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).