VCC1.region: Run the VC-C1 method on a genomic region defined by a start...
In RVPedigree: Methods for Family-Based Rare-Variant Genetic Association Tests

Description Usage Arguments Value Author(s)

Runs the VC-C1 method on a given genomic region

VCC1.region(y = NULL, X = NULL, Phi = NULL, type = "bed",
  filename = NULL, map = NULL, chr = 0, startpos = 0, endpos = 0,
  regionname = NULL, U = NULL, S = NULL, RH.Null = NULL,
  weights = NULL)

`y`	vector of phenotype data (one entry per individual), of length n.
`X`	matrix of covariates including intercept (dimension: n \times p, with p the number of covariates)
`Phi`	Relationship matrix (i.e. twice the kinship matrix); an n \times n square symmetric positive-definite matrix.
`type`	character, `'ped'`, `'bed'` (default) or `'shapeit-haps'` format of input file containing haplotype data
`filename`	character, path to input file containing haplotype data
`map`	object, data.frame contains 3 columns: rsID, chromosome, position in bp as output by e.g. `readMapFile`.
`chr`	character, chromosome number (basically from 1 to 22 as used by Plink), on which the region of interest is located
`startpos`	numeric, start position (in bp, base pairs) of the region of interest (default: 0)
`endpos`	numeric, end position (in bp, base pairs) of the region of interest (default: 0)
`regionname`	(character) Name of the region/gene on which you are running the association test. This name is used in the output of this function and can be used to distinguish different regions if this function is run multiple times.
`U`	(optional) Matrix of Eigenvectors of the relationship matrix obtained from spectral decomposition of the relationship matrix: Φ = U S U^T. If this parameter is not given, it will be computed, so when running this function for many regions time can be saved by specifying not only `Phi`, but also `S` and `U`.
`S`	(optional) Matrix of Eigenvalues of the relationship matrix obtained from spectral decomposition of the relationship matrix: Φ = U S U^T. If this parameter is not given, it will be computed, so when running this function for many regions, time can be saved by specifying not only `Phi`, but also `S` and `U`.
`RH.Null`	(optional) output of `Estim.H0.VCC` function. Practically, you don't need to calculate the null hypothesis for every region. One estimation per trait is enough.
`weights`	optional numeric vector of genotype weights. If this option is not specified, the beta distribution is used for weighting the variants, with each weight given by w_i = dbeta(f_i, 1, 25)^2, with f_i the minor allele frequency (MAF) of variant i. This default is the same as used by the `SKAT` package. This vector is used as the diagonal of the m \times m matrix W, with m the number of variants.

A data frame containing the results of the association test. The data frame contains the following columns:

Score.Test: the score of the given association test
P.value: the p-value of the association test
N.Markers: the number of markers in the region
regionname: Name of the region/gene on which you are running the association test