Imperial College London

Dr Marika Kaakinen

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Visiting Researcher
 
 
 
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Contact

 

+44 (0)20 7594 6537m.kaakinen Website

 
 
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Location

 

E301Burlington DanesHammersmith Campus

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Summary

 

Publications

Publication Type
Year
to

107 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

Lagou V, Mägi R, Hottenga J-J, Grallert H, Perry JRB, Bouatia-Naji N, Marullo L, Rybin D, Jansen R, Min JL, Dimas AS, Ulrich A, Zudina L, Gådin JR, Jiang L, Faggian A, Bonnefond A, Fadista J, Stathopoulou MG, Isaacs A, Willems SM, Navarro P, Tanaka T, Jackson AU, Montasser ME, O'Connell JR, Bielak LF, Webster RJ, Saxena R, Stafford JM, Pourcain BS, Timpson NJ, Salo P, Shin S-Y, Amin N, Smith AV, Li G, Verweij N, Goel A, Ford I, Johnson PCD, Johnson T, Kapur K, Thorleifsson G, Strawbridge RJ, Rasmussen-Torvik LJ, Esko T, Mihailov E, Fall T, Fraser RM, Mahajan A, Kanoni S, Giedraitis V, Kleber ME, Silbernagel G, Meyer J, Müller-Nurasyid M, Ganna A, Sarin A-P, Yengo L, Shungin D, Luan J, Horikoshi M, An P, Sanna S, Boettcher Y, Rayner NW, Nolte IM, Zemunik T, Iperen EV, Kovacs P, Hastie ND, Wild SH, McLachlan S, Campbell S, Polasek O, Carlson O, Egan J, Kiess W, Willemsen G, Kuusisto J, Laakso M, Dimitriou M, Hicks AA, Rauramaa R, Bandinelli S, Thorand B, Liu Y, Miljkovic I, Lind L, Doney A, Perola M, Hingorani A, Kivimaki M, Kumari M, Bennett AJ, Groves CJ, Herder C, Koistinen HA, Kinnunen L, Faire UD, Bakker SJL, Uusitupa M, Palmer CNA, Jukema JW, Sattar N, Pouta A, Snieder H, Boerwinkle E, Pankow JS, Magnusson PK, Krus U, Scapoli C, de Geus EJCN, Blüher M, Wolffenbuttel BHR, Province MA, Abecasis GR, Meigs JB, Hovingh GK, Lindström J, Wilson JF, Wright AF, Dedoussis GV, Bornstein SR, Schwarz PEH, Tönjes A, Winkelmann BR, Boehm BO, März W, Metspalu A, Price JF, Deloukas P, Körner A, Lakka TA, Keinanen-Kiukaanniemi SM, Saaristo TE, Bergman RN, Tuomilehto J, Wareham NJ, Langenberg C, Männistö S, Franks PW, Hayward C, Vitart V, Kaprio J, Visvikis-Siest S, Balkau B, Altshuler D, Rudan I, Stumvoll M, Campbell H, van Duijn CM, Gieger C, Illig T, Ferrucci L, Pedersen NL, Pramstaller PP, Boehnke M, Frayling TM, Shuldiner AR, Peyser PA, Kardia SLR, Palmer LJ, Penninx BW, Meneton P, Harris TB, Navis G, Harst PVD, Smith GD, Forouhi NG, Loos RJF, Salomaa V, Soranzo N, Boomsma DIet al., 2021, Publisher Correction: Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability., Nat Commun, Vol: 12

Journal article

Lagou V, Maegi R, Hottenga J-J, Grallert H, Perry JRB, Bouatia-Naji N, Marullo L, Rybin D, Jansen R, Min JL, Dimas AS, Ulrich A, Zudina L, Gadin JR, Jiang L, Faggian A, Bonnefond A, Fadista J, Stathopoulou MG, Isaacs A, Willems SM, Navarro P, Tanaka T, Jackson AU, Montasser ME, O'Connell JR, Bielak LF, Webster RJ, Saxena R, Stafford JM, Pourcain BS, Timpson NJ, Salo P, Shin S-Y, Amin N, Smith AV, Li G, Verweij N, Goel A, Ford I, Johnson PCD, Johnson T, Kapur K, Thorleifsson G, Strawbridge RJ, Rasmussen-Torvik LJ, Esko T, Mihailov E, Fall T, Fraser RM, Mahajan A, Kanoni S, Giedraitis V, Kleber ME, Silbernagel G, Meyer J, Mueller-Nurasyid M, Ganna A, Sarin A-P, Yengo L, Shungin D, Luan J, Horikoshi M, An P, Sanna S, Boettcher Y, Rayner NW, Nolte IM, Zemunik T, Iperen EV, Kovacs P, Hastie ND, Wild SH, McLachlan S, Campbell S, Polasek O, Carlson O, Egan J, Kiess W, Willemsen G, Kuusisto J, Laakso M, Dimitriou M, Hicks AA, Rauramaa R, Bandinelli S, Thorand B, Liu Y, Miljkovic I, Lind L, Doney A, Perola M, Hingorani A, Kivimaki M, Kumari M, Bennett AJ, Groves CJ, Herder C, Koistinen HA, Kinnunen L, Faire UD, Bakker SJL, Uusitupa M, Palmer CNA, Jukema JW, Sattar N, Pouta A, Snieder H, Boerwinkle E, Pankow JS, Magnusson PK, Krus U, Scapoli C, de Geus EJCN, Blueher M, Wolffenbuttel BHR, Province MA, Abecasis GR, Meigs JB, Hovingh GK, Lindstroem J, Wilson JF, Wright AF, Dedoussis GV, Bornstein SR, Schwarz PEH, Toenjes A, Winkelmann BR, Boehm BO, Maerz W, Metspalu A, Price JF, Deloukas P, Koerner A, Lakka TA, Keinanen-Kiukaanniemi SM, Saaristo TE, Bergman RN, Tuomilehto J, Wareham NJ, Langenberg C, Maennistoe S, Franks PW, Hayward C, Vitart V, Kaprio J, Visvikis-Siest S, Balkau B, Altshuler D, Rudan I, Stumvoll M, Campbell H, van Duijn CM, Gieger C, Illig T, Ferrucci L, Pedersen NL, Pramstaller PP, Boehnke M, Frayling TM, Shuldiner AR, Peyser PA, Kardia SLR, Palmer LJ, Penninx BW, Meneton P, Harris TB, Navis G, Harst PVD, Smith GD, Forouhi NG, Loos RJF, Salomaa V, Soranzo N Bet al., 2021, Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability, NATURE COMMUNICATIONS, Vol: 12, ISSN: 2041-1723

Journal article

Alves AC, De Silva NMG, Karhunen V, Sovio U, Das S, Rob Taal H, Warrington NM, Lewin AM, Kaakinen M, Cousminer DL, Thiering E, Timpson NJ, Bond TA, Lowry E, Brown CD, Estivill X, Lindi V, Bradfield JP, Geller F, Speed D, Coin LJM, Loh M, Barton SJ, Beilin LJ, Bisgaard H, Bønnelykke K, Alili R, Hatoum IJ, Schramm K, Cartwright R, Charles MA, Salerno V, Clément K, Claringbould AAJ, Van Duijn CM, Moltchanova E, Eriksson JG, Elks C, Feenstra B, Flexeder C, Franks S, Frayling TM, Freathy RM, Elliott P, Widén E, Hakonarson H, Hattersley AT, Rodriguez A, Banterle M, Heinrich J, Heude B, Holloway JW, Hofman A, Hyppönen E, Inskip H, Kaplan LM, Hedman AK, Läärä E, Prokisch H, Grallert H, Lakka TA, Lawlor DA, Melbye M, Ahluwalia TS, Marinelli M, Millwood IY, Palmer LJ, Pennell CE, Perry JR, Ring SM, Savolainen MJ, Rivadeneira F, Standl M, Sunyer J, Tiesler CMT, Uitterlinden AG, Schierding W, Sullivan OM, Prokopenko I, Herzig KH, Smith GD, O'Reilly P, Felix JF, Buxton JL, Blakemore AIF, Ong KK, Jaddoe VWV, Grant SFA, Sebert S, McCarthy MI, Järvelin MRet al., 2019, GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI, Science Advances, Vol: 5, ISSN: 2375-2548

Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here we combine genome-wide association studies with modelling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score and co-localization analyses to determine how developmental timings, molecular pathways and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult BMI, with variants associated with adult BMI acting as early as 4-6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.

