Imperial College London

DrWeihuaZhang

Faculty of MedicineSchool of Public Health

Honorary Research Associate
 
 
 
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Contact

 

+44 (0)20 7594 1612weihua.zhang

 
 
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Location

 

165Medical SchoolSt Mary's Campus

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Summary

 

Publications

Publication Type
Year
to

101 results found

Chen J, Spracklen CN, Marenne G, Varshney A, Corbin LJ, Luan J, Willems SM, Wu Y, Zhang X, Horikoshi M, Boutin TS, Magi R, Waage J, Li-Gao R, Chan KHK, Yao J, Anasanti MD, Chu AY, Claringbould A, Heikkinen J, Hong J, Hottenga J-J, Huo S, Kaakinen MA, Louie T, Maerz W, Moreno-Macias H, Ndungu A, Nelson SC, Nolte IM, North KE, Raulerson CK, Ray D, Rohde R, Rybin D, Schurmann C, Sim X, Southam L, Stewart ID, Wang CA, Wang Y, Wu P, Zhang W, Ahluwalia TS, Appel EVR, Bielak LF, Brody JA, Burtt NP, Cabrera CP, Cade BE, Chai JF, Chai X, Chang L-C, Chen C-H, Chen BH, Chitrala KN, Chiu Y-F, de Haan HG, Delgado GE, Demirkan A, Duan Q, Engmann J, Fatumo SA, Gayan J, Giulianini F, Gong JH, Gustafsson S, Hai Y, Hartwig FP, He J, Heianza Y, Huang T, Huerta-Chagoya A, Hwang MY, Jensen RA, Kawaguchi T, Kentistou KA, Kim YJ, Kleber ME, Kooner IK, Lai S, Lange LA, Langefeld CD, Lauzon M, Li M, Ligthart S, Liu J, Loh M, Long J, Lyssenko V, Mangino M, Marzi C, Montasser ME, Nag A, Nakatochi M, Noce D, Noordam R, Pistis G, Preuss M, Raffield L, Rasmussen-Torvik LJ, Rich SS, Robertson NR, Rueedi R, Ryan K, Sanna S, Saxena R, Schraut KE, Sennblad B, Setoh K, Smith AV, Sparso T, Strawbridge RJ, Takeuchi F, Tan J, Trompet S, van den Akker E, van der Most PJ, Verweij N, Vogel M, Wang H, Wang C, Wang N, Warren HR, Wen W, Wilsgaard T, Wong A, Wood AR, Xie T, Zafarmand MH, Zhao J-H, Zhao W, Amin N, Arzumanyan Z, Astrup A, Bakker SJL, Baldassarre D, Beekman M, Bergman RN, Bertoni A, Blueher M, Bonnycastle LL, Bornstein SR, Bowden DW, Cai Q, Campbell A, Campbell H, Chang YC, de Geus EJC, Dehghan A, Du S, Eiriksdottir G, Farmaki AE, Franberg M, Fuchsberger C, Gao Y, Gjesing AP, Goel A, Han S, Hartman CA, Herder C, Hicks AA, Hsieh C-H, Hsueh WA, Ichihara S, Igase M, Ikram MA, Johnson WC, Jorgensen ME, Joshi PK, Kalyani RR, Kandeel FR, Katsuya T, Khor CC, Kiess W, Kolcic I, Kuulasmaa T, Kuusisto J, Lall K, Lam K, Lawlor DA, Lee NR, Lemaitre RN, Li H, Lin S-Y, Lindstrom J, Linneberg A, Liu J, Lorenzoet al., 2021, The trans-ancestral genomic architecture of glycemic traits, NATURE GENETICS, Vol: 53, Pages: 840-+, ISSN: 1061-4036

Journal article

Surendran P, Feofanova EV, Lahrouchi N, Ntalla I, Karthikeyan S, Cook J, Chen L, Mifsud B, Yao C, Kraja AT, Cartwright JH, Hellwege JN, Giri A, Tragante V, Thorleifsson G, Liu DJ, Prins BP, Stewart ID, Cabrera CP, Eales JM, Akbarov A, Auer PL, Bielak LF, Bis JC, Braithwaite VS, Brody JA, Daw EW, Warren HR, Drenos F, Nielsen SF, Faul JD, Fauman EB, Fava C, Ferreira T, Foley CN, Franceschini N, Gao H, Giannakopoulou O, Giulianini F, Gudbjartsson DF, Guo X, Harris SE, Havulinna AS, Helgadottir A, Huffman JE, Hwang S-J, Kanoni S, Kontto J, Larson MG, Li-Gao R, Lindstrom J, Lotta LA, Lu Y, Luan J, Mahajan A, Malerba G, Masca NGD, Mei H, Menni C, Mook-Kanamori DO, Mosen-Ansorena D, Muller-Nurasyid M, Pare G, Paul DS, Perola M, Poveda A, Rauramaa R, Richard M, Richardson TG, Sepulveda N, Sim X, Smith AV, Smith JA, Staley JR, Stanakova A, Sulem P, Theriault S, Thorsteinsdottir U, Trompet S, Varga TV, Velez Edwards DR, Veronesi G, Weiss S, Willems SM, Yao J, Young R, Yu B, Zhang W, Zhao J-H, Zhao W, Zhao W, Evangelou E, Aeschbacher S, Asllanaj E, Blankenberg S, Bonnycastle LL, Bork-Jensen J, Brandslund I, Braund PS, Burgess S, Cho K, Christensen C, Connell J, de Mutsert R, Dominiczak AF, Dorr M, Eiriksdottir G, Farmaki A-E, Gaziano JM, Grarup N, Grove ML, Hallmans G, Hansen T, Have CT, Heiss G, Jorgensen ME, Jousilahti P, Kajantie E, Kamat M, Karajamaki A, Karpe F, Koistinen HA, Kovesdy CP, Kuulasmaa K, Laatikainen I, Lannfelt L, Lee I-T, Lee W-J, Linneberg A, Martin LW, Moitry M, Nadkarni G, Neville MJ, Palmer CNA, Papanicolaou GJ, Pedersen O, Peters J, Poulter N, Rasheed A, Rasmussen KL, Rayner NW, Magi R, Renstrom F, Rettig R, Rossouw J, Schreiner PJ, Sever PS, Sigurdsson EL, Skaaby T, Sun YV, Sundstrom J, Thorgeirsson G, Esko T, Trabetti E, Tsao PS, Tuomi T, Turner ST, Tzoulaki I, Vaartjes I, Vergnaud A-C, Willer CJ, Wilson PWF, Witte DR, Yonova-Doing E, Zhang H, Aliya N, Almgren P, Amouyel P, Asselbergs FW, Barnes MR, Blakemore AI, Boehnke M, Bots ML, Bottinger EP, Buriet al., 2021, Publisher Correction: Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals, Nature Genetics, Vol: 53, Pages: 1-2, ISSN: 1061-4036

