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\begin{document}

\title{Host plant genetic control of associated fungal and insect species in a
Populus hybrid}



\author[1]{Sandra Simon}%
\author[2]{Timothy Tschaplinski}%
\author[3]{Jared Leboldus}%
\author[4]{Ken Keefover-Ring}%
\author[4]{Muhammad Azeem}%
\author[2]{Jin-Gui Chen}%
\author[1]{David Macaya-Sanz}%
\author[1]{William MacDonald}%
\author[2]{Wellington Muchero}%
\author[2]{Gerald Tuskan}%
\author[1]{Stephen DiFazio}%
\affil[1]{West Virginia University}%
\affil[2]{Oak Ridge National Laboratory}%
\affil[3]{Oregon State University}%
\affil[4]{University of Wisconsin Madison}%


\vspace{-1em}



  
  \date{June 08, 2024}


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\selectlanguage{english}
\begin{abstract}
Plants employ a diverse set of defense mechanisms to mediate
interactions with insects and fungi. These relationships can leave
lasting impacts on host plant genome structure such as rapid expansion
of gene families through tandem duplication. These genomic signatures
provide important clues about the complexities of plant/biotic stress
interactions and evolution. We used a pseudo-backcross hybrid family to
identify Quantitative Trait Loci (QTL) controlling associations between
Populus trees and several common Populus diseases and insects. Using
whole genome sequences from each parent, we identified candidate genes
that may mediate these interactions. Candidates were partially validated
using mass spectrometry to identify corresponding QTL for defensive
compounds. We detected significant QTL for two interacting fungal
pathogens and three insects. The QTL intervals contained candidate genes
potentially involved in physical and chemical mechanisms of host-plant
resistance and susceptibility. In particular, we identified overlapping
QTLs for a phenolic glycoside and Phyllocolpa sawfly abundance. There
was also significant enrichment of recent tandem duplications in the
genomic intervals of the native parent, but not the exotic parent.
Tandem gene duplication may be an important mechanism for rapid response
to biotic stressors, enabling trees with long juvenile periods to reach
maturity despite many coevolving biotic stressors.%
\end{abstract}%



\sloppy


\textbf{Keywords}

Community genetics -- Comparative genomics -- Plant-biotic interactions
-- \emph{Populus --} Tandem duplication

\textbf{1 INTRODUCTION}

In natural ecosystems, the dynamics of plant interactions with other
living organisms are complex. This is especially true for organisms that
rely on plants for shelter, nutrition, and reproduction, such as fungi
and insects (Chisholm, Coaker, Day, \& Staskawicz, 2006; Panda \& Khush,
1995). Although fungi and insects can provide some of the same useful
services in return, those that do not can be extremely harmful to plant
productivity. To mitigate the effects of biotic stress, plants employ a
diverse set of defense mechanisms including chemical, protein-derived
molecules, and physical barriers (Panda \& Khush, 1995). Insects and
fungi must develop strategies in parallel to overcome these obstacles to
survive (Chisholm et al., 2006; Mello \& Silva-Filho, 2002). The theory
of gene-for-gene coevolution has frequently been used to describe this
host plant genetic relationship to its arthropod and fungal communities
(Chisholm et al., 2006; Ehrlich \& Raven, 1964; Mello \& Silva-Filho,
2002; Thompson, 1988).

The gene-for-gene theory suggests a very simple dynamic for the genetic
interactions that occur between two species. A gene in the host plant
that is important in biotic relationships has a corresponding,
coevolving gene from a pathogen/insect which can lead to resistance or
susceptibility depending on the life history of the pathogen/insect
(Flor, 1971; Friesen, Meinhardt, \& Faris, 2007). Much of the evidence
for these interactions has been found in crop systems where plant
species often have dominant, single-gene resistance to feeding
(Thompson, 1988). For example, there are over twenty different genes in
wheat (\emph{Triticum aestivum} L.) that each confer resistance to the
Hessian fly, \emph{Mayetiola destructor} (Thompson \& Burdon, 1992).
Exposure of Hessian fly populations to these resistant varieties of
wheat created selection pressure that led to increased virulent gene
combinations in the pest (Gallun, 1977; Panda \& Khush, 1995).
Similarly, in plant-fungal systems breeding for dominant resistance in
cereal crops resulted in new selective forces that increased virulent
gene frequencies in \emph{Puccinia} spp. cereal rusts (X. M. Chen, 2005;
Pretorius, Singh, Wagoire, \& Payne, 2000). This in turn can lead to an
evolutionary arms race between plants, insects, and fungi with the
continual development of mechanisms to overcome both genetic defenses
and virulent attacks (Bergelson, Kreitman, Stahl, \& Tian, 2001;
Thompson \& Burdon, 1992).

The relationships of host plant genetics and biotic association can also
be more complex than these crop breeding systems suggest, and they can
leave a lasting impact on genome structure (Lefebvre \& Ch\selectlanguage{ngerman}èvre, 1995).
Host plant and biotic associations can lead to the expansion of gene
families responsible for the host plant response to biotic stress. For
example, the Kunitz protease inhibitors (KPIs) in \emph{Populus} are
important in defense responses against insects by inhibition of
herbivore digestion (Haruta, Major, Christopher, Patton, \& Constabel,
2001; Major \& Constabel, 2008). The KPI gene family has greatly
expanded in response to insect attack through tandem duplication events
(Philippe, Ralph, Külheim, Jancsik, \& Bohlmann, 2009). Similarly, plant
resistance (\emph{R} ) genes, which are important in the defense
response of plants to fungal pathogen attack, have also expanded through
tandem and segmental duplication events due to biotic pressures
(Hulbert, Webb, Smith, \& Sun, 2001; Leister, 2004). Analyzing how the
genome is structured in the host plant when it associates with fungi and
insects is important for studying these relationships and understanding
the complexities of their genetic interactions.

Given their rapid growth and vegetative reproduction,
\emph{Populus}species have become a focus for research into biofuel
production making them a valuable commercial crop (Meilan et al., 2002;
Stanton, B. Neale, \& Li, 2010; Taylor, 2002). \emph{Populus} has also
become an important genetic model for research into a wide variety of
ecological and adaptive traits (McKown et al., 2014), including
interactions with the biotic community (Crutsinger et al., 2014; Whitham
et al., 2006). In particular, interspecific hybrids of \emph{P.
trichocarpa} x \emph{P. deltoides} segregate for a wide variety of
traits including resistance to insect and fungal attack (G. Newcombe,
1998; George Newcombe \& Ostry, 2001b). Such hybrid family crosses can
be used to identify regions of the genome that are important in
mediating biotic stress.