Journal article

Rhodes CJ, Batai K, Bleda M, Haimel M, Southgate L, Germain M, Pauciulo MW, Hadinnapola C, Aman J, Girerd B, Arora A, Knight J, Hanscombe KB, Karnes JH, Kaakinen M, Gall H, Ulrich A, Harbaum L, Cebola I, Ferrer J, Lutz K, Swietlik EM, Ahmad F, Amouyel P, Archer SL, Argula R, Austin ED, Badesch D, Bakshi S, Barnett C, Benza R, Bhatt N, Bogaard HJ, Burger CD, Chakinala M, Church C, Coghlan JG, Condliffe R, Corris PA, Danesino C, Debette S, Elliott CG, Elwing J, Eyries M, Fortin T, Franke A, Frantz RP, Frost A, Garcia JGN, Ghio S, Ghofrani H-A, Gibbs JSR, Harley J, He H, Hill NS, Hirsch R, Houweling AC, Howard LS, Ivy D, Kiely DG, Klinger J, Kovacs G, Lahm T, Laudes M, Machado RD, Ross RVM, Marsolo K, Martin LJ, Moledina S, Montani D, Nathan SD, Newnham M, Olschewski A, Olschewski H, Oudiz RJ, Ouwehand WH, Peacock AJ, Pepke-Zaba J, Rehman Z, Robbins I, Roden DM, Rosenzweig EB, Saydain G, Scelsi L, Schilz R, Seeger W, Shaffer CM, Simms RW, Simon M, Sitbon O, Suntharalingam J, Tang H, Tchourbanov AY, Thenappan T, Torres F, Toshner MR, Treacy CM, Noordegraaf AV, Waisfisz Q, Walsworth AK, Walter RE, Wharton J, White RJ, Wilt J, Wort SJ, Yung D, Lawrie A, Humbert M, Soubrier F, Trégouët D-A, Prokopenko I, Kittles R, Gräf S, Nichols WC, Trembath RC, Desai AA, Morrell NW, Wilkins MR, UK NIHR BioResource Rare Diseases Consortium, UK PAH Cohort Study Consortium, US PAH Biobank Consortiumet al., 2019, Genetic determinants of risk in pulmonary arterial hypertension: international case-control studies and meta-analysis, Lancet Respiratory Medicine, Vol: 7, Pages: 227-238, ISSN: 2213-2600

BackgroundRare genetic variants cause pulmonary arterial hypertension, but the contribution of common genetic variation to disease risk and natural history is poorly characterised. We tested for genome-wide association for pulmonary arterial hypertension in large international cohorts and assessed the contribution of associated regions to outcomes.MethodsWe did two separate genome-wide association studies (GWAS) and a meta-analysis of pulmonary arterial hypertension. These GWAS used data from four international case-control studies across 11 744 individuals with European ancestry (including 2085 patients). One GWAS used genotypes from 5895 whole-genome sequences and the other GWAS used genotyping array data from an additional 5849 individuals. Cross-validation of loci reaching genome-wide significance was sought by meta-analysis. Conditional analysis corrected for the most significant variants at each locus was used to resolve signals for multiple associations. We functionally annotated associated variants and tested associations with duration of survival. All-cause mortality was the primary endpoint in survival analyses.FindingsA locus near SOX17 (rs10103692, odds ratio 1·80 [95% CI 1·55–2·08], p=5·13 × 10–15) and a second locus in HLA-DPA1 and HLA-DPB1 (collectively referred to as HLA-DPA1/DPB1 here; rs2856830, 1·56 [1·42–1·71], p=7·65 × 10–20) within the class II MHC region were associated with pulmonary arterial hypertension. The SOX17 locus had two independent signals associated with pulmonary arterial hypertension (rs13266183, 1·36 [1·25–1·48], p=1·69 × 10–12; and rs10103692). Functional and epigenomic data indicate that the risk variants near SOX17 alter gene regulation via an enhancer active in endothelial cells. Pulmonary arterial hypertension risk variants determined haplotype-specific enhancer activity, and CRISPR-media

Journal article

Linner RK, Biroli P, Kong E, Meddens FW, Wedow R, Fontana MA, Lebreton M, Tino SP, Abdellaoui A, Hammerschlag AR, Nivard MG, Okbay A, Rietveld CA, Timshel PN, Trzaskowski M, de Vlaming R, Zund CL, Bao Y, Buzdugan L, Caplin AH, Chen C-Y, Eibich P, Fontanillas P, Gonzalez JR, Joshi PK, Karhunen V, Kleinman A, Levin RZ, Lill CM, Meddens GA, Muntane G, Sanchez-Roige S, van Rooij FJ, Taskesen E, Wu Y, Zhang F, Agee M, Alipanahi B, Bell RK, Bryc K, Elson SL, Furlotte NA, Huber KE, Litterman NK, McCreight JC, McIntyre MH, Mountain JL, Northover CAM, Pitts SJ, Sathirapongsasuti JF, Sazonova OV, Shelton JF, Shringarpure S, Tian C, Tung JY, Vacic V, Wilson CH, Agbessi M, Ahsan H, Alves I, Andiappan A, Awadalla P, Battle A, Beutner F, Bonder MJ, Boomsma DI, Christiansen M, Claringbould A, Deelen P, Esko T, Fave M-J, Franke L, Frayling T, Gharib SA, Gibson G, Heijmans B, Hemani G, Jansen R, Kahonen M, Kalnapenkis A, Kasela S, Kettunen J, Kim Y, Kirsten H, Kovacs P, Krohn K, Kronberg-Guzman J, Kukushkina V, Kutalik Z, Lee B, Lehtimaki T, Loeffler M, Marigorta UM, Metspalu A, Milani L, Montgomery GW, Mueller-Nurasyid M, Nauck M, Penninx B, Perola M, Pervjakova N, Pierce B, Powell J, Prokisch H, Psaty BM, Raitakari O, Ring S, Ripatti S, Rotzchke O, Rueger S, Saha A, Scholz M, Schramm K, Seppala I, Stumvoll M, Sullivan P, Hoen P-B, Teumer A, Thiery J, Tong L, Tonjes A, van Dongen J, van Meurs J, Verlouw J, Visscher PM, Voelker U, Vosa U, Westra H-J, Yaghootkar H, Yang J, Zeng B, Lee JJ, Pers TH, Turley P, Chen G-B, Emilsson V, Oskarsson S, Pickrell JK, Thom K, Timshel P, Ahluwalia TS, Bacelis J, Baumbach C, Bjornsdottir G, Brandsma JH, Concas MP, Derringer J, Furlotte NA, Galesloot TE, Girotto G, Gupta R, Hall LM, Harris SE, Hofer E, Horikoshi M, Huffman JE, Kaasik K, Kalafati IP, Kong A, Lahti J, van der Lee SJ, de Leeuw C, Lind PA, Lindgren K-O, Liu T, Mangino M, Marten J, Mihailov E, Miller MB, van der Most PJ, Oldmeadow C, Payton A, Pervjakova N, Peyrot WJ, Qian Y, Raitakari Oet al., 2019, Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences, NATURE GENETICS, Vol: 51, Pages: 245-+, ISSN: 1061-4036