Journal article

Gomez-Alonso MDC, Kretschmer A, Wilson R, Pfeiffer L, Karhunen V, Seppala I, Zhang W, Mittelstrass K, Wahl S, Matias-Garcia PR, Prokisch H, Horn S, Meitinger T, Serrano-Garcia LR, Sebert S, Raitakari O, Loh M, Rathmann W, Mueller-Nurasyid M, Herder C, Roden M, Hurme M, Jarvelin M-R, Ala-Korpela M, Kooner JS, Peters A, Lehtimaki T, Chambers JC, Gieger C, Kettunen J, Waldenberger Met al., 2021, DNA methylation and lipid metabolism: an EWAS of 226 metabolic measures, CLINICAL EPIGENETICS, Vol: 13, ISSN: 1868-7075

Journal article

Surendran P, Gao H, Zhang W, Evangelou E, Poulter N, Sever PJ, Vergnaud A, Chambers JC, Elliott P, Jarvelin M-R, Kooner JS, Howson Jet al., 2020, Discovery of rare variants associated with blood pressure regulation trhough meta-analaysis of 1.3 million individuals, Nature Genetics, Vol: 52, Pages: 1314-1332, ISSN: 1061-4036

Genetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequency, MAF > 0.05). In a meta-analysis of up to >1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (MAF≤ 0.01) variant BP associations (P < 5 × 10-8), of which 32 were in new BP-associated loci and 55 were independent BP-associated SNVs within known BP-associated regions. Average effects of rare variants (44% coding) were ~8 times larger than common variant effects and indicate potential candidate causal genes at new and known loci (e.g.GATA5, PLCB3). BP-associated variants (including rare and common) were enriched in regions of active chromatin in fetal tissues, potentially linking fetal development with BP regulation in later life. Multivariable Mendelian randomization suggested possible inverse effects of elevated systolic and diastolic BP on large artery stroke. Our study demonstrates the utility of rare variant analyses for identifying candidate genes and the results highlight potential therapeutic targets.

Journal article

Spracklen CN, Horikoshi M, Kim YJ, Lin K, Bragg F, Moon S, Suzuki K, Tam CHT, Tabara Y, Kwak S-H, Takeuchi F, Long J, Lim VJY, Chai J-F, Chen C-H, Nakatochi M, Yao J, Choi HS, Iyengar AK, Perrin HJ, Brotman SM, Van De Bunt M, Gloyn AL, Below JE, Boehnke M, Bowden DW, Chambers JC, Mahajan A, McCarthy MI, Ng MCY, Petty LE, Zhang W, Morris AP, Adair LS, Akiyama M, Bian Z, Chan JCN, Chang L-C, Chee M-L, Chen Y-DI, Chen Y-T, Chen Z, Chuang L-M, Du S, Gordon-Larsen P, Gross M, Guo X, Guo Y, Han S, Howard A-G, Huang W, Hung Y-J, Hwang MY, Hwu C-M, Ichihara S, Isono M, Jang H-M, Jiang G, Jonas JB, Kamatani Y, Katsuya T, Kawaguchi T, Khor C-C, Kohara K, Lee M-S, Lee NR, Li L, Liu J, Luk AO, Lv J, Okada Y, Pereira MA, Sabanayagam C, Shi J, Shin DM, So WY, Takahashi A, Tomlinson B, Tsai F-J, van Dam RM, Xiang Y-B, Yamamoto K, Yamauchi T, Yoon K, Yu C, Yuan J-M, Zhang L, Zheng W, Igase M, Cho YS, Rotter JI, Wang Y-X, Sheu WHH, Yokota M, Wu J-Y, Cheng C-Y, Wong T-Y, Shu X-O, Kato N, Park K-S, Tai E-S, Matsuda F, Koh W-P, Ma RCW, Maeda S, Millwood IY, Lee J, Kadowaki T, Walters RG, Kim B-J, Mohlke KL, Sim Xet al., 2020, Identification of type 2 diabetes loci in 433,540 East Asian individuals, NATURE, Vol: 582, Pages: 240-+, ISSN: 0028-0836

Journal article

Ochoa-Rosales C, Portilla-Fernandez E, Nano J, Wilson R, Lehne B, Mishra PP, Gao X, Ghanbari M, Rueda-Ochoa OL, Juvinao-Quintero D, Loh M, Zhang W, Kooner JS, Grabe HJ, Felix SB, Schoettker B, Zhang Y, Gieger C, Mueller-Nurasyid M, Heier M, Peters A, Lehtimaki T, Teumer A, Brenner H, Waldenberger M, Ikram MA, van Meurs JBJ, Franco OH, Voortman T, Chambers J, Stricker BH, Muka Tet al., 2020, Epigenetic Link Between Statin Therapy and Type 2 Diabetes, DIABETES CARE, Vol: 43, Pages: 875-884, ISSN: 0149-5992

Journal article

Clark DW, Zhang W, Gao H, Afaq S, Elliott P, Elliott J, Poulter N, Scott W, Sever P, Tzoulaki I, Lehne B, Chambers J, Evangelou E, Kooner J, Walters R, Wilson Jet al., 2019, Associations of autozygosity with a broad range of human phenotypes, Nature Communications, Vol: 10, ISSN: 2041-1723

In many species, the offspring of related parents suffer reduced vigor, survival and reproductive success, a phenomenon known as inbreeding depression1. In humans, the importance of this effect has remained unclear2, partly because reproduction between close relatives is both rare in many cultures and frequently associated with confounding social factors3. Here, using genomic inbreeding coefficients4 (FROH) for >1.3 million individuals, we show that FROH is significantly associated (P < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. Increased FROH is associated with reduced reproductive success (decreased number and likelihood of having children, older age at first sex and first birth, decreased number of sexual partners), as well as reduced risk-taking behaviour (alcohol intake, ever-smoked, self-reported risk taking) and increased disease risk (self-reported overall health, and risk factors including grip strength and heart rate). The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. These effects are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants causing inbreeding depression are predominantly rare. For a subset of traits, the effect of FROH differs significantly between men and women. Indeed, an increased FROH is associated with decreased total and LDL cholesterol in men, raising the possibility that increases in these traits may have benefited evolutionary fitness, despite being known coronary risk factors. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of environmental confounding. We conclude that inbreeding depression influences a broad range of human phenotypes through the action of rare, recessive variants.

Journal article

Erzurumluoglu AM, Chambers JC, Elliott P, Evangelou E, Kooner JS, Poulter N, Sever P, Zhang W, Howson JMM, Wells Jet al., 2019, Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci, Molecular Psychiatry, Vol: 25, Pages: 2392-2409, ISSN: 1359-4184

Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10−8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10−8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10−3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.

