| Title: | GWAS-based Mendelian Randomization and Path Analyses |
|---|---|
| Description: | Perform Mendelian randomization analysis of multiple SNPs to determine risk factors causing disease of study and to exclude confounding variabels and perform path analysis to construct path of risk factors to the disease. |
| Authors: | Yuan-De Tan |
| Maintainer: | Yuan-De Tan <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.33.0 |
| Built: | 2024-07-15 05:00:24 UTC |
| Source: | https://github.com/bioc/GMRP |
GMRP is used to perform Mendelian randomization analysis of causal variables on disease of study using SNP beta data from GWAS or GWAS meta analysis and furthermore execute path analysis of causal variables onto the disease.
GMRP can perform analyses of Mendelian randomization (MR),correlation, path of causal variables onto disease of interest and SNP annotation summarization analysis. MR includes SNP selection with given criteria and regression analysis of causal variables on the disease to generate beta values of causal variables on the disease. Using the beta values, GMRP performs correlation and path analyses to construct path diagrams of causal variables to the disease. GMRP consists
of 8 functions: chrp, fmerge, mktable, pathdiagram2, pathdiagram, path, snpPositAnnot, ucscannot and 5 datasets: beta.data, cad.data, lpd.data, SNP358.data and SNP368_annot.data. Function chrp is used to separate string vector hg19 into two numeric vectors: chromosome number and SNP position on chromosomes. Function fmerge is used to merge two datasets into one dataset. Function mktable performs SNP selection and creates a standard beta table for
function path to do path analyses. Function pathdiagram is used to create a path diagram of causal variables onto the disease or onto outcome. Function pathdiagram2 can merge two-level pathdiagrams into one nested pathdiagram where inner path diagram is a path diagram of causal variables contributing onto outcome and the outside path diagram is a diagram of path of causal variables including outcome onto the disease. The five datasets provide examples for running these
functions. lpd.data and cad.data provide an example to create a standard beta dataset for path function to do path analysis and SNP data for SNP annotation analysis by performing mktable and fmerge. beta.data are a standard beta dataset for path analysis. SNP358.data provide an example for function snpPositAnnot to do SNP position annotation analysis and SNP368_annot.data are for function ucscannot to do SNP function annotation analysis. It is specially emphasized that except for that making standard beta table using mktable must be done in Unix/Linux system,GMRP can be performed in Windows or Mac OS. This is because GWAS datasets usually are very huge but standard beta table is small. If users'Unix/Linux system has X11 or the other graphics system, then user should perform GMRP in Unix/Linux system, otherwise, user should transfer a standard beta table to a local computer and run GMRP in it.
Yuan-De Tan
[email protected]
Maintainer: Yuan-De Tan
Do, R. et al. 2013. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet 45: 1345-1352.
Sheehan, N.A. et al. 2008. Mendelian randomisation and causal inference in observational epidemiology. PLoS Med 5: e177.
Sheehan, N.A.,et al. 2010. Mendelian randomisation: a tool for assessing causality in observational epidemiology. Methods Mol Biol 713: 153-166.\Wright, S. 1921. Correlation and causation. J. Agricultural Research 20: 557-585.
Wright, S. 1934. The method of path coefficients Annals of Mathematical Statistics 5 (3): 161-215 5: 161-215.
path, mktable, pathdiagram,pathdiagram2, plotmat, plotweb
data(beta.data) mybeta<-DataFrame(beta.data) CAD<-beta.data$cad LDL<-beta.data$ldl HDL<-beta.data$hdl TG<-beta.data$tg TC<-beta.data$tc #par(mfrow=c(2,2)) plot(LDL,CAD,pch=19,col="blue",xlab="beta of SNPs on LDL",ylab="beta of SNP on CAD", main="A",cex.lab=1.5,cex.axis=1.5,cex.main=2) abline(lm(CAD~LDL),col="red",lwd=2) plot(HDL,CAD,pch=19,col="darkgreen",xlab="beta of SNPs on HDL",ylab="beta of SNP on CAD", main="B",cex.lab=1.5,cex.axis=1.5,cex.main=2) abline(lm(CAD~HDL),col="red",lwd=2) plot(TG,CAD,pch=19,col=colors()[96],xlab="beta of SNPs on TG",ylab="beta of SNP on CAD",main="C",cex.lab=1.5,cex.axis=1.5,cex.main=2) abline(lm(CAD~TG),col="red",lwd=2) plot(TC,CAD,pch=19,col=colors()[123],xlab="beta of SNPs on TC",ylab="beta of SNP on CAD",main="D",cex.lab=1.5,cex.axis=1.5,cex.main=2) abline(lm(CAD~TC),col="red",lwd=2) mod<-cad~ldl+hdl+tg+tc pathvalue<-path(betav=mybeta,model=mod,outcome="cad") mypath<-matrix(NA,3,4) mypath[1,]<-c(1.000000,-0.066678, 0.420036,0.764638) mypath[2,]<-c(-0.066678,1.000000,-0.559718,0.496831) mypath[3,]<-c(0.420036,-0.559718,1.000000,0.414346) colnames(mypath)<-c("ldl","hdl","tg","path") mypath<-DataFrame(mypath) #mypath #DataFrame with 3 rows and 4 columns # ldl hdl tg path # <numeric> <numeric> <numeric> <numeric> #1 1.000000 -0.066678 0.420036 0.764638 #2 -0.066678 1.000000 -0.559718 0.496831 #3 0.420036 -0.559718 1.000000 