#'CARV: Comprehrensive Approach to Analyzing Rare Variants
#'
#'The CARV method has been proposed by Hoffmann et al (2010) as an approach
#'that determines an optimal grouping of rare variants while avoiding
#'assumptions required by other methods for grouping such variants. The idea
#'behind CARV is to try multiple models for rare variants, since prior
#'information is generally not very accurate. Statistical significance is
#'obtained by permutation.
#'
#'The argument \code{waf} is used to specify weights of the variants in order
#'to incorporate allele frequency information. When \code{waf=FALSE}, all
#'variants have a constant unit weight. When \code{waf=TRUE}, the weights are
#'calculated as in the function \code{\link{WSS}}, that is, weights are the
#'inverse variance of allele frequency in controls. \cr
#'
#'The argument \code{signs} is used to specify the direction of the variant
#'effect (deleterious or protective). When \code{signs=FALSE}, all variants
#'have a positive sign indicating a likely deleterious effect. When
#'\code{signs=TRUE}, all rare alleles that are more prevalent in controls than
#'cases will have an associated sk=-1 (see paper in the reference); conversely,
#'all the rare alleles that are more prevalent in cases than controls will have
#'an associated sk=1. \cr
#'
#'The argument \code{approach} is used to specify whether the alle belongs in
#'the model for variable selection. When \code{approach="hard"}, only variants
#'below the predefined \code{maf} are included in the analysis. When
#'\code{approach="variable"}, a variable threshold approach is used as in the
#'\code{\link{VT}} method: all possible minor allele frequencies are
#'considered, selecting the maximum statistic among all of them. When
#'\code{approach="stepup"}, the step-up strategy described in Hoffman et al
#'(2010) is applied. \cr
#'
#'There is no imputation for the missing data. Missing values are simply
#'ignored in the computations.
#'
#'@param y numeric vector with phenotype status: 0=controls, 1=cases. No
#'missing data allowed
#'@param X numeric matrix or data frame with genotype data coded as 0, 1, 2.
#'Missing data is allowed
#'@param waf logical value to indicate whether weights for the variants should
#'be calculated as in the \code{WSS} method (default \code{waf=FALSE}). See
#'deatils below
#'@param signs logical value to indicate whether signs for the variants should
#'be calculated based on allele prevalence (\code{signs=FALSE} by default). See
#'details below
#'@param approach character string to indicate the type of approach to be used
#'for variable selection; i.e. whether each variant belongs in the model for
#'variable selection (\code{approach="hard"} by default). Possible options are
#'\code{"hard"}, \code{"variable"}, and \code{"stepup"}. See details below
#'@param maf numeric value between 0 and 1 to indicate the threshold of minor
#'allele frequency for rare variants. Only used when \code{approach="hard"}
#'@param perm positive integer indicating the number of permutations (100 by
#'default)
#'@return An object of class \code{"assoctest"}, basically a list with the
#'following elements:
#'@returnItem carv.stat carv statistic
#'@returnItem perm.pval permuted p-value
#'@returnItem args descriptive information with number of controls, cases,
#'variants, permutations, waf, signs, approach, and maf
#'@returnItem name name of the statistic
#'@author Gaston Sanchez
#'@seealso \code{\link{RARECOVER}}
#'@references Hoffmann TJ, Marini NJ, Witte JS (2010) Comprehensive Approach to
#'Analyzing Rare Genetic Variants. \emph{PLoS One}, \bold{5(11)}: e13584
#'@examples
#'
#' \dontrun{
#'