Journal article

Tin A, Marten J, Kuhns VLH, Li Y, Wuttke M, Kirsten H, Sieber KB, Qiu C, Gorski M, Yu Z, Giri A, Sveinbjornsson G, Li M, Chu AY, Hoppmann A, O'Connor LJ, Prins B, Nutile T, Noce D, Akiyama M, Cocca M, Ghasemi S, van Der Most PJ, Horn K, Xu Y, Fuchsberger C, Sedaghat S, Afaq S, Amin N, Arnlov J, Bakker SJL, Bansal N, Baptista D, Bergmann S, Biggs ML, Biino G, Boerwinkle E, Bottinger EP, Boutin TS, Brumat M, Burkhardt R, Campana E, Campbell A, Campbell H, Carroll RJ, Catamo E, Chambers JC, Ciullo M, Concas MP, Coresh J, Corre T, Cusi D, Felicita SC, de Borst MH, De Grandi A, de Mutsert R, de Vries APJ, Delgado G, Demirkan A, Devuyst O, Dittrich K, Eckardt K-U, Ehret G, Endlich K, Evans MK, Gansevoort RT, Gasparini P, Giedraitis V, Gieger C, Girotto G, Ggele M, Gordon SD, Gudbjartsson DF, Gudnason V, Haller T, Hamet P, Harris TB, Hayward C, Hicks AA, Hofer E, Holm H, Huang W, Hutri-Kahonen N, Hwang S-J, Ikram MA, Lewis RM, Ingelsson E, Jakobsdottir J, Jonsdottir I, Jonsson H, Joshi PK, Josyula NS, Jung B, Kahonen M, Kamatani Y, Kanai M, Kerr SM, Kiess W, Kleber ME, Koenig W, Kooner JS, Korner A, Kovacs P, Kramer BK, Kronenberg F, Kubo M, Kuhnel B, La Bianca M, Lange LA, Lehne B, Lehtimaki T, Liu J, Loeffler M, Loos RJF, Lyytikainen L-P, Magi R, Mahajan A, Martin NG, Marz W, Mascalzoni D, Matsuda K, Meisinger C, Meitinger T, Metspalu A, Milaneschi Y, ODonnell CJ, Wilson OD, Gaziano JM, Mishra PP, Mohlke KL, Mononen N, Montgomery GW, Mook-Kanamori DO, Mueller-Nurasyid M, Nadkarni GN, Nalls MA, Nauck M, Nikus K, Ning B, Nolte IM, Noordam R, O'Connell JR, Olafsson I, Padmanabhan S, Penninx BWJH, Perls T, Peters A, Pirastu M, Pirastu N, Pistis G, Polasek O, Ponte B, Porteous DJ, Poulain T, Preuss MH, Rabelink TJ, Raffield LM, Raitakari OT, Rettig R, Rheinberger M, Rice KM, Rizzi F, Robino A, Rudan I, Krajcoviechova A, Cifkova R, Rueedi R, Ruggiero D, Ryan KA, Saba Y, Salvi E, Schmidt H, Schmidt R, Shaffer CM, Smith A, Smith BH, Spracklen CN, Strauch K, Stumvoll M, Sulem Pet al., 2019, Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels, NATURE GENETICS, Vol: 51, Pages: 1459-+, ISSN: 1061-4036

Journal article

Sung YJ, de Las Fuentes L, Winkler TW, Chasman DI, Bentley AR, Kraja AT, Ntalla I, Warren HR, Guo X, Schwander K, Manning AK, Brown MR, Aschard H, Feitosa MF, Franceschini N, Lu Y, Cheng C-Y, Sim X, Vojinovic D, Marten J, Musani SK, Kilpeläinen TO, Richard MA, Aslibekyan S, Bartz TM, Dorajoo R, Li C, Liu Y, Rankinen T, Smith AV, Tajuddin SM, Tayo BO, Zhao W, Zhou Y, Matoba N, Sofer T, Alver M, Amini M, Boissel M, Chai JF, Chen X, Divers J, Gandin I, Gao C, Giulianini F, Goel A, Harris SE, Hartwig FP, He M, Horimoto ARVR, Hsu F-C, Jackson AU, Kammerer CM, Kasturiratne A, Komulainen P, Kühnel B, Leander K, Lee W-J, Lin K-H, Luan J, Lyytikäinen L-P, McKenzie CA, Nelson CP, Noordam R, Scott RA, Sheu WHH, Stančáková A, Takeuchi F, van der Most PJ, Varga TV, Waken RJ, Wang H, Wang Y, Ware EB, Weiss S, Wen W, Yanek LR, Zhang W, Zhao JH, Afaq S, Alfred T, Amin N, Arking DE, Aung T, Barr RG, Bielak LF, Boerwinkle E, Bottinger EP, Braund PS, Brody JA, Broeckel U, Cade B, Campbell A, Canouil M, Chakravarti A, Cocca M, Collins FS, Connell JM, de Mutsert R, de Silva HJ, Dörr M, Duan Q, Eaton CB, Ehret G, Evangelou E, Faul JD, Forouhi NG, Franco OH, Friedlander Y, Gao H, Gigante B, Gu CC, Gupta P, Hagenaars SP, Harris TB, He J, Heikkinen S, Heng C-K, Hofman A, Howard BV, Hunt SC, Irvin MR, Jia Y, Katsuya T, Kaufman J, Kerrison ND, Khor CC, Koh W-P, Koistinen HA, Kooperberg CB, Krieger JE, Kubo M, Kutalik Z, Kuusisto J, Lakka TA, Langefeld CD, Langenberg C, Launer LJ, Lee JH, Lehne B, Levy D, Lewis CE, Li Y, Lifelines Cohort Study, Lim SH, Liu C-T, Liu J, Liu J, Liu Y, Loh M, Lohman KK, Louie T, Mägi R, Matsuda K, Meitinger T, Metspalu A, Milani L, Momozawa Y, Mosley TH, Nalls MA, Nasri U, O'Connell JR, Ogunniyi A, Palmas WR, Palmer ND, Pankow JS, Pedersen NL, Peters A, Peyser PA, Polasek O, Porteous D, Raitakari OT, Renström F, Rice TK, Ridker PM, Robino A, Robinson JG, Rose LM, Rudan I, Sabanayagam C, Salako BL, Sandow K, Schmidt CO, Schreiner PJ, Scott WR, Sever P, Sims M, Sitet al., 2019, A multi-ancestry genome-wide study incorporating gene-smoking interactions identifies multiple new loci for pulse pressure and mean arterial pressure, Human Molecular Genetics, Vol: 28, Pages: 2615-2633, ISSN: 0964-6906

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

Journal article

Brazel DM, Jiang Y, Hughey JM, Turcot V, Zhan X, Gong J, Batini C, Weissenkampen JD, Liu MZ, Surendran P, Young R, Barnes DR, Nielsen SF, Rasheed A, Samuel M, Zhao W, Kontto J, Perola M, Caslake M, de Craen AJM, Trompet S, Uria-Nickelsen M, Malarstig A, Reily DF, Hoek M, Vogt T, Jukema JW, Sattar N, Ford I, Packard CJ, Alam DS, Majumder AAS, Di Angelantonio E, Chowdhury R, Amouyel P, Arveiler D, Blankenberg S, Ferrières J, Kee F, Kuulasmaa K, Müller-Nurasyid M, Veronesi G, Virtamo J, EPIC-CVD Consortium, Frossard P, Nordestgaard BG, Saleheen D, Danesh J, Butterworth AS, Howson JMM, Erzurumluoglu AM, Jackson VE, Melbourne CA, Varga TV, Warren HR, Tragante V, Tachmazidou I, Harris SE, Evangelou E, Marten J, Zhang W, Altmaier E, Luan J, Langenberg C, Scott RA, Yaghootkar H, Stirrups K, Kanoni S, Marouli E, Karpe F, Dominiczak AF, Sever P, Poulter N, Rolandsson O, Baumbach C, Afaq S, Chambers JC, Kooner JS, Wareham NJ, Renström F, Hallmans G, Marioni RE, Corley J, Starr JM, Verweij N, de Boer RA, van der Meer P, Yavas E, Vaartjes I, Bots ML, Asselbergs FW, Grabe HJ, Völzke Het al., 2019, Exome chip meta-analysis fine maps causal variants and elucidates the genetic architecture of rare coding variants in smoking and alcohol use, Biological Psychiatry, Vol: 85, Pages: 946-955, ISSN: 0006-3223

Background: Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk. Methods: We analyzed ∼250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci. Results: Meta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals. Conclusions: Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior.

