qtl (version 1.66)

summary.scanoneperm: 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 scanoneperm
summary(object, alpha=c(0.05, 0.10),
                    controlAcrossCol=FALSE, ...)

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.

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.

Author

Karl W Broman,

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 $$\alpha_X = 1 - (1-\alpha)^{L_X/L_T}$$ 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. See Burrage et al. (2010), right column on page 118.

References

Broman KW, Sen Ś, Owens SE, Manichaikul A, Southard-Smith EM, Churchill GA (2006) The X chromosome in quantitative trait locus mapping. Genetics, 174, 2151--2158.

Burrage LC, Baskin-Hill AE, Sinasac DS, Singer JB, Croniger CM, Kirby A, Kulbokas EJ, Daly MJ, Lander ES, Broman KW, Nadeau JH (2010) Genetic resistance to diet-induced obesity in chromosome substitution strains of mice. Mamm Genome, 21, 115--129.

Churchill GA, Doerge RW (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 <- subset(fake.f2, chr=c(18:19,"X"))
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))