# drgene.gm: causal models with G*E and E In IFP: Identifying Functional Polymorphisms

## Description

provides concordance probabilities of relative pairs for a causal model with G*E and E components

## Usage

 `1` ``` drgene(fdg,frg,eg,e) ```

## Arguments

 `fdg` an array (size=number of dominant genes+recessive genes) of dominant gene frequencies including 0 values of recessive genes of G component of G*E interacting with E of G*E `frg` an array (size=number of dominant genes+recessive genes) of recessive gene frequencies including 0 values of dominant genes of G component of G*E interacting with E of G*E `eg` a proportion of population who are exposed to environmental cause of G*E interacting with genetic cause of G*E during their entire life `e` a proportion of population who are exposed to environmental cause during their entire life

## Value

matrix of NN, ND, and DD probabilities of 9 relative pairs: 1:mzt,2:parent-offspring,3:dzt,4:sibling,5:2-direct(grandparent-grandchild),6:3rd(uncle-niece),7:3-direct(great-grandparent-great-grandchild),8:4th (causin),9:4d(great-great-grandparent-great-great-grandchild)

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```### PLI=0.01. ppt<-0.01 ### g*e+e model pge<-0.007 # the proportion of G*E component in total populations e<-1-(1-ppt)/(1-pge) # the proportion of E component in total populations ppe<-0.5 ppg<-pge/ppe fd<-0.0005 # one dominant gene tt<-3 # the number of recessive genes temp<-sqrt(1-((1-ppg)/(1-fd)^2)^(1/tt)) fr<-c(array(0,length(fd)),array(temp,tt)) fd<-c(fd,array(0,tt)) drgene(fd,fr,ppe,e) ```