load("dbp.R")<br data-attributes="%20/" />ls()<br data-attributes="%20/" />dbp[1:5,]<br data-attributes="%20/" />#<br data-attributes="%20/" />result.snp12 = glm (affection ~ rs1112, family=binomial("logit"), data=dbp)<br data-attributes="%20/" />print (result.snp12)<br data-attributes="%20/" />print ( class (result.snp12) )<br data-attributes="%20/" />print ( summary(result.snp12) )<br data-attributes="%20/" />#<br data-attributes="%20/" />dev.geno = anova (result.snp12, test="Chi")<br data-attributes="%20/" />lrt.pvalue = pchisq(dev.geno[dim(dev.geno)[1],"Deviance"],<br data-attributes="%20/" /> df=2, ncp=0, FALSE)<br data-attributes="%20/" />print ( lrt.pvalue )<br data-attributes="%20/" />#<br data-attributes="%20/" />print ( summary(result.snp12)$coefficients )<br data-attributes="%20/" />snp.beta = summary(result.snp12)$coefficients[2:3,1]<br data-attributes="%20/" />print ( snp.beta )<br data-attributes="%20/" />print ( exp(snp.beta) )<br data-attributes="%20/" />ci = confint (result.snp12)<br data-attributes="%20/" />print (ci)<br data-attributes="%20/" />print ( exp(ci) )<br data-attributes="%20/" />#<br data-attributes="%20/" />snp.data = dbp[,c("affection", "rs1112")]<br data-attributes="%20/" />summary(snp.data)<br data-attributes="%20/" />snp.data[,"rs1112"]<br />summary(snp.data)<br data-attributes="%20/" />#<br data-attributes="%20/" />result.all = glm (affection ~ rs1112, family=binomial("logit"),<br data-attributes="%20/" /> data=snp.data)<br data-attributes="%20/" />dev.all = anova (result.all, test="Chi")<br data-attributes="%20/" />summary(result.all)<br data-attributes="%20/" />print(dev.all)<br data-attributes="%20/" />#<br data-attributes="%20/" />snp.data = dbp[,c("affection", "trait","sex", "age", "rs1112", "rs1117")]<br data-attributes="%20/" />summary(snp.data)<br data-attributes="%20/" />snp.data[,"rs1112"]<br />snp.data[,"rs1117"]
#<br data-attributes="%20/" />result.adj = glm (affection ~ sex + rs1112 , family=binomial("logit"),<br data-attributes="%20/" /> data=snp.data)<br data-attributes="%20/" />summary(result.adj)<br data-attributes="%20/" />#<br data-attributes="%20/" />result.adj = glm (affection ~ age + rs1112 , family=binomial("logit"),<br data-attributes="%20/" /> data=snp.data)<br data-attributes="%20/" />summary(result.adj)<br data-attributes="%20/" />#<br data-attributes="%20/" />result.adj = glm (affection ~ sex + age + rs1112, family=binomial("logit"),<br data-attributes="%20/" /> data=snp.data)<br data-attributes="%20/" />summary(result.adj)<br data-attributes="%20/" />#<br data-attributes="%20/" />result.adj = glm (affection ~ rs1117 + rs1112, family=binomial("logit"),<br data-attributes="%20/" /> data=snp.data)<br data-attributes="%20/" />summary(result.adj)<br data-attributes="%20/" />anova (result.adj, test="Chi")<br data-attributes="%20/" />result.adj = glm (affection ~ rs1112 + rs1117, family=binomial("logit"),<br data-attributes="%20/" /> data=snp.data)<br data-attributes="%20/" />summary(result.adj)<br data-attributes="%20/" />anova (result.adj, test="Chi")<br data-attributes="%20/" />#<br data-attributes="%20/" />result.adj = lm (trait ~ rs1112, data=snp.data)<br data-attributes="%20/" />summary(result.adj)<br data-attributes="%20/" />result.adj = lm (trait ~ sex + rs1112, data=snp.data)<br data-attributes="%20/" />summary(result.adj)<br data-attributes="%20/" />#<br data-attributes="%20/" />result.inter = glm (affection ~ sex * rs1112, family=binomial("logit"),<br data-attributes="%20/" /> data=snp.data)<br data-attributes="%20/" />summary(result.inter)<br data-attributes="%20/" />result.inter = glm (affection ~ age * rs1112, family=binomial("logit"),<br data-attributes="%20/" /> data=snp.data)<br data-attributes="%20/" />summary(result.inter)<br data-attributes="%20/" />#<br data-attributes="%20/" />result.inter = glm (affection ~ rs1112 * rs1117, family=binomial("logit"),<br data-attributes="%20/" /> data=snp.data)<br data-attributes="%20/" />summary(result.inter)<br data-attributes="%20/" />#<br data-attributes="%20/" />q()
==RV-TDT exercise==
for g in `ls rvtdt_exercise_data | grep tped | cut -d"." -f1`
do
echo "runing rvTDT on gene "${g}
./rvTDT exercise_proj -G ./rvtdt_exercise_data/${g}.tped \
-P ./rvtdt_exercise_data/rvtdt_exercise.phen \
-M ./rvtdt_exercise_data/${g}.map \
--adapt 500 --alpha 0.00001 --permut 2000 \
--lower_cutoff 0 --upper_cutoff 100 \
--minVariants 3 \
--maxMissRatio 1
done
==SEQPower exercise==
spower -h<br data-attributes="%20/" />spower LOGIT -h<br data-attributes="%20/" />spower LOGIT Kryukov2009European1800.sfs --sample_size 1000 --OR_rare_detrimental 1.5 --method "CFisher --name CMC" -r 100 -j 4 -l 1 -o exercise
<br data-attributes="%20/" />spower show exercise.SEQPowerDB LOGIT method power title --condition "where power between 0.25 and 0.95"<br data-attributes="%20/" />
<br data-attributes="%20/" />for i in 1 1.5 2 2.5 3 3.5 4; do<br data-attributes="%20/" />spower LOGIT Kryukov2009European1800.sfs --sample_size 1000 --OR_rare_detrimental $i --method "CFisher --name CMC$i" --title FixedOR$i -r 100 -j 4 -l 1 -o exercise2<br data-attributes="%20/" />done
==VAT exercise==