Genome-wide SNP identification in Fraxinus linking genetic characteristics to tolerance of Agrilus planipennis

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Hale, Mark A. Jordan, Gloria Iriarte, Andrew J. Storer, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.2.14056/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Sep, 2021 Read the published version in Ecology and Evolution → Version 1 posted You are reading this latest preprint version Abstract Background Ash ( Fraxinus spp.) is one of the most widely distributed tree genera in North America. Populations of ash in the United States and Canada have been decimated by the introduced pest, Agrilus planipennis (Coleoptera: Buprestidae; emerald ash borer), having both negative impacts on forest ecosystems and economic interests. The majority of trees succumb to attack by A. planipennis , but some trees have been found to be tolerant to infestation despite years of exposure. Restriction site-associated DNA (RAD) sequencing was used to sequence ash individuals, both tolerant and susceptible to A. planipennis attack, in order to identify SNP patterns related to tolerance and health declines. Results A de novo reference genome was assembled and single nucleotide polymorphisms (SNPs) were called using SAMtools. After filtering criteria were implemented, a set of 17,807 SNPs were generated. Principle component analysis (PCA) of SNPs aligned individual trees into clusters related to geography, however, five tolerant trees clustered together despite geographic diversity. A subset of 32 outlier SNPs identified within this group, as well as a subset of 17 SNPs identified based on vigor rating, are candidates for selection on host tolerance. Conclusions Identifying genetic markers associated with host tolerance through genome-wide association has the potential to restore populations with cultivars that are able to withstand A. planipennis infestation. This study was successful in using RAD-sequencing in order to identify SNPs that are potential candidates to identify tolerance to A. planipennis . This was a first step toward uncovering the genetic basis for host tolerance to A. planipennis . Future studies are needed to identify the functionality of the loci where these SNPs occur and how they may be related to tolerance of A. planipennis attack. Epigenetics & Genomics Ash emerald ash borer RAD-seq tolerance survival Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Agrilus planipennis Fairmaire (Coleoptera: Buprestidae; emerald ash borer) is a metallic green beetle native to northeastern Asia that has become a pest to North American ash ( Fraxinus spp. L.) [1]. This pest was introduced into the Detroit/Windsor area of Michigan, USA/Ontario, Canada, and quickly dispersed via human assistance, including movement of firewood, nursery stock, and wood packing material [2,3]. In its native range, A. planipennis coevolved with Manchurian ash ( F. mandshurica Rupr.) and is a secondary pest in this tree species requiring a primary stressor for successful attack [4,5]. Ash species in North America lack this natural resistance and succumb to attack, regardless of the presence of a primary stressor, often within one to four years after initial attack [4,6]. While black ( F. nigra Marsh.), green ( F. pennsylvanica Marsh.), and white ( F. americana L.) ash are the most susceptible in the introduced range of A. planipennis, all North American ash are susceptible [2,7,8]. Blue ash ( F. quadrangulata Michx.) has the lowest susceptibility to A. planipennis attack among North American ash species [9]. In North America, the life cycle of A. planipennis is typically completed within one year [10]. Females and males mature as they feed on canopy leaves. Males identify suitable mates via visual and contact cues, and females feed on foliage for an additional five to seven days after mating before oviposition begins [8,11,12]. Eggs are typically laid in bark cracks and crevices, with larvae subsequently tunneling into the bark to feed on the phloem and vascular cambium of the tree. Phloem consumption creates serpentine-shaped galleries, which severs photosynthate transport leading to eventual mortality. While a one-year life cycle is most common, a two-year life cycle does occur, especially in more northern latitudes, with larvae overwintering in intermediate instars within the phloem [13]. The proliferation of A. planipennis throughout forests in North America has caused the mortality of millions of ash trees, producing devastating ecological and economic impacts [8,14,15]. These impacts have created long lasting changes to North American forest ecosystems, requiring substantial restoration efforts [8,10]. Additional negative impacts include eradication of wood products produced from ash and diminished aesthetics in urban and suburban neighborhoods [8,14]. The cost of removal and replacement of ash trees in urban landscapes has been estimated at $12.5 billion from 2010–2020 [15]. Additionally, the estimated loss by timberlands in the United States is $300 billion [8]. Given the large-scale distribution of A. planipennis in both natural and urban landscapes, management options for control of the pest remain limited. Therefore, a long-term solution to preserving ash will depend on successfully identifying resilient genetic variants of ash. Resistance to wood-boring beetles is typically a function of female host selection and larval survival rate [17]. Therefore, resistance mechanisms can be placed into three general categories: antixenosis, antibiosis, and tolerance [18,19]. Antixenosis traits are aimed at decreasing preferences for feeding and/or ovipositioning, while antibiosis results from traits that negatively affect insect growth, survival, and/or fecundity. Lastly, tolerance is the ability of the host to withstand infestation while remaining relatively healthy compared to other individuals undergoing the same level of attack. There is evidence of antixenotic traits in the interaction between A. planipennis and hosts. Adults of A. planipennis express variation in both feeding and oviposition host preferences. When given a choice, adult beetles preferentially feed on white, green, and black ash compared to Manchurian, blue, and European ash ( F. excelsior L.) [20]. North American ash species receive more eggs compared to Manchurian ash, suggesting a female choice of susceptible hosts in order to increase larval performance [21,22]. Within North American ash species, inter- and intraspecific variation of volatile emissions and oviposition preferences of A. planipennis have been shown to play a role in