In this study, we investigated the genome composition of loci associated
with insect and fungal species in an interspecific \emph{Populus}family.
We surveyed insects and fungal pathogens in a \emph{P. deltoides x P.
trichocarpa} pseudo-backcross family and used quantitative trait locus
(QTL) analysis and comparative genomics to address three main questions:
(1) Is there heritable, host genetic control of fungal and insect
species? (2) What protein domains and gene ontology terms are enriched
in the QTL intervals in the genomes of each \emph{Populus}species? (3)
What candidate genes in the intervals are unique to each species when
comparing the \emph{P. trichocarpa} and \emph{P. deltoides}genomes?

\textbf{2 METHODS}

\textbf{2.1 QTL mapping pedigree}

The 52-124 family was developed by crossing a male \emph{Populus
deltoides} , ILL-101 from southern Illinois, with a female \emph{Populus
trichocarpa} clone, 93-968 from western Washington State. The
F\textsubscript{1} clone, 52-225, was then crossed with a
male\emph{Populus deltoides} clone, D124 from Minnesota, to generate the
final 749 progeny in the pseudo-backcross population.

\textbf{2.2 Field Site Descriptions}

Clonal cuttings of the 52-124 progeny were obtained from the University
of Minnesota and the University of Florida. The individuals were
propagated in the West Virginia University (WVU) greenhouses in March
2006. A hay production field was tilled and disked at the WVU Agronomy
farm (39°39\selectlanguage{english}'32\selectlanguage{english}"N 79deg54\selectlanguage{english}'19\selectlanguage{english}"W) in Morgantown, West Virginia for planting.
In July 2007 the rooted cuttings were planted with two clonal replicates
for each of 749 genotypes at 2 m x 2 m spacing in an interlocking block
design. The plantation was thinned in December of 2008 by removing 50\%
of the trees in a diamond fashion resulting in 2.83 m x 4 m spacing of
the remaining trees. A plantation of the same family cross was
established at the Westport Research Station in the Columbia River
floodplain (46deg07'58.8''N 123deg22'05.0''W) in April, 2016. The trial was
planted with a total of 339 progeny replicated in a randomized complete
block design with three blocks.

\textbf{2.3 SNP genotyping and genetic map construction}

SNP loci were selected from whole genome resequencing data generated for
the parent trees of the pedigree, focusing on loci that were fixed for
different alleles in \emph{P. deltoides} and \emph{P. trichocarpa} .
This yielded loci that were maternally informative for the SNP derived
from\emph{P. trichocarpa} . These were incorporated into an Illumina
Infinium Bead Array which was used to genotype 3,568 SNP loci in 692 of
the progeny. This data was then used to create the genetic map which was
composed of 19 linkage groups corresponding to 19
\emph{Populus}chromosomes. Genotyping and map construction are described
in more detail elsewhere (Muchero et al., 2015).

\textbf{2.4 Family 52-124 Parent and Progeny Phenotyping}

In order to identify regions of the \emph{Populus} genome associated
with biotic stress, a variety of fungal pathogens and insect herbivore
species were surveyed in the WVU Morgantown plantation
site.\emph{Melampsora} sp. leaf rust (Figure 1a) and \emph{Sphaerulina
musiva} fungal leaf spot symptoms (Figure 1b) were scored on a 0-3 scale
of disease severity, with 0 indicating absence of symptoms and 3
indicating high degree of pathogen leaf damage, in the 2008 growing
season for all 1353 tree canopies. In the fall of 2014, stem canker
symptoms caused by the same fungus \emph{Sphaerulina musiva} (Figure 1c)
were scored on a 0-5 disease severity scale for a subset 498 unique
genotypes and a total of 580 trees. Upon further examination of field
conditions for \emph{S. musiva} disease severity it was determined that
none of the progeny displayed complete resistance. The original 0-5
scale was binarized with scores from 2.5-5 scaled to 1 and 0-2 scaled to
0. The new scale indicated the progression of infection with 1
specifying severe canker development and 0 indicating less aggressive
canker symptoms.

Two abundant species of galling aphid were also surveyed during the 2016
growing season. To equalize the biomass surveyed for the aphid insect
observations on each tree, branches of equal diameter were selected for
insect counts and the remaining branches in the canopy were not
surveyed. Tree canopy presence or absence for the petiole galling
aphid\emph{Pemphigus~populitransversus} (Figure 1d) and the branch
galling aphid \emph{Mordwilkoja vagabunda} (Figure 1e) was recorded for
201 unique genotypes and a total of 218 trees. For all of the damage
scores in every survey year, 0 indicated a complete absence of fungal or
insect presence in tree canopy.

In early August 2017 at the Westport site full canopies were scored by
counting galls of the leaf folding sawfly \emph{Phyllocolpa} sp. (Figure
1f), on 534 unique genotypes and a total of 1020 trees. To estimate
productivity of individuals and confirm that availability of resources
did not drive insect attraction or feeding, main stem diameter in
millimeters was recorded for all trees to be used as a covariate in the
analysis.

\textbf{2.5 Leaf Metabolites}

The metabolomic profiles for 211 genotypes of Family 52-124 were
determined for leaves (leaf plastochron index 7 +- 2) sampled on
September 14, 2006 at a USDA-FS field site near Grand Rapids, MI from
plants in their first growing season in the field. Leaves were quickly
collected and flash-frozen on dry ice, prior to shipping back to Oak
Ridge National Laboratory, where \textasciitilde{}200 mg fresh weight
per sample were extracted in 80\% ethanol (aqueous), as described in
Zhao \emph{et al.} , (2010) and analyzed for the major aromatic
metabolites following trimethylsilylation and analysis by gas
chromatography-mass spectrometry (GC-MS) using electron impact
ionization (70 eV). The analytical and data extraction protocols were
similar to that reported previously in Weston \emph{et al.} , (2015).
The major phenolic metabolites quantified included salicortin,
\selectlanguage{greek}α-\selectlanguage{english}salicyloylsalicin, salicyl-salicylic acid-2-O-glucoside, salirepin
(gentisyl alcohol 2-O-glucoside), gentisic acid 5-O-glucoside,
1,2,4-benzenetriol, and gentisyl alcohol 5-O-glucoside.

