life Sciences, University of Tampere, Finland (L.-P.L., T.L.); Wellcome Trust Centre for Human Genetics (A. Mahajan, A.G., M. Farrall, T.F., C.M.L., H.W., A.P.M.), and Division of Cardiovascular Medicine, Radcliffe Department of Medicine (A.G., M. Farrall, H.W.), University of Oxford, United Kingdom; MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, United Kingdom (N.J.W., J.L., C.L., R.J.F.L., R.A.S., J.H.Z.); Clinical Division of Neurogeriatrics, Department of Neurology (E.H., R. Schmidt), Institute of Medical Informatics, Statistics and Documentation (E.H.), and Department of Neurology (H. Schmidt), Medical University Graz, Austria; Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics (P.K.J., H.C., I.R., S.W., J.F.W.), Centre for Cognitive Ageing and Cognitive Epidemiology (L.M.L., S.E.H., G.D., A.J.G., D.C.M.L., J.M.S., I.J.D.), Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine (A. Campbell), Generation Scotland, Centre for Genomic and Experimental Medicine (A. Campbell, S.P., C.H.), Department of Psychology (G.D., D.C.M.L., A. Pattie, I.J.D.), Alzheimer Scotland Dementia Research Centre (J.M.S.), and Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine (C.H.), University of Edinburgh, Scotland, United Kingdom; Department of Health (K.K., A.S.H., T. Niiranen, P.J., A.J., S. Koskinen, P.K., V.S., M.P.), and Chronic Disease Prevention Unit (J.T.), National Institute for Health and Welfare (THL), Helsinki, Finland; Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milano, Italy (M.T., C.M.B., C.F.S., D.T.); Data Tecnica International, Glen Echo, MD (M.A.N.); Medical Genetics, IRCCS-Burlo Garofolo Children Hospital, Trieste, Italy (D.V., G.G., P.G.); Department of Medical, Surgical and Health Sciences, University of Trieste, Italy (D.V., I.G., M. Brumat, M. Cocca, A. Morgan, G.G., P.G.); Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy (F.D.G.M., P.P.P., A.S.P., A.A.H.); Department of Genetics and Genomic Sciences (K.L.A.), The Charles Bronfman Institute for Personalized Medicine (Y.L., E.P.B., R.J.F.L.), and Mindich Child health Development Institute (R.J.F.L.), Icahn School of Medicine at Mount Sinai, New York; Cardiovascular Epidemiology and Genetics, IMIM, and CIBERCV, Barcelona, Spain (J. Marrugat, R.E.); Institute of Genetics and Biophysics A. Buzzati-Traverso, CNR, Napoli, Italy (D.R., T. Nutile, R. Sorice, M. Ciullo); Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin (L.M.L.); UCD Conway Institute, Centre for Proteome Research (L.M.L.), and School of Medicine, Conway Institute (D.C.S.), University College Dublin, Belfield, Ireland; Department of Immunology, Genetics and Pathology, Uppsala Universitet, Science for Life Laboratory, Sweden (S.E., Å. Johansson, U.G.); Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor (A.U.J., M. Boehnke); NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester United Kingdom (C.P.N., P.S.B., N.J.S.); MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine (J.E.H., V.V., J. Marten, A.F.W., J.F.W.), and Medical Genetics Section, Centre for Genomic and Experimental Medicine and MRC Institute of Genetics and Molecular Medicine (S.E.H.), University of Edinburgh, Western General Hospital, Scotland, United Kingdom; Department of Epidemiology and Biostatistics, School of Public Health (W.Z., E.E., J.C.C., H.G., B.L., I.T., A.-C.V.), MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health (M.-R.J., P.E.), School of Public Health (N.P.), International Centre for Circulatory Health (S. Thom), and National Heart and Lung Institute (P.S.), Imperial College London, United Kingdom; Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Southall, United Kingdom (W.Z., J.C.C., J.S.K.); Department of Medical Biology, Faculty of Medicine, University of Split, Croatia (T.Z.); Department of Hygiene and Epidemiology, University of Ioannina Medical School, Greece (E.E.); Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Scotland, United Kingdom (N. Shah, A.S.F.D., C.N.A.P.); Department of Pharmacy, COMSATS Institute of Information Technology, Abbottabad, Pakistan (N. Shah); National Institute for Health Research Biomedical Research Centre, London, United Kingdom (M.M.); Department of Human Genetics, Wellcome Trust Sanger Institute, United Kingdom (B.P.P., E.Z.); INSERM U 1219, Bordeaux Population Health Center, France (G.C., C.T., S.D.); Bordeaux University, France (G.C., C.T., S.D.); Hunter Medical Research Institute, New Lambton, NSW, Australia (C.O., E.G.H., R. Scott, J.A.); Center for Statistical Genetics, Department of Biostatistics, Ann Arbor, MI (G.A.); Department of Genetics and Molecular Biology, Isfahan University of Medical Sciences, Iran (M.A.); Busselton Population Medical Research Institute, Western Australia (J.B., J.H.); PathWest Laboratory Medicine of Western Australia, Nedlands (J.B., J.H.); School of Pathology and Laboratory Medicine (J.B., J.H.), School of Population and Global Health (J.H.), and School of Medicine and Pharmacology (A. James), The University of Western Australia, Nedlands; Imperial College Healthcare NHS Trust, London, United Kingdom (J.C.C., J.S.K.); University of Dundee, Ninewells Hospital & Medical School, United Kingdom (J.C.); Institute of Genetic Medicine (H.J.C.), and Institute of Health and Society (C. Mamasoula), Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Pathology, Amsterdam Medical Center, The Netherlands (J.J.D.); Department of Numerical Analysis and Computer Science, Stockholm University, Sweden (M. Frånberg); Department of Public Health and Caring Sciences, Geriatrics, Uppsala, Sweden (V. Giedraitis); Helmholtz Zentrum Muenchen, Deutsches Forschungszentrum fuer Gesundheit und Umwelt (GmbH), Neuherberg, Germany (C.G.); Department of Psychology, School of Social Sciences, Heriot-Watt University, Edinburgh, United Kingdom (A.J.G.); Intramural Research Program, Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging (T.B.H., L.J.L.); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (A. Hofman); Center For Life-Course Health Research (M.