Journal article

Prokopenko I, Miyakawa G, Zheng B, Heikkinen J, Petrova Quayle D, Udeh-Momoh C, Claringbould A, Neumann J, Haytural H, Kaakinen MA, Loizidou E, Meissner E, Bertram L, BIOS consortium, Gveric DO, Gentleman SM, Attems J, Perneczky R, Arzberger T, Muglia P, Lill CM, Parkkinen L, Middleton LTet al., 2019, Alzheimer's disease pathology explains association between dementia with Lewy bodies and APOE-ε4/TOMM40 long poly-T repeat allele variants., Alzheimers & Dementia, Vol: 5, Pages: 814-824, ISSN: 1552-5260

Introduction: The role of TOMM40-APOE 19q13.3 region variants is well documented in Alzheimer's disease (AD) but remains contentious in dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD). Methods: We dissected genetic profiles within the TOMM40-APOE region in 451 individuals from four European brain banks, including DLB and PDD cases with/without neuropathological evidence of AD-related pathology and healthy controls. Results: TOMM40-L/APOE-ε4 alleles were associated with DLB (OR TOMM40 -L = 3.61; P value = 3.23 × 10-9; OR APOE -ε4 = 3.75; P value = 4.90 × 10-10) and earlier age at onset of DLB (HR TOMM40 -L = 1.33, P value = .031; HR APOE -ε4 = 1.46, P value = .004), but not with PDD. The TOMM40-L/APOE-ε4 effect was most pronounced in DLB individuals with concomitant AD pathology (OR TOMM40 -L = 4.40, P value = 1.15 × 10-6; OR APOE -ε4 = 5.65, P value = 2.97 × 10-8) but was not significant in DLB without AD. Meta-analyses combining all APOE-ε4 data in DLB confirmed our findings (ORDLB = 2.93, P value = 3.78 × 10-99; ORDLB+AD = 5.36, P value = 1.56 × 10-47). Discussion: APOE-ε4/TOMM40-L alleles increase susceptibility and risk of earlier DLB onset, an effect explained by concomitant AD-related pathology. These findings have important implications in future drug discovery and development efforts in DLB.

Journal article

Kaakinen M, Prelot L, Draisma H, Anasanti MD, Jarvelin M-R, Prokopenko Iet al., 2018, Machine learning in multi-omics data to assess longitudinal predictors of glycaemic trait levels, 27th Annual Meeting of the International-Genetic-Epidemiology-Society (IGES), Publisher: WILEY, Pages: 709-709, ISSN: 0741-0395

Conference paper

Macare C, Ducci F, Zhang Y, Ruggeri B, Jia T, Kaakinen M, Kalsi G, Charoen P, Casoni F, Peters J, Bromberg U, Hil M, Buxton J, Blakemore A, Veijola J, Buchel C, Banaschewski T, Bokde ALW, Conrod P, Flor H, Frouin V, Gallinat J, Garavan H, Gowland PA, Heinz A, Itternnann B, Lathrop M, Martinot J-L, Paus T, Desrivieres S, Munafo M, Jarvelin M-R, Schumanna Get al., 2018, A neurobiological pathway to smoking in adolescence: TTC12-ANKK1-DRD2 variants and reward response, European Neuropsychopharmacology, Vol: 28, Pages: 1103-1114, ISSN: 0924-977X

The TTC12-ANKK1-DRD2 gene-cluster has been implicated in adult smoking. Here, we investigated the contribution of individual genes in the TTC12-ANKK1-DRD2 cluster in smoking and their association with smoking-associated reward processing in adolescence. A meta-analysis of TTC12-ANKK1-DRD2 variants and self-reported smoking behaviours was performed in four European adolescent cohorts (N = 14,084). The minor G-allele of rs2236709, mapping TTC12, was associated with self-reported smoking (p = 5.0 × 10−4) and higher plasma cotinine levels (p = 7.0 × 10−5). This risk allele was linked to an increased ventral-striatal blood-oxygen level-dependent (BOLD) response during reward anticipation (n = 1,263) and with higher DRD2 gene expression in the striatum (p = 0.013), but not with TTC12 or ANKK gene expression. These data suggest a role for the TTC12-ANKK1-DRD2 gene-cluster in adolescent smoking behaviours, provide evidence for the involvement of DRD2 in the early stages of addiction and support the notion that genetically-driven inter-individual differences in dopaminergic transmission mediate reward sensitivity and risk to smoking.

Journal article

Kaakinen M, Jiang L, Lagou V, Gutierrez KS, Prokopenko Iet al., 2018, Large-scale genetic meta-analysis and correlation analysis in up to 61,457 Europeans show large genetic overlap between fasting and random plasma glucose levels, 50th European-Society-of-Human-Genetics (ESHG) Conference, Publisher: NATURE PUBLISHING GROUP, Pages: 311-311, ISSN: 1018-4813

Conference paper

Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M, Tuan AN-V, Bowers P, Sidorenko J, Linner RK, Fontana MA, Kundu T, Lee C, Li H, Li R, Royer R, Timshel PN, Walters RK, Willoughby EA, Yengo L, Alver M, Bao Y, Clark DW, Day FR, Furlotte NA, Joshi PK, Kemper KE, Kleinman A, Langenberg C, Magi R, Trampush JW, Verma SS, Wu Y, Lam M, Zhao JH, Zheng Z, Boardman JD, Campbell H, Freese J, Harris KM, Hayward C, Herd P, Kumari M, Lencz T, Luan J, Malhotra AK, Metspalu A, Milani L, Ong KK, Perry JRB, Porteous DJ, Ritchie MD, Smart MC, Smith BH, Tung JY, Wareham NJ, Wilson JF, Beauchamp JP, Conley DC, Esko T, Lehrer SF, Magnusson PKE, Oskarsson S, Pers TH, Robinson MR, Thom K, Watson C, Chabris CF, Meyer MN, Laibson DI, Yang J, Johannesson M, Koellinger PD, Turley P, Visscher PM, Benjamin DJ, Cesarini Det al., 2018, Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals, NATURE GENETICS, Vol: 50, Pages: 1112-+, ISSN: 1061-4036

Journal article

Draisma HHM, Wielscher M, Hassan S, Balkhiyarova Z, Jarvelin M-R, Kaakinen M, Prokopenko Iet al., 2018, Multi-phenotype epigenome-wide association analysis of fasting glucose and insulin in 981 Finnish individuals, ESHG 2018, Publisher: Nature Publishing Group, ISSN: 1018-4813

Conference paper

Draisma HHM, Wielscher M, Hassan S, Balkhiyarova Z, Jarvelin M-R, Kaakinen M, Prokopenko Iet al., 2017, Multi–phenotype epigenome-wide association analysis of fasting glucose and insulin in 981 Finns, IC Genomics Symposium 2017, Publisher: F1000 Research Ltd, ISSN: 2046-1402

Conference paper

Rietschel L, Streit F, Zhu G, McAloney K, Frank J, Couvy-Duchesne B, Witt SH, Binz TM, Bolton JL, Hayward C, Direk N, Anderson A, Huffman J, Wilson JF, Campbell H, Rudan I, Wright A, Hastie N, Wild SH, Velders FP, Hofman A, Uitterlinden AG, Lahti J, Räikkönen K, Kajantie E, Widen E, Palotie A, Eriksson JG, Kaakinen M, Järvelin MR, Timpson NJ, Davey Smith G, Ring SM, Evans DM, St Pourcain B, Tanaka T, Milaneschi Y, Bandinelli S, Ferrucci L, Van Der Harst P, Rosmalen JG, Bakker SJ, Verweij N, Dullaart RP, Mahajan A, Lindgren CM, Morris A, Lind L, Ingelsson E, Anderson LN, Pennell CE, Lye SJ, Matthews SG, Eriksson J, Mellstrom D, Ohlsson C, Price JF, Strachan MW, Reynolds RM, Tiemeier H, Ripke S, Mattheisen M, Abdellaoui A, Adams MJ, Agerbo E, Air TM, Andlauer TF, Bacanu SA, Bækvad-Hansen M, Beekman AT, Bennett DA, Berger K, Bigdeli TB, Bybjerg-Grauholm Jet al., 2017, Hair cortisol in twins: heritability and genetic overlap with psychological variables and stress-system genes, Scientific Reports, Vol: 7, ISSN: 2045-2322

