Last updated: 2020-06-23
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Knit directory: PSYMETAB/
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| File | Version | Author | Date | Message |
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| Rmd | a56ae32 | Sjaarda Jennifer Lynn | 2020-01-22 | add GWAS functions |
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| Rmd | 16f6c28 | Sjaarda Jennifer Lynn | 2019-12-06 | add gwas Rmd |
The following document outlines and summarizes the GWAS analyses run in PSYMETAB.
found that GYYEHMDR is listed as both Male and Female -> should be female only.
After discussion with others, it was decided to define drugs into three classes
AP1 pour faible risque de prise de poids: Amisulpride, Aripiprazole, Brexpiprazole, Cariprazine, Carbamazepine, Chlorprothixene, Flupentixol, Fluphenazine, Haloperidol, Lurasidone, Pipampérone, Promazine, Sertindole, Sulpiride, Tiapride
AP2 pour risque intermédiaire de prise de poids: Amitriptyline, Asenapine, Clomipramine, Dibenzepine, Doxepine, Imipramine, Lévomépromazine, Lithium, Mirtazapine, Nortriptyline, Opipramol, Palipéridone, Quétiapine, Rispéridone, Trimipramine, Zuclopenthixol
AP3 pour risque élevé de prise de poids: Clozapine, Olanzapine, Valproate
Specifically, high, medium and low inducers were defined as follows:
high_inducers[1] "Olanzapine" "Clozapine" "Valproate" med_inducers [1] "Amitriptyline" "Asenapine" "Clomipramine" "Doxepine"
[5] "Levomepromazine" "Lithium" "Mirtazapine" "Paliperidone"
[9] "Risperidone" "Quetiapine" "Trimipramine" "Zuclopenthixol" low_inducers [1] "Amisulpride" "Aripiprazole" "Brexpiprazole" "Cariprazine"
[5] "Carbamazepine" "Chlorprothixene" "Flupentixol" "Fluphenazine"
[9] "Haloperidol" "Lurasidone" "Pipamperone" "Sertindole"
[13] "Sulpiride" "Tiapride" Plus or minus on follow-up to double check that the “Mois” column make sense.
leeway_time <- 90 For example, if one visit is marked month 3 on September 5/15; and the previous visit marked month 0 on June 15/19, the script below will check if: the difference between the two dates (September 5/15 - June 15/19) is within the difference between the two months (3 = 90 days) +/- the ‘leeway_time’
min_follow_up <- 14
Three types of GWAS were run
het_out <-
fread(
paste0("analysis/GWAS/subgroup/", eth, "/", output, "_", suffix,
"_", eth, ".", outcome_var, ".het"),
header = F,
data.table = F
)
sig_list <- which(as.numeric(het_out[, 2]) < 5e-08)
length(sig_list)
colnames(het_out) <- c("ID", "P_HET")
result <- cbind(filter, "P_HET" = het_out$P_HET)
png(
paste0("analysis/GWAS/subgroup/", eth, "/", output, "_", suffix, "_", eth, ".", outcome_var, ".het.manhattan.png"),
width = 2000,
height = 1000,
pointsize = 18
)
manhattan(result, p = "P_HET", chr = "CHROM")
dev.off()
png(
paste0(
"analysis/GWAS/subgroup/", eth, "/", output, "_", suffix, "_", eth, ".", outcome_var, ".het.qq.png"),
width = 2000,
height = 1000,
pointsize = 18
)
qq(result$P_HET)
dev.off()
#### GWAS
sig_nodrug <-nodrug[ which(nodrug$P < 5e-08),]
sig_drug <- drug[which(drug$P < 5e-08),]
for(data in c("nodrug", "drug"))
{
gwas_result <- get(data)
joint <- reduce(list(gwas_result,freq,info), full_join, by = "ID")
sig <- joint %>%
mutate_at("P", as.numeric) %>%
filter(P < 5e-06) %>%
filter(ALT_FREQS > maf_threshold & ALT_FREQS < (1 - maf_threshold)) %>%
filter(R2 > info_threshold)
joint_maf <- joint %>% filter(ALT_FREQS > maf_threshold & ALT_FREQS < (1- maf_threshold))%>% mutate_at("P", as.numeric)
sig <- joint_maf %>% filter(P < gw_sig)
png("man_interaction.png", width=2000, height=1000, pointsize=18)
manhattan(joint_maf)
dev.off()
png("interaction_qq2.png", width=2000, height=1000, pointsize=18)
qq(joint_maf$P)
dev.off()
qq(gwas_result2$P)
# sig_nodrug <- sig
}
