Detecting rare recessive and compound heterozygote alleles in genome wide association.

cocohet {GenABEL}R Documentation

Detecting rare recessive and compound heterozygote alleles in genome wide association.

Description

Detecting rare recessive and compound heterozygote alleles in genome wide association.

Usage

cocohet(data, trait, window, return_all_result=TRUE, manhettan_filename="manhettan_plot.jpeg", test="CHI2", min_expected_cut_off=-1)

Arguments

data Genotype data for analysis. Object of class snp.data
trait Vector with binary trait data. Object of class integer or numeric.
window Number of SNPs on the "right" of a given SNP which are used in analysis with a SNP. Object of class integer
return_all_result If FALSE then return only a vector where each element is a chisq obtained as a maximum chisq between a given SNP and SNPs on the right within a window. If TRUE then return also a matrix where chisq's for all tests are stored. Object of class logical
manhettan_filename File name where manhettan plot will be saved after analysis. Object of class character
test Name of the test to be performed. Available tests are "CHI2", "YATES" (chi2 with Yates correction), and "FISHER". Object of class character
min_expected_cut_off In case this is >=0 and test is NOT Pearson's chisq test then Pearson's chisq test (!) is performed only for SNPs which produce acontingency table where the expected number of subjects in each field is >min_expected_cut_off. Otherwise the specified test is performed. Object of class integer or numeric

Details

The function is an inplementation of the method aimed to detect a gene-phenotype association caused by recessive and compound heterozygote genotype states of multiple rare variants at a particular gene locus. The paper 'Detecting rare recessive and compound heterozygote alleles in genome wide association and sequencing studies with red hair color as example'; Fan Liu, Maksim V. Struchalin, Kate van Duijn, Albert Hofman, Andre Uitterlinden2 Yurii S. Aulchenko2 and Manfred Kayser1. Submited to 'PLoS Genetics'.

The three tests are implemented: Pearson's chi-square test, Pearson's chi-square test with Yates correction, Fisher exact test. In case when the input parameter min_expected_cut_off is <0 the choosen in the input parameter "test" test is performed. If min_expected_cut_off >= 0 then always Pearson's chi-square test is performed exept of the cases when expected number of subjects in a field of contingency table is <min_expected_cut_off. In this case the test choosen in the input parameter test is performed.

Value

A list is returned.

chi2_max A vector where each element is a test statistic choosen as a maximum chisq among tests where a SNP and SNPs on the right within a window are involved.
chi2_all Statistics of all tests done in the analysis. Each row of the matrix contains tests statistics for a SNP and all SNPs on the right of him within of a given window. For example: the ellement chi2_all[1,1] stands for a test

Author(s)

Maksim Struchalin

Examples


data(srdta)
chis2_nocorrection <- cocohet(data=srdta@gtdata, trait=srdta@phdata$bt, window=3, test="CHI2")		


[Package GenABEL version 1.6-7 Index]