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
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 putamen 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
The dataset used in this study (Bulk RNA-Seq) was previously published and can be found at NCBI GEO (GSE182321), this analysis was conducted by GEO2R to compare control and OUD samples, only top differentially expressed genes are reported. To understand mechanisms and identify potential targets for intervention in the current crisis of opioid use disorder (OUD), postmortem brains represent an under-utilized resource. To refine previously reported gene signatures of neurobiological alterations in OUD from the dorsolateral prefrontal cortex (Brodmann Area 9, BA9), we explored the role of microRNAs (miRNA) as powerful epigenetic regulators of gene function.
The dataset used in this study (Bulk RNA-Seq) was previously published and can be found at NCBI GEO (GSE182321), this analysis was conducted by GEO2R to compare control and OUD samples, only top differentially expressed genes are reported. To understand mechanisms and identify potential targets for intervention in the current crisis of opioid use disorder (OUD), postmortem brains represent an under-utilized resource. To refine previously reported gene signatures of neurobiological alterations in OUD from the dorsolateral prefrontal cortex (Brodmann Area 9, BA9), we explored the role of microRNAs (miRNA) as powerful epigenetic regulators of gene function.
Postmortem tissue samples of the dorsolateral prefrontal cortex (DLPFC) from 153 deceased individuals (Mage = 35.4; 62% male; 77% European ancestry). Study groups included 72 brain samples from individuals who died of acute opioid intoxication, 53 psychiatric controls, and 28 normal controls. Whole transcriptome RNA-sequencing was used to generate exon counts, and differential expression was tested using limma-voom. Analyses were adjusted for relevant sociodemographic characteristics, technical covariates, and cryptic relatedness using quality surrogate variables. Weighted correlation network analysis and gene set enrichment analyses also were conducted.
Authors:
David W Sosnowski, Andrew E Jaffe, Ran Tao, Amy Deep-Soboslay, Chang Shu, Sarven Sabunciyan, Joel E Kleinman, Thomas M Hyde, Brion S Maher
Human induced pluripotent stem cell (iPSC) lines, A and B, derived from two healthy adult male individuals, were used to generate hCOs for RNA-sequencing. Methodone treatment began on Day 9 of organoid culture, the first day of the neural proliferation stage, and concluded at Day 60. Nuclease-free water was used as a vehicular control. Cortical organoids were collected 2 months (60 days) after initiating organoid culture. Each well of hCOs (15–20 organoids) was a separate biological replicate for a given treatment condition (i.e., treated or untreated). RNA was extracted from frozen organoid pellets using the Direct-Zol Miniprep Plus Kit (Zymo, Irvine, CA) according to the manufacturer’s instructions. Samples were multiplexed and sequenced on the Illumina NovaSeq 6000 S4 to produce approximately 100 million, 100 base pair, paired end reads per sample. 3 control and 3 methadone-treated samples were sequenced from cell line A, and 4 control and 4 treated samples from cell line B. Raw fastq file quality assessment and read alignment to the hg19 genome (GRCh37, RefSeq GCF_000001405.13) were performed. Significantly differentially expressed genes (DEGs) were selected based on the confident effect size of their log2(Fold Change) values at FDR<0.05. Genes presented are without cutoffs and were obtained using the GEO2R tool by GW curators (GEO accession: GSE210682).
Authors:
Ila Dwivedi, Andrew B Caldwell, Dan Zhou, Wei Wu, Shankar Subramaniam, Gabriel G Haddad
DEG methadone human cortical organoids cell line B_pvalue
Description:
Human induced pluripotent stem cell (iPSC) lines, A and B, derived from two healthy adult male individuals, were used to generate hCOs for RNA-sequencing. Methodone treatment began on Day 9 of organoid culture, the first day of the neural proliferation stage, and concluded at Day 60. Nuclease-free water was used as a vehicular control. Cortical organoids were collected 2 months (60 days) after initiating organoid culture. Each well of hCOs (15–20 organoids) was a separate biological replicate for a given treatment condition (i.e., treated or untreated). RNA was extracted from frozen organoid pellets using the Direct-Zol Miniprep Plus Kit (Zymo, Irvine, CA) according to the manufacturer’s instructions. Samples were multiplexed and sequenced on the Illumina NovaSeq 6000 S4 to produce approximately 100 million, 100 base pair, paired end reads per sample. 3 control and 3 methadone-treated samples were sequenced from cell line A, and 4 control and 4 treated samples from cell line B. Raw fastq file quality assessment and read alignment to the hg19 genome (GRCh37, RefSeq GCF_000001405.13) were performed. Significantly differentially expressed genes (DEGs) were selected based on the confident effect size of their log2(Fold Change) values at FDR<0.05. Genes presented are without cutoffs and were obtained using the GEO2R tool by GW curators (GEO accession: GSE210682).