\textbf{Statistical Analysis}

\textbf{2.6 Within-Family Broad-Sense Heritability
(H\textsuperscript{2}) Calculation}

Surveys of the \emph{Melampsora} sp. leaf rust, \emph{S. musiva} leaf
spot and counts of the \emph{Phyllocolpa} sp. galls had within-garden
microsite variation removed using thin-plate spline regression
(\emph{fields} R package). Residuals of the spatial correction were
added to the mean of each survey dataset for each tree observation to
spatially correct and rescale the values. These corrected values were
then used to calculate the proportion of variance in the fungal and
insect distributions that was due to genotype using a linear-mixed model
(\emph{lmer} function of the \emph{lme4} R package) with insect counts
and fungal scores as the response, tree genotype as the predictor, and
stem diameter biomass estimates as a covariate where applicable.
Broad-sense heritability was calculated as\(\ H^{2}=\ \delta_{g}^{2}/{(\delta}_{g}^{2}+\delta_{e}^{2})\),
where~\(\delta_{g}^{2}\)~is the genetic variance due to genotype
and~\(\delta_{e}^{2}\)~is the residual variance.~Rapid, simulation-based
exact likelihood ratio tests were used to evaluate the significance of
variation due to genotype for each linear model
(\emph{exactRLRT}function of the \emph{RLRsim} R package).

SAS software version 9.4 (2013) was used to test for the normality of
all data sets and for all subsequent statistical analyses,
transformations were conducted when necessary. Data from all chemical
assays were analyzed by single-factor ANOVAs (PROC GLM) to test for
differences between low and high \emph{Phyllocolpa} sp. abundance.

\textbf{2.7 Quantitative Trait Loci (QTL) Analysis}

The R software package \emph{R/qtl} (Broman, Wu, Sen, \& Churchill,
2003) was used for all QTL analyses described below. Composite interval
mapping (CIM) was used to associate \emph{Melampsora} sp. leaf
rust,\emph{S. musiva} leaf spot, and \emph{Phyllocolpa} sp. to QTL
positions (\emph{cim} function). Single QTL mapping was used to
associate the binary scores for the \emph{S. musiva} canker, \emph{M.
vagabunda} and\emph{P. populitransversus} to QTL positions
(\emph{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 (\emph{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 (\emph{effectplot} function).

\textbf{2.8 Physical Genome Intervals}

Physical genome intervals in the \emph{P. trichocarpa} genome (v3.0)
were examined for each significant QTL for biotic associations. The
intervals were defined as 1 Mb regions centered on the marker with the
highest LOD score. Fixed physical genome sizes were used rather than
intervals defined based on LOD scores due to the large variation in
magnitude of LOD observed for the significant QTL. For example,
intervals of 1 LOD centered on the QTL ranged in size from 169 to 4620
kb. Much of this variation was likely due to variation in marker density
and local recombination rates, in addition to phenotyping and genotyping
error. We believe that a fixed 1 Mb interval is a more consistent and
conservative approach. On average, this represents approximately 6.34
cM, based on a total map size of 2617 cM and a total assembled genome
length of 420 Mb.

Orthologous intervals were identified in the \emph{P. deltoides} clone
WV94 reference genome (v2.1) obtained from Phytozome (Goodstein et al.,
2012). Orthology was determined using a combination of protein sequence
conservation and synteny using MCScanX (Wang et al., 2012). Briefly, all
proteins were compared in all-vs-all searches using blastp both within
genomes and between genomes. These were then chained into collinear
segments using the MCScanX algorithm. Orthologous segments were
identified based on the presence of large numbers of gene pairs in
collinear order with high sequence identity (median blastp E score
\textless{}1e-180) (Figure 2). Synonymous (Ks) and nonsynonymous Ka)
nucleotide substitution rates were calculated using the Bioperl
DNAstatistics module (Stajich, 2002) (Table S1), domain composition
(Table S2), and Gene Ontology (GO) terms (Table S3) were obtained for
each genome from Phytozome (v12.1). Intervals were customized for the
grandparents of the pseudo backcross progeny (clones 93-968 for \emph{P.
trichocarpa} and D124 for \emph{P. deltoides} ) using
\textasciitilde{}150X of 2x250 paired end Illumina sequences. These were
aligned to the respective reference genome for each species using bwa
mem with default parameters. SNPs and small indels were identified using
samtools mpileup (Li, 2011; Li et al., 2009), and sequence depth was
extracted using vcftools (Danecek et al., 2011). Sequences were
converted using the vcftools utility vcf-consensus. Genes with no
coverage in the alignments were excluded from the intervals for each
species.

\textbf{2.9 Tandem Duplications}

Tandemly duplicated genes were identified using all-vs-all blastp
searches within each genome for biotic stress associated intervals
(Table S4). Genes with blastp E scores \textless{} 1e-180 that were
located within 500 kb of one another were considered to be recent tandem
duplications. The window size was determined by testing a range of
values and choosing a window size at which the number of
newly-discovered tandem duplicates began to decline (Figure 3). The
stringent E score cutoff was intended to focus the analysis on genes
that are recently duplicated and therefore potentially differentially
duplicated between the species. The QTL intervals were tested for
significant enrichment of tandem duplicates by using a Monte Carlo
simulation. Sets of contiguous genes equal in number to those contained
in each QTL interval were randomly selected from the whole genome, and
the number of sampled tandem duplications was counted for each
iteration. This was repeated 10,000 times, and the observed number of
tandem duplicates was compared to the simulated distribution to derive
an empirical P-value.

\textbf{3 RESULTS}

\textbf{3.1 Heritability of Fungal and Insect Associations}

Clonal repeatability (or within-family broad-sense heritability) was
estimated for each categorical survey trait (Table 1). There was a
significant host plant genetic contribution of moderate effect
controlling the association of \emph{S. musiva} leaf spot disease
severity (H\textsuperscript{2} = 0.227, p-value \textless{} 0.0001).
There was a strong effect of host plant genetics on associations
of\emph{Melampsora} sp. disease severity (H\textsuperscript{2} = 0.631,
p-value \textless{} 0.0001) and the continuous count of leaf-folding
galls of \emph{Phyllocolpa} sp. (H\textsuperscript{2} = 0.391, p-value
\textless{} 0.0001).

\textbf{3.2 QTL Mapping of Fungal and Insect Surveys}

Three QTL intervals containing a total of 26 markers were significantly
associated with the abundance of fungal pathogens (Table 2, Figure
4a,b). An overlapping QTL on Chr04 (Figure 4a) was associated with
both\emph{Melampsora} sp. (marker position = 3.50687, p-value = 0.001)
and\emph{S. musiva} leaf spot disease severity (marker position =
15.4912824, p-value = 0.026). The marker with the highest LOD score
explained 54.1\% of the variance in the disease severity
of\emph{Melampsora} sp. within the Chr04 QTL interval, whereas the top
marker within the overlapping interval only explained 3.31\% of the
variance in the disease severity of \emph{S. musiva} . The positive
allele contributing to an increase in fungal infection for both species
was derived from \emph{P. trichocarpa} . Binary presence of the canker
symptoms caused by \emph{S. musiva} (Figure 4b) was found to be
associated with a QTL located on Chr16 (marker position = 60.81429,
p-value = 0.022). The positive allele contributing to increase in
presence of \emph{S. musiva} canker was from \emph{P. trichocarpa} .

