GeneDrops: simulate genotypes on a pedigree
In rJPSGCS: R-Interface to Gene Drop Simulation from JPSGCS
Description
Usage
Arguments
Value
Note
Author(s)
See Also
Examples
Description
This function is an R wrapper for the JPSGCS multi-locus gene-drop
program.
Usage
1
2
Arguments
ld.par
Name of a LINKAGE parameter file or an LD model file output by FitGMLD.
ped
Name of the post-MAKEPED pedfile (see the explanation in the
documentation for read.pedfile)
giving the pedigree relationships and
marker data availability to simulate.
n
Number of pedigrees to simulate.
complete.data
Should complete data be simulated; i.e., should we ignore the marker
data availability in ped? Default is FALSE.
gd.ped
Name of the output file of simulated marker data in post-MAKEPED pedigree
file format.
compress.pedfiles
Should the output pedigree file be compressed? Default is FALSE.
Value
No value. The output is in the file named by gd.ped.
Note
This function can require large amounts of memory
for large data sets (e.g. whole chromosomes of SNPs at the marker
density of the HapMap project). The computations
are done in Java, using functions from Alun
Thomas' suite of Java Programs for Statistical Genetics and
Computational Statistics (JPSGCS). The JPSGCS Java programs are accessed
by R-wrappers provided by the rJPSGCS package, which
initializes the Java virtual machine (JVM) if not already
done so by another package.
To over-ride the default amount of
memory Java allocates for heap space,
initialize the JVM before loading rJPSGCS as
follows:
1
2
3
4
5
Author(s)
Sigal Blay, Jinko Graham and Brad McNeney
See Also
Examples
1
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# 1. Gene-drop assuming independent loci in the founders, using allele
# frequencies and map information from the example data set.
data(exdat)
# Print out the data to examine missing data pattern.
exdat$markers
#Coerce to snp.matrix
sdat<-as(exdat$markers,"snp.matrix")
# Need LINKAGE parameter and pedigree files as input.
# For independent loci in the founders use a standard LINKAGE parameter file.
write.parfile(snp.data=sdat, map=exdat$map, file="indep.par")
# To construct the pedigree file for unrelated individuals, use
# the pedinf="unrelated" option.
write.pedfile(pedinf="unrelated",snp.data=sdat,file="unrel.ped")
GeneDrops(ld.par="indep.par",ped="unrel.ped",n=10,gd.ped="gd.ped")
# Read the SNP data back into R.
simdat<-read.pedfile("gd.ped")$snp.data
# Look at the simulated data: need to coerce to numeric matrix to see
# values of individual genotypes, rather than the usual snp.matrix summary
as(simdat,"numeric")
# 2. Simulate data at 5 independent SNPs 0.1 cM apart on a parent-child trio.
map=(1:5)/10
# Marker data on each individual as follows:
mom=c(1,2,1,1,1) # complete data
dad=c(0,NA,0,1,0) # missing data at SNP 2
child=c(NA,1,1,2,NA) #missing data at SNPs 1 and 5
sdat<-rbind(mom,dad,child)
colnames(sdat)<-paste("SNP",1:5,sep="")
sdat<-as(sdat,"snp.matrix")
write.parfile(snp.data=sdat, map=map, file="trio.par")
# Block of code setting up pedigree information for mom, dad and child.
# See the Details section of the write.pedfile help file for
# information on the file format.
trioinf<-matrix(c(
1, 1, 0, 0, 0, 0, 0, 2, 1, #mom: ID=1, no parents in pedigree
1, 2, 0, 0, 0, 0, 0, 1, 1, #dad: ID=2, no parents in pedigree
1, 3, 2, 1, 0, 0, 0, 2, 1),#child: ID=3, dadID=2, momID=1
byrow=TRUE, ncol=9, nrow=3)
write.pedfile(pedinf=trioinf, snp.data=sdat,file="trio.ped")
GeneDrops(ld.par="trio.par",ped="trio.ped",n=10,gd.ped="gd.ped")
#Read the SNP data back into R.