resistance [23–25]. The bark of blue ash has a phenolic composition that may contribute to its resistance relative to white, green, and black ash [5]. Bark smoothness, as a phenotypic characteristic, may be a limiting factor in oviposition locations and subsequently limits the number of larvae that could attack a tree at a given time [26]. Additionally, variability in ash growth rates have been related to susceptibility to A. planipennis, with trees tolerant of attack having more rapid and constant growth compared to susceptible trees [27]. Antibiosis interactions also exist in larval development. Mechanisms that affect larval performance mainly focus on variation in phenolic and defense protein chemistry [5,28–30]. Previous studies comparing phenolic and lignin profiles of ash species found that Manchurian ash contains unique profiles that may contribute to their resistance to A. planipennis [5,28,30]. Four potential defense-related proteins are expressed more than five-fold higher in Manchurian ash than in other species, and may contribute to resistance [30]. Mechanisms of tolerance are more difficult to quantify and therefore have not been as well studied. Identifying the genetic variants that allow these surviving trees in North America to tolerate infestation would greatly aide in the conservation of ash [29]. Even with severe levels of ash mortality in the introduced range, certain trees have been able to survive after years of repeated exposure [26]. This has led to the identification of trees with differing apparent tolerance levels to A. planipennis attack. Trees classified as tolerant survive in spite of signs of A. planipennis attack and damage [26]. The objectives of this study were to (1) identify ash single nucleotide polymorphisms associated with the tolerance-susceptibility gradient to A. planipennis, (2) identify phenotypic and genotypic relationships between trees relative to this tolerance-susceptibility gradient, and (3) test the hypothesis that tolerance and susceptibility are linked to identifiable genetic markers. Results Phenotypic Classification The 47 ash trees selected for this study were sampled from six different geographic locations (Figure 1A). Thirty-eight green ash and nine white ash individuals were used for analysis. These trees were classified into two major groups: tolerant or susceptible to A. planipennis attack using both vigor and dieback. This categorization resulted in 28 trees being classified as tolerant and 19 as susceptible. Twenty-two (57.9%) green ash trees were categorized as tolerant, while six (66.7%) of the white ash trees were categorized as tolerant. Categorization (tolerant or susceptible) was independent of species (green or white) (χ 2 = 0.23, df = 1, P-value = 0.630). Pooled across species, vigor rating categories were evenly represented within each category (Figure 1B). However, dieback was skewed right, with fewer trees having greater values of dieback (Figure 1C). Overall, signs of A. planipennis infestation (i.e. bark splits, exit holes, woodpecker damage, epicormic sprouts) were present in 37 individuals, 31 of which had more than one sign present. Of the ten trees lacking signs, eight were categorized as tolerant and two were categorized as susceptible. RAD-Sequencing Restriction site-associated DNA sequencing was used to sequence the genomes of 47 ash trees in order to identify SNPs that are correlated with tolerant or susceptible phenotypes. BLASTN analysis of the 1000 random sequences from each individual revealed that 60.7% of the genomic sequences were potentially ash-specific (no hits discovered). A high percentage of mitochondrial reads (18.0%) were found, potentially diverting reads from nuclear loci. Of sequences mapped, 17.7% aligned to Populus tremula, 0.5% Sesamum spp., and 0.5% Olea europaea. Due to the high mitochondrial hit counts, a reference of just polymorphic loci was created and BLASTN analysis was performed on those sequences. Out of this entire subset, only eight loci mapped to mitochondrial sequences. After SNP calling and initial filtering, a set of 23,243 SNPs were produced. Application of the more stringent filtering criteria (Table 1) generated a final set of 17,807 SNPs. Individuals were assessed for read depth. Mean read depth over all individuals was 91.5 and means ranged from 30–150 (Figure 2). Principal Component Analysis For major clusters (labeled as right, lower, upper, and middle) were identified in the PCA based on 17,807 SNPs (Figure 3A). Diverging substantially from all other groups, the cluster on the right contained trees from different geographic locations, including Houghton, Kensington, Oakwoods, and Willow. Furthermore, all the individuals in this cluster were classified as tolerant to A. planipennis attack. Clustering within the PCA did not appear to be influenced by tree species, with white and green ash occurring together in two of the four clusters (Figure 3C). Outlier SNPs Outlier detection based on the four PCA clusters identified 32 outlier SNPs within 28 different loci (Table 2). Outlier detection based on vigor rating identified 17 outlier SNPs with 13 different loci (Table 3). All outlier F ST values were skewed to the right and outliers were relatively high, suggesting directional selection (Figure 4). There were no outliers detected by analysis when trees were grouped based on tolerance and susceptibility. Out of all 41 outlier loci, only one matched to a known sequence from the NCBI nucleotide database (locus 30133_137; top blast hit: XM_011468015.1). This locus mapped to a PTI1-like tyrosine-protein kinase receptor. Of the outliers detected between the PCA clusters, ten had a clear pattern of the polymorphic nucleotide being predominantly present in the five right cluster individuals (Figure 5). These patterns were slightly offset by similar genetic trends between the middle and right clusters, however, the right cluster clearly had the highest occurrence of these polymorphisms. Interestingly, one set of outlier SNPs, all occurring at the locus 16669_22, was present in all trees except the five in the right group and two trees from the middle group (Figure 6). These trees retained the reference nucleotide in this case, not the polymorphic nucleotide. For the outlier SNPs present at this locus, each individual either had all four polymorphic nucleotides or retained all four reference nucleotides. Of the outliers detected between groups based on vigor rating, four had a clear pattern of the polymorphic nucleotide being present exclusively in trees with high vigor (Figure 7). Three of these SNPs were present at one locus (4467_128). One outlier SNP at locus 10225_13 displayed a pattern of the reference nucleotide occurring more frequently in trees with high vigor, whereas, trees with low vigor all had the polymorphic nucleotide (Figure 8). Phenotypic Analysis PCA based on all phenotypic data resulted in no distinct clustering between geographic locations or species (Figure 3B). Tolerant and susceptible trees did separate in this PCA, however, this is due to the categorization being defined by the same phenotypic data used to calculate the PCA. The PC1 axes of both the SNP and phenotype PCA analyses were not correlated (r = –0.16, P-value = 0.270). There was no correlation between the SNP PC1 axis and vigor (r = –0.22, P-value = 0.132). Likewise, there was no correlation between the SNP PC1 axis and dieback (r = –0.16, P-value = 0.271). Additionally, there was overlap between green and white ash (Figure 3D). Discussion This study identified polymorphic loci in Fraxinus spp. using RAD-sequencing genotyping-by-sequencing. The filter settings were selected to ensure high quality nucleotide data with sufficient coverage across individuals [31,32]. The resulting SNPs were used to highlight insights into a potential genetic basis for host tolerance to A. planipennis. Patterns of Genetic Variation PCA plots provided visual representation of genetic divergence among individuals. There was no clear relationship between the PCA clusters and geographic distribution, as three of the four clusters contained trees from multiple locations. Whiteand green ash did not separate out on the PCA as expected. This is not the first occurrence of genetic overlap of these two species. White ashis a polyploid species (2n = 46, 92, and 138) and hybridization appears to confound genetic results [33,34]. In some cases, white ash individuals group with green ashin phylogenetic analyses, a result that was attributed to the white ashsamples likely being a polyploid hybrid with green ash34]. Additionally, there is low genetic differentiation between white , velvet ( F. velutina Torr.), and green ash [35]. Rapid radiation or recent exchange of genetic material could have led to these relationships [35]. The co-occurrence of white and green ash in all sampling locations presents the possibility of hybridization between the two species; therefore, low genetic differentiation could have resulted between individuals identified as the two different species based on phenotypic characters if hybridization occurred. Across the PCA, there was little separation based on tolerance and susceptibility categories applied by field assessment data. The exception to this pattern was the right cluster, which contained five tolerant individuals from various geographic locations in Michigan. Four of those individuals were in close proximity to the de facto A. planipennis introduction epicenter [36], indicating they have been exposed to A. planipennis for nearly 20 years and are still able to tolerate infestation. These five trees were located in Houghton County, Kensington, and Oakwoods (the one individual from Willow was grouped with Oakwoods for some analyses). For this reason, outlier SNPs were identified between the four clusters on the PCA to determine which SNPs were likely causing the variation in this group. SNP Candidates for Tolerance Selection All outlier SNPs detected between the PCA clusters had high F ST values and appeared to be responsible for the divergence of the right cluster. However, subsequent PCA on SNP variation with these outliers removed (results not shown) revealed that the five individuals in the right cluster still displayed the same pattern of divergence, indicating that these 28 loci are not the only source of variation within this group. Throughout the outliers identified, there were similar genetic trends between trees in the middle and right clusters. The similarities between these two clusters are evident when looking at just the ten polymorphic loci that showed a pattern of almost exclusive presence in the right group. For seven of the ten loci, one to two trees from the middle cluster also had the polymorphic nucleotide. Two of these trees from the middle cluster were classified as susceptible; however, all of the trees from the middle group that had genotypic similarities with the five right group trees had no signs of A. planipennis attack (i.e. lacking bark spits, exit holes, woodpecker activity, and sprouting), despite being located in areas where A. planipennis is present. The outlier locus 30133_137 mapped to a PTI1-like tyrosine-protein kinase 2. This protein is known to be involved in growth and development, as well as defense responses [37,38]. PTI1 serine/threonine protein kinases were described to be key components of speck disease resistance in tomatoes by amplifying signaling pathways [39]. This gene may also play a role in defense against A. planipennis by amplifying pathways necessary to tolerate infestation. Locus 16669_22 is another potentially important gene for host tolerance. Four outlier SNPs at this locus had distinctive patterns in individuals with either all present as the polymorphism or all in their reference form. For the five individuals in the right cluster, the reference nucleotides were retained for all four SNPs at this locus. Unfortunately, BLAST analysis did not map this locus to any known genes. Additionally, two trees that clustered in the middle PCA group also had the reference nucleotides at this locus. These two trees were classified as susceptible, but interestingly, they were the only two susceptible trees with no signs of A. planipennis attack. This exemplifies the coarseness of categorizing tolerance based on phenotypic characteristics of vigor and dieback. These two trees with poor vigor and high dieback may simply be displaying other disease manifestations not associated with A. planipennis (i.e. Houghton County trees were along a highway and subject to salt spray). Outliers detected between trees based on vigor rating resulted in an additional 13 loci being identified as potential candidates for host tolerance. None of these mapped to any known functional genes, however, the five outliers that did show a pattern of either the polymorphic or the reference nucleotide being present exclusively in high vigor trees are of particular interest. Future analyses on characterizing the functionality of the outlier loci detected in this study could expose the importance of these genes and the role they may play in tolerance. A clear link between genotypic and phenotypic data was not identified. Most likely, this was due to the coarseness of