Computational protocol: The protocadherin 17 gene affects cognition, personality, amygdala structure and function, synapse development and risk of major mood disorders

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Protocol publication

[…] Subcortical brain regions form circuits with cortical areas to learning, memory and motivation, and altered circuits can lead to abnormal behavior and disease. To investigate how common genetic variants affect the structure of these brain regions, ENIGMA2 consortium conducted GWASs on the volumes of several subcortical regions derived from magnetic resonance images (MRI).We focused on two phenotypes (amygdala volume and hippocampal volume) closely relevant to risk of mood disorders, and obtained the statistical results from the ENIGMA2 GWAS discovery sample. In short, the discovery sample includes 13 171 European subjects, the subcortical brain measures (amygdala and hippocampus) were delineated in the brain using well-validated, freely available brain segmentation software packages: FIRST, part of the FMRIB Software Library (FSL), or FreeSurfer. The standardized protocols for image analysis and quality assurance are openly available online ( For each SNP, the additive dosage value was regressed against the trait of interest separately using a multiple linear regression framework controlling for age, age, sex, four MDS components, ICV and diagnosis (when applicable). For studies with data collected from several centers or scanners, dummy-coded covariates were also included in the model. Detailed information on the samples, imaging procedures and genotyping methods can be found in the original GWAS. [...] fMRI images were processed using Statistical Parametric Mapping (SPM8, The procedures followed our previously published studies with the same task., In brief, the preprocessing included realignment, slice timing correction, normalization to the Montreal Neurological Institute (MNI) space with voxel size 3 × 3 × 3 mm3, and spatial smoothing with a 9 mm full-width at half-maximum (FWHM) Gaussian kernel. The preprocessed images were then analyzed at two levels. At the first level, images for each individual were analyzed using general linear models (GLM), where the boxcar vectors for task conditions (convolved with the standard SPM hemodynamic response function) were included as regressors of interest and the six head motion parameters from the realignment step were included as regressors of no interest. The data were high-pass filtered (cutoff, 128 s) and individual maps for the ‘face-matching>shape-matching’ contrast were computed. The contrast images were then used for a second-level random effects analysis. To test for genetic association, these contrast images were analyzed using the multiple regression model including the three allelic groups (labelled as 0,1,2) as variable of interest and age, sex and scanner site as the nuisance covariates. Significance was measured at P<0.05 family-wise error corrected across an a priori defined anatomical mask of the bilateral amygdala from the Automated Anatomical Labeling atlas. To probe more precisely which subregion the peak voxel was located, we further extracted three amygdala subdivisions (superficial, latero-basal and centro-medial complex) from the Anatomy toolbox, and corrected the peak voxel across the three subregional masks. The corrected P-values for each of the masks were reported. [...] We downloaded raw RNA-sequencing reads from the SMRI data set ( in the FASTQ file format. The RNA-seq data were from frontal cortex (15 BPD, 15 MDD and 15 healthy controls) generated by SMRI neuropathology collection. Reads after adaptors and low quality filtering using btrim64 were aligned to human reference genome (hg38, through Tophat2 v2.0.14 () with mismatches, gap length as well as edit distance all no more than 3 bases. Cufflinks v2.2.1 () was then applied to call new transcripts and quantify both the new and old ones with default parameters. For replicate samples, accepted hits bam files from Tophat2 alignment were merged by Samtools v0.1.18 () and the merged files were utilized for the following Cufflinks quantification. Only reads uniquely mapped to genes were used to calculate the gene expression level. To quantify mRNA expression, FPKM (Fragments per Kilobase per Million mapped reads) was calculated to measure gene-level expression according to the formula: FPKM=R × 103/L × 106/N; where F is the number of fragments mapping to the gene annotation, L is the length of the gene structure in nucleotides, and N is the total number of sequence reads mapped to the genome of chromosome.Statistical analyses of mRNA expression associated with diagnosis were conducted in R 3.0.1 using linear regression, covaring for RNA integrity number, sex, age, race, duration of illness, brain pH, post-mortem interval, suicide status and batch number in each sample. All reported two-sided P-value s were calculated from t statistics computed from the log fold change and its standard error from each multiple regression model, and therefore represent covariate-adjusted P-values. […]

Pipeline specifications

Software tools FSL, FreeSurfer, SPM, AAL
Applications Magnetic resonance imaging, Functional magnetic resonance imaging
Organisms Homo sapiens