Data
XSun
2023-11-13
Last updated: 2024-12-12
Checks: 6 1
Knit directory: multigroup_ctwas_analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 6b46378 | XSun | 2024-12-11 | update |
| html | 6b46378 | XSun | 2024-12-11 | update |
| Rmd | afa8021 | XSun | 2024-11-22 | update |
| html | afa8021 | XSun | 2024-11-22 | update |
| Rmd | ec8887f | XSun | 2024-11-21 | update |
| html | ec8887f | XSun | 2024-11-21 | update |
| Rmd | c420007 | XSun | 2024-11-21 | update |
| html | c420007 | XSun | 2024-11-21 | update |
| Rmd | bb59ace | XSun | 2023-12-05 | updadate |
| html | bb59ace | XSun | 2023-12-05 | updadate |
| Rmd | ddf474a | Xiaotong Sun | 2023-11-13 | update |
| html | ddf474a | Xiaotong Sun | 2023-11-13 | update |
GWAS
Round 1
| Sample size | Ancestry | ID | Year | ref | link | |
|---|---|---|---|---|---|---|
| IBD (same with ctwas paper) | 59,957 | Mixed | ebi-a-GCST004131 | 2017 | PMID: 28067908 | https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST004131/ |
| SBP (same with ctwas paper) | 317,754 | European | ukb-a-360 | 2017 | Rapid GWAS Project. In: http://www.nealelab.is/uk-biobank (accessed in 2021). | https://gwas.mrcieu.ac.uk/datasets/ukb-a-360/ |
| LDL (same with ctwas paper) | 343,621 | European | ukb-d-30780_irnt | 2018 | same with above | https://gwas.mrcieu.ac.uk/datasets/ukb-d-30780_irnt/ |
| SCZ | 127,906 | European | ieu-b-5102 | 2022 | PMID: 35396580 | https://gwas.mrcieu.ac.uk/datasets/ieu-b-5102/ |
| AFib | 1,030,836 | European | ebi-a-GCST006414 | 2018 | PMID: 30061737 | https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST006414/ |
| Asthma | 361,194 | European | ukb-d-J10_ASTHMA | 2018 | http://www.nealelab.is/uk-biobank/ (cite this website) | https://gwas.mrcieu.ac.uk/datasets/ukb-d-J10_ASTHMA/ |
| WBC | 563,946 | European | ieu-b-30 | 2020 | PMID: 32888494 | https://gwas.mrcieu.ac.uk/datasets/ieu-b-30/ |
Extended 1
Munro’s traits (GTEx)
https://zenodo.org/records/3629742#.XjCh9OF7m90
gtex <- data.table::fread("/project/xinhe/xsun/multi_group_ctwas/data/gwas/gwas_metadata_gtex.txt")
#gwas_selected_gtex <- readxl::read_excel("/project/xinhe/xsun/multi_group_ctwas/data/gwas/gwas_selected.xlsx",sheet = 4)
gwas_ukb <- gtex[gtex$Consortium == "UK Biobank",]
gwas_ukb_show <- gwas_ukb[,c("Category","new_Phenotype","new_abbreviation","Sample_Size","Cases","Tag")]
gwas_ukb_show <- gwas_ukb_show[order(gwas_ukb_show$Category),]
DT::datatable(gwas_ukb_show,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;',"Munro's GWAS (GTEx, only UKB are shown)"),options = list(pageLength = 10) )gwas_noukb <- gtex[gtex$Consortium != "UK Biobank",]
gwas_noukb <- gwas_noukb[,c("Category","new_Phenotype","new_abbreviation","Sample_Size","Cases","Tag")]
gwas_noukb_show <- gwas_noukb[order(gwas_noukb$Category),]
DT::datatable(gwas_noukb_show,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;',"Munro's GWAS (GTEx, non-UKB are shown)"),options = list(pageLength = 10) )Pan-UKB
https://www.medrxiv.org/content/10.1101/2024.03.13.24303864v2.full.pdf
https://pan.ukbb.broadinstitute.org/downloads/index.html
load("/project/xinhe/xsun/multi_group_ctwas/data/gwas/panukb.rdata")
