burden.mlogit.subscores: Logistic or multinomial regression on a multiple genetic scores within a genomic region

Description

Performs burden tests with subscores in the regression on categorical phenotypes

Usage

burden.mlogit.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:

OR: The OR value(s) associated to the subscores in the regression. If there are more than two groups, there will be one OR value per group compared to the reference group
l.lower: The lower bound of the confidence interval of each OR
l.upper: The upper bound of the confidence interval of each OR

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.

If only two groups of individuals are present, a classical logistic regression is performed. If more than two groups of individuals are present, a non-ordinal multinomial regression is performed, comparing each group of individuals to the reference group indicated by the argument ref.level in NullObject.parameters. The choice of the reference group won't affect the p-values, but only the Odds Ratios. In both types of regression, the p-value is estimated using the Likelihood Ratio test and the function burden.mlogit.

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)

#run null model, using the 1000Genome population as "outcome"
#x1.H0 <- NullObject.parameters(pheno = x1@ped$pop, ref.level = "CEU",
#                               RVAT = "burden", pheno.type = "categorical")

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