stmgeplink {stmgp}

Smooth-threshold multivariate genetic prediction (incorporating gene-environment interactions) for genome-wide association or whole-genome sequencing data in PLINK format

Description

Build prediction model from training data and predict test data phenotype through smooth-threshold multivariate genetic prediction (STMGP) method incorporating gene-environment (GxE) interactions, in which GxE interaction effects are linearly added to the STMGP model with marginal effects. Data must be in PLINK binary format and marginal test p-values (i.e. test for each variant) are computed by PLINK software, which enables rapid computation even for data having very large number of variants. An optimal p-value cutoff is selected by Cp-type criterion. Both quantitative and binary phenotypes are acceptable, in which data must be in PLINK fam file format or in a separate file (PLINK format, i.e. FID and IID are needed). Environment variables need be in covariate file by specifying the column names.

Usage

stmgeplink(trainbed, Z ,Enames ,Zte = NULL, testbed = NULL, gamma = 1, taun = NULL,
   lambda = 1, plink = "plink --noweb", maf = 0.01, hwe = 1e-4, geno = 0.1,
   fout = "stp", trainfout = "train", testfout = "test", ll = 50, maxal = NULL,
   alc = NULL, tdir = NULL, Znames = NULL, trainphenofile = NULL, testphenofile = NULL, 
   phenoname = NULL, Assc = FALSE, AsscGE = FALSE, centerE = TRUE)

Arguments

trainbed

A training data file name in PLINK binary format or a vector of three file names of .bed, .bim, .fam; Binary phenotype must be coded as 1 or 2 in PLINK fam file. Missing values in .fam file are -9 as usual.

Z

A covariate file name for training data including environment variables (PLINK format, i.e. FID and IID are needed) or data matrix, missing values are "-9".

Enames

Vector of (column) names of environment variables in the covariate file specified in Z.

Zte

A covariate file name for test data including environment variables (PLINK format, i.e. FID and IID are needed) or data matrix, missing values are "-9"; NULL (default) means unspecified.

testbed

A test data file name in PLINK binary format or a vector of three file names of .bed, .bim, .fam; NULL (default) means unspecified. Missing values in .fam are -9 as usual.

gamma

gamma parameter; gamma=1 is default as suggested in Ueki and Tamiya (2016).

taun

tau parameter divided by (sample size) (allowed to be a vector object; optimal parameter is chosen by Cp); NULL (default) specifies tau=n/log(n)^0.5 as suggested in Ueki and Tamiya (2016).

lambda

lambda parameter (default=1).

plink

PLINK command, e.g. "plink2", "./plink –noweb", or "plink1.9 –memory 100000" (default is plink --noweb) where options can be added; PLINK must be installed.

maf

Minor allele frequency (MAF) cutoff for --maf option in PLINK.

hwe

Hardy-Weinberg equilibrium (HWE) cutoff for --hwe option in PLINK.

geno

Missing call rate cutoff for --geno option in PLINK (default=0.1).

fout

An output file name (default="stp").

trainfout

An output file name for training data (default="train").

testfout

An output file name for test data (default="test").

ll

Number of candidate p-value cutoffs for search (default=50) as determined by 10^seq( log10(maxal),log10(5e-8), length=ll).

maxal

Maximum p-value cutoff for search (default=NULL); If most variants are null maxal*(number of variables to be selected) gives approximate number of filtered variants, which is useful for rapid computation even for data with large number of variants.

alc

User-specified candidate p-value cutoffs for search; ll option is effective if alc=NULL.

tdir

Location of temporary files (default=tempdir()).

Znames

Name(s) of covariate used; NULL (default) means unspecified.

trainphenofile

A phenotype file name for training data (PLINK format, i.e. FID and IID are needed) with header columns (i.e. FID, IID, phenoname1, phenoname2, ...) missing values are "-9"; NULL (default) means unspecified.

testphenofile

A phenotype file name for test data (PLINK format, i.e. FID and IID are needed) with header columns (i.e. FID, IID, phenoname1, phenoname2, ...) missing values are "-9"; NULL (default) means unspecified.

phenoname

Phenotype name in trainphenofile; NULL (default) means unspecified but users should provide if trainphenofile is provided.

Assc

Whether the marginal association result is stored or not (default=FALSE).

AsscGE

Whether the gene-environment interaction result is stored or not (default=FALSE).

centerE

Whether environment variables are centered or not by subtracting their mean (default=TRUE).

Details

See Ueki and Tamiya (2016).

