luochihiro
commented
5 years ago Closed luochihiro closed 4 years ago
luochihiro
commented
5 years ago 
MaVdb
commented
5 years ago You can try the following:
devtools::install_github('MarkEdmondson1234/googleAuthR@v0.8.1') library(googleAuthR) ao <- available_outcomes()
This worked for me
meghanmorrison
commented
4 years ago devtools::install_github('MarkEdmondson1234/googleAuthR@v0.8.1')
Skipping install of 'googleAuthR' from a github remote, the SHA1 (cb974f8c) has not changed since last install.
Use force = TRUE to force installation
library(googleAuthR) ao <- available_outcomes() Error in available_outcomes() : could not find function "available_outcomes"
Does anyone know how to help?
explodecomputer
commented
4 years ago Need to call library(TwoSampleMR) ?
From: meghanmorrison notifications@github.com Reply to: MRCIEU/TwoSampleMR reply@reply.github.com Date: Wednesday, 25 September 2019 at 14:58 To: MRCIEU/TwoSampleMR TwoSampleMR@noreply.github.com Cc: Subscribed subscribed@noreply.github.com Subject: Re: [MRCIEU/TwoSampleMR] ao <- available_outcomes() have a bug (#119)
devtools::install_github('MarkEdmondson1234/googleAuthR@v0.8.1') Skipping install of 'googleAuthR' from a github remote, the SHA1 (cb974f8c) has not changed since last install. Use force = TRUE to force installation
library(googleAuthR) ao <- available_outcomes() Error in available_outcomes() : could not find function "available_outcomes"
Does anyone know how to help?
— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub, or mute the thread.
meghanmorrison
commented
4 years ago library(TwoSampleMR) Welcome to TwoSampleMR. [>] Full documentation: https://mrcieu.github.io/TwoSampleMR [>] Check news(package='TwoSampleMR') for bug fixes and updates [>] By generating access tokens to retrieve data from the MR-Base database you consent to having your email address logged on our servers. For info on how this is used see logging_info() [>] NOTE: We will be rolling out extensive changes to the database in the next few weeks. To ensure backwards compatibility please keep the R package updated. devtools::install_github('MarkEdmondson1234/googleAuthR@v0.8.1') Skipping install of 'googleAuthR' from a github remote, the SHA1 (cb974f8c) has not changed since last install. Use
force = TRUEto force installation library(googleAuthR) ao <- available_outcomes() Error: Cannot read token from alleged .rds file: mrbase.oauthError in readRDS(token_path): error reading from connection
Thank you for your quick reply! I still seem to be having problems connecting unfortunately, do you know what I'm doing wrong?
explodecomputer
commented
4 years ago Hmm, try
access_token=NULL argument for any functions that call the API e.g. available_outcomes(access_token=NULL)meghanmorrison
commented
4 years ago library(TwoSampleMR) Welcome to TwoSampleMR. [>] Full documentation: https://mrcieu.github.io/TwoSampleMR [>] Check news(package='TwoSampleMR') for bug fixes and updates [>] By generating access tokens to retrieve data from the MR-Base database you consent to having your email address logged on our servers. For info on how this is used see logging_info() [>] NOTE: We will be rolling out extensive changes to the database in the next few weeks. To ensure backwards compatibility please keep the R package updated.
devtools::install_github('MarkEdmondson1234/googleAuthR@v0.8.1') Skipping install of 'googleAuthR' from a github remote, the SHA1 (cb974f8c) has not changed since last install. Use
force = TRUEto force installation library(googleAuthR) ao <- available_outcomes() 2019-09-25 15:16:39> No httr_oauth_cache file found at mrbase.oauth - creating new file. Error: package ‘curl’ does not have a namespace available_outcomes(accesstoken=NULL) access author category consortium 1 public Dastani Z Risk factor ADIPOGen 2 public Jostins L Disease IIBDGC 3 public Randall JC Risk factor GIANT 4 public Okbay Risk factor SSGAC 5 public Okbay Risk factor SSGAC 6 public Kilpelainen Risk factor7 public Kilpelainen Risk factor .meta_.public.release.txt.tab 2 CD.gwas_ichip_meta_release.txt.gz.tab 3 GIANT_Randall2013PlosGenet_stage1_publicrelease_HapMapCeuFreq_HIPadjBMI_MEN_N.txt.tab 4 DS_Full.txt.gz.tab 5 EduYears_Main.txt.gz.tab 6 Leptin_Adjusted_for_BMI.txt.tab 7 Leptin_Not_Adjusted_for_BMI.txt.tab 8 Menopause_HapMap2_DayNG2015_18112015.txt.tab 9 MESA_MetaAnallogemph950Model2.csv.tab 10 MPV.txt.tab 11 Neuroticism_Full.txt.gz.tab 12 PLT.txt.tab 13 SWB_Full.txt.gz.tab 14 GIANT_Randall2013PlosGenet_stage1_publicrelease_HapMapCeuFreq_HIPadjBMI_WOMEN_N.txt.tab 