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# Genome scan with a single-QTL and linear mixed model
#
# called by scan1max() when kinship() is provided.
scan1max_pg <-
function(genoprobs, pheno, kinship, addcovar=NULL, Xcovar=NULL,
intcovar=NULL, weights=NULL, reml=TRUE, hsq=NULL,
by_chr=FALSE, cores=1, ...)
{
# deal with the dot args
dotargs <- list(...)
tol <- grab_dots(dotargs, "tol", 1e-12)
if(!is_pos_number(tol)) stop("tol should be a single positive number")
intcovar_method <- grab_dots(dotargs, "intcovar_method", "lowmem",
c("highmem", "lowmem"))
quiet <- grab_dots(dotargs, "quiet", TRUE)
max_batch <- grab_dots(dotargs, "max_batch", NULL)
if(!is.null(max_batch) && !is_pos_number(max_batch)) stop("max_batch should be a single positive integer")
check_boundary <- grab_dots(dotargs, "check_boundary", TRUE)
check_extra_dots(dotargs, c("tol", "intcovar_method", "check_boundary", "quiet", "max_batch"))
# check that the objects have rownames
check4names(pheno, addcovar, Xcovar, intcovar)
# force things to be matrices
if(!is.matrix(pheno)) {
pheno <- as.matrix(pheno)
if(!is.numeric(pheno)) stop("pheno is not numeric")
}
if(is.null(colnames(pheno))) # force column names
colnames(pheno) <- paste0("pheno", seq_len(ncol(pheno)))
if(!is.null(addcovar)) {
if(!is.matrix(addcovar)) addcovar <- as.matrix(addcovar)
if(!is.numeric(addcovar)) stop("addcovar is not numeric")
}
if(!is.null(Xcovar)) {
if(!is.matrix(Xcovar)) Xcovar <- as.matrix(Xcovar)
if(!is.numeric(Xcovar)) stop("Xcovar is not numeric")
}
if(!is.null(intcovar)) {
if(!is.matrix(intcovar)) intcovar <- as.matrix(intcovar)
if(!is.numeric(intcovar)) stop("intcovar is not numeric")
}
# check that kinship matrices are square with same IDs
kinshipIDs <- check_kinship(kinship, length(genoprobs))
# multiply kinship matrix by 2; rest is using 2*kinship
# see Almasy & Blangero (1998) https://doi.org/10.1086/301844
kinship <- double_kinship(kinship)
# take square-root of weights
weights <- sqrt_weights(weights)
# find individuals in common across all arguments
# and drop individuals with missing covariates or missing *all* phenotypes
ind2keep <- get_common_ids(genoprobs, addcovar, Xcovar, intcovar,
kinshipIDs, weights, complete.cases=TRUE)
ind2keep <- get_common_ids(ind2keep, pheno[rowSums(is.finite(pheno)) > 0,,drop=FALSE])
if(length(ind2keep)<=2) {
if(length(ind2keep)==0)
stop("No individuals in common.")
else
stop("Only ", length(ind2keep), " individuals in common: ",
paste(ind2keep, collapse=":"))
}
# make sure addcovar is full rank when we add an intercept
addcovar <- drop_depcols(addcovar, TRUE, tol)
# make sure columns in intcovar are also in addcovar
addcovar <- force_intcovar(addcovar, intcovar, tol)
# drop things from Xcovar that are already in addcovar
Xcovar <- drop_xcovar(addcovar, Xcovar, tol)
# batch phenotypes by missing values
phe_batches <- batch_cols(pheno[ind2keep,,drop=FALSE], max_batch)
# drop cols in genotype probs that are all 0 (just looking at the X chromosome)
genoprob_Xcol2drop <- genoprobs_col2drop(genoprobs)
is_x_chr <- attr(genoprobs, "is_x_chr")
if(is.null(is_x_chr)) is_x_chr <- rep(FALSE, length(genoprobs))
# set up parallel analysis
cores <- setup_cluster(cores)
if(!quiet && n_cores(cores)>1) {
message(" - Using ", n_cores(cores), " cores")
quiet <- TRUE # make the rest quiet
}
n_chr <- length(genoprobs)
# to contain the results
result <- matrix(nrow=n_chr, ncol=ncol(pheno))
dimnames(result) <- list(names(genoprobs), colnames(pheno))
# number of chr to consider under null
if(is_kinship_list(kinship)) n_null_chr <- length(kinship)
else if(!is.null(Xcovar)) n_null_chr <- any(is_x_chr) + any(!is_x_chr)
else n_null_chr <- 1
if(is.null(hsq)) {
hsq <- matrix(nrow=n_null_chr, ncol=ncol(pheno))
dimnames(hsq) <- hsq_dimnames(kinship, Xcovar, is_x_chr, pheno)
estimate_hsq <- TRUE
} else {
if(length(hsq) != n_null_chr * ncol(pheno)) {
stop("hsq should be NULL or a matrix of size ", n_null_chr, " x ", ncol(pheno))
}
if(!is.matrix(hsq)) hsq <- as.matrix(hsq, ncol=ncol(pheno))
if(nrow(hsq) != n_null_chr) {
stop("hsq should be NULL or a matrix of size ", n_null_chr, " x ", ncol(pheno))
}
if(any(hsq < 0 | hsq > 1)) {
stop("hsq values should be between 0 and 1.")
