Kohannim et al. Discovering genes that affect brain images however, ﬁts into a ﬁltering rather than predictive framework and similar data-adaptive ﬁltering followed by F -tests in the same dataset has been done in previous work (Chen et al., 2011; Hibar et al., 2011a). This approach is potentially unfair, as ﬁtting in LASSO is followed by another ﬁtting with multiple linear regres- sion (F -tests) in the same dataset, whereas ﬁtting is only performed once in a univariate scheme. As GWAS are sensitive to sample size, a nested cross-validation scheme, though more robust, would most likely yield no signiﬁcant results. We observed this power limita- tion, as we attempted nested approaches with varying numbers of folds, and were unable to obtain boosted gene-based associations. This may change in the future, as larger datasets become more widely available. Our use of a replication cohort, however, does add credibility to the top result, as the same set of SNPs selected in the discovery sample show signiﬁcant, spatial effects on brain scans from a completely independent (non-overlapping) group of subjects scanned with a different scanner on a different continent. Another limitation of our approach is that we focus on genes, but exclude promoter and intergenic SNPs. This has the drawback of missing potentially important regulatory elements in the genome. Our work has several possible future directions, biologically and methodologically. Further investigation is needed to clar- ify the roles of the genes we identiﬁed. We did create voxelwise maps for the top genes, but one could also use a more com- putationally demanding imaging GWAS approach by re-running the gene-centric, LASSO at each voxel in the brain (Stein et al., 2010b; Vounou et al., 2010; Hibar et al., 2011a), instead of run- ning it on summary measures derived from the images. Sparse coding, used here to reduce the dimensionality of the genomic data, could also be used to zero in on the most promising voxels in the images, leading to a set of phenotypes in the images that show greatest association. Vounou et al. (2010, 2012), in partic- ular, have proposed a general “reduced rank” method that distils a set of genes and brain measures from regions of interest into a more manageable set for assessing associations. Other approaches for dimension reduction, within both the image and the genome, involve variants of independent components analysis (Liu et al., 2009). In a recent advance, Chiang et al. (2011b) proposed to use genetic correlations to identify pairs of voxels in an image with common genetic determination, rather than simply phenotypic correlation. This could be more promising in principle than using phenotypic covariance, as it seems voxel sets are inﬂuenced by common (partially overlapping) sets of genes. By clustering these voxels into regions of interest, Chiang et al. (2011b) were able to boost power to detect genome-wide associations in a large DTI study. Clearly, the promise of multivariate methods for imaging genomics is high. Several variants of linear regression, penalized regression, and machine learning are now being adapted to handle images, with the main goal of boosting power and reducing the very large samples typically considered necessary for replicable ﬁndings in genetics (The ENIGMA Consortium, 2011). In addition to GWAS, an alternative more hypothesis-driven approach is to use candidate gene studies to study the inﬂu- ence of genetic variants on brain structure. These have recently been successful in implicating genes as associated with brain white matter integrity measures derived from diffusion tensor imaging (e.g., CLU, Braskie et al., 2011; BDNF, Chiang et al., 2011a; HFE, Jahanshad et al., 2012). Furthermore, it will be interesting to study interactions between genes discovered through GWAS by consid- ering the overall pathways or regulatory networks in which they act (Inkster et al., 2010; Potkin et al., 2010). Another type of genetic information, not considered here, is rare variants on the genome (Schork et al., 2009), or copy number variants, which may also be relevant in the determination of brain structure. Ongoing imaging studies are beginning to include proteomic and gene expression data, as well. Such studies may begin to integrate genetic infor- mation from different sources to probe the mechanisms of brain pathology and identify means to intervene and resist it. ACKNOWLEDGMENTS Data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, Glax- oSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pﬁzer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non- proﬁt partners the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern Cal- ifornia Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Califor- nia Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation. We also thank the many contributors to ADNI-1 genotyping sample curation at NCRAD (Kelley Faber), performing BeadChip assays at TGen and initial quality control of genotypes (David Craig, April Allen, Jill Gerber, and Jason Corneveaux), and bioinfor- matics problem solving (Indiana U: Kwangsik Nho; Pﬁzer: Bryan DeChairo). The neuroimaging and genetic analysis of the twin dataset was supported by grant number RO1 HD050735 from the National Institute of Child Health and Human Development, USA, and Project Grant 496682 from the National Health and Medical Research Council, Australia. The collection of IQ data and zygosity typing was supported by the Australian Research Council (A7960034, A79906588, A79801419, and DP0212016). Greig I. de Zubicaray is supported by an Australian Research Council Future Fellowship (FT0991634). Additional support for algorithm devel- opment was provided by the NIA, NIBIB, and the National Center for Research Resources (AG016570, EB01651, and RR019771 to Paul M. Thompson). We are also grateful to the twins for their willingness to participate in our studies, and research nurses, Marlene Grace and Ann Eldridge, Queensland Institute of Med- ical Research, for twin recruitment. Derrek P. Hibar is partially supported by NSF GRFP grant DGE-0707424. Omid Kohannim is supported by NIH F30 AG041681 and the UCLA Graduate Division. www.frontiersin.org August 2012 | Volume 6 | Article 115 | 9