Hair cortisol concentration (HCC) is a promising measure of long-Term hypothalamus-pituitary-Adrenal (HPA) axis activity. Previous research has suggested an association between HCC and psychological variables, and initial studies of inter-individual variance in HCC have implicated genetic factors. However, whether HCC and psychological variables share genetic risk factors remains unclear. The aims of the present twin study were to: (i) assess the heritability of HCC; (ii) estimate the phenotypic and genetic correlation between HPA axis activity and the psychological variables perceived stress, depressive symptoms, and neuroticism; using formal genetic twin models and molecular genetic methods, i.e. polygenic risk scores (PRS). HCC was measured in 671 adolescents and young adults. These included 115 monozygotic and 183 dizygotic twin-pairs. For 432 subjects PRS scores for plasma cortisol, major depression, and neuroticism were calculated using data from large genome wide association studies. The twin model revealed a heritability for HCC of 72%. No significant phenotypic or genetic correlation was found between HCC and the three psychological variables of interest. PRS did not explain variance in HCC. The present data suggest that HCC is highly heritable. However, the data do not support a strong biological link between HCC and any of the investigated psychological variables.

Journal article

Scott RA, Scott LJ, Mägi R, Marullo L, Gaulton KJ, Kaakinen M, Pervjakova N, Pers TH, Johnson AD, Eicher JD, Jackson AU, Ferreira T, Lee Y, Ma C, Steinthorsdottir V, Thorleifsson G, Qi L, Van Zuydam NR, Mahajan A, Chen H, Almgren P, Voight BF, Grallert H, Müller-Nurasyid M, Ried JS, Rayner WN, Robertson N, Karssen LC, van Leeuwen EM, Willems SM, Fuchsberger C, Kwan P, Teslovich TM, Chanda P, Li M, Lu Y, Dina C, Thuillier D, Yengo L, Jiang L, Sparso T, Kestler HA, Chheda H, Eisele L, Gustafsson S, Frånberg M, Strawbridge RJ, Benediktsson R, Hreidarsson AB, Kong A, Sigurðsson G, Kerrison ND, Luan J, Liang L, Meitinger T, Roden M, Thorand B, Esko T, Mihailov E, Fox C, Liu CT, Rybin D, Isomaa B, Lyssenko V, Tuomi T, Couper DJ, Pankow JS, Grarup N, Have CT, Jørgensen ME, Jørgensen T, Linneberg A, Cornelis MC, van Dam RM, Hunter DJ, Kraft P, Sun Q, Edkins S, Owen KR, Perry JR, Wood AR, Zeggini E, Tajes-Fernandes J, Abecasis GR, Bonnycastle LL, Chines PS, Stringham HM, Koistinen HA, Kinnunen L, Sennblad B, Mühleisen TW, Nöthen MM, Pechlivanis S, Baldassarre D, Gertow K, Humphries SE, Tremoli E, Klopp N, Meyer J, Steinbach G, Wennauer R, Eriksson JG, Mӓnnistö S, Peltonen L, Tikkanen E, Charpentier G, Eury E, Lobbens S, Gigante B, Leander K, McLeod O, Bottinger EP, Gottesman O, Ruderfer D, Blüher M, Kovacs P, Tonjes A, Maruthur NM, Scapoli C, Erbel R, Jöckel KH, Moebus S, de Faire U, Hamsten A, Stumvoll M, Deloukas P, Donnelly PJ, Frayling TM, Hattersley AT, Ripatti S, Salomaa V, Pedersen NL, Boehm BO, Bergman RN, Collins FS, Mohlke KL, Tuomilehto J, Hansen T, Pedersen O, Barroso I, Lannfelt L, Ingelsson E, Lind L, Lindgren CM, Cauchi S, Froguel P, Loos RJ, Balkau B, Boeing H, Franks PW, Gurrea AB, Palli D, van der Schouw YT, Altshuler D, Groop LC, Langenberg C, Wareham NJ, Sijbrands E, van Duijn CM, Florez JC, Meigs JB, Boerwinkle E, Gieger C, Strauch K, Metspalu A, Morris AD, Palmer CN, Hu FB, Thorsteinsdottir U, Stefansson K, Dupuis J, Morris AP, Boehnke M, McCarthy MI Pet al., 2017, An expanded genome-wide association study of Type 2 diabetes in Europeans, Diabetes, Vol: 66, Pages: 2888-2902, ISSN: 0012-1797

To characterise type 2 diabetes (T2D) associated variation across the allele frequency spectrum, we conducted a meta-analysis of genome-wide association data from 26,676 T2D cases and 132,532 controls of European ancestry after imputation using the 1000 Genomes multi-ethnic reference panel. Promising association signals were followed-up in additional data sets (of 14,545 or 7,397 T2D cases and 38,994 or 71,604 controls). We identified 13 novel T2D-associated loci (p<5×10(-8)), including variants near the GLP2R, GIP, and HLA-DQA1 genes. Our analysis brought the total number of independent T2D associations to 128 distinct signals at 113 loci. Despite substantially increased sample size and more complete coverage of low-frequency variation, all novel associations were driven by common SNVs. Credible sets of potentially causal variants were generally larger than those based on imputation with earlier reference panels, consistent with resolution of causal signals to common risk haplotypes. Stratification of T2D-associated loci based on T2D-related quantitative trait associations revealed tissue-specific enrichment of regulatory annotations in pancreatic islet enhancers for loci influencing insulin secretion, and in adipocytes, monocytes and hepatocytes for insulin action-associated loci. These findings highlight the predominant role played by common variants of modest effect and the diversity of biological mechanisms influencing T2D pathophysiology.

Journal article

Kaakinen M, Magi R, Fischer K, Heikkinen J, Jarvelin M-R, Morris AP, Prokopenko Iet al., 2017, A rare-variant test for high-dimensional data, European Journal of Human Genetics, Vol: 25, Pages: 988-994, ISSN: 1018-4813

Genome-wide association studies have facilitated the discovery of thousands of loci for hundreds of phenotypes. However, the issue of missing heritability remains unsolved for most complex traits. Locus discovery could be enhanced with both improved power through multi-phenotype analysis (MPA) and use of a wider allele frequency range, including rare variants (RVs). MPA methods for single-variant association have been proposed, but given their low power for RVs, more efficient approaches are required. We propose multi-phenotype analysis of rare variants (MARV), a burden test-based method for RVs extended to the joint analysis of multiple phenotypes through a powerful reverse regression technique. Specifically, MARV models the proportion of RVs at which minor alleles are carried by individuals within a genomic region as a linear combination of multiple phenotypes, which can be both binary and continuous, and the method accommodates directly the genotyped and imputed data. The full model, including all phenotypes, is tested for association for discovery, and a more thorough dissection of the phenotype combinations for any set of RVs is also enabled. We show, via simulations, that the type I error rate is well controlled under various correlations between two continuous phenotypes, and that the method outperforms a univariate burden test in all considered scenarios. Application of MARV to 4876 individuals from the Northern Finland Birth Cohort 1966 for triglycerides, high- and low-density lipoprotein cholesterols highlights known loci with stronger signals of association than those observed in univariate RV analyses and suggests novel RV effects for these lipid traits.

