Imperial College London


Faculty of MedicineSchool of Public Health

Reader in Cardiometabolic Disease Epidemiology



+44 (0)20 7594 3347a.dehghan CV




157Norfolk PlaceSt Mary's Campus





Publication Type

270 results found

Pazoki R, Evangelos E, David M, Rui P, Ibrahim K, Paul B, Dipender G, Verena Z, Paul E, Ioanna T, Abbas Det al., GWAS for urinary sodium and potassium excretion highlights pathways shared with cardiovascular traits, Nature Communications, ISSN: 2041-1723

Urinary sodium and potassium excretion are associated with blood pressure (BP) and cardiovascular disease (CVD). The exact biological link between these traits is yet to be elucidated. Here, we identify 51 loci for sodium and 13 for potassium excretion in a large-scale genome-wide association study (GWAS) on urinary sodium and potassium excretion using data from 446,237 individuals of European descent from the UK Biobank study. We extensively interrogate the results using multiple analyses such as Mendelian randomization, functional assessment, co localization, genetic risk score, and pathway analyses. We identify a shared genetic component between urinary sodium and potassium expression and cardiovascular traits. Ingenuity pathway analysis shows that urinary sodium and potassium excretion loci are over represented in behavioural response to stimuli. Our study highlights pathways that are shared between urinary sodium and potassium excretion and cardiovascular traits.

Journal article

Gill D, Benyamin B, Moore LSP, Monori G, Zhou A, Fotios K, Evangelou E, Laffan M, Walker AP, Tsilidis KK, Dehghan A, Elliott P, Hyppönen E, Tzoulaki Iet al., 2019, Associations of genetically determined iron status across the phenome: a mendelian randomization study, PLoS Medicine, Vol: 16, ISSN: 1549-1277

BackgroundIron is integral to many physiological processes and variations in its levels, even within the normal range, can have implications for health. The objective of this study was to explore the broad clinical effects of varying iron status.Methods and FindingsGenome-wide association study summary data obtained from 48,972 European individuals (55% female) across 19 cohorts in the Genetics of Iron Status Consortium were used to identify three genetic variants (rs1800562 and rs1799945 in the hemochromatosis gene, and rs855791 in the transmembrane protease serine 6 gene) that associate with increased serum iron, ferritin and transferrin saturation, and decreased transferrin levels, thus serving as instruments for systemic iron status. Phenome-wide association study (PheWAS) of these instruments was performed on 415,482 European individuals (54% female) in the UK Biobank that were aged 40-69 years when recruited from 2006 to 2010, with their genetic data linked to Hospital Episode Statistics from April 1995 to March 2016. Two-sample summary data Mendelian randomization (MR) analysis was performed to investigate the effect of varying iron status on outcomes across the human phenome. MR-PheWAS analysis for the three iron status genetic instruments was performed separately and then pooled by meta-analysis. Correction was made for testing of multiple correlated phenotypes using a 5% false discovery rate threshold. Heterogeneity between MR estimates for different instruments was used to indicate possible bias due to effects of the genetic variants through pathways unrelated to iron status. There were 904 distinct phenotypes included in the MR-PheWAS analyses. After correcting for multiple testing, the three genetic instruments for systemic iron status demonstrated consistent evidence of a causal effect of higher iron status on decreasing risk of traits related to anemia (iron deficiency anemia: odds ratio [OR] scaled to a standard deviation increase in genetically dete

Journal article

Liu J, Carnero-Montoro E, van Dongen J, Lent S, Nedeljkovic I, Ligthart S, Tsai P-C, Martin TC, Mandaviya PR, Jansen R, Peters MJ, Duijts L, Jaddoe VWV, Tiemeier H, Felix JF, Willemsen G, de Geus EJC, Chu AY, Levy D, Hwang S-J, Bressler J, Gondalia R, Salfati EL, Herder C, Hidalgo BA, Tanaka T, Moore AZ, Lemaitre RN, Jhun MA, Smith JA, Sotoodehnia N, Bandinelli S, Ferrucci L, Arnett DK, Grallert H, Assimes TL, Hou L, Baccarelli A, Whitsel EA, van Dijk KW, Amin N, Uitterlinden AG, Sijbrands EJG, Franco OH, Dehghan A, Spector TD, Dupuis J, Hivert M-F, Rotter J, Meigs JB, Pankow JS, van Meurs JBJ, Isaacs A, Boomsma D, Bell JT, Demirkan A, van Duijn CMet al., 2019, An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis, NATURE COMMUNICATIONS, Vol: 10, ISSN: 2041-1723

Journal article

Ward-Caviness CK, de Vries PS, Wiggins KL, Huffman JE, Yanek LR, Bielak LF, Giulianini F, Guo X, Kleber ME, Kacprowski T, Gross S, Petersman A, Smith GD, Hartwig FP, Bowden J, Hemani G, Mueller-Nuraysid M, Strauch K, Koenig W, Waldenberger M, Meitinger T, Pankratz N, Boerwinkle E, Tang W, Fu Y-P, Johnson AD, Song C, de Maat MPM, Uitterlinden AG, Franco OH, Brody JA, McKnight B, Chen Y-DI, Psaty BM, Mathias RA, Becker DM, Peyser PA, Smith JA, Bielinski SJ, Ridker PM, Taylor KD, Yao J, Tracy R, Delgado G, Trompet S, Sattar N, Jukema JW, Becker LC, Kardia SLR, Rotter J, Maerz W, Doerr M, Chasman D, Dehghan A, O'Donnell CJ, Smith NL, Peters A, Morrison ACet al., 2019, Mendelian randomization evaluation of causal effects of fibrinogen on incident coronary heart disease, PLOS ONE, Vol: 14, ISSN: 1932-6203

Journal article

Portilla-Fernandez E, Ghanbari M, van Meurs JBJ, Danser AHJ, Franco OH, Muka T, Roks A, Dehghan Aet al., 2019, Dissecting the association of autophagy-related genes with cardiovascular diseases and intermediate vascular traits: A population-based approach, PLOS ONE, Vol: 14, ISSN: 1932-6203

Journal article

Inker LA, Grams ME, Levey AS, Coresh J, Cirillo M, Collins JF, Gansevoort RT, Gutierrez OM, Hamano T, Heine GH, Ishikawa S, Jee SH, Kronenberg F, Landray MJ, Miura K, Nadkarni GN, Peralta CA, Rothenbacher D, Schaeffner E, Sedaghat S, Shlipak MG, Zhang L, van Zuilen AD, Hallan SI, Kovesdy CP, Woodward M, Levin A, CKD Prognosis Consortiumet al., 2019, Relationship of Estimated GFR and Albuminuria to Concurrent Laboratory Abnormalities: An Individual Participant Data Meta-analysis in a Global Consortium., Am J Kidney Dis, Vol: 73, Pages: 206-217