15 EduYears_Main_Men.txt.gz.tab 16 EduYears_Main_Women.txt.gz.tab 17 gwama_1_fixed.out.plasma_cortisol.tab 18 phs000086.pha002871.csv.tab 19 GWASsummary_Okada_PMID21196492_20160919.txt.gz.tab 20 Brain_2015_Goris_OCB.meta.tab 21 Brain_2015_Goris_IgG.meta.tab.all_neg 22 SWB_excl_ALSPAC_TEDS_23andMe_ldscGC_sumstats.txt.tab 23 TGEN_mig_bp_log_frq_hwe.txt.tab 24 GIANT_Randall2013PlosGenet_stage1_publicrelease_HapMapCeuFreq_WC_MEN_N.txt.tab 25 MESA_hispaniclogemph950Model2_21112011filter0.8.csv.tab 26 MESA_chineselogemph950Model2_21112011filter0.8.csv.tab 27 MESA_whitelogemph950Model2_21112011filter0.8.csv.tab 28 MESA_blacklogemph950Model2_21112011filter0.8.csv.tab 29 WTCCC2_2011.txt.tab 30 IChip_2013.txt.new.tab.all_pos 31 AAA_MetaGWAS.tab 32 phs000184.pha002870.csv.tab 33 EAGLE_aggression_Public.txt.tab 34 meta3.INTsnplist_F.txt.tab 35 GIANT_Randall2013PlosGenet_stage1_publicrelease_HapMapCeuFreq_WC_WOMEN_N.txt.tab 36 Pubertal_growth_10F.txt.gz.tab 37 Pubertal_growth_12M.txt.gz.tab 38 Pubertal_growth_10F_12M_combined.txt.gz.tab 39 Pubertal_growth_PGF.txt.gz.tab 40 Pubertal_growth_PGM.txt.gz.tab 41 Pubertal_growth_PGF_PGM_combined.txt.gz.tab 42 Pubertal_growth_PTF.txt.gz.tab 43 Pubertal_growth_PTM.txt.gz.tab 44 GIANT_Randall2013PlosGenet_stage1_publicrelease_HapMapCeuFreq_WCadjBMI_MEN_N.txt.tab 45 Pubertal_growth_PTF_PTM_combined.txt.gz.tab id mr ncase ncontrol 1 1 1 NA NA 2 10 1 14763 15977 3 100 1 NA NA 4 1000 1 NA NA 5 1001 1 NA NA 6 1002 1 NA NA 7 1003 1 NA NA 8 1004 1 NA NA 9 1005 1 NA NA 10 1006 1 NA NA 11 1007 1 NA NA 12 1008 1 NA NA 13 1009 1 NA NA 14 101 1 NA NA 15 1010 1 NA NA 16 1011 1 NA NA 17 1012 1 NA NA 18 1014 0 NA NA 19 1015 1 NA NA 20 1016 1 363 2663 21 1017 1 NA NA 22 1018 1 NA NA 23 1019 1 460 924 24 102 1 NA NA 25 1020 1 NA NA 26 1021 1 NA NA 27 1022 1 NA NA 28 1023 1 NA NA 29 1024 1 9722 17376 30 1025 1 14498 24091 31 1026 0 4972 99858 32 1027 0 NA NA 33 1028 0 NA NA 34 1029 1 NA NA 35 103 1 NA NA 36 1032 1 NA NA 37 1033 1 NA NA 38 1034 1 NA NA 39 1035 1 NA NA 40 1036 1 NA NA 41 1037 1 NA NA 42 1038 1 NA NA 43 1039 1 NA NA 44 104 1 NA NA 45 1040 1 NA NA note 18 public Day Risk factor ReproGen 9 public Manichaikul A Risk factor MESA 10 public Gieger C Risk factor HaemGen 11 public Okbay Risk factor SSGAC 12 public Gieger C Risk factor HaemGen 13 public Okbay Risk factor SSGAC 14 public Randall JC Risk factor GIANT 15 public Okbay Risk factor SSGAC 16 public Okbay Risk factor SSGAC 17 public Bolton JL Risk factor CORNET 18 public Paterson AD Risk factor DCCT & EDIC 19 public Okada Y Risk factor BioBank Japan Project 20 public Goris A Risk factor 21 public Goris A Risk factor 22 public Okbay Risk factor SSGAC 23 public Jacobsen KK Risk factor 24 public Randall JC Risk factor GIANT 25 public Manichaikul A Risk factor MESA 26 public Manichaikul A Risk factor MESA 27 public Manichaikul A Risk factor MESA 28 public Manichaikul A Risk factor MESA 29 public Sawcer S Disease IMSGC 30 public Beecham Disease IMSGC 31 public Disease IAC 32 public Ober Risk factor 33 public Pappa Disease EAGLE 34 public Benke Disease EAGLE 35 public Randall JC Risk factor GIANT 36 public Cousminer Risk factor EGG 37 public Cousminer Risk factor EGG 38 public Cousminer Risk factor EGG 39 public Cousminer Risk factor EGG 40 public Cousminer Risk factor EGG 41 public Cousminer Risk factor EGG 42 public Cousminer Risk factor EGG 43 public Cousminer Risk factor EGG 44 public Randall JC Risk factor GIANT 45 public Cousminer Risk factor EGG filename 1 adipogen.discovery.eur 2 3 Adjusted for BMI 4 5 6 Adjusted for BMI; effect allele frequencies are missing 7 Effect allele frequencies are missing 8 9 10 Effect allele frequencies are missing 11 12 13 14 Adjusted for BMI 15 16 17 18 Only P values available; all subjects had type 1 diabetes 19 20 All subjects had multiple sclerosis; effect allele frequeinces are missing 21 All subjects had multiple sclerosis; effect allele frequeinces are missing 22 ALSPAC; TEDS and 23andMe excluded (ALSPAC=5649; 23andME=93454; TEDS=2143) 23 24 25 26 27 28 29 30 31 Only P-values available 32 Only P-values available 33 Only P values are available 34 In preschool; effect allele frequencies are missing 35 36 Age 10; effect allele frequencies are missing 37 Age 12; effect allele frequencies are missing 38 Females at age 10 and males at age 12; effect allele frequencies are missing 39 Childhood at age 8; effect allele frequencies are missing 40 Childhood at age 8; effect allele frequencies are missing 41 Childhood at age 8; effect allele frequencies are missing 42 