gwas_selected_panukb <- readxl::read_excel("/project/xinhe/xsun/multi_group_ctwas/data/gwas/gwas_selected.xlsx",sheet = 3)
gwas_detail <- merge(gwas_selected_panukb, panukb_cut, by.x = "ID", by.y = "filename", all.x = TRUE)
gwas_detail <- gwas_detail[match(gwas_selected_panukb$traits,gwas_detail$traits),]
gwas_detail <- gwas_detail[c(setdiff(names(gwas_detail), "ID"), "ID")]
gwas_detail <- gwas_detail[,c("trait_category","sub_category","traits","abbreviation","trait_type","trait_efo_terms","description","category","pops","n_cases_EUR","n_controls_EUR","wget","ID")]
gwas_detail <- gwas_detail[complete.cases(gwas_detail$wget),]
DT::datatable(gwas_detail,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','GWAS selected from pan-ukb'),options = list(pageLength = 10) )panukb_cut <- panukb_cut[,c("trait_type","trait_efo_terms","description","category","pops","n_cases_EUR","n_controls_EUR","wget")]
DT::datatable(panukb_cut,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','All GWAS from pan-ukb'),options = list(pageLength = 5) )Warning in instance$preRenderHook(instance): It seems your data is too
big for client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.htmlUKB+Finn
https://www.nature.com/articles/s41588-021-00931-x#Sec9
load("/project/xinhe/xsun/multi_group_ctwas/data/gwas/traits_ukb.rdata")
gwas_selected_ukbfin <- readxl::read_excel("/project/xinhe/xsun/multi_group_ctwas/data/gwas/gwas_selected.xlsx",sheet = 2)
gwas_detail <- merge(gwas_selected_ukbfin, gwas_catalog, by.x = "ID", by.y = "id", all.x = TRUE)
gwas_detail <- gwas_detail[match(gwas_selected_ukbfin$traits,gwas_detail$traits),]
gwas_detail <- gwas_detail[c(setdiff(names(gwas_detail), "ID"), "ID")]
DT::datatable(gwas_detail,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','GWAS selected from ukb+finn meta'),options = list(pageLength = 10) )TGFM
gwas <- read.table("/project/xinhe/xsun/multi_group_ctwas/data/gwas/TGFM.txt",header = T,fill = T, sep = "\t")
DT::datatable(gwas,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','GWAS used in TGFM paper'),options = list(pageLength = 10) )Weights
PredictDB weights
storaged in /project2/xinhe/shared_data/multigroup_ctwas/weights/predictdb_nolnc/mashr_“$WEIGHT”_nolnc.db
LD ref
from UKBB
storaged in
hg38 /project2/mstephens/wcrouse/UKB_LDR_0.1/
hg37 /project2/mstephens/wcrouse/UKB_LDR_0.1_b37/
download: https://uchicago.box.com/s/jqocacd2fulskmhoqnasrknbt59x3xkn
Manhattan plots for the GWAS data
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
locale:
[1] C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] Rcpp_1.0.12 cellranger_1.1.0 pillar_1.9.0 compiler_4.2.0
[5] bslib_0.3.1 later_1.3.0 jquerylib_0.1.4 git2r_0.30.1
[9] workflowr_1.7.0 tools_4.2.0 digest_0.6.29 jsonlite_1.8.0
[13] evaluate_0.15 lifecycle_1.0.4 tibble_3.2.1 pkgconfig_2.0.3
[17] rlang_1.1.2 cli_3.6.1 rstudioapi_0.13 crosstalk_1.2.0
[21] yaml_2.3.5 xfun_0.41 fastmap_1.1.0 stringr_1.5.1
[25] knitr_1.39 fs_1.5.2 vctrs_0.6.5 sass_0.4.1
[29] htmlwidgets_1.5.4 rprojroot_2.0.3 DT_0.22 glue_1.6.2
[33] data.table_1.14.2 R6_2.5.1 fansi_1.0.3 readxl_1.4.0
[37] rmarkdown_2.25 magrittr_2.0.3 whisker_0.4 promises_1.2.0.1
[41] htmltools_0.5.2 httpuv_1.6.5 utf8_1.2.2 stringi_1.7.6