Advertisement

Behavior Genetics

, Volume 49, Issue 1, pp 112–121 | Cite as

A Fast Method for Estimating Statistical Power of Multivariate GWAS in Real Case Scenarios: Examples from the Field of Imaging Genetics

  • Baptiste Couvy-Duchesne
  • Lachlan T. Strike
  • Katie L. McMahon
  • Greig I. de Zubicaray
  • Paul M. Thompson
  • Nicholas G. Martin
  • Sarah E. Medland
  • Margaret J. Wright
Original Research

Abstract

In GWAS of imaging phenotypes (e.g., by the ENIGMA and CHARGE consortia), the growing number of phenotypes considered presents a statistical challenge that other fields are not experiencing (e.g. psychiatry and the Psychiatric Genetics Consortium). However, the multivariate nature of MRI measurements may also be an advantage as many of the MRI phenotypes are correlated and multivariate methods could be considered. Here, we compared the statistical power of a multivariate GWAS versus the current univariate approach, which consists of multiple univariate analyses. To do so, we used results from twin models to estimate pertinent vectors of SNP effect sizes on brain imaging phenotypes, as well as the residual correlation matrices, necessary to estimate analytically the statistical power. We showed that for subcortical structure volumes and hippocampal subfields, a multivariate GWAS yields similar statistical power to the current univariate approach. Our analytical approach is as accurate but ~ 1000 times faster than simulations and we have released the code to facilitate the investigation of other scenarios, may they be outside the field of imaging genetics.

Keywords

Twin models Statistical power Multivariate Univariate GWAS MRI imaging 

Notes

Acknowledgements

We are very grateful to the twins for their generosity of time and willingness to participate in our studies. We thank research nurses Marlene Grace and Ann Eldridge for twin recruitment, Richard Parker for data collection, research assistants Lachlan Strike, Kori Johnson, Aaron Quiggle, and Natalie Garden, and radiographers Matthew Meredith, Peter Hobden, Kate Borg, Aiman Al Najjar, and Anita Burns for data acquisition, David Butler, Daniel Park, David Smyth and Anthony Conciatore for IT support.

Funding

The MRI imaging of QTIM was supported by grants from National Institute of Health (NIH) (R01 HD050735, U54 EB020403) and the National Health and Medical Research Council (NHMRC) (496682, 1009064). PT and MJW are supported in part by NIH Grant to the ENIGMA Center for Worldwide Medicine, Imaging & Genomics. SEM is supported by an NHMRC fellowship (APP1103623). The funding was supported by Australian Research Council (Grant Nos. A7960034, A79906588, A79801419, DP0212016).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Informed consent

Written informed consent was obtained from all participants including a parent or guardian for those aged less than 18 years.

Research involving human and animal rights

The QTIM study was approved by the ethics review boards of the Queensland Institute of Medical Research, the University of Queensland, and Uniting Health Care, Wesley Hospital, Brisbane. QTIM participants received an honorarium in appreciation of their time. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Supplementary material

10519_2018_9936_MOESM1_ESM.docx (6.8 mb)
Supplementary material 1 (DOCX 6918 KB)

