scan1max: Maximum LOD score from genome scan with a single-QTL model

Description

Maximum LOD score from genome scan with a single-QTL model by Haley-Knott regression or a linear mixed model, with possible allowance for covariates.

Usage

scan1max(
  genoprobs,
  pheno,
  kinship = NULL,
  addcovar = NULL,
  Xcovar = NULL,
  intcovar = NULL,
  weights = NULL,
  reml = TRUE,
  model = c("normal", "binary"),
  hsq = NULL,
  by_chr = FALSE,
  cores = 1,
  ...
)

Value

Either a vector of genome-wide maximum LOD scores, or if by_chr is TRUE, a matrix with the chromosome-specific maxima, with the rows being the chromosomes and the columns being the phenotypes.

Arguments

genoprobs: Genotype probabilities as calculated by calc_genoprob().
pheno: A numeric matrix of phenotypes, individuals x phenotypes.
kinship: Optional kinship matrix, or a list of kinship matrices (one per chromosome), in order to use the LOCO (leave one chromosome out) method.
addcovar: An optional numeric matrix of additive covariates.
Xcovar: An optional numeric matrix with additional additive covariates used for null hypothesis when scanning the X chromosome.
intcovar: An numeric optional matrix of interactive covariates.
weights: An optional numeric vector of positive weights for the individuals. As with the other inputs, it must have names for individual identifiers.
reml: If kinship provided: if reml=TRUE, use REML; otherwise maximum likelihood.
model: Indicates whether to use a normal model (least squares) or binary model (logistic regression) for the phenotype. If model="binary", the phenotypes must have values in \([0, 1]\).
hsq: Considered only if kinship is provided, in which case this is taken as the assumed value for the residual heritability. It should be a vector with length corresponding to the number of columns in pheno, or (if kinship corresponds to a list of LOCO kinship matrices) a matrix with dimension length(kinship) x ncol(pheno).
by_chr: If TRUE, save the individual chromosome maxima.
cores: Number of CPU cores to use, for parallel calculations. (If 0, use parallel::detectCores().) Alternatively, this can be links to a set of cluster sockets, as produced by parallel::makeCluster().
...: Additional control parameters; see Details.

Details

Equivalent to running scan1() and then saving the column maxima, with some savings in memory usage.

Examples

Run this code

# read data
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
iron <- iron[,c("19", "X")] # subset to chr 19 and X

# insert pseudomarkers into map
map <- insert_pseudomarkers(iron$gmap, step=1)

# calculate genotype probabilities
probs <- calc_genoprob(iron, map, error_prob=0.002)

# grab phenotypes and covariates; ensure that covariates have names attribute
pheno <- iron$pheno
covar <- match(iron$covar$sex, c("f", "m")) # make numeric
names(covar) <- rownames(iron$covar)
Xcovar <- get_x_covar(iron)

# perform genome scan
out <- scan1max(probs, pheno, addcovar=covar, Xcovar=Xcovar)