Three QTL intervals containing a total of 40 markers were significantly
associated with insect abundance (Table 2, Figure 4c,d). A QTL on Chr05
(marker position = 21.83874, p-value = 0.001) was associated with binary
presence of \emph{M. vagabunda} branch galls (Figure 4c). The positive
allele contributing to an increase in presence of \emph{M. vagabunda}was
from \emph{P. trichocarpa} . Two QTLs, one located on Chr10 (marker
position = 57.69914343, p-value = 0.015) and one located on Chr13
(marker position = 84.43857329, p-value = 0.045) were associated with
the number of leaf-folding galls from \emph{Phyllocolpa} sp. (Figure
4d). The marker with the highest LOD score explained 9.65\% of the
variance in oviposition gall count while 8.82\% of the variance was
explained by the top marker located on Chr13. For both markers, the
positive allele contributing to an increase in the number of female
oviposition galls was from \emph{P. deltoides} . There were no QTL
intervals that passed the permutation threshold for the binary presence
of \emph{P. populitransversus} petiole galls (Figure 4c).

\textbf{3.3 QTL Mapping of Leaf Metabolites}

Only one tested compound, gentisyl alcohol 5-O-glucoside levels, yielded
significant QTL. Three QTL intervals containing a total of 56
significant markers were significantly associated with gentisyl alcohol
5-O-glucoside levels (Table 2, Figure 4d). This included markers on
chromosome 17 (marker position = 21.54739208, p-value \textless{}
0.0001) which explained 25.8\% of variation, Chr10 (marker position =
109.8469079, p-value \textless{} 0.0001) which explained 23.1\% of
variation, and chromosome 14 (marker position = 91.51828521, p-value =
0.0100) which explained 7.30\% of variation. The positive allele that
contributed to an increase in gentisyl alcohol 5-O-glucoside levels for
all markers was derived from \emph{P. trichocarpa} .

\textbf{3.4 Interaction between \emph{Melampsora} sp. and \emph{S.
musiva} Leaf Infection}

\emph{Melampsora} sp. leaf rust infection severity was dependent upon
the disease severity of \emph{S. musiva} leaf spot symptoms (F = 35.2,
p-value = 0.0001) (Table 3). The severity of \emph{Melampsora} sp.
infection for individuals with no \emph{S. musiva} leaf spot was
significantly higher than for individuals with \emph{S. musiva} leaf
spot symptoms (Figure 5). Similarly, \emph{S. musiva} leaf spot disease
severity was dependent upon the levels of \emph{Melampsora} sp. leaf
rust (F = 31.5, p-value \emph{=} 0.0001). \emph{S. musiva} leaf spot
infection severity was significantly lower for individuals with
a\emph{Melampsora} sp. leaf spot score of 3 compared to individuals with
less severe \emph{Melampsora} sp. leaf spot symptoms (Figure 5).

\textbf{3.5 PFAM, GO Term and Tandem Duplication Enrichment}

The total number of genes present in the QTL intervals of each parental
species was 174 on Chr04, 81 on Chr16, 161 on Chr10, 445 on Chr13, and
156 on Chr05 (Table 4). There were 40 PFAM domains enriched relative to
the rest of the \emph{P. trichocarpa} genome across all QTL intervals
(alpha = 0.05; p-value threshold \textless{} 0.00014), and 25 PFAM
domains enriched in the \emph{P. deltoides} QTL intervals (alpha = 0.05,
p-value \textless{} 0.000059) (Table S2). Additionally, there were 21 GO
terms enriched in the \emph{P. trichocarpa} intervals (alpha = 0.05,
p-value \textless{} 0.000226) and 46 GO terms enriched in the \emph{P.
deltoides} intervals (alpha = 0.05, p-value \textless{} 0.000214) (Table
S3). Finally seven of the genes in these intervals showed evidence of
positive selection based on the ratio of nonsynonymous to synonymous
nucleotide substitutions between the \emph{P. deltoides} and \emph{P.
trichocarpa} orthologs (Table 5).

In total, there were 107 recent tandem duplicates in the \emph{P.
trichocarpa} intervals and 195 in the \emph{P. deltoides} intervals for
all biotic QTLs (Figure 6, Table 6). This includes 23 and 41 recent
tandem duplicates for the \emph{Melampsora sp.} intervals; 6 and 10 for
the \emph{S. musiva} intervals; 32 and 96 for the \emph{Phyllocolpa} sp.
intervals; and 47 and 46 for the \emph{M. vagabunda} intervals,
for\emph{P. trichocarpa} and \emph{P. deltoides} , respectively. The
total number of recent tandem duplicates was significantly enriched
relative to tandem counts for random intervals of the same size as the
QTL intervals for the \emph{P. deltoides} grandparent (p-value = 0.0118)
but not for the \emph{P. trichocarpa} grandparent (p-value = 0.1191).

\textbf{4 DISCUSSION}

The goal of our research was to utilize QTL analysis as a tool to
identify regions of the \emph{Populus} genome that were important in
mediating biotic interactions. Upon identification of these regions we
were able to directly compare the parental genomes of the hybrid cross
to look for similarity in content and potential gene-for-gene
interactions reflected in recent tandem duplication expansion. We found
that the host plant genotype had a significant effect on fungi and
insects in our study. The progeny segregated for varying resistance to
fungal and insect pathogens and pests that were inherited from the
grandparents and parents of the 52-124 family cross. Novel alleles (i.e.
those from the grandparent that was not native to the region where the
trials occurred) were important for resistance to \emph{Melampsora} sp.
fungus, \emph{M. vagabunda} galls, and \emph{Phyllocolpa} sp.
oviposition. In contrast, the interval associated with \emph{S.
musiva}canker symptoms was the only case in which susceptibility to the
fungus was dominant and inherited from the non-coevolved host (\emph{P.
trichocarpa} ), as has been previously observed (Muchero et al., 2018;
George Newcombe \& Ostry, 2001a).

