Imperial College London

DrRahaPazoki

Faculty of MedicineSchool of Public Health

Honorary Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 1174r.pazoki

 
 
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Location

 

VC7Praed StreetSt Mary's Campus

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Summary

 

Publications

Publication Type
Year
to

44 results found

Evangelou E, Suzuki H, Bai W, Pazoki R, Gao H, Matthews P, Elliott Pet al., 2021, Alcohol consumption in the general population is associated with structural changes in multiple organ systems., eLife, Vol: 10, Pages: 1-15, ISSN: 2050-084X

Background:Excessive alcohol consumption is associated with damage to various organs, but its multi-organ effects have not been characterised across the usual range of alcohol drinking in a large general population sample.Methods:We assessed global effect sizes of alcohol consumption on quantitative magnetic resonance imaging phenotypic measures of the brain, heart, aorta, and liver of UK Biobank participants who reported drinking alcohol.Results:We found a monotonic association of higher alcohol consumption with lower normalised brain volume across the range of alcohol intakes (–1.7 × 10−3 ± 0.76 × 10−3 per doubling of alcohol consumption, p=3.0 × 10−14). Alcohol consumption was also associated directly with measures of left ventricular mass index and left ventricular and atrial volume indices. Liver fat increased by a mean of 0.15% per doubling of alcohol consumption.Conclusions:Our results imply that there is not a ‘safe threshold’ below which there are no toxic effects of alcohol. Current public health guidelines concerning alcohol consumption may need to be revisited.

Journal article

Pazoki R, Elliott J, Evangelou E, Gill D, Pinto R, Zuber V, Said S, Dehghan A, Tzoulaki I, Jarvelin MR, Thursz M, Elliott Pet al., 2021, Genetic analysis in European ancestry individuals identifies 517 loci associated with liver enzymes, Nature Communications, Vol: 12, ISSN: 2041-1723

Serum concentration of hepatic enzymes are linked to liver dysfunction, metabolic and cardiovascular diseases. We perform genetic analysis on serum levels of alanine transaminase (ALT), alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT) using data on 437,438 UK Biobank participants. Replication in 315,572 individuals from European descent from the Million Veteran Program, Rotterdam Study and Lifeline study confirms 517 liver enzyme SNPs. Genetic risk score analysis using the identified SNPs is strongly associated with serum activity of liver enzymes in two independent European descent studies (The Airwave Health Monitoring study and the Northern Finland Birth Cohort 1966). Gene-set enrichment analysis using the identified SNPs highlights involvement in liver development and function, lipid metabolism, insulin resistance, and vascular formation. Mendelian randomization analysis shows association of liver enzyme variants with coronary heart disease and ischemic stroke. Genetic risk score for elevated serum activity of liver enzymes is associated with higher fat percentage of body, trunk, and liver and body mass index. Our study highlights the role of molecular pathways regulated by the liver in metabolic disorders and cardiovascular disease.

Journal article

Robinson O, Chadeau Hyam M, Karaman I, Climaco Pinto R, Ala-Korpela M, Handakas E, Fiorito G, Gao H, Heard A, Jarvelin M-R, Lewis M, Pazoki R, Polidoro S, Tzoulaki I, Wielscher M, Elliott P, Vineis Pet al., 2020, Determinants of accelerated metabolomic and epigenetic ageing in a UK cohort, Aging Cell, Vol: 19, Pages: 1-13, ISSN: 1474-9718

Markers of biological aging have potential utility in primary care and public health. We developed a model of age based on untargeted metabolic profiling across multiple platforms, including nuclear magnetic resonance spectroscopy and liquid chromatography–mass spectrometry in urine and serum, within a large sample (N = 2,239) from the UK Airwave cohort. We validated a subset of model predictors in a Finnish cohort including repeat measurements from 2,144 individuals. We investigated the determinants of accelerated aging, including lifestyle and psychological risk factors for premature mortality. The metabolomic age model was well correlated with chronological age (mean r = .86 across independent test sets). Increased metabolomic age acceleration (mAA) was associated after false discovery rate (FDR) correction with overweight/obesity, diabetes, heavy alcohol use and depression. DNA methylation age acceleration measures were uncorrelated with mAA. Increased DNA methylation phenotypic age acceleration (N = 1,110) was associated after FDR correction with heavy alcohol use, hypertension and low income. In conclusion, metabolomics is a promising approach for the assessment of biological age and appears complementary to established epigenetic clocks.

Journal article

Schmidt AF, Holmes MV, Preiss D, Swerdlow DI, Denaxas S, Fatemifar G, Faraway R, Finan C, Valentine D, Fairhurst-Hunter Z, Hartwig FP, Horta BL, Hypponen E, Power C, Moldovan M, van Iperen E, Hovingh K, Demuth I, Norman K, Steinhagen-Thiessen E, Demuth J, Bertram L, Lill CM, Coassin S, Willeit J, Kiechl S, Willeit K, Mason D, Wright J, Morris R, Wanamethee G, Whincup P, Ben-Shlomo Y, McLachlan S, Price JF, Kivimaki M, Welch C, Sanchez-Galvez A, Marques-Vidal P, Nicolaides A, Panayiotou AG, Onland-Moret NC, van der Schouw YT, Matullo G, Fiorito G, Guarrera S, Sacerdote C, Wareham NJ, Langenberg C, Scott RA, Luan J, Bobak M, Malyutina S, Pająk A, Kubinova R, Tamosiunas A, Pikhart H, Grarup N, Pedersen O, Hansen T, Linneberg A, Jess T, Cooper J, Humphries SE, Brilliant M, Kitchner T, Hakonarson H, Carrell DS, McCarty CA, Lester KH, Larson EB, Crosslin DR, de Andrade M, Roden DM, Denny JC, Carty C, Hancock S, Attia J, Holliday E, Scott R, Schofield P, O'Donnell M, Yusuf S, Chong M, Pare G, van der Harst P, Said MA, Eppinga RN, Verweij N, Snieder H, Lifelines Cohort authors, Christen T, Mook-Kanamori DO, ICBP Consortium, Gustafsson S, Lind L, Ingelsson E, Pazoki R, Franco O, Hofman A, Uitterlinden A, Dehghan A, Teumer A, Baumeister S, Dörr M, Lerch MM, Völker U, Völzke H, Ward J, Pell JP, Meade T, Christophersen IE, Maitland-van der Zee AH, Baranova EV, Young R, Ford I, Campbell A, Padmanabhan S, Bots ML, Grobbee DE, Froguel P, Thuillier D, Roussel R, Bonnefond A, Cariou B, Smart M, Bao Y, Kumari M, Mahajan A, Hopewell JC, Seshadri S, METASTROKE Consortium of the ISGC, Dale C, Costa RPE, Ridker PM, Chasman DI, Reiner AP, Ritchie MD, Lange LA, Cornish AJ, Dobbins SE, Hemminki K, Kinnersley B, Sanson M, Labreche K, Simon M, Bondy M, Law P, Speedy H, Allan J, Li N, Went M, Weinhold N, Morgan G, Sonneveld P, Nilsson B, Goldschmidt H, Sud A, Engert A, Hansson M, Hemingway H, Asselbergs FW, Patel RS, Keating BJ, Sattar N, Houlston R, Casas JP, Hingorani ADet al., 2019, Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9, BMC Cardiovascular Disorders, Vol: 19, ISSN: 1471-2261