phenotype classification, which then failed to correlate with complex genetic diversity. Phenotypes were defined by tree assessments, which included categorical tree health observations and presence or absence of signs of A. planipennis attack. A future study may have more success if these signs are quantified at finer scales (i.e. number of exit holes per square meter, area of phloem regrowth, and location of bark splits) as opposed to whole tree values. Detailed phenotypic data would allow for more robust analyses linking genotype and phenotype, such as a mixed-linear model. Finally, the power of genome-wide associations are affected by the genetic complexity and heritability of a trait [40]. As tolerance is expected to be a complex genetic trait, this increases the chance of false positive associations. To remedy this issue in future studies, as many genotypes as possible should be used along with high quality nucleotide data. Conclusions Agrilus planipennis has devastated populations of Fraxinus in North America, however, the survival of some individuals despite years of exposure to A. planipennis is evidence of host tolerance. Understanding the mechanisms of host tolerance through genome-wide association has the potential to restore populations with cultivars that are able to persist in the presence of A. planipennis. Despite the caveats presented above, this study was successful in using RAD-sequencing in order to identify SNPs that are potential candidates for tolerance to A. planipennis. This was a first step toward uncovering the genetic basis for host tolerance to A. planipennis. Future studies are needed to identify the functionality of the outlier loci detected in this study. METHODS Study Species and Sample Collection Trees were selected from Fraxinus spp. individuals within Fort Wayne, Indiana USA (n = 3), Huron-Clinton Metroparks, Michigan USA (n = 39), and Houghton County, Michigan USA (n = 5) (Figure 1). Within most of these locations, green ashwas the dominant species with white ash being less common. However, in Houghton County, white ashdominated. Leaf, bud, and bark morphological characteristics were used to identify species (C. E. Hale and J. M. Marshall). Vouchers were not collected. Selection of trees was based on their occurrence along an apparent gradient from high tolerance to high susceptibility (i.e. low tolerance) to A. planipennis attack. Apical buds were collected from trees and placed in liquid nitrogen immediately after collection. Fort Wayne and Huron-Clinton Metropark collections were made in July 2014. Houghton County collections were made in August 2016. Once returned to the lab, samples were stored at –80 °C. Tree Assessment Selected trees were assessed on vigor (overall tree health: categorical 1–5 with 1 being high vigor [crown with relatively few dead twigs; normal foliage color and density] and 5 being low vigor [more than half of crown dead]), crown dieback (percent of dead branch tips: 5–100%), and signs of A. planipennis attack (presence/absence: bark splits, exit holes, woodpecker damage, epicormic sprouts). Assessments followed those conducted in previous studies [26,41–44], which were derived from Millers et al. [45]. After assessment, 47 individuals were selected for analysis and given an overall categorization of tolerant or susceptible to A. planipennis infestation. This tolerant-susceptible categorization was similar to [46]. Individuals with a vigor ≤ 3 and dieback of ≤ 30 were considered tolerant. Individuals with a vigor of ≥ 3 and dieback > 30 were considered susceptible. Chi-squared analysis was used to test the null hypothesis that tolerance categorization was independent of species. DNA Extraction and Quantification Entire bud samples (two to three buds) were homogenized using sterile ceramic mortars and pestles, which were first cooled with liquid nitrogen. DNeasy Plant Mini Kit (QIAGEN) was used to extract total genomic DNA following the manufacturer’s protocol. DNA from each sample was quantified using UV spectrophotometry (NanoDrop 1000) absorbance. All samples were subsequently diluted to a concentration of 25 ng/μl. Library Creation and SNP Discovery Genomic DNA was converted into nextRAD genotyping-by-sequencing libraries (SNPsaurus, LLC) as described by Russello et al. [47]. Briefly, genomic DNA was first fragmented with Nextera reagent (Illumina, Inc), which also ligates short adapter sequences to the ends of the fragments. The Nextera reaction was scaled for fragmenting 7 ng of genomic DNA, although 14 ng of genomic DNA was used for input to compensate for the amount of degraded DNA in the samples and to increase fragment sizes. Fragmented DNA was then amplified for 25 cycles at 75 °C, with one of the primers matching the adapter and extending eight nucleotides into the genomic DNA with the selective sequence TGCAGGAG. Thus, only fragments starting with a sequence that can be hybridized by the selective sequence of the primer will be efficiently amplified. The nextRAD libraries were sequenced on a HiSeq 4000 with one lane of 150 bp reads (University of Oregon). The genotyping analysis used custom scripts (SNPsaurus, LLC) that trimmed the reads using bbduk (BBMap tools, http://sourceforge.net/projects/bbmap/ ) . Command was as follows: bash bbmap/bbduk.sh in = $file out = $outfile ktrim = r k = 17 hdist = 1 mink = 8 ref = bbmap/resources/nextera.fa.gz minlen = 100 ow = t qtrim = r trimq = 10 Next, a de novo reference genome was created by collecting 10 million reads in total, evenly from the samples, and excluding reads that had counts fewer than 7 or more than 700. The remaining loci were then aligned to each other to identify allelic loci and collapse allelic haplotypes to a single representative. All reads were mapped to the reference with an alignment identity threshold of 95% using bbmap (BBMap tools). In order to assess the proportion of sequence reads that originated from Fraxinus spp.versus other species, 1000 high-quality reads from each sample were subject to BLASTN analysis in the NCBI database. Genotype calling was done using SAMtools and BCFtools [48]. Command was as follows: samtools mpileup -gu -Q 12 -t DP, DPR -f ref.fasta -b samples.txt | bcftools call -cv - > genotypes.vcf The VCF file was filtered to remove alleles with a population frequency of less than 0.03. Loci were removed that were heterozygous in all samples or had more than two alleles in a sample (suggesting collapsed paralogs). The absence of artifacts was checked by counting SNPs at each read nucleotide position and determining that SNP number did not increase with reduced base quality at the