2.7 Quantitative Trait Loci (QTL) Analysis
The R software package R/qtl (Broman, Wu, Sen, & Churchill,
2003) was used for all QTL analyses described below. Composite interval
mapping (CIM) was used to associate Melampsora sp. leaf rust,S. musiva leaf spot, and Phyllocolpa sp. to QTL positions
(cim function). Single QTL mapping was used to associate the
binary scores for the S. musiva canker, M. vagabunda andP. populitransversus to QTL positions (scanone function).
The method used for both mapping approaches was the
expectation-maximization (EM) algorithm. Estimation of QTL interval
significance was completed by performing 1000 permutations. Intervals
with logarithm of odds (LOD) scores that were above the p-value
threshold (alpha = 0.05), as determined from the permutation tests, were
selected for further analysis. The percent variance explained by
significant markers for fungal and insect surveys, that were mapped
using CIM, were calculated by extracting significant marker positions
and creating a fit QTL model (fitQTL function). The positive
allele contributing to an increase in susceptibility to fungi and
insects was found by generating effect plots for each phenotype and its
significant marker position (effectplot function).