RATIONALE & OBJECTIVE: Chronic kidney disease (CKD) is complicated by abnormalities that reflect disruption in filtration, tubular, and endocrine functions of the kidney. Our aim was to explore the relationship of specific laboratory result abnormalities and hypertension with the estimated glomerular filtration rate (eGFR) and albuminuria CKD staging framework. STUDY DESIGN: Cross-sectional individual participant-level analyses in a global consortium. SETTING & STUDY POPULATIONS: 17 CKD and 38 general population and high-risk cohorts. SELECTION CRITERIA FOR STUDIES: Cohorts in the CKD Prognosis Consortium with data for eGFR and albuminuria, as well as a measurement of hemoglobin, bicarbonate, phosphorus, parathyroid hormone, potassium, or calcium, or hypertension. DATA EXTRACTION: Data were obtained and analyzed between July 2015 and January 2018. ANALYTICAL APPROACH: We modeled the association of eGFR and albuminuria with hemoglobin, bicarbonate, phosphorus, parathyroid hormone, potassium, and calcium values using linear regression and with hypertension and categorical definitions of each abnormality using logistic regression. Results were pooled using random-effects meta-analyses. RESULTS: The CKD cohorts (n=254,666 participants) were 27% women and 10% black, with a mean age of 69 (SD, 12) years. The general population/high-risk cohorts (n=1,758,334) were 50% women and 2% black, with a mean age of 50 (16) years. There was a strong graded association between lower eGFR and all laboratory result abnormalities (ORs ranging from 3.27 [95% CI, 2.68-3.97] to 8.91 [95% CI, 7.22-10.99] comparing eGFRs of 15 to 29 with eGFRs of 45 to 59mL/min/1.73m2), whereas albuminuria had equivocal or weak associations with abnormalities (ORs ranging from 0.77 [95% CI, 0.60-0.99] to 1.92 [95% CI, 1.65-2.24] comparing urinary albumin-creatinine ratio > 300 vs < 30mg/g). LIMITATIONS: Variations in study era, health care delivery system, typical diet, and laboratory

Journal article

Adlam D, Olson TM, Combaret N, Kovacic JC, Iismaa SE, Al-Hussaini A, O'Byrne MM, Bouajila S, Georges A, Mishra K, Braund PS, d'Escamard V, Huang S, Margaritis M, Nelson CP, de Andrade M, Kadian-Dodov D, Welch CA, Mazurkiewicz S, Jeunemaitre X, DISCO Consortium, Wong CMY, Giannoulatou E, Sweeting M, Muller D, Wood A, McGrath-Cadell L, Fatkin D, Dunwoodie SL, Harvey R, Holloway C, Empana J-P, Jouven X, CARDIoGRAMPlusC4D Study Group, Olin JW, Gulati R, Tweet MS, Hayes SN, Samani NJ, Graham RM, Motreff P, Bouatia-Naji Net al., 2019, Association of the PHACTR1/EDN1 Genetic Locus With Spontaneous Coronary Artery Dissection., J Am Coll Cardiol, Vol: 73, Pages: 58-66

BACKGROUND: Spontaneous coronary artery dissection (SCAD) is an increasingly recognized cause of acute coronary syndromes (ACS) afflicting predominantly younger to middle-aged women. Observational studies have reported a high prevalence of extracoronary vascular anomalies, especially fibromuscular dysplasia (FMD) and a low prevalence of coincidental cases of atherosclerosis. PHACTR1/EDN1 is a genetic risk locus for several vascular diseases, including FMD and coronary artery disease, with the putative causal noncoding variant at the rs9349379 locus acting as a potential enhancer for the endothelin-1 (EDN1) gene. OBJECTIVES: This study sought to test the association between the rs9349379 genotype and SCAD. METHODS: Results from case control studies from France, United Kingdom, United States, and Australia were analyzed to test the association with SCAD risk, including age at first event, pregnancy-associated SCAD (P-SCAD), and recurrent SCAD. RESULTS: The previously reported risk allele for FMD (rs9349379-A) was associated with a higher risk of SCAD in all studies. In a meta-analysis of 1,055 SCAD patients and 7,190 controls, the odds ratio (OR) was 1.67 (95% confidence interval [CI]: 1.50 to 1.86) per copy of rs9349379-A. In a subset of 491 SCAD patients, the OR estimate was found to be higher for the association with SCAD in patients without FMD (OR: 1.89; 95% CI: 1.53 to 2.33) than in SCAD cases with FMD (OR: 1.60; 95% CI: 1.28 to 1.99). There was no effect of genotype on age at first event, P-SCAD, or recurrence. CONCLUSIONS: The first genetic risk factor for SCAD was identified in the largest study conducted to date for this condition. This genetic link may contribute to the clinical overlap between SCAD and FMD.

Journal article

Pazoki R, Dehghan A, Evangelou E, Warren H, Gao H, Caulfield M, Elliott P, Tzoulaki I, Tzoulaki I, Dehghan A, Pazoki R, Evangelou E, Elliott P, Gao Het al., 2019, Correction to: Genetic predisposition to high blood pressure and lifestyle factors: associations with midlife blood pressure levels and cardiovascular events, Circulation, Vol: 139, Pages: E2-E2, ISSN: 0009-7322

Background: High blood pressure (BP) is a major risk factor for cardiovascular diseases (CVDs), the leading cause of mortality worldwide. Both heritable and lifestyle risk factors contribute to elevated BP levels. We aimed to investigate the extent to which lifestyle factors could offset the effect of an adverse BP genetic profile and its effect on CVD risk.Methods: We constructed a genetic risk score for high BP by using 314 published BP loci in 277 005 individuals without previous CVD from the UK Biobank study, a prospective cohort of individuals aged 40 to 69 years, with a median of 6.11 years of follow-up. We scored participants according to their lifestyle factors including body mass index, healthy diet, sedentary lifestyle, alcohol consumption, smoking, and urinary sodium excretion levels measured at recruitment. We examined the association between tertiles of genetic risk and tertiles of lifestyle score with BP levels and incident CVD by using linear regression and Cox regression models, respectively.Results: Healthy lifestyle score was strongly associated with BP (P<10–320) for systolic and diastolic BP and CVD events regardless of the underlying BP genetic risk. Participants with a favorable in comparison with an unfavorable lifestyle (bottom versus top tertile lifestyle score) had 3.6, 3.5, and 3.6 mm Hg lower systolic BP in low, middle, and high genetic risk groups, respectively (P for interaction=0.0006). Similarly, favorable in comparison with unfavorable lifestyle showed 30%, 31%, and 33% lower risk of CVD among participants in low, middle, and high genetic risk groups, respectively (P for interaction=0.99).Conclusions: Our data further support population-wide efforts to lower BP in the population via lifestyle modification. The advantages and disadvantages of disclosing genetic predisposition to high BP for risk stratification needs careful evaluation.

Journal article

Franceschini N, Giambartolomei C, de Vries PS, Finan C, Bis JC, Huntley RP, Lovering RC, Tajuddin SM, Winkler TW, Graff M, Kavousi M, Dale C, Smith AV, Hofer E, van Leeuwen EM, Nolte IM, Lu L, Scholz M, Sargurupremraj M, Pitkänen N, Franzén O, Joshi PK, Noordam R, Marioni RE, Hwang S-J, Musani SK, Schminke U, Palmas W, Isaacs A, Correa A, Zonderman AB, Hofman A, Teumer A, Cox AJ, Uitterlinden AG, Wong A, Smit AJ, Newman AB, Britton A, Ruusalepp A, Sennblad B, Hedblad B, Pasaniuc B, Penninx BW, Langefeld CD, Wassel CL, Tzourio C, Fava C, Baldassarre D, O'Leary DH, Teupser D, Kuh D, Tremoli E, Mannarino E, Grossi E, Boerwinkle E, Schadt EE, Ingelsson E, Veglia F, Rivadeneira F, Beutner F, Chauhan G, Heiss G, Snieder H, Campbell H, Völzke H, Markus HS, Deary IJ, Jukema JW, de Graaf J, Price J, Pott J, Hopewell JC, Liang J, Thiery J, Engmann J, Gertow K, Rice K, Taylor KD, Dhana K, Kiemeney LALM, Lind L, Raffield LM, Launer LJ, Holdt LM, Dörr M, Dichgans M, Traylor M, Sitzer M, Kumari M, Kivimaki M, Nalls MA, Melander O, Raitakari O, Franco OH, Rueda-Ochoa OL, Roussos P, Whincup PH, Amouyel P, Giral P, Anugu P, Wong Q, Malik R, Rauramaa R, Burkhardt R, Hardy R, Schmidt R, de Mutsert R, Morris RW, Strawbridge RJ, Wannamethee SG, Hägg S, Shah S, McLachlan S, Trompet S, Seshadri S, Kurl S, Heckbert SR, Ring S, Harris TB, Lehtimäki T, Galesloot TE, Shah T, de Faire U, Plagnol V, Rosamond WD, Post W, Zhu X, Zhang X, Guo X, Saba Y, MEGASTROKE Consortium, Dehghan A, Seldenrijk A, Morrison AC, Hamsten A, Psaty BM, van Duijn CM, Lawlor DA, Mook-Kanamori DO, Bowden DW, Schmidt H, Wilson JF, Wilson JG, Rotter JI, Wardlaw JM, Deanfield J, Halcox J, Lyytikäinen L-P, Loeffler M, Evans MK, Debette S, Humphries SE, Völker U, Gudnason V, Hingorani AD, Björkegren JLM, Casas JP, O'Donnell CJet al., 2018, GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes., Nat Commun, Vol: 9

