Kohannim et al. Discovering genes that affect brain images GOLM1,6 TRPC4AP,7 LRAT,8 ATXN1,9 CD33,10 and FAM113B11 (Coon et al., 2007; Abraham et al., 2008; Bertram et al., 2008; Li et al., 2008; Beecham et al., 2009; Podulso et al., 2009). Most of these GWAS studies search for SNPs with alleles that are over-represented in very large samples of diseased versus matched control populations (often numbering tens of thousands of subjects). Diagnostic status, as deﬁned in these case-control studies, is often ascertained based on a battery of cognitive tests, which are relatively remote from the molecular mechanisms of gene action in the brain. As such, studies of genetic variants that are over-represented in certain diagnostic groups often require sam- ples of 30,000 subjects to ﬁnd and replicate associations (Frayling et al., 2007). Neuroimaging measures have been proposed as intermediate phenotypes (also known as endophenotypes) to overcome these limitations (Gottesman and Gould, 2003; Meyer-Lindenberg and Weinberger, 2006; Hall and Smoller, 2010). Several projects, such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI12) have also sought biomarkers that are most highly associated with dis- ease risk or with accelerated progression of the disease (Beckett et al., 2010). Imaging measures may offer several advantages for genetic analyses. As an entire 3D image of measurements is col- lected, rather than one single measure, the full arsenal of statistical methods developed for images – including voxel-based statistics and multivariate methods – may be used to identify spatial patterns of gene effects. Image-wide searches may also detect promising associations. Voxel-set selection may then be used to boost the power of replication attempts, by focusing on promising brain regions that may show larger effect sizes (Vounou et al., 2010, 2012). Such approaches boost statistical power by carrying only the most promising voxels into secondary analyses of independent datasets (Chen et al., 2010; Thompson et al., 2010). Several neuroimaging measures are associated with AD (Frisoni et al., 2010; Jack et al., 2011), and some of these have already been analyzed using GWAS. Genome-wide studies of the ADNI dataset have identiﬁed new candidate AD risk genes including TOMM4013 (Potkin et al., 2009) and GRIN2B14 (Stein et al., 2010a). Tradition- ally, GWAS studies consider each genotype’s effect independently; they generally ignore the statistical interdependence between vari- ants, such as their linkage disequilibrium (LD) structure, which makes certain variants more likely to be inherited together. More sophisticated GWAS approaches, using information on genes and even pathways, jointly consider groups of genetic variants that are correlated (Neale and Sham, 2004; Luo et al., 2010). Recently, neu- roimaging studies have also begun to use these multi-SNP methods (Inkster et al., 2010; Hibar et al., 2011a,b; Kohannim et al., 2011; Wang et al., 2012). Here, we implement a gene-centric approach based on the least absolute shrinkage and selection operator 6Golgi membrane protein 1 7Transient receptor potential cation channel, subfamily C, member 4 associated protein 8Lecithin retinol acyltransferase 9Spinocerebellar ataxia type 1 protein 10Cluster of differentiation 33 11Family with sequence similarity 113, member B 12http://adni.loni.ucla.edu 13Translocase of outer mitochondrial membrane 40 homolog 14Glutamate receptor, ionotropic, N -methyl D-aspartate 2B Frontiers in Neuroscience | Neurogenomics (LASSO), to detect and model genetic inﬂuences on neuroimaging measures. LASSO is a form of regularized regression (Tibshi- rani, 1996), that assesses the combined effect of many correlated variables through a sparsity (or efﬁciency)-driven L1 penalty. Here, we use the LASSO algorithm to select sparse groups of SNPs within genes. We then model the effects of the resulting SNPs jointly, to discover associations with neuroimaging measures in the ADNI dataset. Our aim was to detect more genes that inﬂuence brain structure with replication potential, as individual SNP vari- ants have individual effect sizes that are usually hard to detect in neuroimaging datasets of the size available today, unless very large meta-analyses are performed (The ENIGMA Consortium, 2011; Stein et al., 2012). LASSO and similar penalized regres- sion techniques have been successfully applied in the context of GWAS or candidate gene studies for selection of SNPs (Ayers and Cordell, 2010; Shi et al., 2011), detection of gene-gene interactions (D’Angelo et al., 2009; Li et al., 2011), and risk prediction from top GWAS hits (Kooperberg et al., 2010). We hypothesized that using this approach, we would reduce the number of variants of interest in a gene to sparse sets of SNPs and thereby identify genes reliably associated with temporal lobe volume. (The method would work with other brain measures too; temporal lobe volume is just an example of speciﬁc interest, due to its role as a biomarker of neurodegenerative disease.) We tested the reliability and reproducibility of our top genetic association ﬁndings in an independent non-overlapping young adult cohort. Our goal was to see if the results could be replicated in a cohort scanned on a different continent, with a different scanner ﬁeld strength, and with a roughly 50-year difference in mean age. Our motivation was to ﬁnd gene effects that might persist across the human lifespan. Clearly, such a second sample presents challenges for replication, and we selected it to demonstrate the robustness of the results. We admit that this very stringent approach is only likely to ﬁnd gene effects with an enduring inﬂuence throughout life, and would not serve to replicate biologically important effects present at only one part of the lifespan. MATERIALS AND METHODS SUBJECTS Neuroimaging and genetic data were obtained from 818 subjects as part of ADNI, a 5-year study launched in 2004 by the NIH, private pharmaceutical companies, and non-proﬁt organizations, as a public–private partnership. The goal of ADNI is to determine biological markers of Alzheimer’s disease through neuroimaging, genetics, neuropsychological tests, and other measures in order to develop and monitor new therapies, and reduce the time of clinical trials. Subjects were recruited from 58 sites across North America. The study was conducted according to the Good Clinical Prac- tice guidelines, the Declaration of Helsinki, and U.S. 21 CFR Part 50 – Protection of Human Subjects, and Part 56 – Institutional Review Boards. Written informed consent was obtained from all participants before protocol-speciﬁc procedures were performed. All data acquired as part of this study are publicly available (see text footnote 12). All ADNI subjects underwent thorough clinical and cognitive assessment at the time of scan acquisition to establish diagnosis. In this study, only the baseline 1.5-T scans were used (not the longitu- dinal follow-up data). The Mini-Mental State Exam (MMSE) was August 2012 | Volume 6 | Article 115 | 2