Value

Muhat

Estimated phenotypes from linear model evaluated at each candidate tuning parameters (al and tau) whose size is of (sample size) x (length of al) x (length of tau).

gdf

Generalized degrees of freedom (GDF, Ye 1998) whose size is of (length of al) x (length of tau).

sig2hat

Error variance estimates (=1 for binary traits) whose size is of (length of al) x (length of tau).

df

Number of nonzero regression coefficients whose size is of (length of al) x (length of tau).

al

Candidate p-value cutoffs for search.

lopt

An optimal tuning parameter indexes for al and tau selected by Cp-type criterion, CP

BA

Estimated regression coefficient matrix whose size is of (1 + number of columns of Z + number of columns of X) x (length of al)) x (length of tau)); the first element, the second block and third block correspond to intercept, Z and X, respectively.

Loss

Loss (sum of squared residuals or -2*loglikelihood) whose size is of (length of al) x (length of tau).

sig2hato

An error variance estimate (=1 for binary traits) used in computing the variance term of Cp-type criterion.

tau

Candidate tau parameters for search.

CP

Cp-type criterion whose size is of (length of al) x (length of tau).

PE

PLINK .fam file for training data with additional column including the predicted phenotype from linear model.

PEte

PLINK .fam file for test data with additional column including the predicted phenotype from linear model estimated from training data.

nonzero

Variants with nonzero regression coefficients at the optimal parameter in PLINK file.

DataTr

Training dataset used (y, X and Z)

lapprox

lapprox values for gene-environment interaction tests (discrepancy from 1 suggests model misspecification)

ASSC

Marginal association result from PLINK if Assc is TRUE

ASSCGE

Gene-environment interaction result (approx test) from PLINK if AsscGE is TRUE

ASSCGEa

Gene-environment interaction result (all tests) from PLINK if AsscGE is TRUE

References

Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira M, Bender D, Maller J, Sklar P, de Bakker P, Daly MJ, Sham PC. (2007) PLINK: A tool set for whole-genome and population-based linkage analyses. Am J Hum Genet 81:559-75.

Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ. (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4.

Ueki M, Fujii M, Tamiya G. (2019) Quick assessment for systematic test statistic inflation/deflation due to null model misspecifications in genome-wide environment interaction studies. PLoS ONE 14: e0219825.

Examples

## Not run: 
wd = system.file("extdata",package="stmgp")


# quantitative traits
# training data (plink format)
trainbed = paste(wd,"train",sep="/")
# test data (plink format)
testbed = paste(wd,"test",sep="/")
# number of SNPs
#n.snp = length(readLines(paste(trainbed,".bim",sep="")))
n.snp = 80000


#> head(read.table(paste0(trainbed,".fam")))
# training sample .fam file (quantitative phenotype in the 6th column)
#       V1      V2 V3 V4 V5    V6
#1 id1_100 id2_100  0  0  1 -1.23
#2 id1_101 id2_101  0  0  1  1.48
#3 id1_102 id2_102  0  0  1 -4.27
#4 id1_103 id2_103  0  0  1 -2.61
#5 id1_104 id2_104  0  0  1 -0.27
#6 id1_105 id2_105  0  0  1 -0.50


#> head(read.table(paste0(testbed,".fam")))
# test sample .fam file
# (quantitative phenotype in the 6th column but missing (i.e. "-9") allowed)
#     V1    V2 V3 V4 V5    V6
#1 id1_0 id2_0  0  0  1 -0.59
#2 id1_1 id2_1  0  0  1  1.11
#3 id1_2 id2_2  0  0  1 -2.45
#4 id1_3 id2_3  0  0  1  0.11
#5 id1_4 id2_4  0  0  1 -1.17
#6 id1_5 id2_5  0  0  1  2.08


# using covariates files

Zf = paste(wd,"train.cov",sep="/")
Ztef = paste(wd,"test.cov",sep="/")


#> head(read.table(Zf,header=TRUE))   # covariate for training sample
#      FID     IID        COV1 COV2 qphen bphen
#1 id1_340 id2_340  1.27203944    1 -2.47     2
#2 id1_430 id2_430 -0.44144482    1 -0.71     2
#3 id1_199 id2_199 -0.18200011    1 -3.42     2
#4 id1_473 id2_473  0.03965880    0  0.32     1
#5 id1_105 id2_105  0.20418279    0 -0.50     2
#6 id1_188 id2_188 -0.04838519    0  2.98     1


#> head(read.table(Ztef,header=TRUE))   # covariate for test sample
#     FID    IID       COV1 COV2
#1 id1_80 id2_80 -0.2057512    0
#2 id1_53 id2_53 -0.8627601    1
#3 id1_59 id2_59 -0.2973529    1
#4 id1_71 id2_71  1.4728727    1
#5 id1_92 id2_92  3.5614472    0
#6 id1_25 id2_25  0.5135032    1