simdat<-read.pedfile("gd.ped")$snp.data
# Look at the missing data patterns in simdat
as(simdat,"numeric")
## Not run:
# 3. Repeat gene drop for the trio, but this time use an LD model fit
# by FitGMLD. FitGMLD requires a LINKAGE parameter file and a transposed
# pedigree file as input. Can use the parameter file trio.par again.
# The transposed pedigree file can be generated as follows:
write.pedfile(pedinf=trioinf,snp.data=sdat,file="f.ped",transpose=TRUE)
FitGMLD("trio.par","f.ped","out.ld.par")
#Call to GeneDrops uses the output of FitGMLD as the parameter file and
# the regular (not transposed) pedigree file
GeneDrops(ld.par="out.ld.par",ped="trio.ped",n=10,gd.ped="gd.ped")
simdat<-read.pedfile("gd.ped")$snp.data
unlink(c("f.ped","out.ld.par"))
## End(Not run)
unlink(c("unrel.ped","trio.ped","indep.par","trio.par", "gd.ped"))
Example output
Loading required package: rJava
Loading required package: chopsticks
Loading required package: survival
Warning message:
In methods:::bind_activation(TRUE) :
methods:::bind_activation(TRUE) is deprecated;
you should rather provide methods for cbind2() / rbind2()
SNP1 SNP2 SNP3 SNP4 SNP5 SNP6 SNP7 SNP8 SNP9 SNP10
subject1 2 1 2 2 1 0 2 2 2 1
subject2 1 0 0 2 1 2 2 1 2 1
subject3 1 2 2 1 1 2 2 1 1 2
subject4 2 2 2 2 1 0 0 2 1 2
subject5 2 2 NA NA 2 1 2 0 2 1
[1] 5 10
Warning: parameter file has no LD model appended.
Assuming linkage equilirbiurm and given allele frequencies.
1 2 3 4 5 6 7 8 9 10
Family.1.Individual.1 1 0 1 0 1 1 1 0 0 1
Family.2.Individual.2 0 1 0 0 1 1 0 0 1 0
Family.3.Individual.3 0 1 1 0 1 0 1 0 1 0
Family.4.Individual.4 0 1 0 1 2 0 0 0 0 2
Family.5.Individual.5 0 0 NA NA 1 2 0 1 0 0
Family.1.Individual.1 0 1 1 0 1 0 1 1 0 0
Family.2.Individual.2 0 1 0 0 0 2 0 1 1 0
Family.3.Individual.3 0 0 1 1 0 0 0 0 0 2
Family.4.Individual.4 1 0 2 0 0 2 0 0 0 1
Family.5.Individual.5 0 0 NA NA 1 1 0 1 1 0
Family.1.Individual.1 0 0 2 1 1 1 0 1 0 0
Family.2.Individual.2 0 0 0 0 1 1 0 1 0 0
Family.3.Individual.3 0 2 0 0 1 2 0 2 0 1
Family.4.Individual.4 1 2 0 0 1 2 1 1 0 0
Family.5.Individual.5 1 1 NA NA 0 0 1 1 0 0
Family.1.Individual.1 0 1 0 0 0 1 0 0 1 1
Family.2.Individual.2 0 0 0 0 0 0 0 1 0 1
Family.3.Individual.3 0 2 0 0 1 1 0 1 0 0
Family.4.Individual.4 1 1 0 0 1 2 0 1 0 2
Family.5.Individual.5 1 0 NA NA 1 1 0 1 0 1
Family.1.Individual.1 1 2 0 0 0 1 0 0 0 1
Family.2.Individual.2 1 0 0 0 2 2 0 0 1 1
Family.3.Individual.3 1 0 1 1 0 1 0 1 0 0
Family.4.Individual.4 1 0 0 0 2 0 0 0 0 0
Family.5.Individual.5 1 2 NA NA 0 0 2 0 0 0
Family.1.Individual.1 0 2 1 1 1 1 0 1 0 0