BACKGROUND: We characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9. METHODS: Published and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Seventeen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentration. RESULTS: The PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95% CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95% CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95% CI 0.57; 1.22) for the GS, compared to 0.85 (95% CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95% CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer's disease - outcomes for which large-scale trial data were unavailable. CONCLUSIONS: Genetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. While indicating an increased risk of T2DM, no other possible safety concerns were shown; although precision was moderate.

Journal article

Evangelou E, Gao H, Blakeley P, Pazoki R, Suzuki H, Elliott J, Karaman I, Jarvelin MR, Tzoulaki I, Bell JD, Matthews PM, Elliott Pet al., 2019, New alcohol-related genes suggest shared genetic mechanisms with neuropsychiatric disorders, Nature Human Behaviour, Vol: 3, Pages: 950-961, ISSN: 2397-3374

Excessive alcohol consumption is one of the main causes of death and disability worldwide. Alcohol consumption is a heritable complex trait. Here we conducted a meta-analysis of genome-wide association studies of alcohol consumption (g d−1) from the UK Biobank, the Alcohol Genome-Wide Consortium and the Cohorts for Heart and Aging Research in Genomic Epidemiology Plus consortia, collecting data from 480,842 people of European descent to decipher the genetic architecture of alcohol intake. We identified 46 new common loci and investigated their potential functional importance using magnetic resonance imaging data and gene expression studies. We identify genetic pathways associated with alcohol consumption and suggest genetic mechanisms that are shared with neuropsychiatric disorders such as schizophrenia.

Journal article

Pazoki R, Evangelou E, Mosen-Ansorena D, Pinto R, Karaman I, Blakeley P, Gill D, Zuber V, Elliott P, Tzoulaki I, Dehghan Aet al., 2019, GWAS for urinary sodium and potassium excretion highlights pathways shared with cardiovascular traits, Nature Communications, Vol: 10, 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

Pazoki R, 2019, Cardiovascular disease, ABO locus, and markers of platelet functionality., Int J Cardiol, Vol: 286, Pages: 162-163

Journal article

de Vries PS, Brown MR, Bentley AR, Sung YJ, Winkler TW, Ntalla I, Schwander K, Kraja AT, Guo X, Franceschini N, Cheng C-Y, Sim X, Vojinovic D, Huffman JE, Musani SK, Li C, Feitosa MF, Richard MA, Noordam R, Aschard H, Bartz TM, Bielak LF, Deng X, Dorajoo R, Lohman KK, Manning AK, Rankinen T, Smith AV, Tajuddin SM, Evangelou E, Graff M, Alver M, Boissel M, Chai JF, Chen X, Divers J, Gandin I, Gao C, Goel A, Hagemeijer Y, Harris SE, Hartwig FP, He M, Horimoto ARVR, Hsu F-C, Jackson AU, Kasturiratne A, Komulainen P, Kühnel B, Laguzzi F, Lee JH, Luan J, Lyytikäinen L-P, Matoba N, Nolte IM, Pietzner M, Riaz M, Said MA, Scott RA, Sofer T, Stancáková A, Takeuchi F, Tayo BO, van der Most PJ, Varga TV, Wang Y, Ware EB, Wen W, Yanek LR, Zhang W, Zhao JH, Afaq S, Amin N, Amini M, Arking DE, Aung T, Ballantyne C, Boerwinkle E, Broeckel U, Campbell A, Canouil M, Charumathi S, Chen Y-DI, Connell JM, de Faire U, de Las Fuentes L, de Mutsert R, de Silva HJ, Ding J, Dominiczak AF, Duan Q, Eaton CB, Eppinga RN, Faul JD, Fisher V, Forrester T, Franco OH, Friedlander Y, Ghanbari M, Giulianini F, Grabe HJ, Grove ML, Gu CC, Harris TB, Heikkinen S, Heng C-K, Hirata M, Hixson JE, Howard BV, Ikram MA, InterAct Consortium, Jacobs DR, Johnson C, Jonas JB, Kammerer CM, Katsuya T, Khor CC, Kilpeläinen TO, Koh W-P, Koistinen HA, Kolcic I, Kooperberg C, Krieger JE, Kritchevsky SB, Kubo M, Kuusisto J, Lakka TA, Langefeld CD, Langenberg C, Launer LJ, Lehne B, Lemaitre RN, Li Y, Liang J, Liu J, Liu K, Loh M, Louie T, Mägi R, Manichaikul AW, McKenzie CA, Meitinger T, Metspalu A, Milaneschi Y, Milani L, Mohlke KL, Mosley TH, Mukamal KJ, Nalls MA, Nauck M, Nelson CP, Sotoodehnia N, O'Connell JR, Palmer ND, Pazoki R, Pedersen NL, Peters A, Peyser PA, Polasek O, Poulter N, Raffel LJ, Raitakari OT, Reiner AP, Rice TK, Rich SS, Robino A, Robinson JG, Rose LM, Rudan I, Schmidt CO, Schreiner PJ, Scott WR, Sever P, Shi Y, Sidney S, Sims M, Smith BH, Smith JA, Snieder H, Starr JM, Strauch K, Tan N, Taylor KDet al., 2019, Multi-ancestry genome-wide association study of lipid levels incorporating gene-alcohol interactions, American Journal of Epidemiology, Vol: 188, Pages: 1033-1054, ISSN: 1476-6256

An individual'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 multi-ancestry 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 five ancestry groups. Genetic main and interaction effects were jointly assessed by 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 Stage 1 and Stage 2, 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 (PCSK5, VEGFB, and A1CF) with a putative role in lipid metabolism based on existing evidence from cellular and experimental models.

