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03_submit_SAIGE_gene_step2.r
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03_submit_SAIGE_gene_step2.r
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library(data.table)
options(scipen=999)
# R wrapper to submit all SAIGE gene step2 jobs
# Need to loop over all of the required phenotype columns in each of the files that Teresa passed to us.
TRANCHE <- "200k"
# Phenotypes
cts_phenotypes <- c(
"Alanine_aminotransferase_residual",
"Albumin_residual",
"Alkaline_Phosphatase_residual",
"Apolipoprotein_A_residual",
"Apolipoprotein_B_residual",
"Aspartate_aminotransferase_residual",
"C_reactive_Protein_residual",
"Calcium_residual",
"Cholesterol_residual",
"Creatinine_Serum_residual",
"Creatinine_Urine_residual",
"Cystatin_C_Serum_residual",
"Direct_Bilirubin_residual",
"Gamma_glutamyltransferase_residual",
"Glucose_residual",
"HbA1c_residual",
"HDL_Cholesterol_residual",
"IGF_1_residual",
"Lipoprotein_A_residual",
"Microalbumin_Urine_residual",
"Oestradiol_residual",
"Phosphate_residual",
"Potassium_Urine_residual",
"Rheumatoid_factor_residual",
"SHBG_residual",
"Sodium_Urine_residual",
"Testosterone_residual",
"Total_Bilirubin_residual",
"Total_Protein_residual",
"Triglyceride_residual",
"Urate_residual",
"Urea_residual",
"Vitamin_D_residual",
# "Alanine_aminotransferase_M_residual",
# "Albumin_M_residual",
# "Alkaline_Phosphatase_M_residual",
# "Apolipoprotein_A_M_residual",
# "Apolipoprotein_B_M_residual",
# "Aspartate_aminotransferase_M_residual",
# "C_reactive_Protein_M_residual",
# "Calcium_M_residual",
# "Cholesterol_M_residual",
# "Creatinine_Serum_M_residual",
# "Creatinine_Urine_M_residual",
# "Cystatin_C_Serum_M_residual",
# "Direct_Bilirubin_M_residual",
# "Gamma_glutamyltransferase_M_residual",
# "Glucose_M_residual",
# "HbA1c_M_residual",
# "HDL_Cholesterol_M_residual",
# "IGF_1_M_residual",
# "Lipoprotein_A_M_residual",
# "Microalbumin_Urine_M_residual",
# "Oestradiol_M_residual",
# "Phosphate_M_residual",
# "Potassium_Urine_M_residual",
# "Rheumatoid_factor_M_residual",
# "SHBG_M_residual",
# "Sodium_Urine_M_residual",
# "Testosterone_M_residual",
# "Total_Bilirubin_M_residual",
# "Total_Protein_M_residual",
# "Triglyceride_M_residual",
# "Urate_M_residual",
# "Urea_M_residual",
# "Vitamin_D_M_residual",
# "Alanine_aminotransferase_F_residual",
# "Albumin_F_residual",
# "Alkaline_Phosphatase_F_residual",
# "Apolipoprotein_A_F_residual",
# "Apolipoprotein_B_F_residual",
# "Aspartate_aminotransferase_F_residual",
# "C_reactive_Protein_F_residual",
# "Calcium_F_residual",
# "Cholesterol_F_residual",
# "Creatinine_Serum_F_residual",
# "Creatinine_Urine_F_residual",
# "Cystatin_C_Serum_F_residual",
# "Direct_Bilirubin_F_residual",
# "Gamma_glutamyltransferase_F_residual",
# "Glucose_F_residual",
# "HbA1c_F_residual",
# "HDL_Cholesterol_F_residual",
# "IGF_1_F_residual",
# "Lipoprotein_A_F_residual",
# "Microalbumin_Urine_F_residual",
# "Oestradiol_F_residual",
# "Phosphate_F_residual",
# "Potassium_Urine_F_residual",
# "Rheumatoid_factor_F_residual",
# "SHBG_F_residual",
# "Sodium_Urine_F_residual",
# "Testosterone_F_residual",
# "Total_Bilirubin_F_residual",
# "Total_Protein_F_residual",
# "Triglyceride_F_residual",
# "Urate_F_residual",
# "Urea_F_residual",
# "Vitamin_D_F_residual",
"BMI",
"BMI_imp",
"standing_height",
"VAT",
"AFR",
"AMRA",
"body_fat_percentage",
"waist_circumference",
"hip_circumference",
"WHR",
"WHR_adj_BMI" #,
# "WHR_adj_BMI_M"
# "WHR_adj_BMI_F"
)
binary_phenotypes <- c(
"BC_combined",
"BC_combined_primary_care",
"CAD_combined",
"CAD_combined_primary_care",
"COPD_combined",
"COPD_combined_primary_care",
"CLD_combined",
"CLD_combined_primary_care",
"CC_combined",
"CC_combined_primary_care",
"DEM_combined",
"DEM_combined_primary_care",
"INF_combined",
"INF_combined_primary_care",
"LC_combined",
"LC_combined_primary_care",
"NAFLD_combined",
"NAFLD_combined_primary_care",
"RF_combined",
"RF_combined_primary_care",
"RF_acute_combined",
"RF_acute_combined_primary_care",
"RF_chronic_combined",
"RF_chronic_combined_primary_care",
"STR_combined",
"STR_combined_primary_care",
