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2.2_LDSR_ss_regress_wave1.sb
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2.2_LDSR_ss_regress_wave1.sb
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#!/bin/bash --login
########## SBATCH Lines for Resource Request ##########
#SBATCH --time=4:00:00 # limit of wall clock time - how long the job will run (same as -t)
#SBATCH --nodes=1-24 # number of different nodes - could be an exact number or a range of nodes (same as -N)
#SBATCH --ntasks=24 # number of tasks - how many tasks (nodes) that you require (same as -n)
#SBATCH --cpus-per-task=2 # number of CPUs (or cores) per task (same as -c)
#SBATCH --mem-per-cpu=64G # memory required per allocated CPU (or core) - amount of memory (in bytes)
#SBATCH --job-name Wave2.2 # you can give your job a name for easier identification (same as -J)
#### NOTES ####
## This LDSR program is ridiculously finicky. You can't have any modules loaded, or anything in your path
## it doesn't expect. I also had to uninstall linuxbrew completely, because for some reason it was
## still trying to use the linuxbrew glibc *even though it was absolutly not in my path anymore* I
## don't even know how that's possible.
#######################
##### For interactive testing:
#screen
#qsub -I -N MyJobName -l nodes=1:ppn=8,mem=64gb,walltime=04:00:00,feature='intel18'
#######################
########## Command Lines to Run ##########
module purge
#module load GCC/6.4.0-2.28 OpenMPI ### load necessary modules, e.g.
cd $SLURM_SUBMIT_DIR || exit
export PATH=$PATH:$PWD/tools/bin/
module load Anaconda2/4.2.0
#conda create --name ldsr python=2
source activate ldsr
# pip install pandas==0.17.0 numpy==1.8.0 scipy==0.11.0 bitarray==0.8.3
# ======== prepare summary stats ========
## Raw summary stat files should be saved in the ss folder
# folder for munged sumstats files
mkdir -p mss
mkdir -p wmss
mkdir -p wss
##############################################
## Psychiatric Traits
## Each of these traits (Anxiety, Bipolar, etc) has it's own version of the code, because the GWAS summary file are all formatted
## differently, so you can't just loop through them. For most of them, there are 6 lines of code to run. They are labeled in the
## Anxiety code below, and explained in detail here:
## 1. run bash src/s5-provide-summary-statistics.sh on it. This is a script Jory wrote, and it essentially looks to see how
## different that summary stat file is from the expected format, and tells you what columns it couldn't locate. It will
## print the first line of the file to the screen, and also give you a copy-pasteable line of code for running munge_sumstats.py
## with asterisks where you need to fill in info. Just paste the correct column name for each set of asterisks so it can find the
## the correct columns.
## Lines 2-4: Mark wanted to Winsorize the pvalues to see if it changes the results. Lines 2-4 do that operation. It replaces any pvalue
## less than 1e-7 with 1e-7. In this code $X specifies a column, by number and "\t" in the print statement inserts a tab character.
## 2. Prints the column names from the original file to the new winsorized file
## 3. Uses awk to do a find and replace for the pvalues and print it all to the new winsorized file.
## 4. Zips new winsorized file
## 5. Runs munge_sumstats.py on the original, non-winsorized file
## 6. Runs munge_sumstats.py on the new, winsorized file
## To make a new code block so you can add a new summary stats file. Copy the Anxiety code block and then:
## In line:
## 1. Change the file name anxiety.meta.full.fs.tbl.gz to name_of_new_file
## 2. Change both instances of anxiety.meta.full.fs.tbl.gz to name_of_new_file
## 3. Change both instances of anxiety.meta.full.fs.tbl.gz to name_of_new_file AND
## Change the first $9 to $X where X is the number of the column where the pvalues are in the new file AND
## Move the 0.0000001 in the first print statement to replace the $X AND
## Replace the space left by 0.0000001 with $9 AND
## Add or subtract instances of tabs and column calls in both print statements so that all columns from the origianl file
## are printed and have tabs between them
## 4. Change the file name anxiety.meta.full.fs.tbl.gz to name_of_new_file
## 5. Actually manually run the first line of code, copy out the line indicated, edit as necessary for this file
## 6. Copy the line from 5, but change the directories as follows:
## ss becomes wss
## mss becomes wmss
### Anxiety
# Code line 1: view columns
bash src/s5-provide-summary-statistics.sh ss/anxiety.meta.full.fs.tbl.gz
# Winsorizing the pvalues from the trait file. All values less than 1e-7 get changes to 1e-7. Winsorized files in wss and wmss folders
# Code line 2: Get the column names
zcat ss/anxiety.meta.full.fs.tbl.gz | head -n 1 > wss/anxiety.meta.full.fs.tbl
# Code line 3: Replace the values in the pvalue column with 1e-7 if they are less than 1e-7
zcat ss/anxiety.meta.full.fs.tbl.gz | awk 'FNR > 1{
