Generalized Multi-SNP Mediation Intersection-Union Test

Description

Testing the mediation effect of multiple SNPs on an outcome following an exponential family distribution or a survival outcome through a continuous mediator.

Usage

GSMUT(G,mediator,outcome,covariates=NULL,outcome_type,
approxi=TRUE,verbose=FALSE)

Arguments

Value

Author(s)

Wujuan Zhong

Examples

library(SMUT)
# load the Genotype data included in this R package
data("Genotype_data")


##### for a binary outcome #####
set.seed(1)

# generate two covariates
covariate_1=rnorm(nrow(Genotype_data),0,1)
covariate_2=sample(c(0,1),size=nrow(Genotype_data),replace = TRUE)
covariates=cbind(covariate_1,covariate_2)

# generate a mediator 
beta=rnorm(ncol(Genotype_data),0,0.5)
tau_M=c(-0.3,0.2)
e1 = rnorm(nrow(Genotype_data), 0, 1)
mediator = 1 + eigenMapMatMult(Genotype_data,beta) + 
           eigenMapMatMult(covariates, tau_M) + e1

#### generate a binary outcome ####
theta=1
gamma=rnorm(ncol(Genotype_data),0,0.5)
tau=c(-0.2,0.2)
eta=1 + eigenMapMatMult(Genotype_data, gamma) + 
    eigenMapMatMult(covariates, tau) + theta * mediator
pi=1/(1+exp( -(eta ) ))
outcome=rbinom(length(pi),size=1,prob=pi) 
result=GSMUT(G=Genotype_data,mediator=mediator,outcome=outcome,
covariates=covariates,outcome_type="binary")
print(result)
# p_value_IUT is the p value for the mediation effect.

## Not run: 

##### generate a count outcome #####
theta=1
gamma=rnorm(ncol(Genotype_data),0,0.5)
tau=c(-0.2,0.2)
eta=1 + eigenMapMatMult(Genotype_data, gamma) + 
    eigenMapMatMult(covariates, tau) + theta * mediator
mu_param=exp(eta) # the mean parameter
phi_param=10  # the shape parameter
outcome=rnbinom(length(mu_param),size=phi_param,mu=mu_param)
result=GSMUT(G=Genotype_data,mediator=mediator,outcome=outcome,
covariates=covariates,outcome_type="count")
print(result)
# p_value_IUT is the p value for the mediation effect.

##### generate a survival outcome #####
theta=2
gamma=rnorm(ncol(Genotype_data),0,0.5)
tau=c(-0.2,0.2)
eta=1 + eigenMapMatMult(Genotype_data, gamma) + 
    eigenMapMatMult(covariates, tau) + theta * mediator
v=runif(nrow(Genotype_data))
lambda=0.01; rho=1; rateC=0.001
Tlat=(- log(v) / (lambda * exp( eta  )))^(1 / rho)
# censoring times
C= rexp(nrow(Genotype_data), rate=rateC)
# follow-up times and event indicators
time= pmin(Tlat, C)
status= as.numeric(Tlat <= C)
outcome=cbind(time,status)      
colnames(outcome)=c("time","status")
result=GSMUT(G=Genotype_data,mediator=mediator,outcome=outcome,
covariates=covariates,outcome_type="survival")
print(result)
# p_value_IUT is the p value for the mediation effect.


## End(Not run) 

Testing coefficient of mediator in the outcome model for an outcome following an exponential family distribution or a survival outcome

Description

Testing coefficient of mediator, namely theta, in the outcome model. The outcome model is the following.
outcome ~ intercept + G*gamma + mediator*theta + error

Usage

Generalized_Testing_coefficient_of_mediator(G,mediator,outcome,
covariates=NULL,outcome_type,
approxi=TRUE,verbose=FALSE)

Arguments

Value

Author(s)

Wujuan Zhong

Example genotype data for SMUT

Description

Example genotype data for SMUT. It is a matrix with 100 rows and 200 columns. Each row is an individual; each column is a SNP.

Format

It is a matrix with 100 rows and 200 columns. Each row is an individual; each column is a SNP.

Multi-SNP Mediation Intersection-Union Test

Description

Testing the mediation effect of multiple SNPs on an outcome through a mediator.

