Dataset simulated according to Broman and Speed (2002).
An object of class
cross. List with two components:
geno is a list with elements corresponding to chromosomes.
names(geno) contains the names of the chromosomes. There are two
components for each chromosome:
data, a matrix whose rows are
individuals and whose columns are markers, and
map, a vector of
marker positions in cM. There is no missing data, and genotypes are coded as 1=AA, 2=AB
pheno is a data frame of size (250 x 2) containing the trait data. The first trait is generated from a random normal distribution with variance 1.0 and mean determined by the QTL genotypes as described below. The second trait
is an ID for each individual
The data was generated for a sample size of 250 from a map with 9 chromosomes. Each chromosome had length 100 cM and contained 11 equally spaced markers (spaced 10 cM apart). The background phenotypic variation was 1.0 and there was no missing data. The QTL were located as follows:
Chr 1: 2 QTL located at 30 and 70 cM, both with effect size of 0.76
Chr 2: 2 QTL located at 30 and 70 cM with effect size of 0.76 and -0.76
Chr 3: 1 QTL located at 50 cM with effect size of 0.76
Chr 4: 1 QTL located at 30 cM with effect size of 0.76
Chr 5: 1 QTL located at 0 cM with effect size of 0.76
Broman, KW and Speed TP. 2002. A model selection approach for the identification of quantitative trait loci in experimental crosses. JRSS-B 64:641-656.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
data(BSdat) library(qtl) # Summary of chromosomes and markers nchr(BSdat) nmar(BSdat) # linkage map of data plot.map(BSdat) # interval mapping BSgp <- calc.genoprob(BSdat, step=2) BSim <- scanone(BSgp) # composite interval mapping BScim <- cim(BSgp, n.marcov=5, method="hk") # LOD profile from CIM plot(BScim) # LOD threshold for 5 cofactors from paper abline(h=3.56)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.