Carotid artery intima media thickness (cIMT) and carotid plaque are measures of subclinical atherosclerosis associated with ischemic stroke and coronary heart disease (CHD). Here, we undertake meta-analyses of genome-wide association studies (GWAS) in 71,128 individuals for cIMT, and 48,434 individuals for carotid plaque traits. We identify eight novel susceptibility loci for cIMT, one independent association at the previously-identified PINX1 locus, and one novel locus for carotid plaque. Colocalization analysis with nearby vascular expression quantitative loci (cis-eQTLs) derived from arterial wall and metabolic tissues obtained from patients with CHD identifies candidate genes at two potentially additional loci, ADAMTS9 and LOXL4. LD score regression reveals significant genetic correlations between cIMT and plaque traits, and both cIMT and plaque with CHD, any stroke subtype and ischemic stroke. Our study provides insights into genes and tissue-specific regulatory mechanisms linking atherosclerosis both to its functional genomic origins and its clinical consequences in humans.

Journal article

't Hart LM, Vogelzangs N, Mook-Kanamori DO, Brahimaj A, Nano J, van der Heijden AAWA, Willems van Dijk K, Slieker RC, Steyerberg EW, Ikram MA, Beekman M, Boomsma DI, van Duijn CM, Slagboom PE, Stehouwer CDA, Schalkwijk CG, Arts ICW, Dekker JM, Dehghan A, Muka T, van der Kallen CJH, Nijpels G, van Greevenbroek MMJet al., 2018, Blood Metabolomic Measures Associate With Present and Future Glycemic Control in Type 2 Diabetes., J Clin Endocrinol Metab, Vol: 103, Pages: 4569-4579

Objective: We studied whether blood metabolomic measures in people with type 2 diabetes (T2D) are associated with insufficient glycemic control and whether this association is influenced differentially by various diabetes drugs. We then tested whether the same metabolomic profiles were associated with the initiation of insulin therapy. Methods: A total of 162 metabolomic measures were analyzed using a nuclear magnetic resonance-based method in people with T2D from four cohort studies (n = 2641) and one replication cohort (n = 395). Linear and logistic regression analyses with adjustment for potential confounders, followed by meta-analyses, were performed to analyze associations with hemoglobin A1c levels, six glucose-lowering drug categories, and insulin initiation during a 7-year follow-up period (n = 698). Results: After Bonferroni correction, 26 measures were associated with insufficient glycemic control (HbA1c >53 mmol/mol). The strongest association was with glutamine (OR, 0.66; 95% CI, 0.61 to 0.73; P = 7.6 × 10-19). In addition, compared with treatment-naive patients, 31 metabolomic measures were associated with glucose-lowering drug use (representing various metabolite categories; P ≤ 3.1 × 10-4 for all). In drug-stratified analyses, associations with insufficient glycemic control were only mildly affected by different glucose-lowering drugs. Five of the 26 metabolomic measures (apolipoprotein A1 and medium high-density lipoprotein subclasses) were also associated with insulin initiation during follow-up in both discovery and replication. The strongest association was observed for medium high-density lipoprotein cholesteryl ester (OR, 0.54; 95% CI, 0.42 to 0.71; P = 4.5 × 10-6). Conclusion: Blood metabolomic measures were associated with present and future glycemic control and might thus provide relevant cues to identify those at increased risk of treatment failure.

Journal article

Sedaghat S, Darweesh SKL, Verlinden VJA, Van Der Geest JN, Dehghan A, Franco OH, Hoorn EJ, Ikram MAet al., 2018, Kidney function, gait pattern and fall in the general population: A cohort study, Nephrology Dialysis Transplantation, Vol: 33, Pages: 2165-2172, ISSN: 0931-0509

© The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. Background. Gait disturbance is proposed as a mechanism for higher risk of fall in kidney disease patients. We investigated the association of kidney function with gait pattern in the general population and tested whether the association between impaired kidney function and fall is more pronounced in subjects with lower gait function. Methods. We included 1430 participants (mean age: 60 years) from the Rotterdam Study. Kidney function was assessed using estimated glomerular filtration rate (eGFR) and albumin-to-creatinine ratio (ACR). We assessed global gait, gait velocity and seven independent gait domains: Rhythm, Phases, Variability, Pace, Tandem, Turning and Base of Support. Regression models adjusted for cardiometabolic and neurological factors were used. We evaluated whether participants with impaired kidney function and impaired gait fell more in the previous year. Results. The study population had a median (interquartile range) ACR of 3.6 (2.5-6.2) mg/g and mean 6 SD eGFR of 87.6 6 15 mL/min/1.73 m 2 . Higher ACR and lower eGFR were associated with lower global gait score [per doubling of ACR: 0.10, 95% confidence interval (CI): 0.14 to 0.06, and per SD eGFR:0.09, 95% CI: 0.14 to 0.03] and slower gait speed (ACR: 1.44 cm/s, CI: 2.12 to 0.76; eGFR: 1.55 cm/s, CI: 2.43 to 0.67). Worse kidney function was associated with lower scores in Variability domain. The association between impaired kidney function and history of fall was present only in participants with lower gait scores [odds ratio (95% CI): ACR: 1.34 (1.09-1.65); eGFR: 1.58 (1.07-2.33)]. Conclusions. We observed a graded association between lower kidney function and impaired gait suggesting that individuals with decreased kidney function, even at an early stage, need to be evaluated for gait abnormalities and might benefit from fall prevention programmes.

Journal article

Gill D, Monori G, Tzoulaki I, Dehghan Aet al., 2018, Iron status and risk of stroke, Stroke, Vol: 49, Pages: 2815-2821, ISSN: 0039-2499

Background and Purpose- Both iron deficiency and excess have been associated with stroke risk in observational studies. However, such associations may be attributable to confounding from environmental factors. This study uses the Mendelian randomization technique to overcome these limitations by investigating the association between genetic variants related to iron status and stroke risk. Methods- A study of 48 972 subjects performed by the Genetics of Iron Status consortium identified genetic variants with concordant relations to 4 biomarkers of iron status (serum iron, transferrin saturation, ferritin, and transferrin) that supported their use as instruments for overall iron status. Genetic estimates from the MEGASTROKE consortium were used to investigate the association between the same genetic variants and stroke risk. The 2-sample ratio method Mendelian randomization approach was used for the main analysis, with the MR-Egger and weighted median techniques used in sensitivity analyses. Results- The main results, reported as odds ratio (OR) of stroke per SD unit increase in genetically determined iron status biomarker, showed a detrimental effect of increased iron status on stroke risk (serum iron OR, 1.07; 95% CI, 1.01-1.14; [log-transformed] ferritin OR, 1.18; 95% CI, 1.02-1.36; and transferrin saturation OR, 1.06; 95% CI, 1.01-1.11). A higher transferrin, indicative of lower iron status, was also associated with decreased stroke risk (OR, 0.92; 95% CI, 0.86-0.99). Examining ischemic stroke subtypes, we found the detrimental effect of iron status to be driven by cardioembolic stroke. These results were supported in statistical sensitivity analyses more robust to the inclusion of pleiotropic variants. Conclusions- This study provides Mendelian randomization evidence that higher iron status is associated with increased stroke risk and, in particular, cardioembolic stroke. Further work is required to investigate the underlying mechanism and whether this can

