DESCRIPTION:
"Overlap between significant genes in MAGMA, S-PrediXcan, and COJO SNP analyses. We amassed a set of phenotype-specific GWAS summary statistics for different externalizing phenotypes, either by collecting existing results or by performing GWAS in UK Biobank (UKB) (Supplementary Information section 2). The multivariate method “genomic structural equation modelling” (Genomic SEM) was applied on a subset of the summary statistics (N = 53,293–1,251,809) deemed adequately heritable and statistically powered, in order to estimate a series of model specifications representing different genetic factor structures (Supplementary Information section 3). The best-fitting and most parsimonious solution (“the preferred model specification”) specified a single common genetic factor with seven indicator phenotypes (which we hereafter refer to as “the latent genetic externalizing factor”, or simply, “the externalizing factor”). The 7 phenotypes eventually used to estimate the latent genetic externalizing factor were (1) ADHD, (2) age at first sexual intercourse (FSEX), (3) problematic alcohol use (ALCP), (4) lifetime cannabis use (CANN), (5) lifetime smoking initiation (SMOK), (6) general risk tolerance (RISK), and (7) number of sexual partners (NSEX). The externalizing GWAS results were first clumped and then subjected to “conditional and joint multiple-SNP analysis” (GCTA-COJO) to identify a set of “579 jointly associated lead SNPs”, which we consider to be our main GWAS findings. The method FUMA (version 1.3.5e) was applied to explore the functional consequences of the 579 SNPs (Supplementary Table 9), which included ANNOVAR categories (that is, the functional consequence of SNPs on genes), combined annotation dependent depletion scores, RegulomeDB scores, expression quantitative trait loci and chromatin states. We used S-PrediXcan v0.6.222 to analyze gene expression levels in multiple brain tissues, and to test whether the gene expression correlated with the genetic liability of externalizing. We used pre-computed tissue weights from the Genotype-Tissue Expression (GTEx, v8) project database (https://www.gtexportal.org/) as the reference transcriptome dataset. As input data, we used the summary statistics for the externalizing GWAS, transcriptome tissue data, and covariance matrices of the SNPs within each gene model (based on HapMap SNP set; available to download at the PredictDB Data Repository, http://predictdb.org) from 13 brain tissues: anterior cingulate cortex, amygdala, caudate basal ganglia, cerebellar hemisphere, cerebellum, cortex, frontal cortex, hippocampus, hypothalamus, nucleus accumbens basal ganglia, putamen basal ganglia, spinal cord and substantia nigra. We used a transcriptome-wide significance threshold of P < 2.73×10–7, which is the Bonferroni-corrected threshold when adjusting for 13 tissues times 14,095 tested genes (183,235 gene-tissue pairs). We performed competitive gene-based association analyses using the genome-wide summary statistics from the externalizing GWAS by applying the method “multi-marker analysis of genomic annotation” (MAGMA v1.08). We evaluated Bonferroni-corrected significance, adjusted for testing 18,235 genes (one-sided P < 2.74×10–6). We used an extension of MAGMA v1.08, “Hi-C coupled MAGMA” or “H-MAGMA” (version June 14, 2019), to assign non-coding (intergenic and intronic) SNPs to cognate genes based on their chromatin interactions. Exonic and promoter SNPs were assigned to genes based on physical position. We used four Hi-C datasets derived from adult brain, fetal brain, and iPSC derived neurons and astrocytes. We evaluated Bonferroni corrected P-value thresholds, adjusted for multiple testing within each analysis (one-sided P < 9.84×10–7). From supplementary table 22."
LABEL:
Unique sig. genes H-MAGMA adult brain EXT GWAS
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Binary
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