Family.2.Individual.2 0 1 1 1 1 0 0 1 1 0
Family.3.Individual.3 1 1 0 0 1 2 1 0 0 1
Family.4.Individual.4 0 1 1 0 1 0 0 1 0 1
Family.5.Individual.5 2 0 NA NA 1 1 0 1 0 1
Family.1.Individual.1 0 0 0 0 2 2 1 2 1 0
Family.2.Individual.2 0 1 1 0 1 2 0 1 0 0
Family.3.Individual.3 0 0 1 1 1 1 1 0 0 0
Family.4.Individual.4 1 1 1 0 1 0 0 1 0 2
Family.5.Individual.5 0 1 NA NA 2 1 1 1 2 0
Family.1.Individual.1 0 1 0 1 0 2 1 2 0 1
Family.2.Individual.2 0 1 1 1 2 1 0 1 0 2
Family.3.Individual.3 1 1 0 1 0 1 0 1 0 0
Family.4.Individual.4 1 0 1 0 2 1 1 0 0 2
Family.5.Individual.5 0 1 NA NA 2 1 1 1 1 1
Family.1.Individual.1 1 1 0 0 1 1 1 2 1 0
Family.2.Individual.2 0 1 0 1 0 1 1 1 0 1
Family.3.Individual.3 0 2 0 0 1 1 0 1 0 1
Family.4.Individual.4 0 1 1 0 0 0 0 0 0 0
Family.5.Individual.5 0 0 NA NA 1 0 1 1 1 0
Family.1.Individual.1 0 0 0 0 1 2 1 2 0 0
Family.2.Individual.2 0 1 1 0 2 1 0 1 1 1
Family.3.Individual.3 0 0 0 0 0 0 0 0 0 0
Family.4.Individual.4 0 0 0 0 0 2 2 1 0 1
Family.5.Individual.5 0 0 NA NA 0 1 1 0 1 1
[1] 3 5
Warning: parameter file has no LD model appended.
Assuming linkage equilirbiurm and given allele frequencies.
1 2 3 4 5
Family.1.Individual.1 0 1 2 2 1
Family.1.Individual.2 2 NA 2 2 0
Family.1.Individual.3 NA 0 2 2 NA
Family.1.Individual.1 1 0 1 1 0
Family.1.Individual.2 1 NA 0 0 1
Family.1.Individual.3 NA 1 0 1 NA
Family.1.Individual.1 1 0 0 0 1
Family.1.Individual.2 0 NA 1 2 1
Family.1.Individual.3 NA 1 0 1 NA
Family.1.Individual.1 1 0 0 0 0
Family.1.Individual.2 0 NA 1 0 1
Family.1.Individual.3 NA 0 1 0 NA
Family.1.Individual.1 0 2 0 2 0
Family.1.Individual.2 1 NA 0 0 0
Family.1.Individual.3 NA 1 0 1 NA
Family.1.Individual.1 1 0 1 0 0
Family.1.Individual.2 1 NA 1 1 1
Family.1.Individual.3 NA 0 1 1 NA
Family.1.Individual.1 0 2 0 0 2
Family.1.Individual.2 1 NA 0 0 1
Family.1.Individual.3 NA 1 0 0 NA
Family.1.Individual.1 0 0 2 0 2
Family.1.Individual.2 1 NA 0 0 0
Family.1.Individual.3 NA 1 1 0 NA
Family.1.Individual.1 1 1 1 1 2
Family.1.Individual.2 2 NA 1 0 1
Family.1.Individual.3 NA 1 1 0 NA
Family.1.Individual.1 0 0 2 0 1
Family.1.Individual.2 2 NA 0 0 0
Family.1.Individual.3 NA 1 1 0 NA
[1] 3 5
Setup time = 0.013000011444091797 0.013000011444091797
--------------------------------------------------
--------------------------------------------------+++++
+++++
Done = 7.347999811172485 7.360999822616577
rJPSGCS documentation built on May 1, 2019, 11:10 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Note Author(s) See Also Examples
Description
This function is an R wrapper for the JPSGCS multi-locus gene-drop program.