if ((0+$9)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" 0.0000001 "\t" $10;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10;
}' >> wss/anxiety.meta.full.fs.tbl
# Code line 4: Zip the winsorized file
gzip -f wss/anxiety.meta.full.fs.tbl
# munge data
# Code line 5: Run munge_sumstats on the non-winsorized file
python tools/ldsc/munge_sumstats.py --sumstats ss/anxiety.meta.full.fs.tbl.gz --out mss/munged.anxiety.meta.full.fs.tbl --merge-alleles s5/w_hm3.snplist --a1-inc --N 18186 --snp SNPID --a1 Allele1 --a2 Allele2 --p P.value
# Code line 6: Run munge_sumstats on the non-winsorized file
python tools/ldsc/munge_sumstats.py --sumstats wss/anxiety.meta.full.fs.tbl.gz --out wmss/munged.winz.anxiety.meta.full.fs.tbl --merge-alleles s5/w_hm3.snplist --a1-inc --N 18186 --snp SNPID --a1 Allele1 --a2 Allele2 --p P.value
### Autism
# view columns for Autism and munge
# N from Table S6 of https://www.biorxiv.org/content/10.1101/428391v2.full
bash src/s5-provide-summary-statistics.sh ss/iPSYCH-PGC_ASD_Nov2017.gz
zcat ss/iPSYCH-PGC_ASD_Nov2017.gz | head -n 1 > wss/iPSYCH-PGC_ASD_Nov2017
zcat ss/iPSYCH-PGC_ASD_Nov2017.gz | awk 'FNR > 1{
if ((0+$9)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" 0.0000001 ;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9;
}' >> wss/iPSYCH-PGC_ASD_Nov2017
gzip -f wss/iPSYCH-PGC_ASD_Nov2017
python tools/ldsc/munge_sumstats.py --sumstats ss/iPSYCH-PGC_ASD_Nov2017.gz --out mss/munged.iPSYCH-PGC_ASD_Nov2017 --merge-alleles s5/w_hm3.snplist --a1-inc --N-cas 18381 --N-con 27969
python tools/ldsc/munge_sumstats.py --sumstats wss/iPSYCH-PGC_ASD_Nov2017.gz --out wmss/munged.winz.iPSYCH-PGC_ASD_Nov2017 --merge-alleles s5/w_hm3.snplist --a1-inc --N-cas 18381 --N-con 27969
### Bipolar
# view columns for Bipolar and munge
bash src/s5-provide-summary-statistics.sh ss/daner_PGC_BIP32b_mds7a_0416a.gz
# Winsorizing the pvalues from the trait file. All values less than 1e-7 get changes to 1e-7. Winsorized files in wss and wmss folders
zcat ss/daner_PGC_BIP32b_mds7a_0416a.gz | head -n 1 > wss/daner_PGC_BIP32b_mds7a_0416a
zcat ss/daner_PGC_BIP32b_mds7a_0416a.gz | awk 'FNR > 1{
if ((0+$11)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10 "\t" 0.0000001 "\t" $12 "\t" $13 "\t" $14 "\t" $15 "\t" $16 "\t" $17 "\t" $18 "\t" $19;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10 "\t" $11 "\t" $12 "\t" $13 "\t" $14 "\t" $15 "\t" $16 "\t" $17 "\t" $18 "\t" $19;
}' >> wss/daner_PGC_BIP32b_mds7a_0416a
gzip -f wss/daner_PGC_BIP32b_mds7a_0416a
# let it calculate N from daner columns
python tools/ldsc/munge_sumstats.py --sumstats ss/daner_PGC_BIP32b_mds7a_0416a.gz --out mss/munged.daner_PGC_BIP32b_mds7a_0416a --a1-inc --daner-n --snp SNP --a1 A1 --a2 A2 --p P
python tools/ldsc/munge_sumstats.py --sumstats wss/daner_PGC_BIP32b_mds7a_0416a.gz --out wmss/munged.winz.daner_PGC_BIP32b_mds7a_0416a --a1-inc --daner-n --snp SNP --a1 A1 --a2 A2 --p P
### MDD (major depressive disorder)
# view columns for MDD and munge
bash src/s5-provide-summary-statistics.sh ss/MDD2018_ex23andMe.gz
zcat ss/MDD2018_ex23andMe.gz | head -n 1 > wss/MDD2018_ex23andMe
zcat ss/MDD2018_ex23andMe.gz | awk 'FNR > 1{
if ((0+$11)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10 "\t" 0.0000001 "\t" $12 "\t" $13 "\t" $14 "\t" $15 "\t" $16 "\t" $17 "\t" $18 "\t" $19;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10 "\t" $11 "\t" $12 "\t" $13 "\t" $14 "\t" $15 "\t" $16 "\t" $17 "\t" $18 "\t" $19;
}' >> wss/MDD2018_ex23andMe
gzip -f wss/MDD2018_ex23andMe
python tools/ldsc/munge_sumstats.py --sumstats ss/MDD2018_ex23andMe.gz --out mss/munged.MDD2018_ex23andMe.gz --a1-inc --N-cas 59851 --N-con 113154
python tools/ldsc/munge_sumstats.py --sumstats wss/MDD2018_ex23andMe.gz --out wmss/munged.winz.MDD2018_ex23andMe.gz --a1-inc --N-cas 59851 --N-con 113154
### Schizophrenia
# view columns for schizophrenia and munge and do winsorized version
bash src/s5-provide-summary-statistics.sh ss/pgc.scz2.gz
zcat ss/pgc.scz2.gz | head -n 1 > wss/pgc.scz2
zcat ss/pgc.scz2.gz | awk 'FNR > 1{
if ((0+$9)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" 0.0000001 "\t" $10;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10;
}' >> wss/pgc.scz2
gzip -f wss/pgc.scz2
python tools/ldsc/munge_sumstats.py --sumstats ss/pgc.scz2.gz --out mss/munged.pgc.scz2 --merge-alleles s5/w_hm3.snplist --a1-inc --N-cas 36989 --N-con 113075 --snp snpid --a1 a1 --a2 a2 --p p
python tools/ldsc/munge_sumstats.py --sumstats wss/pgc.scz2.gz --out wmss/munged.winz.pgc.scz2 --merge-alleles s5/w_hm3.snplist --a1-inc --N-cas 36989 --N-con 113075 --snp snpid --a1 a1 --a2 a2 --p p
### ADHD
# view columns for schizophrenia and munge and do winsorized version
# file is zipped
unzip package.zip -d ss/
bash src/s5-provide-summary-statistics.sh ss/adhd_jul2017.gz
zcat ss/adhd_jul2017.gz | head -n 1 > wss/adhd_jul2017
