Imaging genetics --- towards discovery neuroscience

Abstract

Imaging genetics is an emerging field aimed at identifying and characterizing genetic variants that influence measures derived from anatomical or functional brain images, which are in turn related to brain-related illnesses or fundamental cognitive, emotional and behavioral processes, and are affected by environmental factors. Here we review the recent evolution of statistical approaches and outstanding challenges in imaging genetics, with a focus on population-based imaging genetic association studies. We show the trend in imaging genetics from candidate approaches to pure discovery science, and from univariate to multivariate analyses. We also discuss future directions and prospects of imaging genetics for ultimately helping understand the genetic and environmental underpinnings of various neuropsychiatric disorders and turning basic science into clinical strategies.

Graphical abstract

Keywords

imaging genetics / association study / multiple testing / 5-O approach

References

[1] Gottesman, I. I. and Shields, J. (1972) Schizophrenia Genetics: A Twin Study Vantage Point. New York: Academic Press

[2] Gottesman, I. I. and Gould, T. D. (2003) The endophenotype concept in psychiatry: etymology and strategic intentions. Am. J. Psychiatry, 160, 636–645

[3] Meyer-Lindenberg, A. and Weinberger, D. R. (2006) Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nat. Rev. Neurosci., 7, 818–827

[4] Akil, H., Brenner, S., Kandel, E., Kendler, K. S., King, M. C., Scolnick, E., Watson, J. D. and Zoghbi, H. Y. (2010) Medicine. the future of psychiatric research: genomes and neural circuits. Science, 327, 1580–1581

[5] Ashburner, J. and Friston, K. J. (2000) Voxel-based morphometry—the methods. Neuroimage, 11, 805–821

[6] Dale, A. M., Fischl, B. and Sereno, M. I. (1999) Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage, 9, 179–194

[7] Fischl, B., Sereno, M. I. and Dale, A. M. (1999) Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage, 9, 195–207

[8] Fischl, B. (2012) FreeSurfer. Neuroimage, 62, 774–781

[9] Rimol, L. M., Panizzon, M. S., Fennema-Notestine, C., Eyler, L. T., Fischl, B., Franz, C. E., Hagler, D. J., Lyons, M. J., Neale, M. C., Pacheco, J., (2010) Cortical thickness is influenced by regionally specific genetic factors. Biol. Psychiatry, 67, 493–499

[10] Winkler, A. M., Kochunov, P., Blangero, J., Almasy, L., Zilles, K., Fox, P. T., Duggirala, R. and Glahn, D. C. (2010) Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. Neuroimage, 53, 1135–1146

[11] Fox, M. D. and Raichle, M. E. (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci., 8, 700–711

[12] Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., (2009) Correspondence of the brain’s functional architecture during activation and rest. Proc. Natl. Acad. Sci. U.S.A., 106, 13040–13045

[13] Thompson, P. M., Ge, T., Glahn, D. C., Jahanshad, N. and Nichols, T. E. (2013) Genetics of the connectome. Neuroimage, 80, 475–488

[14] Chiang, M. C., Barysheva, M., Shattuck, D. W., Lee, A. D., Madsen, S. K., Avedissian, C., Klunder, A. D., Toga, A. W., McMahon, K. L., de Zubicaray, G. I., (2009) Genetics of brain fiber architecture and intellectual performance. J. Neurosci., 29, 2212–2224

[15] Kochunov, P., Glahn, D. C., Lancaster, J. L., Winkler, A. M., Smith, S., Thompson, P. M., Almasy, L., Duggirala, R., Fox, P. T. and Blangero, J. (2010) Genetics of microstructure of cerebral white matter using diffusion tensor imaging. Neuroimage, 53, 1109–1116

[16] Thomason, M. E. and Thompson, P. M. (2011) Diffusion imaging, white matter, and psychopathology. Annu Rev Clin Psychol, 7, 63–85

[17] Jbabdi, S. and Johansen-Berg, H. (2011) Tractography: where do we go from here? Brain Connect, 1, 169–183

[18] Glahn, D. C., Winkler, A. M., Kochunov, P., Almasy, L., Duggirala, R., Carless, M. A., Curran, J. C., Olvera, R. L., Laird, A. R., Smith, S. M., (2010) Genetic control over the resting brain. Proc. Natl. Acad. Sci. U.S.A., 107, 1223–1228

