burden.continuous.subscores: 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)

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

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.

Examples

Run this code

#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