zcat ss/adhd_jul2017.gz | awk 'FNR > 1{
if ((0+$9)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" 0.0000001;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9;
}' >> wss/adhd_jul2017
gzip -f wss/adhd_jul2017
python tools/ldsc/munge_sumstats.py --sumstats ss/adhd_jul2017.gz --out mss/munged.adhd_jul2017 --merge-alleles s5/w_hm3.snplist --a1-inc --N 55374
python tools/ldsc/munge_sumstats.py --sumstats wss/adhd_jul2017.gz --out wmss/munged.winz.adhd_jul2017 --merge-alleles s5/w_hm3.snplist --a1-inc --N 55374
##############################################
## Substance Use traits
### Alcohol Use Disorder
# NOTE must prepare.pgc.aud.sh to clean original data
cd ss/ || exit
pgc_alcdep.eur_discovery.aug2018_release.txt.gz
gzip -dc pgc_alcdep.eur_discovery.aug2018_release.txt.gz | awk -F' ' -v OFS='\t' 'NR==1 {print($1,$2,$3,$4,$5,$6,$7,$8)} NR>1 {split($2,b,":"); print($1,b[1],$3,$4,$5,$6,$7,$8)}' > cleaned.pgc.aud
gzip -c cleaned.pgc.aud > pgc.aud.gz
cd .. || exit
# view columns for AUD (alcoholism) and munge
bash src/s5-provide-summary-statistics.sh ss/pgc.aud.gz
zcat ss/pgc.aud.gz | head -n 1 > wss/pgc.aud
zcat ss/pgc.aud.gz | awk 'FNR > 1{
if ((0+$7)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" 0.0000001 "\t" $8 ;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8;
}' >> wss/pgc.aud
gzip -f wss/pgc.aud
python tools/ldsc/munge_sumstats.py --sumstats ss/pgc.aud.gz --out mss/munged.pgc.aud --a1-inc --N-cas 11569 --N-con 34999 --snp SNP --a1 A1 --a2 A2 --p P
python tools/ldsc/munge_sumstats.py --sumstats wss/pgc.aud.gz --out wmss/munged.winz.pgc.aud --a1-inc --N-cas 11569 --N-con 34999 --snp SNP --a1 A1 --a2 A2 --p P
##############################################
## Behavioral traits
### Educational Attainment
# view columns for Education and munge
# N from http://ssgac.org/documents/README_EA3.txt
bash src/s5-provide-summary-statistics.sh ss/GWAS_EA_excl23andMe.txt.gz
zcat ss/GWAS_EA_excl23andMe.txt.gz | head -n 1 > wss/GWAS_EA_excl23andMe.txt
zcat ss/GWAS_EA_excl23andMe.txt.gz | awk 'FNR > 1{
if ((0+$9)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" 0.0000001;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9;
}' >> wss/GWAS_EA_excl23andMe.txt
gzip -f wss/GWAS_EA_excl23andMe.txt
python tools/ldsc/munge_sumstats.py --sumstats ss/GWAS_EA_excl23andMe.txt.gz --out mss/munged.GWAS_EA_excl23andMe --merge-alleles s5/w_hm3.snplist --a1-inc --N 766345 --snp MarkerName --a1 A1 --a2 A2 --p Pval
python tools/ldsc/munge_sumstats.py --sumstats wss/GWAS_EA_excl23andMe.txt.gz --out wmss/munged.winz.GWAS_EA_excl23andMe --merge-alleles s5/w_hm3.snplist --a1-inc --N 766345 --snp MarkerName --a1 A1 --a2 A2 --p Pval
### Extraversion
# view columns for Extraversion and munge
bash src/s5-provide-summary-statistics.sh ss/GPC-2.EXTRAVERSION.full.txt.gz
zcat ss/GPC-2.EXTRAVERSION.full.txt.gz | head -n 1 > wss/GPC-2.EXTRAVERSION.full.txt
zcat ss/GPC-2.EXTRAVERSION.full.txt.gz | awk 'FNR > 1{
if ((0+$8)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" 0.0000001 "\t" $9 "\t" $10;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10;
}' >> wss/GPC-2.EXTRAVERSION.full.txt
gzip -f wss/GPC-2.EXTRAVERSION.full.txt
python tools/ldsc/munge_sumstats.py --sumstats ss/GPC-2.EXTRAVERSION.full.txt.gz --out mss/munged.GPC-2.EXTRAVERSION.full --merge-alleles s5/w_hm3.snplist --a1-inc --N 63030 --snp RSNUMBER --a1 Allele1 --a2 Allele2 --p PVALUE
python tools/ldsc/munge_sumstats.py --sumstats wss/GPC-2.EXTRAVERSION.full.txt.gz --out wmss/munged.winz.GPC-2.EXTRAVERSION.full --merge-alleles s5/w_hm3.snplist --a1-inc --N 63030 --snp RSNUMBER --a1 Allele1 --a2 Allele2 --p PVALUE
### IQ
# view columns for IQ and munge
bash src/s5-provide-summary-statistics.sh ss/SavageJansen_2018_intelligence_metaanalysis.txt.gz
zcat ss/SavageJansen_2018_intelligence_metaanalysis.txt.gz | head -n 1 > wss/SavageJansen_2018_intelligence_metaanalysis.txt
zcat ss/SavageJansen_2018_intelligence_metaanalysis.txt.gz | awk 'FNR > 1{
if ((0+$11)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10 "\t" 0.0000001 "\t" $12 "\t" $13 "\t" $14;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10 "\t" $11 "\t" $12 "\t" $13 "\t" $14;
}' >> wss/SavageJansen_2018_intelligence_metaanalysis.txt
gzip -f wss/SavageJansen_2018_intelligence_metaanalysis.txt
python tools/ldsc/munge_sumstats.py --sumstats ss/SavageJansen_2018_intelligence_metaanalysis.txt.gz --out mss/munged.SavageJansen_2018_intelligence_metaanalysis.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N 269867
python tools/ldsc/munge_sumstats.py --sumstats wss/SavageJansen_2018_intelligence_metaanalysis.txt.gz --out wmss/munged.winz.SavageJansen_2018_intelligence_metaanalysis.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N 269867
### Neuroticism
# This file has two columns that fit the auto-nameing of munge_sumstats.py, which breaks it. Here, we're removing the column we don't need.