Journal article

Kaakinen M, Magi R, Fischer K, Heikkinen J, Jarvelin M-R, Morris AP, Prokopenko Iet al., 2017, MARV: a tool for genome-wide multi-phenotype analysis of rare variants, BMC BIOINFORMATICS, Vol: 18, ISSN: 1471-2105

Background:Genome-wide association studies have enabled identification of thousands of loci for hundreds of traits. Yet, for most human traits a substantial part of the estimated heritability is unexplained. This and recent advances in technology to produce high-dimensional data cost-effectively have led to method development beyond standard common variant analysis, including single-phenotype rare variant and multi-phenotype common variant analysis, with the latter increasing power for locus discovery and providing suggestions of pleiotropic effects. However, there are currently no optimal methods and tools for the combined analysis of rare variants and multiple phenotypes.Results:We propose a user-friendly software tool MARV for Multi-phenotype Analysis of Rare Variants. The tool is based on a method that collapses rare variants within a genomic region and models the proportion of minor alleles in the rare variants on a linear combination of multiple phenotypes. MARV provides analyses of all phenotype combinations within one run and calculates the Bayesian Information Criterion to facilitate model selection. The running time increases with the size of the genetic data while the number of phenotypes to analyse has little effect both on running time and required memory. We illustrate the use of MARV with analysis of triglycerides (TG), fasting insulin (FI) and waist-to-hip ratio (WHR) in 4,721 individuals from the Northern Finland Birth Cohort 1966. The analysis suggests novel multi-phenotype effects for these metabolic traits at APOA5 and ZNF259, and at ZNF259 provides stronger support for association (P TG+FI = 1.8 × 10−9) than observed in single phenotype rare variant analyses (P TG = 6.5 × 10−8 and P FI = 0.27).Conclusions:MARV is a computationally efficient, flexible and user-friendly software tool allowing rapid identification of rare variant effects on multiple phenot

Journal article

Mägi R, Suleimanov YV, Clarke GM, Kaakinen M, Fischer K, Prokopenko I, Morris APet al., 2017, SCOPA and META-SCOPA: software for the analysis and aggregation of genome-wide association studies of multiple correlated phenotypes, BMC Bioinformatics, Vol: 18, ISSN: 1471-2105

BACKGROUND: Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits. RESULTS: We have developed the SCOPA software to enable GWAS analysis of multiple correlated phenotypes. The software implements "reverse regression" methodology, which treats the genotype of an individual at a SNP as the outcome and the phenotypes as predictors in a general linear model. SCOPA can be applied to quantitative traits and categorical phenotypes, and can accommodate imputed genotypes under a dosage model. The accompanying META-SCOPA software enables meta-analysis of association summary statistics from SCOPA across GWAS. Application of SCOPA to two GWAS of high-and low-density lipoprotein cholesterol, triglycerides and body mass index, and subsequent meta-analysis with META-SCOPA, highlighted stronger association signals than univariate phenotype analysis at established lipid and obesity loci. The META-SCOPA meta-analysis also revealed a novel signal of association at genome-wide significance for triglycerides mapping to GPC5 (lead SNP rs71427535, p = 1.1x10(-8)), which has not been reported in previous large-scale GWAS of lipid traits. CONCLUSIONS: The SCOPA and META-SCOPA software enable discovery and dissection of multiple phenotype association signals through implementation of a powerful reverse regression approach.

Journal article

Ehret GB, Ferreira T, Chasman DI, Jackson AU, Schmidt EM, Johnson T, Thorleifsson G, Luan J, Donnelly LA, Kanoni S, Petersen AK, Pihur V, Strawbridge RJ, Shungin D, Hughes MF, Meirelles O, Kaakinen M, Bouatia-Naji N, Kristiansson K, Shah S, Kleber ME, Guo X, Lyytikäinen LP, Fava C, Eriksson N, Nolte IM, Magnusson PK, Salfati EL, Rallidis LS, Theusch E, Smith AJ, Folkersen L, Witkowska K, Pers TH, Joehanes R, Kim SK, Lataniotis L, Jansen R, Johnson AD, Warren H, Kim YJ, Zhao W, Wu Y, Tayo BO, Bochud M, CHARGE-EchoGen Consortium, CHARGE-HF Consortium, Wellcome Trust Case Control Consortium, Absher D, Adair LS, Amin N, Arking DE, Axelsson T, Baldassarre D, Balkau B, Bandinelli S, Barnes MR, Barroso I, Bevan S, Bis JC, Bjornsdottir G, Boehnke M, Boerwinkle E, Bonnycastle LL, Boomsma DI, Bornstein SR, Brown MJ, Burnier M, Cabrera CP, Chambers JC, Chang IS, Cheng CY, Chines PS, Chung RH, Collins FS, Connell JM, Döring A, Dallongeville J, Danesh J, de Faire U, Delgado G, Dominiczak AF, Doney AS, Drenos F, Edkins S, Eicher JD, Elosua R, Enroth S, Erdmann J, Eriksson P, Esko T, Evangelou E, Evans A, Fall T, Farrall M, Felix JF, Ferrières J, Ferrucci L, Fornage M, Forrester T, Franceschini N, Franco OH, Franco-Cereceda A, Fraser RM, Ganesh SK, Gao H, Gertow K, Gianfagna F, Gigante B, Giulianini F, Goel A, Goodall AH, Goodarzi MO, Gorski M, Gräßler J, Groves CJ, Gudnason V, Gyllensten U, Hallmans G, Hartikainen AL, Hassinen M, Havulinna AS, Hayward C, Hercberg S, Herzig KH, Hicks AA, Hingorani AD, Hirschhorn JN, Hofman A, Holmen J, Holmen OL, Hottenga JJ, Howard P, Hsiung CA, Hunt SC, Ikram MA, Illig T, Iribarren C, Jensen RA, Kähönen M, Kang HM, Kathiresan S, Keating BJ, Khaw KT, Kim YK, Kim E, Kivimaki M, Klopp N, Kolovou G, Komulainen P, Kooner JS, Kosova G, Krauss RM, Kuh D, Kutalik Z, Kuusisto J, Kvaløy K, Lakka TA, Lee NR, Lee IT, Lee WJ, Levy D, Li X, Liang KW, Lin H, Lin L, Lindström J, Lobbens S, Männistö S, Müller G, Müller-Nurasyid M, Mach F, Markus HS, Marouli Eet al., 2016, The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals, Nature Genetics, Vol: 48, Pages: 1171-1184, ISSN: 1546-1718

To dissect the genetic architecture of blood pressure and assess effects on target organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry, and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure-associated loci, of which 17 were new; 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target organ damage in multiple tissues but with minor effects in the kidney. Our findings expand current knowledge of blood pressure-related pathways and highlight tissues beyond the classical renal system in blood pressure regulation.