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

Wuttke M, Li Y, Li M, Sieber KB, Feitosa MF, Gorski M, Tin A, Wang L, Chu AY, Hoppmann A, Kirsten H, Giri A, Chai J-F, Sveinbjornsson G, Tayo BO, Nutile T, Fuchsberger C, Marten J, Cocca M, Ghasemi S, Xu Y, Horn K, Noce D, Van der Most PJ, Sedaghat S, Yu Z, Akiyama M, Afaq S, Ahluwalia TS, Almgren P, Amin N, Arnlov J, Bakker SJL, Bansal N, Baptista D, Bergmann S, Biggs ML, Biino G, Boehnke M, Boerwinkle E, Boissel M, Bottinger EP, Boutin TS, Brenner H, Brumat M, Burkhardt R, Butterworth AS, Campana E, Campbell A, Campbell H, Canouil M, Carroll RJ, Catamo E, Chambers JC, Chee M-L, Chee M-L, Chen X, Cheng C-Y, Cheng Y, Christensen K, Cifkova R, Ciullo M, Concas MP, Cook JP, Coresh J, Corre T, Sala CF, Cusi D, Danesh J, Daw EW, De Borst MH, De Grandi A, De Mutsert R, De Vries APJ, Degenhardt F, Delgado G, Demirkan A, Di Angelantonio E, Dittrich K, Divers J, Dorajoo R, Eckardt K-U, Ehret G, Elliott P, Endlich K, Evans MK, Felix JF, Foo VHX, Franco OH, Franke A, Freedman BI, Freitag-Wolf S, Friedlander Y, Froguel P, Gansevoort RT, Gao H, Gasparini P, Gaziano JM, Giedraitis V, Gieger C, Girotto G, Giulianini F, Gogele M, Gordon SD, Gudbjartsson DF, Gudnason V, Haller T, Hamet P, Harris TB, Hartman CA, Hayward C, Hellwege JN, Heng C-K, Hicks AA, Hofer E, Huang W, Hutri-Kahonen N, Hwang S-J, Ikram MA, Indridason OS, Ingelsson E, Ising M, Jaddoe VWV, Jakobsdottir J, Jonas JB, Joshi PK, Josyula NS, Jung B, Kahonen M, Kamatani Y, Kammerer CM, Kanai M, Kastarinen M, Kerr SM, Khor C-C, Kiess W, Kleber ME, Koenig W, Kooner JS, Korner A, Kovacs P, Kraja AT, Krajcoviechova A, Kramer H, Kramer BK, Kronenberg F, Kubo M, Kuhnel B, Kuokkanen M, Kuusisto J, La Bianca M, Laakso M, Lange LA, Langefeld CD, Lee JJ-M, Lehne B, Lehtimaki T, Lieb W, Lim S-C, Lind L, Lindgren CM, Liu J, Liu J, Loeffler M, Loos RJF, Lucae S, Lukas MA, Lyytikainen L-P, Magi R, Magnusson PKE, Mahajan A, Martin NG, Martins J, Marz W, Mascalzoni D, Matsuda K, Meisinger C, Meitinger T, Melander O, Metspalu A, Mikaelset al., 2019, A catalog of genetic loci associated with kidney function from analyses of a million individuals, Nature Genetics, Vol: 51, Pages: 957-972, ISSN: 1061-4036

Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.

Journal article

De Silva NMG, Borges MC, Hingorani A, Engmann J, Shah T, Zhang X, Luan J, Langenberg C, Wong A, Kuh D, Chambers JC, Zhang W, Jarvelin M-R, Sebert S, Auvinen J, UCLEB consortium, Gaunt TR, Lawlor DAet al., 2019, Liver function and risk of type 2 diabetes: bidirectional mendelian randomization study., Diabetes, Vol: 68, Pages: 1681-1691, ISSN: 0012-1797

Liver dysfunction and type 2 diabetes (T2D) are consistently associated. However, it is currently unknown whether liver dysfunction contributes to, results from or is merely correlated with T2D due to confounding. We used Mendelian randomization (MR) to investigate the presence and direction of any causal relation between liver function and T2D risk including up to 64,094 T2D cases and 607,012 controls. Several biomarkers were used as proxies of liver function [i.e. alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and gamma-glutamyl transferase (GGT)]. Genetic variants strongly associated with each liver function marker were used to investigate the effect of liver function on T2D risk. In addition, genetic variants strongly associated with T2D risk and with fasting insulin were used to investigate the effect of predisposition to T2D and insulin resistance, respectively, on liver function. Genetically predicted higher circulating ALT and AST were related to increased risk of T2D. There was a modest negative association of genetically predicted ALP with T2D risk and no evidence of association between GGT and T2D risk. Genetically predisposition to higher fasting insulin, but not to T2D, was related to increased circulating ALT. Since circulating ALT and AST are markers of NAFLD, these findings provide some support for insulin resistance resulting in NAFLD, which in turn increases T2D risk.

