Package SimGbyE. July 20, 2009

Transcription

1 Package SimGbyE July 20, 2009 Type Package Title Simulated case/control or survival data sets with genetic and environmental interactions. Author Melanie Wilson Maintainer Melanie Wilson Description The functions in this package create simulated case/control or survival data sets with one or more of the following assumed effects: genetic main effects (G), environmental main effects (E), Gene by Gene interactions (GbyG), Gene by environment interactions (GbyE). The assumed genetic one and two locus models of epistasis are chosen randomly from a set of models chosen from Li and Reich. Then given a set of assumed coefficients on the effects mentioned above, an outcome variable is simulated (case/control or survival) based on a set user specified distribution parameters. Version License Unlimited Depends R topics documented: gene.data sim sim.gbye sim.geno Index 9 1

2 2 sim gene.data Example Genetic Data Description Genotypes for 155 individuals at 5 different SNPs. Usage data(gene.data) Format A data frame with 155 observations on 5 SNPs. Details A set of genetic data on 155 individuals that is taken as input in the function sim.gbye and used to create a simulated survival data set and a case/control data set. Examples data(gene.data) sim Wrapper function to simulate genotypes and output Description This wrapper function is used to simulate genotypes with sim.geno.r for a set of chromo. and positions and then simulate output with sim.gbye.r. Usage sim<-function(chromo.pos,popn="ceu",n=100, outpath="./", exepath="/home/fac/iversen/bin/linux/", mappath="/local/recombrates/", happath="/local/hapmap.phased/", perlpath="/proj/design/simstudy/rcode/", rr1=1.0,rr2=1.0,minmaf=0.05, eniv,family="case.control",pop.char, G=0,E=0,GbyG=0,GbyE=0, dist.coef,outfile=null)

3 sim 3 Arguments chromo.pos Vector of three values (1) Integer in c(1:22) giving the specific chromo. to sample from (2) Nucleotide position of start of region on the given chromo and (3) Nucleotide position of end of region on the given chromo. (Note: chr17: is the 40KB region centered on TP53) popn n outpath exepath mappath happath perlpath rr1 rr2 minmaf The population in HapMap to simulate the genetic samples from: must be one of ("CEU","YRI","JPT+CHB") Number of individuals to simulate the genetic data on Path to directory where outfiles will be deposited Path to hapgen executable file Path to oxford genetic map Path to hapmap phased genotype data Path to PERL script Heterozygote relative risk (should be 1.0) for population samples Rare homozygote relative risk (should be 1.0) for population samples Smallest minor allele freq for disease locus eniv matrix of environmental data of dim (m X q) family pop.char G E Value GbyG GbyE coef.dist outfile Character vector giving the family type where "case.control" will assume a binomial family and "survival" assumes a weibull family If family="case.control" the user must specify the fraction of cases in the simulated data set and if family="survival" the user must specify the median survival time, and scale and shape parameters of the baseline survival function respectively. Integer giving the number of genetic main effects Integer giving the number of eniv. main effects Interger giving the number of GXG interactions Interger giving the number of GXE interactions Discrete distribution on the coefficients of each effect specified as a list of two vectors: the first vector giving the possible values for the coefficients and the second vector indicating the support for each of these values. File path for the simulation files once they are created This function outputs a list of the following values if a file "outfile" is not given: model A list of two matrices, the first is a matrix of dimension (2 X (G+E+GbyG+GbyE)) that specifics the variables (genetic or environmental) that are involved in each effect. Where (1) Genetic main effect: 1 SNP is specified in the first row and "NA" in the second row, (2) Eniv. main effect: a eniv. variable in the first row and a "NA" in the second row, (3) GbyG: two SNPs in row one and two, and (4) GbyE a SNP in the first row and a eniv. variable in the second row. The SNP variables are specified by their rs numbers. Each SNP genotype is normally