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, Lill CM, 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, Elliottet al., 2019, Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function (vol 9, 2098, 2018), NATURE COMMUNICATIONS, Vol: 10, ISSN: 2041-1723

Journal article

Kilpelainen TO, Bentley AR, Noordam R, Sung YJ, Schwander K, Winkler TW, Jakupovic H, Chasman DI, Manning A, Ntalla I, Aschard H, Brown MR, de las Fuentes L, Franceschini N, Guo X, Vojinovic D, Aslibekyan S, Feitosa MF, Kho M, Musani SK, Richard M, Wang H, Wang Z, Bartz TM, Bielak LF, Campbell A, Dorajoo R, Fisher V, Hartwig FP, Horimoto ARVR, Li C, Lohman KK, Marten J, Sim X, Smith AV, Tajuddin SM, Alver M, Amini M, Boissel M, Chai JF, Chen X, Divers J, Evangelou E, Gao C, Graff M, Harris SE, He M, Hsu F-C, Jackson AU, Zhao JH, Kraja AT, Kuehnel B, Laguzzi F, Lyytikainen L-P, Nolte IM, Rauramaa R, Riaz M, Robino A, Rueedi R, Stringham HM, Takeuchi F, van der Most PJ, Varga TV, Verweij N, Ware EB, Wen W, Li X, Yanek LR, Amin N, Arnett DK, Boerwinkle E, Brumat M, Cade B, Canouil M, Chen Y-DI, Concas MP, Connell J, de Mutsert R, de Silva HJ, de Vries PS, Demirkan A, Ding J, Eaton CB, Faul JD, Friedlander Y, Gabriel KP, Ghanbari M, Giulianini F, Gu CC, Gu D, Harris TB, He J, Heikkinen S, Heng C-K, Hunt SC, Ikram MA, Jonas JB, Koh W-P, Komulainen P, Krieger JE, Kritchevsky SB, Kutalik Z, Kuusisto J, Langefeld CD, Langenberg C, Launer LJ, Leander K, Lemaitre RN, Lewis CE, Liang J, Alizadeh BZ, Boezen HM, Franke L, Navis G, Rots M, Swertz M, Wolffenbuttel BHR, Wijmenga C, Liu J, Magi R, Manichaikul A, Meitinger T, Metspalu A, Milaneschi Y, Mohlke KL, Mosley TH, Murray AD, Nalls MA, Nang E-EK, Nelson CP, Nona S, Norris JM, Nwuba CV, O'Connell J, Palmer ND, Papanicolau GJ, Pazoki R, Pedersen NL, Peters A, Peyser PA, Polasek O, Porteous DJ, Poveda A, Raitakari OT, Rich SS, Risch N, Robinson JG, Rose LM, Rudan I, Schreiner PJ, Scott RA, Sidney SS, Sims M, Smith JA, Snieder H, Sofer T, Starr JM, Sternfeld B, Strauch K, Tang H, Taylor KD, Tsai MY, Tuomilehto J, Uitterlinden AG, van der Ende MY, van Heemst D, Voortman T, Waldenberger M, Wennberg P, Wilson G, Xiang Y-B, Yao J, Yu C, Yuan J-M, Zhao W, Zonderman AB, Becker DM, Boehnke M, Bowden DW, de Faire U, Deary IJ, Elliott Pet al., 2019, Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity, Nature Communications, Vol: 10, ISSN: 2041-1723

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels.

Journal article

Pazoki R, Dehghan A, Evangelou E, Warren H, Gao H, Caulfield M, Elliott P, Tzoulaki Iet al., 2019, Genetic Predisposition to High Blood Pressure and Lifestyle Factors: Associations With Midlife Blood Pressure Levels and Cardiovascular Events (vol 137, pg 653, 2018), CIRCULATION, Vol: 139, Pages: E2-E2, ISSN: 0009-7322

Journal article

Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, Ntritsos G, Dimou N, Cabrera CP, Karaman I, Fu LN, Evangelou M, Witkowska K, Tzanis E, Hellwege JN, Giri A, Edwards DRV, Sun YV, Cho K, Gaziano JM, Wilson PWF, Tsao PS, Kovesdy CP, Esko T, Magi R, Milani L, Almgren P, Boutin T, Debette S, Ding J, Giulianini F, Holliday EG, Jackson AU, Li-Gao R, Lin W-Y, Luan J, Mangino M, Oldmeadow C, Prins BP, Qian Y, Sargurupremraj M, Shah N, Surendran P, Theriault S, Verweij N, Willems SM, Zhao J-H, Amouyel P, Connell J, de Mutsert R, Doney ASF, Farrall M, Menni C, Morris AD, Noordam R, Pare G, Poulter NR, Shields DC, Stanton A, Thom S, Abecasis G, Amin N, Arking DE, Ayers KL, Barbieri CM, Batini C, Bis JC, Blake T, Bochud M, Boehnke M, Boerwinkle E, Boomsma DI, Bottinger EP, Braund PS, Brumat M, Campbell A, Campbell H, Chakravarti A, Chambers JC, Chauhan G, Ciullo M, Cocca M, Collins F, Cordell HJ, Davies G, de Borst MH, de Geus EJ, Deary IJ, Deelen J, Del Greco FM, Demirkale CY, Dorr M, Ehret GB, Elosua R, Enroth S, Erzurumluoglu AM, Ferreira T, Franberg M, Franco OH, Gandin I, Gasparini P, Giedraitis V, Gieger C, Girotto G, Goel A, Gow AJ, Gudnason V, Guo X, Gyllensten U, Hamsten A, Harris TB, Harris SE, Hartman CA, Havulinna AS, Hicks AA, Hofer E, Hofman A, Hottenga J-J, Huffman JE, Hwang S-J, Ingelsson E, James A, Jansen R, Jarvelin M-R, Joehanes R, Johansson A, Johnson AD, Joshi PK, Jousilahti P, Jukema JW, Jula A, Kahonen M, Kathiresan S, Keavney BD, Khaw K-T, Knekt P, Knight J, Kolcic I, Kooner JS, Koskinen S, Kristiansson K, Kutalik Z, Laan M, Larson M, Launer LJ, Lehne B, Lehtimaki T, Liewald DCM, Lin L, Lind L, Lindgren CM, Liu Y, Loos RJF, Lopez LM, Lu Y, Lyytikainen L-P, Mahajan A, Mamasoula C, Marrugat J, Marten J, Milaneschi Y, Morgan A, Morris AP, Morrison AC, Munson PJ, Nalls MA, Nandakumar P, Nelson CP, Niiranen T, Nolte IM, Nutile T, Oldehinkel AJ, Oostra BA, O'Reilly PF, Org E, Padmanabhan S, Palmas W, Palotie A, Pattie A, Penninx BWJH, Perolet al., 2018, Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018), NATURE GENETICS, Vol: 50, Pages: 1755-1755, ISSN: 1061-4036