gwas_result <- fread(result, data.table=F, stringsAsFactors=F)
gwas_result2 <- gwas_result %>% rename(CHR = "#CHROM") %>% rename(BP = POS) %>% filter(!is.na(P))
#### GWAS
sig_nodrug <-nodrug[ which(nodrug$P < 5e-08),]
sig_drug <- drug[which(drug$P < 5e-08),]
for(data in c("nodrug", "drug"))
{
gwas_result <- get(data)
gwas_munge <- gwas_result %>% rename(CHR = "#CHROM") %>% rename(BP = POS) %>% filter(!is.na(P))
joint <- reduce(list(gwas_munge,freq,info), full_join, by = "ID")
sig <- joint %>%
mutate_at("P", as.numeric) %>%
filter(P < 5e-06) %>%
filter(ALT_FREQS > maf_threshold & ALT_FREQS < (1 - maf_threshold)) %>%
filter(R2 > info_threshold)
# sig_nodrug <- sig
}loadd(pheno_raw)
# temp <- pheno_raw %>%
# mutate(Date = as.Date(Date, format = '%d.%m.%Y')) %>% filter(!is.na(Date)) %>% arrange(Date) %>%
# mutate(AP1 = gsub(" ", "_",AP1)) %>% mutate_at("AP1",as.factor) %>% mutate(AP1 = gsub("_.*$","", AP1)) %>% mutate(AP1 = na_if(AP1, "")) %>%## merge retard/depot with original
# group_by(GEN) %>% mutate(sex = check_sex(Sexe))
# temp %>% filter(is.na(sex)) %>% dplyr::select(sex, Sexe) %>% filter(!is.na(Sexe))
#
# GEN sex Sexe
# <chr> <chr> <chr>
# 1 GYYEHMDR NA M
# 2 GYYEHMDR NA M
# 3 GYYEHMDR NA M
# 4 GYYEHMDR NA F
# 5 GYYEHMDR NA F
sessionInfo()R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /data/sgg2/jenny/bin/R-3.5.3/lib64/R/lib/libRblas.so
LAPACK: /data/sgg2/jenny/bin/R-3.5.3/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tidylog_1.0.1 OpenImageR_1.1.6 fuzzyjoin_0.1.5
[4] kableExtra_1.1.0 R.utils_2.9.2 R.oo_1.23.0
[7] R.methodsS3_1.7.1 TwoSampleMR_0.4.25 reader_1.0.6
[10] NCmisc_1.1.6 optparse_1.6.4 readxl_1.3.1
[13] ggthemes_4.2.0 tryCatchLog_1.1.6 futile.logger_1.4.3
[16] DataExplorer_0.8.0 taRifx_1.0.6.1 qqman_0.1.4
[19] MASS_7.3-51.5 bit64_0.9-7 bit_1.1-14
[22] rslurm_0.5.0 rmeta_3.0 devtools_2.2.1
[25] usethis_1.5.1 data.table_1.12.8 clustermq_0.8.8.1
[28] future.batchtools_0.8.1 future_1.15.1 rlang_0.4.5
[31] knitr_1.26 drake_7.12.0.9000 forcats_0.4.0
[34] stringr_1.4.0 dplyr_0.8.3 purrr_0.3.3
[37] readr_1.3.1 tidyr_1.0.3 tibble_2.1.3
[40] ggplot2_3.2.1 tidyverse_1.3.0 pacman_0.5.1
[43] processx_3.4.1 workflowr_1.6.0
loaded via a namespace (and not attached):
[1] backports_1.1.6 plyr_1.8.5 igraph_1.2.5
[4] lazyeval_0.2.2 storr_1.2.1 listenv_0.8.0
[7] digest_0.6.25 htmltools_0.4.0 tiff_0.1-5
[10] fansi_0.4.1 magrittr_1.5 checkmate_1.9.4
[13] memoise_1.1.0 base64url_1.4 remotes_2.1.0
[16] globals_0.12.5 modelr_0.1.5 prettyunits_1.1.0
[19] jpeg_0.1-8.1 colorspace_1.4-1 rvest_0.3.5
[22] rappdirs_0.3.1 haven_2.2.0 xfun_0.11
[25] callr_3.4.0 crayon_1.3.4 jsonlite_1.6
[28] brew_1.0-6 glue_1.4.0 gtable_0.3.0
[31] webshot_0.5.2 pkgbuild_1.0.6 scales_1.1.0
[34] futile.options_1.0.1 DBI_1.1.0 Rcpp_1.0.3
[37] xtable_1.8-4 viridisLite_0.3.0 progress_1.2.2
[40] txtq_0.2.0 clisymbols_1.2.0 htmlwidgets_1.5.1
[43] httr_1.4.1 getopt_1.20.3 calibrate_1.7.5
[46] ellipsis_0.3.0 pkgconfig_2.0.3 dbplyr_1.4.2
[49] tidyselect_0.2.5 reshape2_1.4.3 later_1.0.0
[52] munsell_0.5.0 cellranger_1.1.0 tools_3.5.3
[55] cli_2.0.1 generics_0.0.2 broom_0.5.3
[58] fastmap_1.0.1 evaluate_0.14 yaml_2.2.0
[61] fs_1.3.1 packrat_0.5.0 nlme_3.1-143
[64] mime_0.8 whisker_0.4 formatR_1.7
[67] proftools_0.99-2 xml2_1.2.2 compiler_3.5.3
[70] rstudioapi_0.10 png_0.1-7 filelock_1.0.2
[73] testthat_2.3.1 reprex_0.3.0 stringi_1.4.5
[76] ps_1.3.0 desc_1.2.0 lattice_0.20-38
[79] vctrs_0.2.4 pillar_1.4.3 lifecycle_0.1.0
[82] networkD3_0.4 httpuv_1.5.2 R6_2.4.1
[85] promises_1.1.0 gridExtra_2.3 sessioninfo_1.1.1
[88] codetools_0.2-16 lambda.r_1.2.4 assertthat_0.2.1
[91] pkgload_1.0.2 rprojroot_1.3-2 withr_2.1.2
[94] batchtools_0.9.12 parallel_3.5.3 hms_0.5.3
[97] grid_3.5.3 rmarkdown_1.18 git2r_0.26.1
[100] shiny_1.4.0 lubridate_1.7.4