[19] Fornito, A., Zalesky, A., Bassett, D. S., Meunier, D., Ellison-Wright, I., Yücel, M., Wood, S. J., Shaw, K., O’Connor, J., Nertney, D., (2011) Genetic influences on cost-efficient organization of human cortical functional networks. J. Neurosci., 31, 3261–3270

[20] van den Heuvel, M. P., van Soelen, I. L. C., Stam, C. J., Kahn, R. S., Boomsma, D. I. and Hulshoff Pol, H. E. (2013) Genetic control of functional brain network efficiency in children. Eur Neuropsychopharmacol, 23, 19–23

[21] Rubinov, M. and Sporns, O. (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52, 1059–1069

[22] Glahn, D. C., Curran, J. E., Winkler, A. M., Carless, M. A., Kent, J. W. Jr, Charlesworth, J. C., Johnson, M. P., Göring, H. H., Cole, S. A., Dyer, T. D., (2012) High dimensional endophenotype ranking in the search for major depression risk genes. Biol. Psychiatry, 71, 6–14

[23] Frazer, K. A., Ballinger, D. G., Cox, D. R., Hinds, D. A., Stuve, L. L., Gibbs, R. A., Belmont, J. W., Boudreau, A., Hardenbol, P., Leal, S. M., , and the International HapMap Consortium. (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature, 449, 851–861

[24] Freedman, M. L., Reich, D., Penney, K. L., McDonald, G. J., Mignault, A. A., Patterson, N., Gabriel, S. B., Topol, E. J., Smoller, J. W., Pato, C. N., (2004) Assessing the impact of population stratification on genetic association studies. Nat. Genet., 36, 388–393

[25] Marchini, J., Cardon, L. R., Phillips, M. S. and Donnelly, P. (2004) The effects of human population structure on large genetic association studies. Nat. Genet., 36, 512–517

[26] Devlin, B. and Roeder, K. (1999) Genomic control for association studies. Biometrics, 55, 997–1004

[27] Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A. and Reich, D. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet., 38, 904–909

[28] Kang, H. M., Sul, J. H., Service, S. K., Zaitlen, N. A., Kong, S. Y., Freimer, N. B., Sabatti, C. and Eskin, E. (2010) Variance component model to account for sample structure in genome-wide association studies. Nat. Genet., 42, 348–354

[29] Nymberg, C., Jia, T., Lubbe, S., Ruggeri, B., Desrivieres, S., Barker, G., Büchel, C., Fauth-Buehler, M., Cattrell, A., Conrod, P., , and the IMAGEN Consortium. (2013) Neural mechanisms of attention-deficit/hyperactivity disorder symptoms are stratified by MAOA genotype. Biol. Psychiatry, 74, 607–614

[30] Joyner, A. H., J, C. R., Bloss, C. S., Bakken, T. E., Rimol, L. M., Melle, I., Agartz, I., Djurovic, S., Topol, E. J., Schork, N. J., (2009) A common MECP2 haplotype associates with reduced cortical surface area in humans in two independent populations. Proc. Natl. Acad. Sci. U.S.A., 106, 15483–15488

[31] Meyer-Lindenberg, A. (2009) Neural connectivity as an intermediate phenotype: brain networks under genetic control. Hum Brain Mapp, 30, 1938–1946

[32] Brown, J. A., Terashima, K. H., Burggren, A. C., Ercoli, L. M., Miller, K. J., Small, G. W. and Bookheimer, S. Y. (2011) Brain network local interconnectivity loss in aging APOE-4 allele carriers. Proc. Natl. Acad. Sci. U.S.A., 108, 20760–20765

[33] Dennis, E. L., Jahanshad, N., Rudie, J. D., Brown, J. A., Johnson, K., McMahon, K. L., de Zubicaray, G. I., Montgomery, G., Martin, N. G., Wright, M. J., (2011) Altered structural brain connectivity in healthy carriers of the autism risk gene, CNTNAP2. Brain Connect, 1, 447–459

[34] Loth, E., Poline, J. B., Thyreau, B., Jia, T., Tao, C., Lourdusamy, A., Stacey, D., Cattrell, A., Desrivières, S., Ruggeri, B., , and the IMAGEN Consortium. (2013) Oxytocin receptor genotype modulates ventral striatal activity to social cues and response to stressful life events. Biol. Psychiatry, PMID: 24120094