#' # number of cases
#' cases = 500
#'
#' # number of controls
#' controls = 500
#'
#' # total (cases + controls)
#' total = cases + controls
#'
#' # phenotype vector
#' phenotype = c(rep(1, cases), rep(0, controls))
#'
#' # define genotype matrix with 10 variants (random data)
#' set.seed(1234)
#' genotype = matrix(rbinom(total*10, 2, 0.051), nrow=total, ncol=10)
#'
#' # apply CARV with "hard" approach and maf=0.05
#' mycarv1 = CARV(phenotype, genotype, waf=FALSE, signs=FALSE,
#' approach="hard", maf=0.05, perm=500)
#' mycarv1
#'
#' # apply CARV with "variable" approach and waf=TRUE
#' mycarv2 = CARV(phenotype, genotype, waf=TRUE, signs=FALSE,
#' approach="variable", perm=500)
#' mycarv2
#'
#' # apply CARV with "stepup" approach, waf=TRUE, and signs=TRUE
#' mycarv3 = CARV(phenotype, genotype, waf=TRUE, signs=TRUE,
#' approach="stepup", perm=500)
#' mycarv3
#'
#' }
#'
CARV <-
function(y, X, waf=FALSE, signs=FALSE, approach="hard", maf=0.05, perm=100)
{
## checking arguments
Xy_perm = my_check(y, X, perm)
y = Xy_perm$y
X = Xy_perm$X
perm = Xy_perm$perm
if (!is.logical(waf))
stop("argument 'waf' must be TRUE or FALSE")
if (!is.logical(signs))
stop("argument 'signs' must be TRUE or FALSE")
if (!(approach %in% c("stepup", "variable", "hard")))
stop("argument 'approach' must be one of 'stepup', 'variable' or 'hard'")
if (mode(maf) != "numeric" || length(maf) != 1 || maf <= 0 || maf > 1)
maf = 0.05
# how many variants
M = ncol(X)
# weights 'ak' to incorporate allele frequencies
ak = rep(1, M)
if (waf) ak = my_weights_wss(y, X) # weights a la madsen & browning
## signs 'sk' of the variant effect
sk = rep(1, M)
if (signs)
{
nAs = apply(X[y==1,], 2, function(x) sum(!is.na(x)))
nUs = apply(X[y==0,], 2, function(x) sum(!is.na(x)))
maf.A = colSums(X[y==1,], na.rm=TRUE) / (2*nAs)
maf.U = colSums(X[y==0,], na.rm=TRUE) / (2*nUs)
# more prevalent in cases than controls
sk[maf.U > maf.A] = -1
}
## prepare data for approaches
ymean = mean(y)
y.cen = y - ymean
X.cen = scale(X, scale=FALSE)
w = ak * sk
## hard approach
if (approach == "hard")
{
# get minor allele frequencies
MAFs = colMeans(X, na.rm=TRUE) / 2
# are there any rare variants?
if (sum(MAFs < maf) == 0)
stop(paste("\n", "Ooops: No rare variants below maf=",
maf, " were detected. Try a larger maf", sep=""))
carv.stat = my_hard_approach(y.cen, X.cen, w, MAFs, maf)
}
## variable approach
if (approach == "variable")
{
# get minor allele frequencies
MAFs = colMeans(X, na.rm=TRUE) / 2
several.maf = sort(unique(MAFs))
carv.stat = my_variable_approach(y.cen, X.cen, w, MAFs, several.maf)
}
## step-up approach
if (approach == "stepup") {
carv.stat = my_stepup_approach(y.cen, X.cen, w)
}
## permutations
perm.pval = NA
if (perm > 0)
{
x.perm = rep(0, perm)
for (i in 1:perm)
{
perm.sample = sample(1:length(y))
# center phenotype y
y.perm = y[perm.sample] - ymean
# get score vector
x.perm[i] = switch(approach,
"hard" = my_hard_approach(y.perm, X.cen, w, MAFs, maf),
"variable" = my_variable_approach(y.perm, X.cen, w, MAFs, several.maf),
"stepup" = my_stepup_approach(y.perm, X.cen, w)
)
}
# p-value
perm.pval = sum(x.perm >= carv.stat) / perm
}
## results
if (waf) mywaf="TRUE" else mywaf="FALSE"
if (signs) mysigns="TRUE" else mysigns="FALSE"
name = "CARV: Comprehensive Approach to Analyzing Rare Genetic Variants"
arg.spec = c(sum(y), length(y)-sum(y), ncol(X), perm, mywaf, mysigns, approach, maf)
names(arg.spec) = c("cases", "controls", "variants", "n.perms",
"waf", "signs", "approach", "maf")
res = list(carv.stat = carv.stat,
perm.pval = perm.pval,
args = arg.spec,
name = name)
class(res) = "assoctest"
return(res)
}
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