end of the read. All polymorphic sequences retained were subject to BLASTN analysis in the NCBI database. VCFtools [49] was used to further filter SNPs based on the following criteria: (1) Phred-quality score, (2) minor allele frequency, (3) maximum missing genotype, and (4) minimum mean read depth (Table 1). Loci that failed to meet the quantification threshold for any of the filtering criteria were removed and excluded from subsequent analyses. Samples were not filtered based on Hardy-Weinberg expectations because the goal of this study was to identify polymorphic loci under selection, which are expected to deviate from equilibrium. The VCF file was converted into file formats necessary for analysis using PGDSpider 2.1.1.3 [50]. Principal Component Analysis The packages vcfR v1.7.0 and adegenet v2.1.1 in R v3.4.2 [51–53] were used to perform an individual-based principal component analysis (PCA) to characterize structure based on SNP variation. PCA was also used to visualize relationships between individuals based on phenotypic characteristics (vigor, dieback, and signs of A. planipennis attack) using prcomp R base function. Pearson’s correlation was used to test the hypothesis that the first principle component axis for SNP PCA and the first principle component axis for phenotypic PCA had a linear relationship. Similarly, linear correlations were tested between the first principle component axis for SNP PCA with vigor and dieback values. Detection of Markers under Selection BAYESCAN v2.1 [57] was used to identify outlier loci based on populations defined by the PCA clusters, as well as populations defined by vigor rating. BAYESCAN uses a hierarchal-Bayesian method to estimate population-specific F ST coefficients, described by Beaumont and Balding [58]. A more conservative neutral model available in BAYESCAN (prior odds = 1000) was used to minimize the number of false positives. Prior odds or prior probability is the likelihood of the null hypothesis being true before the test is performed. This increase in prior odds corresponds to the selection model being 1000 times less likely than the neutral model, which was a more appropriate assumption given the number of SNPs included in this analysis [59]. After 100,000 iterations, SNPs with a posterior distribution over 0.95 were considered outliers. High F ST values (outliers) suggest that the locus has undergone directional selection (in contrast to balancing selection). Abbreviations BLASTN: Standard nucleotide basic local alignment search tool F ST : Fixation index PCA: Principle component analysis RAD: Restriction site associated DNA SNP: Single nucleotide polymorphism Declarations Acknowledgements The authors would like to thank Nick White for field assistance and SNPsaurus, LLC for contributing to a portion of the methods. The authors would like to thank Kirk Broders for comments on an earlier version of the manuscript. Funding Financial support for this work was partially provided by Purdue University Fort Wayne Institute for Research, Scholarship, and Creative Endeavors Collaborative Grant Program; funds were used for data acquisition. Availability of data and materials The datasets generated and/or analysed during the current study are available in the GenBank repository as BioProject ID: PRJNA561365, https://www.ncbi.nlm.nih.gov/bioproject/561365 Authors’ contributions Conceived and designed the experiments: AJS, VJN and JMM. Performed the experiments: CEH, VJN and JMM. Analyzed the data: CEH, MAJ, GB, VJN and JMM. Wrote the manuscript: CEH, VJN and JMM. All authors have read and approved the manuscript. Ethics approval and consent to participate Not applicable. Consent for Publication Not applicable. Competing Interests The authors declare that they have no competing interests. References McCullough DG, Katovich SA. Pest Alert. Emerald Ash Borer. NA-PR–02–04, U.S. Department of Agriculture, Forest Service. Northeastern Area, Newton Square, PA; 2004. Cappaert D, McCullough DG, Poland TM, Siegert NW. Emerald ash borer in Northern America: a research and regulatory challenge. Am Entomol. 2005;51:152–165. 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Efficacy of trap and lure types for detection of Agrilus planipennis (Col., Buprestidae) at low density. J Appl Entomol. 2010;134:296–302. Marshall JM, Porter MJ, Storer AJ. Predicting emerald ash borer, Agrilus planipennis (Coleoptera: Buprestidae), landing behavior on unwounded ash. Great Lakes Entomol. 2012;45:29–39. Millers I, Lachangce D, Burkman WG, Allen DC. North American sugar maple decline project: organization and field methods. USDA Forest Service Gen. Tech. Rep. NE–154; 1991. http://www.fs.fed.us/ne/newtown_square/publications/technical_reports/pdfs/scanned/gtr154.pdf. Hietala KG. Evaluation and monitoring of ash (Fraxinus spp.) tolerant to long-term emerald ash borer (Agrilus planipennis [Coleoptera: Buprestidae]) exposure. MS thesis. USA: Michigan Technological University; 2013 Russello MA, Waterhouse MD, Etter PD, Johnson EA. From promise to practice: pairing non-invasive sampling with genomics in conservation. PeerJ. 2015;3:e1106. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–2079. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G. The variant call format and VCFtools. Bioinformatics. 2011;27:2156–2158. Lischer HEL, Excoffier L. 2012. PGDSpider: An automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics. 2012;28:298–299. Knaus BJ, Grünwald NJ. VCFR: a package to manipulate and visualize variant call format data in R. Mol Ecol Res. 2017;17:44–53. Jombart T. Adegenet: an R package for the multivariate analysis of genetic markers. Bioinformatics. 2008;24:1403–1405. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2017 https://www.R-project.org/. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155:945–959. Earl DA. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Con Genet Resour. 2012;4:359–361. Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol Ecol Resour. 