1. GWAS Analysis Pipeline
GWAS Data
Less time
Less errors
Easier
Module 1
Module 2
Module 3 …
Analysis Options
(universal interface)
Results

2. Pipeline Structure Input (data + analysis options) job1 job2 ….. Job N
gwaOption_R => R module
gwaOption_sas => SAS module
gwaOption_C => C module
Perl, Java, Matlab, …
Result 1
Result 2
…..
Result N
Final Results

3. An Example gwas.sh options.gwa gwas.R job1 job2 ….. Job N
sas -sysparm ‘optionfile.jobi’ glm.sas (single machine mode)
bsub –J jobname sas -sysparm ‘optionfile.jobi’ glm.sas (cluster mode)
Result 1
Result 2
…..
Result N
Final Results

4. gwas.sh options.gwa [DATA] database=SAS
genotype_dir=/dsg1/gwas/fhsgeno
genotype_file=chr6_2
phenotype_dir=/dsg1/gwas/fhspheno
phenotype_file=fhs100
markerinfo_dir= /dsg1/gwas/fhsmap
markerinfo_file=mapall
marker_selection=MAF<0.01
pedigree_dir= /dsg1/gwas/fhspedi
pedigree_file=pediall
subjectID=subject
pedgreeID=famid
markername=snp
globbing=*/1:22 or 1,2,3,5:10
[ANALYSIS]
phenolist_file=
pheno_list=bmi/qt
covariates=
program=SASGLM
analysis=mixed
[OUTPUT]
output_dir=/dsguser/qunyuan/fhs/bmi
output_file=
output_replace=no
[RUN]
clusterjobname=bmimixed
memsize=1000M …
#!/bin/sh
OPFILE=$1
R --no-save < /dsguser/qunyuan/gwas/code/gwas.R $OPFILE > gwas.rlog &
genotype_dir=IN_FILE
genotype_file=genolist.txt
Pheno type covar program analysis run
Bmi qt age,sex SASGLM mixed YES
Obes ql NA SASGLM gee YES
HD ql age SASGLM gee NO
Age …
Sex … …
Program language location Maintainer
SASGLM SAS /dsg1/code/sas/glm.sas Q.Zhang
GSTAT R /dsg1/code/R/gstat.R Q.Zhang
sasPLINK SAS /dsg1/code/sas/plink.sas J. Czajkowski …

5. gwas.R sasOption.job1 sasOption.job2 sasOption.job3 … sasOption.jobN
%let genotype_dir=/dsguser/qunyuan/gwas/cluster/impudata/;
%let genotype_file=mldosechr6_12;
%let phenotype_dir=/dsguser/qunyuan/gwas/cluster/data/;
%let phenotype_file=pheno100;
%let markerinfo_dir=;
%let markerinfo_file=;
%let marker_selection=MAF<0.01;
%let pedigree_dir=;
%let pedigree_file=;
%let subjectID=subject;
%let pedgreeID=famid;
%let markername=snp;
%let phenolist_file=;
%let phenolist=bmi;
%let phenodata_type=qt;
%let covariates=;
%let program=SASGLM;
%let analysis=mixed;
%let output_dir=/dsguser/qunyuan/gwas/run/run2/mixck/;
%let output_replace=yes;
%let clusterjobname=;
%let memsize=1000M;
source("/dsguser/qunyuan/gwas/code/gwaslib.R")
option.file=commandArgs()[3]
read.table(option.file,sep="=",stringsAsFactors=F)->x
x$V1=gsub(" ","",x$V1)
x$V2=gsub(" ","",x$V2)
rownames(x)=x$V1
gf=x["genotype_file",2]
if (nchar(gf)>0) strsplit(gf,split=",")[[1]]->gfs
if (nchar(gf)==0) {
gfs=dir(x["genotype_dir",2])
if (x["output_replace",2]=="no")
outfs=dir(x["output_dir",2])
gfs=gfs[!(gfs %in% outfs)] }
gfs=gsub(".sas7bdat","",gfs)
(dir.create(x["output_dir",2],recursive=T))
for (fi in gfs)
x["genotype_file",2]=fi
xi=paste("%let ", x$V1,"=", x$V2, ";",sep="")
optfile=paste(fi,".opt",sep="")
write.table(xi,file=optfile,quote=F,row.names=F,col.names=F)
cmd=paste("sas -memsize",x["memsize",2],"-sysparm",paste("'",optfile,"'",sep=""),x["program",2])
if (nchar(x["clusterjobname",2])>0) cmd=paste ("bsub -J",x["clusterjobname",2],cmd)
system(cmd)
sasOption.job1
sasOption.job2
sasOption.job3 …
sasOption.jobN
sas –memsize 1000 M-sysparm ‘sasOption.job1’ /dsg1/code/sas/glm.sas &
bsub –J jobname sas –sysparm ‘sasOption.job1’ /dsg1/code/sas/glm.sas
%include “&sysparm”; .

6. Programming Tips
Get all information that your program needs from one option file, pass the option file to your program via command line (e.g. sas –sysparm ‘optionfile’ yourscript.sas), or at least define all the options using variables at the top your script.
For any information that may change in different analyses, instead of fixed names, use variables. (e.g. set my data(keep=&markname) )
Keep a good structure, use functionally independent macros and/or functions to organize your code. Don’t put every thing in one macro/function.
%MACRO main();
%macro1(); *get p value for markers;
%macro2(); *merge with marker positions & allele frequencies;
%MEND main;
%main();
Avoid name conflict. Name your variables and datasets using unique prefixes (e.g. QZ_temp).
Write as little as you can.
chrom loop; pheno loop; geno loop; yourmacro(indat=,y=,x=,covar=,method=,outdat=); end;end;end;
Error handling.