[35] Friston, K. J., Ashburner, J. T., Kiebel, S. J., Nichols, T. E. and Penny, W. D. (2006) Statistical Parametric Mapping: The Analysis of Functional Brain Images. New York: Academic Press

[36] Filippini, N., Rao, A., Wetten, S., Gibson, R. A., Borrie, M., Guzman, D., Kertesz, A., Loy-English, I., Williams, J., Nichols, T., (2009) Anatomically-distinct genetic associations of APOE ε4 allele load with regional cortical atrophy in Alzheimer’s disease. Neuroimage, 44, 724–728

[37] Coon, K. D., Myers, A. J., Craig, D. W., Webster, J. A., Pearson, J. V., Lince, D. H., Zismann, V. L., Beach, T. G., Leung, D., Bryden, L., (2007) A high-density whole-genome association study reveals that APOE is the major susceptibility gene for sporadic late-onset Alzheimer’s disease. J Clin Psychiatry, 68, 613–618

[38] Farrer, L. A., Cupples, L. A., Haines, J. L., Hyman, B., Kukull, W. A., Mayeux, R., Myers, R. H., Pericak-Vance, M. A., Risch, N. and van Duijn, C. M., and the APOE and Alzheimer Disease Meta Analysis Consortium. (1997) Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. JAMA, 278, 1349–1356

[39] Ho, A. J., Stein, J. L., Hua, X., Lee, S., Hibar, D. P., Leow, A. D., Dinov, I. D., Toga, A. W., Saykin, A. J., Shen, L., , and the Alzheimer’s Disease Neuroimaging Initiative. (2010) A commonly carried allele of the obesity-related FTO gene is associated with reduced brain volume in the healthy elderly. Proc. Natl. Acad. Sci. U.S.A., 107, 8404–8409

[40] Frayling, T. M., Timpson, N. J., Weedon, M. N., Zeggini, E., Freathy, R. M., Lindgren, C. M., Perry, J. R., Elliott, K. S., Lango, H., Rayner, N. W., (2007) A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science, 316, 889–894

[41] Braskie, M. N., Jahanshad, N., Stein, J. L., Barysheva, M., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Wright, M. J., Ringman, J. M., Toga, A. W., (2011) Common Alzheimer’s disease risk variant within the CLU gene affects white matter microstructure in young adults. J. Neurosci., 31, 6764–6770

[42] Westlye, E. T., Lundervold, A., Rootwelt, H., Lundervold, A. J. and Westlye, L. T. (2011) Increased hippocampal default mode synchronization during rest in middle-aged and elderly APOE ε4 carriers: relationships with memory performance. J. Neurosci., 31, 7775–7783

[43] Filippini, N., MacIntosh, B. J., Hough, M. G., Goodwin, G. M., Frisoni, G. B., Smith, S. M., Matthews, P. M., Beckmann, C. F. and Mackay, C. E. (2009) Distinct patterns of brain activity in young carriers of the APOE-ε4 allele. Proc. Natl. Acad. Sci. U.S.A., 106, 7209–7214

[44] Trachtenberg, A. J., Filippini, N., Ebmeier, K. P., Smith, S. M., Karpe, F. and Mackay, C. E. (2012) The effects of APOE on the functional architecture of the resting brain. Neuroimage, 59, 565–572

[45] Chiang, M. C., Barysheva, M., Toga, A. W., Medland, S. E., Hansell, N. K., James, M. R., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Wright, M. J., (2011) BDNF gene effects on brain circuitry replicated in 455 twins. Neuroimage, 55, 448–454

[46] Thomason, M. E., Dougherty, R. F., Colich, N. L., Perry, L. M., Rykhlevskaia, E. I., Louro, H. M., Hallmayer, J. F., Waugh, C. E., Bammer, R., Glover, G. H., (2010) COMT genotype affects prefrontal white matter pathways in children and adolescents. Neuroimage, 53, 926–934

[47] Liu, B., Song, M., Li, J., Liu, Y., Li, K., Yu, C. and Jiang, T. (2010) Prefrontal-related functional connectivities within the default network are modulated by COMT val158met in healthy young adults. J. Neurosci., 30, 64–69