2015;15:1179–1191. Foll M, Gaggiotti O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics. 2008;180:977–993. Beaumont MA, Balding EJ. Identifying adaptive genetic divergence among populations from genome scans. Mol Ecol. 2004;13:969–980. Lotterhos KE, Whitlock MC. Evaluation of demographic history and neutral parameterization on the performance of FST outlier tests. Mol Ecol. 2014;23:2178–2192. Tables Table 1. Filtering criteria for polymorphic loci. Loci failing to meet the quantification threshold for any of the criteria were excluded from subsequent analysis. Filtering Criteria Quantification Heterozygous in all samples False More than 2 alleles in a sample False Phred-like quality score > 20 Minor allele frequency > 0.1 Maximum missing genotype 0.5 Minimum mean read depth 14 Table 2. Summary of outlier loci detected between four clusters within PCA. Includes single nucleotide polymorphism (SNP) location within loci, reference/polymorphic nucleotide pairs, and F ST and q Values. False discovery rate of < 0.05 was used. Locus SNP Location SNP F ST q Value 2690_69 86 G/A 0.472 0.045 3535_59 67 A/G 0.478 0.024 6512_10 66 A/G 0.472 0.042 8409_8 142 A/G 0.533 0.002 9230_115 99 C/G 0.479 0.029 12476_19 13 C/A 0.466 0.019 13284_148 49 G/A 0.504 0.031 13980_8 103 T/C 0.505 0.006 16669_22 10 T/C 0.532 0.004 16669_22 13 A/G 0.532 0.003 16669_22 49 T/C 0.531 0.004 16669_22 104 T/C 0.527 0.004 17461_7 34 C/T 0.441 0.047 18944_8 106 T/C 0.437 0.05 22135_11 3 T/A 0.476 0.008 24473_11 46 T/A 0.505 0.002 30133_137 109 G/A 0.486 0.014 31830_23 118 C/T 0.479 0.026 34843_20 109 G/A 0.465 0.007 37961_17 133 C/T 0.531 0.001 38762_25 57 G/A 0.474 0.035 39733_10 132 G/A 0.478 0.041 50806_23 97 C/G 0.563 0.0002 51756_10 107 A/C 0.469 0.011 56570_57 87 T/A 0.474 0.039 56570_57 132 C/A 0.471 0.037 56747_9 86 C/T 0.441 0.033 57187_42 84 G/A 0.536 0.003 59640_10 101 G/A 0.506 0.005 61718_43 96 T/C 0.485 0.022 67007_71 5 C/A 0.461 0.017 88261_24 115 T/C 0.51 0.004 Table 3. Summary of outlier loci detected between populations based on vigor rating. Includes single nucleotide polymorphism (SNP) location within loci, reference/polymorphic nucleotide pairs, and F ST and q Values. False discovery rate of < 0.05 was used. Locus SNP Location SNP F ST q Value 4467_128 20 A/G 0.19 0.004 4467_128 28 T/C 0.199 0.002 4467_128 55 A/G 0.192 0.005 10225_13 51 C/T 0.163 0.047 19593_14 42 T/A 0.174 0.029 25780_8 16 T/C 0.186 0.019 39536_56 126 C/T 0.237 0.001 41669_24 113 T/C 0.157 0.033 46716_44 9 C/T 0.204 0.012 46716_44 11 G/A 0.206 0.015 49707_11 44 A/G 0.209 0.003 50319_14 27 C/T 0.16 0.037 50319_14 102 C/T 0.166 0.026 56942_24 101 C/T 0.179 0.009 58136_88 134 A/G 0.194 0.017 71869_12 51 G/A 0.162 0.042 90283_36 123 C/T 0.157 0.022 Cite Share Download PDF Status: Published Journal Publication published 27 Sep, 2021 Read the published version in Ecology and Evolution → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4755","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":148180,"identity":"9e8058f8-1c1c-46bf-b022-b50a0cfd723a","order_by":1,"name":"Cecelia E. Hale","email":"","orcid":"","institution":"Purdue University Fort Wayne","correspondingAuthor":false,"prefix":"","firstName":"Cecelia","middleName":"E.","lastName":"Hale","suffix":""},{"id":148181,"identity":"fa01f964-d111-43c2-9cd0-0df236e7e65d","order_by":2,"name":"Mark A. Jordan","email":"","orcid":"","institution":"Purdue University Fort Wayne","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"A.","lastName":"Jordan","suffix":""},{"id":148182,"identity":"d4b52d2f-489a-4025-8540-75595b6d1d85","order_by":3,"name":"Gloria Iriarte","email":"","orcid":"","institution":"Smithsonian Tropical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Gloria","middleName":"","lastName":"Iriarte","suffix":""},{"id":148183,"identity":"1b78657b-7e6e-4c7b-8e81-3e777a734343","order_by":4,"name":"Andrew J. Storer","email":"","orcid":"","institution":"Michigan Technological University","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"J.","lastName":"Storer","suffix":""},{"id":148184,"identity":"b9112aed-2157-4108-9a42-f840c5343e39","order_by":5,"name":"Vamsi J. Nalam","email":"","orcid":"","institution":"Colorado State University","correspondingAuthor":false,"prefix":"","firstName":"Vamsi","middleName":"J.","lastName":"Nalam","suffix":""},{"id":148185,"identity":"27e94c00-732a-40cf-bd4f-90e29cf002e2","order_by":6,"name":"Jordan Marshall","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYJACZgYGC2Z+BsYHEDaRWiSYJRuYDUjTwmBwgFgtuu1nH38uqJFgN77dzCbBUGGd2EBIi9mZdDPpGcckmM3uHAZqOZNOhJYDaWzMPGxALTfyj91gbDtMhJbzz5g/8/yTYDaekcx2g/EfMVpupDFI87ZJMBtIgLQ0EKXlGZs0b58Es8Sdw+w/Eo6lGxPhsDSgw77ZJPPPbmY2+FBjLUtQCwwkA6OGgSGBWOUgYAfWMgpGwSgYBaMAGwAAqhI4T6gqI0wAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-6024-3758","institution":"Purdue University Fort Wayne","correspondingAuthor":true,"prefix":"","firstName":"Jordan","middleName":"","lastName":"Marshall","suffix":""}],"badges":[],"createdAt":"2019-09-03 14:20:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.2.14056/v1","doiUrl":"https://doi.org/10.21203/rs.2.14056/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1002/ece3.8163","type":"published","date":"2021-09-27T05:23:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":494657,"identity":"17ca3940-93a7-4b26-98ef-ab2b6b1d6366","added_by":"auto","created_at":"2020-02-14 14:53:15","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":643063,"visible":true,"origin":"","legend":"Location of sampled trees in Fort Wayne, Indiana USA (A); Huron-Clinton Metroparks, Michigan USA (Kensington, Lower Huron, Oakwoods, and Willow); and Houghton County, Michigan USA. Number of trees per category of vigor (B) and dieback (C) determined by field assessments.","description":"","filename":"HaleetalFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/dc18daee-356f-4923-8334-514ef7a29e8a/v1/Hale_etal_Figure1.jpg"},{"id":494658,"identity":"d833fe96-9a3a-4953-9570-e12a7973e9b4","added_by":"auto","created_at":"2020-02-14 14:53:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123278,"visible":true,"origin":"","legend":"Boxplot of read depth for all sampled ash individuals.","description":"","filename":"HaleetalFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/dc18daee-356f-4923-8334-514ef7a29e8a/v1/Hale_etal_Figure2.jpg"},{"id":494659,"identity":"8cd881fa-f6b6-4280-9710-187aa82d1435","added_by":"auto","created_at":"2020-02-14 14:53:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":376197,"visible":true,"origin":"","legend":"Principle component analysis (PCA) of filtered single nucleotide polymorphisms (SNPs) (A,C) and phenotypic data (B,D) from ash individuals in Fort Wayne, Indiana USA; Huron-Clinton Metroparks, Michigan USA (Kensington, Lower Huron, Oakwoods, and Willow); and Houghton County, Michigan USA. Closed symbols represent tolerant trees and open symbols represent susceptible trees. Ellipses are arbitrary, represent visual clustering, and are presented for labelling of cluster locations in PCA.","description":"","filename":"HaleetalFigure3.jpg","url":"https://assets-eu.researchsquare.com/files/dc18daee-356f-4923-8334-514ef7a29e8a/v1/Hale_etal_Figure3.jpg"},{"id":494660,"identity":"52bcb4d0-6ab8-4555-abcb-b181ee2cceae","added_by":"auto","created_at":"2020-02-14 