Authors:
Ila Dwivedi, Andrew B Caldwell, Dan Zhou, Wei Wu, Shankar Subramaniam, Gabriel G Haddad
DEG methadone human cortical organoids cell line B_qvalue
Description:
Human induced pluripotent stem cell (iPSC) lines, A and B, derived from two healthy adult male individuals, were used to generate hCOs for RNA-sequencing. Methodone treatment began on Day 9 of organoid culture, the first day of the neural proliferation stage, and concluded at Day 60. Nuclease-free water was used as a vehicular control. Cortical organoids were collected 2 months (60 days) after initiating organoid culture. Each well of hCOs (15–20 organoids) was a separate biological replicate for a given treatment condition (i.e., treated or untreated). RNA was extracted from frozen organoid pellets using the Direct-Zol Miniprep Plus Kit (Zymo, Irvine, CA) according to the manufacturer’s instructions. Samples were multiplexed and sequenced on the Illumina NovaSeq 6000 S4 to produce approximately 100 million, 100 base pair, paired end reads per sample. 3 control and 3 methadone-treated samples were sequenced from cell line A, and 4 control and 4 treated samples from cell line B. Raw fastq file quality assessment and read alignment to the hg19 genome (GRCh37, RefSeq GCF_000001405.13) were performed. Significantly differentially expressed genes (DEGs) were selected based on the confident effect size of their log2(Fold Change) values at FDR<0.05. Genes presented are without cutoffs and were obtained using the GEO2R tool by GW curators (GEO accession: GSE210682).
Authors:
Ila Dwivedi, Andrew B Caldwell, Dan Zhou, Wei Wu, Shankar Subramaniam, Gabriel G Haddad
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
List of positional candidate genes after correcting for multiple testing and controlling the false discovery rate from genome wide association studies (GWAS) retrieved from the NHGRI-EBI Catalog of published genome-wide association studies (http://www.ebi.ac.uk/gwas/). The disease/trait examined in this study, as reported by the authors, was Maximal oxygen uptake response. The EFO term maximal oxygen uptake measurement was annotated to this set after curation by NHGRI-EBI. Intergenic SNPS were mapped to both the upstream and downstream gene. P-value uploaded. This gene set was generated using gwas2gs v. 0.1.8 and the GWAS Catalog v. 1.0.1.
Authors:
I Ahmetov, N Kulemin, D Popov, V Naumov, E Akimov, Y Bravy, E Egorova, A Galeeva, E Generozov, E Kostryukova, A Larin, Lj Mustafina, E Ospanova, A Pavlenko, L Starnes, P Żmijewski, D Alexeev, O Vinogradova, V Govorun
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
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
DE lncRNA from human NAc associated with AD vs control_pvalue
Description:
At the single gene expression analysis (p ≤ 0.10), we identified a total of 6,469 probes differentially expressed between alcohol dependency (AD) cases and controls, representing 3,645 long non-coding (lncRNA) and 2,725 protein-coding genes (PCG) that compromise 5.9% and 4.4% of the total gene pool assayed, respectively. Among the differentially expressed genes, we also identified 99 pseudogenes.
Authors:
John Drake, Gowon O McMichael, Eric Sean Vornholt, Kellen Cresswell, Vernell Williamson, Chris Chatzinakos, Mohammed Mamdani, Siddharth Hariharan, Kenneth S Kendler, Gursharan Kalsi, Brien P Riley, Mikhail Dozmorov, Michael F Miles, Silviu-Alin Bacanu, Vladimir I Vladimirov
DE lncRNA from human NAc associated with AD vs control_logFC
Description:
At the single gene expression analysis (p ≤ 0.10), we identified a total of 6,469 probes differentially expressed between alcohol dependency (AD) cases and controls, representing 3,645 long non-coding (lncRNA) and 2,725 protein-coding genes (PCG) that compromise 5.9% and 4.4% of the total gene pool assayed, respectively. Among the differentially expressed genes, we also identified 99 pseudogenes.
Authors:
John Drake, Gowon O McMichael, Eric Sean Vornholt, Kellen Cresswell, Vernell Williamson, Chris Chatzinakos, Mohammed Mamdani, Siddharth Hariharan, Kenneth S Kendler, Gursharan Kalsi, Brien P Riley, Mikhail Dozmorov, Michael F Miles, Silviu-Alin Bacanu, Vladimir I Vladimirov
"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 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
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
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
"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
"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
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
Bipolar disorder a is highly heritable mental illness that has definable behavioral traits associated with it. Dorsal striatum is linked to impulsivity and risk-taking behavior. This brain structure was collected from human (postmortem) brain samples and was mapped using RNA sequencing, specifically DESeq2. 1468 genes were identified as differentially expressed at nominal significance between the BD striatal group vs. the control group.
List of positional candidate genes after correcting for multiple testing and controlling the false discovery rate from genome wide association studies (GWAS) retrieved from the NHGRI-EBI Catalog of published genome-wide association studies (http://www.ebi.ac.uk/gwas/). The disease/trait examined in this study, as reported by the authors, was Parental longevity (combined parental age at death). The EFO term parental longevity was annotated to this set after curation by NHGRI-EBI. Intergenic SNPS were mapped to both the upstream and downstream gene. P-value uploaded. This gene set was generated using gwas2gs v. 0.1.8 and the GWAS Catalog v. 1.0.1.
Authors:
LC Pilling, JL Atkins, K Bowman, SE Jones, J Tyrrell, RN Beaumont, KS Ruth, MA Tuke, H Yaghootkar, AR Wood, RM Freathy, A Murray, MN Weedon, L Xue, K Lunetta, JM Murabito, LW Harries, JM Robine, C Brayne, GA Kuchel, L Ferrucci, TM Frayling, D Melzer
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