-R.J.), and Biocenter Oulu (M.-R.J.), University of Oulu, Finland; Unit of Primary Care, Oulu University Hospital, Finland (M.-R.J.); National Heart, Lung and Blood Institute, Cardiovascular Epidemiology and Human Genomics Branch, Bethesda, MD (A.D.J.); Department of Clinical Physiology, Tampere University Hospital, Finland (M.K.); Department of Clinical Physiology, Faculty of Medicine and Life Sciences, University of Tampere, Finland (M.K.); Cardiovascular Research Center (S. Kathiresan, C.N.-C.); Center for Human Genetics (S. Kathiresan), and Center for Human Genetic Research (C.N.-C.), Massachusetts General Hospital, Boston; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA (S. Kathiresan, C.N.-C.); Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, United Kingdom (K.-T.K.); Department of Public Health, Faculty of Medicine, University of Split, Croatia (I.K., O.P.); Cardiology, Department of Specialties of Medicine, Geneva University Hospital, Switzerland (L. Lin, F.M., G.B.E.); Department of Medical Sciences, Cardiovascular Epidemiology (L. Lind, J.S.), and Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory (E.I.), Uppsala University, Sweden; Department of Psychiatry, EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands (Y.M., B.W.J.H.P.); School of Molecular, Genetic and Population Health Sciences, University of Edinburgh, Medical School, Teviot Place, Scotland, United Kingdom (A.D.M.); Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston (A.C.M.); British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences (S.P.), and Institute of Cardiovascular and Medical Sciences, Faculty of Medicine (D.J.S.), University of Glasgow, United Kingdom; Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland (A. Palotie, S.R., A.-P.S., M.P.); Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada (G.P., S. Thériault); Department of Neurology, General Central Hospital, Bolzano, Italy (P.P.P.); Department of Neurology, University of Lübeck, Germany (P.P.P.); Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Finland (O.T.R.); Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Finland (O.T.R.); Department of Cardiology, Fujian Provincial Hospital, Fujian Medical University, Fuzhou, China (M.R.); Harvard Medical School, Boston, MA (P.M.R., D.I.C.); Institute for Maternal and Child Health IRCCS Burlo Garofolo, Trieste, Italy (A.R.); Institute of Molecular Biology and Biochemistry, Centre for Molecular Medicine, Medical University of Graz, Austria (Y.S., H. Schmidt); INSERM U1078, Etablissement Français du Sang, Brest Cedex, France (A.S.P.); Faculty of Health, University of Newcastle, Callaghan, NSW, Australia (R. Scott, J.A.); John Hunter Hospital, New Lambton, NSW, Australia (R. Scott, J.A.); The New York Academy of Medicine, New York (D.S.); IRCCS Neuromed, Pozzilli, Isernia, Italy (R. Sorice, M. Ciullo); Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin 2, Ireland (A.S.); Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA (K.D.T.); Division of Genetic Outcomes, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA (K.D.T.); Department of Public Health (C.T.), and Department of Neurology (S.D.), Bordeaux University Hospital, France; Department of Internal Medicine, Lausanne University Hospital, CHUV, Switzerland (P.V.); Population Health Research Institute, McMaster University, Hamilton Ontario, Canada (D.C.); National Heart and Lung Institute, Imperial College London, Hammersmith Hospital Campus, United Kingdom (J.S.K.); Dasman Diabetes Institute, Kuwait (J.T.); Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia (J.T.); Department of Neurosciences and Preventive Medicine, Danube-University Krems, Austria (J.T.); Division of Cardiovascular Sciences, The University of Manchester and Central Manchester University Hospitals NHS Foundation Trust, United Kingdom (B.D.K.); Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem (Y.M.L.); Kaiser Permanent Washington Health Research Institute, Seattle, WA (B.M.P.); Institute of Physiology, University Medicine Greifswald, Karlsburg, Germany (R.R); Department of Pulmonary Physiology and Sleep, Sir Charles Gairdner Hospital, Nedlands, Western Australia (A. James); Population Health Research Institute, St George's, University of London, United Kingdom (D.P.S.); Department of Medicine, Columbia University Medical Center, New York (W.P.); Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, CA (E.I.); Data Science Institute and Lancaster Medical School, Lancaster University, United Kingdom (J.K.); and Department of Biostatistics, University of Liverpool, United Kingdom (A.P.M.).