0.414346 #> pathdiagram(pathdata=mypath,disease="cad",R2=0.988243,range=c(1:3)) #Loading required package: shape #Error in pathcad$path : $ operator is invalid for atomic vectors mypath<-as.data.frame(mypath) pathdiagram(pathdata=mypath,disease="cad",R2=0.988243,range=c(1:3))data(beta.data) mybeta<-DataFrame(beta.data) CAD<-beta.data$cad LDL<-beta.data$ldl HDL<-beta.data$hdl TG<-beta.data$tg TC<-beta.data$tc #par(mfrow=c(2,2)) plot(LDL,CAD,pch=19,col="blue",xlab="beta of SNPs on LDL",ylab="beta of SNP on CAD", main="A",cex.lab=1.5,cex.axis=1.5,cex.main=2) abline(lm(CAD~LDL),col="red",lwd=2) plot(HDL,CAD,pch=19,col="darkgreen",xlab="beta of SNPs on HDL",ylab="beta of SNP on CAD", main="B",cex.lab=1.5,cex.axis=1.5,cex.main=2) abline(lm(CAD~HDL),col="red",lwd=2) plot(TG,CAD,pch=19,col=colors()[96],xlab="beta of SNPs on TG",ylab="beta of SNP on CAD",main="C",cex.lab=1.5,cex.axis=1.5,cex.main=2) abline(lm(CAD~TG),col="red",lwd=2) plot(TC,CAD,pch=19,col=colors()[123],xlab="beta of SNPs on TC",ylab="beta of SNP on CAD",main="D",cex.lab=1.5,cex.axis=1.5,cex.main=2) abline(lm(CAD~TC),col="red",lwd=2) mod<-cad~ldl+hdl+tg+tc pathvalue<-path(betav=mybeta,model=mod,outcome="cad") mypath<-matrix(NA,3,4) mypath[1,]<-c(1.000000,-0.066678, 0.420036,0.764638) mypath[2,]<-c(-0.066678,1.000000,-0.559718,0.496831) mypath[3,]<-c(0.420036,-0.559718,1.000000,0.414346) colnames(mypath)<-c("ldl","hdl","tg","path") mypath<-DataFrame(mypath) #mypath #DataFrame with 3 rows and 4 columns # ldl hdl tg path # <numeric> <numeric> <numeric> <numeric> #1 1.000000 -0.066678 0.420036 0.764638 #2 -0.066678 1.000000 -0.559718 0.496831 #3 0.420036 -0.559718 1.000000 0.414346 #> pathdiagram(pathdata=mypath,disease="cad",R2=0.988243,range=c(1:3)) #Loading required package: shape #Error in pathcad$path : $ operator is invalid for atomic vectors mypath<-as.data.frame(mypath) pathdiagram(pathdata=mypath,disease="cad",R2=0.988243,range=c(1:3))
Beta data are a matrix dataset consisting of 5 columns: cad, ldl, hdl, tg, and tc with 368 rows.
data("beta.data")data("beta.data")
A data frame with 368 observations on the following 5 variables.
cada numeric vector
ldla numeric vector
hdla numeric vector
tga numeric vector
tca numeric vector
Beta data are a matrix consisting of regression coefficients of 368 SNPs on cad, ldl, hdl, tg, tc where cad is coronary artery disease, ldl is low-density lipoprotein cholesterol, hdl, high-density lipoprotein cholesterol, tg,triglycerides and tc, total cholesterol in plasma. These 368 SNPs were obtained by using mktable from GWAS meta-analyzed data.
A set of real regression coefficients of 368 SNPs on disease and causal variables.
http://csg.sph.umich.edu//abecasis/public/lipids2013/
\http://www.cardiogramplusc4d.org/downloads/
Willer CJ et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 2013. doi:10.1038/ng.2797.
\Schunkert, H. et al. 2011. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet 43: 333-338.[online]
\Schunkert H, Konig IR, Kathiresan S, Reilly MP, Assimes TL, Holm H et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet. 2011 43: 333-338.
data(beta.data) ## maybe str(beta.data) ; plot(beta.data) ...data(beta.data) ## maybe str(beta.data) ; plot(beta.data) ...
cad.data are a matrix dataset consisting of 12 variables such as SNPID, SNP position on chromosomes, allele and alternative allele, allelic frequencies and 1069 SNPs.
data("cad.data")data("cad.data")
A data frame with 1609 observations on the following 12 variables.
SNPa character vector
chr_pos_b36a character vector
reference_allelea character vector
other_allelea character vector
ref_allele_frequencya numeric vector
pvaluea numeric vector
het_pvaluea numeric vector
log_oddsa numeric vector
log_odds_sea numeric vector
N_casea numeric vector
N_controla numeric vector
modela character vector
cad.data, also called CARDIoGRAM GWAS, are a meta-analyzed GWAS data from GWAS studies of European descent imputed to HapMap2 involving 22,233 cases and 64,762 controls. The data were downloaded from the following website.
A data sheet consisting of 1609 rows(SNPs) and 12 columns(character vectors such as SNPID and allele, numeric vector such as allele frequency and beta coefficient. See data format above).
http://www.cardiogramplusc4d.org/downloads/
Schunkert H, Konig IR, Kathiresan S, Reilly MP, Assimes TL, Holm H et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet. 2011 43: 333-338.
data(cad.data) ## maybe str(cad.data) ; plot(cad.data) ...data(cad.data) ## maybe str(cad.data) ; plot(cad.data) ...
Split string hg19 into two numeric columns: chr and posit.
chrp(hg)chrp(hg)
hg |
character vector represented with codechr##.####### where chr## is chromosome number and |
chrp can convert chr##.####### into two numeric columns: chr(chromosome number) and posit(SNP position)
Return two numeric vectors: chrosomome number and SNP position
If there is chrX.######### in the data sheet, then user should change chrX.######## to chr23.#######.
hg may also be hg18. User can also use packages GenomicRanges to retrieve chromosome # and SNP position.
