| burden {Ravages} | R Documentation |
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$genomic.region, burden,
maf.threshold = 0.5, get.effect.size = FALSE, alpha = 0.05, cores = 10,
verbose = TRUE)
Arguments
x |
A bed matrix, only needed if |
NullObject |
A list returned from |
genomic.region |
A factor containing the genomic region of each SNP, |
burden |
"CAST" or "WSS" to directly compute the CAST or the WSS genetic score, or a matrix with one row per individual and one column per |
maf.threshold |
The MAF threshold to use for the definition of a rare variant in the CAST score. Set at 0.5 by default |
get.effect.size |
TRUE/FALSE: whether to return effect sizes of the tested |
alpha |
The alpha threshold to use for the OR confidence interval |
cores |
How many cores to use, set at 10 by default. |
verbose |
Whether to display information about the function actions |
Details
This function will return results from the regression of the phenotype on the genetic score for each genomic region.
If only two groups of individuals are present, a classical logistic regression is performed.
If more than two groups of individuals are present, a non-ordinal multinomial regression is performed,
comparing each group of individuals to the reference group indicated by the argument ref.level in NullObject.parameters.
The choice of the reference group won't affect the p-values, but only the Odds Ratios.
In both types of regression, the p-value is estimated using the Likelihood Ratio test and the function burden.mlogit.
If the phenotype is continuous, a linear regression is performed using the function burden.continuous.
The type of phenotype is determined from NullObject$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.
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$pheno.type = "categorical" and get.OR.value=TRUE, additional columns are present:
OR/beta |
The OR/beta value(s) associated to the regression. For categorical phenotypes, if there are more than two groups, there will be one OR value per group compared to the reference group |
l.lower |
The lower bound of the confidence interval of each OR/beta |
l.upper |
The upper bound of the confidence interval of each OR/beta |
References
Bocher O, et al. DOI: 10.1002/gepi.22210. Rare variant association testing for multicategory phenotype. Genet.Epidemiol. 2019;43:646–656.
See Also
NullObject.parameters, burden.continuous, burden.mlogit, CAST, WSS, burden.weighted.matrix
Examples
#Import data in a bed matrix
x <- as.bed.matrix(x=LCT.matrix.bed, fam=LCT.matrix.fam, bim=LCT.snps)
#Add population
x@ped[,c("pop", "superpop")] <- LCT.matrix.pop1000G[,c("population", "super.population")]
#Select EUR superpopulation
x <- select.inds(x, superpop=="EUR")
x@ped$pop <- droplevels(x@ped$pop)
#Group variants within known genes
x <- set.genomic.region(x)
#Filter of rare variants: only non-monomorphic variants with
#a MAF lower than 2.5%
#keeping only genomic regions with at least 10 SNPs
x1 <- filter.rare.variants(x, filter = "whole", maf.threshold = 0.025, min.nb.snps = 10)
#run null model, using the 1000Genome population as "outcome"
x1.H0 <- NullObject.parameters(pheno = x1@ped$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)