scantwo {qtl}

Two-dimensional genome scan with a two-QTL model

Description

Perform a two-dimensional genome scan with a two-QTL model, with possible allowance for covariates.

Usage

scantwo(cross, chr, pheno.col=1, model=c("normal","binary"),
        method=c("em","imp","hk","mr","mr-imp","mr-argmax"),
        addcovar=NULL, intcovar=NULL, weights=NULL,
        use=c("all.obs", "complete.obs"),
        incl.markers=FALSE, clean.output=FALSE,
        clean.nmar=1, clean.distance=0,
        maxit=4000, tol=1e-4,
        verbose=TRUE, n.perm, perm.Xsp=FALSE, perm.strata=NULL,
        assumeCondIndep=FALSE, batchsize=250, n.cluster=1)

Arguments

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 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 or a model for binary traits.

method

Indicates whether to use the the EM algorithm, imputation, Haley-Knott regression, or marker regression. 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.

intcovar

Interactive covariates (interact with QTL genotype).

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.

incl.markers

If FALSE, do calculations only at points on an evenly spaced grid. If calc.genoprob or sim.geno were run with stepwidth="variable" or stepwidth="max", we force incl.markers=TRUE.

clean.output

If TRUE, clean the output with clean.scantwo, replacing LOD scores for pairs of positions that are not well separated with 0. In permutations, this will be done for each permutation replicate. This can be important for the case of method="em", as there can be difficulty with algorithm convergence in these regions.

clean.nmar

If clean.output=TRUE, this is the number of markers that must separate two positions.

clean.distance

If clean.output=TRUE, this is the cM distance that must separate two positions.

maxit

Maximum number of iterations; used only with method "em".

tol

Tolerance value for determining convergence; used only with method "em".

verbose

If TRUE, display information about the progress of calculations. For method "em", if verbose is an integer above 1, further details on the progress of the algorithm will be displayed.

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.

assumeCondIndep

If TRUE, assume conditional independence of QTL genotypes given marker genotypes. This is an approximation, but it may speed things up.

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.

Details

Standard interval mapping (method="em") and Haley-Knott regression (method="hk") require that multipoint genotype probabilities are first calculated using calc.genoprob. The imputation method uses the results of sim.geno.

The method "em" is standard interval mapping by the EM algorithm (Dempster et al. 1977; Lander and Botstein 1989). Marker regression (method="mr") is simply linear regression of phenotypes on marker genotypes (individuals with missing genotypes are discarded). Haley-Knott regression (method="hk") uses the regression of phenotypes on multipoint genotype probabilities. The imputation method (method="imp") uses the pseudomarker algorithm described by Sen and Churchill (2001).

Individuals with missing phenotypes are dropped.

In the presence of covariates, the full model is

y = \mu + \beta_{q_1} + \beta_{q_2} + \beta_{q_1 \times q_2} + A \gamma + Z \delta_{q_1} + Z \delta_{q_2} + Z \delta_{q_1 \times q_2} + \epsilon

where q_1 and q_2 are the unknown QTL genotypes at two locations, A is a matrix of covariates, and Z is a matrix of covariates that interact with QTL genotypes. The columns of Z are forced to be contained in the matrix A.

The above full model is compared to the additive QTL model,

y = \mu + \beta_{q_1} + \beta_{q_2} + A \gamma + Z \delta_{q_1} + Z \delta_{q_2} + \epsilon

and also to the null model, with no QTL,

y = \mu + A \gamma + \epsilon

In the case that n.perm is specified, the R function scantwo is called repeatedly.

For model="binary", a logistic regression model is used.

Value

If n.perm is missing, the function returns a list with class "scantwo" and containing three components. The first component is a matrix of dimension [tot.pos x tot.pos]; the upper triangle contains the LOD scores for the additive model, and the lower triangle contains the LOD scores for the full model. The diagonal contains the results of scanone. The second component of the output is a data.frame indicating the locations at which the two-QTL LOD scores were calculated. The first column is the chromosome identifier, the second column is the position in cM, the third column is a 1/0 indicator for ease in later pulling out only the equally spaced positions, and the fourth column indicates whether the position is on the X chromosome or not. The final component is a version of the results of scanone including sex and/or cross direction as additive covariates, which is needed for a proper calculation of conditional LOD scores.

If n.perm is specified, the function returns a list with six different LOD scores from each of the permutation replicates. First, the maximum LOD score for the full model (two QTLs plus an interaction). Second, for each pair of chromosomes, we take the difference between the full LOD and the maximum single-QTL LOD for those two chromosomes, and then maximize this across chromosome pairs. Third, for each pair of chromosomes we take the difference between the maximum full LOD and the maximum additive LOD, and then maximize this across chromosome pairs. Fourth, the maximum LOD score for the additive QTL model. Fifth, for each pair of chromosomes, we take the difference between the additive LOD and the maximum single-QTL LOD for those two chromosomes, and then maximize this across chromosome pairs. Finally, the maximum single-QTL LOD score (that is, from a single-QTL scan). The latter is not used in summary.scantwo, but does get calculated at each permutation, so we include it for the sake of completeness.

If n.perm is specified and perm.Xsp=TRUE, the result is a list with the permutation results for the regions A:A, A:X, and X:X, each of which is a list with the six different LOD scores. Independent permutations are performed in each region, n.perm is the number of permutations for the A:A region; additional permutations are are used for the A:X and X:X parts, as estimates of quantiles farther out into the tails are needed.

X chromosome

The X chromosome must be treated specially in QTL mapping.

As in scanone, if both males and females are included, male hemizygotes are allowed to be different from female homozygotes, and the null hypothesis must be changed in order to ensure that sex- or pgm-differences in the phenotype do not results in spurious linkage to the X chromosome. (See the help file for scanone.)

If n.perm is specified and perm.Xsp=TRUE, X-chromosome-specific permutations are performed, to obtain separate thresholds for the regions A:A, A:X, and X:X.

Author(s)

Karl W Broman, [e-mail]; Hao Wu

References

Churchill, G. A. and Doerge, R. W. (1994) Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971.

Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. B, 39, 1–38.

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

Lander, E. S. and Botstein, D. (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121, 185–199.

Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371–387.

Soller, M., Brody, T. and Genizi, A. (1976) On the power of experimental designs for the detection of linkage between marker loci and quantitative loci in crosses between inbred lines. Theor. Appl. Genet. 47, 35–39.

See Also

plot.scantwo, summary.scantwo, scanone, max.scantwo, summary.scantwoperm, c.scantwoperm

Examples

data(fake.f2)

fake.f2 <- calc.genoprob(fake.f2, step=5)
out.2dim <- scantwo(fake.f2, method="hk")
plot(out.2dim)

# permutations

## Not run: permo.2dim <- scantwo(fake.f2, method="hk", n.perm=1000)
summary(permo.2dim, alpha=0.05)

# summary with p-values
summary(out.2dim, perms=permo.2dim, pvalues=TRUE,
        alphas=c(0.05, 0.10, 0.10, 0.05, 0.10))

# covariates
data(fake.bc)

fake.bc <- calc.genoprob(fake.bc, step=10)

ac <- pull.pheno(fake.bc, c("sex","age"))
ic <- pull.pheno(fake.bc, "sex")

out <- scantwo(fake.bc, method="hk", pheno.col=1,
               addcovar=ac, intcovar=ic)
plot(out)