References

  1. Adams HH, Hibar DP, Chouraki V, Stein JL, Nyquist PA, Renteria ME, Trompet S, Arias-Vasquez A, Seshadri S, Desrivieres S, Beecham AH, Jahanshad N, Wittfeld K, Van der Lee SJ, Abramovic L, Alhusaini S, Amin N, Andersson M, Arfanakis K, Aribisala BS, Armstrong NJ, Athanasiu L, Axelsson T, Beiser A, Bernard M, Bis JC, Blanken LM, Blanton SH, Bohlken MM, Boks MP, Bralten J, Brickman AM, Carmichael O, Chakravarty MM, Chauhan G, Chen Q, Ching CR, Cuellar-Partida G, Braber AD, Doan NT, Ehrlich S, Filippi I, Ge T, Giddaluru S, Goldman AL, Gottesman RF, Greven CU, Grimm O, Griswold ME, Guadalupe T, Hass J, Haukvik UK, Hilal S, Hofer E, Hoehn D, Holmes AJ, Hoogman M, Janowitz D, Jia T, Kasperaviciute D, Kim S, Klein M, Kraemer B, Lee PH, Liao J, Liewald DC, Lopez LM, Luciano M, Macare C, Marquand A, Matarin M, Mather KA, Mattheisen M, Mazoyer B, McKay DR, McWhirter R, Milaneschi Y, Mirza-Schreiber N, Muetzel RL, Maniega SM, Nho K, Nugent AC, Loohuis LM, Oosterlaan J, Papmeyer M, Pappa I, Pirpamer L, Pudas S, Putz B, Rajan KB, Ramasamy A, Richards JS, Risacher SL, Roiz-Santianez R, Rommelse N, Rose EJ, Royle NA, Rundek T, Samann PG, Satizabal CL, Schmaal L, Schork AJ, Shen L, Shin J, Shumskaya E, Smith AV, Sprooten E, Strike LT, Teumer A, Thomson R, Tordesillas-Gutierrez D, Toro R, Trabzuni D, Vaidya D, Van der Grond J, Van der Meer D, Van Donkelaar MM, Van Eijk KR, Van Erp TG, Van Rooij D, Walton E, Westlye LT, Whelan CD, Windham BG, Winkler AM, Woldehawariat G, Wolf C, Wolfers T, Xu B, Yanek LR, Yang J, Zijdenbos A, Zwiers MP, Agartz I, Aggarwal NT, Almasy L, Ames D, Amouyel P, Andreassen OA, Arepalli S, Assareh AA, Barral S, Bastin ME, Becker DM, Becker JT, Bennett DA, Blangero J, van Bokhoven H, Boomsma DI, Brodaty H, Brouwer RM, Brunner HG, Buckner RL, Buitelaar JK, Bulayeva KB, Cahn W, Calhoun VD, Cannon DM, Cavalleri GL, Chen C, Cheng CY, Cichon S, Cookson MR, Corvin A, Crespo-Facorro B, Curran JE, Czisch M, Dale AM, Davies GE, De Geus EJ, De Jager PL, de Zubicaray GI, Delanty N, Depondt C, DeStefano AL, Dillman A, Djurovic S, Donohoe G, Drevets WC, Duggirala R, Dyer TD, Erk S, Espeseth T, Evans DA, Fedko IO, Fernandez G, Ferrucci L, Fisher SE, Fleischman DA, Ford I, Foroud TM, Fox PT, Francks C, Fukunaga M, Gibbs JR, Glahn DC, Gollub RL, Goring HH, Grabe HJ, Green RC, Gruber O, Gudnason V, Guelfi S, Hansell NK, Hardy J, Hartman CA, Hashimoto R, Hegenscheid K, Heinz A, Le Hellard S, Hernandez DG, Heslenfeld DJ, Ho BC, Hoekstra PJ, Hoffmann W, Hofman A, Holsboer F, Homuth G, Hosten N, Hottenga JJ, Pol HE, Ikeda M, Ikram MK, Jack CR Jr, Jenkinson M, Johnson R, Jonsson EG, Jukema JW, Kahn RS, Kanai R, Kloszewska I, Knopman DS, Kochunov P, Kwok JB, Lawrie SM, Lemaitre H, Liu X, Longo DL, Longstreth WT Jr, Lopez OL, Lovestone S, Martinez O, Martinot JL, Mattay VS, McDonald C, McIntosh AM, McMahon KL, McMahon FJ, Mecocci P, Melle I, Meyer-Lindenberg A, Mohnke S, Montgomery GW, Morris DW, Mosley TH, Muhleisen TW, Muller-Myhsok B, Nalls MA, Nauck M, Nichols TE, Niessen WJ, Nothen MM, Nyberg L, Ohi K, Olvera RL, Ophoff RA, Pandolfo M, Paus T, Pausova Z, Penninx BW, Pike GB, Potkin SG, Psaty BM, Reppermund S, Rietschel M, Roffman JL, Romanczuk-Seiferth N, Rotter JI, Ryten M, Sacco RL, Sachdev PS, Saykin AJ, Schmidt R, Schofield PR, Sigurdsson S, Simmons A, Singleton A, Sisodiya SM, Smith C, Smoller JW, Soininen H, Srikanth V, Steen VM, Stott DJ, Sussmann JE, Thalamuthu A, Tiemeier H, Toga AW, Traynor BJ, Troncoso J, Turner JA, Tzourio