Nothing
# Calculate QTL effects in scan along one chromosome, adjusting for polygenes with an LMM
#
scan1coef_pg <-
function(genoprobs, pheno, kinship,
addcovar=NULL, nullcovar=NULL, intcovar=NULL,
weights=NULL, contrasts=NULL, zerosum=TRUE, se=FALSE,
hsq=NULL, reml=TRUE, ...)
{
# 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")
check_extra_dots(dotargs, "tol")
# check that the objects have rownames
check4names(pheno, addcovar, NULL, intcovar, nullcovar)
# force things to be matrices
if(!is.null(addcovar)) {
if(!is.matrix(addcovar)) addcovar <- as.matrix(addcovar)
if(!is.numeric(addcovar)) stop("addcovar is not numeric")
}
if(!is.null(nullcovar)) {
if(!is.matrix(nullcovar)) nullcovar <- as.matrix(nullcovar)
if(!is.numeric(nullcovar)) stop("nullcovar is not numeric")
}
if(!is.null(intcovar)) {
if(!is.matrix(intcovar)) intcovar <- as.matrix(intcovar)
if(!is.numeric(intcovar)) stop("intcovar is not numeric")
}
if(!is.null(contrasts)) {
if(!is.matrix(contrasts)) contrasts <- as.matrix(contrasts)
if(!is.numeric(contrasts)) stop("contrasts is not numeric")
}
# make sure pheno is a vector
if(is.matrix(pheno) || is.data.frame(pheno)) {
if(ncol(pheno) > 1)
warning("Considering only the first phenotype.")
rn <- rownames(pheno)
pheno <- pheno[,1]
names(pheno) <- rn
if(!is.numeric(pheno)) stop("pheno is not numeric")
}
# genoprobs has more than one chromosome?
if(length(genoprobs) > 1)
warning("Using only the first chromosome, ", names(genoprobs)[1])
genoprobs <- genoprobs[[1]]
# make sure contrasts is square n_genotypes x n_genotypes
if(!is.null(contrasts)) {
ng <- ncol(genoprobs)
if(ncol(contrasts) != ng || nrow(contrasts) != ng)
stop("contrasts should be a square matrix, ", ng, " x ", ng)
}
# make sure kinship is for a single chromosome and get IDs
did_decomp <- is_kinship_decomposed(kinship)
kinship <- check_kinship_onechr(kinship)
kinshipIDs <- check_kinship(kinship, 1)
# multiply kinship matrix by 2; rest is using 2*kinship
# see Almasy & Blangero (1998) https://doi.org/10.1086/301844
kinship <- double_kinship(kinship)
# find individuals in common across all arguments
# and drop individuals with missing covariates or missing *all* phenotypes
ind2keep <- get_common_ids(genoprobs, pheno, kinshipIDs, weights,
addcovar, nullcovar, intcovar, complete.cases=TRUE)
if(length(ind2keep)<=2) {
if(length(ind2keep)==0)
stop("No individuals in common.")
else
stop("Only ", length(ind2keep), " individuals in common: ",
paste(ind2keep, collapse=":"))
}
if(did_decomp) { # if did decomposition already, make sure it was with exactly
if(length(kinshipIDs) != length(ind2keep) ||
any(sort(kinshipIDs) != sort(ind2keep)))
stop("Decomposed kinship matrix was with different individuals")
else
ind2keep <- kinshipIDs # force them in same order
}
# omit individuals not in common
genoprobs <- genoprobs[ind2keep,,,drop=FALSE]
pheno <- pheno[ind2keep]
if(!is.null(addcovar)) addcovar <- addcovar[ind2keep,,drop=FALSE]
if(!is.null(nullcovar)) nullcovar <- nullcovar[ind2keep,,drop=FALSE]
if(!is.null(intcovar)) intcovar <- intcovar[ind2keep,,drop=FALSE]
if(!is.null(weights)) weights <- weights[ind2keep]
# 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)
# square-root of weights (only if model="normal")
weights <- sqrt_weights(weights) # also check >0 (and if all 1's, turn to NULL)
# multiply stuff by weights
kinship <- weight_kinship(kinship, weights)
addcovar <- weight_matrix(addcovar, weights)
intcovar <- weight_matrix(intcovar, weights)
nullcovar <- weight_matrix(nullcovar, weights)
pheno <- weight_matrix(pheno, weights)
# eigen decomposition of kinship matrix
if(!did_decomp)
kinship <- decomp_kinship(kinship[ind2keep, ind2keep])
# estimate hsq if necessary
if(is.null(hsq)) {
nullresult <- calc_hsq_clean(Ke=kinship, pheno=as.matrix(pheno),
addcovar=cbind(addcovar, nullcovar), Xcovar=NULL,
weights=weights, is_x_chr=FALSE, reml=reml,
cores=1, check_boundary=TRUE, tol=tol)
hsq <- as.numeric(nullresult$hsq)
}
# eigen-vectors and weights
eigenvec <- kinship$vectors
wts <- 1/sqrt(hsq*kinship$values + (1-hsq))
# multiply genoprobs by contrasts
if(!is.null(contrasts))
genoprobs <- genoprobs_by_contrasts(genoprobs, contrasts)
# multiply genoprobs by weights
genoprobs <- weight_array(genoprobs[ind2keep,,,drop=FALSE], weights)
if(se) { # also calculate SEs
if(is.null(intcovar)) { # just addcovar
if(is.null(addcovar)) addcovar <- matrix(nrow=length(ind2keep), ncol=0)
result <- scancoefSE_pg_addcovar(genoprobs, pheno, addcovar, eigenvec, wts, tol)
}
else { # intcovar
result <- scancoefSE_pg_intcovar(genoprobs, pheno, addcovar, intcovar,
eigenvec, wts, tol)
}
SE <- t(result$SE) # transpose to positions x coefficients
result <- result$coef
} else { # don't calculate SEs
if(is.null(intcovar)) { # just addcovar
if(is.null(addcovar)) addcovar <- matrix(nrow=length(ind2keep), ncol=0)
result <- scancoef_pg_addcovar(genoprobs, pheno, addcovar, eigenvec, wts, tol)
}
else { # intcovar
result <- scancoef_pg_intcovar(genoprobs, pheno, addcovar, intcovar,
eigenvec, wts, tol)
}
SE <- NULL
}
result <- t(result) # transpose to positions x coefficients
# add names
dimnames(result) <- list(dimnames(genoprobs)[[3]],
scan1coef_names(genoprobs, addcovar, intcovar))
if(se) dimnames(SE) <- dimnames(result)
# center the QTL effects at zero and add an intercept
if(zerosum && is.null(contrasts)) {
ng <- dim(genoprobs)[2]
whcol <- seq_len(ng)
mu <- rowMeans(result[,whcol,drop=FALSE], na.rm=TRUE)
result <- cbind(result, intercept=mu)
result[,whcol] <- result[,whcol] - mu
if(se) {
SE <- cbind(SE, intercept=sqrt(rowMeans(SE[,whcol,drop=FALSE]^2, na.rm=TRUE)))
}
}
# add some attributes with details on analysis
attr(result, "sample_size") <- length(ind2keep)
attr(result, "SE") <- SE # include only if not NULL
class(result) <- c("scan1coef", "scan1", "matrix")
result
}
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