DOI: 10.5061/dryad.mcvdnck0x
title: R/QTL datasets for fusiform rust resistance QTL mapping

description: Fusiform rust disease, caused by the endemic fungus Cronartium quercuum f.
 sp. fusiforme, is the most damaging disease affecting economically
 important pine species in the southeast United States. In this report, we
 detail the genomic localization and sequence-level discovery of candidate
 race-nonspecific broad-spectrum fusiform rust resistance genes in Pinus
 taeda L. Two full-sib families, each with ~1000 progeny, were challenged
 with a complex inoculum consisting of over 150 pathogen isolates.
 High-density linkage mapping revealed three QTL distributed on two linkage
 groups. The two QTL on linkage group 2 were additive with respect to their
 effects on the probability of disease outcome. All three QTL were
 validated using a population of 2057 cloned pine genotypes in a
 six-year-old multi-environmental field trial. As a complement to the QTL
 mapping approach, bulked segregant RNAseq analysis revealed a small number
 of candidate nucleotide binding leucine rich repeat genes harboring SNP
 significantly associated with disease resistance. The results of this
 study demonstrate that single qualitative resistance genes can confer
 effective resistance against genetically diverse mixtures of an endemic
 pathogen.
description: Linkage maps were produced using the two-way pseudo-testcross
 design for both families. Briefly, prior to linkage mapping, the genotype
 of each parent at each marker was ascertained. For each cross, a linkage
 map was produced using markers in either backcross configuration (AB:BB or
 BB:AB), resulting in separate maps for the maternal and paternal genomes.
 Markers in the intercross configuration (AB:AB) were used to generate the
 sex averaged map for each LG, but these were dropped from the dataset
 prior to QTL analysis since the linkage phase of heterozygous genotypes
 for which both parents are heterozygous is unknown in an outbred F1 cross.
 The consensus map combining the linkage maps from all four parents was
 generated through linear programming methods. Details of these procedures
 are presented in <b>Supplementary Materials</b>.
 All QTL analysis was conducted using the R package R/QTL (Broman
 & Sen, 2009). Interval mapping was conducted via the Expectation
 Maximization algorithm, using a logistic regression model implemented in
 the <i>scanone</i> function of R/QTL. LOD thresholds for
 declaring QTL were determined using 1000 permutations of the phenotypic
 data. Peaks which surpassed the LOD threshold were declared as putative
 QTL, and their genomic positions under a conditional model (with more than
 one QTL) were determined using the <i>refineqtl</i> function.
 For identified QTL, genotype probabilities for each individual were
 estimated by <i>calc.genoprob</i>. Individuals were assigned
 to two genotype classes (<i>Rr, rr</i>) in family E9, and
 assigned to four genotype classes (<i>rr/rr, Rr/rr, rr/Rr,
 Rr/Rr</i>) in family E4. These genotype classes were used as
 categorical variables in generalized linear models.<br> <br>
 The amount of variance explained by each QTL was estimated using the
 <i>fitqtl</i> function in R/QTL, which utilizes a simple
 transformation of the conditional LOD score. Additive effects for QTL
 haplotypes and specific contrasts were estimated using the GLIMMIX
 procedure of SAS/STAT software v9.1 (SAS Institute, Cary NC, 2013). In
 these models, the susceptible haplotype (either one- or two-QTL
 susceptible haplotype) was declared as the reference level.
description: The R/QTL datasets can be read directly into R. They are
 "format = csv". To read the datasets into
 R/QTL, use the function "read.cross", specifying the full
 directory to the file, and using the "format=csv"
 argument.