[48] Scott-Van Zeeland, A. A., Abrahams, B. S., Alvarez-Retuerto, A. I., Sonnenblick, L. I., Rudie, J. D., Ghahremani, D., Mumford, J. A., Poldrack, R. A., Dapretto, M., Geschwind, D. H., (2010) Altered functional connectivity in frontal lobe circuits is associated with variation in the autism risk gene CNTNAP2. Sci. Transl. Med., 2, 56ra80

[49] Rudie, J. D., Hernandez, L. M., Brown, J. A., Beck-Pancer, D., Colich, N. L., Gorrindo, P., Thompson, P. M., Geschwind, D. H., Bookheimer, S. Y., Levitt, P., (2012) Autism-associated promoter variant in MET impacts functional and structural brain networks. Neuron, 75, 904–915

[50] Braskie, M. N., Jahanshad, N., Toga, A. W., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Wright, M. J. and Thompson, P. M. (2012) How a common variant in the growth factor receptor gene, NTRK1, affects white matter. Bioarchitecture, 2, 181–184

[51] Esslinger, C., Kirsch, P., Haddad, L., Mier, D., Sauer, C., Erk, S., Schnell, K., Arnold, C., Witt, S. H., Rietschel, M., (2011) Cognitive state and connectivity effects of the genome-wide significant psychosis variant in ZNF804A. Neuroimage, 54, 2514–2523

[52] Johnson, A. D. and O’Donnell, C. J. (2009) An open access database of genome-wide association results. BMC Med. Genet., 10, 6

[53] Visscher, P. M., Brown, M. A., McCarthy, M. I. and Yang, J. (2012) Five years of GWAS discovery. Am. J. Hum. Genet., 90, 7–24

[54] Potkin, S. G., Turner, J. A., Fallon, J. A., Lakatos, A., Keator, D. B., Guffanti, G. and Macciardi, F. (2009) Gene discovery through imaging genetics: identification of two novel genes associated with schizophrenia. Mol. Psychiatry, 14, 416–428

[55] Stein, J. L., Hibar, D. P., Madsen, S. K., Khamis, M., McMahon, K. L., de Zubicaray, G. I., Hansell, N. K., Montgomery, G. W., Martin, N. G., Wright, M. J., , and the Alzheimer’s Disease Neuroimaging Initiative Investigators. (2011) Discovery and replication of dopamine-related gene effects on caudate volume in young and elderly populations (N=1198) using genome-wide search. Mol. Psychiatry, 16, 927–937, 881

[56] Potkin, S. G., Guffanti, G., Lakatos, A., Turner, J. A., Kruggel, F., Fallon, J. H., Saykin, A. J., Orro, A., Lupoli, S., Salvi, E., , and the Alzheimer’s Disease Neuroimaging Initiative. (2009) Hippocampal atrophy as a quantitative trait in a genome-wide association study identifying novel susceptibility genes for Alzheimer’s disease. PLoS ONE, 4, e6501

[57] Stein, J. L., Hua, X., Morra, J. H., Lee, S., Hibar, D. P., Ho, A. J., Leow, A. D., Toga, A. W., Sul, J. H., Kang, H. M., , and the Alzheimer’s Disease Neuroimaging Initiative. (2010) Genome-wide analysis reveals novel genes influencing temporal lobe structure with relevance to neurodegeneration in Alzheimer’s disease. Neuroimage, 51, 542–554

[58] Chiang, M. C., Barysheva, M., McMahon, K. L., de Zubicaray, G. I., Johnson, K., Montgomery, G. W., Martin, N. G., Toga, A. W., Wright, M. J., Shapshak, P., (2012) Gene network effects on brain microstructure and intellectual performance identified in 472 twins. J. Neurosci., 32, 8732–8745

[59] Bralten, J., Arias-Vásquez, A., Makkinje, R., Veltman, J. A., Brunner, H. G., Fernández, G., Rijpkema, M. and Franke, B. (2011) Association of the Alzheimer’s gene SORL1 with hippocampal volume in young, healthy adults. Am J Psychiatry, 168, 1083–1089

[60] Shen, L., Kim, S., Risacher, S. L., Nho, K., Swaminathan, S., West, J. D., Foroud, T., Pankratz, N., Moore, J. H., Sloan, C. D., , and the Alzheimer’s Disease Neuroimaging Initiative. (2010) Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort. Neuroimage, 53, 1051–1063