Journal article

Sabater-Lleal M, Huffman JE, de Vries PS, Marten J, Mastrangelo MA, Song C, Pankratz N, Ward-Caviness CK, Yanek LR, Trompet S, Delgado GE, Guo X, Bartz TM, Martinez-Perez A, Germain M, de Haan HG, Ozel AB, Polasek O, Smith AV, Eicher JD, Reiner AP, Tang W, Davies NM, Stott DJ, Rotter JI, Tofler GH, Boerwinkle E, de Maat MPM, Kleber ME, Welsh P, Brody JA, Chen M-H, Vaidya D, Soria JM, Suchon P, van Hylckama Vlieg A, Desch KC, Kolcic I, Joshi PK, Launer LJ, Harris TB, Campbell H, Rudan I, Becker DM, Li JZ, Rivadeneira F, Uitterlinden AG, Hofman A, Franco OH, Cushman M, Psaty BM, Morange P-E, McKnight B, Chong MR, Fernandez-Cadenas I, Rosand J, Lindgren A, Gudnason V, Wilson JF, Hayward C, Ginsburg D, Fornage M, Rosendaal FR, Souto JC, Becker LC, Jenny NS, Maerz W, Jukema JW, Dehghan A, Tregouet D-A, Morrison AC, Johnson AD, O'Donnell CJ, Strachan DP, Lowenstein CJ, Smith NLet al., 2018, Genome-Wide Association Transethnic Meta-Analyses Identifies Novel Associations Regulating Coagulation Factor VIII and von Willebrand Factor Plasma Levels, CIRCULATION, Vol: 139, Pages: 620-635, ISSN: 0009-7322

Journal article

Mustafa R, Ghanbari M, Evangelou M, Dehghan Aet al., 2018, An enrichment analysis for cardiometabolic traits suggests non-random assignment of genes to microRNAs, International Journal of Molecular Sciences, Vol: 19, ISSN: 1422-0067

MicroRNAs (miRNAs) regulate the expression of majority of genes. However, it is not known whether they regulate genes in random or are organized according to their function. To this end, we chose cardiometabolic disorders as an example and investigated whether genes associated with cardiometabolic disorders are regulated by a random set of miRNAs or a limited number of them. Single-nucleotide polymorphisms (SNPs) reaching genome-wide level significance were retrieved from most recent genome-wide association studies on cardiometabolic traits, which were cross-referenced with Ensembl to identify related genes and combined with miRNA target prediction databases (TargetScan, miRTarBase, or miRecords) to identify miRNAs that regulate them. We retrieved 520 SNPs, of which 355 were intragenic, corresponding to 304 genes. While we found a higher proportion of genes reported from all GWAS that were predicted targets for miRNAs in comparison to all protein coding genes (75.1%), the proportion was even higher for cardiometabolic genes (80.6%). Enrichment analysis was performed within each database. We found that cardiometabolic genes were over-represented in target genes for 29 miRNAs (based on TargetScan) and 3 miRNAs (miR-181a, miR-302d, and miR-372) (based on miRecords) after Benjamini-Hochberg correction for multiple testing. Our work provides evidence for non-random assignment of genes to miRNAs and supports the idea that miRNAs regulate sets of genes that are functionally related.

Journal article

Iotchkova V, Huang J, Morris JA, Jain D, Barbieri C, Walter K, Min JL, Chen L, Astle W, Cocca M, Deelen P, Elding H, Farmaki A-E, Franklin CS, Franberg M, Gaunt TR, Hofman A, Jiang T, Kleber ME, Lachance G, Luan J, Malerba G, Matchan A, Mead D, Memari Y, Ntalla I, Panoutsopoulou K, Pazoki R, Perry JRB, Rivadeneira F, Sabater-Lleal M, Sennblad B, Shin S-Y, Southam L, Traglia M, van Dijk F, van Leeuwen EM, Zaza G, Zhang W, Amin N, Butterworth A, Chambers JC, Dedoussis G, Dehghan A, Franco OH, Franke L, Frontini M, Gambaro G, Gasparini P, Hamsten A, Isaacs A, Kooner JS, Kooperberg C, Langenberg C, Marz W, Scott RA, Swertz MA, Toniolo D, Uitterlinden AG, van Duijn CM, Watkins H, Zeggini E, Maurano MT, Timpson NJ, Reiner AP, Auer PL, Soranzo Net al., 2018, Author Correction: Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps, Nature Genetics, Vol: 50, Pages: 1752-1752, ISSN: 1061-4036

Correction to: Nature Genetics, published online 26 September 2016.In the version of the article published, the surname of author Aaron Isaacs is misspelled as Issacs.

Journal article

Ligthart S, Vaez A, Vosa U, Stathopoulou MG, de Vries PS, Prins BP, Van der Most PJ, Tanaka T, Naderi E, Rose LM, Wu Y, Karlsson R, Barbalic M, Lin H, Pool R, Zhu G, Mace A, Sidore C, Trompet S, Mangino M, Sabater-Lleal M, Kemp JP, Abbasi A, Kacprowski T, Verweij N, Smith AV, Huang T, Marzi C, Feitosa MF, Lohman KK, Kleber ME, Milaneschi Y, Mueller C, Huq M, Vlachopoulou E, Lyytikainen L-P, Oldmeadow C, Deelen J, Perola M, Zhao JH, Feenstra B, Amini M, Lahti J, Schraut KE, Fornage M, Suktitipat B, Chen W-M, Li X, Nutile T, Malerba G, Luan J, Bak T, Schork N, Del Greco FM, Thiering E, Mahajan A, Marioni RE, Mihailov E, Eriksson J, Ozel AB, Zhang W, Nethander M, Cheng Y-C, Aslibekyan S, Ang W, Gandin I, Yengo L, Portas L, Kooperberg C, Hofer E, Rajan KB, Schurmann C, den Hollander W, Ahluwalia TS, Zhao J, Draisma HHM, Ford I, Timpson N, Teumer A, Huang H, Wahl S, Liu Y, Huang J, Uh H-W, Geller F, Joshi PK, Yanek LR, Trabetti E, Lehne B, Vozzi D, Verbanck M, Biino G, Saba Y, Meulenbelt I, O'Connell JR, Laakso M, Giulianini F, Magnusson PKE, Ballantyne CM, Hottenga JJ, Montgomery G, Rivadineira F, Rueedi R, Steri M, Herzig K-H, Stott DJ, Menni C, Franberg M, St Pourcain B, Felix SB, Pers TH, Bakker SJL, Kraft P, Peters A, Vaidya D, Delgado G, Smit JH, Grossmann V, Sinisalo J, Seppala I, Williams SR, Holliday EG, Moed M, Langenberg C, Raikkonen K, Ding J, Campbell H, Sale MM, Chen Y-DI, James AL, Ruggiero D, Soranzo N, Hartman CA, Smith EN, Berenson GS, Fuchsberger C, Hernandez D, Tiesler CMT, Giedraitis V, Liewald D, Fischer K, Mellstrom D, Larsson A, Wang Y, Scott WR, Lorentzon M, Beilby J, Ryan KA, Pennell CE, Vuckovic D, Balkau B, Concas MP, Schmidt R, de Leon CFM, Bottinger EP, Kloppenburg M, Paternoster L, Boehnke M, Musk AW, Willemsen G, Evans DM, Madden PAF, Kahonen M, Kutalik Z, Zoledziewska M, Karhunen V, Kritchevsky SB, Sattar N, Lachance G, Clarke R, Harris TB, Raitakari OT, Attia JR, Van Heemst D, Kajantie E, Sorice R, Gambaro G, Scott RA, Hicks AA, Ferrucciet al., 2018, Genome analyses of >200,000 individuals identify 58 loci for chronic inflammation and highlight pathways that link inflammation and complex disorders, American Journal of Human Genetics, Vol: 103, Pages: 691-706, ISSN: 0002-9297

C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5 × 10−8). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences.