C, Uitterlinden AG, Hernandez MC, Van der Brug M, Van der Lugt A, Van der Wee NJ, Van Duijn CM, Van Haren NE, Van TED, Van Tol MJ, Vardarajan BN, Veltman DJ, Vernooij MW, Volzke H, Walter H, Wardlaw JM, Wassink TH, Weale ME, Weinberger DR, Weiner MW, Wen W, Westman E, White T, Wong TY, Wright CB, Zielke HR, Zonderman AB, Deary IJ, DeCarli C, Schmidt H, Martin NG, De Craen AJ, Wright MJ, Launer LJ, Schumann G, Fornage M, Franke B, Debette S, Medland SE, Ikram MA, Thompson PM (2016) Novel genetic loci underlying human intracranial volume identified through genome-wide association. Nat Neurosci 19(12):1569–1582CrossRefGoogle Scholar
  2. Boker S, Neale M, Maes H, Wilde M, Spiegel M, Brick T, Spies J, Estabrook R, Kenny S, Bates T, Mehta P, Fox J (2011) OpenMx: an open source extended structural equation modeling framework. Psychometrika 76(23258944):306–317CrossRefGoogle Scholar
  3. Breteler MMB, Stöcker T, Pracht E, Brenner D, Stirnberg R (2014) MRI in the Rhineland study: a novel protocol for population neuroimaging. Alzheimer’s Dement J Alzheimer’s Assoc 10(4):P92CrossRefGoogle Scholar
  4. Cole DA, Maxwell SE, Arvey R, Salas E (1994) How the power of manova can both increase and decrease as a function of the intercorrelations among the dependent-variables. Psychol Bull 115(3):465–474CrossRefGoogle Scholar
  5. de Zubicaray GI, Chiang MC, McMahon KL, Shattuck DW, Toga AW, Martin NG, Wright MJ, Thompson PM (2008) Meeting the challenges of neuroimaging genetics. Brain Imaging Behav 2(4):258–263CrossRefGoogle Scholar
  6. Ferreira MA, Purcell SM (2009) A multivariate test of association. Bioinformatics 25(1):132–133CrossRefGoogle Scholar
  7. Fischl B (2012) FreeSurfer. NeuroImage 62(2):774–781CrossRefGoogle Scholar
  8. Galesloot TE, van Steen K, Kiemeney LA, Janss LL, Vermeulen SH (2014) A comparison of multivariate genome-wide association methods. PLoS ONE 9(4):e95923CrossRefGoogle Scholar
  9. Greiser KH, Linseisen J, Schmidt B, Wichmann HE, Consortium GNCG (2014) The German National Cohort: aims, study design and organization. Eur J Epidemiol 29(5):371–382CrossRefGoogle Scholar
  10. Hibar DP, Stein JL, Renteria ME, Arias-Vasquez A, Desrivières S, Jahanshad N, Toro R, Wittfeld K, Abramovic L, Andersson M, Aribisala BS, Armstrong NJ, Bernard M (2015) Common genetic variants influence human subcortical brain structures. Nature 520:224CrossRefGoogle Scholar
  11. Iglesias JE, Augustinack JC, Nguyen K, Player CM, Player A, Wright M, Roy N, Frosch MP, McKee AC, Wald LL, Fischl B, Van Leemput K, Neuroimaging AD (2015) A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI. NeuroImage 115:117–137CrossRefGoogle Scholar
  12. Li J, Ji L (2005) Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 95(3):221–227CrossRefGoogle Scholar
  13. Li MX, Yeung JM, Cherny SS, Sham PC (2012) Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum Genet 131(5):747–756CrossRefGoogle Scholar
  14. Marchini J, Howie B, Myers S, McVean G, Donnelly P (2007) A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 39(7):906–913CrossRefGoogle Scholar
  15. Medland SE, Neale MC (2010) An integrated phenomic approach to multivariate allelic association. Eur J Hum Genet 18(2):233–239CrossRefGoogle Scholar
  16. Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E, Xu J, Bartsch AJ, Jbabdi S, Sotiropoulos SN, Andersson JL, Griffanti L, Douaud G, Okell TW, Weale P, Dragonu I, Garratt S, Hudson S, Collins R, Jenkinson M, Matthews PM, Smith SM (2016) Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci 19(11):1523–1536CrossRefGoogle Scholar