Usage
1 2 |
Arguments
ld.par |
Name of a LINKAGE parameter file or an LD model file output by |
ped |
Name of the post-MAKEPED pedfile (see the explanation in the
documentation for |
n |
Number of pedigrees to simulate. |
complete.data |
Should complete data be simulated; i.e., should we ignore the marker
data availability in |
gd.ped |
Name of the output file of simulated marker data in post-MAKEPED pedigree file format. |
compress.pedfiles |
Should the output pedigree file be compressed? Default is |
Value
No value. The output is in the file named by gd.ped.
Note
This function can require large amounts of memory for large data sets (e.g. whole chromosomes of SNPs at the marker density of the HapMap project). The computations are done in Java, using functions from Alun Thomas' suite of Java Programs for Statistical Genetics and Computational Statistics (JPSGCS). The JPSGCS Java programs are accessed by R-wrappers provided by the rJPSGCS package, which initializes the Java virtual machine (JVM) if not already done so by another package. To over-ride the default amount of memory Java allocates for heap space, initialize the JVM before loading rJPSGCS as follows:
1 2 3 4 5 |
Author(s)
Sigal Blay, Jinko Graham and Brad McNeney
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | # 1. Gene-drop assuming independent loci in the founders, using allele
# frequencies and map information from the example data set.
data(exdat)
# Print out the data to examine missing data pattern.
exdat$markers
#Coerce to snp.matrix
sdat<-as(exdat$markers,"snp.matrix")
# Need LINKAGE parameter and pedigree files as input.
# For independent loci in the founders use a standard LINKAGE parameter file.
write.parfile(snp.data=sdat, map=exdat$map, file="indep.par")
# To construct the pedigree file for unrelated individuals, use
# the pedinf="unrelated" option.
write.pedfile(pedinf="unrelated",snp.data=sdat,file="unrel.ped")
GeneDrops(ld.par="indep.par",ped="unrel.ped",n=10,gd.ped="gd.ped")
# Read the SNP data back into R.
simdat<-read.pedfile("gd.ped")$snp.data
# Look at the simulated data: need to coerce to numeric matrix to see
# values of individual genotypes, rather than the usual snp.matrix summary
as(simdat,"numeric")
# 2. Simulate data at 5 independent SNPs 0.1 cM apart on a parent-child trio.
map=(1:5)/10
# Marker data on each individual as follows:
mom=c(1,2,1,1,1) # complete data
dad=c(0,NA,0,1,0) # missing data at SNP 2
child=c(NA,1,1,2,NA) #missing data at SNPs 1 and 5
sdat<-rbind(mom,dad,child)
colnames(sdat)<-paste("SNP",1:5,sep="")
sdat<-as(sdat,"snp.matrix")
write.parfile(snp.data=sdat, map=map, file="trio.par")
# Block of code setting up pedigree information for mom, dad and child.
# See the Details section of the write.pedfile help file for
# information on the file format.
trioinf<-matrix(c(
1, 1, 0, 0, 0, 0, 0, 2, 1, #mom: ID=1, no parents in pedigree
1, 2, 0, 0, 0, 0, 0, 1, 1, #dad: ID=2, no parents in pedigree
1, 3, 2, 1, 0, 0, 0, 2, 1),#child: ID=3, dadID=2, momID=1
byrow=TRUE, ncol=9, nrow=3)
write.pedfile(pedinf=trioinf, snp.data=sdat,file="trio.ped")
GeneDrops(ld.par="trio.par",ped="trio.ped",n=10,gd.ped="gd.ped")
#Read the SNP data back into R.