Journal article

Bentley AR, Sung YJ, Brown MR, Winkler TW, Kraja AT, Ntalla I, Schwander K, Chasman D, Lim E, Deng X, Guo X, Liu J, Lu Y, Cheng C-Y, Sim X, Vojinovic D, Huffman JE, Musani SK, Li C, Feitosa MF, Richard MA, Noordam R, Baker J, Chen G, Aschard H, Bartz TM, Ding J, Dorajoo R, Manning AK, Rankinen T, Smith A, Tajuddin SM, Zhao W, Graff M, Alver M, Boissel M, Chai JF, Chen X, Divers J, Evangelou E, Gao C, Goel A, Hagemeijer Y, Harris SE, Hartwig FP, He M, Horimoto ARVR, Hsu F-C, Hung Y-J, Jackson AU, Kasturiratne A, Komulainen P, Kuehnel B, Leander K, Lin K-H, Luan J, Lyytikainen L-P, Matoba N, Nolte IM, Pietzner M, Prins B, Riaz M, Robino A, Said MA, Schupf N, Scott RA, Sofer T, Stancakova A, Takeuchi F, Tayo BO, van der Most PJ, Varga TV, Wang T-D, Wang Y, Ware EB, Wen W, Xiang Y-B, Yanek LR, Zhang W, Zhao JH, Adeyemo A, Afaq S, Amin N, Amini M, Arking DE, Arzumanyan Z, Aung T, Ballantyne C, Barr RG, Bielak LF, Boerwinkle E, Bottinger EP, Broeckel U, Brown M, Cade BE, Campbell A, Canouil M, Charumathi S, Chen Y-DI, Christensen K, Concas MP, Connell JM, de las Fuentes L, de Silva HJ, de Vries PS, Doumatey A, Duan Q, Eaton CB, Eppinga RN, Faul JD, Floyd JS, Forouhi NG, Forrester T, Friedlander Y, Gandin I, Gao H, Ghanbari M, Gharib SA, Gigante B, Giulianini F, Grabe HJ, Gu CC, Harris TB, Heikkinen S, Heng C-K, Hirata M, Hixson JE, Ikram MA, Jia Y, Joehanes R, Johnson C, Jonas JB, Justice AE, Katsuya T, Khor CC, Kilpelainen TO, Koh W-P, Kolcic I, Kooperberg C, Krieger JE, Kritchevsky SB, Kubo M, Kuusisto J, Lakka TA, Langefeld CD, Langenberg C, Launer LJ, Lehne B, Lewis CE, Li Y, Liang J, Lin S, Liu C-T, Liu J, Liu K, Loh M, Lohman KK, Louie T, Luzzi A, Magi R, Mahajan A, Manichaikul AW, McKenzie CA, Meitinger T, Metspalu A, Milaneschi Y, Milani L, Mohlke KL, Momozawa Y, Morris AP, Murray AD, Nalls MA, Nauck M, Nelson CP, North KE, O'Connell JR, Palmer ND, Papanicolau GJ, Pedersen NL, Peters A, Peyser PA, Polasek O, Poulter N, Raitakari OT, Reiner AP, Renstrom F, Rice TKet al., 2019, Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids, Nature Genetics, Vol: 51, Pages: 636-648, ISSN: 1061-4036

The concentrations of high- and low-density-lipoprotein cholesterol and triglycerides are influenced by smoking, but it is unknown whether genetic associations with lipids may be modified by smoking. We conducted a multi-ancestry genome-wide gene–smoking interaction study in 133,805 individuals with follow-up in an additional 253,467 individuals. Combined meta-analyses identified 13 new loci associated with lipids, some of which were detected only because association differed by smoking status. Additionally, we demonstrate the importance of including diverse populations, particularly in studies of interactions with lifestyle factors, where genomic and lifestyle differences by ancestry may contribute to novel findings.

Journal article

Karasik D, Zillikens MC, Hsu Y-H, Aghdassi A, Akesson K, Amin N, Barroso I, Bennett DA, Bertram L, Bochud M, Borecki IB, Broer L, Buchman AS, Byberg L, Campbell H, Campos-Obando N, Cauley JA, Cawthon PM, Chambers JC, Chen Z, Cho NH, Choi HJ, Chou W-C, Cummings SR, de Groot LCPGM, De Jager PL, Demuth I, Diatchenko L, Econs MJ, Eiriksdottir G, Enneman AW, Eriksson J, Eriksson JG, Estrada K, Evans DS, Feitosa MF, Fu M, Gieger C, Grallert H, Gudnason V, Lenore LJ, Hayward C, Hofman A, Homuth G, Huffman KM, Husted LB, Illig T, Ingelsson E, Ittermann T, Jansson J-O, Johnson T, Biffar R, Jordan JM, Jula A, Karlsson M, Khaw K-T, Kilpelainen TO, Klopp N, Kloth JSL, Koller DL, Kooner JS, Kraus WE, Kritchevsky S, Kutalik Z, Kuulasmaa T, Kuusisto J, Laakso M, Lahti J, Lang T, Langdahl BL, Lerch MM, Lewis JR, Lill C, Lind L, Lindgren C, Liu Y, Livshits G, Ljunggren O, Loos RJF, Lorentzon M, Luan J, Luben RN, Malkin I, McGuigan FE, Medina-Gomez C, Meitinger T, Melhus H, Mellstrom D, Michaelsson K, Mitchell BD, Morris AP, Mosekilde L, Nethander M, Newman AB, O'Connell JR, Oostra BA, Orwoll ES, Palotie A, Peacock M, Perola M, Peters A, Prince RL, Psaty BM, Raikkonen K, Ralston SH, Ripatti S, Rivadeneira F, Robbins JA, Rotter JI, Rudan I, Salomaa V, Satterfield S, Schipf S, Shin CS, Smith AV, Smith SB, Soranzo N, Spector TD, Stancakova A, Stefansson K, Steinhagen-Thiessen E, Stolk L, Streeten EA, Styrkarsdottir U, Swart KMA, Thompson P, Thomson CA, Thorleifsson G, Thorsteinsdottir U, Tikkanen E, Tranah GJ, Uitterlinden AG, van Duijn CM, van Schoor NM, Vandenput L, Vollenweider P, Volzke H, Wactawski-Wende J, Walker M, Wareham NJ, Waterworth D, Weedon MN, Wichmann H-E, Widen E, Williams FMK, Wilson JF, Wright NC, Yerges-Armstrong LM, Yu L, Zhang W, Zhao JH, Zhou Y, Nielson CM, Harris TB, Demissie S, Kiel DP, Ohlsson Cet al., 2019, Disentangling the genetics of lean mass, AMERICAN JOURNAL OF CLINICAL NUTRITION, Vol: 109, Pages: 276-287, ISSN: 0002-9165

BackgroundLean body mass (LM) plays an important role in mobility and metabolic function. We previously identified five loci associated with LM adjusted for fat mass in kilograms. Such an adjustment may reduce the power to identify genetic signals having an association with both lean mass and fat mass.ObjectivesTo determine the impact of different fat mass adjustments on genetic architecture of LM and identify additional LM loci.MethodsWe performed genome-wide association analyses for whole-body LM (20 cohorts of European ancestry with n = 38,292) measured using dual-energy X-ray absorptiometry) or bioelectrical impedance analysis, adjusted for sex, age, age2, and height with or without fat mass adjustments (Model 1 no fat adjustment; Model 2 adjustment for fat mass as a percentage of body mass; Model 3 adjustment for fat mass in kilograms).ResultsSeven single-nucleotide polymorphisms (SNPs) in separate loci, including one novel LM locus (TNRC6B), were successfully replicated in an additional 47,227 individuals from 29 cohorts. Based on the strengths of the associations in Model 1 vs Model 3, we divided the LM loci into those with an effect on both lean mass and fat mass in the same direction and refer to those as “sumo wrestler” loci (FTO and MC4R). In contrast, loci with an impact specifically on LM were termed “body builder” loci (VCAN and ADAMTSL3). Using existing available genome-wide association study databases, LM increasing alleles of SNPs in sumo wrestler loci were associated with an adverse metabolic profile, whereas LM increasing alleles of SNPs in “body builder” loci were associated with metabolic protection.ConclusionsIn conclusion, we identified one novel LM locus (TNRC6B). Our results suggest that a genetically determined increase in lean mass might exert either harmful or protective effects on metabolic traits, depending on its relation to fat mass.

