R: Summarize the results of a two-dimensional genome scan

summary.scantwo {qtl} | R Documentation

## Summarize the results of a two-dimensional genome scan

### Description

Summarize the interesting aspects of the results of `scantwo`.

### Usage

    ## S3 method for class 'scantwo'
    summary(object, thresholds,
            what=c("best", "full", "add", "int"),
            perms, alphas, lodcolumn=1, pvalues=FALSE,
            allpairs=TRUE, ...)

### Arguments

`object` | An object of class `scantwo`, the output of the function `scantwo`.
`thresholds` | A vector of length 5, giving LOD thresholds for the full, conditional-interactive, interaction, additive, and conditional-additive LOD scores. See Details, below.
`what` | Indicates for which LOD score the maximum should be reported. See Details, below.
`perms` | Optional permutation results used to derive thresholds or to calculate genome-scan-adjusted p-values. This must be consistent with the `object` input, in that it must have the same number of LOD score columns, though it can have just one column of permutation results, in which case they are assumed to apply to any chosen LOD score column.
`alphas` | If perms are included, these are the significance levels used to calculate thresholds for determining which peaks to pull out. It should be a vector of length 5, giving significance levels for the full, conditional-interactive, interaction, additive, and conditional-additive LOD scores. (It can also be a single number, in which case it is assumed that the same value is used for all five LOD scores.) If `thresholds` is specified, `alphas` should not be.
`lodcolumn` | If the scantwo results contain LOD scores for multiple phenotypes, this argument indicates which to use in the summary. Only one LOD score column may be considered at a time.
`pvalues` | If TRUE, include columns with genome-scan-adjusted p-values in the results. This requires that `perms` be provided.
`allpairs` | If TRUE, all pairs of chromosomes are considered. If FALSE, only self-self pairs are considered, so that one may more conveniently check for possible linked QTL.
`...` | Ignored at this point.

### Details

If `what="best"`, we calculate, for each pair of chromosomes, the maximum LOD score for the full model (two QTL plus interaction) and the maximum LOD score for the additive model. The difference between these is a LOD score for a test for interaction. We also calculate the difference between the maximum full LOD and the maximum single-QTL LOD score for the two chromosomes; this is the LOD score for a test for a second QTL, allowing for epistasis, which we call either the conditional-interactive or "fv1" LOD score. Finally, we calculate the difference between the maximum additive LOD score and the maximum single-QTL LOD score for the two chromosomes; this is the LOD score for a test for a second QTL, assuming that the two QTL act additively, which we call either the conditional-additive or "av1" LOD score. Note that the maximum full LOD and additive LOD are allowed to occur in different places.

If `what="full"`, we find the maximum full LOD and extract the additive LOD at the corresponding pair of positions; we derive the other three LOD scores for that fixed pair of positions.

If `what="add"`, we find the maximum additive LOD and extract the full LOD at the corresponding pair of positions; we derive the other three LOD scores for that fixed pair of positions.

If `what="int"`, we find the pair of positions for which the difference between the full and additive LOD scores is largest, and then calculate the five LOD scores at that pair of positions.

If `thresholds` or `alphas` is provided (and note that when `alphas` is provided, `perms` must also), we extract just those pairs of chromosomes for which either (a) the full LOD score exceeds its thresholds and either the conditional-interactive LOD or the interaction LOD exceed their threshold, or (b) the additive LOD score exceeds its threshold and the conditional-additive LOD exceeds its threshold. The thresholds or alphas must be given in the order full, cond-int, int, add, cond-add.

Thresholds may be obtained by a permutation test with `scantwo`, but these are extremely time-consuming. For a mouse backcross, we suggest the thresholds (6.0, 4.7, 4.4, 4.7, 2.6) for the full, conditional-interactive, interaction, additive, and conditional-additive LOD scores, respectively. For a mouse intercross, we suggest the thresholds (9.1, 7.1, 6.3, 6.3, 3.3) for the full, conditional-interactive, interaction, additive, and conditional-additive LOD scores, respectively. These were obtained by 10,000 simulations of crosses with 250 individuals, markers at a 10 cM spacing, and analysis by Haley-Knott regression.

### Value

An object of class `summary.scantwo`, to be printed by `print.summary.scantwo`;

### Output of addpair

Note that, for output from `addpair` in which the new loci are indicated explicitly in the formula, the summary provided by `summary.scantwo` is somewhat special.

All arguments except `allpairs` and `thresholds` (and, of course, the input `object`) are ignored.

If the formula is symmetric in the two new QTL, the output has just two LOD score columns: `lod.2v0` comparing the full model to the model with neither of the new QTL, and `lod.2v1` comparing the full model to the model with just one new QTL.

If the formula is not symmetric in the two new QTL, the output has three LOD score columns: `lod.2v0` comparing the full model to the model with neither of the new QTL, `lod.2v1b` comparing the full model to the model in which the first of the new QTL is omitted, and `lod.2v1a` comparing the full model to the model with the second of the new QTL omitted.

The `thresholds` argument should have length 1 or 2, rather than the usual 5. Rows will be retained if `lod.2v0` is greater than `thresholds[1]` and `lod.2v1` (or either of `lod.2v1a` or `lod.2v1b`) is greater than `thresholds[2]`. (If a single thresholds is given, we assume that `thresholds[2]==0`.)

### The older version

The previous version of this function is still available, though it is now named `summaryScantwoOld`.

We much prefer the revised function. However, while we are confident that this function (and the permutations in `scantwo`) are calculating the relevant statistics, the appropriate significance levels for these relatively complex series of statistical tests is not yet completely clear.

### Author(s)

Karl W Broman, broman@wisc.edu

### See Also

`scantwo`, `plot.scantwo`, `max.scantwo`, `condense.scantwo`

### Examples

    data(fake.f2)

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

    # All pairs of chromosomes
    summary(out.2dim)

    # Chromosome pairs meeting specified criteria
    summary(out.2dim, thresholds=c(9.1, 7.1, 6.3, 6.3, 3.3))

    # Similar, but ignoring the interaction LOD score in the rule
    summary(out.2dim, thresholds=c(9.1, 7.1, Inf, 6.3, 3.3))

    # Pairs having largest interaction LOD score, if it's > 4
    summary(out.2dim, thresholds=c(0, Inf, 4, Inf, Inf), what="int")

    # permutation test to get thresholds; run in two batches
    #     and then combined with c.scantwoperm
    ## Not run: operm.2dimA <- scantwo(fake.f2, method="hk", n.perm=500)
    operm.2dimB <- scantwo(fake.f2, method="hk", n.perm=500)
    operm.2dim <- c(operm.2dimA, operm.2dimB)
    ## End(Not run)

    # estimated LOD thresholds
    summary(operm.2dim)

    # Summary, citing significance levels and so estimating thresholds
    #     from the permutation results
    summary(out.2dim, perms=operm.2dim, alpha=rep(0.05, 5))

    # Similar, but ignoring the interaction LOD score in the rule
    summary(out.2dim, perms=operm.2dim, alpha=c(0.05, 0.05, 0, 0.05, 0.05))

    # Similar, but also getting genome-scan-adjusted p-values
    summary(out.2dim, perms=operm.2dim, alpha=c(0.05, 0.05, 0, 0.05, 0.05),
            pvalues=TRUE)

[Package qtl version 1.46-2 Index]