riskScore: Function to compute genetic risk scores.

Description

The function computes unweighted or weighted genetic risk scores. The relative effects (or weights) of genetic variants can either come from beta coefficients of a risk model or from a vector of beta coefficients imported into R, e.g., when beta cofficients are obtained from meta-analysis.

Usage

riskScore(weights, data, cGenPreds, Type)

Arguments

weights

The vector that includes the weights given to the genetic variants. See details for more informations.

data

Data frame or matrix that includes the outcome and predictors variables.

cGenPreds

Column numbers of the genetic variables on the basis of which the risk score is computed.

Type

Specification of the type of risk scores that will be computed. Type can be weighted (Type="weighted") or unweighted (Type="unweighted").

Value

The function returns a vector of risk scores.

Details

The function calculates unweighted or weighted genetic risk scores. The unweighted genetic risk score is a simple risk allele count assuming that all alleles have the same effect. For this calculation, it is required that the genetic variables are coded as the number of risk alleles. Beta coefficients are used to determine which allele is the risk allele. When the sign of the beta coefficient is negative, the allele coding is reversed. The weighted risk score is a sum of the number of risk alleles multiplied by their beta coefficients.

The beta coefficients can come from two different sources, either beta coefficients of a risk model or a vector of beta coefficients imported into R, e.g., when beta cofficients are obtained from meta-analysis. This vector of beta coefficients should be a named vector containing the same names as mentioned in genetic variants. A logistic regression model can be constructed using fitLogRegModel from this package.

Examples

Run this code

# NOT RUN {
# specify dataset with outcome and predictor variables
data(ExampleData) 
# specify column numbers of genetic predictors
cGenPred <- c(11:16)

# fit a logistic regression model
# all steps needed to construct a logistic regression model are written in a function
# called 'ExampleModels', which is described on page 4-5
riskmodel <- ExampleModels()$riskModel2

# compute unweighted risk scores 
riskScore <- riskScore(weights=riskmodel, data=ExampleData, 
cGenPreds=cGenPred, Type="unweighted")
# }