[61] Jahanshad, N., Rajagopalan, P., Hua, X., Hibar, D. P., Nir, T. M., Toga, A. W., Jack, C. R. Jr, Saykin, A. J., Green, R. C., Weiner, M. W., , and the Alzheimer’s Disease Neuroimaging Initiative. (2013) Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity. Proc. Natl. Acad. Sci. U.S.A., 110, 4768–4773

[62] Stein, J. L., Hua, X., Lee, S., Ho, A. J., Leow, A. D., Toga, A. W., Saykin, A. J., Shen, L., Foroud, T., Pankratz, N., , and the Alzheimer’s Disease Neuroimaging Initiative. (2010) Voxelwise genome-wide association study (vGWAS). Neuroimage, 53, 1160–1174

[63] Leow, A., Huang, S. C., Geng, A., Becker, J. and Davis, S., et al. (2005) Inverse consistent mapping in 3D deformable image registration: its construction and statistical properties. In: Information Processing in Medical Imaging, pps, 493–503. Berlin: Springer

[64] Hibar, D.P., Kohannim, O., Stein, J.L., Chiang, M. C. and Thompson. P.M. (2011) Multilocus genetic analysis of brain images. Frontiers in Genetics, 2, 73

[65] Wu, M. C., Kraft, P., Epstein, M. P., Taylor, D. M., Chanock, S. J., Hunter, D. J. and Lin, X. (2010) Powerful SNP-set analysis for case-control genome-wide association studies. Am. J. Hum. Genet., 86, 929–942

[66] Hoh, J., Wille, A. and Ott, J. (2001) Trimming, weighting, and grouping SNPs in human case-control association studies. Genome Res., 11, 2115–2119

[67] Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I., Daly, M. J., (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet., 81, 559–575

[68] Morgenthaler, S. and Thilly, W.G. (2007) A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST). Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis, 615, 28–56

[69] Madsen, B. E. and Browning, S. R. (2009) A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet., 5, e1000384

[70] Morris, A. P. and Zeggini, E. (2010) An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet. Epidemiol., 34, 188–193

[71] Li, B. and Leal, S. M. (2008) Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet., 83, 311–321

[72] Hoerl, R. W. (1985) Ridge analysis 25 years later. Am. Stat., 39, 186–192.

[73] Kohannim, O., Hibar, D. P., Stein, J. L., Jahanshad, N., Jack, C. R., Weiner, M. W., Toga, A. W. and Thompson, P. M. (2011) Boosting power to detect genetic associations in imaging using multi-locus, genome-wide scans and ridge regression. In Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium, IEEE, 1855–1859

[74] Tibshirani, R. (1996) Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Series B Stat. Methodol., 58, 267–288.

[75] Kohannim, O., Hibar, D.P., Stein, J.L., Jahanshad, N., Hua, X., (2012) Discovery and replication of gene influences on brain structure using LASSO regression. Frontiers in Neuroscience, 6, 115

[76] Zou, H. and Hastie, T. (2005) Regularization and variable selection via the elastic net. J. R. Stat. Soc. Series B Stat. Methodol., 67, 301–320.

[77] Kohannim, O., Hibar, D. P., Jahanshad, N., Stein, J. L., Hua, X., Toga, A. W., Jack, C. R., Weinen, M. W. and Thompson, P. M. (2012) Predicting temporal lobe volume on MRI from genotypes using l¹-l² regularized regression. In Biomedical Imaging: From Nano to Macro, 2012 IEEE International Symposium, IEEE, 1160–1163.

[78] Hibar, D. P., Stein, J. L., Kohannim, O., Jahanshad, N., Saykin, A. J., Shen, L., Kim, S., Pankratz, N., Foroud, T., Huentelman, M. J., , and the Alzheimer’s Disease Neuroimaging Initiative. (2011) Voxelwise gene-wide association study (vGeneWAS): multivariate gene-based association testing in 731 elderly subjects. Neuroimage, 56, 1875–1891

[79] Ge, T., Feng, J., Hibar, D. P., Thompson, P. M. and Nichols, T. E. (2012) Increasing power for voxel-wise genome-wide association studies: the random field theory, least square kernel machines and fast permutation procedures. Neuroimage, 63, 858–873

[80] Liu, D., Lin, X. and Ghosh, D. (2007) Semiparametric regression of multidimensional genetic pathway data: least-squares kernel machines and linear mixed models. Biometrics, 63, 1079–1088

[81] Wu, M. C., Lee, S., Cai, T., Li, Y., Boehnke, M. and Lin, X. (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet., 89, 82–93

[82] Kwee, L. C., Liu, D., Lin, X., Ghosh, D. and Epstein, M. P. (2008) A powerful and flexible multilocus association test for quantitative traits. Am. J. Hum. Genet., 82, 386–397

[83] Lin, X. (1997) Variance component testing in generalised linear models with random effects. Biometrika, 84, 309–326.