  17. Minica CC, Boomsma DI, van der Sluis S, Dolan CV (2010) Genetic association in multivariate phenotypic data: power in five models. Twin Res Hum Genet 13(6):525–543CrossRefGoogle Scholar
  18. Muller KE, Peterson BL (1984) Practical methods for computing power in testing the multivariate general linear-hypothesis. Comput Stat Data Anal 2(2):143–158CrossRefGoogle Scholar
  19. Nagarsenker BN, Suniaga J (1983) Distributions of a class of statistics useful in multivariate-analysis. J Am Stat Assoc 78(382):472–475CrossRefGoogle Scholar
  20. O’Reilly PF, Hoggart CJ, Pomyen Y, Calboli FCF, Elliott P, Jarvelin MR, Coin LJM (2012) MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS. PLoS ONE 7(5):e34861CrossRefGoogle Scholar
  21. Olson CL (1974) Comparative robustness of 6 tests in multivariate-analysis of variance. J Am Stat Assoc 69(348):894–908CrossRefGoogle Scholar
  22. Olson CL (1976) Choosing a test statistic in multivariate-analysis of variance. Psychol Bull 83(4):579–586CrossRefGoogle Scholar
  23. Olson CL (1979) Practical considerations in choosing a Manova test statistic—a rejoinder to Stevens. Psychol Bull 86(6):1350–1352CrossRefGoogle Scholar
  24. Pillai KCS (1956) On the distribution of the largest or the smallest root of a matrix in multivariate analysis. Biometrika 43(1–2):122–127CrossRefGoogle Scholar
  25. Pillai KCS, Jayachandran K (1967) Power comparisons of tests of 2 multivariate hypotheses based on 4 criteria. Biometrika 54:195–210CrossRefGoogle Scholar
  26. Porter HF, O’Reilly PF (2017) Multivariate simulation framework reveals performance of multi-trait GWAS methods. Sci Rep 7:38837CrossRefGoogle Scholar
  27. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3):559–575CrossRefGoogle Scholar
  28. Rao CR (1973) Linear statistical inference and its applications. Wiley, New YorkCrossRefGoogle Scholar
  29. Renteria ME, Hansell NK, Strike LT, McMahon KL, de Zubicaray GI, Hickie IB, Thompson PM, Martin NG, Medland SE, Wright MJ (2014) Genetic architecture of subcortical brain regions: common and region-specific genetic contributions. Genes Brain Behav 13(8):821–830CrossRefGoogle Scholar
  30. Schram MT, Sep SJ, van der Kallen CJ, Dagnelie PC, Koster A, Schaper N, Henry RM, Stehouwer CD (2014) The Maastricht Study: an extensive phenotyping study on determinants of type 2 diabetes, its complications and its comorbidities. Eur J Epidemiol 29(6):439–451CrossRefGoogle Scholar
  31. Stein JL, Medland SE, Vasquez AA, Hibar DP, Senstad RE, Winkler AM, Toro R, Appel K, Bartecek R, Bergmann O, Bernard M, Brown AA, Cannon DM, Chakravarty MM, Christoforou A, Domin M, Grimm O, Hollinshead M, Holmes AJ, Homuth G, Hottenga JJ, Langan C, Lopez LM, Hansell NK, Hwang KS, Kim S, Laje G, Lee PH, Liu X, Loth E, Lourdusamy A, Mattingsdal M, Mohnke S, Maniega SM, Nho K, Nugent AC, O’Brien C, Papmeyer M, Putz B, Ramasamy A, Rasmussen J, Rijpkema M, Risacher SL, Roddey JC, Rose EJ, Ryten M, Shen L, Sprooten E, Strengman E, Teumer A, Trabzuni D, Turner J, van Eijk K, van Erp TG, van Tol MJ, Wittfeld K, Wolf C, Woudstra S, Aleman A, Alhusaini S, Almasy L, Binder EB, Brohawn DG, Cantor RM, Carless MA, Corvin A, Czisch M, Curran JE, Davies G, de Almeida MA, Delanty N, Depondt C, Duggirala R, Dyer TD, Erk S, Fagerness J, Fox PT, Freimer NB, Gill M, Goring HH, Hagler DJ, Hoehn D, Holsboer F, Hoogman M, Hosten N, Jahanshad N, Johnson MP, Kasperaviciute D, Kent JW Jr, Kochunov P, Lancaster