Journal article

Ashar FN, Mitchell RN, Albert CM, Newton-Cheh C, Brody JA, Mueller-Nurasyid M, Moes A, Meitinger T, Mak A, Huikuri H, Junttila MJ, Goyette P, Pulit SL, Pazoki R, Tanck M, Blom MT, Zhao X, Havulinna AS, Jabbari R, Glinge C, Tragante V, Escher SA, Chakravarti A, Ehret G, Coresh J, Li M, Prineas RJ, Franco OH, Kwok P-Y, Lumley T, Dumas F, McKnight B, Rotter JI, Lemaitre RN, Heckbert SR, O'Donnell CJ, Hwang S-J, Tardif J-C, VanDenburgh M, Uitterlinden AG, Hofman A, Stricker BHC, de Bakker PIW, Franks PW, Jansson J-H, Asselbergs FW, Halushka MK, Maleszewski JJ, Tfelt-Hansen J, Engstrom T, Salomaa V, Virmani R, Kolodgie F, Wilde AAM, Tan HL, Bezzina CR, Eijgelsheim M, Rioux JD, Jouven X, Kaeaeb S, Psaty BM, Siscovick DS, Arking DE, Sotoodehnia Net al., 2018, A comprehensive evaluation of the genetic architecture of sudden cardiac arrest, EUROPEAN HEART JOURNAL, Vol: 39, Pages: 3961-+, ISSN: 0195-668X

Journal article

Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, Ntritsos G, Dimou N, Cabrera CP, Karaman I, Fu LN, Evangelou M, Witkowska K, Tzanis E, Hellwege JN, Giri A, Edwards DRV, Sun YV, Cho K, Gaziano JM, Wilson PWF, Tsao PS, Kovesdy CP, Esko T, Magi R, Milani L, Almgren P, Boutin T, Debette S, Ding J, Giulianini F, Holliday EG, Jackson AU, Li-Gao R, Lin W-Y, Luan J, Mangino M, Oldmeadow C, Prins BP, Qian Y, Sargurupremraj M, Shah N, Surendran P, Theriault S, Verweij N, Willems SM, Zhao J-H, Amouyel P, Connell J, de Mutsert R, Doney ASF, Farrall M, Menni C, Morris AD, Noordam R, Pare G, Poulter NR, Shields DC, Stanton A, Thom S, Abecasis G, Amin N, Arking DE, Ayers KL, Barbieri CM, Batini C, Bis JC, Blake T, Bochud M, Boehnke M, Boerwinkle E, Boomsma DI, Bottinger EP, Braund PS, Brumat M, Campbell A, Campbell H, Chakravarti A, Chambers JC, Chauhan G, Ciullo M, Cocca M, Collins F, Cordell HJ, Davies G, de Borst MH, de Geus EJ, Deary IJ, Deelen J, Del Greco FM, Demirkale CY, Dorr M, Ehret GB, Elosua R, Enroth S, Erzurumluoglu AM, Ferreira T, Franberg M, Franco OH, Gandin I, Gasparini P, Giedraitis V, Gieger C, Girotto G, Goel A, Gow AJ, Gudnason V, Guo X, Gyllensten U, Hamsten A, Harris TB, Harris SE, Hartman CA, Havulinna AS, Hicks AA, Hofer E, Hofman A, Hottenga J-J, Huffman JE, Hwang S-J, Ingelsson E, James A, Jansen R, Jarvelin M-R, Joehanes R, Johansson A, Johnson AD, Joshi PK, Jousilahti P, Jukema JW, Jula A, Kahonen M, Kathiresan S, Keavney BD, Khaw K-T, Knekt P, Knight J, Kolcic I, Kooner JS, Koskinen S, Kristiansson K, Kutalik Z, Laan M, Larson M, Launer LJ, Lehne B, Lehtimaki T, Liewald DCM, Lin L, Lind L, Lindgren CM, Liu Y, Loos RJF, Lopez LM, Lu Y, Lyytikainen L-P, Mahajan A, Mamasoula C, Marrugat J, Marten J, Milaneschi Y, Morgan A, Morris AP, Morrison AC, Munson PJ, Nalls MA, Nandakumar P, Nelson CP, Niiranen T, Nolte IM, Nutile T, Oldehinkel AJ, Oostra BA, O'Reilly PF, Org E, Padmanabhan S, Palmas W, Palotie A, Pattie A, Penninx BWJH, Perolet al., 2018, Publisher correction: Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits, Nature Genetics, Vol: 50, Pages: 1755-1755, ISSN: 1061-4036

Correction to: Nature Genetics https://doi.org/10.1038/s41588-018-0205-x, published online 17 September 2018.

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 https://doi.org/10.1038/ng.3668, published online 26 September 2016.In the version of the article published, the surname of author Aaron Isaacs is misspelled as Issacs.

Journal article

Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, Ntritsos G, Dimou N, Cabrera CP, Karaman I, Fu LN, Evangelou M, Witkowska K, Tzanis E, Hellwege JN, Giri A, Edwards DRV, Sun YV, Cho K, Gaziano JM, Wilson PWF, Tsao PS, Kovesdy CP, Esko T, Magi R, Milani L, Almgren P, Boutin T, Debette S, Ding J, Giulianini F, Holliday EG, Jackson AU, Li-Gao R, Lin W-Y, Luan J, Mangino M, Oldmeadow C, Prins BP, Qian Y, Sargurupremraj M, Shah N, Surendran P, Theriault S, Verweij N, Willems SM, Zhao J-H, Amouyel P, Connell J, de Mutsert R, Doney ASF, Farrall M, Menni C, Morris AD, Noordam R, Pare G, Poulter NR, Shields DC, Stanton A, Thom S, Abecasis G, Amin N, Arking DE, Ayers KL, Barbieri CM, Batini C, Bis JC, Blake T, Bochud M, Boehnke M, Boerwinkle E, Boomsma DI, Bottinger EP, Braund PS, Brumat M, Campbell A, Campbell H, Chakravarti A, Chambers JC, Chauhan G, Ciullo M, Cocca M, Collins F, Cordell HJ, Davies G, de Borst MH, de Geus EJ, Deary IJ, Deelen J, Del Greco FM, Demirkale CY, Dorr M, Ehret GB, Elosua R, Enroth S, Erzurumluoglu AM, Ferreira T, Franberg M, Franco OH, Gandin I, Gasparini P, Giedraitis V, Gieger C, Girotto G, Goel A, Gow AJ, Gudnason V, Guo X, Gyllensten U, Hamsten A, Harris TB, Harris SE, Hartman CA, Havulinna AS, Hicks AA, Hofer E, Hofman A, Hottenga J-J, Huffman JE, Hwang S-J, Ingelsson E, James A, Jansen R, Jarvelin M-R, Joehanes R, Johansson A, Johnson AD, Joshi PK, Jousilahti P, Jukema JW, Jula A, Kahonen M, Kathiresan S, Keavney BD, Khaw K-T, Knekt P, Knight J, Kolcic I, Kooner JS, Koskinen S, Kristiansson K, Kutalik Z, Laan M, Larson M, Launer LJ, Lehne B, Lehtimaki T, Liewald DCM, Lin L, Lind L, Lindgren CM, Liu Y, Loos RJF, Lopez LM, Lu Y, Lyytikainen L-P, Mahajan A, Mamasoula C, Marrugat J, Marten J, Milaneschi Y, Morgan A, Morris AP, Morrison AC, Munson PJ, Nalls MA, Nandakumar P, Nelson CP, Niiranen T, Nolte IM, Nutile T, Oldehinkel AJ, Oostra BA, O'Reilly PF, Org E, Padmanabhan S, Palmas W, Palotie A, Pattie A, Penninx BWJH, Perolet al., 2018, Genetic analysis of over one million people identifies 535 new loci associated with blood pressure traits, Nature Genetics, Vol: 50, Pages: 1412-1425, ISSN: 1061-4036