[84] Hotelling, H. (1936) Relations between two sets of variates. Biometrika, 28, 321–377

[85] Wold, S., Martens, H. and Wold, H. (1983) The multivariate calibration problem in chemistry solved by the PLS method. Matrix Pencils, 973, 286–293

[86] Le Floch, E., Guillemot, V., Frouin, V., Pinel, P., Lalanne, C., Trinchera, L., Tenenhaus, A., Moreno, A., Zilbovicius, M., Bourgeron, T., (2012) Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares. Neuroimage, 63, 11–24

[87] Chi, E. C., Allen, G. I., Zhou, H., Kohannim, O., Lange, K., (2013) Imaging genetics via sparse canonical correlation analysis. In: Biomedical Imaging: From Nano to Macro, 2013 IEEE International Symposium, IEEE, 740–743

[88] Liu, J., Pearlson, G., Windemuth, A., Ruano, G., Perrone-Bizzozero, N. I. and Calhoun, V. (2009) Combining fMRI and SNP data to investigate connections between brain function and genetics using parallel ICA. Hum Brain Mapp, 30, 241–255

[89] Wang, H., Nie, F., Huang, H., Kim, S., Nho, K., Risacher, S. L., Saykin, A. J. and Shen, L., and the Alzheimer’s Disease Neuroimaging Initiative. (2012) Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort. Bioinformatics, 28, 229–237

[90] Wang, H., Nie, F., Huang, H., Risacher, S. L., Saykin, A. J. and Shen, L., and the Alzheimer’s Disease Neuroimaging Initiative. (2012) Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning. Bioinformatics, 28, i127–i136

[91] Vounou, M., Nichols, T. E. and Montana, G., and the Alzheimer’s Disease Neuroimaging Initiative. (2010) Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach. Neuroimage, 53, 1147–1159

[92] Vounou, M., Janousova, E., Wolz, R., Stein, J. L., Thompson, P. M., Rueckert, D. and Montana, G., and the Alzheimer’s Disease Neuroimaging Initiative. (2012) Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer’s disease. Neuroimage, 60, 700–716

[93] Silver, M., Janousova, E., Hua, X., Thompson, P. M. and Montana, G., and the Alzheimer’s Disease Neuroimaging Initiative. (2012) Identification of gene pathways implicated in Alzheimer’s disease using longitudinal imaging phenotypes with sparse regression. Neuroimage, 63, 1681–1694

[94] Stingo, F. C., Guindani, M., Vannucci, M. and Calhoun, V. D. (2013) An integrative Bayesian modeling approach to imaging genetics. J. Am. Stat. Assoc., 108, 876–891

[95] Batmanghelich, N. K., Dalca, A. V., Sabuncu, M. R. and Golland, P. (2013) Joint modeling of imaging and genetics. In Information Processing in Medical Imaging, Berlin: Springer, pps 766–777

[96] Guyon, I., Weston, J., Barnhill, S. and Vapnik, V. (2002) Gene selection for cancer classification using support vector machines. Mach. Learn., 46, 389–422

[97] Zhang, H. H. and Lv, J. (2008) Discussion of “Sure Independence Screening for Ultra-High Dimensional Feature Space”. J. R. Stat. Soc. Series B Stat. Methodol., 70, 849–911

[98] Fan, J. and Song, R. (2010) Sure independence screening in generalized linear models with NP-dimensionality. Ann. Stat., 38, 3567–3604.

[99] He, Q. and Lin, D. Y. (2011) A variable selection method for genome-wide association studies. Bioinformatics, 27, 1–8

[100] Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol., 57, 289–300.