We detected a major QTL for \emph{Melampsora sp.} resistance on Chr04.
This region is known to contain the \emph{MXC3} locus which confers
resistance to infection of many species of the \emph{Melampsora} leaf
rusts (George Newcombe, Stirling, \& Bradshaw, 2001; Yin et al., 2004).
Based on mean parental infection scores and the allelic effects at the
QTL, progeny in family 52-124 inherited this resistance from the\emph{P.
trichocarpa} grandmother 93-968. Furthermore, the QTL interval contained
two tandem repeats of stigma-specific proteins (Stig1) in both the
\emph{P. trichocarpa} genome and the orthologous interval in the\emph{P.
deltoides} genome. Several of these showed evidence of positive
selection based on Ka/Ks ratios (Table 5). Stigma-specific proteins,
specifically Stig1, have been found to be associated with female
sterility in tobacco (\emph{Nicotiana tabacum} ) and petunia
(\emph{Petunia hybrida} ) (Goldman, Goldberg, \& Mariani, 1994;
Verhoeven et al., 2005). Stig1 is known to mediate secretion of exudate
lipids in the intercellular spaces and high expression of the protein
inhibits pollen grains from penetrating style tissue preventing
fertilization (Verhoeven et al., 2005).~Most lipid transfer proteins,
such as Stig1, are important in plant cell-wall loosening and their
expression can prevent penetration of plant tissues (Nieuwland et al.,
2005). Diversification of the protein family containing Stig1 may play
an important role in lowering fungal infection in \emph{Populus} by
providing a physical barrier to resist the \emph{Melampsora} sp. hyphae.
Another protein domain that was found to be enriched in the \emph{P.
trichocarpa} Chr04 interval was the malonyl-CoA decarboxylase C-terminal
domain. Similarly, the gene ontology function for malonyl-CoA
decarboxylase activity was also enriched in \emph{P. trichocarpa} in the
same interval. Genes which are capable of transforming malonyl-CoA could
be beneficial in resistance to \emph{Melampsora} sp. as it is an
important precursor in the production of several pathogen defensive
compounds, such as isoprenoids~in the Mevalonate pathway (H. Chen, Kim,
Weng, \& Browse, 2011; Dixon, 2001).

Interestingly, an overlapping interval on Chr04 was found to be
associated with the activity of the leaf spot symptoms of the \emph{S.
musiva} fungus. Upon further investigation we found that trees that were
not infected by the \emph{S. musiva} leaf spot had more severe symptoms
of the \emph{Melampsora} sp. leaf rust. This suggests that a competitive
interaction occurred between the two pathogens in the field during the
year of survey, and was reflected in a false association on Chr04 with
the \emph{S. musiva} leaf spot score. Competition among fungal pathogens
is not uncommon in field conditions with most examples focused on
different genotypes within the same fungal species (Abdullah et al.,
2017). The outcome of within host tissue colonization of multiple
strains or species often relies upon the genetic similarity of the
pathogens (Abdullah et al., 2017; Koskella, Giraud, \& Hood, 2006). In
our case, we had two very different pathogens that were utilizing the
leaf tissue in vastly dissimilar ways. \emph{S. musiva} is a
necrotrophic fungus, which requires dead tissue to reproduce,
and\emph{Melampsora} sp. is biotrophic, requiring living tissue to
reproduce. The presence of \emph{S. musiva} on the trees had a much
larger effect on the occurrence of \emph{Melampsora} sp. infection. This
may indicate that the \emph{S. musiva} fungus had a competitive
advantage over \emph{Melampsora} sp. in utilization of the\emph{Populus}
leaf tissue at our site. Further supporting this finding was the
complete loss of \emph{Melampsora} sp. symptoms at the plantation site
over the course of ten years and the continual presence of the \emph{S.
musiva} leaf spot. A similar interaction has been reported in wheat
between the biotroph \emph{Blumeria graminis} ~f.sp.~\emph{tritici}
~(\emph{Bgt} ), the powdery mildew pathogen, and necrotroph
\emph{Zymoseptoria tritici} , the cause of\emph{Septoria tritici} blotch
(Orton \& Brown, 2016). Similarly, the outcome of the interaction of the
two pathogens in wheat was competitive. However, the necrotroph was
found to actually be capable of reducing the reproductive capability of
the biotroph which indicates pathogen-pathogen interactions can be more
direct rather than relying solely on the host plant genetics (Orton \&
Brown, 2016).

The \emph{S. musiva} stem canker symptoms were found to be associated
with a QTL interval on Chr16. In this study, susceptibility to the
necrotroph appeared to be originating from the presence of \emph{P.
trichocarpa} alleles in the progeny. Previous work on a
similar\emph{Populus} hybrid cross supports this with susceptibility
to\emph{S. musiva} stem canker originating primarily from dominant
alleles derived from \emph{P. trichocarpa} (G. Newcombe, 1998; George
Newcombe \& Ostry, 2001a). Interestingly, the loci conferring
susceptibility in family 52-124 did not overlap with the four loci that
were uncovered in a previous genome-wide association study of \emph{S.
musiva}susceptibility in \emph{P. trichocarpa} (Muchero et al., 2018),
suggesting that different mechanisms may be involved in hybrid
interactions with this pathogen. However, the QTL did contain a tandem
repeat of a G-type lectin receptor-like protein kinase that was expanded
in \emph{P. deltoides} . This protein could play a similar role to a
receptor-like kinase from the same family (Yang et al., 2016) that was
associated with susceptibility to \emph{S. musiva} in the \emph{P.
trichocarpa} study (Muchero et al., 2018).

\emph{M. vagabunda} has been recorded completing its life cycle on
several species of \emph{Populus} including \emph{P. deltoides}
and\emph{P. tremuloides} (Floate, 2010). Although the aphid's life
history has been documented, little is known about the influence of host
plant genetics on gall formation or resistance to feeding (Floate, 2010;
Ignoffo \& Granovsky, 1961). We detected a QTL on Chr05 in which alleles
inherited from \emph{P. deltoides} were positively associated with gall
occurrence. Genes conferring lipoxygenase and oxidoreductase activity
were enriched in both the \emph{P. deltoides} as well as the \emph{P.
trichocarpa} QTL intervals. Lipoxygenase genes are known to be
associated with the \emph{Populus} response to both abiotic and biotic
stressors (Cheng et al., 2006; Ralph et al., 2006). They are often
upregulated in the presence of mechanical damage, fungal pathogen
invasion, and exposure to simulated insect feeding (F. Chen, Liu,
Tschaplinski, \& Zhao, 2009; Cheng et al., 2006). The lipoxygenases are
important in the formation of jasmonic acid, the signaling molecule that
upregulates plant defenses against herbivore feeding (F. Chen et al.,
2009).