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.

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

Pazoki R, Dehghan A, Evangelou E, Warren H, Gao H, Caulfield M, Elliott P, Tzoulaki Iet al., 2018, Genetic predisposition to high blood pressure and lifestyle factors. Associations with midlife blood pressure levels and cardiovascular events, Circulation, Vol: 137, Pages: 653-661, 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 4.9, 4.3, and 4.1 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%, 33%, and 31% 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

Pazoki R, 2018, Methods for Polygenic Traits., Pages: 145-156

An important aspect of public health is disease prediction and health promotion through better targeting of preventive strategies. Well-targeted preventive strategies will eventually decrease burden of diseases and thus precise prediction plays a crucial role in public health. Many investigators put efforts into finding models that improve prediction using known risk factors of diseases. Recently with the overwhelming load of genetic loci discovered for complex diseases through genome-wide association studies (GWAS), much of attention has been focused on the role of these genetic loci to improve prediction models. Genetic loci in solo explain little variance of diseases. It is thus necessary to create new genetic parameters that combine the effect of as many genetic loci as possible. Such new parameters aim to better distinguish individuals who will develop a disease from those who will not. In this chapter, various polygenic methods that use multiple genetic loci to directly or indirectly improve precision of genetic prediction are discussed.

Book chapter

Visser AE, Pazoki R, Pulit SL, van Rheenen W, Raaphorst J, van der Kooi AJ, Ricaño-Ponce I, Wijmenga C, Otten HG, Veldink JH, van den Berg LHet al., 2017, No association between gluten sensitivity and amyotrophic lateral sclerosis., J Neurol, Vol: 264, Pages: 694-700

To examine evidence for a role of gluten sensitivity (GS) or celiac disease (CD) in ALS etiology, we included participants from a population-based case-control study in The Netherlands between January 2006 and December 2015. We compared levels and seroprevalence of IgA antibodies to tissue transglutaminase 6 (TG6) in 359 ALS patients and 359 controls, and to transglutaminase 2 (TG2) and endomysium (EMA) in 199 ALS patients and 199 controls. Questionnaire data on 1829 ALS patients and 3920 controls were examined for CD or gluten-free diets (GFD). Genetic correlation and HLA allele frequencies were analyzed using two genome-wide association studies: one on ALS (12,577 cases, 23,475 controls), and one on CD (4533 cases, 10,750 controls). We found one patient with TG6, TG2 and EMA antibodies who had typical ALS and no symptoms of GS. TG6 antibody concentrations and positivity, CD prevalence and adherence to a GFD were similar in patients and controls (p > 0.66) and in these patients disease progression was compatible with typical ALS. CD and ALS were not found to be genetically correlated (p > 0.37). CD-associated HLA allele frequencies were similar in patients and controls (p > 0.28). In conclusion, we found no serological evidence for involvement of gluten-related antibodies in ALS etiology nor did we observe an association between CD and ALS in medical history or genetic data, indicating that there is no evidence in our data for an association between the two diseases. Hence, a role for a GFD in the ALS treatment seems unlikely.

Journal article

Schmidt AF, Swerdlow DI, Holmes MV, Patel RS, Fairhurst-Hunter Z, Lyall DM, Hartwig FP, Horta BL, Hypponen E, Power C, Moldovan M, van Iperen E, Hovingh GK, Demuth I, Norman K, Steinhagen-Thiessen E, Demuth J, Bertram L, Liu T, Coassin S, Willeit J, Kiechl S, Willeit K, Mason D, Wright J, Morris R, Wanamethee G, Whincup P, Ben-Shlomo Y, McLachlan S, Price JF, Kivimaki M, Welch C, Sanchez-Galvez A, Marques-Vidal P, Nicolaides A, Panayiotou AG, Onland-Moret NC, van der Schouw YT, Matullo G, Fiorito G, Guarrera S, Sacerdote C, Wareham NJ, Langenberg C, Scott R, Luan J, Bobak M, Malyutina SA, Pajak A, Kubinova R, Tamosiunas A, Pikhart H, Husemoen LLN, Grarup N, Pedersen O, Hansen T, Linneberg A, Simonsen KS, Cooper J, Humphries SE, Brilliant M, Kitchner T, Hakonarson H, Carrell DS, McCarty CA, Kirchner HL, Larson EB, Crosslin DR, de Andrade M, Roden DM, Denny JC, Carty C, Hancock S, Attia J, Holliday E, Donnell MO, Yusuf S, Chong M, Pare G, van der Harst P, Said MA, Eppinga RN, Verweij N, Snieder H, Christen T, Mook-Kanamori DO, Gustafsson S, Lind L, Ingelsson E, Pazoki R, Franco O, Hofman A, Uitterlinden A, Dehghan A, Teumer A, Baumeister S, Doerr M, Lerch MM, Voelker U, Voelzke H, Ward J, Pell JP, Smith DJ, Meade T, Maitland-van der Zee AH, Baranova EV, Young R, Ford I, Campbell A, Padmanabhan S, Bots ML, Grobbee DE, Froguel P, Thuillier D, Balkau B, Bonnefond A, Cariou B, Smart M, Bao Y, Kumari M, Mahajan A, Ridker PM, Chasman DI, Reiner AP, Lange LA, Ritchie MD, Asselbergs FW, Casas J-P, Keating BJ, Preiss D, Hingorani AD, Sattar Net al., 2016, PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study, Lancet Diabetes and Endocrinology, Vol: 5, Pages: 97-105, ISSN: 2213-8587

