chr6p21
Genes in cytogenetic band chr6p21
c1 - Positional genesets for each human chromosome and cytogenetic band.
Molecular Signatures Database (MSigDB) Geneset. This geneset was imported from one of the MSigDB collections.
gene2msig v. 0.1.0
Last updated 2015.08.31
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 Neuroticism. The EFO term neuroticism 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:
A Okbay, BM Baselmans, JE De Neve, P Turley, MG Nivard, MA Fontana, SF Meddens, RK Linnér, CA Rietveld, J Derringer, J Gratten, JJ Lee, JZ Liu, R de Vlaming, TS Ahluwalia, J Buchwald, A Cavadino, AC Frazier-Wood, NA Furlotte, V Garfield, MH Geisel, JR Gonzalez, S Haitjema, R Karlsson, SW van der Laan, KH Ladwig, J Lahti, SJ van der Lee, PA Lind, T Liu, L Matteson, E Mihailov, MB Miller, CC Minica, IM Nolte, D Mook-Kanamori, PJ van der Most, C Oldmeadow, Y Qian, O Raitakari, R Rawal, A Realo, R Rueedi, B Schmidt, AV Smith, E Stergiakouli, T Tanaka, K Taylor, J Wedenoja, J Wellmann, HJ Westra, SM Willems, W Zhao, N Amin, A Bakshi, PA Boyle, S Cherney, SR Cox, G Davies, OS Davis, J Ding, N Direk, P Eibich, RT Emeny, G Fatemifar, JD Faul, L Ferrucci, A Forstner, C Gieger, R Gupta, TB Harris, JM Harris, EG Holliday, JJ Hottenga, PL De Jager, MA Kaakinen, E Kajantie, V Karhunen, I Kolcic, M Kumari, LJ Launer, L Franke, R Li-Gao, M Koini, A Loukola, P Marques-Vidal, GW Montgomery, MA Mosing, L Paternoster, A Pattie, KE Petrovic, L Pulkki-Råback, L Quaye, K Räikkönen, I Rudan, RJ Scott, JA Smith, AR Sutin, M Trzaskowski, AE Vinkhuyzen, L Yu, D Zabaneh, JR Attia, DA Bennett, K Berger, L Bertram, DI Boomsma, H Snieder, SC Chang, F Cucca, IJ Deary, CM van Duijn, JG Eriksson, U Bültmann, EJ de Geus, PJ Groenen, V Gudnason, T Hansen, CA Hartman, CM Haworth, C Hayward, AC Heath, DA Hinds, E Hyppönen, WG Iacono, MR Järvelin, KH Jöckel, J Kaprio, SL Kardia, L Keltikangas-Järvinen, P Kraft, LD Kubzansky, T Lehtimäki, PK Magnusson, NG Martin, M McGue, A Metspalu, M Mills, R de Mutsert, AJ Oldehinkel, G Pasterkamp, NL Pedersen, R Plomin, O Polasek, C Power, SS Rich, FR Rosendaal, HM den Ruijter, D Schlessinger, H Schmidt, R Svento, R Schmidt, BZ Alizadeh, TI Sørensen, TD Spector, A Steptoe, A Terracciano, AR Thurik, NJ Timpson, H Tiemeier, AG Uitterlinden, P Vollenweider, GG Wagner, DR Weir, J Yang, DC Conley, GD Smith, A Hofman, M Johannesson, DI Laibson, SE Medland, MN Meyer, JK Pickrell, T Esko, RF Krueger, JP Beauchamp, PD Koellinger, DJ Benjamin, M Bartels, D Cesarini
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 Iron status biomarkers. The EFO term iron biomarker 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:
B Benyamin, AF McRae, G Zhu, S Gordon, AK Henders, A Palotie, L Peltonen, NG Martin, GW Montgomery, JB Whitfield, PM Visscher
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 Neuroticism. The EFO term neuroticism 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:
MH de Moor, SM van den Berg, KJ Verweij, RF Krueger, M Luciano, A Arias Vasquez, LK Matteson, J Derringer, T Esko, N Amin, SD Gordon, NK Hansell, AB Hart, I Seppälä, JE Huffman, B Konte, J Lahti, M Lee, M Miller, T Nutile, T Tanaka, A Teumer, A Viktorin, J Wedenoja, GR Abecasis, DE Adkins, A Agrawal, J Allik, K Appel, TB Bigdeli, F Busonero, H Campbell, PT Costa, G Davey Smith, G Davies, H de Wit, J Ding, BE Engelhardt, JG Eriksson, IO Fedko, L Ferrucci, B Franke, I Giegling, R Grucza, AM Hartmann, AC Heath, K Heinonen, AK Henders, G Homuth, JJ Hottenga, WG Iacono, J Janzing, M Jokela, R Karlsson, JP Kemp, MG Kirkpatrick, A Latvala, T Lehtimäki, DC Liewald, PA Madden, C Magri, PK Magnusson, J Marten, A Maschio, SE Medland, E Mihailov, Y Milaneschi, GW Montgomery, M Nauck, KG Ouwens, A Palotie, E Pettersson, O Polasek, Y Qian, L Pulkki-Råback, OT Raitakari, A Realo, RJ Rose, D Ruggiero, CO Schmidt, WS Slutske, R Sorice, JM Starr, B St Pourcain, AR Sutin, NJ Timpson, H Trochet, S Vermeulen, E Vuoksimaa, E Widen, J Wouda, MJ Wright, L Zgaga, D Porteous, A Minelli, AA Palmer, D Rujescu, M Ciullo, C Hayward, I Rudan, A Metspalu, J Kaprio, IJ Deary, K Räikkönen, JF Wilson, L Keltikangas-Järvinen, LJ Bierut, JM Hettema, HJ Grabe, CM van Duijn, DM Evans, D Schlessinger, NL Pedersen, A Terracciano, M McGue, BW Penninx, NG Martin, DI Boomsma
"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
"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
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
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
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
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