stmgp: Smooth-threshold multivariate genetic prediction

In stmgp: Rapid and Accurate Genetic Prediction Modeling for Genome-Wide Association or Whole-Genome Sequencing Study Data

Description

Smooth-threshold multivariate genetic prediction (STMGP) method, which is based on the smooth-threshold estimating equations (Ueki 2009). Variable selection is performed based on marginal association test p- values (i.e. test of nonzero slope parameter in univariate regression for each predictor variable) with an optimal p-value cutoff selected by a Cp-type criterion. Quantitative and binary phenotypes are modeled via linear and logistic regression, respectively.

Usage

stmgp(y, X, Z = NULL, tau, qb, maxal, gamma = 1, ll = 50,
  lambda = 1, alc = NULL, pSum = NULL)

Arguments

y	A response variable, either quantitative or binary (coded 0 or 1); Response type is specified by qb.
X	Predictor variables subjected to variable selection.
Z	Covariates; Z=NULL means unspecified.
tau	tau parameter (allowed to be a vector object); NULL (default) specifies tau=n/log(n)^0.5 as suggested in Ueki and Tamiya (2016).
qb	Type of response variable, qb="q" and "b" specify quantitative and binary traits, respectively.
maxal	Maximum p-value cutoff for search.
gamma	gamma parameter; gamma=1 is default as suggested in Ueki and Tamiya (2016).
ll	Number of candidate p-value cutoffs for search (default=50) as determined by 10^seq( log10(maxal),log10(5e-8), length=ll).
lambda	lambda parameter (default=1).
alc	User-specified candidate p-value cutoffs for search; ll option is effective if alc=NULL.
pSum	User-specified p-values matrix from other studies that are independent of the study data (optional, default=NULL), a matrix object having rows with the same size of X and columns for each study (multiple studies are capable). Missing p-values must be coded as NA. Summary p-values are combined with p-values in the study data by the Fisher's method.

Details

See Ueki and Tamiya (2016).

Value

Muhat	Estimated phenotypic values 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).

References

Ye J. (1988) On measuring and correcting the effects of data mining and model selection. J Am Stat Assoc 93:120-31.

Ueki M. (2009) A note on automatic variable selection using smooth-threshold estimating equations. Biometrika 96:1005-11.

Examples

## Not run: 


set.seed(22200)

wd = system.file("extdata",package="stmgp")

D = read.table(unzip(paste(wd,"snps.raw.zip",sep="/"),exdir=tempdir()),header=TRUE)

X = D[,-(1:6)]
X = (X==1) + 2*(X==2)
p = ncol(X)
n = nrow(X)
ll = 30
p0 = 50; b0 = log(rep(1.2,p0))
iA0 = sample(1:p,p0)
Z = as.matrix(cbind(rnorm(n),runif(n)))  # covariates
eta = crossprod(t(X[,iA0]),b0) - 4 + crossprod(t(Z),c(0.5,0.5))


# quantitative trait
mu = eta
sig = 1.4
y = mu + rnorm(n)*sig
STq = stmgp(y,X,Z,tau=n*c(1),qb="q",maxal=0.1,gamma=1,ll=ll)
boptq = STq$BA[,STq$lopt[1],STq$lopt[2]]  # regression coefficient in selected model
nonzeroXq = which( boptq[(1+ncol(Z))+(1:p)]!=0 )  # nonzero regression coefficient
# check consistency
cor( STq$Muhat[,STq$lopt[1],STq$lopt[2]], crossprod(t(cbind(1,Z,X)),boptq) )
cor( STq$Muhat[,STq$lopt[1],STq$lopt[2]], eta)  # correlation with true function
# proportion of correctly identified true nonzero regression coefficients
length(intersect(which(boptq[-(1:(ncol(Z)+1))]!=0),iA0))/length(iA0)


# binary trait
mu = 1/(1+exp(-eta))
Y = rbinom(n,size=1,prob=mu)
STb = stmgp(Y,X,Z,tau=n*c(1),qb="b",maxal=0.1,gamma=1,ll=ll)
boptb = STb$BA[,STb$lopt[1],STb$lopt[2]]  # regression coefficient in selected model
nonzeroXb = which( boptb[(1+ncol(Z))+(1:p)]!=0 )  # nonzero regression coefficient
# check consistency
cor( STb$Muhat[,STb$lopt[1],STb$lopt[2]], crossprod(t(cbind(1,Z,X)),boptb) )
Prob = 1/(1+exp(-STb$Muhat[,STb$lopt[1],STb$lopt[2]]))  # Pr(Y=1) (logistic regression)
cor( STb$Muhat[,STb$lopt[1],STb$lopt[2]], eta)  # correlation with true function
# proportion of correctly identified true nonzero regression coefficients 
length(intersect(which(boptb[-(1:(ncol(Z)+1))]!=0),iA0))/length(iA0)



# simulated summary p-values
pSum = cbind(runif(ncol(X)),runif(ncol(X)));
pSum[iA0,1] = pchisq(rnorm(length(iA0),5,1)^2,df=1,low=F); # study 1 summary p-values
pSum[iA0,2] = pchisq(rnorm(length(iA0),6,1)^2,df=1,low=F); # study 2 summary p-values
pSum[sample(1:length(pSum),20)] = NA
head(pSum)


# quantitative trait using summary p-values
STqs = stmgp(y,X,Z,tau=n*c(1),qb="q",maxal=0.1,gamma=1,ll=ll,pSum=pSum)
boptqs = STqs$BA[,STqs$lopt[1],STqs$lopt[2]]  # regression coefficient in selected model
nonzeroXqs = which( boptqs[(1+ncol(Z))+(1:p)]!=0 )  # nonzero regression coefficient
# check consistency
cor( STqs$Muhat[,STqs$lopt[1],STqs$lopt[2]], crossprod(t(cbind(1,Z,X)),boptqs) )
cor( STqs$Muhat[,STqs$lopt[1],STqs$lopt[2]], eta)  # correlation with true function
# proportion of correctly identified true nonzero regression coefficients 
length(intersect(which(boptqs[-(1:(ncol(Z)+1))]!=0),iA0))/length(iA0)



# binary trait using summary p-values
STbs = stmgp(Y,X,Z,tau=n*c(1),qb="b",maxal=0.1,gamma=1,ll=ll,pSum=pSum)
boptbs = STbs$BA[,STbs$lopt[1],STbs$lopt[2]]  # regression coefficient in selected model
nonzeroXbs = which( boptbs[(1+ncol(Z))+(1:p)]!=0 )  # nonzero regression coefficient
# check consistency
cor( STbs$Muhat[,STbs$lopt[1],STbs$lopt[2]], crossprod(t(cbind(1,Z,X)),boptbs) )
Prob = 1/(1+exp(-STbs$Muhat[,STbs$lopt[1],STbs$lopt[2]]))  # Pr(Y=1) (logistic regression)
cor( STbs$Muhat[,STbs$lopt[1],STbs$lopt[2]], eta)  # correlation with true function
# proportion of correctly identified true nonzero regression coefficients 
length(intersect(which(boptbs[-(1:(ncol(Z)+1))]!=0),iA0))/length(iA0)






## End(Not run)

stmgp documentation built on July 18, 2021, 9:06 a.m.