JL, Lawrie SM, Liewald DC, Mandl R, Matarin M, Mattheisen M, Meisenzahl E, Melle I, Moses EK, Muhleisen TW, Nauck M, Nothen MM, Olvera RL, Pandolfo M, Pike GB, Puls R, Reinvang I, Renteria ME, Rietschel M, Roffman JL, Royle NA, Rujescu D, Savitz J, Schnack HG, Schnell K, Seiferth N, Smith C, Steen VM, Valdes Hernandez MC, Van den Heuvel M, van der Wee NJ, Van Haren NE, Veltman JA, Volzke H, Walker R, Westlye LT, Whelan CD, Agartz I, Boomsma DI, Cavalleri GL, Dale AM, Djurovic S, Drevets WC, Hagoort P, Hall J, Heinz A, Jack CR Jr, Foroud TM, Le Hellard S, Macciardi F, Montgomery GW, Poline JB, Porteous DJ, Sisodiya SM, Starr JM, Sussmann J, Toga AW, Veltman DJ, Walter H, Weiner MW, Alzheimer’s Disease Neuroimaging Initiative, EPIGEN Consortium, Consortium IMAGEN, Saguenay Youth Study Group, Bis JC, Ikram MA, Smith AV, Gudnason V, Tzourio C, Vernooij MW, Launer LJ, DeCarli C, Seshadri S, Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium, Andreassen OA, Apostolova LG, Bastin ME, Blangero J, Brunner HG, Buckner RL, Cichon S, Coppola G, de Zubicaray GI, Deary IJ, Donohoe G, de Geus EJ, Espeseth T, Fernandez G, Glahn DC, Grabe HJ, Hardy J, Hulshoff Pol HE, Jenkinson M, Kahn RS, McDonald C, McIntosh AM, McMahon FJ, McMahon KL, Meyer-Lindenberg A, Morris DW, Muller-Myhsok B, Nichols TE, Ophoff RA, Paus T, Pausova Z, Penninx BW, Potkin SG, Samann PG, Saykin AJ, Schumann G, Smoller JW, Wardlaw JM, Weale ME, Martin NG, Franke B, Wright MJ, Thompson PM, Enhancing Neuro Imaging Genetics through Meta-Analysis Consortium (2012) Identification of common variants associated with human hippocampal and intracranial volumes. Nat Genet 44(5):552–561CrossRefGoogle Scholar
  32. Stephens M (2013) A unified framework for association analysis with multiple related phenotypes. PLoS ONE 8(7):e65245CrossRefGoogle Scholar
  33. Stevens J (1979) Choosing a test statistic in multivariate-analysis of variance—comment. Psychol Bull 86(2):355–360CrossRefGoogle Scholar
  34. Stevens JP (1980) Power of the multivariate-analysis of variance tests. Psychol Bull 88(3):728–737CrossRefGoogle Scholar
  35. Strike LT, Couvy-Duchesne B, Hansell NK, Cuellar-Partida G, Medland SE, Wright MJ (2015) Genetics and brain morphology. Neuropsychol Rev 25(1):63–96CrossRefGoogle Scholar
  36. Thompson PM, Stein JL, Medland SE, Hibar DP, Vasquez AA, Renteria ME, Toro R, Jahanshad N, Schumann G, Franke B, Wright MJ, Martin NG, Agartz I, Alda M, Alhusaini S, Almasy L, Almeida J, Alpert K, Andreasen NC, Andreassen OA, Apostolova LG, Appel K, Armstrong NJ, Aribisala B, Bastin ME, Bauer M, Bearden CE, Bergmann O, Binder EB, Blangero J, Bockholt HJ, Boen E, Bois C, Boomsma DI, Booth T, Bowman IJ, Bralten J, Brouwer RM, Brunner HG, Brohawn DG, Buckner RL, Buitelaar J, Bulayeva K, Bustillo JR, Calhoun VD, Cannon DM, Cantor RM, Carless MA, Caseras X, Cavalleri GL, Chakravarty MM, Chang KD, Ching CR, Christoforou A, Cichon S, Clark VP, Conrod P, Coppola G, Crespo-Facorro B, Curran JE, Czisch M, Deary IJ, de Geus EJ, den Braber A, Delvecchio G, Depondt C, de Haan L, de Zubicaray GI, Dima D, Dimitrova R, Djurovic S, Dong H, Donohoe G, Duggirala R, Dyer TD, Ehrlich S, Ekman CJ, Elvsashagen T, Emsell L, Erk S, Espeseth T, Fagerness J, Fears S, Fedko I, Fernandez G, Fisher SE, Foroud T, Fox PT, Francks C, Frangou S, Frey EM, Frodl T, Frouin V, Garavan H, Giddaluru S, Glahn DC, Godlewska B, Goldstein RZ, Gollub RL, Grabe HJ, Grimm O, Gruber O, Guadalupe T, Gur RE, Gur RC, Goring HH, Hagenaars S, Hajek