LOD thresholds from scanone permutation results
Print the estimated genome-wide LOD thresholds on the basis of
permutation results from scanone (with
n.perm > 0).
## S3 method for class 'scanoneperm'
summary(object, alpha=c(0.05, 0.10),
controlAcrossCol=FALSE, ...)object |
Output from the function |
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. |
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. See Burrage et al. (2010),
right column on page 118.
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.
Karl W Broman, broman@wisc.edu
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.
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))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.