"STR_hem_combined",
"STR_hem_combined_primary_care",
"STR_isc_combined",
"STR_isc_combined_primary_care",
"ADHD_combined",
"ADHD_combined_primary_care",
"AD_combined",
"AD_combined_primary_care",
"DEP_combined",
"DEP_combined_primary_care",
"HAM_combined",
"HAM_combined_primary_care",
"AUT_combined",
"AUT_combined_primary_care",
"CIRR_combined",
"CIRR_combined_primary_care",
"CD_combined",
"CD_combined_primary_care",
"IBD_combined",
"IBD_combined_primary_care",
"NASH_combined",
"NASH_combined_primary_care",
"PSOR_combined",
"PSOR_combined_primary_care",
"HEM_combined",
"HEM_combined_primary_care",
"PRO_combined",
"PRO_combined_primary_care",
"OLI_combined",
"OLI_combined_primary_care",
"PRE_combined",
"PRE_combined_primary_care",
"EP_combined",
"EP_combined_primary_care",
"PKD_combined",
"PKD_combined_primary_care",
"HAB_combined",
"HAB_combined_primary_care",
"POI_combined",
"POI_combined_primary_care",
"HYP_combined",
"HYP_combined_primary_care",
"DM_T1D",
"DM_T2D",
"DM_GD")
# annotations <- c('pLoF', 'damaging_missense|LC', 'pLoF|damaging_missense|LC', 'pLoF|damaging_missense', 'damaging_missense', 'other_missense', 'synonymous')
annotations <- c('pLoF', 'damaging_missense', 'other_missense', 'synonymous')
submission_script <- "03_SAIGE_gene_step2.sh"
# Quantitative traits
# Loop and submit the jobs separately using qsub
# For each phenotype, wes want to run gene-based tests for distinct MAF cutoffs
minMAF_maxMAFforGroupTest <- data.table(
minMAF = c(0),
maxMAFforGroupTest = c(0.01)
)
vcfFile <- paste0("/well/lindgren/UKBIOBANK/dpalmer/wes_", TRANCHE, "/ukb_wes_qc/data/final_mt/10_european.strict_filtered_chr@.vcf.bgz")
vcfFileIndex <- paste0("/well/lindgren/UKBIOBANK/dpalmer/wes_", TRANCHE, "/ukb_wes_qc/data/final_mt/10_european.strict_filtered_chr@.vcf.bgz.csi")
phenotypeFolder <- paste0("/well/lindgren/UKBIOBANK/dpalmer/ukb_wes_SAIGE_output/", TRANCHE)
for (annotation in annotations) {
if (annotation %in% c('pLoF', 'damaging_missense|LC', 'pLoF|damaging_missense|LC', 'pLoF|damaging_missense', 'damaging_missense', 'synonymous')) {
queue <- "short.qe@@short.hge"
} else {
queue <- "long.qf@@long.hgf"
}
annotation_filename <- gsub("\\|", "_", annotation)
groupFile <- paste0("/well/lindgren/UKBIOBANK/dpalmer/ukb_wes_variants_vep/", TRANCHE, "/SAIGE_gene_input/ukb_wes_", TRANCHE, "_filtered_chr@_", annotation_filename, "_saige_gene.tsv.gz")
for (phenotype in cts_phenotypes) {
for (row in 1:nrow(minMAF_maxMAFforGroupTest)) {
job_options <-paste(
"-q", queue,
"-v",
paste0(
"vcfFile=\"", vcfFile, "\",",
"vcfFileIndex=\"", vcfFileIndex, "\",",
"phenotypeFolder=\"", phenotypeFolder, "\",",
"phenotype=\"", phenotype, "\",",
"groupFile=\"", groupFile, "\",",
"minMAF=\"", minMAF_maxMAFforGroupTest$minMAF[row], "\",",
"maxMAFforGroupTest=\"", minMAF_maxMAFforGroupTest$maxMAFforGroupTest[row], "\""
)
)
job_submission <- paste("qsub", job_options, submission_script)
system(job_submission)
}
}
}
# Binary traits
# Loop and submit the jobs separately using qsub
# For each phenotype, we want to run gene-based tests for distinct MAF cutoffs
for (annotation in annotations) {
if (annotation %in% c('pLoF', 'damaging_missense|LC', 'pLoF|damaging_missense|LC', 'pLoF|damaging_missense', 'damaging_missense', 'synonymous')) {
queue <- "short.qe@@short.hge"
} else {
queue <- "long.qf@@long.hgf"
}
annotation_filename <- gsub("\\|", "_", annotation)
groupFile <- paste0("/well/lindgren/UKBIOBANK/dpalmer/ukb_wes_variants_vep/", TRANCHE, "/SAIGE_gene_input/ukb_wes_", TRANCHE, "_filtered_chr@_", annotation_filename, "_saige_gene.tsv.gz")
for (phenotype in binary_phenotypes) {
for (row in 1:nrow(minMAF_maxMAFforGroupTest)) {
job_options <- paste(
"-q", queue,
"-v",
paste0(
"vcfFile=\"", vcfFile, "\",",
"vcfFileIndex=\"", vcfFileIndex, "\",",
"phenotypeFolder=\"", phenotypeFolder, "\",",
"phenotype=\"", phenotype, "\",",
"groupFile=\"", groupFile, "\",",
"minMAF=\"", minMAF_maxMAFforGroupTest$minMAF[row], "\",",
"maxMAFforGroupTest=\"", minMAF_maxMAFforGroupTest$maxMAFforGroupTest[row], "\""
)
)
job_submission <- paste("qsub", job_options, submission_script)
system(job_submission)
}
}
}