T, Hall GB, Hall J, Hardy J, Hartman CA, Hass J, Hatton SN, Haukvik UK, Hegenscheid K, Heinz A, Hickie IB, Ho BC, Hoehn D, Hoekstra PJ, Hollinshead M, Holmes AJ, Homuth G, Hoogman M, Hong LE, Hosten N, Hottenga JJ, Hulshoff Pol HE, Hwang KS, Jack CR Jr, Jenkinson M, Johnston C, Jonsson EG, Kahn RS, Kasperaviciute D, Kelly S, Kim S, Kochunov P, Koenders L, Kramer B, Kwok JB, Lagopoulos J, Laje G, Landen M, Landman BA, Lauriello J, Lawrie SM, Lee PH, Le Hellard S, Lemaitre H, Leonardo CD, Li CS, Liberg B, Liewald DC, Liu X, Lopez LM, Loth E, Lourdusamy A, Luciano M, Macciardi F, Machielsen MW, Macqueen GM, Malt UF, Mandl R, Manoach DS, Martinot JL, Matarin M, Mather KA, Mattheisen M, Mattingsdal M, Meyer-Lindenberg A, McDonald C, McIntosh AM, McMahon FJ, McMahon KL, Meisenzahl E, Melle I, Milaneschi Y, Mohnke S, Montgomery GW, Morris DW, Moses EK, Mueller BA, Munoz Maniega S, Muhleisen TW, Muller-Myhsok B, Mwangi B, Nauck M, Nho K, Nichols TE, Nilsson LG, Nugent AC, Nyberg L, Olvera RL, Oosterlaan J, Ophoff RA, Pandolfo M, Papalampropoulou-Tsiridou M, Papmeyer M, Paus T, Pausova Z, Pearlson GD, Penninx BW, Peterson CP, Pfennig A, Phillips M, Pike GB, Poline JB, Potkin SG, Putz B, Ramasamy A, Rasmussen J, Rietschel M, Rijpkema M, Risacher SL, Roffman JL, Roiz-Santianez R, Romanczuk-Seiferth N, Rose EJ, Royle NA, Rujescu D, Ryten M, Sachdev PS, Salami A, Satterthwaite TD, Savitz J, Saykin AJ, Scanlon C, Schmaal L, Schnack HG, Schork AJ, Schulz SC, Schur R, Seidman L, Shen L, Shoemaker JM, Simmons A, Sisodiya SM, Smith C, Smoller JW, Soares JC, Sponheim SR, Sprooten E, Starr JM, Steen VM, Strakowski S, Strike L, Sussmann J, Samann PG, Teumer A, Toga AW, Tordesillas-Gutierrez D, Trabzuni D, Trost S, Turner J, Van den Heuvel M, van der Wee NJ, van Eijk K, van Erp TG, van Haren NE, van ‘t Ent D, van Tol MJ, Valdes Hernandez MC, Veltman DJ, Versace A, Volzke H, Walker R, Walter H, Wang L, Wardlaw JM, Weale ME, Weiner MW, Wen W, Westlye LT, Whalley HC, Whelan CD, White T, Winkler AM, Wittfeld K, Woldehawariat G, Wolf C, Zilles D, Zwiers MP, Thalamuthu A, Schofield PR, Freimer NB, Lawrence NS, Drevets W, Alzheimer’s Disease Neuroimaging Initiative ECICSYSG (2014) The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav 8(2):153–182Google Scholar
  37. van der Sluis S, Posthuma D, Dolan CV (2013) TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies. PLoS Genetics 9(1):e1003235CrossRefGoogle Scholar
  38. Zhou X, Stephens M (2014) Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat Methods 11(4):407–409CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Baptiste Couvy-Duchesne
    • 1
    • 2
    • 3
  • Lachlan T. Strike
    • 2
  • Katie L. McMahon
    • 4
    • 5
  • Greig I. de Zubicaray
    • 6
  • Paul M. Thompson
    • 7
  • Nicholas G. Martin
    • 3
  • Sarah E. Medland
    • 3
  • Margaret J. Wright
    • 2
    • 5
  1. 1.Institute of Molecular BioscienceThe University of QueenslandBrisbaneAustralia
  2. 2.Queensland Brain InstituteThe University of QueenslandBrisbaneAustralia
  3. 3.QIMR Berghofer Medical Research InstituteBrisbaneAustralia
  4. 4.Herston Imaging Research Facility (HIRF)Queensland Institute of TechnologyBrisbaneAustralia
  5. 5.Centre for Advanced ImagingThe University of QueenslandBrisbaneAustralia
  6. 6.Institute of Health and Biomedical InnovationsQueensland Institute of TechnologyBrisbaneAustralia
  7. 7.Imaging Genetics Center, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesUSA

Personalised recommendations