}
estimate_hsq <- FALSE
}
n <- rep(NA, ncol(pheno)); names(n) <- colnames(pheno)
chr_index <- rep(seq_len(n_chr), sapply(genoprobs, function(a) dim(a)[3]))
# loop over batches of phenotypes with the same pattern of NAs
for(batch in seq_along(phe_batches)) {
# info about batch
omit <- phe_batches[[batch]]$omit # ind to omit
phecol <- phe_batches[[batch]]$cols # phenotype columns in batch
# individuals to keep in this batch
these2keep <- ind2keep
if(length(omit)>0) these2keep <- ind2keep[-omit]
n[phecol] <- length(these2keep)
if(length(these2keep) <= 2) next # not enough individuals; skip this batch
# subset the rest
K <- subset_kinship(kinship, ind=these2keep)
ac <- addcovar; if(!is.null(ac)) { ac <- ac[these2keep,,drop=FALSE]; ac <- drop_depcols(ac, TRUE, tol) }
Xc <- Xcovar; if(!is.null(Xc)) Xc <- Xc[these2keep,,drop=FALSE]
ic <- intcovar; if(!is.null(ic)) { ic <- ic[these2keep,,drop=FALSE]; ic <- drop_depcols(ic, TRUE, tol) }
wts <- weights; if(!is.null(wts)) wts <- wts[these2keep]
ph <- pheno[these2keep,phecol,drop=FALSE]
# multiply stuff by the weights
K <- weight_kinship(K, wts)
ac <- weight_matrix(ac, wts)
Xc <- weight_matrix(Xc, wts)
ph <- weight_matrix(ph, wts)
# eigen decomposition of kinship matrix
Ke <- decomp_kinship(K, cores=cores)
# fit LMM for each phenotype, one at a time
if(estimate_hsq) {
nullresult <- calc_hsq_clean(Ke=Ke, pheno=ph, addcovar=ac, Xcovar=Xc,
is_x_chr=is_x_chr, weights=wts, reml=reml,
cores=cores, check_boundary=check_boundary, tol=tol)
hsq[, phecol] <- nullresult$hsq
}
else {
# for the log likelihood, calculate the reml=FALSE version
loglik <- calc_nullLL_clean(hsq=hsq[,phecol,drop=FALSE], Ke=Ke, pheno=ph,
addcovar=ac, Xcovar=Xc,
is_x_chr=is_x_chr, weights=wts, reml=FALSE,
cores=cores)
nullresult <- list(hsq=hsq[,phecol,drop=FALSE],
loglik=loglik)
}
# weighted least squares genome scan, using cluster_lapply across chromosomes
lod <- scan1_pg_clean(genoprobs, these2keep, Ke, ph, ac, ic, is_x_chr,
wts, genoprob_Xcol2drop,
nullresult$hsq, nullresult$loglik, reml, cores,
intcovar_method, tol)
result[,phecol] <- apply(lod, 2, tapply, chr_index, max, na.rm=TRUE)
}
if(!by_chr) result <- apply(result, 2, max, na.rm=TRUE)
# add attributes
attr(result, "hsq") <- hsq
attr(result, "sample_size") <- n
result
}
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