Journal article

Mahajan A, Taliun D, Thurner M, Robertson NR, Torres JM, Rayner NW, Payne AJ, Steinthorsdottir V, Scott RA, Grarup N, Cook JP, Schmidt EM, Wuttke M, Sarnowski C, Magill R, Nano J, Gieger C, Trompet S, Lecoeur C, Preuss MH, Prins BP, Guo X, Bielak LF, Below JE, Bowden DW, Chambers JC, Kim YJ, Ng MCY, Petty LE, Sim X, Zhang W, Bennett AJ, Bork-Jensen J, Brummett CM, Canouil M, Kardt K-UE, Fischer K, Kardia SLR, Kronenberg F, Lall K, Liu C-T, Locke AE, Luan J, Ntalla L, Nylander V, Schoenherr S, Schurmann C, Yengo L, Bottinger EP, Brandslund I, Christensen C, Dedoussis G, Florez JC, Ford I, France OH, Frayling TM, Giedraitis V, Hackinger S, Hattersley AT, Herder C, Ikram MA, Ingelsson M, Jorgensen ME, Jorgensen T, Kriebel J, Kuusisto J, Ligthart S, Lindgren CM, Linneberg A, Lyssenko V, Mamakou V, Meitinger T, Mohlke KL, Morris AD, Nadkarni G, Pankow JS, Peters A, Sattar N, Stancakova A, Strauch K, Taylor KD, Thorand B, Thorleifsson G, Thorsteinsdottir U, Tuomilehto J, Witte DR, Dupuis J, Peyser PA, Zeggini E, Loos RJF, Froguel P, Ingelsson E, Lind L, Groop L, Laakso M, Collins FS, Jukema JW, Palmer CNA, Grallert H, Metspalu A, Dehghan A, Koettgen A, Abecasis GR, Meigs JB, Rotter J, Marchini J, Pedersen O, Hansen T, Langenberg C, Wareham NJ, Stefansson K, Gloyn AL, Morris AP, Boehnke M, McCarthy Met al., 2018, Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps, Nature Genetics, Vol: 50, Pages: 1505-1515, ISSN: 1061-4036

We expanded GWAS discovery for type 2 diabetes (T2D) by combining data from 898,130 European-descent individuals (9% cases), after imputation to high-density reference panels. With these data, we (i) extend the inventory of T2D-risk variants (243 loci, 135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency <5%, 14 with estimated allelic odds ratio >2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence).

Journal article

Gill D, Georgakis MK, Laffan M, Sabater-Lleal M, Malik R, Tzoulaki I, Veltkamp R, Dehghan Aet al., 2018, Genetically determined FXI (Factor XI) levels and risk of stroke, Stroke, Vol: 49, Pages: 2761-2763, ISSN: 0039-2499

Background and Purpose—FXI (factor XI) is involved in thrombus propagation and stabilization. It is unknown whether lower FXI levels have a protective effect on risk of ischemic stroke (IS) or myocardial infarction. This study investigated the effect of genetically determined FXI levels on risk of IS, myocardial infarction, and intracerebral hemorrhage.Methods—Two-sample Mendelian randomization analysis was performed. Instruments and genetic association estimates for FXI levels were obtained from a genome-wide association study of 16 169 individuals. Genetic association estimates for IS and its etiological subtypes were obtained from a study of 16 851 cases and 32 473 controls. For myocardial infarction, estimates were obtained from a study of 43 676 cases and 123 504 controls and for intracerebral hemorrhage from a study of 1545 cases and 1481 controls.Results—After applying a Bonferroni correction for multiple testing, the Mendelian randomization analysis supported a causal effect of higher, genetically determined FXI levels on risk of any IS (odds ratio [OR] per 1-unit increase in natural logarithm-transformed FXI levels, 2.54; 95% CI, 1.68–3.84; P=1×10−5) but not myocardial infarction (OR, 1.01; 95% CI, 0.76–1.34; P=0.94) or intracerebral hemorrhage (OR, 1.81; 95% CI, 0.44–7.38; P=0.41). Examining IS subtypes, the main results supported an effect of higher, genetically determined FXI levels on risk of cardioembolism (OR, 4.23; 95% CI, 1.94–9.19; P=3×10−4) and IS of undetermined cause (OR, 3.44; 95% CI, 1.79–6.60; P=2×10−4) but not large artery atherosclerosis (OR, 2.73; 95% CI, 1.15–6.45; P=0.02) or small artery occlusion (OR, 1.19; 95% CI, 0.50–2.82; P=0.69). However, the statistically significant result for IS of undetermined cause was not replicated in all sensitivity analyses.Conclusions—We find Mendelian randomization evidence supporting FXI as a possible t

Journal article

Ward-Caviness CK, Huffman JE, Everett K, Germain M, van Dongen J, Hill WD, Jhun MA, Brody JA, Ghanbari M, Du L, Roetker NS, de Vries PS, Waldenberger M, Gieger C, Wolf P, Prokisch H, Koenig W, O'Donnell CJ, Levy D, Liu C, Truong V, Wells PS, Trégouët D-A, Tang W, Morrison AC, Boerwinkle E, Wiggins KL, McKnight B, Guo X, Psaty BM, Sotoodenia N, Boomsma DI, Willemsen G, Ligthart L, Deary IJ, Zhao W, Ware EB, Kardia SLR, Van Meurs JBJ, Uitterlinden AG, Franco OH, Eriksson P, Franco-Cereceda A, Pankow JS, Johnson AD, Gagnon F, Morange P-E, de Geus EJC, Starr JM, Smith JA, Dehghan A, Björck HM, Smith NL, Peters Aet al., 2018, DNA methylation age is associated with an altered hemostatic profile in a multiethnic meta-analysis., Blood, Vol: 132, Pages: 1842-1850

Many hemostatic factors are associated with age and age-related diseases; however, much remains unknown about the biological mechanisms linking aging and hemostatic factors. DNA methylation is a novel means by which to assess epigenetic aging, which is a measure of age and the aging processes as determined by altered epigenetic states. We used a meta-analysis approach to examine the association between measures of epigenetic aging and hemostatic factors, as well as a clotting time measure. For fibrinogen, we performed European and African ancestry-specific meta-analyses which were then combined via a random effects meta-analysis. For all other measures we could not estimate ancestry-specific effects and used a single fixed effects meta-analysis. We found that 1-year higher extrinsic epigenetic age as compared with chronological age was associated with higher fibrinogen (0.004 g/L/y; 95% confidence interval, 0.001-0.007; P = .01) and plasminogen activator inhibitor 1 (PAI-1; 0.13 U/mL/y; 95% confidence interval, 0.07-0.20; P = 6.6 × 10-5) concentrations, as well as lower activated partial thromboplastin time, a measure of clotting time. We replicated PAI-1 associations using an independent cohort. To further elucidate potential functional mechanisms, we associated epigenetic aging with expression levels of the PAI-1 protein encoding gene (SERPINE1) and the 3 fibrinogen subunit-encoding genes (FGA, FGG, and FGB) in both peripheral blood and aorta intima-media samples. We observed associations between accelerated epigenetic aging and transcription of FGG in both tissues. Collectively, our results indicate that accelerated epigenetic aging is associated with a procoagulation hemostatic profile, and that epigenetic aging may regulate hemostasis in part via gene transcription.

