burden.continuous.subscores {Ravages}

Linear regression on a multiple genetic scores within a genomic region

Description

Performs burden tests with subscores in the regression on continuous phenotypes

Usage

burden.continuous.subscores(x, NullObject, genomic.region = x@snps$genomic.region, 
                            SubRegion = x@snps$SubRegion, burden.function = WSS, 
                            maf.threshold = 0.5, get.effect.size = FALSE, 
                            alpha = 0.05, cores = 10)

Arguments

x

A bed matrix, only needed if burden="CAST" or burden="WSS"

NullObject

A list returned from NullObject.parameters

genomic.region

A factor containing the genomic region of each SNP, x@snps$genomic.region by default, for example the CADD regions

SubRegion

A vector containing subregions within each genomic.region, x@snps$SubRegion by default, for example genomic categories

burden.function

A function to compute the genetic score, WSS by default.

maf.threshold

The MAF threshold to use for the definition of a rare variant in the CAST score. Set at 0.5 by default

get.effect.size

TRUE/FALSE: whether to return effect sizes of the tested genomic.region (OR for categorical phenotypes, betas for continuous phenotypes)

alpha

The alpha threshold to use for the OR confidence interval

cores

How many cores to use, set at 10 by default. Only needed if NullObject$pheno.type = "categorical"

Details

This function will return results from the regression of the phenotype on the genetic score(s) for each genomic region. Within each genomic region, a subscore will be computed for each SubRegion and one test will be performed for each genomic.region.

Value

A dataframe with one row per genomic region and two columns:

p.value

The p.value of the regression

is.err

0/1: whether there was a convergence problem with the regression

If get.effect.size=TRUE, a list is returned with the previous dataframe in $Asso and with effect, a list containing matrices with three columns:

beta

The beta value(s) associated to the subscores in the regression

l.lower

The lower bound of the confidence interval of each beta

l.upper

The upper bound of the confidence interval of each beta

See Also

NullObject.parameters, burden.subscores, CAST, WSS

Examples

#Import data in a bed matrix
#x <- as.bed.matrix(x=LCT.matrix.bed, fam=LCT.matrix.fam, bim=LCT.snps)

#Add population
#x@ped[,c("pop", "superpop")] <- LCT.matrix.pop1000G[,c("population", "super.population")]

#Select EUR superpopulation
#x <- select.inds(x, superpop=="EUR")
#x@ped$pop <- droplevels(x@ped$pop)

#Group variants within CADD regions and genomic categories
#x <- set.CADDregions(x)

#Filter of rare variants: only non-monomorphic variants with
#a MAF lower than 2.5%
#and with a adjusted CADD score greater than the median
#x1 <- filter.adjustedCADD(x, filter = "whole", maf.threshold = 0.025)

#Simulation of a covariate + Sex as a covariate
#sex <- x1@ped$sex
#set.seed(1) ; u <- runif(nrow(x1))
#covar <- cbind(sex, u)

#Null model with the covariate sex and a continuous phenotype
#x1.H0.covar <- NullObject.parameters(pheno = x1@ped$pheno <- rnorm(nrow(x1)),
#                                     RVAT = "burden", pheno.type = "continuous",
#                                     data = covar, formula = ~ sex)

#WSS test
#res.subscores <-burden.continuous.subscores(x1, NullObject = x1.H0.covar, 
#                                            burden = WSS, get.effect.size=TRUE, cores = 1)
#res.subscores$Asso # p-values
#res.subscores$effect #beta values