4 4 sim design coef data parametrized as a 0 for the rare hom. genotype (aa), 1 for the het. genotype (aa) and 2 for the common hom. genotype (AA). If the SNP rs number is specified with a ".c" after the name then the reverse parameterization is used (0 for AA, 1 for aa and 2 for aa). The second matrix is of dimension (9 X (G+E+GbyG+GbyE)) and is a matrix specifying the genetic epistasis models of the genetic markers in the case of the genetic main effects, GbyG interactions and GbyE interactions. Each model is specified using a vector of length 9 that is the "penetrance" at each possible set of genotypes for two makers (aa/bb,aa/bb,aa/bb,aa/bb,aa/bb,aa/bb,aa/bb,aa/bb,aa/bb) this is the same model specification as in pages of Li and Reich. For the (1) Environmental main effects no model effect specification is given, for (2) the GbyE effects, the model effect specification that is given is the specification for the genetic main effect that is interacting with the given environmental variable. The design matrix created based on the models described above and used for the simulation A vector of beta coefficients for each of the effects in the simulation (of length G+E+GbyG+GbyE) The simulated data set If a file is given the above items are saved to the files outfile.model.out, outfile.design.out, outfile.data.out, and outfile.coef.out respectively. Author(s) Melanie Wilson <maw27@stat.duke.edu> References Li W and Reich J (1999). A complete enumeration and classification of two-locus disease models. Human Heredity; 50: Examples ##Simulated Genotypes for TP53 Region chromo = 17 pos1 = pos2 = chromo.pos = c(chromo,pos1,pos2) popn = "CEU" n = 100 outpath = "./" exepath = "/home/fac/iversen/bin/linux/" mappath = "/local/recombrates/" happath = "/local/hapmap.phased/" ##make the environment data ##AGE age = rnorm(n,mean=55,sd=11) eniv = cbind(age) colnames(eniv) = c("age")

5 sim.gbye 5 eniv = data.frame(eniv) pop.char =.4coef.dist = list(log(c(1.25,1.5,1.75)),c(1/3,1/3,1/3)) p53.assoc = sim(chromo.pos,popn=popn,n=n,outpath=outpath, exepath=exepath,mappath=mappath,happath=happath, eniv=eniv,family="case.control",pop.char=pop.char, G = 3,E=1,GbyG=0,GbyE=0,dist.coef=coef.dist) sim.gbye Simulation Function for case/control or Survival Data sets with genetic and environmental interactions Description Usage This function takes genetic data and environmental data for a set of individuals and simulations either a case/control data set or a survival data set that assumes GbyG and GbyE interactions. genetic.sim = function(gene,eniv,family,n,pop.char, G=0,E=0,GbyG=0,GbyE=0, dist.coef,outpath="./") Arguments gene matrix of genetic data of dim (m X p) where m >= n (the total number of individuals wanted in the final simulated data set) eniv matrix of environmental data of dim (m X q) family n pop.char G E GbyG GbyE coef.dist outpath Character vector giving the family type where "case.control" will assume a binomial family and "survival" assumes a weibull family The total number of individuals requested in the final simulated data set where n <= m If family="case.control" the user must specify the fraction of cases in the simulated data set and if family="survival" the user must specify the median survival time, and scale and shape parameters of the baseline survival function respectively. Integer giving the number of genetic main effects Integer giving the number of eniv. main effects Interger giving the number of GXG interactions Interger giving the number of GXE interactions Discrete distribution on the coefficients of each effect specified as a list of two vectors: the first vector giving the possible values for the coefficients and the second vector indicating the support for each of these values. Path that the genetic data is written to in the sim.geno function

6 6 sim.gbye Value This function outputs a list of the following values: model design coef data A list of two matrices, the first is a matrix of dimension (2 X (G+E+GbyG+GbyE)) that specifics the variables (genetic or environmental) that are involved in each effect. Where (1) Genetic main effect: 1 SNP is specified in the first row and "NA" in the second row, (2) Eniv. main effect: a eniv. variable in the first row and a "NA" in the second row, (3) GbyG: two SNPs in row one and two, and (4) GbyE a SNP in the first row and a eniv. variable in the second row. The SNP variables are specified by their rs numbers. Each SNP genotype is normally parametrized as a 0 for the rare hom. genotype (aa), 1 for the het. genotype (aa) and 2 for the common hom. genotype (AA). If the SNP rs number is specified with a ".c" after the name then the reverse parameterization is used (0 for AA, 1 for aa and 2 for aa). The second matrix is of dimension (9 X (G+E+GbyG+GbyE)) and is a matrix specifying the genetic epistasis models of the genetic markers in the case of the genetic main effects, GbyG interactions and GbyE interactions. Each model is specified using a vector of length 9 that is the "penetrance" at each possible set of genotypes for two makers (aa/bb,aa/bb,aa/bb,aa/bb,aa/bb,aa/bb,aa/bb,aa/bb,aa/bb) this is the same model specification as in pages of Li and Reich. For the (1) Environmental main effects no model effect specification is given, for (2) the GbyE effects, the model effect specification that is given is the specification for the genetic main effect that is interacting with the given environmental variable. The design matrix created based on the models described above and used for the simulation A vector of beta coefficients for each of the effects in the simulation (of length G+E+GbyG+GbyE) The simulated data set Author(s) Melanie Wilson <maw27@stat.duke.edu> References Li W and Reich J (1999). A complete enumeration and classification of two-locus disease models. Human Heredity; 50: Examples ##Load the genetic data (155 individuals and 5 SNPs) data(gene.data) gene = gene.data ##make the environment data ##AGE age = rnorm(155,mean=55,sd=11) eniv = cbind(age) colnames(eniv) = c("age")