Adolescence at age 14; effect allele frequencies are missing 43 Adolescence at age 14; effect allele frequencies are missing 44 Adjusted for BMI 45 Adolescence at age 14; effect allele frequencies are missing nsnp 1 2675209 2 13898 3 2725796 4 6524475 5 8146841 6 2474010 7 2473865 8 2418696 9 2968584 10 2690859 11 6524433 12 2703394 13 2268675 14 2725796 15 7781138 16 7643301 17 2660192 18 841305 19 2178019 20 485523 21 242775 22 2264177 23 723225 24 2745544 25 2585998 26 2011196 27 2390490 28 2681851 29 465435 30 156632 31 5341028 32 290329 33 2188529 34 2821735 35 2740385 36 2393631 37 2500338 38 2479700 39 2390840 40 2391721 41 2384832 42 2400928 43 2401290 44 2744366 45 2401290 path 1 /projects/MRC-IEU/publicdata/GWAS_summary_data/ADIPOGEN 2 /projects/MRC-IEU/publicdata/GWAS_summary_data/IBD 3 /projects/MRC-IEU/publicdata/GWAS_summary_data/GIANT_2010_2012_2013 4 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/SSGAC_27089181 5 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/SSGAC_education_educational_attainment_2016/ 6 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/Kilpelainen_Leptin 7 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/Kilpelainen_Leptin 8 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/ReproGen_Menopause 9 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/MESA_emphysema 10 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/HaemGen_platelets/data 11 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/SSGAC_27089181 12 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/HaemGen_platelets/data 13 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/dev/release_candidate/data/results/passqc 14 /projects/MRC-IEU/publicdata/GWAS_summary_data/GIANT_2010_2012_2013 15 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/SSGAC_education_educational_attainment_2016/ 16 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/SSGAC_education_educational_attainment_2016/ 17 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/Bolton25010111/ 18 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/Paterson_19729612 19 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/Okada21196492/ 20 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/Goris25616667/Brain_2015_Goris/ 21 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/Goris25616667/Brain_2015_Goris/ 22 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/SSGAC_27089181 23 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/Jacobsen25451450 24 /projects/MRC-IEU/publicdata/GWAS_summary_data/GIANT_2010_2012_2013 25 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/MESA_emphysema 26 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/MESA_emphysema 27 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/MESA_emphysema 28 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/MESA_emphysema 29 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/IMSGC_21833088_24076602 30 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/IMSGC_21833088_24076602 31 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/IAC_Abdominal_aortic_aneurysm/ 32 /projects/MRC-IEU/publicdata/GWAS_summary_data/dbGAP_collections/Lp_a 33 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EAGLE_ADHD_Aggression_Internalizing_problems/ 34 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EAGLE_ADHD_Aggression_Internalizing_problems/ 35 /projects/MRC-IEU/publicdata/GWAS_summary_data/GIANT_2010_2012_2013 36 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ 37 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ 38 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ 39 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ 40 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ 41 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ 42 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ 43 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ 44 /projects/MRC-IEU/publicdata/GWAS_summary_data/GIANT_2010_2012_2013 45 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ pmid population priority sample_size sd sex 1 22479202 Mixed 1 39883 0.5700000 Males and females 2 23128233 European 1 30740 NA Males and females 3 23754948 European 15 60586 8.4548000 Males 4 27089181 European 1 161460 NA Males and females 5 27225129 European 1 293723 3.7100000 Males and females 6 26833098 European 1 32161 NA Males and females 7 26833098 European 2 32161 NA Males and females 8 26414677 European 1 69360 3.9300000 Females 9 24383474 Mixed 1 7667 0.7080000 Males and females 10 22139419 European 1 66867 