scan1perm: Permutation test for genome scan with a single-QTL model
In rqtl/qtl2scan: Genome Scans for QTL Experiments

Description Usage Arguments Details Value References See Also Examples

Permutation test for a enome scan with a single-QTL model by Haley-Knott regression or a linear mixed model, with possible allowance for covariates.

scan1perm(genoprobs, pheno, kinship = NULL, addcovar = NULL,
  Xcovar = NULL, intcovar = NULL, weights = NULL, reml = TRUE,
  model = c("normal", "binary"), n_perm = 1, perm_Xsp = FALSE,
  perm_strata = NULL, chr_lengths = NULL, cores = 1, ...)

`genoprobs`	Genotype probabilities as calculated by `qtl2geno::calc_genoprob()`.
`pheno`	A 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 matrix of additive covariates.
`Xcovar`	An optional matrix with additional additive covariates used for null hypothesis when scanning the X chromosome.
`intcovar`	An optional matrix of interactive covariates.
`weights`	An optional vector of positive weights for the individuals. As with the other inputs, it must have `names` for individual identifiers. Ignored if `kinship` is provided.
`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].
`n_perm`	Number of permutation replicates.
`perm_Xsp`	If TRUE, do separate permutations for the autosomes and the X chromosome.
`perm_strata`	Vector of strata, for a stratified permutation test. Should be named in the same way as the rows of `pheno`. The unique values define the strata.
`chr_lengths`	Lengths of the chromosomes; needed only if `perm_Xsp=TRUE`. See `chr_lengths()`.
`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.

If kinship is not provided, so that analysis proceeds by Haley-Knott regression, we permute the rows of the phenotype data; the same permutations are also applied to the rows of the covariates (addcovar, Xcovar, and intcovar) are permuted.

If kinship is provided, we instead permute the rows of the genotype data and fit an LMM with the same residual heritability (estimated under the null hypothesis of no QTL).

If Xcovar is provided and perm_strata=NULL, we do a stratified permutation test with the strata defined by the rows of Xcovar. If a simple permutation test is desired, provide perm_strata that is a vector containing a single repeated value.

The ... argument can contain three additional control parameters; suspended for simplicity (or confusion, depending on your point of view). tol is used as a tolerance value for linear regression by QR decomposition (in determining whether columns are linearly dependent on others and should be omitted); default 1e-12. intcovar_method indicates whether to use a high-memory (but potentially faster) method or a low-memory (and possibly slower) method, with values "highmem" or "lowmem"; default "lowmem". Finally, max_batch indicates the maximum number of phenotypes to run together; default is 1000.

If perm_Xsp=FALSE, the result is matrix of genome-wide maximum LOD scores, permutation replicates x phenotypes. If perm_Xsp=TRUE, the result is a list of two matrices, one for the autosomes and one for the X chromosome.

Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138:963–971.

Manichaikul A, Palmer AA, Sen S, Broman KW (2007) Significance thresholds for quantitative trait locus mapping under selective genotyping. Genetics 177:1963–1966.

Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324.

Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723.

scan1(), chr_lengths(), mat2strata()

# load qtl2geno package for data and genoprob calculation
library(qtl2geno)

# read data
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2geno"))


# 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)

# strata for permutations
perm_strata <- mat2strata(Xcovar)

# permutations with genome scan
## Not run: 
operm <- scan1perm(probs, pheno, addcovar=covar, Xcovar=Xcovar,
                   n_perm=1000, perm_Xsp=TRUE, perm_strata=perm_strata,
                   chr_lengths=chr_lengths(iron$gmap))
## End(Not run)

summary(operm)

# leave-one-chromosome-out kinship matrices
kinship <- calc_kinship(probs, "loco")

# genome scan with a linear mixed model
## Not run: 
operm_lmm <- scan1perm(probs, pheno, kinship, covar, Xcovar, n_perm=1000,
                       perm_Xsp=TRUE, perm_strata=perm_strata,
                       chr_lengths=chr_lengths(map))
## End(Not run)

summary(operm_lmm)