"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). 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). Displaying genes with P value less than 1E–5. From supplementary table 17."
Authors:
Richard Karlsson Linnér, Travis T Mallard, Peter B Barr, Sandra Sanchez-Roige, James W Madole, Morgan N Driver, Holly E Poore, Ronald de Vlaming, Andrew D Grotzinger, Jorim J Tielbeek, Emma C Johnson, Mengzhen Liu, Sara Brin Rosenthal, Trey Ideker, Hang Zhou, Rachel L Kember, Joëlle A Pasman, Karin J H Verweij, Dajiang J Liu, Scott Vrieze, , Henry R Kranzler, Joel Gelernter, Kathleen Mullan Harris, Elliot M Tucker-Drob, Irwin D Waldman, Abraham A Palmer, K Paige Harden, Philipp D Koellinger, Danielle M Dick
Sig. H-MAGMA genes in adult brain from EXT GWAS_pvalue
Description:
"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). 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. Genes with significant corrected p-values shown here (one-sided P < 9.84×10–7). From supplementary table 17."
Authors:
Richard Karlsson Linnér, Travis T Mallard, Peter B Barr, Sandra Sanchez-Roige, James W Madole, Morgan N Driver, Holly E Poore, Ronald de Vlaming, Andrew D Grotzinger, Jorim J Tielbeek, Emma C Johnson, Mengzhen Liu, Sara Brin Rosenthal, Trey Ideker, Hang Zhou, Rachel L Kember, Joëlle A Pasman, Karin J H Verweij, Dajiang J Liu, Scott Vrieze, , Henry R Kranzler, Joel Gelernter, Kathleen Mullan Harris, Elliot M Tucker-Drob, Irwin D Waldman, Abraham A Palmer, K Paige Harden, Philipp D Koellinger, Danielle M Dick
H-MAGMA genes in iPSC neurons from EXT GWAS_pvalue
Description:
"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). 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). Displaying genes with P value less than 1E–5. From supplementary table 19."
Authors:
Richard Karlsson Linnér, Travis T Mallard, Peter B Barr, Sandra Sanchez-Roige, James W Madole, Morgan N Driver, Holly E Poore, Ronald de Vlaming, Andrew D Grotzinger, Jorim J Tielbeek, Emma C Johnson, Mengzhen Liu, Sara Brin Rosenthal, Trey Ideker, Hang Zhou, Rachel L Kember, Joëlle A Pasman, Karin J H Verweij, Dajiang J Liu, Scott Vrieze, , Henry R Kranzler, Joel Gelernter, Kathleen Mullan Harris, Elliot M Tucker-Drob, Irwin D Waldman, Abraham A Palmer, K Paige Harden, Philipp D Koellinger, Danielle M Dick
H-MAGMA genes in iPSC astrocytes from EXT GWAS_pvalue
Description:
"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). 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). Displaying genes with P value less than 1E–5. From supplementary table 20."
Authors:
Richard Karlsson Linnér, Travis T Mallard, Peter B Barr, Sandra Sanchez-Roige, James W Madole, Morgan N Driver, Holly E Poore, Ronald de Vlaming, Andrew D Grotzinger, Jorim J Tielbeek, Emma C Johnson, Mengzhen Liu, Sara Brin Rosenthal, Trey Ideker, Hang Zhou, Rachel L Kember, Joëlle A Pasman, Karin J H Verweij, Dajiang J Liu, Scott Vrieze, , Henry R Kranzler, Joel Gelernter, Kathleen Mullan Harris, Elliot M Tucker-Drob, Irwin D Waldman, Abraham A Palmer, K Paige Harden, Philipp D Koellinger, Danielle M Dick
Sig. H-MAGMA genes in iPSC astrocytes from EXT GWAS_pvalue
Description:
"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). 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. Genes with significant corrected p-values shown here (one-sided P < 9.84×10–7). From supplementary table 20."
Authors:
Richard Karlsson Linnér, Travis T Mallard, Peter B Barr, Sandra Sanchez-Roige, James W Madole, Morgan N Driver, Holly E Poore, Ronald de Vlaming, Andrew D Grotzinger, Jorim J Tielbeek, Emma C Johnson, Mengzhen Liu, Sara Brin Rosenthal, Trey Ideker, Hang Zhou, Rachel L Kember, Joëlle A Pasman, Karin J H Verweij, Dajiang J Liu, Scott Vrieze, , Henry R Kranzler, Joel Gelernter, Kathleen Mullan Harris, Elliot M Tucker-Drob, Irwin D Waldman, Abraham A Palmer, K Paige Harden, Philipp D Koellinger, Danielle M Dick
Postmortem human brain tissue from the caudate nucleus region of a total of 48 individuals with Alcohol Use Disorder (AUD) and 51 control individuals were taken and RNA extracted from frozen tissue. Sequencing was carried out using the NovaSeq 6000 (Illumina) platform, and gene expression analysis was carried out with respect to AUD and control samples. Gene symbols from Entrez ids are used and Logbase2 FC as provided by the authors are annotated.
