R: LOD thresholds from scanone permutation results

summary.scanoneperm {qtl} | R Documentation

## LOD thresholds from scanone permutation results

### Description

Print the estimated genome-wide LOD thresholds on the basis of permutation results from `scanone` (with `n.perm` > 0).

### Usage

    ## S3 method for class 'scanoneperm'
    summary(object, alpha=c(0.05, 0.10),
                        controlAcrossCol=FALSE, ...)

### Arguments

`object` | Output from the function `scanone` with `n.perm` > 0.
`alpha` | Genome-wide significance levels.
`controlAcrossCol` | If TRUE, control error rate not just across the genome but also across the columns of LOD scores.
`...` | Ignored at this point.

### Details

If there were autosomal data only or `scanone` was run with `perm.Xsp=FALSE`, genome-wide LOD thresholds are given; these are the 1-alpha quantiles of the genome-wide maximum LOD scores from the permutations.

If there were autosomal and X chromosome data and `scanone` was run with `perm.Xsp=TRUE`, autosome- and X-chromsome-specific LOD thresholds are given, by the method described in Broman et al. (2006). Let L_A and L_X be total the genetic lengths of the autosomes and X chromosome, respectively, and let L_T = L_A + L_X Then in place of alpha, we use

alpha_A = 1 - (1 - alpha)^(L_A/L_T)

as the significance level for the autosomes and

alpha_x = 1 - (1 - alpha)^(LX/LT)

as the significance level for the X chromosome. The result is a list with two matrices, one for the autosomes and one for the X chromosome.

If `controlAcrossCol=TRUE`, we use a trick to control the error rate not just across the genome but also across the LOD score columns. Namely, we convert each column of permutation results to ranks, and then for each permutation replicate we find the maximum rank across the columns. We then find the appropriate quantile of the maximized ranks, and then backtrack to the corresponding LOD score within each of the columns.

### Value

An object of class `summary.scanoneperm`, to be printed by `print.summary.scanoneperm`. If there were X chromosome data and `scanone` was run with `perm.Xsp=TRUE`, there are two matrices in the results, for the autosome and X-chromosome LOD thresholds.

### Author(s)

Karl W Broman, kbroman@biostat.wisc.edu

### References

Broman, K. W., Sen, Ś, Owens, S. E., Manichaikul, A., Southard-Smith, E. M. and Churchill G. A. (2006) The X chromosome in quantitative trait locus mapping. Genetics, 174, 2151–2158.

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

### See Also

`scanone`, `summary.scanone`, `plot.scanoneperm`

### Examples

    data(fake.f2)

    fake.f2 <- calc.genoprob(fake.f2, step=2.5)

    operm1 <- scanone(fake.f2, n.perm=100, method="hk")
    summary(operm1)

    operm2 <- scanone(fake.f2, n.perm=100, method="hk", perm.Xsp=TRUE)
    summary(operm2)

    # Add noise column
    fake.f2$pheno$noise <- rnorm(nind(fake.f2))
    operm3 <- scanone(fake.f2, pheno.col=c("phenotype", "noise"), n.perm=10, method="hk")
    summary(operm3)
    summary(operm3, controlAcrossCol=TRUE, alpha=c(0.05, 0.36))

[Package qtl version 1.41-6 Index]