# model building from training data
# (incorporating COV1xG interaction as well as two covariates, COV1 and COV2)
sge1p0 = stmgeplink(trainbed=trainbed,Z=Zf,Enames="COV1",
                 maxal=5000/n.snp,Znames=c("COV1","COV2"))
head(sge1p0$PE)

# model building from training data and predicting test data
# (incorporating COV1xG interaction as well as two covariates, COV1 and COV2)
sge1p = stmgeplink(trainbed=trainbed,testbed=testbed,Z=Zf,Zte=Ztef,Enames="COV1",
                 maxal=5000/n.snp,Znames=c("COV1","COV2"))
head(sge1p$PEte)
head(sge1p$nonzero)


# model building from training data
# (incorporating COV1xG and COV2xG interactions as well as two covariates, COV1 and COV2)
sge12p0 = stmgeplink(trainbed=trainbed,Z=Zf,Enames=c("COV1","COV2"),
                 maxal=5000/n.snp,Znames=c("COV1","COV2"))
head(sge12p0$PE)

# model building from training data and predicting test data
# (incorporating COV1xG and COV2xG interactions as well as two covariates, COV1 and COV2)
sge12p = stmgeplink(trainbed=trainbed,testbed=testbed,Z=Zf,Zte=Ztef,Enames=c("COV1","COV2"),
                 maxal=5000/n.snp,Znames=c("COV1","COV2"))
head(sge12p$PEte)
head(sge12p$nonzero)





#### binary traits ####
# training data (plink format)
trainbed = paste(wd,"train",sep="/")
# test data (plink format)
testbed = paste(wd,"test",sep="/")
# number of SNPs
#n.snp = length(readLines(paste(trainbed,".bim",sep="")))
n.snp = 80000


#> head(read.table(paste0(trainbed,"b.fam"))) 
# training sample .fam file (binary phenotype (1 or 2) in the 6th column)
#       V1      V2 V3 V4 V5 V6
#1 id1_100 id2_100  0  0  1  2
#2 id1_101 id2_101  0  0  1  1
#3 id1_102 id2_102  0  0  1  2
#4 id1_103 id2_103  0  0  1  2
#5 id1_104 id2_104  0  0  1  2
#6 id1_105 id2_105  0  0  1  2


#> head(read.table(paste0(testbed,"b.fam")))  
# test sample .fam file (binary phenotype (1 or 2) in the 6th column
# but missing (i.e. "-9") allowed)
#     V1    V2 V3 V4 V5 V6
#1 id1_0 id2_0  0  0  1  2
#2 id1_1 id2_1  0  0  1  1
#3 id1_2 id2_2  0  0  1  2
#4 id1_3 id2_3  0  0  1  1
#5 id1_4 id2_4  0  0  1  2
#6 id1_5 id2_5  0  0  1  1


# using covariates files

# model building from training data
# (incorporating COV1xG interaction as well as two covariates, COV1 and COV2)
sge1p0b = stmgeplink(trainbed=paste0(trainbed,c(".bed",".bim","b.fam")),
       Z=paste(wd,"train.cov",sep="/"),Enames="COV1",
       maxal=5000/n.snp,Znames=c("COV1","COV2"))
head(sge1p0b$PE)

# model building from training data and predicting test data
# (incorporating COV1xG interaction as well as two covariates, COV1 and COV2)
sge1pb = stmgeplink(trainbed=paste0(trainbed,c(".bed",".bim","b.fam")),
       testbed=paste0(testbed,c(".bed",".bim","b.fam")),
       Z=paste(wd,"train.cov",sep="/"),Zte=paste(wd,"test.cov",sep="/"),Enames="COV1",
       maxal=5000/n.snp,Znames=c("COV1","COV2"))
head(sge1pb$PEte)
head(sge1pb$nonzero)


# model building from training data
# (incorporating COV1xG and COV2xG interactions as well as two covariates, COV1 and COV2)
sge12p0b = stmgeplink(trainbed=paste0(trainbed,c(".bed",".bim","b.fam")),
       Z=paste(wd,"train.cov",sep="/"),Enames=c("COV1","COV2"),
       maxal=5000/n.snp,Znames=c("COV1","COV2"))
head(sge12p0b$PE)

# model building from training data and predicting test data
# (incorporating COV1xG and COV2xG interactions as well as two covariates, COV1 and COV2)
sge12pb = stmgeplink(trainbed=paste0(trainbed,c(".bed",".bim","b.fam")),
       testbed=paste0(testbed,c(".bed",".bim","b.fam")),
       Z=paste(wd,"train.cov",sep="/"),Zte=paste(wd,"test.cov",sep="/"),
       Enames=c("COV1","COV2"),maxal=5000/n.snp,Znames=c("COV1","COV2"))
head(sge12pb$PEte)
head(sge12pb$nonzero)







## End(Not run)