Background: Statin treatment and variants in the gene encoding HMG-CoA reductase are associated with reductionsin both the concentration of LDL cholesterol and the risk of coronary heart disease, but also with modesthyperglycaemia, increased bodyweight, and modestly increased risk of type 2 diabetes, which in no way off sets theirsubstantial benefi ts. We sought to investigate the associations of LDL cholesterol-lowering PCSK9 variants with type 2diabetes and related biomarkers to gauge the likely eff ects of PCSK9 inhibitors on diabetes risk.Methods: In this mendelian randomisation study, we used data from cohort studies, randomised controlled trials,case control studies, and genetic consortia to estimate associations of PCSK9 genetic variants with LDL cholesterol,fasting blood glucose, HbA1c, fasting insulin, bodyweight, waist-to-hip ratio, BMI, and risk of type 2 diabetes, usinga standardised analysis plan, meta-analyses, and weighted gene-centric scores.Findings: Data were available for more than 550 000 individuals and 51 623 cases of type 2 diabetes. Combined analysesof four independent PCSK9 variants (rs11583680, rs11591147, rs2479409, and rs11206510) scaled to 1 mmol/L lowerLDL cholesterol showed associations with increased fasting glucose (0·09 mmol/L, 95% CI 0·02 to 0·15), bodyweight(1·03 kg, 0·24 to 1·82), waist-to-hip ratio (0·006, 0·003 to 0·010), and an odds ratio for type diabetes of 1·29 (1·11 to 1·50).Based on the collected data, we did not identify associations with HbA1c (0·03%, –0·01 to 0·08), fasting insulin (0·00%,–0·06 to 0·07), and BMI (0·11 kg/m², –0·09 to 0·30).Interpretation: PCSK9 variants associated with lower LDL cholesterol were also associated with circulating higherfasting glucose concentration, bodyweight, and waist-to-hip ratio, and an increased risk of type 2 diab

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, Issacs 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., 2016, Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps, Nature Genetics, Vol: 48, Pages: 1303-1312, ISSN: 1061-4036

Large-scale whole genome sequence datasets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole genome sequence data from the UK10K and the 1000 Genomes Projects into 35,981 study participants of European ancestry, followed by association analysis with twenty quantitative cardiometabolic and hematologic traits. We describe 17 novel associations, including six rare (minor allele frequency [MAF]<1%) or low frequency variants (1%<MAF<5%) with platelet count (PLT), red cell indices (MCH, MCV) and high-density lipoprotein (HDL) cholesterol. Applying fine-mapping analysis to 233 known and novel loci associated with the twenty traits, we resolve associations of 59 loci to credible sets of 20 or less variants, and describe trait enrichments within regions of predicted regulatory function. These findings augment understanding of the allelic architecture of risk factors for cardiometabolic and hematologic diseases, and provide additional functional insights with the identification of potentially novel biological targets.

Journal article

Polfus LM, Khajuria RK, Schick UM, Pankratz N, Pazoki R, Brody JA, Chen MH, Auer PL, Floyd JS, Huang J, Lange L, van Rooij FJA, Gibbs RA, Metcalf G, Muzny D, Veeraraghavan N, Walter K, Chen L, Yanek L, Becker LC, Peloso GM, Wakabayashi A, Kals M, Metspalu A, Esko T, Fox K, Wallace R, Franceschini N, Matijevic N, Rice KM, Bartz TM, Lyytikäinen LP, Kähönen M, Lehtimäki T, Raitakari OT, Li-Gao R, Mook-Kanamori DO, Lettre G, van Duijn CM, Franco OH, Rich SS, Rivadeneira F, Hofman A, Uitterlinden AG, Wilson JG, Psaty BM, Soranzo N, Dehghan A, Boerwinkle E, Zhang X, Johnson AD, O'Donnell CJ, Johnsen JM, Reiner AP, Ganesh SK, Sankaran VGet al., 2016, Erratum: Whole-Exome Sequencing Identifies Loci Associated with Blood Cell Traits and Reveals a Role for Alternative GFI1B Splice Variants in Human Hematopoiesis (American Journal of Human Genetics (2016) 99(2) (481–488)(S0002929716302208)(10.1016/j.ajhg.2016.06.016)), American Journal of Human Genetics, Vol: 99, Pages: 785-785, ISSN: 0002-9297

© 2016 American Society of Human Genetics (The American Journal of Human Genetics 99, 481–488; August 4, 2016) In the originally published version of this paper, Nora Franceschini's surname was misspelled. It has now been corrected online. The authors apologize for the error.

Journal article

Polfus LM, Khajuria RK, Schick UM, Pankratz N, Pazoki R, Brody JA, Chen M-H, Auer PL, Floyd JS, Huang J, Lange L, van Rooij FJA, Gibbs RA, Metcalf G, Muzny D, Veeraraghavan N, Walter K, Chen L, Yanek L, Becker LC, Peloso GM, Wakabayashi A, Kals M, Metspalu A, Esko T, Fox K, Wallace R, Franceschini N, Matijevic N, Rice KM, Bartz TM, Lyytikainen L-P, Kahonen M, Lehtimaki T, Raitakari OT, Li-Gao R, Mook-Kanamori DO, Lettre G, van Duijn CM, Franco OH, Rich SS, Rivadeneira F, Hofman A, Uitterlinden AG, Wilson JG, Psaty BM, Soranzo N, Dehghan A, Boerwinkle E, Zhang X, Johnson AD, O'Donnell CJ, Johnsen JM, Reiner AP, Ganesh SK, Sankaran VGet al., 2016, Whole-Exome Sequencing Identifies Loci Associated with Blood Cell Traits and Reveals a Role for Alternative GFI1B Splice Variants in Human Hematopoiesis, AMERICAN JOURNAL OF HUMAN GENETICS, Vol: 99, Pages: 481-488, ISSN: 0002-9297