The \emph{M. vagabunda} QTL interval on Chr05 also contained a tandem
array of resistance genes (R-genes) that encoded disease resistance
proteins (TIR-NBS-LRR class) that was greatly expanded in \emph{P.
trichocarpa} compared to \emph{P. deltoides} , as well as repeats of the
leucine-rich repeat protein kinase family proteins in both species.
These protein families are well known for their roles in the recognition
and upregulation of host plant defenses against bacterial and fungal
infection (Bergelson et al., 2001; Martin, Bogdanove, \& Sessa, 2003).
Diversification of their function through tandem duplication due to
gene-for-gene coevolution has also been demonstrated in many
plant-fungal pathosystems (Leister, 2004). Although R-genes have not
been recognized as frequently in plant-insect interactions, those that
have been discovered typically mediate interactions with
piercing-sucking insects such as aphids and whiteflies (Kaloshian,
2004).

In addition to the R-genes, there were a series of genes encoding
cytochrome P450 family proteins in both species as well as a unique
tandem set that was only present in the \emph{P. trichocarpa} genome.
P450 enzymes are important in the production of many classes of
secondary metabolites such as furanocoumarins and terpenoids which are
highly toxic to insects (Keeling \& Bohlmann, 2006; Schuler, 2011).
Alternatively, cytochrome P450's may also be implicated in the
susceptibility of the host plant to galling aphids. They are important
in the synthesis of fatty acids and production of suberin in plant
tissues (H\selectlanguage{ngerman}öfer et al., 2008; Pinot \& Beisson, 2011). Typically, suberin
is important in separation of different tissues as well as in the
establishment of apoplastic barriers that restrict nutrient/water loss
as well as pathogen invasion (Höfer et al., 2008; Qin \& LeBoldus,
2014). Several insects are known to produce suberized spherical galls on
leaves including hymenopteran pests of Rosacea species and dipteran
pests of Fabaceae (Krishnan \& Franceschi, 1988; Oliveira et al., 2016).
If aphids induce the suberization mechanism of the plant genome it may
lead to the increased toughening of aphid galls, much like the woody
structures \emph{M. vagabunda} leaves behind on branches once they have
finished feeding.

We detected two QTL for \emph{Phyllocolpa} sp. with \emph{P.
trichocarpa} alleles being positively associated with leaf fold
occurrence in both cases. The \emph{P. trichocarpa} genomic interval
corresponding to the Chr10 QTL was enriched for several domains and GO
terms that could be involved in gall development. For example, the
interval was enriched for sugar transporters as well as GO terms for
sucrose transporter activity. Often in the case of galling insects plant
tissue is modified in such a way as to act as a sugar sink, thereby
enhancing its nutritional value to larvae (Larson \& Whitham, 1991;
Nyman, Widmer, \& Roininen, 2000; Wool, 2004) . The presence of these
combinations of sugar transporter genes may be mediating a similar
interaction between the \emph{Phyllocolpa} sp. female sawflies and their
chosen \emph{Populus} hosts.

\emph{Phyllocolpa} sp. galls are formed early in the season when a
female sawfly selects a leaf and injects the longitudinal fold with
small amounts of fluid on the underside of young leaves (Fritz \& Price,
1988; Kopelke, 2007). The adult sawfly will proceed to oviposit near the
base of the leaf and after 1-2 days the leaf fold forms and the newly
hatched larvae feeds on the inside of the gall (Smith \& Fritz, 1996).
The \emph{Phyllocolpa} sp. galls were a unique biotic phenotype to this
study as they were an estimate of female sawfly ovipositional choice
rather than feeding success. Host selection for oviposition is initially
driven by visual cues and reinforced by females assessing the nutrition
and chemical cues of foliage (Boeckler, Gershenzon, \& Unsicker, 2011;
Panda \& Khush, 1995). Previous research in \emph{Salix} (closely
related to \emph{Populus} ) has shown that common phenolic glycosides in
leaf tissue are important in host choice in both the
free-feeding\emph{Nematus oligospilus} and galling \emph{Euura amerinae}
specialist sawflies (Fernández et al., 2019; Kolehmainen, Roininen,
Julkunen-Tiitto, \& Tahvanainen, 1994). We therefore pursued QTL mapping
of metabolites to determine if phenolic compounds might be important
determinants of the potential ovipositional relationship.

We detected three significant QTL for gentisyl alcohol 5-O-glucoside
levels. One of these overlapped with the \emph{Phyllocolpa} sp. leaf
fold QTL on Chr10. The QTL on Chr14 also overlapped with a suggestive
QTL peak for \emph{Phyllocolpa sp} . (LOD=3.96). This is the first case
of an association of gentisyl alcohol 5-\emph{O} -glucoside with a
specialist arthropod in \emph{Populus} , and is, in fact, the first
report of this metabolite in \emph{Populus} . Salirepin (gentisyl
alcohol 2-\emph{O} -glucoside), a closely related metabolite, is a
well-known constituent in \emph{Populus} sp, (Busov et al., 2006;
Tschaplinski et al., 2019; Veach et al., 2018) and leaves of \emph{P.
deltoides} and\emph{P. trichocarpa x deltoides} have a lower abundance,
later eluting metabolite with a nearly identical fragmentation pattern
to salirepin that we tentatively identify as gentisyl alcohol 2-\emph{O}
-glucoside. The QTL contains three putative candidate genes, including
an aldehyde dehydrogenase 5F1 (Podel.10G175800) that may be involved in
the reduction of gentisyl aldehyde to the alcohol, and two
UDP-glycosyltransferases (Podel.10G184800, Podel.10G185000) that may be
involved in the gentisyl alcohol conjugation to glucose. Specific
substrates have yet to be determined for these genes, but a previous
report suggests aldehyde dehydrogenase 5F1 genes are likely involved in
the basic metabolism

of \emph{Populus} (Tian et al., 2015).

\emph{Phyllocolpa} sp. sawflies are considered a keystone species as the
abandoned or unused leaf folds are often used as a habitat for many
other species such as aphids and spiders (Bailey \& Whitham, 2007). The
presence of folds in aspen forests is associated with a two-fold
increase in arthropod species richness and around a four-fold increase
in arthropod abundance relative to forests where the insect is absent
(Bailey \& Whitham, 2003). This in turn makes the host plant and sawfly
relationship important in examining how shifts in the genes of a
population ultimately structure whole communities, effectively linking
ecology and evolutionary biology. Further investigation of this
potential relationship could be key to connecting \emph{Populus}genetics
to the assemblage of the surrounding communities of organisms.