0.1090000 Males and females 11 27089181 European 1 170911 NA Males and females 12 22139419 European 1 66867 59.9340000 Males and females 13 27089181 European 1 298420 NA Males and females 14 23754948 European 16 73137 8.4548000 Females 15 27225129 European 3 146631 3.7100000 Males 16 27225129 European 4 182286 3.7100000 Females 17 25010111 European 1 12597 NA Males and females 18 19729612 European 1 685 0.1026116 Males and females 19 21196492 Japanese 1 10112 NA Males and females 20 25616667 European 1 3026 NA Males and females 21 25616667 European 1 938 3.4400000 Males and females 22 27089181 European 3 197174 NA Males and females 23 25451450 European 1 1374 NA Males and females 24 23754948 European 7 60586 12.5200000 Males 25 24383474 Hispanic 4 2065 0.6630000 Males and females 26 24383474 Chinese 5 702 0.6970000 Males and females 27 24383474 European 2 2431 0.7190000 Males and females 28 24383474 African American 3 2469 0.7360000 Males and females 29 21833088 European 1 27098 NA Males and females 30 24076602 European 1 38589 NA Males and females 31 NA European 1 104830 NA Males and females 32 19124843 European 1 357 NA Males and females 33 26087016 European 1 18988 NA Males and females 34 24839885 European 1 4596 NA Males and females 35 23754948 European 8 73137 12.5200000 Females 36 23449627 European 1 6974 NA Females 37 23449627 European 1 6986 NA Males 38 23449627 European 1 13960 NA Males and females 39 23449627 European 1 5756 NA Females 40 23449627 European 1 5043 NA Males 41 23449627 European 1 10799 NA Males and females 42 23449627 European 1 4946 NA Females 43 23449627 European 1 4282 NA Males 44 23754948 European 15 60586 12.5200000 Males 45 23449627 European 1 9228 NA Males and females subcategory 1 Protein 2 Autoimmune / inflammatory 3 Anthropometric 4 Psychiatric / neurological 5 Education 6 Hormone 7 Hormone 8 Reproductive aging 9 Lung disease 10 Haemotological 11 Personality 12 Haemotological 13 Psychiatric / neurological 14 Anthropometric 15 Education 16 Education 17 Hormone 18 Immune system 19 Immune system 20 Immune system 21 Immune system 22 Psychiatric / neurological 23 Psychiatric / neurological 24 Anthropometric 25 Lung disease 26 Lung disease 27 Lung disease 28 Lung disease 29 Autoimmune / inflammatory 30 Autoimmune / inflammatory 31 Cardiovascular 32 Lipid 33 Psychiatric / neurological 34 Psychiatric / neurological 35 Anthropometric 36 Anthropometric 37 Anthropometric 38 Anthropometric 39 Anthropometric 40 Anthropometric 41 Anthropometric 42 Anthropometric 43 Anthropometric 44 Anthropometric 45 Anthropometric trait unit 1 Adiponectin ln(mg/dL) 2 Crohn's disease log odds 3 Hip circumference SD (cm) 4 Depressive symptoms SD 5 Years of schooling SD (years) 6 Leptin log ng/ml 7 Leptin log ng/ml 8 Age at menopause years 9 Percent emphysema log (% emphysema + 1) 10 Mean platelet volume log fl 11 Neuroticism SD 12 Platelet count 1x10^9/l 13 Subjective well being SD 14 Hip circumference SD (cm) 15 Years of schooling SD (years) 16 Years of schooling SD (years) 17 Plasma cortisol SD 18 E-selectin log ng/ml 19 C-reactive protein SD 20 Oligoclonal band status log odds 21 Immunoglobulin G index levels log2 IgG index ratio 22 Subjective well being SD 23 Migraine in bipolar disorder log odds 24 Waist circumference SD (cm) 25 Percent emphysema log (% emphysema + 1) 26 Percent emphysema log (% emphysema + 1) 27 Percent emphysema log (% emphysema + 1) 28 Percent emphysema log (% emphysema + 1) 29 Multiple sclerosis log odds 30 Multiple sclerosis log odds 31 Abdominal aortic aneurysm 32 Lp(a) Levels 33 Aggression 34 Internalizing problems 35 Waist circumference SD (cm) 36 Height SD 37 Height SD 38 Height SD 39 Difference in height between childhood and adulthood SD 40 Difference in height between childhood and adulthood SD 41 Difference in height between childhood and adulthood SD 42 Difference in height between adolescence and adulthood SD 43 Difference in height between adolescence and adulthood SD 44 Waist circumference SD (cm) 45 Difference in height between adolescence and adulthood SD year 1 2012 2 2012 3 2013 4 2016 5 2016 6 2016 7 2016 8 2015 9 2014 10 2011 11 2016 12 2011 13 2016 14 2013 15 2016 16 2016 17 2014 18 2009 19 2016 20 2015 21 2015 22 2016 23 2015 24 2013 25 2014 26 2014 27 2014 28 2014 29 2011 30 2013 31 NA 32 2009 33 2016 34 2014 35 2013 36 2013 37 2013 38 2013 39 2013 40 2013 41 2013 42 2013 43 2013 44 2013 45 2013 [ reached 'max' / getOption("max.print") -- omitted 21237 rows ]
I think it worked - thank you for your help I really appreciate it!