[101] Genovese, C. R., Lazar, N. A. and Nichols, T. (2002) Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage, 15, 870–878

[102] Holmes, A. P., Blair, R. C., Watson, J. D. and Ford, I. (1996) Nonparametric analysis of statistic images from functional mapping experiments. J. Cereb. Blood Flow Metab., 16, 7–22

[103] Nichols, T. and Hayasaka, S. (2003) Controlling the familywise error rate in functional neuroimaging: a comparative review. Stat Methods Med Res, 12, 419–446

[104] Knijnenburg, T. A., Wessels, L. F. A., Reinders, M. J. T. and Shmulevich, I. (2009) Fewer permutations, more accurate P-values. Bioinformatics, 25, i161–i168

[105] Friston, K. J., Holmes, A., Poline, J. B., Price, C. J. and Frith, C. D. (1996) Detecting activations in PET and fMRI: levels of inference and power. Neuroimage, 4, 223–235

[106] Worsley, K. J., Marrett, S., Neelin, P., Vandal, A. C., Friston, K. J. and Evans, A. C. (1996) A unified statistical approach for determining significant signals in images of cerebral activation. Hum Brain Mapp, 4, 58–73

[107] Cao, J. (1999) The size of the connected components of excursion sets of χ², T and F fields. Adv. Appl. Probab., 31, 579–595.

[108] Smith, S. M. and Nichols, T. E. (2009) Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage, 44, 83–98

[109] Zhang, H., Nichols, T. E. and Johnson, T. D. (2009) Cluster mass inference via random field theory. Neuroimage, 44, 51–61

[110] Chumbley, J., Worsley, K., Flandin, G. and Friston, K. (2010) Topological FDR for neuroimaging. Neuroimage, 49, 3057–3064

[111] Zhang, C., Fan, J. and Yu, T. (2011) Multiple testing via FDR_Lfor large scale imaging data. Ann Stat, 39, 613–642

[112] Gao, X., Starmer, J. and Martin, E. R. (2008) A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet. Epidemiol., 32, 361–369

[113] Gao, X., Becker, L. C., Becker, D. M., Starmer, J. D. and Province, M. A. (2010) Avoiding the high Bonferroni penalty in genome-wide association studies. Genet. Epidemiol., 34, 100–105

[114] Fan, J., Han, X. and Gu, W. (2012) Estimating false discovery proportion under arbitrary covariance dependence. J. Am. Stat. Assoc., 107, 1019–1035.

[115] Efron, B. (2013) Large-scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. Cambridge: Cambridge University Press

[116] Eklund, A., Andersson, M.and Knutsson. H. (2011) Fast random permutation tests enable objective evaluation of methods for single-subject fMRI analysis. International Journal of Biomedical Imaging. http://dx.doi.org/10.1155/2011/627947

[117] Nichols, T. E. (2012) Multiple testing corrections, nonparametric methods, and random field theory. Neuroimage, 62, 811–815

[118] Ioannidis, J. P. A. (2005) Why most published research findings are false. PLoS Med., 2, e124

[119] Sullivan, P. F. (2010) The psychiatric GWAS consortium: big science comes to psychiatry. Neuron, 68, 182–186

[120] Schumann, G., Loth, E., Banaschewski, T., Barbot, A., Barker, G., Büchel, C., Conrod, P. J., Dalley, J. W., Flor, H., Gallinat, J., , and the IMAGEN consortium. (2010) The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol. Psychiatry, 15, 1128–1139

[121] Stein, J. L., Medland, S. E., Vasquez, A. A., Hibar, D. P., Senstad, R. E., Winkler, A. M., Toro, R., Appel, K., Bartecek, R., Bergmann, Ø., , and the Alzheimer’s Disease Neuroimaging Initiative, and the EPIGEN Consortium, and the IMAGEN Consortium, and the Saguenay Youth Study Group, and the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium, and the Enhancing Neuro Imaging Genetics through Meta-Analysis Consortium. (2012) Identification of common variants associated with human hippocampal and intracranial volumes. Nat. Genet., 44, 552–561

[122] Jahanshad, N., Kochunov, P. V., Sprooten, E., Mandl, R. C., Nichols, T. E., Almasy, L., Blangero, J., Brouwer, R. M., Curran, J. E., de Zubicaray, G. I., (2013) Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: a pilot project of the ENIGMA-DTI working group. Neuroimage, 81, 455–469

[123] Novak, N. M., Stein, J. L., Medland, S. E., Hibar, D. P., Thompson, P. M. and Toga, A. W. (2012) EnigmaVis: online interactive visualization of genome-wide association studies of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. Twin Res. Hum. Genet., 15, 414–418

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