Ravages (version 1.1.3)

GRR.matrix: GRR matrix for genetic data simulation

Description

Computes a GRR matrix based on a simulation model

Usage

GRR.matrix(genes.maf = Kryukov, n.case.groups = 2, 
           GRR = c("SKAT", "constant", "variable"), 
           GRR.value, GRR.function, GRR.multiplicative.factor, select.gene)

Value

A matrix containing the GRR values with one column per variant and one line per cases group

Arguments

genes.maf

A dataframe containing at least the MAF in the general population (column maf) with their associated gene (column gene). By default, maf from the file Kryukov are used

n.case.groups

The number of cases groups (set at 2 by default), i.e. the number of groups where variants will have a GRR greater than 1

GRR

How to calculate the GRR

GRR.value

GRR value if GRR="constant"

GRR.function

A function indicating how to calculate the GRR depending on MAF in the general population, only needed if GRR="variable"

GRR.multiplicative.factor

A vector of size (n.case.groups-1) containing the multiplicative factor for the GRR for each group of cases compared to the first group of cases

select.gene

The gene(s) to be selected from the file genes.maf if multiple genes are present. If missing, the first level of genes.maf$gene is kept.

Details

The GRR can be computed in three ways using the argument GRR.

If GRR="constant", the same GRR is given to all the variants, its value being specified to GRR.value. If GRR="SKAT", the GRR are calculating using the formula from the paper presenting the SKAT method and thus depend on MAF. If GRR="variable", the GRR are calculating using a function given by the user to GRR.function depending only on the MAF in the general population.

The argument multiplicative.factor contains n.case.groups-1 values; if multiplicative.factor=1, GRR will be the same between the different groups of cases.

The two dataframes Kryukov (used by default) and GnomADgenes (containing MAF in the NFE population) can be used as genes.maf.

GRR.matrix returns a matrix that can be used in other simulation functions such as rbm.GRR.

See Also

rbm.GRR, GnomADgenes, Kryukov

Examples

#GRR calculated on the MAF from the first unit of the file Kryukov
#using the formula from the SKAT paper, with the second group of cases 
#having GRR values twice as high as the first one
GRR.del <- GRR.matrix(GRR = "SKAT", genes.maf = Kryukov,
                      GRR.multiplicative.factor=2, select.gene = "R1")