Journal article

Tin A, Li Y, Brody JA, Nutile T, Chu AY, Huffman JE, Yang Q, Chen M-H, Robinson-Cohen C, Macé A, Liu J, Demirkan A, Sorice R, Sedaghat S, Swen M, Yu B, Ghasemi S, Teumer A, Vollenweider P, Ciullo M, Li M, Uitterlinden AG, Kraaij R, Amin N, van Rooij J, Kutalik Z, Dehghan A, McKnight B, van Duijn CM, Morrison A, Psaty BM, Boerwinkle E, Fox CS, Woodward OM, Köttgen Aet al., 2018, Large-scale whole-exome sequencing association studies identify rare functional variants influencing serum urate levels, Nature Communications, Vol: 9, ISSN: 2041-1723

Elevated serum urate levels can cause gout, an excruciating disease with suboptimal treatment. Previous GWAS identified common variants with modest effects on serum urate. Here we report large-scale whole-exome sequencing association studies of serum urate and kidney function among ≤19,517 European ancestry and African-American individuals. We identify aggregate associations of low-frequency damaging variants in the urate transporters SLC22A12 (URAT1; p = 1.3 × 10-56) and SLC2A9 (p = 4.5 × 10-7). Gout risk in rare SLC22A12 variant carriers is halved (OR = 0.5, p = 4.9 × 10-3). Selected rare variants in SLC22A12 are validated in transport studies, confirming three as loss-of-function (R325W, R405C, and T467M) and illustrating the therapeutic potential of the new URAT1-blocker lesinurad. In SLC2A9, mapping of rare variants of large effects onto the predicted protein structure reveals new residues that may affect urate binding. These findings provide new insights into the genetic architecture of serum urate, and highlight molecular targets in SLC22A12 and SLC2A9 for lowering serum urate and preventing gout.

Journal article

Tylee DS, Sun J, Hess JL, Tahir MA, Sharma E, Malik R, Worrall BB, Levine AJ, Martinson JJ, Nejentsev S, Speed D, Fischer A, Mick E, Walker BR, Crawford A, Grant SFA, Polychronakos C, Bradfield JP, Sleiman PMA, Hakonarson H, Ellinghaus E, Elder JT, Tsoi LC, Trembath RC, Barker JN, Franke A, Dehghan A, 23 and Me Research Team, Inflammation Working Group of the CHARGE Consortium, METASTROKE Consortium of the International Stroke Genetics Consortium, Netherlands Twin Registry, neuroCHARGE Working Group, Obsessive Compulsive and Tourette Syndrome Working Group of the Psychiatric Genomics Consortium, Faraone SV, Glatt SJet al., 2018, Genetic correlations among psychiatric and immune-related phenotypes based on genome-wide association data., Am J Med Genet B Neuropsychiatr Genet, Vol: 177, Pages: 641-657

Individuals with psychiatric disorders have elevated rates of autoimmune comorbidity and altered immune signaling. It is unclear whether these altered immunological states have a shared genetic basis with those psychiatric disorders. The present study sought to use existing summary-level data from previous genome-wide association studies to determine if commonly varying single nucleotide polymorphisms are shared between psychiatric and immune-related phenotypes. We estimated heritability and examined pair-wise genetic correlations using the linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics methods. Using LDSC, we observed significant genetic correlations between immune-related disorders and several psychiatric disorders, including anorexia nervosa, attention deficit-hyperactivity disorder, bipolar disorder, major depression, obsessive compulsive disorder, schizophrenia, smoking behavior, and Tourette syndrome. Loci significantly mediating genetic correlations were identified for schizophrenia when analytically paired with Crohn's disease, primary biliary cirrhosis, systemic lupus erythematosus, and ulcerative colitis. We report significantly correlated loci and highlight those containing genome-wide associations and candidate genes for respective disorders. We also used the LDSC method to characterize genetic correlations among the immune-related phenotypes. We discuss our findings in the context of relevant genetic and epidemiological literature, as well as the limitations and caveats of the study.

Journal article

Walton E, Hemani G, Dehghan A, Relton C, Smith GDet al., 2018, Systematic evaluation of the causal relationship between DNA methylation and C-reactive protein, Publisher: Cold Spring Harbor Laboratory

<jats:title>Abstract</jats:title><jats:p>Elevated C-reactive protein (CRP) levels are an indicator of chronic low-grade inflammation. Epigenetic modifications, including DNA methylation, have been linked to CRP, but systematic investigations into potential underlying causal relationships have not yet been performed.</jats:p><jats:p>We systematically performed two-sample Mendelian randomization and colocalization analysis between CRP and DNA methylation levels, using GWAS and EWAS summary statistics as well as individual level data available through the ARIES subset of the Avon Longitudinal Study of Parents and Children (ALSPAC; 1,616 participants).</jats:p><jats:p>We found no convincing examples for a causal association from CRP to DNA methylation. Testing for the reverse (a putative causal effect of DNA methylation on CRP), we found three CpG sites that had shared genetic effects with CRP levels after correcting for multiple testing (cg26470501 (offspring: beta=0.07 [0.03, 0.11]; mothers: beta=0.08 [0.04, 0.13]), cg27023597 (offspring: beta=0.18 [0.10, 0.25]; mothers: beta=0.20 [0.12, 0.28]) and cg12054453 (offspring: beta=0.09 [0.05, 0.13])) influenced CRP levels. For all three CpG sites, linked to the genes <jats:italic>TMEM49, BCL3</jats:italic> and <jats:italic>MIR21</jats:italic>, increased methylation related to an increase in CRP levels. Two CpGs (cg27023597 and cg12054453) were influenced by SNPs in genomic regions that had not previously been implicated in CRP GWASs, implicating them as novel genetic associations.</jats:p><jats:p>Overall, our findings suggest that CRP associations with DNA methylation are more likely to be driven by either confounding or causal influences of DNA methylation on CRP levels, rather than the reverse.</jats:p>

Working paper

Dhana K, Braun KVE, Nano J, Voortman T, Demerath EW, Guan W, Fornage M, van Meurs JBJ, Uitterlinden AG, Hofman A, Franco OH, Dehghan Aet al., 2018, An Epigenome-Wide Association Study of Obesity-Related Traits., Am J Epidemiol, Vol: 187, Pages: 1662-1669

We conducted an epigenome-wide association study on obesity-related traits. We used data from 2 prospective, population-based cohort studies: the Rotterdam Study (RS) (2006-2013) and the Atherosclerosis Risk in Communities (ARIC) Study (1990-1992). We used the RS (n = 1,450) as the discovery panel and the ARIC Study (n = 2,097) as the replication panel. Linear mixed-effect models were used to assess the cross-sectional associations between genome-wide DNA methylation in leukocytes and body mass index (BMI) and waist circumference (WC), adjusting for sex, age, smoking, leukocyte proportions, array number, and position on array. The latter 2 variables were modeled as random effects. Fourteen 5'-C-phosphate-G-3' (CpG) sites were associated with BMI and 26 CpG sites with WC in the RS after Bonferroni correction (P < 1.07 × 10-7), of which 12 and 13 CpGs were replicated in the ARIC Study, respectively. The most significant novel CpGs were located on the Musashi RNA binding protein 2 gene (MSI2; cg21139312) and the leucyl-tRNA synthetase 2, mitochondrial gene (LARS2; cg18030453) and were associated with both BMI and WC. CpGs at BRDT, PSMD1, IFI44L, MAP1A, and MAP3K5 were associated with BMI. CpGs at LGALS3BP, MAP2K3, DHCR24, CPSF4L, and TMEM49 were associated with WC. We report novel associations between methylation at MSI2 and LARS2 and obesity-related traits. These results provide further insight into mechanisms underlying obesity-related traits, which can enable identification of new biomarkers in obesity-related chronic diseases.