zcat ss/sumstats_neuroticism_ctg_format.txt.gz | cut -f 2 --complement > ss/sumstats_neuroticism_ctg_format.test
gzip -f ss/sumstats_neuroticism_ctg_format.test
# view columns for neuroticism and munge
bash src/s5-provide-summary-statistics.sh ss/sumstats_neuroticism_ctg_format.test.gz
zcat ss/sumstats_neuroticism_ctg_format.test.gz | head -n 1 > wss/sumstats_neuroticism_ctg_format.test
zcat ss/sumstats_neuroticism_ctg_format.test.gz | awk 'FNR > 1{
if ((0+$10)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" 0.0000001 "\t" $11 "\t" $12;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10 "\t" $11 "\t" $12;
}' >> wss/sumstats_neuroticism_ctg_format.test
gzip -f wss/sumstats_neuroticism_ctg_format.test
python tools/ldsc/munge_sumstats.py --sumstats ss/sumstats_neuroticism_ctg_format.test.gz --out mss/munged.sumstats_neuroticism_ctg_format.test --merge-alleles s5/w_hm3.snplist --a1-inc --N 380506 --snp RSID --a1 A1 --a2 A2 --p P
python tools/ldsc/munge_sumstats.py --sumstats wss/sumstats_neuroticism_ctg_format.test.gz --out wmss/munged.winz.sumstats_neuroticism_ctg_format.test --merge-alleles s5/w_hm3.snplist --a1-inc --N 380506 --snp RSID --a1 A1 --a2 A2 --p P
### Risky Behavior
# view columns for risky behavior and munge
bash src/s5-provide-summary-statistics.sh ss/RISK_GWAS_MA_UKB+replication.txt.gz
zcat ss/RISK_GWAS_MA_UKB+replication.txt.gz | head -n 1 > wss/RISK_GWAS_MA_UKB+replication.txt
zcat ss/RISK_GWAS_MA_UKB+replication.txt.gz | awk 'FNR > 1{
if ((0+$9)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" 0.0000001;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9;
}' >> wss/RISK_GWAS_MA_UKB+replication.txt
gzip -f wss/RISK_GWAS_MA_UKB+replication.txt
python tools/ldsc/munge_sumstats.py --sumstats ss/RISK_GWAS_MA_UKB+replication.txt.gz --out mss/munged.RISK_GWAS_MA_UKB+replication.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N 466571 --snp MarkerName --a1 A1 --a2 A2 --p Pval
python tools/ldsc/munge_sumstats.py --sumstats wss/RISK_GWAS_MA_UKB+replication.txt.gz --out wmss/munged.winz.RISK_GWAS_MA_UKB+replication.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N 466571 --snp MarkerName --a1 A1 --a2 A2 --p Pval
### Well-being
# view columns for well being and munge
bash src/s5-provide-summary-statistics.sh ss/SWB_Full.txt.gz
zcat ss/SWB_Full.txt.gz | head -n 1 > wss/SWB_Full.txt
zcat ss/SWB_Full.txt.gz | awk 'FNR > 1{
if ((0+$9)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" 0.0000001;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9;
}' >> wss/SWB_Full.txt
gzip -f wss/SWB_Full.txt
python tools/ldsc/munge_sumstats.py --sumstats ss/SWB_Full.txt.gz --out mss/munged.SWB_Full.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N 298420 --snp MarkerName --a1 A1 --a2 A2 --p Pval
python tools/ldsc/munge_sumstats.py --sumstats wss/SWB_Full.txt.gz --out wmss/munged.winz.SWB_Full.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N 298420 --snp MarkerName --a1 A1 --a2 A2 --p Pval