7 sim.geno 7 eniv = data.frame(eniv) ##Case control simulation parameters family="case.control" n = 155 pop.char =.4 coef.dist = list(log(c(1.25,1.5,1.75)),c(1/3,1/3,1/3)) ##create one simulation with only 2 genetic main effects and 1 environmental effect sim = genetic.sim(gene=gene,eniv=eniv,family=family,n=n,pop.char=pop.char, G=2,E=1,GbyG=0,GbyE=0,dist.coef=coef.dist) sim.geno Simulation Function for Genetic data Description This function creates genetic data for a population using R binary versions of hapmap and recomb map input files. Usage sim.geno<-function(regnum=1,chromo=17,pos1= ,pos2= ,popn="ceu",n=100, outpath="./", exepath="/home/fac/iversen/bin/linux/", mappath="/local/recombrates/", happath="/local/hapmap.phased/", rr1=1.0,rr2=1.0,minmaf=0.05) Arguments regnum chromo pos1 pos2 popn n outpath exepath mappath happath rr1 rr2 minmaf Integer specifying the index of the chromo. region Integer in c(1:22) giving the specific chromo. to sample from Nucleotide position of start of region on the given chromo. Nucleotide position of end of region on the given chromo. (Note: chr17: is the 40KB region centered on TP53) The population in HapMap to simulate the genetic samples from: must be one of ("CEU","YRI","JPT+CHB") Number of individuals to simulate the genetic data on Path to directory where outfiles will be deposited Path to hapgen executable file Path to oxford genetic map Path to hapmap phased genotype data Heterozygote relative risk (should be 1.0) for population samples Rare homozygote relative risk (should be 1.0) for population samples Smallest minor allele freq for disease locus

8 8 sim.geno Details Value Function Calls hapgen to simulate one replicate from a specified chromosomal region given data from one of the HapMap II populations. The code generates samples of genotypes in a contiguous range of DNA using hapmap release 21 (NCBI build 35) data (it drops all evidently monomorphic variants). The default (rr1=1.0, rr2=1.0) is to generate population-based samples. If one or the other of rr1 and rr2 are >1.0, it (hapgen) will randomly choose a variant with MAF in (minmaf,0.5) as the disease allele and generate a case-control sample (I ve forced n.case=n.control=(n/2)). So this code can be used for generating main-effects simulations that have 1 or 0 associated SNPs per region generated. The position range may encompase an entire chromosome or simply bracket a gene or locus of interest. Generating a candidate gene/pathway sample or a genome-wide sample will require multiple calls to this function, one for each independent chromosomal range (if there is ld between variants in two ranges, they should be combined into one). This function outputs a list of the following values: geno An n*p matrix of genotypes coded as the number of minor alleles present (0, 1 or 2) where p is the number of non-monomorphic SNPs in the chosen chromo. range y legend An n-vector of zeros and ones denoting case/control status (1/0) even when both rr1 and rr2 are set to 1.0 (in that case, the case/control designation is meaningless) The hapmap legend file for the in-range SNPs, columns are rs number, chromosomal position, allele 0 and allele 1 assoc Data from the hapgen.aux file: when one or more of rr1 and rr2 are not 1.0: rows are index of the disease SNP in the geno matrix), disease SNP position, minor allele (allele 0 or 1), estimated MAF, assumed rr s (specified rr1 and rr2) Author(s) Melanie Wilson <maw27@stat.duke.edu> Examples ##Simulated Genotypes for TP53 Region chromo = 17 pos1 = pos2 = popn = "CEU" n = 100 outpath = "./" exepath = "/home/fac/iversen/bin/linux/" mappath = "/local/recombrates/" happath = "/local/hapmap.phased/" p53.null = sim.geno(n=10000)

9 Index Topic datasets gene.data, 1 Topic models sim, 2 sim.gbye, 5 sim.geno, 7 gene.data, 1 sim, 2 sim.gbye, 2, 5 sim.geno, 7 9