Journal article

Kaakinen M, Claringbould A, Hagenbeek F, Magi R, Ala-Korpela M, Jarvelin M-R, Prokopenko Iet al., 2016, Genome-wide multiphenotype and eQTL analyses provide novel insights into omega fatty acid metabolism and Type 2 diabetes, 76th Scientific Sessions of the American-Diabetes-Association, Publisher: American Diabetes Association, Pages: A52-A52, ISSN: 0012-1797

Conference paper

Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA, Turley P, Chen G-B, Emilsson V, Meddens SFW, Oskarsson S, Pickrell JK, Thom K, Timshel P, de Vlaming R, Abdellaoui A, Ahluwalia TS, Bacelis J, Baumbach C, Bjornsdottir G, Brandsma JH, Concas MP, Derringer J, Furlotte NA, Galesloot TE, Girotto G, Gupta R, Hall LM, Harris SE, Hofer E, Horikoshi M, Huffman JE, Kaasik K, Kalafati IP, Karlsson R, Kong A, Lahti J, van der Lee SJ, de Leeuw C, Lind PA, Lindgren K-O, Liu T, Mangino M, Marten J, Mihailov E, Miller MB, van der Most PJ, Oldmeadow C, Payton A, Pervjakova N, Peyrot WJ, Qian Y, Raitakari O, Rueedi R, Salvi E, Schmidt B, Schraut KE, Shi J, Smith AV, Poot RA, St Pourcain B, Teumer A, Thorleifsson G, Verweij N, Vuckovic D, Wellmann J, Westra H-J, Yang J, Zhao W, Zhu Z, Alizadeh BZ, Amin N, Bakshi A, Baumeister SE, Biino G, Bonnelykke K, Boyle PA, Campbell H, Cappuccio FP, Davies G, De Neve J-E, Deloukas P, Demuth I, Ding J, Eibich P, Eisele L, Eklund N, Evans DM, Faul JD, Feitosa MF, Forstner AJ, Gandin I, Gunnarsson B, Halldorsson BV, Harris TB, Heath AC, Hocking LJ, Holliday EG, Homuth G, Horan MA, Hottenga J-J, de Jager PL, Joshi PK, Jugessur A, Kaakinen MA, Kahonen M, Kanoni S, Keltigangas-Jarvinen L, Kiemeney LALM, Kolcic I, Koskinen S, Kraja AT, Kroh M, Kutalik Z, Latvala A, Launer LJ, Lebreton MP, Levinson DF, Lichtenstein P, Lichtner P, Liewald DCM, Loukola A, Madden PA, Magi R, Maki-Opas T, Marioni RE, Marques-Vidal P, Meddens GA, McMahon G, Meisinger C, Meitinger T, Milaneschi Y, Milani L, Montgomery GW, Myhre R, Nelson CP, Nyholt DR, Ollier WER, Palotie A, Paternoster L, Pedersen NL, Petrovic KE, Porteous DJ, Raikkonen K, Ring SM, Robino A, Rostapshova O, Rudan I, Rustichini A, Salomaa V, Sanders AR, Sarin A-P, Schmidt H, Scott RJ, Smith BH, Smith JA, Staessen JA, Steinhagen-Thiessen E, Strauch K, Terracciano A, Tobin MD, Ulivi S, Vaccargiu S, Quaye L, van Rooij FJA, Venturini C, Vinkhuyzen AAE, Volker U, Volzke H, Vonk JM, Vozzi D, Waage Jet al., 2016, Genome-wide association study identifies 74 loci associated with educational attainment, Nature, Vol: 533, Pages: 539-542, ISSN: 0028-0836

Journal article

Okbay A, Baselmans BML, De Neve J-E, Turley P, Nivard MG, Fontana MA, Meddens SFW, Linner RK, Rietveld CA, Derringer J, Gratten J, Lee JJ, Liu JZ, de Vlaming R, Ahluwalia TS, Buchwald J, Cavadino A, Frazier-Wood AC, Furlotte NA, Garfield V, Geisel MH, Gonzalez JR, Haitjema S, Karlsson R, van der Laan SW, Ladwig K-H, Lahti J, van der Lee SJ, Lind PA, Liu T, Matteson L, Mihailov E, Miller MB, Minica CC, Nolte IM, Mook-Kanamori D, van der Most PJ, Oldmeadow C, Qian Y, Raitakari O, Rawal R, Realo A, Rueedi R, Schmidt B, Smith AV, Stergiakouli E, Tanaka T, Taylor K, Wedenoja J, Wellmann J, Westra H-J, Willems SM, Zhao W, Amin N, Bakshi A, Boyle PA, Cherney S, Cox SR, Davies G, Davis OSP, Ding J, Direk N, Eibich P, Emeny RT, Fatemifar G, Faul JD, Ferrucci L, Forstner A, Gieger C, Gupta R, Harris TB, Harris JM, Holliday EG, Hottenga J-J, De Jager PL, Kaakinen MA, Kajantie E, Karhunen V, Kolcic I, Kumari M, Launer LJ, Franke L, Li-Gao R, Koini M, Loukola A, Marques-Vidal P, Montgomery GW, Mosing MA, Paternoster L, Pattie A, Petrovic KE, Pulkki-Raback L, Quaye L, Raikkonen K, Rudan I, Scott RJ, Smith JA, Sutin AR, Trzaskowski M, Vinkhuyzen AE, Yu L, Zabaneh D, Attia JR, Bennett DA, Berger K, Bertram L, Boomsma DI, Snieder H, Chang S-C, Cucca F, Deary IJ, van Duijn CM, Eriksson JG, Bultmann U, de Geus EJC, Groenen PJF, Gudnason V, Hansen T, Hartman CA, Haworth CMA, Hayward C, Heath AC, Hinds DA, Hypponen E, Iacono WG, Jarvelin M-R, Jockel K-H, Kaprio J, Kardia SLR, Keltikangas-Jarvinen L, Kraft P, Kubzansky LD, Lehtimaki T, Magnusson PKE, Martin NG, Mcgue M, Metspalu A, Mills M, de Mutsert R, Oldehinkel AJ, Pasterkamp G, Pedersen NL, Plomin R, Polasek O, Power C, Rich SS, Rosendaal FR, den Ruijter HM, Schlessinger D, Schmidt H, Svento R, Schmidt R, Alizadeh BZ, Sorensen TIA, Spector TD, Steptoe A, Terracciano A, Thurik AR, Timpson NJ, Tiemeier H, Uitterlinden AG, Vollenweider P, Wagner GG, Weir DR, Yang J, Conley DC, Smith GD, Hofman A, Johannesson M, Laibson DI, Medland SEet al., 2016, Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses, Nature Genetics, Vol: 48, Pages: 624-633, ISSN: 1061-4036

Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 161,460), and neuroticism (n = 170,911). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (|ρˆ| ≈ 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.

Journal article

Linneberg A, Jacobsen RK, Skaaby T, Taylor AE, Fluharty ME, Jeppesen JL, Bjorngaard JH, Asvold BO, Gabrielsen ME, Campbell A, Marioni RE, Kumari M, Marques-Vidal P, Kaakinen M, Cavadino A, Postmus I, Ahluwalia TS, Wannamethee SG, Lahti J, Raikkonen K, Palotie A, Wong A, Dalgard C, Ford I, Ben-Shlomo Y, Christiansen L, Kyvik KO, Kuh D, Eriksson JG, Whincup PH, Mbarek H, de Geus EJC, Vink JM, Boomsma DI, Smith GD, Lawlor DA, Kisialiou A, McConnachie A, Padmanabhan S, Jukema JW, Power C, Hyppoenen E, Preisig M, Waeber G, Vollenweider P, Korhonen T, Laatikainen T, Salomaa V, Kaprio J, Kivimaki M, Smith BH, Hayward C, Sorensen TIA, Thuesen BH, Sattar N, Morris RW, Romundstad PR, Munafo MR, Jarvelin M-R, Husemoen LLNet al., 2015, Effect of smoking on blood pressure and resting heart rate: a Mendelian randomization meta-analysis in the CARTA consortium, Circulation-Cardiovascular Genetics, Vol: 8, Pages: 832-841, ISSN: 1942-325X

Background—Smoking is an important cardiovascular disease risk factor, but the mechanisms linking smoking to blood pressure are poorly understood.Methods and Results—Data on 141 317 participants (62 666 never, 40 669 former, 37 982 current smokers) from 23 population-based studies were included in observational and Mendelian randomization meta-analyses of the associations of smoking status and smoking heaviness with systolic and diastolic blood pressure, hypertension, and resting heart rate. For the Mendelian randomization analyses, a genetic variant rs16969968/rs1051730 was used as a proxy for smoking heaviness in current smokers. In observational analyses, current as compared with never smoking was associated with lower systolic blood pressure and diastolic blood pressure and lower hypertension risk, but with higher resting heart rate. In observational analyses among current smokers, 1 cigarette/day higher level of smoking heaviness was associated with higher (0.21 bpm; 95% confidence interval 0.19; 0.24) resting heart rate and slightly higher diastolic blood pressure (0.05 mm Hg; 95% confidence interval 0.02; 0.08) and systolic blood pressure (0.08 mm Hg; 95% confidence interval 0.03; 0.13). However, in Mendelian randomization analyses among current smokers, although each smoking increasing allele of rs16969968/rs1051730 was associated with higher resting heart rate (0.36 bpm/allele; 95% confidence interval 0.18; 0.54), there was no strong association with diastolic blood pressure, systolic blood pressure, or hypertension. This would suggest a 7 bpm higher heart rate in those who smoke 20 cigarettes/day.Conclusions—This Mendelian randomization meta-analysis supports a causal association of smoking heaviness with higher level of resting heart rate, but not with blood pressure. These findings suggest that part of the cardiovascular risk of smoking may operate through increasing resting heart rate.

