Kohannim et al. Discovering genes that affect brain images FIGURE 3 | MACROD2’s effects in ADNI temporal lobes and partial replication in a younger cohort. The SNPs selected from MACROD2 in ADNI are strongly associated with mean temporal lobe volumes (p D 7.94 10 12), and reveal extensive and signiﬁcant effects on TBM maps of temporal lobes (A). We studied the joint effect of the same group of SNPs in MACROD2 on temporal lobe maps of the Brisbane young adult dataset. The gene effect showed reproducibility, including effects at some speciﬁc locations overlapping with the ADNI ﬁndings, after correction for multiple comparisons across the temporal lobe voxels (B). A sagittal slice from the left hemisphere is shown; warmer colors represent more signiﬁcant associations. Both (A,B) are taken from the same slice (x D 53 mm in Montreal Neurological Institute coordinates). The slices do not appear identical due to the age range differences between the populations [e.g., atrophy in (A)], but almost all of the signiﬁcant voxels in the more central cluster in (B) overlap with the signiﬁcant voxels in (A), implying replication. regulate PLD2, a gene coding for a member of a protein family with signiﬁcant implications for ASD. MACROD2 has also been associ- ated with schizophrenia, as the gene corresponding to a rare, copy number variant in a linkage analysis (Xu et al., 2009). The same gene has also been associated with MRI-deﬁned brain infarcts, as the gene comprising the top SNP (p < 5 10 7) in a meta-analysis GWAS of >9,000 mostly white, European subjects from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium with an average age of 69.7 years (Debette et al., 2010). Interestingly, this study also found a suggestive association for a SNP in GALNTL4, another top gene in our list. We additionally found boosted associations for another gene, GRIN2B, coding for a subunit of an N -methyl-d-aspartate (NMDA)-type glutamate receptor, and NRXN3, coding for a neurexin, important for synaptic function, both of which were previously identiﬁed in the same dataset with standard GWAS (Stein et al., 2010a). We also found signiﬁcant associations for SORCS2 and MAGI2, which are in the AlzGene18 database of genes that show promising associations with the risk for developing AD based on the literature (Rogaeva et al., 2007; Potkin et al., 2009). Additionally, NPAS3 has been linked to schizophrenia and bipolar disorder (Pickard et al., 2009), CLSTN2 has been associated with memory performance (Papassotiropoulos et al., 2006) and with Alzheimer’s disease (Liu et al., 2007), and RBFOX1 (A2BP1) has been very recently dis- covered as a splicing regulator of neuronal excitation and calcium homeostasis in the brain (Gehman et al., 2011). RBFOX1 has been 18http://www.alzgene.org/ Frontiers in Neuroscience | Neurogenomics associated with autism, among other brain disorders (Martin et al., 2007), and an RBFOX1 variant has interestingly been detected in another sparse regression study in ADNI (Vounou et al., 2012). The discovery and replication populations in this study are quite different: the ADNI cohort consists of elderly subjects within the spectrum of Alzheimer’s disease and the Brisbane cohort con- sists of healthy young adults. Though the cohorts share a Caucasian background, which minimizes genetic heterogeneity, the age, and health differences between them implies that their brain structure may be inﬂuenced by genetic risks and mechanisms that partially overlap but also partially differ. By choosing cohorts that differ in age, we can ﬁnd polymorphisms that are of enduring relevance over the lifespan, but may be less able to conﬁrm gene effects that only matter in old age. Unfortunately, another elderly cohort with GWAS and MRI scans was not available to us at this time; we requested published GWAS data from other elderly cohorts whose MRI scans we have already analyzed, but our request was declined. However, as discussed in Stein et al. (2011) who discovered and replicated genetic variants on MRI-derived caudate volume in the same two cohorts, there may be genes with persistent effects over the human lifespan, so any such replication may be even stronger than one observed between more similar populations. In addition, though the genes we identiﬁed here may not all be AD genes, the effect of a well-known AD risk conferring polymorphism in the clusterin gene (CLU ), and that of GAB2, another AD gene, have been replicated as showing associations with brain structure in the same young adult cohort (Braskie et al., 2011; Hibar et al., 2012). In other words, we knew in advance that genes associated with brain structure in the elderly may also exert detectable effects in scans from younger people. This was also the case for some SNP effects that were reliably replicated by two very large GWAS consortia analyzing brain scans from cohorts across the lifespan (Stein et al., 2012) or in elderly cohorts (Bis et al., 2012). In this study, we were similarly interested to see if our top gene’s effects on temporal lobe structure would replicate in the young, adult cohort, suggesting a more lasting inﬂuence on brain structure across a person’s lifetime. In our study, SNPs were coded additively, i.e., using a value of 0, 1, or 2 for the number of minor alleles. This coding makes the assumption that all SNPs considered in the analysis exert their effects in an additive fashion, as opposed to alternative models such as recessive or dominant. This is certainly not the case for all SNPs, and the assumption likely affects the statistical power of our results, since greatest power is obtained when the true model of a causal allele is implemented (Lettre et al., 2007). This is a potential limitation of our study. We chose the additive model as previous genome-wide analyses of the same dataset relevant to our work implement the same allelic coding and were successful in ﬁnding genetic associations that were later replicated (Stein et al., 2010a; Hibar et al., 2011a). Furthermore, the additive model is the most commonly used association model. It is the model assumed in her- itability calculations and has been argued to be closest to actual risk models for complex traits, such as our quantitative imaging-based measure (Balding, 2006). A possible limitation of our results is that we do not imple- ment a nested cross-validation approach, in which SNPs selected from LASSO regression would have been included in F -tests in non-overlapping subjects. Our implementation of LASSO here, August 2012 | Volume 6 | Article 115 | 8