Journal article

Parmar P, Lowry E, Cugliari G, Suderman M, Wilson R, Karhunen V, Andrew T, Wiklund P, Wielscher M, Guarrera S, Teumer A, Lehne B, Milani L, de Klein N, Mishra PP, Melton PE, Mandaviya PR, Kasela S, Nano J, Zhang W, Zhang Y, Uitterlinden AG, Peters A, Schoettker B, Gieger C, Anderson D, Boomsma D, Grabe HJ, Panico S, Veldink JH, van Meurs JBJ, van den Berg L, Beilin LJ, Franke L, Loh M, van Greevenbroek MMJ, Nauck M, Kahonen M, Hurme MA, Raitakari OT, Franco OH, Slagboom PE, van der Harst P, Kunze S, Felix SB, Zhang T, Chen W, Mori TA, Bonnefond A, Heijmans BT, Muka T, Kooner JS, Fischer K, Waldenberger M, Froguel P, Huang R-C, Lehtimaki T, Rathmann W, Relton CL, Matullo G, Brenner H, Verweij N, Li S, Chambers JC, Jarvelin M-R, Sebert Set al., 2018, Association of maternal prenatal smoking GFI1-locus and cardiometabolic phenotypes in 18,212 adults, EBioMedicine, Vol: 38, Pages: 206-216, ISSN: 2352-3964

BackgroundDNA methylation at the GFI1-locus has been repeatedly associated with exposure to smoking from the foetal period onwards. We explored whether DNA methylation may be a mechanism that links exposure to maternal prenatal smoking with offspring's adult cardio-metabolic health.MethodsWe meta-analysed the association between DNA methylation at GFI1-locus with maternal prenatal smoking, adult own smoking, and cardio-metabolic phenotypes in 22 population-based studies from Europe, Australia, and USA (n = 18,212). DNA methylation at the GFI1-locus was measured in whole-blood. Multivariable regression models were fitted to examine its association with exposure to prenatal and own adult smoking. DNA methylation levels were analysed in relation to body mass index (BMI), waist circumference (WC), fasting glucose (FG), high-density lipoprotein cholesterol (HDL—C), triglycerides (TG), diastolic, and systolic blood pressure (BP).FindingsLower DNA methylation at three out of eight GFI1-CpGs was associated with exposure to maternal prenatal smoking, whereas, all eight CpGs were associated with adult own smoking. Lower DNA methylation at cg14179389, the strongest maternal prenatal smoking locus, was associated with increased WC and BP when adjusted for sex, age, and adult smoking with Bonferroni-corrected P < 0·012. In contrast, lower DNA methylation at cg09935388, the strongest adult own smoking locus, was associated with decreased BMI, WC, and BP (adjusted 1 × 10−7 < P < 0.01). Similarly, lower DNA methylation at cg12876356, cg18316974, cg09662411, and cg18146737 was associated with decreased BMI and WC (5 × 10−8 < P < 0.001). Lower DNA methylation at all the CpGs was consistently associated with higher TG levels.InterpretationEpigenetic changes at the GFI1 were linked to smoking exposure in-utero/in-adulthood and robustly associated with cardio-metabolic risk factors.

Journal article

Takeuchi F, Akiyama M, Matoba N, Katsuya T, Nakatochi M, Tabara Y, Narita A, Saw W-Y, Moon S, Spracklen CN, Chai J-F, Kim Y-J, Zhang L, Wang C, Li H, Li H, Wu J-Y, Dorajoo R, Nierenberg JL, Wang YX, He J, Bennett DA, Takahashi A, Momozawa Y, Hirata M, Matsuda K, Rakugi H, Nakashima E, Isono M, Shirota M, Hozawa A, Ichihara S, Matsubara T, Yamamoto K, Kohara K, Igase M, Han S, Gordon-Larsen P, Huang W, Lee NR, Adair LS, Hwang MY, Lee J, Chee ML, Sabanayagam C, Zhao W, Liu J, Reilly DF, Sun L, Huo S, Edwards TL, Long J, Chang L-C, Chen C-H, Yuan J-M, Koh W-P, Friedlander Y, Kelly TN, Wei WB, Xu L, Cai H, Xiang Y-B, Lin K, Clarke R, Walters RG, Millwood IY, Li L, Chambers JC, Kooner JS, Elliott P, van der Harst P, Chen Z, Sasaki M, Shu X-O, Jonas JB, He J, Heng C-K, Chen Y-T, Zheng W, Lin X, Teo Y-Y, Tai E-S, Cheng C-Y, Wong TY, Sim X, Mohlke KL, Yamamoto M, Kim B-J, Miki T, Nabika T, Yokota M, Kamatani Y, Kubo M, Kato Net al., 2018, Interethnic analyses of blood pressure loci in populations of East Asian and European descent., Nature Communications, Vol: 9, ISSN: 2041-1723

Blood pressure (BP) is a major risk factor for cardiovascular disease and more than 200 genetic loci associated with BP are known. Here, we perform a multi-stage genome-wide association study for BP (max N = 289,038) principally in East Asians and meta-analysis in East Asians and Europeans. We report 19 new genetic loci and ancestry-specific BP variants, conforming to a common ancestry-specific variant association model. At 10 unique loci, distinct non-rare ancestry-specific variants colocalize within the same linkage disequilibrium block despite the significantly discordant effects for the proxy shared variants between the ethnic groups. The genome-wide transethnic correlation of causal-variant effect-sizes is 0.898 and 0.851 for systolic and diastolic BP, respectively. Some of the ancestry-specific association signals are also influenced by a selective sweep. Our results provide new evidence for the role of common ancestry-specific variants and natural selection in ethnic differences in complex traits such as BP.

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

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

Feitosa M, Kraja A, Zhang W, Evangelou E, Gao H, Scott W, Sever P, Chambers J, Froguel P, Scott J, Elliott P, Levy Det al., 2018, Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570,000 individuals across multiple ancestries, PLoS ONE, Vol: 13, ISSN: 1932-6203

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

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

Sung YJ, Lehne B, Scott WR, Sever P, Chambers J, Froguel P, Kooner JS, Scott J, Elliott P, Chasman DIet al., 2018, A large-scale multi-ancestry genome-wide study accounting for smoking bahavior identifies multiple genome-wide significant loci for systolic and diastolic blood pressure, American Journal of Human Genetics, Vol: 102, Pages: 375-400, ISSN: 0002-9297

Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify novel BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactionsin 610,091 individuals. Stage 1 analysis examined ~18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-upanalysis of promising variants in 480,178 additional individuals from five ancestries. Weidentified 15 new loci that were genome-wide significant (P < 5×10-8) in Stage 1 and formally replicated in Stage 2. A combined Stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci ( 13, 35, and 18 loci in European, African and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (P < 5×10-8).O f the newly identified loci, 10 showed significant interaction with smoking status, but none of them were replicated in Stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies(SDCCAG8,RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling (MSRA, EBF2)

