Usage
NormalizedASKAT.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)
Arguments
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:
$\Phi = U S U^T$. 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:
$\Phi = U S U^T$. 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.NormalizedASKAT function. Practically,
you don't need to calculate th enull 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.