Journal article

Miettunen J, Nordstrom T, Kaakinen M, Ahmed AOet al., 2015, Latent variable mixture modeling in psychiatric research - a review and application, Psychological Medicine, Vol: 46, Pages: 457-467, ISSN: 0033-2917

Latent variable mixture modeling represents a flexible approach to investigating population heterogeneity by sortingcases into latent but non-arbitrary subgroups that are more homogeneous. The purpose of this selective review is to providea non-technical introduction to mixture modeling in a cross-sectional context. Latent class analysis is used to classifyindividuals into homogeneous subgroups (latent classes). Factor mixture modeling represents a newer approach thatrepresents a fusion of latent class analysis and factor analysis. Factor mixture models are adaptable to representing categoricaland dimensional states of affairs. This article provides an overview of latent variable mixture models and illustratesthe application of these methods by applying them to the study of the latent structure of psychotic experiences. Theflexibility of latent variable mixture models makes them adaptable to the study of heterogeneity in complex psychiatricand psychological phenomena. They also allow researchers to address research questions that directly compare the viabilityof dimensional, categorical and hybrid conceptions of constructs.

Journal article

Winkler TW, Justice AE, Graff M, Barata L, Feitosa MF, Chu S, Czajkowski J, Esko T, Fall T, Kilpelainen TO, Lu Y, Magi R, Mihailov E, Pers TH, Rueeger S, Teumer A, Ehret GB, Ferreira T, Heard-Costa NL, Karjalainen J, Lagou V, Mahajan A, Neinast MD, Prokopenko I, Simino J, Teslovich TM, Jansen R, Westra H-J, White CC, Absher D, Ahluwalia TS, Ahmad S, Albrecht E, Alves AC, Bragg-Gresham JL, de Craen AJM, Bis JC, Bonnefond A, Boucher G, Cadby G, Cheng Y-C, Chiang CWK, Delgado G, Demirkan A, Dueker N, Eklund N, Eiriksdottir G, Eriksson J, Feenstra B, Fischer K, Frau F, Galesloot TE, Geller F, Goel A, Gorski M, Grammer TB, Gustafsson S, Haitjema S, Hottenga J-J, Huffman JE, Jackson AU, Jacobs KB, Johansson A, Kaakinen M, Kleber ME, Lahti J, Leach IM, Lehne B, Liu Y, Lo KS, Lorentzon M, Luan J, Madden PAF, Mangino M, McKnight B, Medina-Gomez C, Monda KL, Montasser ME, Mueller G, Mueller-Nurasyid M, Nolte IM, Panoutsopoulou K, Pascoe L, Paternoster L, Rayner NW, Renstrom F, Rizzi F, Rose LM, Ryan KA, Salo P, Sanna S, Scharnagl H, Shi J, Smith AV, Southam L, Stancakova A, Steinthorsdottir V, Strawbridge RJ, Sung YJ, Tachmazidou I, Tanaka T, Thorleifsson G, Trompet S, Pervjakova N, Tyrer JP, Vandenput L, van der Laan SW, van der Velde N, van Setten J, van Vliet-Ostaptchouk JV, Verweij N, Vlachopoulou E, Waite LL, Wang SR, Wang Z, Wild SH, Willenborg C, Wilson JF, Wong A, Yang J, Yengo L, Yerges-Armstrong LM, Yu L, Zhang W, Zhao JH, Andersson EA, Bakker SJL, Baldassarre D, Banasik K, Barcella M, Barlassina C, Bellis C, Benaglio P, Blangero J, Blueher M, Bonnet F, Bonnycastle LL, Boyd HA, Bruinenberg M, Buchman AS, Campbell H, Chen Y-DI, Chines PS, Claudi-Boehm S, Cole J, Collins FS, de Geus EJC, de Groot LCPGM, Dimitriou M, Duan J, Enroth S, Eury E, Farmaki A-E, Forouhi NG, Friedrich N, Gejman PV, Gigante B, Glorioso N, Go AS, Gottesman O, Graessler J, Grallert H, Grarup N, Gu Y-M, Broer L, Ham AC, Hansen T, Harris TB, Hartman CA, Hassinen M, Hastie N, Hattersley AT, Heath ACet al., 2015, The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study, PLOS GENETICS, Vol: 11, ISSN: 1553-7404

Journal article

Morris RW, Taylor AE, Fluharty ME, Bjorngaard JH, Asvold BO, Gabrielsen ME, Campbell A, Marioni R, Kumari M, Korhonen T, Mannisto S, Marques-Vidal P, Kaakinen M, Cavadino A, Postmus I, Husemoen LLN, Skaaby T, Ahluwalia TVS, Treur JL, Willemsen G, Dale C, Wannamethee SG, Lahti J, Palotie A, Raikkonen K, McConnachie A, Padmanabhan S, Wong A, Dalgard C, Paternoster L, Ben-Shlomo Y, Tyrrell J, Horwood J, Fergusson DM, Kennedy MA, Nohr EA, Christiansen L, Kyvik KO, Kuh D, Watt G, Eriksson JG, Whincup PH, Vink JM, Boomsma DI, Smith GD, Lawlor D, Linneberg A, Ford I, Jukema JW, Power C, Hypponen E, Jarvelin M-R, Preisig M, Borodulin K, Kaprio J, Kivimaki M, Smith BH, Hayward C, Romundstad PR, Sorensen TIA, Munafo MR, Sattar Net al., 2015, Heavier smoking may lead to a relative increase in waist circumference: evidence for a causal relationship from a Mendelian randomisation meta-analysis. The CARTA consortium, BMJ Open, Vol: 5, ISSN: 2044-6055

Objectives To investigate, using a Mendelian randomisation approach, whether heavier smoking is associated with a range of regional adiposity phenotypes, in particular those related to abdominal adiposity.Design Mendelian randomisation meta-analyses using a genetic variant (rs16969968/rs1051730 in the CHRNA5-CHRNA3-CHRNB4 gene region) as a proxy for smoking heaviness, of the associations of smoking heaviness with a range of adiposity phenotypes.Participants 148 731 current, former and never-smokers of European ancestry aged ≥16 years from 29 studies in the consortium for Causal Analysis Research in Tobacco and Alcohol (CARTA).Primary outcome measures Waist and hip circumferences, and waist-hip ratio.Results The data included up to 66 809 never-smokers, 43 009 former smokers and 38 913 current daily cigarette smokers. Among current smokers, for each extra minor allele, the geometric mean was lower for waist circumference by −0.40% (95% CI −0.57% to −0.22%), with effects on hip circumference, waist-hip ratio and body mass index (BMI) being −0.31% (95% CI −0.42% to −0.19), −0.08% (−0.19% to 0.03%) and −0.74% (−0.96% to −0.51%), respectively. In contrast, among never-smokers, these effects were higher by 0.23% (0.09% to 0.36%), 0.17% (0.08% to 0.26%), 0.07% (−0.01% to 0.15%) and 0.35% (0.18% to 0.52%), respectively. When adjusting the three central adiposity measures for BMI, the effects among current smokers changed direction and were higher by 0.14% (0.05% to 0.22%) for waist circumference, 0.02% (−0.05% to 0.08%) for hip circumference and 0.10% (0.02% to 0.19%) for waist-hip ratio, for each extra minor allele.Conclusions For a given BMI, a gene variant associated with increased cigarette consumption was associated with increased waist circumference. Smoking in an effort to control weight may lead to accumulation of central adiposity.