Yuan-De Tan [email protected]
\Dajiang Liu
mktable,link{GenomicRanges}[Granges],link{GenomicRanges}[IRangs],link{GenomicRanges}[DataFrame]
data(lpd.data) lpd<-lpd.data hg19<-lpd$SNP_hg19.HDL chr<-chrp(hg=hg19)data(lpd.data) lpd<-lpd.data hg19<-lpd$SNP_hg19.HDL chr<-chrp(hg=hg19)
fmerg can be used to merge two GWAS result data sheets with the same key ID(SNP ID) into one data sheet.
fmerge(fl1, fl2,ID1, ID2, A, B, method)fmerge(fl1, fl2,ID1, ID2, A, B, method)
fl1 |
R object: data file 1 |
fl2 |
R object: data file 2 |
ID1 |
key id (SNP ID such as rsid) in file 1 |
ID2 |
key id (SNP ID such as rsid) in file 2 |
A |
postfix for file 1: A=".W1". W1 may be any identifier in file 1. Default is A="". |
B |
postfix for file 2: B=".W2". W2 may be any identifier in file2. Default is B="". |
method |
method for merging. See details. |
fl1 and fl2 are two GWAS result data files from different studies or with different risk variables. They contain SNPID, hg18, hg19(positions), beta values, allele, frequency, and so on. The method has four options: method="No","NO" or "no" means that all data with unmatch SNPs are not saved in the merged file; method="All","ALL" or "all" lets fmerge save all the data with unmatched SNPs from two files but they are not paired one-by-one. This is
different from R merge function. method="file1" will save the data with unmatched SNPs only from file 1 in the merged file and method="file2" allows function fmerge to save the data with unmatched SNPs from file2 in the merged file.
Return a joined data sheet.
Function fmerg can also be applied to the other types of data.
Yuan-De Tan [email protected]
data1<-matrix(NA,20,4) data2<-matrix(NA,30,7) SNPID1<-paste("rs",seq(1:20),sep="") SNPID2<-paste("rs",seq(1:30),sep="") data1[,1:4]<-c(round(runif(20),4),round(runif(20),4),round(runif(20),4),round(runif(20),4)) data2[,1:4]<-c(round(runif(30),4),round(runif(30),4),round(runif(30),4),round(runif(30),4)) data2[,5:7]<-c(round(seq(30)*runif(30),4),round(seq(30)*runif(30),4),seq(30)) data1<-cbind(SNPID1,as.data.frame(data1)) data2<-cbind(SNPID2,as.data.frame(data2)) dim(data1) dim(data2) colnames(data1)<-c("SNP","var1","var2","var3","var4") colnames(data2)<-c("SNP","var1","var2","var3","var4","V1","V2","V3") data12<-fmerge(fl1=data1,fl2=data2,ID1="SNP",ID2="SNP",A=".dat1",B=".dat2",method="No") #data12[1:3,] # SNP.dat1 var1.dat1 var2.dat1 var3.dat1 var4.dat1 SNP.dat2 var1.dat2 var2.dat2 #1 rs1 0.9152 0.9853 0.9879 0.9677 rs1 0.5041 0.5734 #2 rs10 0.3357 0.116 0.3408 0.1867 rs10 0.9147 0.9294 #3 rs11 0.8004 0.8856 0.2236 0.4642 rs11 0.9262 0.5831 # var3.dat2 var4.dat2 V1 V2 V3 #1 0.4933 0.6766 0.1864 0.6836 1 #2 0.4104 0.1327 3.2192 1.4166 10 #3 0.8541 0.6228 1.1803 1.9044 11data1<-matrix(NA,20,4) data2<-matrix(NA,30,7) SNPID1<-paste("rs",seq(1:20),sep="") SNPID2<-paste("rs",seq(1:30),sep="") data1[,1:4]<-c(round(runif(20),4),round(runif(20),4),round(runif(20),4),round(runif(20),4)) data2[,1:4]<-c(round(runif(30),4),round(runif(30),4),round(runif(30),4),round(runif(30),4)) data2[,5:7]<-c(round(seq(30)*runif(30),4),round(seq(30)*runif(30),4),seq(30)) data1<-cbind(SNPID1,as.data.frame(data1)) data2<-cbind(SNPID2,as.data.frame(data2)) dim(data1) dim(data2) colnames(data1)<-c("SNP","var1","var2","var3","var4") colnames(data2)<-c("SNP","var1","var2","var3","var4","V1","V2","V3") data12<-fmerge(fl1=data1,fl2=data2,ID1="SNP",ID2="SNP",A=".dat1",B=".dat2",method="No") #data12[1:3,] # SNP.dat1 var1.dat1 var2.dat1 var3.dat1 var4.dat1 SNP.dat2 var1.dat2 var2.dat2 #1 rs1 0.9152 0.9853 0.9879 0.9677 rs1 0.5041 0.5734 #2 rs10 0.3357 0.116 0.3408 0.1867 rs10 0.9147 0.9294 #3 rs11 0.8004 0.8856 0.2236 0.4642 rs11 0.9262 0.5831 # var3.dat2 var4.dat2 V1 V2 V3 #1 0.4933 0.6766 0.1864 0.6836 1 #2 0.4104 0.1327 3.2192 1.4166 10 #3 0.8541 0.6228 1.1803 1.9044 11
lpd.data are standard GWAS Meta-analyzed dataset of lipoprotein cholesterols. It was constructed by merging four datasets: Mc_HDL.txt,Mc_LDL.txt, Mc_TC.txt and Mc_TG.txt.
data("lpd.data")data("lpd.data")
A data frame with 1609 observations on the following 40 variables.