Journal article

Flannick J, Fuchsberger C, Mahajan A, Teslovich TM, Agarwala V, Gaulton KJ, Caulkins L, Koesterer R, Ma C, Moutsianas L, McCarthy DJ, Rivas MA, Perry JRB, Sim X, Blackwell TW, Robertson NR, Rayner NW, Cingolani P, Locke AE, Tajes JF, Highland HM, Dupuis J, Chines PS, Lindgren CM, Hartl C, Jackson AU, Chen H, Huyghe JR, van de Bunt M, Pearson RD, Kumar A, Mueller-Nurasyid M, Grarup N, Stringham HM, Gamazon ER, Lee J, Chen Y, Scott RA, Below JE, Chen P, Huang J, Go MJ, Stitzel ML, Pasko D, Parker SCJ, Varga TV, Green T, Beer NL, Day-Williams AG, Ferreira T, Fingerlin T, Horikoshi M, Hu C, Huh I, Ikram MK, Kim B-J, Kim Y, Kim YJ, Kwon M-S, Lee J, Lee S, Lin K-H, Maxwell TJ, Nagai Y, Wang X, Welch RP, Yoon J, Zhang W, Barzilai N, Voight BF, Han B-G, Jenkinson CP, Kuulasmaa T, Kuusisto J, Manning A, Ng MCY, Palmer ND, Balkau B, Stancakova A, Abboud HE, Boeing H, Giedraitis V, Prabhakaran D, Gottesman O, Scott J, Carey J, Kwan P, Grant G, Smith JD, Neale BM, Purcell S, Butterworth AS, Howson JMM, Lee HM, Lu Y, Kwak S-H, Zhao W, Danesh J, Lam VKL, Park KS, Saleheen D, So WY, Tam CHT, Afzal U, Aguilar D, Arya R, Aung T, Chan E, Navarro C, Cheng C-Y, Palli D, Correa A, Curran JE, Rybin D, Farook VS, Fowler SP, Freedman BI, Griswold M, Hale DE, Hicks PJ, Khor C-C, Kumar S, Lehne B, Thuillier D, Lim WY, Liu J, Loh M, Musani SK, Puppala S, Scott WR, Yengo L, Tan S-T, Taylor HA, Thameem F, Wilson G, Wong TY, Njolstad PR, Levy JC, Mangino M, Bonnycastle LL, Schwarzmayr T, Fadista J, Surdulescu GL, Herder C, Groves CJ, Wieland T, Bork-Jensen J, Brandslund I, Christensen C, Koistinen HA, Doney ASF, Kinnunen L, Esko T, Farmer AJ, Hakaste L, Hodgkiss D, Kravic J, Lyssenko V, Hollensted M, Jorgensen ME, Jorgensen T, Ladenvall C, Justesen JM, Karajamaki A, Kriebel J, Rathmann W, Lannfelt L, Lauritzen T, Narisu N, Linneberg A, Melander O, Milani L, Neville M, Orho-Melander M, Qi L, Qi Q, Roden M, Rolandsson O, Swift A, Rosengren AH, Stirrups K, Wood AR, Mihailov E, Blancher C, Carneiroet al., 2018, Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls, Scientific Data, Vol: 5, ISSN: 2052-4463

Journal article

Flannick J, Froguel P, Prokopenko I, Lehne B, Kooner JS, Chambers J, Scott J, Loh M, Elliott P, Zhang W, Scott W, Nagai Yet al., 2017, Sequence data and association statistics from 12,940 type 2 diabetes cases and controls, Scientific Data, Vol: 4, ISSN: 2052-4463

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1–5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

Journal article

Márquez-Luna C, Loh P-R, South Asian Type 2 Diabetes SAT2D Consortium, SIGMA Type 2 Diabetes Consortium, Price ALet al., 2017, Multiethnic polygenic risk scores improve risk prediction in diverse populations., Genet Epidemiol, Vol: 41, Pages: 811-823

Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multiethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size (Neff  = 40k) and Latino training data in small sample size (Neff  = 8k). Here, we attained a >70% relative improvement in prediction accuracy (from R2  = 0.027 to 0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. We predict T2D in a South Asian UK Biobank cohort using European (Neff  = 40k) and South Asian (Neff  = 16k) training data and attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort using European (N = 113k) and African (N = 2k) training data attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non-European target populations.

Journal article

Zillikens MC, Demissie S, Hsu Y-H, Yerges-Armstrong LM, Chou W-C, Stolk L, Livshits G, Broer L, Johnson T, Koller DL, Kutalik Z, Luan J, Malkin I, Ried JS, Smith AV, Thorleifsson G, Vandenput L, Zhao JH, Zhang W, Aghdassi A, Akesson K, Amin N, Baier LJ, Barroso I, Bennett DA, Bertram L, Biffar R, Bochud M, Boehnke M, Borecki IB, Buchman AS, Byberg L, Campbell H, Obanda NC, Cauley JA, Cawthon PM, Cederberg H, Chen Z, Cho NH, Choi HJ, Claussnitzer M, Collins F, Cummings SR, De Jager PL, Demuth I, Dhonukshe-Rutten RAM, Diatchenko L, Eiriksdottir G, Enneman AW, Erdos M, Eriksson JG, Eriksson J, Estrada K, Evans DS, Feitosa MF, Fu M, Garcia M, Gieger C, Girke T, Glazer NL, Grallert H, Grewal J, Han B-G, Hanson RL, Hayward C, Hofman A, Hoffman EP, Homuth G, Hsueh W-C, Hubal MJ, Hubbard A, Huffman KM, Husted LB, Illig T, Ingelsson E, Ittermann T, Jansson J-O, Jordan JM, Jula A, Karlsson M, Khaw K-T, Kilpelanen TO, Klopp N, Kloth JSL, Koistinen HA, Kraus WE, Kritchevsky S, Kuulasmaa T, Kuusisto J, Laakso M, Lahti J, Lang T, Langdahl BL, Launer LJ, Lee J-Y, Lerch MM, Lewis JR, Lind L, Lindgren C, Liu Y, Liu T, Liu Y, Ljunggren O, Lorentzon M, Luben RN, Maixner W, McGuigan FE, Medina-Gomez C, Meitinger T, Melhus H, Mellstrom D, Melov S, Michaelsson K, Mitchell BD, Morris AP, Mosekilde L, Newman A, Nielson CM, O'Connell JR, Oostra BA, Orwoll ES, Palotie A, Parker SCJ, Peacock M, Perola M, Peters A, Polasek O, Prince RL, Raikkonen K, Ralston SH, Ripatti S, Robbins JA, Rotter JI, Rudan I, Salomaa V, Satterfield S, Schadt EE, Schipf S, Scott L, Sehmi J, Shen J, Shin CS, Sigurdsson G, Smith S, Soranzo N, Stancakova A, Steinhagen-Thiessen E, Streeten EA, Styrkarsdottir U, Swart KMA, Tan S-T, Tarnopolsky MA, Thompson P, Thomson CA, Thorsteinsdottir U, Tikkanen E, Tranah GJ, Tuomilehto J, van Schoor NM, Verma A, Vollenweider P, Volzke H, Wactawski-Wende J, Walker M, Weedon MN, Welch R, Wichmann H-E, Widen E, Williams FMK, Wilson JF, Wright NC, Xie W, Yu L, Zhou Y, Chambers JC, Doringet al., 2017, Erratum: Large meta-analysis of genome-wide association studies identifies five loci for lean body mass, Nature Communications, Vol: 8, ISSN: 2041-1723

