burden: Linear, logistic or multinomial regression on a genetic score

Description

Performs burden tests on categorical or continuous phenotypes

Usage

burden(x, NullObject, genomic.region = x@snps$genomic.region, burden,
       maf.threshold = 0.5, get.effect.size = FALSE, alpha = 0.05, cores = 10,
       verbose = TRUE)

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

If NullObject$pheno.type = "categorical" and get.OR.value=TRUE, additional columns are present:

OR/beta: The OR/beta value(s) associated to the regression. For categorical phenotypes, 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/beta
l.upper: The upper bound of the confidence interval of each OR/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, 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 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.
verbose: Whether to display information about the function actions

Details

This function will return results from the regression of the phenotype on the genetic score 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.

If the phenotype is continuous, a linear regression is performed using the function burden.continuous.

The type of phenotype is determined from NullObject$pheno.type.

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.

References

Bocher O, et al. DOI: 10.1002/gepi.22210. Rare variant association testing for multicategory phenotype. Genet.Epidemiol. 2019;43:646–656.

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 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 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
burden(x1, NullObject = x1.H0, burden = "WSS", get.effect.size=TRUE, cores = 1)

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