Kohannim et al. Discovering genes that affect brain images administered to provide a global measure of mental status (Cock- rell and Folstein, 1988). The Clinical Dementia Rating (CDR) was used to assess dementia severity (Morris, 1993). Healthy volunteer status was determined if a subject had MMSE scores between 24 and 30 (inclusive), a CDR of 0, and was non-depressed, non-mild cognitive impairment (MCI), and non-demented. MCI diagno- sis was determined if a subject had MMSE scores between 24 and 30 (inclusive), a memory complaint, objective memory loss measured by education adjusted scores on the Wechsler Mem- ory Scale Logical Memory II, a CDR of 0.5, absence of signiﬁcant levels of impairment in other cognitive domains, essentially pre- served activities of daily living, and an absence of dementia. AD was diagnosed based on the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable AD (McKhann et al., 1984), MMSE scores between 20 and 26 (inclusive), and CDR of 0.5 or 1.0. Deﬁnitive autopsy-based diagnosis of AD was not possible. Our ADNI dataset consisted of 729 subjects (mean age: 75.5 6.8 SD years; 173 AD, 358 MCI and 198 cognitively healthy controls) with available neuroimaging, genome-wide genetic data, and other relevant covariates (age, sex, and population structure parameters derivable from the GWAS). As our replication sam- ple, we analyzed the Brisbane healthy young adult dataset, which consists of neuroimaging and genome-wide genetic data from 564 young adult healthy twins (a mixture of monozygotic and dizy- gotic) and siblings of European descent (mean age: 23.8 2.2 SD years; Wright and Martin, 2004). None of the subjects had a his- tory of signiﬁcant head injury, neurological or psychiatric illness, substance abuse or dependence, or had a ﬁrst-degree relative with a psychiatric disorder. All subjects were screened, using a detailed neurocognitive evaluation (de Zubicaray et al., 2008) to exclude cases of pathology known to affect brain structure. Handedness was assessed based on 12 items from Annett’s Handedness Ques- tionnaire (Annett, 1970). This sample presents some challenges for replication, due to the wide age difference (of roughly 50 years) between it and the ADNI sample. As such we were aware that genes with age-dependent effects might be found in one cohort that might not replicate in the other, for biological reasons rather than limitations in power and sample sizes. NEUROIMAGING Structural brain MRI scans were acquired for ADNI subjects using 1.5 T MRI scanners. A subset of the ADNI subjects was also scanned at 3 T, but their scans were not used here to avoid any confounding effects of ﬁeld strength. A sagittal 3D MP- RAGE sequence was used, that had been optimized for con- sistency across sites (Jack et al., 2008; TR/TE D 2,400/1,000 ms; ﬂip angle D 8˚; FOV D 24 cm; ﬁnal reconstructed voxel resolu- tion D 0.9375 0.9375 1.2 mm3). All scans were then linearly registered to the stereotaxic space deﬁned by the International Consortium for Brain Mapping (ICBM-53; Mazziotta et al., 2001) using a 9-parameter transformation (three translations, three rota- tions, and three scales; Collins et al., 1994). Three-dimensional maps of regional brain volumes, computed relative to an average brain template, were generated with a method known as tensor- based morphometry (TBM), a well-established method for map- ping volumetric differences in the brain (Hua et al., 2008a,b), using www.frontiersin.org a minimal deformation template (MDT) from the healthy elderly group as reference. Temporal lobe volumes were obtained by inte- grating the Jacobian determinants of the deformation transform over the region deﬁned as the temporal lobe on the mean anatom- ical template. Average temporal lobe volumes (average of lobar volumes in the left and right hemisphere) were considered as phe- notypes for genome-wide association, and 3D maps were retained for use in post hoc analyses. The same temporal lobe volume phenotype was used in a prior study by Stein et al. (2010a). In the Brisbane young adult cohort, all subjects were imaged on one scanner with structural whole-brain MRI at 4 T (Bruker Med- spec). T 1-weighted images were acquired with an inversion recov- ery rapid gradient echo sequence [TI/TR/TE D 700/1,500/3.35 ms; ﬂip angle D 8°; slice thickness D 0.9 mm, with a 2563 acquisition matrix]. All images were corrected for intensity non-uniformity (Sled et al., 1998) within an automatically delineated mask of the brain (Smith, 2002). Images were spatially normalized to the ICBM-152 template (Mazziotta et al., 2001) using a 9-parameter (global) transformation that rotated and scaled each image to min- imize a normalized mutual information cost function (Jenkinson et al., 2002). Images were then resampled in the space of the tem- plate using sinc interpolation to yield 1 mm3 isotropic voxels. In this way, each brain was globally matched in size and mutually aligned, but local differences in shape and size remained intact. Similarly to ADNI, TBM was used to create maps of volumet- ric differences for each subject, using a reference MDT specially constructed for this young adult cohort. GENOTYPING The ADNI genotyping procedures are thoroughly described in Saykin et al. (2010). Brieﬂy, genotypes were imputed, using Mach (version 1.0)15 as in Stein et al. (2010b). Imputation is com- monly used in genetics to infer or impute unmeasured genotypes, based on available genotypes and known patterns of correlation among markers. We used imputation in ADNI to estimate hap- lotype phasing and remove missing genotype calls, yielding a set of SNPs without missing data. We used the Plink software16 to extract SNPs that had minor allele frequencies greater than 0.1 (10%), and Hardy–Weinberg equilibrium p-values less than 5.7 10 7. Hardy–Weinberg equilibrium refers to the principle that frequencies of alleles, and their corresponding genotypes, are in the expected proportions, under certain assumptions about the population. We only considered alleles with a minor allele fre- quency > 0.1 due to the very large sample sizes needed to detect effects of less common variants (Flint et al., 2010). As in Stein et al. (2010b), we also considered only unrelated Caucasian subjects identiﬁed by self-report and veriﬁed by multidimensional scaling (MDS) analysis (Stein et al., 2010a). This was done to decrease the effects of population stratiﬁcation, although we additionally included MDS parameters as covariates in this study. As detailed in Hibar et al. (2011a), we extracted and grouped all intragenic SNPs, excluding those not located in any genes. Only SNPs within tran- scripts (i.e., introns and exons, including untranslated regions) were included; SNPs upstream or downstream from genes were not included to avoid arbitrary window sizes. Similarly, imputation of 15http://www.sph.umich.edu/csg/abecasis/MaCH/index.html 16http://pngu.mgh.harvard.edu/spurcell/plink/ August 2012 | Volume 6 | Article 115 | 3