Authors:
Lea Zillich, Eric Poisel, Josef Frank, Jerome C Foo, Marion M Friske, Fabian Streit, Lea Sirignano, Stefanie Heilmann-Heimbach, André Heimbach, Per Hoffmann, Franziska Degenhardt, Anita C Hansson, Georgy Bakalkin, Markus M Nöthen, Marcella Rietschel, Rainer Spanagel, Stephanie H Witt
Postmortem human brain tissue from the ventral striatum from postmortem human brain tissue with Alcohol Use Disorder (AUD) region of a total of 48 individuals with Alcohol Use Disorder (AUD) and 51 control individuals were taken and RNA extracted from frozen tissue. Sequencing was carried out using the NovaSeq 6000 (Illumina) platform, and gene expression analysis was carried out with respect to AUD and control samples. Gene symbols from Entrez ids are used and Logbase2 FC as provided by the authors are annotated.
Authors:
Lea Zillich, Eric Poisel, Josef Frank, Jerome C Foo, Marion M Friske, Fabian Streit, Lea Sirignano, Stefanie Heilmann-Heimbach, André Heimbach, Per Hoffmann, Franziska Degenhardt, Anita C Hansson, Georgy Bakalkin, Markus M Nöthen, Marcella Rietschel, Rainer Spanagel, Stephanie H Witt
Differentially expressed genes from RPE compared to Normal Retina
Description:
Transcriptome profiling from macular retina and RPE/choroid samples from 27 unrelated eye tissue donors, was performed using RNA-sequencing. Human donor eye collection were obtained from Utah Lions Eye Bank within a 6-hour post-mortem interval and donors aged 60-90 years. Sample types were Normal Retina, Intermediate AMD Retina, Neovascular AMD Retina, Normal macular retina pigment epithelium (RPE), Intermediate AMD RPE, and Neovascular AMD RPE. Age Related Macular Degeneration (AMD) phenotyping was determined using the Age-Related Eye Disease Study (AREDS) severity grading scale, where AREDS category 0/1 was considered normal, AREDS category 3 intermediate AMD, and AREDS category 4b neovascular AMD. Samples from Normal RPE were compared to Normal Retina, and are presented with fold change > 1.5 and and P < 0.05. This gene set was annotated from the Supplementry Table of BioRxiv pre-print paper ‘Patterns of gene expression and allele-specific expression vary among macular tissues and clinical stages of Age-related Macular Degeneration’ by Zhang et.al (2022) doi: https://doi.org/10.1101/2022.12.19.521092
Data from GEO GSE194368 and analyzed using GEO2R, only top gene shown. Authors identified transcriptional adaptations of GR signaling in the amygdala of humans with OUD. Thus, GRs, their coregulators and downstream systems may represent viable therapeutic targets to treat the “stress side” of OUD.
Authors:
Stephanie A Carmack, Janaina C M Vendruscolo, M Adrienne McGinn, Jorge Miranda-Barrientos, Vez Repunte-Canonigo, Gabriel D Bosse, Daniele Mercatelli, Federico M Giorgi, Yu Fu, Anthony J Hinrich, Francine M Jodelka, Karen Ling, Robert O Messing, Randall T Peterson, Frank Rigo, Scott Edwards, Pietro P Sanna, Marisela Morales, Michelle L Hastings, George F Koob, Leandro F Vendruscolo
Data from GEO GSE194368 and analyzed using GEO2R, only top gene shown. Authors identified transcriptional adaptations of GR signaling in the amygdala of humans with OUD. Thus, GRs, their coregulators and downstream systems may represent viable therapeutic targets to treat the “stress side” of OUD.
Authors:
Stephanie A Carmack, Janaina C M Vendruscolo, M Adrienne McGinn, Jorge Miranda-Barrientos, Vez Repunte-Canonigo, Gabriel D Bosse, Daniele Mercatelli, Federico M Giorgi, Yu Fu, Anthony J Hinrich, Francine M Jodelka, Karen Ling, Robert O Messing, Randall T Peterson, Frank Rigo, Scott Edwards, Pietro P Sanna, Marisela Morales, Michelle L Hastings, George F Koob, Leandro F Vendruscolo
RNA sequencing of a limited number of archived patients' specimens with extended opioid exposure or non-opioid exposure was performed. Immune infiltration and changes in the microenvironment were evaluated using CIBERSORT.