explodecomputer
commented
4 years ago No worries =]
From: meghanmorrison notifications@github.com Reply to: MRCIEU/TwoSampleMR reply@reply.github.com Date: Wednesday, 25 September 2019 at 15:20 To: MRCIEU/TwoSampleMR TwoSampleMR@noreply.github.com Cc: gibran hemani explodecomputer@gmail.com, Comment comment@noreply.github.com Subject: Re: [MRCIEU/TwoSampleMR] ao <- available_outcomes() have a bug (#119)
library(TwoSampleMR) Welcome to TwoSampleMR. [>] Full documentation: https://mrcieu.github.io/TwoSampleMR [>] Check news(package='TwoSampleMR') for bug fixes and updates [>] By generating access tokens to retrieve data from the MR-Base database you consent to having your email address logged on our servers. For info on how this is used see logging_info() [>] NOTE: We will be rolling out extensive changes to the database in the next few weeks. To ensure backwards compatibility please keep the R package updated.
devtools::install_github('MarkEdmondson1234/googleAuthR@v0.8.1') Skipping install of 'googleAuthR' from a github remote, the SHA1 (cb974f8c) has not changed since last install. Use force = TRUE to force installation library(googleAuthR) ao <- available_outcomes() 2019-09-25 15:16:39> No httr_oauth_cache file found at mrbase.oauth - creating new file. Error: package ‘curl’ does not have a namespace available_outcomes(accesstoken=NULL) access author category consortium 1 public Dastani Z Risk factor ADIPOGen 2 public Jostins L Disease IIBDGC 3 public Randall JC Risk factor GIANT 4 public Okbay Risk factor SSGAC 5 public Okbay Risk factor SSGAC 6 public Kilpelainen Risk factor 7 public Kilpelainen Risk factor 8 public Day Risk factor ReproGen 9 public Manichaikul A Risk factor MESA 10 public Gieger C Risk factor HaemGen 11 public Okbay Risk factor SSGAC 12 public Gieger C Risk factor HaemGen 13 public Okbay Risk factor SSGAC 14 public Randall JC Risk factor GIANT 15 public Okbay Risk factor SSGAC 16 public Okbay Risk factor SSGAC 17 public Bolton JL Risk factor CORNET 18 public Paterson AD Risk factor DCCT & EDIC 19 public Okada Y Risk factor BioBank Japan Project 20 public Goris A Risk factor 21 public Goris A Risk factor 22 public Okbay Risk factor SSGAC 23 public Jacobsen KK Risk factor 24 public Randall JC Risk factor GIANT 25 public Manichaikul A Risk factor MESA 26 public Manichaikul A Risk factor MESA 27 public Manichaikul A Risk factor MESA 28 public Manichaikul A Risk factor MESA 29 public Sawcer S Disease IMSGC 30 public Beecham Disease IMSGC 31 public Disease IAC 32 public Ober Risk factor 33 public Pappa Disease EAGLE 34 public Benke Disease EAGLE 35 public Randall JC Risk factor GIANT 36 public Cousminer Risk factor EGG 37 public Cousminer Risk factor EGG 38 public Cousminer Risk factor EGG 39 public Cousminer Risk factor EGG 40 public Cousminer Risk factor EGG 41 public Cousminer Risk factor EGG 42 public Cousminer Risk factor EGG 43 public Cousminer Risk factor EGG 44 public Randall JC Risk factor GIANT 45 public Cousminer Risk factor EGG filename 1 adipogen.discovery.eur.meta_.public.release.txt.tab 2 CD.gwas_ichip_meta_release.txt.gz.tab 3 GIANT_Randall2013PlosGenet_stage1_publicrelease_HapMapCeuFreq_HIPadjBMI_MEN_N.txt.tab 4 DS_Full.txt.gz.tab 5 EduYears_Main.txt.gz.tab 6 Leptin_Adjusted_for_BMI.txt.tab 7 Leptin_Not_Adjusted_for_BMI.txt.tab 8 Menopause_HapMap2_DayNG2015_18112015.txt.tab 9 MESA_MetaAnallogemph950Model2.csv.tab 10 MPV.txt.tab 11 Neuroticism_Full.txt.gz.tab 12 PLT.txt.tab 13 SWB_Full.txt.gz.tab 14 GIANT_Randall2013PlosGenet_stage1_publicrelease_HapMapCeuFreq_HIPadjBMI_WOMEN_N.txt.tab 15 EduYears_Main_Men.txt.gz.tab 16 EduYears_Main_Women.txt.gz.tab 17 gwama_1_fixed.out.plasma_cortisol.tab 18 phs000086.pha002871.csv.tab 19 GWASsummary_Okada_PMID21196492_20160919.txt.gz.tab 20 Brain_2015_Goris_OCB.meta.tab 21 Brain_2015_Goris_IgG.meta.tab.all_neg 22 SWB_excl_ALSPAC_TEDS_23andMe_ldscGC_sumstats.txt.tab 23 TGEN_mig_bp_log_frq_hwe.txt.tab 24 GIANT_Randall2013PlosGenet_stage1_publicrelease_HapMapCeuFreq_WC_MEN_N.txt.tab 