A striking finding in this study was enrichment of recent tandem
duplications in the \emph{P. deltoides} genome but not the \emph{P.
trichocarpa} genome for the biotic QTL intervals. Out of the six
chromosomes that yielded significant QTL results, four were associated
with phenotypes that were fungi and insects native to the distribution
of \emph{P. deltoides} , but not \emph{P. trichocarpa} . Given
that\emph{P. deltoides} has been co-evolving with the majority of the
surveyed fungi and insects, it was not unexpected that there were more
recent tandem duplicates in biotic intervals in its genome as there is
more selective pressure on the native species to overcome biotic stress
(Constabel \& Lindroth, 2010; George Newcombe, Martin, \& Kohler, 2010).
However, given the high amount of novel resistance occurring in the
progeny, recent tandem duplication may also be important in naïve host
resistance.

Our study has demonstrated that many recent tandem duplications, found
across biotic stress QTL intervals, have functional annotations that are
involved in physical, chemical, and tolerance mechanisms of host plant
resistance as well as a few that may be implicated in host plant
susceptibility. The enrichment of recent tandem duplications may be a
signature of gene-for-gene interactions, and a mechanism that protects
long-lived plants such as trees, enabling them to reach maturity despite
many coevolving biotic stressors. In addition to the direct effect of
the host plant genetics on associated organisms, we have also
demonstrated an indirect community effect that was mediated by
the\emph{Populus} progeny (Whitham et al., 2006). The fungal pathogen
succession highlights the complexities of how host plant-pathogen
coevolution can affect multiple species interactions in more subtle ways
than expected in a natural environment.

\textbf{ACKNOWLEDGEMENTS}

We would like to thank Tanita Cheevaphantusri, Luke Evans, Nesatalu
Hiese, Jacob Miller, Kathleen Haiby, and Richard Shuren for help with
field work. This work was supported by BioEnergy Science Center, Center
for Bioenergy Innovation and the Plant-Microbe Interfaces Scientific
Focus Area by the Office of Biological and Environmental Research in the
U.S. Department of Energy Office of Science. Oak Ridge National
Laboratory is managed by UT-Battelle, LLC, for the United States
Department of Energy under contract DE-AC05-00OR22725. This study was
supported by Department of Energy (DOE) Office of Science, Office of
Biological and Environmental Research (BER) Grant DE-SC0018196, US
Department of Agriculture Grant 2012-34103-19771 (to J.M.L.), and the
National Science Foundation Dimensions of Biodiversity Program (1542509
to S.D. and 1542479 to K.K.-R.).

\textbf{CONFLICT OF INTEREST}

None declared.

\textbf{AUTHORS' CONTRIBUTIONS}

S.S., S.D., and D.M.-S. identified, collected, and analyzed insect
surveys. W.M., J.L., J.C., and W.M. identified and collected fungal
species datasets. K.K.-R., M.A., and T.T. collected/analyzed metabolite
data. S.S. and S.D. analyzed the data. W.M., G.T. and S.D. designed and
supervised the study. S.S., S.D., J.L., T.T., and K.K.-R. wrote the
manuscript.

\textbf{DATA ACCESSIBILITY}

DNA sequences are deposited at NCBI Sequence Read Archive under the
following accession numbers: SRP112750, SRP109803 and SRP190316. The
annotated \emph{P. deltoides} clone WV94 genome assembly is available
through Phytozome (https://phytozome.jgi.doe.gov/pz/portal.html).

Table 1. Linear-mixed model output for biotic surveys. Broad-sense
heritability (\emph{H} \textsuperscript{2}) denotes the contribution of
all host plant genetic factors to total variance in the biotic
phenotype. R package RLRsim exactRLRT function was used to test
significance of effects in mixed-model.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Fungus/Insect & \emph{H}\textsuperscript{2} & Genetic variance & Error
variance & RLRatio & p-value\tabularnewline
\midrule
\endhead
\emph{Melampsora sp.} leaf rust & 0.622 & 0.631 & 0.382 & 294 &
\textless{} 0.0001\tabularnewline
\emph{S. musiva} leaf spot & 0.227 & 0.099 & 0.337 & 32.0 & \textless{}
0.0001\tabularnewline
\emph{Phyllocolpa} sp. & 0.391 & 11.9 & 18.5 & 129 & \textless{}
0.0001\tabularnewline
\bottomrule
\end{longtable}

Table 2. Summary of QTL permutation test output. Percent variance in
surveys for nsects and fungi explained by significant marker indicated
for composite interval mapping models. Positive (+) allele specifies
genotype at significant interval that results in an increase in
susceptibility. D indicates progeny are homozygous for \emph{P.
deltoides} alleles and T indicates progeny are heterozygous for \emph{P.
deltoides} and \emph{P. trichocarpa} alleles.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Model\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Chrom.\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Position\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
LOD score\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
p-value\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\% variance\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
+ allele\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Garden\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Melampsora} sp. leaf rust\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chr04\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.50\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
15.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.001\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
54.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
D\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Morgantown\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{S. musiva} leaf spot\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chr04\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
15.49\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.75\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.026\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.31\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
D\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Morgantown\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{S. musiva} canker\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chr16\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
60.81\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.34\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.022\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
T\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Morgantown\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{M. vagabunda}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chr05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
21.83\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5.39\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.001\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
D\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Morgantown\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{P. populitransversus}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chr03\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
83.06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.70\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textgreater{}0.05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Morgantown\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Phyllocolpa} sp.\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chr10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
57.69\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6.60\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.015\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9.65\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
T\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Westport\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Phyllocolpa} sp.\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chr13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
84.43\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.99\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.045\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
8.82\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
T\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Westport\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Gentisyl alcohol 5-O-glucoside\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chr17\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
21.54\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
12.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textless{}0.0001\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
25.760\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
T\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Grand Rapids\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Gentisyl alcohol 5-O-glucoside\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chr10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
109.84\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textless{}0.0001\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
23.104\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
T\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Grand Rapids\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Gentisyl alcohol 5-O-glucoside\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chr14\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
91.51\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5.96\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.0100\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
7.296\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
T\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Grand Rapids\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Table 3. One-way ANOVA, with Genotype as a covariate, analyzing the
effect of infection severity of the genus of one fungus on the infection
severity of the competing leaf fungi in 2008.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Factor\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
DF\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Sum of squares\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Mean square\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
F-ratio\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
p-value\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Dependent Variable- \emph{Melampsora} sp. leaf rust infection\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Dependent Variable- \emph{Melampsora} sp. leaf rust infection\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Dependent Variable- \emph{Melampsora} sp. leaf rust infection\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Dependent Variable- \emph{Melampsora} sp. leaf rust infection\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Dependent Variable- \emph{Melampsora} sp. leaf rust infection\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Dependent Variable- \emph{Melampsora} sp. leaf rust infection\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Genotype\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
732\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1120\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.53\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4.69\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textless{}0.0001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\emph{S. musiva} score\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
34.4\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
11.5\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
35.2\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textless{}0.0001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Residuals\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
602\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
197\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.326\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Dependent Variable- \emph{S. musiva} leaf spot infection\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Dependent Variable- \emph{S. musiva} leaf spot infection\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Dependent Variable- \emph{S. musiva} leaf spot infection\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Dependent Variable- \emph{S. musiva} leaf spot infection\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Dependent Variable- \emph{S. musiva} leaf spot infection\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Dependent Variable- \emph{S. musiva} leaf spot infection\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Genotype\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
732\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
379\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.518\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.77\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textless{}0.0001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\emph{Melampsora} sp. score\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
27.6\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
9.19\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
31.5\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textless{}0.0001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Residuals\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
602\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
176\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.292\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Table 4. Number of genes in QTL intervals in parental genomes for biotic
associations.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Fungus/Insect & QTL Interval & \# Genes in \emph{P. trichocarpa} & \#
Genes in \emph{P. deltoides}\tabularnewline
\midrule
\endhead
\emph{Melampsora} sp. & Chr04 & 159 & 147\tabularnewline
\emph{S. musiva} canker & Chr16 & 71 & 68\tabularnewline
\emph{Phyllocolpa} sp. & Chr10 & 146 & 154\tabularnewline
\emph{Phyllocolpa} sp. & Chr13 & 252 & 425\tabularnewline
\emph{M. vagabunda} & Chr05 & 129 & 147\tabularnewline
\bottomrule
\end{longtable}