Authors:
Mamatha Garige, Sarah Poncet, Alexis Norris, Chao-Kai Chou, Wells W Wu, Rong-Fong Shen, Jacob W Greenberg, Louis Spencer Krane, Carole Sourbier
RNA sequencing of a limited number of archived patients' specimens with extended opioid exposure or non-opioid exposure was performed. Immune infiltration and changes in the microenvironment were evaluated using CIBERSORT.
Authors:
Mamatha Garige, Sarah Poncet, Alexis Norris, Chao-Kai Chou, Wells W Wu, Rong-Fong Shen, Jacob W Greenberg, Louis Spencer Krane, Carole Sourbier
Transcriptional alterations in dorsolateral prefrontal cortex and nucleus accumbens implicate neuroinflammation and synaptic remodeling in opioid use disorder. Transcriptomic profile of 20 control subjects and 20 OUD subjects in brain region DLPFC and NAC. Analyzed using GEO2R (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174409) separately for each brain region, comparing OUD and control samples.
Authors:
Xiangning Xue, Wei Zong, Jill R Glausier, Sam-Moon Kim, Micah A Shelton, BaDoi N Phan, Chaitanya Srinivasan, Andreas R Pfenning, George C Tseng, David A Lewis, Marianne L Seney, Ryan W Logan
Transcriptional alterations in dorsolateral prefrontal cortex and nucleus accumbens implicate neuroinflammation and synaptic remodeling in opioid use disorder. Transcriptomic profile of 20 control subjects and 20 OUD subjects in brain region DLPFC and NAC. Analyzed using GEO2R (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174409) separately for each brain region, comparing OUD and control samples.
Authors:
Xiangning Xue, Wei Zong, Jill R Glausier, Sam-Moon Kim, Micah A Shelton, BaDoi N Phan, Chaitanya Srinivasan, Andreas R Pfenning, George C Tseng, David A Lewis, Marianne L Seney, Ryan W Logan
Transcriptional alterations in dorsolateral prefrontal cortex and nucleus accumbens implicate neuroinflammation and synaptic remodeling in opioid use disorder. Transcriptomic profile of 20 control subjects and 20 OUD subjects in brain region DLPFC and NAC. Analyzed using GEO2R (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174409) separately for each brain region, comparing OUD and control samples.
Authors:
Xiangning Xue, Wei Zong, Jill R Glausier, Sam-Moon Kim, Micah A Shelton, BaDoi N Phan, Chaitanya Srinivasan, Andreas R Pfenning, George C Tseng, David A Lewis, Marianne L Seney, Ryan W Logan
"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). 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). Displaying genes with P value less than 1E–5. From supplementary table 18."
Authors:
Richard Karlsson Linnér, Travis T Mallard, Peter B Barr, Sandra Sanchez-Roige, James W Madole, Morgan N Driver, Holly E Poore, Ronald de Vlaming, Andrew D Grotzinger, Jorim J Tielbeek, Emma C Johnson, Mengzhen Liu, Sara Brin Rosenthal, Trey Ideker, Hang Zhou, Rachel L Kember, Joëlle A Pasman, Karin J H Verweij, Dajiang J Liu, Scott Vrieze, , Henry R Kranzler, Joel Gelernter, Kathleen Mullan Harris, Elliot M Tucker-Drob, Irwin D Waldman, Abraham A Palmer, K Paige Harden, Philipp D Koellinger, Danielle M Dick
Sig. H-MAGMA genes in fetal brain from EXT GWAS_pvalue
Description:
"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). 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. Genes with significant corrected p-values shown here (one-sided P < 9.84×10–7). From supplementary table 18."
Authors:
Richard Karlsson Linnér, Travis T Mallard, Peter B Barr, Sandra Sanchez-Roige, James W Madole, Morgan N Driver, Holly E Poore, Ronald de Vlaming, Andrew D Grotzinger, Jorim J Tielbeek, Emma C Johnson, Mengzhen Liu, Sara Brin Rosenthal, Trey Ideker, Hang Zhou, Rachel L Kember, Joëlle A Pasman, Karin J H Verweij, Dajiang J Liu, Scott Vrieze, , Henry R Kranzler, Joel Gelernter, Kathleen Mullan Harris, Elliot M Tucker-Drob, Irwin D Waldman, Abraham A Palmer, K Paige Harden, Philipp D Koellinger, Danielle M Dick
Sig. H-MAGMA genes in iPSC neurons from EXT GWAS_pvalue
Description:
"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). 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. Genes with significant corrected p-values shown here (one-sided P < 9.84×10–7). From supplementary table 19."