Journal article

Pankratz N, Schick UM, Zhou Y, Zhou W, Ahluwalia TS, Allende ML, Auer PL, Bork-Jensen J, Brody JA, Chen M-H, Clavo V, Eicher JD, Grarup N, Hagedorn EJ, Hu B, Hunker K, Johnson AD, Leusink M, Lu Y, Lyytikainen L-P, Manichaikul A, Marioni RE, Nalls MA, Pazoki R, Smith AV, van Rooij FJA, Yang M-L, Zhang X, Zhang Y, Asselbergs FW, Boerwinkle E, Borecki IB, Bottinger EP, Cushman M, de Bakker PIW, Deary IJ, Dong L, Feitosa MF, Floyd JS, Franceschini N, Franco OH, Garcia ME, Grove ML, Gudnason V, Hansen T, Harris TB, Hofman A, Jackson RD, Jia J, Kahonen M, Launer LJ, Lehtimaki T, Liewald DC, Linneberg A, Liu Y, Loos RJF, Nguyen VM, Numans ME, Pedersen O, Psaty BM, Raitakari OT, Rich SS, Rivadeneira F, Di Sant AMR, Rotter JI, Starr JM, Taylor KD, Thuesen BH, Tracy RP, Uitterlinden AG, Wang J, Wang J, Dehghan A, Huo Y, Cupples LA, Wilson JG, Proia RL, Zon LI, O'Donnell CJ, Reiner AP, Ganesh SKet al., 2016, Meta-analysis of rare and common exome chip variants identifies S1PR4 and other loci influencing blood cell traits, NATURE GENETICS, Vol: 48, Pages: 867-+, ISSN: 1061-4036

Journal article

Chami N, Chen MH, Slater AJ, Eicher JD, Evangelou E, Tajuddin SM, Love-Gregory L, Kacprowski T, Schick UM, Nomura A, Giri A, Lessard S, Brody JA, Schurmann C, Pankratz N, Yanek LR, Manichaikul A, Pazoki R, Mihailov E, Hill WD, Raffield LM, Burt A, Bartz TM, Becker DM, Becker LC, Boerwinkle E, Bork-Jensen J, Bottinger EP, O'Donoghue ML, Crosslin DR, de Denus S, Dubé MP, Elliott P, Engström G, Evans MK, Floyd JS, Fornage M, Gao H, Greinacher A, Gudnason V, Hansen T, Harris TB, Hayward C, Hernesniemi J, Highland HM, Hirschhorn JN, Hofman A, Irvin MR, Kähönen M, Lange E, Launer LJ, Lehtimäki T, Li J, Liewald DC, Linneberg A, Liu Y, Lu Y, Lyytikäinen LP, Mägi R, Mathias RA, Melander O, Metspalu A, Mononen N, Nalls MA, Nickerson DA, Nikus K, O'Donnell CJ, Orho-Melander M, Pedersen O, Petersmann A, Polfus L, Psaty BM, Raitakari OT, Raitoharju E, Richard M, Rice KM, Rivadeneira F, Rotter JI, Schmidt F, Smith AV, Starr JM, Taylor KD, Teumer A, Thuesen BH, Torstenson ES, Tracy RP, Tzoulaki I, Zakai NA, Vacchi-Suzzi C, van Duijn CM, van Rooij FJ, Cushman M, Deary IJ, Velez Edwards DR, Vergnaud AC, Wallentin L, Waterworth DM, White HD, Wilson JG, Zonderman AB, Kathiresan S, Grarup N, Esko T, Loos RJ, Lange LA, Faraday N, Abumrad NA, Edwards TL, Ganesh SK, Auer PL, Johnson AD, Reiner AP, Lettre Get al., 2016, Exome genotyping identifies pleiotropic variants associated with red blood cell traits, American Journal of Human Genetics, Vol: 99, Pages: 8-21, ISSN: 1537-6605

Red blood cell (RBC) traits are important heritable clinical biomarkers and modifiers of disease severity. To identify coding genetic variants associated with these traits, we conducted meta-analyses of seven RBC phenotypes in 130,273 multi-ethnic individuals from studies genotyped on an exome array. After conditional analyses and replication in 27,480 independent individuals, we identified 16 new RBC variants. We found low-frequency missense variants in MAP1A (rs55707100, minor allele frequency [MAF] = 3.3%, p = 2 × 10(-10) for hemoglobin [HGB]) and HNF4A (rs1800961, MAF = 2.4%, p < 3 × 10(-8) for hematocrit [HCT] and HGB). In African Americans, we identified a nonsense variant in CD36 associated with higher RBC distribution width (rs3211938, MAF = 8.7%, p = 7 × 10(-11)) and showed that it is associated with lower CD36 expression and strong allelic imbalance in ex vivo differentiated human erythroblasts. We also identified a rare missense variant in ALAS2 (rs201062903, MAF = 0.2%) associated with lower mean corpuscular volume and mean corpuscular hemoglobin (p < 8 × 10(-9)). Mendelian mutations in ALAS2 are a cause of sideroblastic anemia and erythropoietic protoporphyria. Gene-based testing highlighted three rare missense variants in PKLR, a gene mutated in Mendelian non-spherocytic hemolytic anemia, associated with HGB and HCT (SKAT p < 8 × 10(-7)). These rare, low-frequency, and common RBC variants showed pleiotropy, being also associated with platelet, white blood cell, and lipid traits. Our association results and functional annotation suggest the involvement of new genes in human erythropoiesis. We also confirm that rare and low-frequency variants play a role in the architecture of complex human traits, although their phenotypic effect is generally smaller than originally anticipated.

Journal article

Tajuddin SM, Schick UM, Eicher JD, Chami N, Giri A, Brody JA, Hill WD, Kacprowski T, Li J, Lyytikäinen LP, Manichaikul A, Mihailov E, O'Donoghue ML, Pankratz N, Pazoki R, Polfus LM, Smith AV, Schurmann C, Vacchi-Suzzi C, Waterworth DM, Evangelou E, Yanek LR, Burt A, Chen MH, van Rooij FJ, Floyd JS, Greinacher A, Harris TB, Highland HM, Lange LA, Liu Y, Mägi R, Nalls MA, Mathias RA, Nickerson DA, Nikus K, Starr JM, Tardif JC, Tzoulaki I, Velez Edwards DR, Wallentin L, Bartz TM, Becker LC, Denny JC, Raffield LM, Rioux JD, Friedrich N, Fornage M, Gao H, Hirschhorn JN, Liewald DC, Rich SS, Uitterlinden A, Bastarache L, Becker DM, Boerwinkle E, de Denus S, Bottinger EP, Hayward C, Hofman A, Homuth G, Lange E, Launer LJ, Lehtimäki T, Lu Y, Metspalu A, O'Donnell CJ, Quarells RC, Richard M, Torstenson ES, Taylor KD, Vergnaud AC, Zonderman AB, Crosslin DR, Deary IJ, Dörr M, Elliott P, Evans MK, Gudnason V, Kähönen M, Psaty BM, Rotter JI, Slater AJ, Dehghan A, White HD, Ganesh SK, Loos RJ, Esko T, Faraday N, Wilson JG, Cushman M, Johnson AD, Edwards TL, Zakai NA, Lettre G, Reiner AP, Auer PLet al., 2016, Large-scale exome-wide association analysis identifies loci for white blood cell traits and pleiotropy with immune-mediated diseases, American Journal of Human Genetics, Vol: 99, Pages: 22-39, ISSN: 1537-6605