Usage

SMUT(G, mediator, outcome, 
outcome_type="continuous", method="score",approxi=TRUE, debug=FALSE)

Arguments

Value

Author(s)

Wujuan Zhong

References

Zhong, W., Spracklen, C. N., Mohlke, K. L., Zheng, X., Fine, J., & Li, Y. (2019). Multi-SNP mediation intersection-union test. Bioinformatics.

Examples

library(SMUT)
# load the Genotype data included in this R package
data("Genotype_data")

# generate one mediator and one outcome

# first example, the mediation effect is significant
set.seed(1)
beta=rnorm(ncol(Genotype_data),1,2)
e1 = rnorm(nrow(Genotype_data), 0, 1)
mediator = 1 + eigenMapMatMult(Genotype_data,beta) + e1

theta=0.8
gamma=rnorm(ncol(Genotype_data),0.5,2)
e2 = rnorm(nrow(Genotype_data), 0, 1)
outcome = 2 + eigenMapMatMult(Genotype_data,gamma) + theta*mediator + e2

p_value=SMUT(G=Genotype_data,mediator=mediator,outcome=outcome)
print(p_value)

# p_value_IUT is the p value for the mediation effect.
# we have significant(at alpha level 0.05) mediation effects (p_value_IUT = 0.001655787).

# second example, the mediation effect is non-significant
set.seed(1)
beta=rnorm(ncol(Genotype_data),1,2)
e1 = rnorm(nrow(Genotype_data), 0, 1)
mediator = 1 + eigenMapMatMult(Genotype_data,beta) + e1

theta=0
gamma=rnorm(ncol(Genotype_data),0.5,2)
e2 = rnorm(nrow(Genotype_data), 0, 1)
outcome = 2 + eigenMapMatMult(Genotype_data,gamma) + theta*mediator + e2

p_value=SMUT(G=Genotype_data,mediator=mediator,outcome=outcome)
print(p_value)

# p_value_IUT is the p value for the mediation effect.
# we have non-significant(at alpha level 0.05) mediation effects (p_value_IUT = 0.3281677).

# third example, the mediation effect is non-significant
set.seed(1)
beta=rep(0,ncol(Genotype_data))
e1 = rnorm(nrow(Genotype_data), 0, 1)
mediator = 1 + eigenMapMatMult(Genotype_data,beta) + e1

theta=0.8
gamma=rnorm(ncol(Genotype_data),0.5,2)
e2 = rnorm(nrow(Genotype_data), 0, 1)
outcome = 2 + eigenMapMatMult(Genotype_data,gamma) + theta*mediator + e2

p_value=SMUT(G=Genotype_data,mediator=mediator,outcome=outcome)
print(p_value)

# p_value_IUT is the p value for the mediation effect.
# we have non-significant(at alpha level 0.05) mediation effects (p_value_IUT = 0.5596977).

# Thanks for using our R package SMUT 

Testing coefficient of mediator in the outcome model

Description

Testing coefficient of mediator, namely theta, in the outcome model. The outcome model is the following.
outcome ~ intercept + G*gamma + mediator*theta + error

Usage

Testing_coefficient_of_mediator(G, mediator, outcome, 
outcome_type="continuous", method="score", approxi=TRUE, debug=FALSE)

Arguments

Value

P value for testing the coefficient of mediator in the outcome model.

Author(s)

Wujuan Zhong

Examples

library(SMUT)
# load the Genotype data included in this R package
data("Genotype_data")

# generate one mediator and one outcome

set.seed(1)
beta=rnorm(ncol(Genotype_data),1,2)
e1 = rnorm(nrow(Genotype_data), 0, 1)
mediator = 1 + eigenMapMatMult(Genotype_data,beta) + e1

theta=0.8
gamma=rnorm(ncol(Genotype_data),0.5,2)
e2 = rnorm(nrow(Genotype_data), 0, 1)
outcome = 2 + eigenMapMatMult(Genotype_data,gamma) + theta*mediator + e2

p_value=Testing_coefficient_of_mediator(G=Genotype_data,mediator=mediator,outcome=outcome)
print(p_value)

# Thanks for using our R package SMUT 

Matrix multiplication using RcppEigen

Description

Matrix multiplication using RcppEigen.

Usage

eigenMapMatMult(A,B)

Arguments

Value

The matrix product. The value is the same as A %*% B

Examples

library(SMUT)
A=matrix(1:9,3,3)
A=A+0
B=as.matrix(c(5.0, 2.0, 0.0))
eigenMapMatMult(A,B)
# the result is the same as A %*% B

# Thanks for using our R package SMUT