Authors:
Richard Karlsson Linnér, Travis T Mallard, Peter B Barr, Sandra Sanchez-Roige, James W Madole, Morgan N Driver, Holly E Poore, Ronald de Vlaming, Andrew D Grotzinger, Jorim J Tielbeek, Emma C Johnson, Mengzhen Liu, Sara Brin Rosenthal, Trey Ideker, Hang Zhou, Rachel L Kember, Joëlle A Pasman, Karin J H Verweij, Dajiang J Liu, Scott Vrieze, , Henry R Kranzler, Joel Gelernter, Kathleen Mullan Harris, Elliot M Tucker-Drob, Irwin D Waldman, Abraham A Palmer, K Paige Harden, Philipp D Koellinger, Danielle M Dick
"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."
Authors:
Richard Karlsson Linnér, Travis T Mallard, Peter B Barr, Sandra Sanchez-Roige, James W Madole, Morgan N Driver, Holly E Poore, Ronald de Vlaming, Andrew D Grotzinger, Jorim J Tielbeek, Emma C Johnson, Mengzhen Liu, Sara Brin Rosenthal, Trey Ideker, Hang Zhou, Rachel L Kember, Joëlle A Pasman, Karin J H Verweij, Dajiang J Liu, Scott Vrieze, , Henry R Kranzler, Joel Gelernter, Kathleen Mullan Harris, Elliot M Tucker-Drob, Irwin D Waldman, Abraham A Palmer, K Paige Harden, Philipp D Koellinger, Danielle M Dick
Cerebellum Gene Expression Correlates for ACTI15_SAL measured in BXD RI Females obtained using SJUT Cerebellum mRNA M430 (Mar05) RMA. The ACTI15_SAL measures Distance traveled (cm) during the third five minute bin after saline under the domain Ethanol. The correlates were thresholded at a p-value of less than 0.001.
Authors:
Philip VM, Duvvuru S, Gomero B, Ansah TA, Blaha CD, Cook MN, Hamre KM, Lariviere WR, Matthews DB, Mittleman G, Goldowitz D, Chesler EJ
Cerebellum Gene Expression Correlates for ACTI15_SAL measured in BXD RI Males obtained using SJUT Cerebellum mRNA M430 (Mar05) RMA. The ACTI15_SAL measures Distance traveled (cm) during the third five minute bin after saline under the domain Ethanol. The correlates were thresholded at a p-value of less than 0.001.
Authors:
Philip VM, Duvvuru S, Gomero B, Ansah TA, Blaha CD, Cook MN, Hamre KM, Lariviere WR, Matthews DB, Mittleman G, Goldowitz D, Chesler EJ
Whole Brain Gene Expression Correlates for ACTI20_ETHA measured in BXD RI Females obtained using INIA Brain mRNA M430 (Jun06) RMA. The ACTI20_ETHA measures Distance traveled (cm) during the fourth five minute bin after ethanol under the domain Ethanol. The correlates were thresholded at a p-value of less than 0.001.
Authors:
Philip VM, Duvvuru S, Gomero B, Ansah TA, Blaha CD, Cook MN, Hamre KM, Lariviere WR, Matthews DB, Mittleman G, Goldowitz D, Chesler EJ
Cerebellum Gene Expression Correlates for ACTITOT_SAL measured in BXD RI Females & Males obtained using SJUT Cerebellum mRNA M430 (Mar05) RMA. The ACTITOT_SAL measures total distance traveled (cm) following saline under the domain Ethanol. The correlates were thresholded at a p-value of less than 0.001.
Authors:
Philip VM, Duvvuru S, Gomero B, Ansah TA, Blaha CD, Cook MN, Hamre KM, Lariviere WR, Matthews DB, Mittleman G, Goldowitz D, Chesler EJ
Hippocampus Gene Expression Correlates for C1HCOUNT30 measured in BXD RI Females obtained using GeneNetwork Hippocampus Consortium M430v2 (Jun06) RMA. The C1HCOUNT30 measures Open Field locomotion 15-30 min post cocaine under the domain Cocaine. The correlates were thresholded at a p-value of less than 0.001.
Authors:
Philip VM, Duvvuru S, Gomero B, Ansah TA, Blaha CD, Cook MN, Hamre KM, Lariviere WR, Matthews DB, Mittleman G, Goldowitz D, Chesler EJ
GeneWeaver