simdat<-read.pedfile("gd.ped")$snp.data
# Look at the missing data patterns in simdat
as(simdat,"numeric")
## Not run:
# 3. Repeat gene drop for the trio, but this time use an LD model fit
# by FitGMLD. FitGMLD requires a LINKAGE parameter file and a transposed
# pedigree file as input. Can use the parameter file trio.par again.
# The transposed pedigree file can be generated as follows:
write.pedfile(pedinf=trioinf,snp.data=sdat,file="f.ped",transpose=TRUE)
FitGMLD("trio.par","f.ped","out.ld.par")
#Call to GeneDrops uses the output of FitGMLD as the parameter file and
# the regular (not transposed) pedigree file
GeneDrops(ld.par="out.ld.par",ped="trio.ped",n=10,gd.ped="gd.ped")
simdat<-read.pedfile("gd.ped")$snp.data
unlink(c("f.ped","out.ld.par"))
## End(Not run)
unlink(c("unrel.ped","trio.ped","indep.par","trio.par", "gd.ped"))
|
Example output
Loading required package: rJava
Loading required package: chopsticks
Loading required package: survival
Warning message:
In methods:::bind_activation(TRUE) :
methods:::bind_activation(TRUE) is deprecated;
you should rather provide methods for cbind2() / rbind2()
SNP1 SNP2 SNP3 SNP4 SNP5 SNP6 SNP7 SNP8 SNP9 SNP10
subject1 2 1 2 2 1 0 2 2 2 1
subject2 1 0 0 2 1 2 2 1 2 1
subject3 1 2 2 1 1 2 2 1 1 2
subject4 2 2 2 2 1 0 0 2 1 2
subject5 2 2 NA NA 2 1 2 0 2 1
[1] 5 10
Warning: parameter file has no LD model appended.
Assuming linkage equilirbiurm and given allele frequencies.
1 2 3 4 5 6 7 8 9 10
Family.1.Individual.1 1 0 1 0 1 1 1 0 0 1
Family.2.Individual.2 0 1 0 0 1 1 0 0 1 0
Family.3.Individual.3 0 1 1 0 1 0 1 0 1 0
Family.4.Individual.4 0 1 0 1 2 0 0 0 0 2
Family.5.Individual.5 0 0 NA NA 1 2 0 1 0 0
Family.1.Individual.1 0 1 1 0 1 0 1 1 0 0
Family.2.Individual.2 0 1 0 0 0 2 0 1 1 0
Family.3.Individual.3 0 0 1 1 0 0 0 0 0 2
Family.4.Individual.4 1 0 2 0 0 2 0 0 0 1
Family.5.Individual.5 0 0 NA NA 1 1 0 1 1 0
Family.1.Individual.1 0 0 2 1 1 1 0 1 0 0
Family.2.Individual.2 0 0 0 0 1 1 0 1 0 0
Family.3.Individual.3 0 2 0 0 1 2 0 2 0 1
Family.4.Individual.4 1 2 0 0 1 2 1 1 0 0
Family.5.Individual.5 1 1 NA NA 0 0 1 1 0 0
Family.1.Individual.1 0 1 0 0 0 1 0 0 1 1
Family.2.Individual.2 0 0 0 0 0 0 0 1 0 1
Family.3.Individual.3 0 2 0 0 1 1 0 1 0 0
Family.4.Individual.4 1 1 0 0 1 2 0 1 0 2
Family.5.Individual.5 1 0 NA NA 1 1 0 1 0 1
Family.1.Individual.1 1 2 0 0 0 1 0 0 0 1
Family.2.Individual.2 1 0 0 0 2 2 0 0 1 1
Family.3.Individual.3 1 0 1 1 0 1 0 1 0 0
Family.4.Individual.4 1 0 0 0 2 0 0 0 0 0