Journal article

Zhou B, Bentham J, Di Cesare M, Bixby HRH, Danaei G, Hajifathalian K, Taddei C, Carrillo-Larco R, Khatibzadeh S, Lugero C, Peykari N, Zhang WZ, Bennett J, Bilano V, Stevens G, Riley L, Cowan M, Chen Z, Hambleton I, Jackson RT, Kengne A-P, Khang Y-H, Laxmaiah A, Liu J, Malekzadeh R, Neuhauser H, Soric M, Starc G, Sundstrom J, Woodward M, Ezzati Met al., 2018, Contributions of mean and shape of blood pressure distribution to worldwide trends and variations in raised blood pressure: a pooled analysis of 1,018 population-based measurement studies with 88.6 million participants, International Journal of Epidemiology, Vol: 47, Pages: 872-883i, ISSN: 1464-3685

BackgroundChange in the prevalence of raised blood pressure could be due to both shifts in the entire distribution of blood pressure (representing the combined effects of public health interventions and secular trends) and changes in its high-blood-pressure tail (representing successful clinical interventions to control blood pressure in the hypertensive population). Our aim was to quantify the contributions of these two phenomena to the worldwide trends in the prevalence of raised blood pressure.MethodsWe pooled 1018 population-based studies with blood pressure measurements on 88.6 million participants from 1985 to 2016. We first calculated mean systolic blood pressure (SBP), mean diastolic blood pressure (DBP) and prevalence of raised blood pressure by sex and 10-year age group from 20–29 years to 70–79 years in each study, taking into account complex survey design and survey sample weights, where relevant. We used a linear mixed effect model to quantify the association between (probit-transformed) prevalence of raised blood pressure and age-group- and sex-specific mean blood pressure. We calculated the contributions of change in mean SBP and DBP, and of change in the prevalence-mean association, to the change in prevalence of raised blood pressure.ResultsIn 2005–16, at the same level of population mean SBP and DBP, men and women in South Asia and in Central Asia, the Middle East and North Africa would have the highest prevalence of raised blood pressure, and men and women in the high-income Asia Pacific and high-income Western regions would have the lowest. In most region-sex-age groups where the prevalence of raised blood pressure declined, one half or more of the decline was due to the decline in mean blood pressure. Where prevalence of raised blood pressure has increased, the change was entirely driven by increasing mean blood pressure, offset partly by the change in the prevalence-mean association.ConclusionsChange in mean bloo

Journal article

Aslibekyan S, Agha G, Colicino E, Do AN, Lahti J, Ligthart S, Marioni RE, Marzi C, Mendelson MM, Tanaka T, Wielscher M, Absher DM, Ferrucci L, Franco OH, Gieger C, Grallert H, Hernandez D, Huan T, Iurato S, Joehanes R, Just AC, Kunze S, Lin H, Liu C, Meigs JB, van Meurs JBJ, Moore AZ, Peters A, Prokisch H, Raikkonen K, Rathmann W, Roden M, Schramm K, Schwartz JD, Starr JM, Uitterlinden AG, Vokonas P, Waldenberger M, Yao C, Zhi D, Baccarelli AA, Bandinelli S, Deary IJ, Dehghan A, Eriksson J, Herder C, Jarvelin M-R, Levy D, Arnett DKet al., 2018, Association of methylation signals with incident coronary heart disease in an epigenome-wide assessment of circulating tumor necrosis factor alpha, JAMA CARDIOLOGY, Vol: 3, Pages: 463-472, ISSN: 2380-6583

Importance Tumor necrosis factor α (TNF-α) is a proinflammatory cytokine with manifold consequences for mammalian pathophysiology, including cardiovascular disease. A deeper understanding of TNF-α biology may enhance treatment precision.Objective To conduct an epigenome-wide analysis of blood-derived DNA methylation and TNF-α levels and to assess the clinical relevance of findings.Design, Setting, and Participants This meta-analysis assessed epigenome-wide associations in circulating TNF-α concentrations from 5 cohort studies and 1 interventional trial, with replication in 3 additional cohort studies. Follow-up analyses investigated associations of identified methylation loci with gene expression and incident coronary heart disease; this meta-analysis included 11 461 participants who experienced 1895 coronary events.Exposures Circulating TNF-α concentration.Main Outcomes and Measures DNA methylation at approximately 450 000 loci, neighboring DNA sequence variation, gene expression, and incident coronary heart disease.Results The discovery cohort included 4794 participants, and the replication study included 816 participants (overall mean [SD] age, 60.7 [8.5] years). In the discovery stage, circulating TNF-α levels were associated with methylation of 7 cytosine-phosphate-guanine (CpG) sites, 3 of which were located in or near DTX3L-PARP9 at cg00959259 (β [SE] = −0.01 [0.003]; P = 7.36 × 10−8), cg08122652 (β [SE] = −0.008 [0.002]; P = 2.24 × 10−7), and cg22930808(β [SE] = −0.01 [0.002]; P = 6.92 × 10−8); NLRC5 at cg16411857 (β [SE] = −0.01 [0.002]; P = 2.14 × 10−13) and cg07839457 (β [SE] = −0.02 [0.003]; P = 6.31 ×

Journal article

Davies G, Lam M, Harris SE, Trampush JW, Luciano M, Hill WD, Hagenaars SP, Ritchie SJ, Marioni RE, Fawns-Ritchie C, Liewald DCM, Okely JA, Ahola-Olli AV, Barnes CLK, Bertram L, Bis JC, Burdick KE, Christoforou A, DeRosse P, Djurovic S, Espeseth T, Giakoumaki S, Giddaluru S, Gustavson DE, Hayward C, Hofer E, Ikram MA, Karlsson R, Knowles E, Lahti J, Leber M, Li S, Mather KA, Melle I, Morris D, Oldmeadow C, Palviainen T, Payton A, Pazoki R, Petrovic K, Reynolds CA, Sargurupremraj M, Scholz M, Smith JA, Smith AV, Terzikhan N, Thalamuthu A, Trompet S, van der Lee SJ, Ware EB, Windham BG, Wright MJ, Yang J, Yu J, Ames D, Amin N, Amouyel P, Andreassen OA, Armstrong NJ, Assareh AA, Attia JR, Attix D, Avramopoulos D, Bennett DA, Boehmer AC, Boyle PA, Brodaty H, Campbell H, Cannon TD, Cirulli ET, Congdon E, Conley ED, Corley J, Cox SR, Dale AM, Dehghan A, Dick D, Dickinson D, Eriksson JG, Evangelou E, Faul JD, Ford I, Freimer NA, Gao H, Giegling I, Gillespie NA, Gordon SD, Gottesman RF, Griswold ME, Gudnason V, Harris TB, Hartmann AM, Hatzimanolis A, Heiss G, Holliday EG, Joshi PK, Kahonen M, Kardia SLR, Karlsson I, Kleineidam L, Knopman DS, Kochan NA, Konte B, Kwok JB, Le Hellard S, Lee T, Lehtimaki T, Li S-C, Liu T, Koini M, London E, Longstreth WT, Lopez OL, Loukola A, Luck T, Lundervold AJ, Lundquist A, Lyytikainen L-P, Martin NG, Montgomery GW, Murray AD, Need AC, Noordam R, Nyberg L, Ollier W, Papenberg G, Pattie A, Polasek O, Poldrack RA, Psaty BM, Reppermund S, Riedel-Heller SG, Rose RJ, Rotter JI, Roussos P, Rovio SP, Saba Y, Sabb FW, Sachdev PS, Satizabal CL, Schmid M, Scott RJ, Scult MA, Simino J, Slagboom PE, Smyrnis N, Soumare A, Stefanis NC, Stott DJ, Straub RE, Sundet K, Taylor AM, Taylor KD, Tzoulaki I, Tzourio C, Uitterlinden A, Vitart V, Voineskos AN, Kaprio J, Wagner M, Wagner H, Weinhold L, Wen KH, Widen E, Yang Q, Zhao W, Adams HHH, Arking DE, Bilder RM, Bitsios P, Boerwinkle E, Chiba-Falek O, Corvin A, De Jager PL, Debette S, Donohoe G, Elliott P, Fitzpet al., 2018, Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function, Nature Communications, Vol: 9, ISSN: 2041-1723

General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16–102) and find 148 genome-wide significant independent loci (P < 5 × 10−8) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function.