25 MESA_hispaniclogemph950Model2_21112011filter0.8.csv.tab 26 MESA_chineselogemph950Model2_21112011filter0.8.csv.tab 27 MESA_whitelogemph950Model2_21112011filter0.8.csv.tab 28 MESA_blacklogemph950Model2_21112011filter0.8.csv.tab 29 WTCCC2_2011.txt.tab 30 IChip_2013.txt.new.tab.all_pos 31 AAA_MetaGWAS.tab 32 phs000184.pha002870.csv.tab 33 EAGLE_aggression_Public.txt.tab 34 meta3.INTsnplist_F.txt.tab 35 GIANT_Randall2013PlosGenet_stage1_publicrelease_HapMapCeuFreq_WC_WOMEN_N.txt.tab 36 Pubertal_growth_10F.txt.gz.tab 37 Pubertal_growth_12M.txt.gz.tab 38 Pubertal_growth_10F_12M_combined.txt.gz.tab 39 Pubertal_growth_PGF.txt.gz.tab 40 Pubertal_growth_PGM.txt.gz.tab 41 Pubertal_growth_PGF_PGM_combined.txt.gz.tab 42 Pubertal_growth_PTF.txt.gz.tab 43 Pubertal_growth_PTM.txt.gz.tab 44 GIANT_Randall2013PlosGenet_stage1_publicrelease_HapMapCeuFreq_WCadjBMI_MEN_N.txt.tab 45 Pubertal_growth_PTF_PTM_combined.txt.gz.tab id mr ncase ncontrol 1 1 1 NA NA 2 10 1 14763 15977 3 100 1 NA NA 4 1000 1 NA NA 5 1001 1 NA NA 6 1002 1 NA NA 7 1003 1 NA NA 8 1004 1 NA NA 9 1005 1 NA NA 10 1006 1 NA NA 11 1007 1 NA NA 12 1008 1 NA NA 13 1009 1 NA NA 14 101 1 NA NA 15 1010 1 NA NA 16 1011 1 NA NA 17 1012 1 NA NA 18 1014 0 NA NA 19 1015 1 NA NA 20 1016 1 363 2663 21 1017 1 NA NA 22 1018 1 NA NA 23 1019 1 460 924 24 102 1 NA NA 25 1020 1 NA NA 26 1021 1 NA NA 27 1022 1 NA NA 28 1023 1 NA NA 29 1024 1 9722 17376 30 1025 1 14498 24091 31 1026 0 4972 99858 32 1027 0 NA NA 33 1028 0 NA NA 34 1029 1 NA NA 35 103 1 NA NA 36 1032 1 NA NA 37 1033 1 NA NA 38 1034 1 NA NA 39 1035 1 NA NA 40 1036 1 NA NA 41 1037 1 NA NA 42 1038 1 NA NA 43 1039 1 NA NA 44 104 1 NA NA 45 1040 1 NA NA note 1 2 3 Adjusted for BMI 4 5 6 Adjusted for BMI; effect allele frequencies are missing 7 Effect allele frequencies are missing 8 9 10 Effect allele frequencies are missing 11 12 13 14 Adjusted for BMI 15 16 17 18 Only P values available; all subjects had type 1 diabetes 19 20 All subjects had multiple sclerosis; effect allele frequeinces are missing 21 All subjects had multiple sclerosis; effect allele frequeinces are missing 22 ALSPAC; TEDS and 23andMe excluded (ALSPAC=5649; 23andME=93454; TEDS=2143) 23 24 25 26 27 28 29 30 31 Only P-values available 32 Only P-values available 33 Only P values are available 34 In preschool; effect allele frequencies are missing 35 36 Age 10; effect allele frequencies are missing 37 Age 12; effect allele frequencies are missing 38 Females at age 10 and males at age 12; effect allele frequencies are missing 39 Childhood at age 8; effect allele frequencies are missing 40 Childhood at age 8; effect allele frequencies are missing 41 Childhood at age 8; effect allele frequencies are missing 42 Adolescence at age 14; effect allele frequencies are missing 43 Adolescence at age 14; effect allele frequencies are missing 44 Adjusted for BMI 45 Adolescence at age 14; effect allele frequencies are missing nsnp 1 2675209 2 13898 3 2725796 4 6524475 5 8146841 6 2474010 7 2473865 8 2418696 9 2968584 10 2690859 11 6524433 12 2703394 13 2268675 14 2725796 15 7781138 16 7643301 17 2660192 18 841305 19 2178019 20 485523 21 242775 22 2264177 23 723225 24 2745544 25 2585998 26 2011196 27 2390490 28 2681851 29 465435 30 156632 31 5341028 32 290329 33 2188529 34 2821735 35 2740385 36 2393631 37 2500338 38 2479700 39 2390840 40 2391721 41 2384832 42 2400928 43 2401290 44 2744366 45 2401290 path 1 /projects/MRC-IEU/publicdata/GWAS_summary_data/ADIPOGEN 2 /projects/MRC-IEU/publicdata/GWAS_summary_data/IBD 3 /projects/MRC-IEU/publicdata/GWAS_summary_data/GIANT_2010_2012_2013 4 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/SSGAC_27089181 5 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/SSGAC_education_educational_attainment_2016/ 6 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/Kilpelainen_Leptin 7 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/Kilpelainen_Leptin 8 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/ReproGen_Menopause 