SNP_hg18.HDLa character vector
SNP_hg19.HDLa character vector
rsid.HDLa character vector
A1.HDLa character vector
A2.HDLa character vector
beta.HDLa numeric vector
se.HDLa numeric vector
N.HDLa numeric vector
P.value.HDLa numeric vector
Freq.A1.1000G.EUR.HDLa numeric vector
SNP_hg18.LDLa character vector
SNP_hg19.LDLa character vector
rsid.LDLa character vector
A1.LDLa character vector
A2.LDLa character vector
beta.LDLa numeric vector
se.LDLa numeric vector
N.LDLa numeric vector
P.value.LDLa numeric vector
Freq.A1.1000G.EUR.LDLa numeric vector
SNP_hg18.TGa character vector
SNP_hg19.TGa character vector
rsid.TGa character vector
A1.TGa character vector
A2.TGa character vector
beta.TGa numeric vector
se.TGa numeric vector
N.TGa numeric vector
P.value.TGa numeric vector
Freq.A1.1000G.EUR.TGa numeric vector
SNP_hg18.TCa character vector
SNP_hg19.TCa character vector
rsid.TCa character vector
A1.TCa character vector
A2.TCa character vector
beta.TCa numeric vector
se.TCa numeric vector
N.TCa numeric vector
P.value.TCa numeric vector
Freq.A1.1000G.EUR.TCa numeric vector
These four GWAS Meta-analyzed data of lipoprotein cholesterols were downloaded from the following website.
A data sheet consisting of 1609 rows (SNPs) and 40 columns(character vectors such as SNPID and allele, numeric vector such as allele frequency, beta coefficient. See data format above).
http://csg.sph.umich.edu//abecasis/public/lipids2013/
Willer CJ et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 2013. doi:10.1038/ng.2797.
data(lpd.data) ## maybe str(lpd.data) ; plot(lpd.data) ...data(lpd.data) ## maybe str(lpd.data) ; plot(lpd.data) ...
mktable is used to choose SNPs with LG, Pv, Pc and Pd and create a standard SNP beta table for Mendelian randomization and path analysis, see details.
mktable(cdata, ddata,rt, varname, LG, Pv, Pc, Pd)mktable(cdata, ddata,rt, varname, LG, Pv, Pc, Pd)
cdata |
causal variable GWAS data or GWAS meta-analysed data containing |
ddata |
disease GWAS data or GWAS meta-analysed data containing |
rt |
a string that specifies type of returning table. It has two options: |
varname |
a required string set that lists names of undefined causal variables for Mendelian randomization and path analyses. The first name is disease name. Here an example given is |
LG |
a numeric parameter. |
Pv |
a numeric parameter. |
Pc |
a numeric parameter. |
Pd |
a numeric parameter. Pd is a given proportion of sample size to the maximum sample size in disease data and used to choose |
The standard GWAS cdata set should have the format with following columns: chrn, posit, rsid, a1.x1, a1.x2, ..., a1.xn, freq.x1, freq.x2, ..., freq.xn, beta.x1, beta.x2, ..., beta.xn, sd.x1, sd.x2, ..., sd.xn, pvj, N.x1, N.x2, ..., N.xn, pcj. The standard GWAS ddata set should havehg.d, SNP.d,a1.d, freq.d, beta.d, N.case,N.ctr,freq.case where x1, x2, ..., xn are causal variables. See example.
is a numeric vector that is a column of beta values for regression of SNPs on variable vector X={x1, x2, ..., xn}.
is a numeric vector that is a column of frequencies of allele 1 with respect to variable vector X={x1, x2, ..., xn}.
is a numeric vector that is a column of standard deviations of variable x1,x2, ..., xn specific to SNP. Note that here sd is not beta standard deviation. If sd is not specifical to SNPs, then sd.xi has the same value for all SNPs in variable i.
denotes disease.
is sample size.
is frequency of disease.
is a numeric vector for chromosome #.
is a numeric vector for SNP positions on chromosome #. Some time, chrn and posit are combined into string vector: hg19/hg18.
is defined as p-value, pcj and pdj as proportions of sample size for SNP j to the maximum sample size in the causal variable data and in disease data, respectively.
Return a standard SNP beta or SNP path table containing m SNPs chosen with LG, Pv, Pc and Pd and n variables and disease for Mendelian randomization and path analysis.
The order of column variables must be chrn posit rsid a1.x1 ... a1.xn freq.x1 ... freq.xn beta.x1 ... beta.x1 ... beta.xn sd.x1 ... sd.xn ... otherwise, mktable would have error. see example.
Yuan-De Tan [email protected]
Do, R. et al. 2013. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet 45: 1345-1352.
Sheehan, N.A. et al. 2008. Mendelian randomisation and causal inference in observational epidemiology. PLoS Med 5: e177.
Sheehan, N.A.,et al. 2010. Mendelian randomisation: a tool for assessing causality in observational epidemiology. Methods Mol Biol 713: 153-166.