### Cognative Function
# This is a zipped folder, not just the reaction time file.
unzip ss/Davies_NC_2018.zip -d ss/
gzip ss/Davies_NC_2018/Davies2018_UKB_RT_summary_results_29052018.txt
# view columns for cognative function and munge
bash src/s5-provide-summary-statistics.sh ss/Davies_NC_2018/Davies2018_UKB_RT_summary_results_29052018.txt.gz
zcat ss/Davies_NC_2018/Davies2018_UKB_RT_summary_results_29052018.txt.gz | head -n 1 > wss/Davies2018_UKB_RT_summary_results_29052018.txt
zcat ss/Davies_NC_2018/Davies2018_UKB_RT_summary_results_29052018.txt.gz | awk 'FNR > 1{
if ((0+$6)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" 0.0000001 "\t" $7 ;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7;
}' >> wss/Davies2018_UKB_RT_summary_results_29052018.txt
gzip -f wss/Davies2018_UKB_RT_summary_results_29052018.txt
python tools/ldsc/munge_sumstats.py --sumstats ss/Davies_NC_2018/Davies2018_UKB_RT_summary_results_29052018.txt.gz --out mss/munged.Davies2018_UKB_RT_summary_results_29052018.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N 282014 --snp MarkerName --a1 Effect_allele --a2 Other_allele --p P
python tools/ldsc/munge_sumstats.py --sumstats wss/Davies2018_UKB_RT_summary_results_29052018.txt.gz --out wmss/munged.winz.Davies2018_UKB_RT_summary_results_29052018.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N 282014 --snp MarkerName --a1 Effect_allele --a2 Other_allele --p P
### Insomnia
# view columns for insomnia and munge
bash src/s5-provide-summary-statistics.sh ss/Insomnia_sumstats_Jansenetal.txt.gz
zcat ss/Insomnia_sumstats_Jansenetal.txt.gz | head -n 1 > wss/Insomnia_sumstats_Jansenetal.txt
zcat ss/Insomnia_sumstats_Jansenetal.txt.gz | awk 'FNR > 1{
if ((0+$10)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" 0.0000001 "\t" $11 "\t" $12;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10 "\t" $11 "\t" $12;
}' >> wss/Insomnia_sumstats_Jansenetal.txt
gzip -f wss/Insomnia_sumstats_Jansenetal.txt
python tools/ldsc/munge_sumstats.py --sumstats ss/Insomnia_sumstats_Jansenetal.txt.gz --out mss/munged.Insomnia_sumstats_Jansenetal.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N-col N
python tools/ldsc/munge_sumstats.py --sumstats wss/Insomnia_sumstats_Jansenetal.txt.gz --out wmss/munged.winz.Insomnia_sumstats_Jansenetal.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N-col N
##############################################
## Neurological Traits
### Alzheimers 1
# view columns for Alzheimers and munge
bash src/s5-provide-summary-statistics.sh ss/AD_sumstats_Jansenetal.txt.gz
zcat ss/AD_sumstats_Jansenetal.txt.gz | head -n 1 > wss/AD_sumstats_Jansenetal.txt
zcat ss/AD_sumstats_Jansenetal.txt.gz | awk 'FNR > 1{
if ((0+$8)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" 0.0000001 "\t" $9 "\t" $10 "\t" $11 "\t" $12 "\t" $13 "\t" $14;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10 "\t" $11 "\t" $12 "\t" $13 "\t" $14;
}' >> wss/AD_sumstats_Jansenetal.txt
gzip -f wss/AD_sumstats_Jansenetal.txt
python tools/ldsc/munge_sumstats.py --sumstats ss/AD_sumstats_Jansenetal.txt.gz --out mss/munged.AD_sumstats_Jansenetal --merge-alleles s5/w_hm3.snplist --a1-inc --N 381761
python tools/ldsc/munge_sumstats.py --sumstats wss/AD_sumstats_Jansenetal.txt.gz --out wmss/munged.winz.AD_sumstats_Jansenetal --merge-alleles s5/w_hm3.snplist --a1-inc --N 381761
### Alzheimers 2
# view columns for Alzheimers and munge
bash src/s5-provide-summary-statistics.sh ss/Kunkle_etal_Stage1_results.txt.gz
zcat ss/Kunkle_etal_Stage1_results.txt.gz | head -n 1 > wss/Kunkle_etal_Stage1_results.txt
zcat ss/Kunkle_etal_Stage1_results.txt.gz | awk 'FNR > 1{
if ((0+$8)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" 0.0000001;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8;
}' >> wss/Kunkle_etal_Stage1_results.txt
gzip -f wss/Kunkle_etal_Stage1_results.txt
python tools/ldsc/munge_sumstats.py --sumstats ss/Kunkle_etal_Stage1_results.txt.gz --out mss/munged.Kunkle_etal_Stage1_results.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N-cas 21982 --N-con 41944 --snp MarkerName --a1 Effect_allele --a2 Non_Effect_allele --p Pvalue
python tools/ldsc/munge_sumstats.py --sumstats wss/Kunkle_etal_Stage1_results.txt.gz --out wmss/munged.winz.Kunkle_etal_Stage1_results.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N-cas 21982 --N-con 41944 --snp MarkerName --a1 Effect_allele --a2 Non_Effect_allele --p Pvalue
### Epilepsy
# view columns for Epilepsy and munge
bash src/s5-provide-summary-statistics.sh ss/all_epilepsy_METAL.gz