Elevated blood pressure is a major risk factor for cardiovascular disease and has a substantial genetic contribution. Genetic variation influencing blood pressure has the potential to identify new pharmacological targets for the treatment of hypertension. To discover additional novel blood pressure loci, we used 1000 Genomes Project-based imputation in 150 134 European ancestry individuals and sought significant evidence for independent replication in a further 228 245 individuals. We report 6 new signals of association in or near , , , , , and , and provide new replication evidence for a further 2 signals in and Combining large whole-blood gene expression resources totaling 12 607 individuals, we investigated all novel and previously reported signals and identified 48 genes with evidence for involvement in blood pressure regulation that are significant in multiple resources. Three novel kidney-specific signals were also detected. These robustly implicated genes may provide new leads for therapeutic innovation.
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http://dx.doi.org/10.1161/HYPERTENSIONAHA.117.09438DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5783787PMC
July 2017

Association analyses based on false discovery rate implicate new loci for coronary artery disease.

Nat Genet 2017 Sep 17;49(9):1385-1391. Epub 2017 Jul 17.

German Heart Center Munich, Clinic at Technische Universität München and Deutsches Zentrum für Herz- und Kreislauferkrankungen (DZHK), partner site Munich Heart Alliance, Munich, Germany.

Genome-wide association studies (GWAS) in coronary artery disease (CAD) had identified 66 loci at 'genome-wide significance' (P < 5 × 10) at the time of this analysis, but a much larger number of putative loci at a false discovery rate (FDR) of 5% (refs. 1,2,3,4). Here we leverage an interim release of UK Biobank (UKBB) data to evaluate the validity of the FDR approach. We tested a CAD phenotype inclusive of angina (SOFT; n = 10,801) as well as a stricter definition without angina (HARD; n = 6,482) and selected cases with the former phenotype to conduct a meta-analysis using the two most recent CAD GWAS. This approach identified 13 new loci at genome-wide significance, 12 of which were on our previous list of loci meeting the 5% FDR threshold, thus providing strong support that the remaining loci identified by FDR represent genuine signals. The 304 independent variants associated at 5% FDR in this study explain 21.2% of CAD heritability and identify 243 loci that implicate pathways in blood vessel morphogenesis as well as lipid metabolism, nitric oxide signaling and inflammation.
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http://dx.doi.org/10.1038/ng.3913DOI Listing
September 2017
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