Willer, C.J. Schmidt, E.M. Sengupta, S. Peloso, G.M. Gustafsson, S. Kanoni, S. Ganna, A. Chen, J.,Buchkovich, M.L. Mora, S. et al (2013) Discovery and refinement of loci associated with lipid levels. Nat Genet 45: 1274-1283.
data(lpd.data) #lpd<-DataFrame(lpd.data) lpd<-lpd.data data(cad.data) #cad<-DataFrame(cad.data) cad<-cad.data # step 1: calculate pvj pvalue.LDL<-lpd$P.value.LDL pvalue.HDL<-lpd$P.value.HDL pvalue.TG<-lpd$P.value.TG pvalue.TC<-lpd$P.value.TC pv<-cbind(pvalue.LDL,pvalue.HDL,pvalue.TG,pvalue.TC) pvj<-apply(pv,1,min) #step 2: construct beta table of undefined causal variables: beta.LDL<-lpd$beta.LDL beta.HDL<-lpd$beta.HDL beta.TG<-lpd$beta.TG beta.TC<-lpd$beta.TC beta<-cbind(beta.LDL,beta.HDL,beta.TG,beta.TC) #step 3: construct a matrix for allele 1 in each undefined causal variable: a1.LDL<-lpd$A1.LDL a1.HDL<-lpd$A1.HDL a1.TG<-lpd$A1.TG a1.TC<-lpd$A1.TC alle1<-cbind(a1.LDL,a1.HDL,a1.TG,a1.TC) #step 4: calculate sample sizes of causal variables and calculate pcj N.LDL<-lpd$N.LDL N.HDL<-lpd$N.HDL N.TG<-lpd$N.TG N.TC<-lpd$N.TC ss<-cbind(N.LDL,N.HDL,N.TG,N.TC) sm<-apply(ss,1,sum) pcj<-sm/max(sm) #step 5: construct a matrix for frequency of allele1 in each undefined causal variable in 1000G.EUR freq.LDL<-lpd$Freq.A1.1000G.EUR.LDL freq.HDL<-lpd$Freq.A1.1000G.EUR.HDL freq.TG<-lpd$Freq.A1.1000G.EUR.TG freq.TC<-lpd$Freq.A1.1000G.EUR.TC freq<-cbind(freq.LDL,freq.HDL,freq.TG,freq.TC) #step 6: construct matrix for sd of each causal variable (here sd is not specific to SNPj) # the sd values were averaged over 63 studies see reference Willer et al(2013) sd.LDL<-rep(37.42,length(pvj)) sd.HDL<-rep(14.87,length(pvj)) sd.TG<-rep(92.73,length(pvj)) sd.TC<-rep(42.74,length(pvj)) sd<-cbind(sd.LDL,sd.HDL,sd.TG,sd.TC) #step 7: retriev SNP ID and position: hg19<-lpd$SNP_hg19.HDL rsid<-lpd$rsid.HDL #step 8: invoke chrp to separate chromosome number and SNP position: chr<-chrp(hg=hg19) #step 9: get new data of causal variables: newdata<-cbind(freq,beta,sd,pvj,ss,pcj) newdata<-cbind(chr,rsid,alle1,as.data.frame(newdata)) dim(newdata) #[1] 120165 25 #step 10: retrieve cad data from cad and calculate pdj and frequency of cad in population hg18.d<-cad$chr_pos_b36 SNP.d<-cad$SNP #SNPID a1.d<-tolower(cad$reference_allele) freq.d<-cad$ref_allele_frequency pvalue.d<-cad$pvalue beta.d<-cad$log_odds N.case<-cad$N_case N.ctr<-cad$N_control N.d<-N.case+N.ctr freq.case<-N.case/N.d #step 11: get new cad data: newcad<-cbind(freq.d,beta.d,N.case,N.ctr,freq.case) newcad<-cbind(hg18.d,SNP.d,a1.d,as.data.frame(newcad)) dim(newcad) #step 12: give variable list varname<-c("CAD","LDL","HDL","TG","TC") #step 3: create beta table with function mktable mybeta<-mktable(cdata=newdata,ddata=newcad,rt="beta",varname=varname,LG=1, Pv=0.00000005, Pc=0.979,Pd=0.979) beta<-mybeta[,4:8] # save beta for path analysis snp<-mybeta[,1:3] # save snp for annotation analysis beta<-DataFrame(beta)data(lpd.data) #lpd<-DataFrame(lpd.data) lpd<-lpd.data data(cad.data) #cad<-DataFrame(cad.data) cad<-cad.data # step 1: calculate pvj pvalue.LDL<-lpd$P.value.LDL pvalue.HDL<-lpd$P.value.HDL pvalue.TG<-lpd$P.value.TG pvalue.TC<-lpd$P.value.TC pv<-cbind(pvalue.LDL,pvalue.HDL,pvalue.TG,pvalue.TC) pvj<-apply(pv,1,min) #step 2: construct beta table of undefined causal variables: beta.LDL<-lpd$beta.LDL beta.HDL<-lpd$beta.HDL beta.TG<-lpd$beta.TG beta.TC<-lpd$beta.TC beta<-cbind(beta.LDL,beta.HDL,beta.TG,beta.TC) #step 3: construct a matrix for allele 1 in each undefined causal variable: a1.LDL<-lpd$A1.LDL a1.HDL<-lpd$A1.HDL a1.TG<-lpd$A1.TG a1.TC<-lpd$A1.TC alle1<-cbind(a1.LDL,a1.HDL,a1.TG,a1.TC) #step 4: calculate sample sizes of causal variables and calculate pcj N.LDL<-lpd$N.LDL N.HDL<-lpd$N.HDL N.TG<-lpd$N.TG N.TC<-lpd$N.TC ss<-cbind(N.LDL,N.HDL,N.TG,N.TC) sm<-apply(ss,1,sum) pcj<-sm/max(sm) #step 5: construct a matrix for frequency of allele1 in each undefined causal variable in 1000G.EUR freq.LDL<-lpd$Freq.A1.1000G.EUR.LDL freq.HDL<-lpd$Freq.A1.1000G.EUR.HDL freq.TG<-lpd$Freq.A1.1000G.EUR.TG freq.TC<-lpd$Freq.A1.1000G.EUR.TC freq<-cbind(freq.LDL,freq.HDL,freq.TG,freq.TC) #step 6: construct matrix for sd of each causal variable (here sd is not specific to SNPj) # the sd values were averaged over 63 studies see reference Willer et al(2013) sd.LDL<-rep(37.42,length(pvj)) sd.HDL<-rep(14.87,length(pvj)) sd.TG<-rep(92.73,length(pvj)) sd.TC<-rep(42.74,length(pvj)) sd<-cbind(sd.LDL,sd.HDL,sd.TG,sd.TC) #step 