zcat ss/all_epilepsy_METAL.gz | head -n 1 > wss/all_epilepsy_METAL
zcat ss/all_epilepsy_METAL.gz | awk 'FNR > 1{
if ((0+$6)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" 0.0000001 "\t" $7 "\t" $8 "\t" $9 "\t" $10 "\t" $11 "\t" $12 "\t" $13;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10 "\t" $11 "\t" $12 "\t" $13;
}' >> wss/all_epilepsy_METAL
gzip -f wss/all_epilepsy_METAL
python tools/ldsc/munge_sumstats.py --sumstats ss/all_epilepsy_METAL.gz --out mss/munged.all_epilepsy_METAL --merge-alleles s5/w_hm3.snplist --a1-inc --N 44889 --snp MarkerName --a1 Allele1 --a2 Allele2 --p P-value
python tools/ldsc/munge_sumstats.py --sumstats wss/all_epilepsy_METAL.gz --out wmss/munged.winz.all_epilepsy_METAL --merge-alleles s5/w_hm3.snplist --a1-inc --N 44889 --snp MarkerName --a1 Allele1 --a2 Allele2 --p P-value
### Multiple Sclerosis : Need to figure out how to get both alleles
# view columns for Multiple Sclerosis and munge
#bash src/s5-provide-summary-statistics.sh ss/imputed.allWhites.combined.clinical_c_G35.chrX.csv.gz
#python tools/ldsc/munge_sumstats.py --sumstats ss/imputed.allWhites.combined.clinical_c_G35.chrX.csv.gz --out mss/munged.imputed.allWhites.combined.clinical_c_G35.chrX.csv --merge-alleles s5/w_hm3.snplist --a1-inc --N-cas 1406 --N-con 450858 --snp SNP --a2 ALLELE --p combined_PV-clinical_c_G35
### Parkinsons Disease
# This file doesn't have rsNumber style SNP ids, but it needs to, so I'm joining it to a partial bed file
tail -n +2 ss/nallsEtAl2019_excluding23andMe_allVariants.tab | sort > ss/nallsEtAl2019_excluding23andMe_allVariants.tab.sorted
sed 's/\(chr[0-9]\+\)\t\([0-9]\+\)\t[0-9]\+\t\(rs[0-9]\+\)/\1:\2\t\3/' RawData/NoMHC_GPHN_SNP.bed | sort > RawData/SNPID.txt
join -1 1 -2 1 ss/nallsEtAl2019_excluding23andMe_allVariants.tab.sorted RawData/SNPID.txt > ss/nallsEtAl2019_excluding23andMe_allVariants.tab.named
sed '1 i\ID A1 A2 freq b se p N_cases N_controls SNP' ss/nallsEtAl2019_excluding23andMe_allVariants.tab.named > ss/nallsEtAl2019_excluding23andMe_allVariants.tab.final
gzip -f ss/nallsEtAl2019_excluding23andMe_allVariants.tab.final
# view columns for Parkinsons and munge
bash src/s5-provide-summary-statistics.sh ss/nallsEtAl2019_excluding23andMe_allVariants.tab.final
zcat ss/nallsEtAl2019_excluding23andMe_allVariants.tab.final.gz | head -n 1 > wss/nallsEtAl2019_excluding23andMe_allVariants.tab.final
zcat ss/nallsEtAl2019_excluding23andMe_allVariants.tab.final.gz | awk 'FNR > 1{
if ((0+$7)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" 0.0000001 "\t" $8 "\t" $9 "\t" $10 ;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10;
}' >> wss/nallsEtAl2019_excluding23andMe_allVariants.tab.final
gzip -f wss/nallsEtAl2019_excluding23andMe_allVariants.tab.final
python tools/ldsc/munge_sumstats.py --sumstats ss/nallsEtAl2019_excluding23andMe_allVariants.tab.final.gz --out mss/munged.nallsEtAl2019_excluding23andMe_allVariants.tab.final --merge-alleles s5/w_hm3.snplist --a1-inc --N-cas-col N_cases --N-con-col N_controls --snp SNP --a1 A1 --a2 A2 --p p
python tools/ldsc/munge_sumstats.py --sumstats wss/nallsEtAl2019_excluding23andMe_allVariants.tab.final.gz --out wmss/munged.winz.nallsEtAl2019_excluding23andMe_allVariants.tab.final --merge-alleles s5/w_hm3.snplist --a1-inc --N-cas-col N_cases --N-con-col N_controls --snp SNP --a1 A1 --a2 A2 --p p
### ALS
# it's a tarfile, and all the chromosomes are individual files that need to be joined together
tar -xzf ss/Summary_Statistics_GWAS_2016.tar
zcat ss/summary_statistics/als.sumstats.meta.chr* > ss/ALS_summary
sed '2,${/^chr/d;}' ss/ALS_summary > ss/ALS_summary.final
gzip ss/ALS_summary.final
# view columns for ALS and munge
bash src/s5-provide-summary-statistics.sh ss/ALS_summary.final.gz
zcat ss/ALS_summary.final.gz | head -n 1 > wss/ALS_summary.final
zcat ss/ALS_summary.final.gz | awk 'FNR > 1{
if ((0+$7)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" 0.0000001 "\t" $10 "\t" $11 "\t" $12 "\t" $13 "\t" $14 "\t" $15;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10 "\t" $11 "\t" $12 "\t" $13 "\t" $14 "\t" $15;
}' >> wss/ALS_summary.final
gzip -f wss/ALS_summary.final
python tools/ldsc/munge_sumstats.py --sumstats ss/ALS_summary.final.gz --out mss/munged.ALS_summary.final --merge-alleles s5/w_hm3.snplist --a1-inc --N-cas-col cases --N-con-col controls --snp snp --a1 a1 --a2 a2 --p p
