burden.continuous {Ravages}

Linear regression on a genetic score

Description

Performs a linear regression on a genetic score

Usage

  burden.continuous(x, NullObject, genomic.region = x@snps$genomic.region, 
                    burden, maf.threshold = 0.5, get.effect.size = F, 
                    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, only needed if burden="CAST" or burden="WSS"

burden

"CAST" or "WSS" to directly compute the CAST or the WSS genetic score, or a matrix with one row per individual and one column per genomic.region if another genetic score is wanted.

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 the beta value

alpha

The alpha threshold to use for the OR confidence interval

cores

How many cores to use for moments computation, set at 10 by default

Details

This function will return results from the regression of the continuous phenotype on the genetic score for each genomic region.

If another genetic score than CAST or WSS is wanted, a matrix with one row per individual and one column per genomic.region containing this score should be given to burden. In this situation, no bed matrix x is needed.

Value

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

p.value

The p.value of the regression

is.err

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

beta

The beta coefficient associated to the tested genomic region

l.lower

The lower bound of the confidence interval of beta

l.upper

The upper bound of the confidence interval of beta

See Also

CAST, WSS, burden.weighted.matrix

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 known genes
x <- set.genomic.region(x)

#Filter of rare variants: only non-monomorphic variants with
#a MAF lower than 2.5%
#keeping only genomic regions with at least 10 SNPs
x1 <- filter.rare.variants(x, filter = "whole", maf.threshold = 0.025, min.nb.snps = 10)

#run burden test WSS, using a random continuous variable as phenotype
x1@ped$pheno <- rnorm(nrow(x1))
#Null model
x1.H0 <- NullObject.parameters(pheno = x1@ped$pheno, 
                               RVAT = "burden", pheno.type = "continuous")
#Get the beta value
burden.continuous(x1, NullObject = x1.H0, burden = "WSS", 
                  get.effect.size = TRUE, cores = 1)