7: retriev SNP ID and position: hg19<-lpd$SNP_hg19.HDL rsid<-lpd$rsid.HDL #step 8: invoke chrp to separate chromosome number and SNP position: chr<-chrp(hg=hg19) #step 9: get new data of causal variables: newdata<-cbind(freq,beta,sd,pvj,ss,pcj) newdata<-cbind(chr,rsid,alle1,as.data.frame(newdata)) dim(newdata) #[1] 120165 25 #step 10: retrieve cad data from cad and calculate pdj and frequency of cad in population hg18.d<-cad$chr_pos_b36 SNP.d<-cad$SNP #SNPID a1.d<-tolower(cad$reference_allele) freq.d<-cad$ref_allele_frequency pvalue.d<-cad$pvalue beta.d<-cad$log_odds N.case<-cad$N_case N.ctr<-cad$N_control N.d<-N.case+N.ctr freq.case<-N.case/N.d #step 11: get new cad data: newcad<-cbind(freq.d,beta.d,N.case,N.ctr,freq.case) newcad<-cbind(hg18.d,SNP.d,a1.d,as.data.frame(newcad)) dim(newcad) #step 12: give variable list varname<-c("CAD","LDL","HDL","TG","TC") #step 3: create beta table with function mktable mybeta<-mktable(cdata=newdata,ddata=newcad,rt="beta",varname=varname,LG=1, Pv=0.00000005, Pc=0.979,Pd=0.979) beta<-mybeta[,4:8] # save beta for path analysis snp<-mybeta[,1:3] # save snp for annotation analysis beta<-DataFrame(beta)
path is used to perform path analysis of multiple causal or risk variables on an outcome or disease of study.
path(betav,model,outcome)path(betav,model,outcome)
betav |
a matrix numeric data with p rows and q columns in which the first column must be outcome and other columns are risk variables. |
model |
a linear model for multivariate linear regression analysis. The model must be given in |
outcome |
a string that is required to give outcome name or disease name. For example, |
path is originally planed to perform causal analysis of risk variables on disease of study based on the results of the Mendelian randomization analysis of SNPs on these risk variables and disease. In the GMRP package, the betav is a matrix of beta coefficients of linear regression analyses of chosen SNPs on the risk (or causal) variables and disease or outcome. The beta values are equivalently quantitative values, so this path function can also be used to analyze direct and indirect contributions of quantitative traits to economic traits.
Return three matrices: beta coefficients of regressions of risk variables on outcome or disease, correlation matrix and path matrix and also return director path coefficients and R-square.
betav may also be a matrix of SNP path coefficients onto risk variables and disease.
Yuan-De Tan [email protected]
Do, R. et al. 2013 Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet 45: 1345-1352.
Sheehan, N.A. et al. 2008 Mendelian randomisation and causal inference in observational epidemiology. PLoS Med 5: e177.
Sheehan, N.A.,et al. 2010 Mendelian randomisation: a tool for assessing causality in observational epidemiology. Methods Mol Biol 713: 153-166.
Wright, S. 1921 Correlation and causation. J.Agricultural Research 20: 557-585.
Wright, S. 1934 The method of path coefficients. Annals of Mathematical Statistics 5 (3): 161-215.
link[base]{lm},link[stats]{cor}
data(beta.data) mybeta<-DataFrame(beta.data) #mybeta<-as.data.frame(beta.data) mod<-cad~ldl+hdl+tg+tc pathvalue<-path(betav=mybeta,model=mod,outcome="cad")data(beta.data) mybeta<-DataFrame(beta.data) #mybeta<-as.data.frame(beta.data) mod<-cad~ldl+hdl+tg+tc pathvalue<-path(betav=mybeta,model=mod,outcome="cad")
Create a directed acyclic diagram to represent causal effects of risk factors on the disease of study.
pathdiagram(pathdata, disease, R2, range)pathdiagram(pathdata, disease, R2, range)
pathdata |
R object that is dataset consisting of correlation matrix of risk factors and a numeric vector of direct path coefficients. |
disease |
a string that specifies outcome name or disease name. If disease name is long or has multiple words, then we suggest an abbreviated name, for example, coronary artery disease may be shortened as |
R2 |
a numeric parameter, is R-square obtained from path analysis. |
range |
range of specified columns for correlation matrix. For example, |
The pathdata contains correlation matrix of risk factors and a numeric vector of direct path coefficients obtained from path analysis of beta data of SNPs on risk factors and disease. Columns must have risk factor names and path.
NULL. pathdiagram will create one-level path diagram labeled with colors.