python tools/ldsc/munge_sumstats.py --sumstats wss/ALS_summary.final.gz --out wmss/munged.winz.ALS_summary.final --merge-alleles s5/w_hm3.snplist --a1-inc --N-cas-col cases --N-con-col controls --snp snp --a1 a1 --a2 a2 --p p
##############################################
## Negative Control
### Age-related macular degeneration
# view columns for ARMD and munge
bash src/s5-provide-summary-statistics.sh ss/Fristche_AMDGene2013_Neovascular_v_Controls.txt.gz
zcat ss/Fristche_AMDGene2013_Neovascular_v_Controls.txt.gz | head -n 1 > wss/Fristche_AMDGene2013_Neovascular_v_Controls.txt
zcat ss/Fristche_AMDGene2013_Neovascular_v_Controls.txt.gz | awk 'FNR > 1{
if ((0+$6)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" 0.0000001 "\t" $7;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7;
}' >> wss/Fristche_AMDGene2013_Neovascular_v_Controls.txt
gzip -f wss/Fristche_AMDGene2013_Neovascular_v_Controls.txt
python tools/ldsc/munge_sumstats.py --sumstats ss/Fristche_AMDGene2013_Neovascular_v_Controls.txt.gz --out mss/munged.Fristche_AMDGene2013_Neovascular_v_Controls.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N-cas 2281 --N-con 8285 --snp Marker --a1 Allele1 --a2 Allele2 --p GC.Pvalue
python tools/ldsc/munge_sumstats.py --sumstats wss/Fristche_AMDGene2013_Neovascular_v_Controls.txt.gz --out wmss/munged.winz.Fristche_AMDGene2013_Neovascular_v_Controls.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N-cas 2281 --N-con 8285 --snp Marker --a1 Allele1 --a2 Allele2 --p GC.Pvalue
### BMI
# view columns for BMI and munge
bash src/s5-provide-summary-statistics.sh ss/Meta-analysis_Locke_et_al+UKBiobank_2018_UPDATED.txt.gz
zcat ss/Meta-analysis_Locke_et_al+UKBiobank_2018_UPDATED.txt.gz | head -n 1 > wss/Meta-analysis_Locke_et_al+UKBiobank_2018_UPDATED.txt
zcat ss/Meta-analysis_Locke_et_al+UKBiobank_2018_UPDATED.txt.gz | awk 'FNR > 1{
if ((0+$9)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" 0.0000001 "\t" $10;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10;
}' >> wss/Meta-analysis_Locke_et_al+UKBiobank_2018_UPDATED.txt
gzip -f wss/Meta-analysis_Locke_et_al+UKBiobank_2018_UPDATED.txt
python tools/ldsc/munge_sumstats.py --sumstats ss/Meta-analysis_Locke_et_al+UKBiobank_2018_UPDATED.txt.gz --out mss/munged.Meta-analysis_Locke_et_al+UKBiobank_2018_UPDATED.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N 681275 --snp SNP --a1 Tested_Allele --a2 Other_Allele --p P
python tools/ldsc/munge_sumstats.py --sumstats wss/Meta-analysis_Locke_et_al+UKBiobank_2018_UPDATED.txt.gz --out wmss/munged.winz.Meta-analysis_Locke_et_al+UKBiobank_2018_UPDATED.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N 681275 --snp SNP --a1 Tested_Allele --a2 Other_Allele --p P
### Height
# view columns for height and munge
bash src/s5-provide-summary-statistics.sh ss/Meta-analysis_Wood_et_al+UKBiobank_2018.txt.gz
zcat ss/Meta-analysis_Wood_et_al+UKBiobank_2018.txt.gz | head -n 1 > wss/Meta-analysis_Wood_et_al+UKBiobank_2018.txt
zcat ss/Meta-analysis_Wood_et_al+UKBiobank_2018.txt.gz | awk 'FNR > 1{
if ((0+$9)<0.0000001)
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" 0.0000001 "\t" $10;
else
print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" $9 "\t" $10;
}' >> wss/Meta-analysis_Wood_et_al+UKBiobank_2018.txt
gzip -f wss/Meta-analysis_Wood_et_al+UKBiobank_2018.txt
python tools/ldsc/munge_sumstats.py --sumstats ss/Meta-analysis_Wood_et_al+UKBiobank_2018.txt.gz --out mss/munged.Meta-analysis_Wood_et_al+UKBiobank_2018.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N 693529 --snp SNP --a1 Tested_Allele --a2 Other_Allele --p P
python tools/ldsc/munge_sumstats.py --sumstats wss/Meta-analysis_Wood_et_al+UKBiobank_2018.txt.gz --out wmss/munged.winz.Meta-analysis_Wood_et_al+UKBiobank_2018.txt --merge-alleles s5/w_hm3.snplist --a1-inc --N 693529 --snp SNP --a1 Tested_Allele --a2 Other_Allele --p P
### Ulcerative Colitis : Need to figure out how to get both alleles
# view columns for UC and munge
#bash src/s5-provide-summary-statistics.sh ss/
##############################################
# ======== perform a regression =========
# perform the regression on original trait files
### Anxiety
bash src/s8-do-regression.sh mss/munged.anxiety.meta.full.fs.tbl.sumstats.gz
### Autism
bash src/s8-do-regression.sh mss/munged.iPSYCH-PGC_ASD_Nov2017.sumstats.gz
### Bipolar
bash src/s8-do-regression.sh mss/munged.daner_PGC_BIP32b_mds7a_0416a.sumstats.gz
### MDD (major depressive disorder)
bash src/s8-do-regression.sh mss/munged.MDD2018_ex23andMe.sumstats.gz