14:53:16","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":174334,"visible":true,"origin":"","legend":"Frequency of FST values of all SNPs computed between populations based on the PCA clusters (A) and populations based on vigor rating (B).","description":"","filename":"HaleetalFigure4.jpg","url":"https://assets-eu.researchsquare.com/files/dc18daee-356f-4923-8334-514ef7a29e8a/v1/Hale_etal_Figure4.jpg"},{"id":494661,"identity":"ea6eeb34-f05f-4033-9bbf-559c571b3194","added_by":"auto","created_at":"2020-02-14 14:53:16","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":450528,"visible":true,"origin":"","legend":"Number of trees in each PCA cluster that had the polymorphic nucleotide (white bars) or the reference nucleotide (black bars) for the ten outlier loci identified as having a clear presence in the right group. Within each locus, there was only one outlier SNP identified. Sample sizes for each cluster were as follows: right (n = 5), lower (n = 7), upper (n = 30), middle (n = 5). Samples with missing data for that loci were not included.","description":"","filename":"HaleetalFigure5.jpg","url":"https://assets-eu.researchsquare.com/files/dc18daee-356f-4923-8334-514ef7a29e8a/v1/Hale_etal_Figure5.jpg"},{"id":494662,"identity":"1252a686-494c-4c02-bab0-f2d1820f5b8a","added_by":"auto","created_at":"2020-02-14 14:53:16","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":79415,"visible":true,"origin":"","legend":"Number of trees in each PCA cluster that had the polymorphic nucleotide (white bars) or the reference nucleotide (black bars) for the locus 16669_22. Four SNPs were identified as outliers within this one locus (Table 3). Sample sizes for each cluster were as follows: right (n = 5), lower (n = 7), upper (n = 30), middle (n = 5). Samples with missing data were not included.","description":"","filename":"HaleetalFigure6.jpg","url":"https://assets-eu.researchsquare.com/files/dc18daee-356f-4923-8334-514ef7a29e8a/v1/Hale_etal_Figure6.jpg"},{"id":494663,"identity":"429666ff-f7f7-4327-92e9-a16810043f4a","added_by":"auto","created_at":"2020-02-14 14:53:16","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":306433,"visible":true,"origin":"","legend":"Number of trees in each vigor rating that had the polymorphic nucleotide (white bars) or the reference nucleotide (black bars) for the four outlier loci identified as having a clear pattern of presence in the high vigor groups. Sample sizes for each group were as follows: 1 (n = 11), 2 (n = 9), 3 (n = 10), 4 (n = 8), 5 (n = 9). Samples with missing data were not included.","description":"","filename":"HaleetalFigure7.jpg","url":"https://assets-eu.researchsquare.com/files/dc18daee-356f-4923-8334-514ef7a29e8a/v1/Hale_etal_Figure7.jpg"},{"id":494664,"identity":"b175add2-72d9-477e-abde-82bb9c324f55","added_by":"auto","created_at":"2020-02-14 14:53:16","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":101217,"visible":true,"origin":"","legend":"Number of trees in each vigor rating that had the polymorphic nucleotide (white bars) or the reference nucleotide (black bars) for the locus 10225_13. Sample sizes for each cluster were as follows: 1 (n = 11), 2 (n = 9), 3 (n = 10), 4 (n = 8), 5 (n = 9). Samples with missing data were not included.","description":"","filename":"HaleetalFigure8.jpg","url":"https://assets-eu.researchsquare.com/files/dc18daee-356f-4923-8334-514ef7a29e8a/v1/Hale_etal_Figure8.jpg"},{"id":14047409,"identity":"89ac32e3-2110-4277-927f-448991069f1b","added_by":"auto","created_at":"2021-09-28 05:23:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":940753,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4755/v1/949e0a0e-82eb-4d5c-96ba-5b7c21ba8d4f.pdf"}],"financialInterests":"","formattedTitle":"Genome-wide SNP identification in Fraxinus linking genetic characteristics to tolerance of Agrilus planipennis","fulltext":[{"header":"Background","content":"\u003cp\u003e\u003cem\u003eAgrilus planipennis \u003c/em\u003eFairmaire (Coleoptera: Buprestidae; emerald ash borer) is a metallic green beetle native to northeastern Asia that has become a pest to North American ash (\u003cem\u003eFraxinus \u003c/em\u003espp. L.) [1]. This pest was introduced into the Detroit/Windsor area of Michigan, USA/Ontario, Canada, and quickly dispersed via human assistance, including movement of firewood, nursery stock, and wood packing material [2,3]. In its native range, \u003cem\u003eA. planipennis\u003c/em\u003e coevolved with Manchurian ash (\u003cem\u003eF. mandshurica \u003c/em\u003eRupr.) and is a secondary pest in this tree species requiring a primary stressor for successful attack [4,5]. Ash species in North America lack this natural resistance and succumb to attack, regardless of the presence of a primary stressor, often within one to four years after initial attack [4,6]. While black (\u003cem\u003eF. nigra\u003c/em\u003e Marsh.), green (\u003cem\u003eF. pennsylvanica \u003c/em\u003eMarsh.), and white (\u003cem\u003eF. americana \u003c/em\u003eL.) ash are the most susceptible in the introduced range of \u003cem\u003eA. planipennis,\u003c/em\u003e all North American ash are susceptible [2,7,8]. Blue ash (\u003cem\u003eF. quadrangulata \u003c/em\u003eMichx.) has the lowest susceptibility to \u003cem\u003eA. planipennis\u003c/em\u003e attack among North American ash species [9].\u003c/p\u003e\n\u003cp\u003eIn North America, the life cycle of \u003cem\u003eA. planipennis\u003c/em\u003e is typically completed within one year [10]. Females and males mature as they feed on canopy leaves. Males identify suitable mates via visual and contact cues, and females feed on foliage for an additional five to seven days after mating before oviposition begins [8,11,12]. Eggs are typically laid in bark cracks and crevices, with larvae subsequently tunneling into the bark to feed on the phloem and vascular cambium of the tree. Phloem consumption creates serpentine-shaped galleries, which severs photosynthate transport leading to eventual mortality. While a one-year life cycle is most common, a two-year life cycle does occur, especially in more northern latitudes, with larvae overwintering in intermediate instars within the phloem [13].\u003c/p\u003e\n\u003cp\u003eThe proliferation of \u003cem\u003eA. planipennis\u003c/em\u003e throughout forests in North America has caused the mortality of millions of ash trees, producing devastating ecological and economic impacts [8,14,15]. These impacts have created long lasting changes to North American forest ecosystems, requiring substantial restoration efforts [8,10]. Additional negative impacts include eradication of wood products produced from ash and diminished aesthetics in urban and suburban neighborhoods [8,14]. The cost of removal and replacement of ash trees in urban landscapes has been estimated at $12.5 billion from 2010–2020 [15]. Additionally, the estimated loss by timberlands in the United States is $300 billion [8].