-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathrun_association_tests_chr3_MAP4_cov_adj_SIM.r
205 lines (166 loc) · 8.23 KB
/
run_association_tests_chr3_MAP4_cov_adj_SIM.r
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
# --- Loading necessary packages and functions ---- #
require(stringr,lib.loc="/home/bulllab/jshin/R/x86_64-unknown-linux-gnu-library/3.0",quietly=T)
require(SKAT,lib.loc="/home/bulllab/jshin/R/x86_64-unknown-linux-gnu-library/3.0",quietly=T)
# source SKAT-functions modified in order to extract extra information
# (such as kurtosis or df estimates)
for (i in 1:length(dir('/home/bulllab/gaw18/gaw19/jshin/scripts/SKAT_R/',full.name=TRUE))) {
source(dir('/home/bulllab/gaw18/gaw19/jshin/scripts/SKAT_R/',full.name=TRUE)[i])
}
assignInNamespace("SKAT_PValue_Logistic_VarMatching",
SKAT_PValue_Logistic_VarMatching_JS,ns="SKAT")
assignInNamespace("KMTest.logistic.Linear.VarMatching",
KMTest.logistic.Linear.VarMatching_JS,ns="SKAT")
assignInNamespace("SKAT_Get_DF_Sim",
SKAT_Get_DF_Sim_JS,ns="SKAT")
assignInNamespace("SKAT_GET_kurtosis",
SKAT_GET_kurtosis_JS,ns="SKAT")
assignInNamespace("SKAT_Logistic_VarMatching_GetParam1_QuantileAdj",
SKAT_Logistic_VarMatching_GetParam1_QuantileAdj_JS,ns="SKAT")
assignInNamespace("SKAT_Logistic_VarMatching_GetParam",
SKAT_Logistic_VarMatching_GetParam_JS,ns="SKAT")
# source penalized-likelihood approach implemented by a previous trainee(NAME?) of SB
for (i in 1:length(dir('/home/bulllab/jshin/pmlr11/R/',full.name=TRUE))){
source(dir('/home/bulllab/jshin/pmlr11/R/',full.name=TRUE)[i])
}
rm(i)
# for parallele computing:
id=Sys.getenv("PBS_ARRAYID")
simfile.id <- as.numeric(id)
ORGPHEN = read.table('/home/bulllab/gaw18/gaw19/data/T2D-GENES_P1_Hispanic_phenotypes.txt',
header=TRUE,stringsAsFactors=FALSE,sep="\t")
SIMPHEN = read.csv(paste('/home/bulllab/gaw18/gaw19/data/SIMPHEN/SIMPHEN.',simfile.id,".csv",sep=""),header=TRUE,stringsAsFactors=FALSE)
# simulated phenotype file does not have info on AGE and SEX, so took the orginal (AGE and SEX were not simulated, so they should be the same between original and simulated data sets)
all(ORGPHEN$ID == SIMPHEN$ID)
SIMPEHN$AGE <- ORGPHEN$AGE
SIMPEHN$SEX <- ORGPHEN$SEX
### THIS NEEDS TO BE DONE OUTSIDE THIS FILE ###
GENO = read.csv('/home/bulllab/gaw18/gaw19/data/chr3_MAP4-dose.csv',header=T,stringsAsFactors=F)
tGENO = t(GENO[,-1])#1943x490 (n-by-m matrix)
colnames(tGENO) <- GENO[,1]
# selecting polymorphic markers#dosage - this can be done just once
ind <- rep(NA,ncol(tGENO)) #319 markers
for( i in 1:length(ind) ){
ind[i] <- length(unique(tGENO[,i]))==1
}
# THE FILE IS IN DOSAGE - SHOULD CHANGE IT TO BEST-GUESS GENOTYPES?
###
out.file = paste("/home/bulllab/gaw18/gaw19/results/chr3_MAP4_res_no_imputation_qsub_SIMPHEN.",simfile.id,".out",sep="")
# ------------------ read in data ------------------#
# (phenotype + genotype - created by 'Make_analysible_data.Rnw'
# in JS's desktop-should mv file in the future)
# each genetic marker column codes for the number of index allele
data <- read.csv('/home/bulllab/gaw18/gaw19/data/chr3_pheno_MAP4_var_sites_MAF.csv',
header=T, stringsAsFactors=F)
print(head(data))
print(dim(data))
marker = names(data)[-c(1:9)]
rs_dist = read.csv('/home/bulllab/gaw18/gaw19/data/chr3_MAP4_positions_from_snpnexus_30132.csv',header=T,stringsAsFactors=F)
rs_dist = rs_dist[,c("SNP","chromPosition")]
dist = str_replace(marker,"var_3_","")
for(i in 1:nrow(rs_dist)){
dist[which(dist==rs_dist$SNP[i])] <- rs_dist$chromPosition[i]
}
dist <- as.numeric(dist) #34 of them do not have distances
map.info <- cbind.data.frame(marker,dist,stringsAsFactors=FALSE)
rm(marker,dist)
# -------------- analysis begins here --------------#
# column numbers where marker data are included
first.G.col = 10
last.G.col = ncol(data) #87 polymorphic markers
#25 output variables
# convert the numeric 'hypt' column into factor for pmlr() function
data$y <- as.factor(data$hypt)
org.data <- data
rm(data)
for(i in first.G.col:last.G.col){
marker = names(org.data)[i]
pos = map.info$dist[(i-9)]
cat((i-9),'-th marker ', marker, "\n", sep="")
#missing genotype rates - in the data set with complet info on hypt.