Journal article

Conlin A, Kyrolainen P, Kaakinen M, Jarvelin M-R, Perttunen J, Svento Ret al., 2015, Personality traits and stock market participation, Journal of Empirical Finance, Vol: 33, Pages: 34-50, ISSN: 0927-5398

We analyze the relationship between personality traits and stock market participation. Our sample comes from combining personality trait scores and socioeconomic status information from the Northern Finland Birth Cohort 1966 with data from Finnish Central Securities Depository, the official register of stock holdings in Finland. We find the traits, and especially the subscales of the traits, to be significant predictors of stock market participation. In particular, exploratory excitability, extravagance, sentimentality, and dependence have large effects. One-standard-deviation changes in the subscale scores have marginal effects of up to 4 percentage points on the probability of participating in the stock market.

Journal article

Warrington NM, Howe LD, Paternoster L, Kaakinen M, Herrala S, Huikari V, Wu YY, Kemp JP, Timpson NJ, St Pourcain B, Smith GD, Tilling K, Jarvelin M-R, Pennell CE, Evans DM, Lawlor DA, Briollais L, Palmer LJet al., 2015, A genome-wide association study of body mass index across early life and childhood, International Journal of Epidemiology, Vol: 44, Pages: 700-712, ISSN: 0300-5771

Background: Several studies have investigated the effect of known adult body mass index (BMI) associated single nucleotide polymorphisms (SNPs) on BMI in childhood. There has been no genome-wide association study (GWAS) of BMI trajectories over childhood.Methods: We conducted a GWAS meta-analysis of BMI trajectories from 1 to 17 years of age in 9377 children (77 967 measurements) from the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Western Australian Pregnancy Cohort (Raine) Study. Genome-wide significant loci were examined in a further 3918 individuals (48 530 measurements) from Northern Finland. Linear mixed effects models with smoothing splines were used in each cohort for longitudinal modelling of BMI.Results: A novel SNP, downstream from the FAM120AOS gene on chromosome 9, was detected in the meta-analysis of ALSPAC and Raine. This association was driven by a difference in BMI at 8 years (T allele of rs944990 increased BMI; PSNP = 1.52 × 10−8), with a modest association with change in BMI over time (PWald(Change) = 0.006). Three known adult BMI-associated loci (FTO, MC4R and ADCY3) and one childhood obesity locus (OLFM4) reached genome-wide significance (PWald < 1.13 × 10−8) with BMI at 8 years and/or change over time.Conclusions: This GWAS of BMI trajectories over childhood identified a novel locus that warrants further investigation. We also observed genome-wide significance with previously established obesity loci, making the novel observation that these loci affected both the level and the rate of change in BMI. We have demonstrated that the use of repeated measures data can increase power to allow detection of genetic loci with smaller sample sizes.

Journal article

Cornelis MC, Byrne EM, Esko T, Nalls MA, Ganna A, Paynter N, Monda KL, Amin N, Fischer K, Renstrom F, Ngwa JS, Huikari V, Cavadino A, Nolte IM, Teumer A, Yu K, Marques-Vidal P, Rawal R, Manichaikul A, Wojczynski MK, Vink JM, Zhao JH, Burlutsky G, Lahti J, Mikkila V, Lemaitre RN, Eriksson J, Musani SK, Tanaka T, Geller F, Luan J, Hui J, Maegi R, Dimitriou M, Garcia ME, Ho W-K, Wright MJ, Rose LM, Magnusson PKE, Pedersen NL, Couper D, Oostra BA, Hofman A, Ikram MA, Tiemeier HW, Uitterlinden AG, van Rooij FJA, Barroso I, Johansson I, Xue L, Kaakinen M, Milani L, Power C, Snieder H, Stolk RP, Baumeister SE, Biffar R, Gu F, Bastardot F, Kutalik Z, Jacobs DR, Forouhi NG, Mihailov E, Lind L, Lindgren C, Michaelsson K, Morris A, Jensen M, Khaw K-T, Luben RN, Wang JJ, Mannisto S, Perala M-M, Kahonen M, Lehtimaki T, Viikari J, Mozaffarian D, Mukamal K, Psaty BM, Doering A, Heath AC, Montgomery GW, Dahmen N, Carithers T, Tucker KL, Ferrucci L, Boyd HA, Melbye M, Treur JL, Mellstrom D, Hottenga JJ, Prokopenko I, Toenjes A, Deloukas P, Kanoni S, Lorentzon M, Houston DK, Liu Y, Danesh J, Rasheed A, Mason MA, Zonderman AB, Franke L, Kristal BS, Karjalainen J, Reed DR, Westra H-J, Evans MK, Saleheen D, Harris TB, Dedoussis G, Curhan G, Stumvoll M, Beilby J, Pasquale LR, Feenstra B, Bandinelli S, Ordovas JM, Chan AT, Peters U, Ohlsson C, Gieger C, Martin NG, Waldenberger M, Siscovick DS, Raitakari O, Eriksson JG, Mitchell P, Hunter DJ, Kraft P, Rimm EB, Boomsma DI, Borecki IB, Loos RJF, Wareham NJ, Vollenweider P, Caporaso N, Grabe HJ, Neuhouser ML, Wolffenbuttel BHR, Hu FB, Hyppoenen E, Jarvelin M-R, Cupples LA, Franks PW, Ridker PM, van Duijn CM, Heiss G, Metspalu A, North KE, Ingelsson E, Nettleton JA, van Dam RM, Chasman DIet al., 2015, Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption, MOLECULAR PSYCHIATRY, Vol: 20, Pages: 647-656, ISSN: 1359-4184

Journal article

Fall T, Hagg S, Ploner A, Maegi R, Fischer K, Draisma HHM, Sarin A-P, Benyamin B, Ladenvall C, Akerlund M, Kals M, Esko T, Nelson CP, Kaakinen M, Huikari V, Mangino M, Meirhaeghe A, Kristiansson K, Nuotio M-L, Kobl M, Grallert H, Dehghan A, Kuningas M, de Vries PS, de Bruijn RFAG, Willems SM, Heikkila K, Silventoinen K, Pietilainen KH, Legry V, Giedraitis V, Goumidi L, Syvanen A-C, Strauch K, Koenig W, Lichtner P, Herder C, Palotie A, Menni C, Uitterlinden AG, Kuulasmaa K, Havulinna AS, Moreno LA, Gonzalez-Gross M, Evans A, Tregouet D-A, Yarnell JWG, Virtamo J, Ferrieres J, Veronesi G, Perola M, Arveiler D, Brambilla P, Lind L, Kaprio J, Hofman A, Stricker BH, van Duijn CM, Ikram MA, Franco OH, Cottel D, Dallongeville J, Hall AS, Jula A, Tobin MD, Penninx BW, Peters A, Gieger C, Samani NJ, Montgomery GW, Whiteld JB, Martin NG, Groop L, Spector TD, Magnusson PK, Amouyel P, Boomsma DI, Nilsson PM, Jarvelin M-R, Lyssenko V, Metspalu A, Strachan DP, Salomaa V, Ripatti S, Pedersen NL, Prokopenko I, McCarthy MI, Ingelsson Eet al., 2015, Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors, DIABETES, Vol: 64, Pages: 1841-1852, ISSN: 0012-1797

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