Family.5.Individual.5 1 2 NA NA 0 0 2 0 0 0
Family.1.Individual.1 0 2 1 1 1 1 0 1 0 0
Family.2.Individual.2 0 1 1 1 1 0 0 1 1 0
Family.3.Individual.3 1 1 0 0 1 2 1 0 0 1
Family.4.Individual.4 0 1 1 0 1 0 0 1 0 1
Family.5.Individual.5 2 0 NA NA 1 1 0 1 0 1
Family.1.Individual.1 0 0 0 0 2 2 1 2 1 0
Family.2.Individual.2 0 1 1 0 1 2 0 1 0 0
Family.3.Individual.3 0 0 1 1 1 1 1 0 0 0
Family.4.Individual.4 1 1 1 0 1 0 0 1 0 2
Family.5.Individual.5 0 1 NA NA 2 1 1 1 2 0
Family.1.Individual.1 0 1 0 1 0 2 1 2 0 1
Family.2.Individual.2 0 1 1 1 2 1 0 1 0 2
Family.3.Individual.3 1 1 0 1 0 1 0 1 0 0
Family.4.Individual.4 1 0 1 0 2 1 1 0 0 2
Family.5.Individual.5 0 1 NA NA 2 1 1 1 1 1
Family.1.Individual.1 1 1 0 0 1 1 1 2 1 0
Family.2.Individual.2 0 1 0 1 0 1 1 1 0 1
Family.3.Individual.3 0 2 0 0 1 1 0 1 0 1
Family.4.Individual.4 0 1 1 0 0 0 0 0 0 0
Family.5.Individual.5 0 0 NA NA 1 0 1 1 1 0
Family.1.Individual.1 0 0 0 0 1 2 1 2 0 0
Family.2.Individual.2 0 1 1 0 2 1 0 1 1 1
Family.3.Individual.3 0 0 0 0 0 0 0 0 0 0
Family.4.Individual.4 0 0 0 0 0 2 2 1 0 1
Family.5.Individual.5 0 0 NA NA 0 1 1 0 1 1
[1] 3 5
Warning: parameter file has no LD model appended.
Assuming linkage equilirbiurm and given allele frequencies.
1 2 3 4 5
Family.1.Individual.1 0 1 2 2 1
Family.1.Individual.2 2 NA 2 2 0
Family.1.Individual.3 NA 0 2 2 NA
Family.1.Individual.1 1 0 1 1 0
Family.1.Individual.2 1 NA 0 0 1
Family.1.Individual.3 NA 1 0 1 NA
Family.1.Individual.1 1 0 0 0 1
Family.1.Individual.2 0 NA 1 2 1
Family.1.Individual.3 NA 1 0 1 NA
Family.1.Individual.1 1 0 0 0 0
Family.1.Individual.2 0 NA 1 0 1
Family.1.Individual.3 NA 0 1 0 NA
Family.1.Individual.1 0 2 0 2 0
Family.1.Individual.2 1 NA 0 0 0
Family.1.Individual.3 NA 1 0 1 NA
Family.1.Individual.1 1 0 1 0 0
Family.1.Individual.2 1 NA 1 1 1
Family.1.Individual.3 NA 0 1 1 NA
Family.1.Individual.1 0 2 0 0 2
Family.1.Individual.2 1 NA 0 0 1
Family.1.Individual.3 NA 1 0 0 NA
Family.1.Individual.1 0 0 2 0 2
Family.1.Individual.2 1 NA 0 0 0
Family.1.Individual.3 NA 1 1 0 NA
Family.1.Individual.1 1 1 1 1 2
Family.1.Individual.2 2 NA 1 0 1
Family.1.Individual.3 NA 1 1 0 NA
Family.1.Individual.1 0 0 2 0 1
Family.1.Individual.2 2 NA 0 0 0
Family.1.Individual.3 NA 1 1 0 NA
[1] 3 5
Setup time = 0.013000011444091797 0.013000011444091797
--------------------------------------------------
--------------------------------------------------+++++
+++++
Done = 7.347999811172485 7.360999822616577
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