White blood cells play diverse roles in innate and adaptive immunity. Genetic association analyses of phenotypic variation in circulating white blood cell (WBC) counts from large samples of otherwise healthy individuals can provide insights into genes and biologic pathways involved in production, differentiation, or clearance of particular WBC lineages (myeloid, lymphoid) and also potentially inform the genetic basis of autoimmune, allergic, and blood diseases. We performed an exome array-based meta-analysis of total WBC and subtype counts (neutrophils, monocytes, lymphocytes, basophils, and eosinophils) in a multi-ancestry discovery and replication sample of ∼157,622 individuals from 25 studies. We identified 16 common variants (8 of which were coding variants) associated with one or more WBC traits, the majority of which are pleiotropically associated with autoimmune diseases. Based on functional annotation, these loci included genes encoding surface markers of myeloid, lymphoid, or hematopoietic stem cell differentiation (CD69, CD33, CD87), transcription factors regulating lineage specification during hematopoiesis (ASXL1, IRF8, IKZF1, JMJD1C, ETS2-PSMG1), and molecules involved in neutrophil clearance/apoptosis (C10orf54, LTA), adhesion (TNXB), or centrosome and microtubule structure/function (KIF9, TUBD1). Together with recent reports of somatic ASXL1 mutations among individuals with idiopathic cytopenias or clonal hematopoiesis of undetermined significance, the identification of a common regulatory 3' UTR variant of ASXL1 suggests that both germline and somatic ASXL1 mutations contribute to lower blood counts in otherwise asymptomatic individuals. These association results shed light on genetic mechanisms that regulate circulating WBC counts and suggest a prominent shared genetic architecture with inflammatory and autoimmune diseases.

Journal article

Eicher JD, Chami N, Kacprowski T, Nomura A, Chen MH, Yanek LR, Tajuddin SM, Schick UM, Slater AJ, Pankratz N, Polfus L, Schurmann C, Giri A, Brody JA, Lange LA, Manichaikul A, Hill WD, Pazoki R, Elliot P, Evangelou E, Tzoulaki I, Gao H, Vergnaud AC, Mathias RA, Becker DM, Becker LC, Burt A, Crosslin DR, Lyytikäinen LP, Nikus K, Hernesniemi J, Kähönen M, Raitoharju E, Mononen N, Raitakari OT, Lehtimäki T, Cushman M, Zakai NA, Nickerson DA, Raffield LM, Quarells R, Willer CJ, Peloso GM, Abecasis GR, Liu DJ, Global Lipids Genetics Consortium, Deloukas P, Samani NJ, Schunkert H, Erdmann J, CARDIoGRAM Exome Consortium, Myocardial Infarction Genetics Consortium, Fornage M, Richard M, Tardif JC, Rioux JD, Dube MP, de Denus S, Lu Y, Bottinger EP, Loos RJ, Smith AV, Harris TB, Launer LJ, Gudnason V, Velez Edwards DR, Torstenson ES, Liu Y, Tracy RP, Rotter JI, Rich SS, Highland HM, Boerwinkle E, Li J, Lange E, Wilson JG, Mihailov E, Mägi R, Hirschhorn J, Metspalu A, Esko T, Vacchi-Suzzi C, Nalls MA, Zonderman AB, Evans MK, Engström G, Orho-Melander M, Melander O, O'Donoghue ML, Waterworth DM, Wallentin L, White HD, Floyd JS, Bartz TM, Rice KM, Psaty BM, Starr JM, Liewald DC, Hayward C, Deary IJ, Greinacher A, Völker U, Thiele T, Völzke H, van Rooij FJ, Uitterlinden AG, Franco OH, Dehghan A, Edwards TL, Ganesh SK, Kathiresan S, Faraday N, Auer PL, Reiner AP, Lettre G, Johnson ADet al., 2016, Platelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals, American Journal of Human Genetics, Vol: 99, Pages: 40-55, ISSN: 1537-6605

Platelet production, maintenance, and clearance are tightly controlled processes indicative of platelets' important roles in hemostasis and thrombosis. Platelets are common targets for primary and secondary prevention of several conditions. They are monitored clinically by complete blood counts, specifically with measurements of platelet count (PLT) and mean platelet volume (MPV). Identifying genetic effects on PLT and MPV can provide mechanistic insights into platelet biology and their role in disease. Therefore, we formed the Blood Cell Consortium (BCX) to perform a large-scale meta-analysis of Exomechip association results for PLT and MPV in 157,293 and 57,617 individuals, respectively. Using the low-frequency/rare coding variant-enriched Exomechip genotyping array, we sought to identify genetic variants associated with PLT and MPV. In addition to confirming 47 known PLT and 20 known MPV associations, we identified 32 PLT and 18 MPV associations not previously observed in the literature across the allele frequency spectrum, including rare large effect (FCER1A), low-frequency (IQGAP2, MAP1A, LY75), and common (ZMIZ2, SMG6, PEAR1, ARFGAP3/PACSIN2) variants. Several variants associated with PLT/MPV (PEAR1, MRVI1, PTGES3) were also associated with platelet reactivity. In concurrent BCX analyses, there was overlap of platelet-associated variants with red (MAP1A, TMPRSS6, ZMIZ2) and white (PEAR1, ZMIZ2, LY75) blood cell traits, suggesting common regulatory pathways with shared genetic architecture among these hematopoietic lineages. Our large-scale Exomechip analyses identified previously undocumented associations with platelet traits and further indicate that several complex quantitative hematological, lipid, and cardiovascular traits share genetic factors.

Journal article

Chaker L, Falla A, van der Lee SJ, Muka T, Imo D, Jaspers L, Colpani V, Mendis S, Chowdhury R, Bramer WM, Pazoki R, Franco OHet al., 2015, The global impact of non-communicable diseases on macro-economic productivity: a systematic review, EUROPEAN JOURNAL OF EPIDEMIOLOGY, Vol: 30, Pages: 357-395, ISSN: 0393-2990

Journal article

Muka T, Imo D, Jaspers L, Colpani V, Chaker L, van der Lee SJ, Mendis S, Chowdhury R, Bramer WM, Falla A, Pazoki R, Franco OHet al., 2015, The global impact of non-communicable diseases on healthcare spending and national income: a systematic review, EUROPEAN JOURNAL OF EPIDEMIOLOGY, Vol: 30, Pages: 251-277, ISSN: 0393-2990

Journal article

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