9 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/MESA_emphysema 10 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/HaemGen_platelets/data 11 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/SSGAC_27089181 12 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/HaemGen_platelets/data 13 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/dev/release_candidate/data/results/passqc 14 /projects/MRC-IEU/publicdata/GWAS_summary_data/GIANT_2010_2012_2013 15 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/SSGAC_education_educational_attainment_2016/ 16 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/SSGAC_education_educational_attainment_2016/ 17 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/Bolton25010111/ 18 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/Paterson_19729612 19 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/Okada21196492/ 20 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/Goris25616667/Brain_2015_Goris/ 21 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/Goris25616667/Brain_2015_Goris/ 22 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/SSGAC_27089181 23 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/Jacobsen25451450 24 /projects/MRC-IEU/publicdata/GWAS_summary_data/GIANT_2010_2012_2013 25 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/MESA_emphysema 26 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/MESA_emphysema 27 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/MESA_emphysema 28 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/MESA_emphysema 29 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/IMSGC_21833088_24076602 30 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/IMSGC_21833088_24076602 31 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/IAC_Abdominal_aortic_aneurysm/ 32 /projects/MRC-IEU/publicdata/GWAS_summary_data/dbGAP_collections/Lp_a 33 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EAGLE_ADHD_Aggression_Internalizing_problems/ 34 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EAGLE_ADHD_Aggression_Internalizing_problems/ 35 /projects/MRC-IEU/publicdata/GWAS_summary_data/GIANT_2010_2012_2013 36 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ 37 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ 38 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ 39 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ 40 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ 41 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ 42 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ 43 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ 44 /projects/MRC-IEU/publicdata/GWAS_summary_data/GIANT_2010_2012_2013 45 /projects/MRC-IEU/research/data/evidencehub/summary/gwas/raw/EGG_PubertalGrowth_23449627/ pmid population priority sample_size sd sex 1 22479202 Mixed 1 39883 0.5700000 Males and females 2 23128233 European 1 30740 NA Males and females 3 23754948 European 15 60586 8.4548000 Males 4 27089181 European 1 161460 NA Males and females 5 27225129 European 1 293723 3.7100000 Males and females 6 26833098 European 1 32161 NA Males and females 7 26833098 European 2 32161 NA Males and females 8 26414677 European 1 69360 3.9300000 Females 9 24383474 Mixed 1 7667 0.7080000 Males and females 10 22139419 European 1 66867 0.1090000 Males and females 11 27089181 European 1 170911 NA Males and females 12 22139419 European 1 66867 59.9340000 Males and females 13 27089181 European 1 298420 NA Males and females 14 23754948 European 16 73137 8.4548000 Females 15 27225129 European 3 146631 3.7100000 Males 16 27225129 European 4 182286 3.7100000 Females 17 25010111 European 1 12597 NA Males and females 18 19729612 European 1 685 0.1026116 Males and females 19 21196492 Japanese 1 10112 NA Males and females 20 25616667 European 1 3026 NA Males and females 21 25616667 European 1 938 3.4400000 Males and females 22 27089181 European 3 197174 NA Males