Yuan-De Tan [email protected]
pathdiagram2, plotmat, plotweb
mypath<-matrix(NA,3,4) mypath[1,]<-c(1.000000,-0.066678, 0.420036,0.764638) mypath[2,]<-c(-0.066678,1.000000,-0.559718,0.496831) mypath[3,]<-c(0.420036,-0.559718,1.000000,0.414346) colnames(mypath)<-c("ldl","hdl","tg","path") #mypath # ldl hdl tg path #1 1.000000 -0.066678 0.420036 0.764638 #2 -0.066678 1.000000 -0.559718 0.496831 #3 0.420036 -0.559718 1.000000 0.414346 mypath<-as.data.frame(mypath) pathdiagram(pathdata=mypath,disease="cad",R2=0.988243,range=c(1:3))mypath<-matrix(NA,3,4) mypath[1,]<-c(1.000000,-0.066678, 0.420036,0.764638) mypath[2,]<-c(-0.066678,1.000000,-0.559718,0.496831) mypath[3,]<-c(0.420036,-0.559718,1.000000,0.414346) colnames(mypath)<-c("ldl","hdl","tg","path") #mypath # ldl hdl tg path #1 1.000000 -0.066678 0.420036 0.764638 #2 -0.066678 1.000000 -0.559718 0.496831 #3 0.420036 -0.559718 1.000000 0.414346 mypath<-as.data.frame(mypath) pathdiagram(pathdata=mypath,disease="cad",R2=0.988243,range=c(1:3))
This function is used to create two-level nested pathdiagram to represent causal effects of risk factors on outcome and on the disease of study. The nested path is a child path, which is related to outcome and the outside path is parent path with respect to disease.
pathdiagram2(pathD, pathO, rangeD, rangeO, disease, R2D, R2O)pathdiagram2(pathD, pathO, rangeD, rangeO, disease, R2D, R2O)
pathD |
R object that is disease path result data consisting of correlation matrix of causal variables to be identified in Mendelian randomization analysis and path coefficient vector of these variables directly causing the disease of study. |
pathO |
R object that is outcome path result data consisting of correlation matrix of causal variables and path coefficient vector of these variables directly contributing to outcome. This outcome variable may be or not be one of risk factors or causal variables in disease path data. These variables are the same with those in pathD. |
rangeD |
specifies column range for correlation coefficient matrix in pathD, for example, |
rangeO |
specifies column range for correlation coefficient matrix in pathO, see example in |
disease |
a string that specifies disease name. If disease name is long or has multiple words, then we suggest an abbreviated name, for example, "coronary artery disease" can be shortened as |
R2D |
a required numeric parameter and its value is R-square obtained from path analysis of disease data. |
R2O |
a required numeric parameter and its value is R-square obtained from path analysis of outcome data. |
Two path datasets must contain correlation matrix of variables detected to be risk factor of disease and a vector of direct path coefficients obtained from path analysis of beta data of SNPs on causal variables and disease. Columns must have shortened variable names and path word (see examples). pathdiagram2 requires two path data have the same causal variable names and the same name order. The outcome in the outcome path data must be the last variable in the correlation matrix in disease path data (see examples). Otherwise, pathdiagram2 would give an error message.
Null. Function pathdiagram2 creates a nested two-level path diagram labeled with color.
Yuan-De Tan [email protected]
mypath<-matrix(NA,3,4) mypath[1,]<-c(1.000000,-0.066678, 0.420036,0.764638) mypath[2,]<-c(-0.066678,1.000000,-0.559718,0.496831) mypath[3,]<-c(0.420036,-0.559718,1.000000,0.414346) colnames(mypath)<-c("ldl","hdl","tg","path") mypath<-DataFrame(mypath) #mypath #DataFrame with 3 rows and 4 columns # ldl hdl tg path # <numeric> <numeric> <numeric> <numeric> #1 1.000000 -0.066678 0.420036 0.764638 #2 -0.066678 1.000000 -0.559718 0.496831 #3 0.420036 -0.559718 1.000000 0.414346 #In this pathdiagram, the outcome is TC pathD<-matrix(NA,4,5) pathD[1,]<-c(1, -0.070161,0.399038,0.907127,1.210474) pathD[2,]<-c(-0.070161, 1,-0.552106,0.212201,0.147933) pathD[3,]<-c(0.399038,-0.552106,1,0.44100,0.64229) pathD[4,]<-c(0.907127 ,0.212201,0.441007,1,-1.035677) colnames(pathD)<-c("ldl","hdl","tg","tc","path") #pathD # LDL HDL TG TC path #1 1.000000 -0.070161 0.399038 0.907127 1.210474 #2 -0.070161 1.000000 -0.552106 0.212201 0.147933 #3 0.399038 -0.552106 1.000000 0.441000 0.642290 #4 0.907127 0.212201 0.441007 1.000000 -1.035677 pathD<-as.data.frame(pathD) ## tc is outcome in my path pathdiagram2(pathD=pathD,pathO=mypath,rangeD=c(1:4),rangeO=c(1:3),disease="CAD", R2D=0.536535,R2O=0.988243)mypath<-matrix(NA,3,4) mypath[1,]<-c(1.000000,-0.066678, 0.420036,0.764638) mypath[2,]<-c(-0.066678,1.000000,-0.559718,0.496831) mypath[3,]<-c(0.420036,-0.559718,1.000000,0.414346) colnames(mypath)<-c("ldl","hdl","tg","path") mypath<-DataFrame(mypath) #mypath #DataFrame with 3 rows and 4 columns # ldl hdl tg path # <numeric> <numeric> <numeric> <numeric> #1 1.000000 -0.066678 0.420036 0.764638 #2 -0.066678 1.000000 -0.559718 0.496831 #3 0.420036 -0.559718 1.000000 0.414346 #In this pathdiagram, the outcome is TC pathD<-matrix(NA,4,5) pathD[1,]<-c(1, -0.070161,0.399038,0.907127,1.210474) pathD[2,]<-c(-0.070161, 1,-0.552106,0.212201,0.147933) pathD[3,]<-c(0.399038,-0.552106,1,0.44100,0.64229) pathD[4,]<-c(0.907127 ,0.212201,0.441007,1,-1.035677) colnames(pathD)<-c("ldl","hdl","tg","tc","path") #pathD # LDL HDL TG TC path #1 1.000000 -0.070161 0.399038 0.907127 1.210474 #2 -0.070161 1.000000 -0.552106 0.212201 0.147933 #3 0.399038 -0.552106 1.000000 0.441000 0.642290 #4 0.907127 0.212201 0.441007 1.000000 -1.035677 pathD<-as.data.frame(pathD) ## tc is outcome in my path pathdiagram2(pathD=pathD,pathO=mypath,rangeD=c(1:4),rangeO=c(1:3),disease="CAD", R2D=0.536535,R2O=0.988243)
SNP358.data were obtained from GWAS Meta-analyzed datasets of lipoprotein cholesterols and coronary artery disease. The data contain three numeric vectors (columns): SNPID(rsid), chromosome number (chr) and SNP position on chromosome (posit).