missing_geno_rate = (sum(is.na(org.data[!is.na(org.data$y),marker]))/sum(!is.na(org.data$y)))
# creating a dataset with complete information
# to prevent SKAT from imputing missing genotypes
no.missing.ind <- !is.na(org.data$y) & !is.na(org.data[,marker])
data = org.data[no.missing.ind,]
n00 = sum(data$hypt==0 & data[,i]==0,na.rm=T)
n01 = sum(data$hypt==0 & data[,i]==1,na.rm=T)
n02 = sum(data$hypt==0 & data[,i]==2,na.rm=T)
n10 = sum(data$hypt==1 & data[,i]==0,na.rm=T)
n11 = sum(data$hypt==1 & data[,i]==1,na.rm=T)
n12 = sum(data$hypt==1 & data[,i]==2,na.rm=T)
n = n00+n01+n02+n10+n11+n12
allele.freq = (n01+n11+2*(n02+n12))/n
if(allele.freq == 0){
beta_MLE <- SE.beta_MLE <- beta_PMLE <- SE.beta_PMLE <- NA
stat_lrt <- stat_plrt <- NA
stat_score <- stat_score_var_adj <- stat_score_var_kurt_adj <- NA
df_score_var_kurt_adj <- NA
pval_lrt <- pval_plrt <- pval_score <- NA
pval_score_var_adj <- pval_score_var_kurt_adj <- NA
}
if(allele.freq > 0 ){
# applying MLE
# fitting an additive model
reg.model = as.formula(paste('y~AGE+SEX+',marker))
print(reg.model)
# standard likelihood ratio test
lrt.res = pmlr(reg.model, data=data, method="likelihood", penalized=F)
stat_lrt = lrt.res$stat[,marker,TRUE]
pval_lrt = lrt.res$pval[,marker,TRUE]
beta_MLE = lrt.res$coef[,,TRUE][marker]
SE.beta_MLE = sqrt(lrt.res$var[marker,marker,TRUE])
# penalized likelihood ratio test
plrt.res = pmlr(reg.model, data=data, method="likelihood", penalized=T)
stat_plrt = plrt.res$stat[,marker,TRUE]
pval_plrt = plrt.res$pval[,marker,TRUE]
beta_PMLE = plrt.res$coef[,,TRUE][marker]
SE.beta_PMLE = sqrt(plrt.res$var[marker,marker,TRUE])
# standard score test
score.res = pmlr(reg.model, data=data, method="score", penalized=F)
stat_score = score.res$stat[,marker,TRUE]
pval_score = score.res$pval[,marker,TRUE]
do.not.run <- function(){
#comparison.score
lm0 = glm(y~AGE+SEX,data=data[!is.na(data[,i]),],family=binomial())
lm1 = glm(reg.model,data=data[!is.na(data[,i]),],family=binomial())
anova(lm0,lm1,test="Rao")
anova(lm0,lm1,test="Chi")
}
#SKAT - small-sample-adjustments to var or to var and kurtosis
Z <- as.matrix(data[,i],ncol=1)
# var-adj test
obj.kurtosis.adj <- SKAT_Null_Model_MomentAdjust(hypt~AGE+SEX, data=data)
obj.no.kurtosis.adj <- SKAT_Null_Model_MomentAdjust(hypt~AGE+SEX, data=data,is_kurtosis_adj=FALSE)
#cutoff of the missing rates of the SNPs - default is 15%
#not sure if I remove SNPs with missing rate >= 15%
missing_rate_threshold = 1 # not filtering anything
#score test with small-sample-adjusted variance
skat.no.kurtosis.adj <- SKAT(Z,obj.no.kurtosis.adj,weights=1,
missing_cutoff=missing_rate_threshold,estimate_MAF=2)
stat_score_var_adj <- sqrt(2*skat.no.kurtosis.adj$param$df)*(skat.no.kurtosis.adj$Q-skat.no.kurtosis.adj$param$muQ)/sqrt(skat.no.kurtosis.adj$param$varQ)+skat.no.kurtosis.adj$param$df
pval_score_var_adj <- skat.no.kurtosis.adj$p.value
#score test with small-sample-adjusted variance and kurtosis
skat.kurtosis.adj <- SKAT(Z,obj.kurtosis.adj,weights=1,
missing_cutoff=missing_rate_threshold,estimate_MAF=2)
df_score_var_kurt_adj <- skat.kurtosis.adj$param$df
stat_score_var_kurt_adj <- sqrt(2*df_score_var_kurt_adj)*(skat.kurtosis.adj$Q-skat.kurtosis.adj$param$muQ)/sqrt(skat.kurtosis.adj$param$varQ)+df_score_var_kurt_adj
pval_score_var_kurt_adj <- skat.kurtosis.adj$p.value
}
res <- cbind.data.frame(marker,pos,allele.freq,
n00,n01,n02,n10,n11,n12,n,missing_geno_rate,
beta_MLE,SE.beta_MLE,beta_PMLE,SE.beta_PMLE,
stat_lrt,stat_plrt,
stat_score,stat_score_var_adj,stat_score_var_kurt_adj,
df_score_var_kurt_adj,
pval_lrt,pval_plrt,pval_score,pval_score_var_adj,pval_score_var_kurt_adj)
if(i==first.G.col){
#printing column names
#do this once - using the default sep=" "
write.table(rbind(names(res)),file=out.file,
quote=F,col.names=F,row.names=F,append=TRUE,sep=" ")
}
write.table(rbind(res),file=out.file,
quote=F,col.names=F,row.names=F,append=TRUE,sep=" ")
}
write.table(map.info, file = "/home/bulllab/gaw18/gaw19/results/chr3_MAP4.map",
quote=F,col.names=T,row.names=F,sep=" ")