Journal article

Kraja AT, Evangelou E, Tzoulaki I, Zhang W, Gao H, Chambers J, Jarvelin MR, Kooner J, Poulter N, Sever P, Vergnaud AC, Elliott P, CHARGE EXOME BP, CHD Exome, Exome BP, GoT2DT2DGenes Consortia, The UK Biobank Cardio-Metabolic Traits Consortium Blood Pressure Working Groupet al., 2017, New blood pressure associated loci identified in meta-analyses of 475,000 individuals, Circulation: Cardiovascular Genetics, Vol: 10, ISSN: 1942-325X

Background—Genome-wide association studies have recently identified >400 loci that harbor DNA sequence variants that influence blood pressure (BP). Our earlier studies identified and validated 56 single nucleotide variants (SNVs) associated with BP from meta-analyses of exome chip genotype data. An additional 100 variants yielded suggestive evidence of association.Methods and Results—Here, we augment the sample with 140 886 European individuals from the UK Biobank, in whom 77 of the 100 suggestive SNVs were available for association analysis with systolic BP or diastolic BP or pulse pressure. We performed 2 meta-analyses, one in individuals of European, South Asian, African, and Hispanic descent (pan-ancestry, ≈475 000), and the other in the subset of individuals of European descent (≈423 000). Twenty-one SNVs were genome-wide significant (P<5×10−8) for BP, of which 4 are new BP loci: rs9678851 (missense, SLC4A1AP), rs7437940 (AFAP1), rs13303 (missense, STAB1), and rs1055144 (7p15.2). In addition, we identified a potentially independent novel BP-associated SNV, rs3416322 (missense, SYNPO2L) at a known locus, uncorrelated with the previously reported SNVs. Two SNVs are associated with expression levels of nearby genes, and SNVs at 3 loci are associated with other traits. One SNV with a minor allele frequency <0.01, (rs3025380 at DBH) was genome-wide significant.Conclusions—We report 4 novel loci associated with BP regulation, and 1 independent variant at an established BP locus. This analysis highlights several candidate genes with variation that alter protein function or gene expression for potential follow-up.

Journal article

Warren HR, Evangelou E, Cabrera CP, Gao H, Ren M, Mifsud B, Ntalla I, Surendran P, Liu C, Cook JP, Kraja AT, Drenos F, Loh M, Verweij N, Marten J, Karaman I, Lepe MPS, O'Reilly PF, Knight J, Snieder H, Kato N, He J, Tai ES, Said MA, Porteous D, Alver M, Poulter N, Farrall M, Gansevoort RT, Padmanabhan S, Magi R, Stanton A, Connell J, Bakker SJL, Metspalu A, Shields DC, Thom S, Brown M, Sever P, Esko T, Hayward C, van der Harst P, Saleheen D, Chowdhury R, Chambers JC, Chasman DI, Chakravarti A, Newton-Cheh C, Lindgren CM, Levy D, Kooner JS, Keavney B, Tomaszewski M, Samani NJ, Howson JMM, Tobin MD, Munroe PB, Ehret GB, Wain LV, Barnes MR, Tzoulaki I, Caulfield MJ, Elliott Pet al., 2017, Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk (vol 49, pg 403, 2017), NATURE GENETICS, Vol: 49, Pages: 1558-1558, ISSN: 1061-4036

Journal article

Mace A, Tuke MA, Deelen P, Kristiansson K, Mattsson H, Noukas M, Sapkota Y, Schick U, Porcu E, Rueger S, McDaid AF, Porteous D, Winkler TW, Salvi E, Shrine N, Liu X, Ang WQ, Zhang W, Feitosa MF, Venturini C, van der Most PJ, Rosengren A, Wood AR, Beaumont RN, Jones SE, Ruth KS, Yaghootkar H, Tyrrell J, Havulinna AS, Boers H, Magi R, Kriebel J, Mueller-Nurasyid M, Perola M, Nieminen M, Lokki M-L, Kahonen M, Viikari JS, Geller F, Lahti J, Palotie A, Koponen P, Lundqvist A, Rissanen H, Bottinger EP, Afaq S, Wojczynski MK, Lenzini P, Nolte IM, Sparso T, Schupf N, Christensen K, Perls TT, Newman AB, Werge T, Snieder H, Spector TD, Chambers JC, Koskinen S, Melbye M, Raitakari OT, Lehtimaki T, Tobin MD, Wain LV, Sinisalo J, Peters A, Meitinger T, Martin NG, Wray NR, Montgomery GW, Medland SE, Swertz MA, Vartiainen E, Borodulin K, Mannisto S, Murray A, Bochud M, Jacquemont S, Rivadeneira F, Hansen TF, Oldehinkel AJ, Mangino M, Province MA, Deloukas P, Kooner JS, Freathy RM, Pennell C, Feenstra B, Strachan DP, Lettre G, Hirschhorn J, Cusi D, Heid IM, Hayward C, Mannik K, Beckmann JS, Loos RJF, Nyholt DR, Metspalu A, Eriksson JG, Weedon MN, Salomaa V, Franke L, Reymond A, Frayling TM, Kutalik Zet al., 2017, CNV-association meta-analysis in 191,161 European adults reveals new loci associated with anthropometric traits, NATURE COMMUNICATIONS, Vol: 8, ISSN: 2041-1723

There are few examples of robust associations between rare copy number variants (CNVs) and complex continuous human traits. Here we present a large-scale CNV association meta-analysis on anthropometric traits in up to 191,161 adult samples from 26 cohorts. The study reveals five CNV associations at 1q21.1, 3q29, 7q11.23, 11p14.2, and 18q21.32 and confirms two known loci at 16p11.2 and 22q11.21, implicating at least one anthropometric trait. The discovered CNVs are recurrent and rare (0.01–0.2%), with large effects on height (>2.4 cm), weight (>5 kg), and body mass index (BMI) (>3.5 kg/m2). Burden analysis shows a 0.41 cm decrease in height, a 0.003 increase in waist-to-hip ratio and increase in BMI by 0.14 kg/m2 for each Mb of total deletion burden (P = 2.5 × 10−10, 6.0 × 10−5, and 2.9 × 10−3). Our study provides evidence that the same genes (e.g., MC4R, FIBIN, and FMO5) harbor both common and rare variants affecting body size and that anthropometric traits share genetic loci with developmental and psychiatric disorders.

Journal article

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