# Logistic or multinomial regression on a genetic score Performs a logistical or a non-ordinal multinomial regression on a genetic score ```r burden.mlogit(x, NullObject, genomic.region = x@snps$genomic.region, burden, maf.threshold = 0.5, get.effect.size = FALSE, alpha = 0.05, cores = 10) ``` ## Arguments - `x`: A bed matrix, only needed if `burden="CAST"` or `burden="WSS"` - `NullObject`: A list returned from `NullObject.parameters` - `genomic.region`: A factor containing the genomic region of each SNP, `x@snps$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 OR values - `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 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. 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. ## Returns 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 `get.effect.size=TRUE`, additional columns are present: - **OR**: The OR value(s) associated to 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 ## See Also `NullObject.parameters`, `CAST`, `WSS`, `burden.weighted.matrix` ## References Bocher O, et al. DOI: 10.1002/gepi.22210. **Rare variant associationtesting for multicategory phenotype.** Genet.Epidemiol. 2019;43:646–656. ## Examples ```r #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 200 SNP x1 <- filter.rare.variants(x, filter = "whole", maf.threshold = 0.025, min.nb.snps = 200) #Simulation of a covariate + Sex as a covariate sex <- x1@ped$sex set.seed(1) ; u <- runif(nrow(x1)) covar <- cbind(sex, u) #run null model, using the 1000Genome population as "outcome" #Null model with the covariate sex x1.H0.covar <- NullObject.parameters(pheno = x1@ped$pop, ref.level = "CEU", RVAT = "burden", pheno.type = "categorical", data = covar, formula = ~ sex) #WSS test burden.mlogit(x1, NullObject = x1.H0.covar, burden = "WSS", get.effect.size=TRUE, cores = 1) ```

burden.mlogit function