Table 5. Candidate genes under positive selection (Ka/Ks\textgreater{}1)
in genetic intervals associated with fungi and insects.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
QTL\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
Arabidopsis function\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\emph{P. trichocarpa} gene\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\emph{P. deltoides} gene\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
Ka/Ks\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Chr04\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Chitin elicitor receptor kinase 1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Potri.004G005800\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Podel.04G004900.1.p\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.35\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Chr04\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Cysteine-rich RLK (RECEPTOR-like protein kinase)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Potri.004G012600\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Podel.04G010900.1.p\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.23\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Chr04\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Stigma-specific Stig1 family protein\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Potri.004G006800\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Podel.04G005700.1.p\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.31\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Chr04\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Stigma-specific Stig1 family protein\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Potri.004G007200.1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Podel.04G006100.1.p\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.00\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Chr04\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Stigma-specific Stig1 family protein\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Potri.004G007200\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Podel.04G006100.1.p\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.00\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Chr05\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Pentatricopeptide repeat (PPR) superfamily protein\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Potri.005G038500.1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Podel.05G042200.1.p\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.52\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Chr10\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Copper amine oxidase family protein\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Potri.010G088700\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Podel.10G084500.1.p\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.19\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Table 6. Tandem duplication profiles for genetic intervals. Number of
copies next to species gene name indicates the size of tandem expansion
for the gene.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Arabidopsis function & Arabidopsis function & P. trichocarpa gene & Copy
\# & P. deltoides gene & Copy \# & QTL Chr\tabularnewline
\midrule
\endhead
ERD (early- responsive to dehydration stress) family protein & ERD
(early- responsive to dehydration stress) family protein &
Potri.004G005900 & 3 & Podel.04G005000.1.p & 3 & Chr04\tabularnewline
Pentatricopeptide repeat (PPR) superfamily protein & Pentatricopeptide
repeat (PPR) superfamily protein & NA & 0 & Podel.04G010100.1.p & 2 &
Chr04\tabularnewline
Peroxidase superfamily protein & Peroxidase superfamily protein &
Potri.004G015300 & 1 & Podel.04G013500.1.p & & Chr04\tabularnewline
Disease resistance protein (TIR-NBS- LRR class) & Disease resistance
protein (TIR-NBS- LRR class) & Potri.005G031900 & 7 &
Podel.05G035500.1.p & 12 & Chr05\tabularnewline
Shikimate O- Hydroxycinnamoyl- transferase & Shikimate O-
Hydroxycinnamoyl- transferase & Potri.005G028000 & 3 &
Podel.05G029500.1.p & 2 & Chr05\tabularnewline
Protein tyrosine kinase (Pkinase\_Tyr) Leucine rich repeat (LRR\_8) &
Protein tyrosine kinase (Pkinase\_Tyr) Leucine rich repeat (LRR\_8) &
Potri.005G030600 & 4 & Podel.05G030600.1.p & 2 & Chr05\tabularnewline
Lipoxygenase & Lipoxygenase & Potri.005G032400 & 4 & Podel.05G037200.1.p
& 3 & Chr05\tabularnewline
Cytochrome P450, family 721, subfamily A, polypeptide 1 & Cytochrome
P450, family 721, subfamily A, polypeptide 1 & Potri.005G034500 & 2 &
Podel.05G039300.1.p & 1 & Chr05\tabularnewline
Cytochrome P450, family 76, subfamily G, polypeptide 1 & Cytochrome
P450, family 76, subfamily G, polypeptide 1 & Potri.005G029200 & 5 &
Podel.05G034500.1.p & 0 & Chr05\tabularnewline
Cytochrome P450, family 93, subfamily D, polypeptide 1 &
Potri.005G037100 & 2 & NA & 0 & Chr05 &\tabularnewline
Cytochrome P450, family 76, subfamily G, polypeptide 1 & NA & 0 &
Podel.05G034500.1.p & 4 & Chr05 &\tabularnewline
Disease resistance protein (TIR-NBS class), putative & Potri.005G032000
& 1 & Podel.05G035800.1.p & 12 & Chr05 &\tabularnewline
Receptor-like kinase in flowers & Potri.005G040200 & 1 &
Podel.05G043500.1.p & 2 & Chr05 &\tabularnewline
Pentatricopeptide repeat (PPR) superfamily protein & Potri.010G083800 &
3 & Podel.10G079300.1.p & 3 & Chr10 &\tabularnewline
Copper amine oxidase family protein & Potri.010G088800 & 3 & NA & 0 &
Chr10 &\tabularnewline
Ankyrin repeat family protein & Potri.013G133400 & 5 &
Podel.13G142400.1.p & 10 & Chr13 &\tabularnewline
O-methyltransferase family protein & Potri.013G143700 & 4 &
Podel.13G181100.1.p & 1 & Chr13 &\tabularnewline
O-methyltransferase family protein & NA & 0 & Podel.13G144000.1.p & 4 &
Chr13 &\tabularnewline
G-type lectin receptor-like protein kinase & Potri.016G102500 & 4 &
Podel.16G106900.1.p & 4 & Chr16 &\tabularnewline
\bottomrule
\end{longtable}

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