### Schizophrenia
bash src/s8-do-regression.sh mss/munged.pgc.scz2.sumstats.gz
### ADHD
bash src/s8-do-regression.sh mss/munged.adhd_jul2017.sumstats.gz
### Alcohol Use Disorder
bash src/s8-do-regression.sh mss/munged.pgc.aud.sumstats.gz
### Educational Attainment
bash src/s8-do-regression.sh mss/munged.GWAS_EA_excl23andMe.sumstats.gz
### Extraversion
bash src/s8-do-regression.sh mss/munged.GPC-2.EXTRAVERSION.full.sumstats.gz
### IQ
bash src/s8-do-regression.sh mss/munged.SavageJansen_2018_intelligence_metaanalysis.txt.sumstats.gz
### Neuroticism
bash src/s8-do-regression.sh mss/munged.sumstats_neuroticism_ctg_format.test.sumstats.gz
### Risky Behavior
bash src/s8-do-regression.sh mss/munged.RISK_GWAS_MA_UKB+replication.txt.sumstats.gz
### Well-being
bash src/s8-do-regression.sh mss/munged.SWB_Full.txt.sumstats.gz
### Cognative Function
bash src/s8-do-regression.sh mss/munged.Davies2018_UKB_RT_summary_results_29052018.txt.sumstats.gz
### Insomnia
bash src/s8-do-regression.sh mss/munged.Insomnia_sumstats_Jansenetal.txt.sumstats.gz
### Alzheimers 1
bash src/s8-do-regression.sh mss/munged.AD_sumstats_Jansenetal.sumstats.gz
### Alzheimers 2
bash src/s8-do-regression.sh mss/munged.Kunkle_etal_Stage1_results.txt.sumstats.gz
### Epilepsy
bash src/s8-do-regression.sh mss/munged.all_epilepsy_METAL.sumstats.gz
### Multiple Sclerosis
### Parkinsons Disease
bash src/s8-do-regression.sh mss/munged.nallsEtAl2019_excluding23andMe_allVariants.tab.final.sumstats.gz
###ALS
bash src/s8-do-regression.sh mss/munged.ALS_summary.final.sumstats.gz
### Age-related macular degeneration
bash src/s8-do-regression.sh mss/munged.Fristche_AMDGene2013_Neovascular_v_Controls.txt.sumstats.gz
### BMI
bash src/s8-do-regression.sh mss/munged.Meta-analysis_Locke_et_al+UKBiobank_2018_UPDATED.txt.sumstats.gz
### Height
bash src/s8-do-regression.sh mss/munged.Meta-analysis_Wood_et_al+UKBiobank_2018.txt.sumstats.gz
### Ulcerative Colitis
# perform the regression on winsorized trait files
### Anxiety
bash src/s8-do-regression.sh wmss/munged.winz.anxiety.meta.full.fs.tbl.sumstats.gz
### Autism
bash src/s8-do-regression.sh wmss/munged.winz.iPSYCH-PGC_ASD_Nov2017.sumstats.gz
### Bipolar
bash src/s8-do-regression.sh wmss/munged.winz.daner_PGC_BIP32b_mds7a_0416a.sumstats.gz
### MDD (major depressive disorder)
bash src/s8-do-regression.sh wmss/munged.winz.MDD2018_ex23andMe.gz.sumstats.gz
### Schizophrenia
bash src/s8-do-regression.sh wmss/munged.winz.pgc.scz2.sumstats.gz
### ADHD
bash src/s8-do-regression.sh wmss/munged.winz.adhd_jul2017.sumstats.gz
### Alcohol Use Disorder
bash src/s8-do-regression.sh wmss/munged.winz.pgc.aud.sumstats.gz
### Educational Attainment
bash src/s8-do-regression.sh wmss/munged.winz.GWAS_EA_excl23andMe.sumstats.gz
### Extraversion
bash src/s8-do-regression.sh wmss/munged.winz.GPC-2.EXTRAVERSION.full.sumstats.gz
### IQ
bash src/s8-do-regression.sh wmss/munged.winz.SavageJansen_2018_intelligence_metaanalysis.txt.sumstats.gz
### Neuroticism
bash src/s8-do-regression.sh wmss/munged.winz.sumstats_neuroticism_ctg_format.test.sumstats.gz
### Risky Behavior
bash src/s8-do-regression.sh wmss/munged.winz.RISK_GWAS_MA_UKB+replication.txt.sumstats.gz
### Well-being
bash src/s8-do-regression.sh wmss/munged.winz.SWB_Full.txt.sumstats.gz
### Cognative Function
bash src/s8-do-regression.sh wmss/munged.winz.Davies2018_UKB_RT_summary_results_29052018.txt.sumstats.gz
### Insomnia
bash src/s8-do-regression.sh wmss/munged.Insomnia_sumstats_Jansenetal.txt.sumstats.gz
### Alzheimers 1
bash src/s8-do-regression.sh wmss/munged.winz.AD_sumstats_Jansenetal.sumstats.gz
### Alzheimers 2
bash src/s8-do-regression.sh wmss/munged.winz.Kunkle_etal_Stage1_results.txt.sumstats.gz
### Epilepsy
bash src/s8-do-regression.sh wmss/munged.winz.all_epilepsy_METAL.sumstats.gz
### Multiple Sclerosis
### Parkinsons Disease
bash src/s8-do-regression.sh wmss/munged.winz.nallsEtAl2019_excluding23andMe_allVariants.tab.final.sumstats.gz
###ALS
bash src/s8-do-regression.sh wmss/munged.winz.ALS_summary.final.sumstats.gz
### Age-related macular degeneration
bash src/s8-do-regression.sh wmss/munged.winz.Fristche_AMDGene2013_Neovascular_v_Controls.txt.sumstats.gz
### BMI
bash src/s8-do-regression.sh wmss/munged.winz.Meta-analysis_Locke_et_al+UKBiobank_2018_UPDATED.txt.sumstats.gz
### Height
bash src/s8-do-regression.sh wmss/munged.winz.Meta-analysis_Wood_et_al+UKBiobank_2018.txt.sumstats.gz
### Ulcerative Colitis
scontrol show job $SLURM_JOB_ID