Journal article

Mahajan A, Wessel J, Willems SM, Zhao W, Robertson NR, Chu AY, Gan W, Kitajima H, Taliun D, Rayner NW, Guo X, Lu Y, Li M, Jensen RA, Hu Y, Huo S, Lohman KK, Zhang W, Cook JP, Prins BP, Flannick J, Grarup N, Trubetskoy VV, Kravic J, Kim YJ, Rybin DV, Yaghootkar H, Mueller-Nurasyid M, Meidtner K, Li-Gao R, Varga TV, Marten J, Li J, Smith AV, An P, Ligthart S, Gustafsson S, Malerba G, Demirkan A, Tajes JF, Steinthorsdottir V, Wuttke M, Lecoeur C, Preuss M, Bielak LF, Graff M, Highland HM, Justice AE, Liu DJ, Marouli E, Peloso GM, Warren HR, Afaq S, Afzal S, Ahlqvist E, Almgren P, Amin N, Bang LB, Bertoni AG, Bombieri C, Bork-Jensen J, Brandslund I, Brody JA, Burtt NP, Canouil M, Chen Y-DI, Cho YS, Christensen C, Eastwood SV, Eckardt K-U, Fischer K, Gambaro G, Giedraitis V, Grove ML, de Haan HG, Hackinger S, Hai Y, Han S, Tybjaerg-Hansen A, Hivert M-F, Isomaa B, Jager S, Jorgensen ME, Jorgensen T, Karajamaki A, Kim B-J, Kim SS, Koistinen HA, Kovacs P, Kriebel J, Kronenberg F, Lall K, Lange LA, Lee J-J, Lehne B, Li H, Lin K-H, Linneberg A, Liu C-T, Liu J, Loh M, Magi R, Mamakou V, McKean-Cowdin R, Nadkarni G, Neville M, Nielsen SF, Ntalla I, Peyser PA, Rathmann W, Rice K, Rich SS, Rode L, Rolandsson O, Schonherr S, Selvin E, Small KS, Stancakova A, Surendran P, Taylor KD, Teslovich TM, Thorand B, Thorleifsson G, Tin A, Tonjes A, Varbo A, Witte DR, Wood AR, Yajnik P, Yao J, Yengo L, Young R, Amouyel P, Boeing H, Boerwinkle E, Bottinger EP, Chowdhury R, Collins FS, Dedoussis G, Dehghan A, Deloukas P, Ferrario MM, Ferrieres J, Florez JC, Frossard P, Gudnason V, Harris TB, Heckbert SR, Howson JMM, Ingelsson M, Kathiresan S, Kee F, Kuusisto J, Langenberg C, Launer LJ, Lindgren CM, Mannisto S, Meitinger T, Melander O, Mohlke KL, Moitry M, Morris AD, Murray AD, de Mutsert R, Orho-Melander M, Owen KR, Perola M, Peters A, Province MA, Rasheed A, Ridker PM, Rivadineira F, Rosendaal FR, Rosengren AH, Salomaa V, Sheu WH-H, Sladek R, Smith BH, Strauch K, Uitterlinden AG, Varma R, Wilet al., 2018, Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes, Nature Genetics, Vol: 50, Pages: 559-559, ISSN: 1061-4036

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10−7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent ‘false leads’ with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.

Journal article

Ghanbari M, Peters MJ, De Vries PS, Boer CG, Van Rooij JGJ, Lee YC, Kumar V, Uitterlinden AG, Ikram MA, Wijmenga C, Ordovas JM, Smith CE, Van Meurs JBJ, Erkeland SJ, Franco OH, Dehghan Aet al., 2018, A systematic analysis highlights multiple long non-coding RNAs associated with cardiometabolic disorders, Journal of Human Genetics, Vol: 63, Pages: 431-446, ISSN: 1434-5161

Genome-wide association studies (GWAS) have identified many susceptibility loci for cardiometabolic disorders. Most of the associated variants reside in non-coding regions of the genome including long non-coding RNAs (lncRNAs), which are thought to play critical roles in diverse biological processes. Here, we leveraged data from the available GWAS meta-analyses on lipid and obesity-related traits, blood pressure, type 2 diabetes, and coronary artery disease and identified 179 associated single-nucleotide polymorphisms (SNPs) in 102 lncRNAs (p-value < 2.3 × 10-7). Of these, 55 SNPs, either the lead SNP or in strong linkage disequilibrium with the lead SNP in the related loci, were selected for further investigations. Our in silico predictions and functional annotations of the SNPs as well as expression and DNA methylation analysis of their lncRNAs demonstrated several lncRNAs that fulfilled predefined criteria for being potential functional targets. In particular, we found evidence suggesting that LOC157273 (at 8p23.1) is involved in regulating serum lipid-cholesterol. Our results showed that rs4841132 in the second exon and cg17371580 in the promoter region of LOC157273 are associated with lipids; the lncRNA is expressed in liver and associates with the expression of its nearby coding gene, PPP1R3B. Collectively, we highlight a number of loci associated with cardiometabolic disorders for which the association may act through lncRNAs.

Journal article

Chaker L, Cremers LGM, Korevaar TIM, de Groot M, Dehghan A, Franco OH, Niessen WJ, Ikram MA, Peeters RP, Vernooij MWet al., 2018, Age-dependent association of thyroid function with brain morphology and microstructural organization: evidence from brain imaging., Neurobiol Aging, Vol: 61, Pages: 44-51

Thyroid hormone (TH) is crucial during neurodevelopment, but high levels of TH have been linked to neurodegenerative disorders. No data on the association of thyroid function with brain imaging in the general population are available. We therefore investigated the association of thyroid-stimulating hormone and free thyroxine (FT4) with magnetic resonance imaging (MRI)-derived total intracranial volume, brain tissue volumes, and diffusion tensor imaging measures of white matter microstructure in 4683 dementia- and stroke-free participants (mean age 60.2, range 45.6-89.9 years). Higher FT4 levels were associated with larger total intracranial volumes (β = 6.73 mL, 95% confidence interval = 2.94-9.80). Higher FT4 levels were also associated with larger total brain and white matter volumes in younger individuals, but with smaller total brain and white matter volume in older individuals (p-interaction 0.02). There was a similar interaction by age for the association of FT4 with mean diffusivity on diffusion tensor imaging (p-interaction 0.026). These results are in line with differential effects of TH during neurodevelopmental and neurodegenerative processes and can improve the understanding of the role of thyroid function in neurodegenerative disorders.

Journal article

Dehghan A, 2018, Genome-Wide Association Studies., Pages: 37-49

Genetic association studies have made a major contribution to our understanding of the genetics of complex disorders over the last 10 years through genome-wide association studies (GWAS). In this chapter, we review the key concepts that underlie the GWAS approach. We will describe the "common disease, common variant" theory, and will review how we finally afforded to capture the common variance in genome to make GWAS possible. Finally, we will go over technical aspects of GWAS such as genotype imputation, epidemiologic designs, analysis methods, and considerations such as genomic inflation, multiple testing, and replication.

Book chapter

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