Scanone: Genome scan with a single QTL model
In qtl.outbred: Interface for Genotype Probabilities from Outbred Intercross

Description Usage Arguments Details Value

Genome scan with a single QTL model, with possible allowance for covariates, using any of several possible models for the phenotype and any of several possible numerical methods.

Scanone(cross, chr, pheno.col=1, model=c("normal","binary","2part","np"),
        method=c("em","imp","hk","ehk","mr","mr-imp","mr-argmax"),
        addcovar=NULL, intcovar=NULL, weights=NULL,
        use=c("all.obs", "complete.obs"), upper=FALSE,
        ties.random=FALSE, start=NULL, maxit=4000,
        tol=1e-4, n.perm, perm.Xsp=FALSE, perm.strata=NULL, verbose,
        batchsize=250, n.cluster=1, ind.noqtl)

`cross`	An object of class `cross`. See `read.cross` for details.
`chr`	Optional vector indicating the chromosomes for which LOD scores should be calculated. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding `-` to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used.
`pheno.col`	Column number in the phenotype matrix which should be used as the phenotype. This can be a vector of integers; for methods `"hk"` and `"imp"` this can be considerably faster than doing them one at a time. One may also give a character strings matching the phenotype names. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations.
`model`	The phenotype model: the usual normal model, a model for binary traits, a two-part model or non-parametric analysis
`method`	Indicates whether to use the EM algorithm, imputation, Haley-Knott regression, the extended Haley-Knott method, or marker regression. Not all methods are available for all models. Marker regression is performed either by dropping individuals with missing genotypes (`"mr"`), or by first filling in missing data using a single imputation (`"mr-imp"`) or by the Viterbi algorithm (`"mr-argmax"`).
`addcovar`	Additive covariates; allowed only for the normal and binary models.
`intcovar`	Interactive covariates (interact with QTL genotype); allowed only for the normal and binary models.
`weights`	Optional weights of individuals. Should be either NULL or a vector of length n.ind containing positive weights. Used only in the case `model="normal"`.
`use`	In the case that multiple phenotypes are selected to be scanned, this argument indicates whether to use all individuals, including those missing some phenotypes, or just those individuals that have data on all selected phenotypes.
`upper`	Used only for the two-part model; if true, the "undefined" phenotype is the maximum observed phenotype; otherwise, it is the smallest observed phenotype.
`ties.random`	Used only for the non-parametric "model"; if TRUE, ties in the phenotypes are ranked at random. If FALSE, average ranks are used and a corrected LOD score is calculated.
`start`	Used only for the EM algorithm with the normal model and no covariates. If `NULL`, use the usual starting values; if length 1, use random initial weights for EM; otherwise, this should be a vector of length n+1 (where n is the number of possible genotypes for the cross), giving the initial values for EM.
`maxit`	Maximum number of iterations for methods `"em"` and `"ehk"`.
`tol`	Tolerance value for determining convergence for methods `"em"` and `"ehk"`.
`n.perm`	If specified, a permutation test is performed rather than an analysis of the observed data. This argument defines the number of permutation replicates.
`perm.Xsp`	If `n.perm` > 0, so that a permutation test will be performed, this indicates whether separate permutations should be performed for the autosomes and the X chromosome, in order to get an X-chromosome-specific LOD threshold. In this case, additional permutations are performed for the X chromosome.
`perm.strata`	If `n.perm` > 0, this may be used to perform a stratified permutation test. This should be a vector with the same number of individuals as in the cross data. Unique values indicate the individual strata, and permutations will be performed within the strata.
`verbose`	In the case `n.perm` is specified, display information about the progress of the permutation tests.
`batchsize`	The number of phenotypes (or permutations) to be run as a batch; used only for methods `"hk"` and `"imp"`.
`n.cluster`	If the package `snow` is available and `n.perm` > 0, permutations are run in parallel using this number of nodes.
`ind.noqtl`	Indicates individuals who should not be allowed a QTL effect (used rarely, if at all); this is a logical vector of same length as there are individuals in the cross.

See "scanone" in "qtl" package for details.

If n.perm is missing, the function returns a data.frame whose first two columns contain the chromosome IDs and cM positions. Subsequent columns contain the LOD scores for each phenotype. In the case of the two-part model, there are three LOD score columns for each phenotype: LOD(p,mu), LOD(p) and LOD(mu). The result is given class "scanone" and has attributes "model", "method", "df" and "type" (the latter is the type of cross analyzed).

If n.perm is specified, the function returns the results of a permutation test and the output has class "scanoneperm". If perm.Xsp=FALSE, the function returns a matrix with n.perm rows, each row containing the genome-wide maximum LOD score for each of the phenotypes. In the case of the two-part model, there are three columns for each phenotype, corresponding to the three different LOD scores. If perm.Xsp=TRUE, the result contains separate permutation results for the autosomes and the X chromosome respectively, and an attribute indicates the lengths of the chromosomes and an indicator of which chromosome is X.

qtl.outbred documentation built on May 2, 2019, 5:56 p.m.