data("SNP358.data")data("SNP358.data")
A data frame with 358 observations on the following 3 variables.
rsida character vector
chra numeric vector
posita numeric vector
These 358 SNPs were chosen by using mktable with Pv = 5x10e-08,Pc = Pd =0.979 from lpd.data and cad.data. They provide a data example for how to perform annotation analysis of SNP positions.
A set of data with 358 rows(SNPs) and 3 columns(SNP ID, chromosome # and SNP position on chromosomes).
data(SNP358.data) ## maybe str(SNP358.data) ; plot(SNP358.data) ...data(SNP358.data) ## maybe str(SNP358.data) ; plot(SNP358.data) ...
368SNPs
The annotation data of 368SNPs are used to construct SNP distribution in gene elements (coding region, introne,UTR, etc). The data contain 12 vectors or variables but only Symbol and function_unit are used by ucscannot.R to build SNP distribution in gene elements.
data("SNP368annot.data")data("SNP368annot.data")
A data frame with 1053 observations on the following 6 variables.
SNPa string vector
Allelea string vector
Stranda numeric vector
Symbola string vector
Genea string vector
function_unita string vector
SNP368annot.data were obtained by performing mktable with PV=5x10e-08,Pc=Pd=0.979 on lpd.data and cad.data and SNP tools. SNP368annot.data provides an practical example for constructing distribution of SNPs in gene elements. Note that function_units are gene elements.
A dataset with 1053 rows and 6 columns for results of SNP annotation analysis. See format above.
http://csg.sph.umich.edu//abecasis/public/lipids2013/
http://snp-nexus.org/test/snpnexus_19427/
data(SNP368annot.data)data(SNP368annot.data)
This function is used to perform position annotation analysis of SNPs chosen from GWAS.
snpPositAnnot(SNPdata, SNP_hg19="chr", main)snpPositAnnot(SNPdata, SNP_hg19="chr", main)
SNPdata |
|
SNP_hg19 |
a string parameter. It may be "hg19" or "chr". If SNP_hg19="hg19",then |
main |
a string which is title of graph. If no title is given, then main="". |
Return a set of numbers of SNPs between which interval length > LG on 23 chromosomes. This function also creates a histogram for averaged distances between SNPs and SNP numbers on chromosomes.
This function can also be applied to hg18 data with SNP_hg19="hg18".
Yuan-De Tan [email protected]
data(SNP358.data) SNP358<-DataFrame(SNP358.data) snpPositAnnot(SNPdata=SNP358,SNP_hg19="chr",main="A")data(SNP358.data) SNP358<-DataFrame(SNP358.data) snpPositAnnot(SNPdata=SNP358,SNP_hg19="chr",main="A")
This function is used to give proportion of SNPs derived from functional elements of genes.
ucscannot(UCSCannot,SNPn,A=3,B=1.9,C=1.3,method=1)ucscannot(UCSCannot,SNPn,A=3,B=1.9,C=1.3,method=1)
UCSCannot |
annotation data obtained by performing |
SNPn |
numeric parameter for number of |
A |
numeric parameter for title size, default=2.5 |
B |
numeric parameter for label size, default=1.5 |
C |
numeric parameter for |
method |
numeric parameter for choosing figure output methods. It has two options: method=1 has no legend but color and pie components are labeled with gene elements, method=2 has legend over pie. The default = 1. |
SNPs chosen by performing mktable should be copied to Batch Query Box in SNP annotation tool. After setting parameters and running by clicking run button, SNP annotation data will be obtained after running for a while. Consequence sheet of UCSC should be copied to excel sheet,"Predicted function" column name is changed to "function_unit" name and save it as csv format. These parametric defaults are used as graph image for publication, user can expand image to the maximum size and copy it to powerpoint that will give ideal effect. User also can use R packagelink{VariantAnnotation} to get SNP annotation result but the result must be constructed a table with function_unit column listing gene elements and Symbol column listing genes, otherwise, ucscannot will get an error.
Create a color pie3D diagram and return a set of numeric values: proportions of code region, intron,3'and 5' UTRs and upstream and downstream etc.
This function just need data of "Predicted function" and "symbol", so the other column data in UCSCannot do not impact the results of analysis.
Yuan-De Tan [email protected]
http://snp-nexus.org/index.html
mktable, pie3D,link{VariantAnnotion}
data(SNP368annot.data) SNP368<-DataFrame(SNP368annot.data) ucscannot(UCSCannot=SNP368,SNPn=368,A=1.5,B=1,C=1.3) ucscannot(UCSCannot=SNP368,SNPn=368,A=1.5,B=1,C=1.3,method=2)data(SNP368annot.data) SNP368<-DataFrame(SNP368annot.data) ucscannot(UCSCannot=SNP368,SNPn=368,A=1.5,B=1,C=1.3) ucscannot(UCSCannot=SNP368,SNPn=368,A=1.5,B=1,C=1.3,method=2)