IFP (version 0.2.4)

lrt: Likelihood Ratio Tests for Identifying Number of Functional Polymorphisms

Description

Compute p-values and likelihoods of all possible models for a given number of functional SNP(s).

Usage

lrt(n.fp, n, x, geno, no.con=nrow(geno))

Arguments

n.fp

number of functional SNPs for tests.

n

array of each total number of case sample chromosomes for SNPs

x

array of each total allele number in case samples

geno

matrix of alleles, such that each locus has a pair of adjacent columns of alleles, and the order of columns corresponds to the order of loci on a chromosome. If there are K loci, then ncol(geno) = 2*K. Rows represent the alleles for each subject. Each allele shoud be represented as numbers (A=1,C=2,G=3,T=4).

no.con

number of control chromosomes.

Value

matrix of likelihood ratio test results. First n.fp rows indicate the model for each set of disease polymorphisms, and followed by p-values, -2 log(likelihood ratio) with corrections for variances, maximum likelihood ratio estimates, and likelihood.

References

L. Park, Identifying disease polymorphisms from case-control genetic association data, Genetica, 2010 138 (11-12), 1147-1159.

See Also

allele.freq hap.freq

Examples

# NOT RUN {
## LRT tests when SNP1 & SNP6 are the functional polymorphisms.

data(apoe)

n<-c(2000, 2000, 2000, 2000, 2000, 2000, 2000) #case sample size = 1000
x<-c(1707, 281,1341, 435, 772, 416, 1797) #allele numbers in case samples 


Z<-2 	#number of functional SNPs for tests
n.poly<-ncol(apoe7)/2 	#total number of SNPs

#control sample generation( sample size = 1000 )
con.samp<-sample(nrow(apoe7),1000,replace=TRUE)
con.data<-array()
for (i in con.samp){
con.data<-rbind(con.data,apoe7[i,])
}
con.data<-con.data[2:1001,]

lrt(1,n,x,con.data)
lrt(2,n,x,con.data)
# }