and females 23 25451450 European 1 1374 NA Males and females 24 23754948 European 7 60586 12.5200000 Males 25 24383474 Hispanic 4 2065 0.6630000 Males and females 26 24383474 Chinese 5 702 0.6970000 Males and females 27 24383474 European 2 2431 0.7190000 Males and females 28 24383474 African American 3 2469 0.7360000 Males and females 29 21833088 European 1 27098 NA Males and females 30 24076602 European 1 38589 NA Males and females 31 NA European 1 104830 NA Males and females 32 19124843 European 1 357 NA Males and females 33 26087016 European 1 18988 NA Males and females 34 24839885 European 1 4596 NA Males and females 35 23754948 European 8 73137 12.5200000 Females 36 23449627 European 1 6974 NA Females 37 23449627 European 1 6986 NA Males 38 23449627 European 1 13960 NA Males and females 39 23449627 European 1 5756 NA Females 40 23449627 European 1 5043 NA Males 41 23449627 European 1 10799 NA Males and females 42 23449627 European 1 4946 NA Females 43 23449627 European 1 4282 NA Males 44 23754948 European 15 60586 12.5200000 Males 45 23449627 European 1 9228 NA Males and females subcategory 1 Protein 2 Autoimmune / inflammatory 3 Anthropometric 4 Psychiatric / neurological 5 Education 6 Hormone 7 Hormone 8 Reproductive aging 9 Lung disease 10 Haemotological 11 Personality 12 Haemotological 13 Psychiatric / neurological 14 Anthropometric 15 Education 16 Education 17 Hormone 18 Immune system 19 Immune system 20 Immune system 21 Immune system 22 Psychiatric / neurological 23 Psychiatric / neurological 24 Anthropometric 25 Lung disease 26 Lung disease 27 Lung disease 28 Lung disease 29 Autoimmune / inflammatory 30 Autoimmune / inflammatory 31 Cardiovascular 32 Lipid 33 Psychiatric / neurological 34 Psychiatric / neurological 35 Anthropometric 36 Anthropometric 37 Anthropometric 38 Anthropometric 39 Anthropometric 40 Anthropometric 41 Anthropometric 42 Anthropometric 43 Anthropometric 44 Anthropometric 45 Anthropometric trait unit 1 Adiponectin ln(mg/dL) 2 Crohn's disease log odds 3 Hip circumference SD (cm) 4 Depressive symptoms SD 5 Years of schooling SD (years) 6 Leptin log ng/ml 7 Leptin log ng/ml 8 Age at menopause years 9 Percent emphysema log (% emphysema + 1) 10 Mean platelet volume log fl 11 Neuroticism SD 12 Platelet count 1x10^9/l 13 Subjective well being SD 14 Hip circumference SD (cm) 15 Years of schooling SD (years) 16 Years of schooling SD (years) 17 Plasma cortisol SD 18 E-selectin log ng/ml 19 C-reactive protein SD 20 Oligoclonal band status log odds 21 Immunoglobulin G index levels log2 IgG index ratio 22 Subjective well being SD 23 Migraine in bipolar disorder log odds 24 Waist circumference SD (cm) 25 Percent emphysema log (% emphysema + 1) 26 Percent emphysema log (% emphysema + 1) 27 Percent emphysema log (% emphysema + 1) 28 Percent emphysema log (% emphysema + 1) 29 Multiple sclerosis log odds 30 Multiple sclerosis log odds 31 Abdominal aortic aneurysm 32 Lp(a) Levels 33 Aggression 34 Internalizing problems 35 Waist circumference SD (cm) 36 Height SD 37 Height SD 38 Height SD 39 Difference in height between childhood and adulthood SD 40 Difference in height between childhood and adulthood SD 41 Difference in height between childhood and adulthood SD 42 Difference in height between adolescence and adulthood SD 43 Difference in height between adolescence and adulthood SD 44 Waist circumference SD (cm) 45 Difference in height between adolescence and adulthood SD year 1 2012 2 2012 3 2013 4 2016 5 2016 6 2016 7 2016 8 2015 9 2014 10 2011 11 2016 12 2011 13 2016 14 2013 15 2016 16 2016 17 2014 18 2009 19 2016 20 2015 21 2015 22 2016 23 2015 24 2013 25 2014 26 2014 27 2014 28 2014 29 2011 30 2013 31 NA 32 2009 33 2016 34 2014 35 2013 36 2013 37 2013 38 2013 39 2013 40 2013 41 2013 42 2013 43 2013 44 2013 45 2013 [ reached 'max' / getOption("max.print") -- omitted 21237 rows ]
I think it worked - thank you for your help I really appreciate it!
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