\u003c/p\u003e\n\u003cp\u003eGiven the large-scale distribution of \u003cem\u003eA. planipennis \u003c/em\u003ein both natural and urban landscapes, management options for control of the pest remain limited. Therefore, a long-term solution to preserving ash will depend on successfully identifying resilient genetic variants of ash. Resistance to wood-boring beetles is typically a function of female host selection and larval survival rate [17]. Therefore, resistance mechanisms can be placed into three general categories: antixenosis, antibiosis, and tolerance [18,19]. Antixenosis traits are aimed at decreasing preferences for feeding and/or ovipositioning, while antibiosis results from traits that negatively affect insect growth, survival, and/or fecundity. Lastly, tolerance is the ability of the host to withstand infestation while remaining relatively healthy compared to other individuals undergoing the same level of attack.\u003c/p\u003e\n\u003cp\u003eThere is evidence of antixenotic traits in the interaction between \u003cem\u003eA. planipennis \u003c/em\u003eand hosts. Adults of \u003cem\u003eA. planipennis\u003c/em\u003e express variation in both feeding and oviposition host preferences. When given a choice, adult beetles preferentially feed on white, green, and black ash compared to Manchurian, blue, and European ash (\u003cem\u003eF. excelsior \u003c/em\u003eL.) [20]. North American ash species receive more eggs compared to Manchurian ash, suggesting a female choice of susceptible hosts in order to increase larval performance [21,22]. Within North American ash species, inter- and intraspecific variation of volatile emissions and oviposition preferences of \u003cem\u003eA. planipennis\u003c/em\u003e have been shown to play a role in resistance [23–25]. The bark of blue ash has a phenolic composition that may contribute to its resistance relative to white, green, and black ash [5]. Bark smoothness, as a phenotypic characteristic, may be a limiting factor in oviposition locations and subsequently limits the number of larvae that could attack a tree at a given time [26]. Additionally, variability in ash growth rates have been related to susceptibility to \u003cem\u003eA. planipennis,\u003c/em\u003e with trees tolerant of attack having more rapid and constant growth compared to susceptible trees [27].\u003c/p\u003e\n\u003cp\u003eAntibiosis interactions also exist in larval development. Mechanisms that affect larval performance mainly focus on variation in phenolic and defense protein chemistry [5,28–30]. Previous studies comparing phenolic and lignin profiles of ash species found that Manchurian ash contains unique profiles that may contribute to their resistance to \u003cem\u003eA. planipennis\u003c/em\u003e [5,28,30]. Four potential defense-related proteins are expressed more than five-fold higher in Manchurian ash than in other species, and may contribute to resistance [30].\u003c/p\u003e\n\u003cp\u003eMechanisms of tolerance are more difficult to quantify and therefore have not been as well studied. Identifying the genetic variants that allow these surviving trees in North America to tolerate infestation would greatly aide in the conservation of ash [29]. Even with severe levels of ash mortality in the introduced range, certain trees have been able to survive after years of repeated exposure [26]. This has led to the identification of trees with differing apparent tolerance levels to \u003cem\u003eA. planipennis\u003c/em\u003e attack. Trees classified as tolerant survive in spite of signs of A.\u003cem\u003e planipennis \u003c/em\u003eattack and damage [26]. The objectives of this study were to (1) identify ash single nucleotide polymorphisms associated with the tolerance-susceptibility gradient to \u003cem\u003eA. planipennis,\u003c/em\u003e (2) identify phenotypic and genotypic relationships between trees relative to this tolerance-susceptibility gradient, and (3) test the hypothesis that tolerance and susceptibility are linked to identifiable genetic markers.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003ePhenotypic Classification\u003c/h2\u003e\n\u003cp\u003eThe 47 ash trees selected for this study were sampled from six different geographic locations (Figure 1A). Thirty-eight green ash and nine white ash individuals were used for analysis. These trees were classified into two major groups: tolerant or susceptible to \u003cem\u003eA. planipennis\u003c/em\u003e attack using both vigor and dieback. This categorization resulted in 28 trees being classified as tolerant and 19 as susceptible. Twenty-two (57.9%) green ash trees were categorized as tolerant, while six (66.7%) of the white ash trees were categorized as tolerant. Categorization (tolerant or susceptible) was independent of species (green or white) (χ\u003csup\u003e2\u003c/sup\u003e = 0.23, df = 1, P-value = 0.630). Pooled across species, vigor rating categories were evenly represented within each category (Figure 1B). However, dieback was skewed right, with fewer trees having greater values of dieback (Figure 1C). Overall, signs of \u003cem\u003eA. planipennis\u003c/em\u003e infestation (i.e. bark splits, exit holes, woodpecker damage, epicormic sprouts) were present in 37 individuals, 31 of which had more than one sign present. Of the ten trees lacking signs, eight were categorized as tolerant and two were categorized as susceptible.\u003c/p\u003e\n\u003ch2\u003eRAD-Sequencing\u003c/h2\u003e\n\u003cp\u003eRestriction site-associated DNA sequencing was used to sequence the genomes of 47 ash trees in order to identify SNPs that are correlated with tolerant or susceptible phenotypes. BLASTN analysis of the 1000 random sequences from each individual revealed that 60.7% of the genomic sequences were potentially ash-specific (no hits discovered). A high percentage of mitochondrial reads (18.0%) were found, potentially diverting reads from nuclear loci. Of sequences mapped, 17.7% aligned to \u003cem\u003ePopulus tremula, \u003c/em\u003e0.5% \u003cem\u003eSesamum \u003c/em\u003espp., and 0.5% \u003cem\u003eOlea europaea. \u003c/em\u003eDue to the high mitochondrial hit counts, a reference of just polymorphic loci was created and BLASTN analysis was performed on those sequences. Out of this entire subset, only eight loci mapped to mitochondrial sequences.\u003c/p\u003e\n\u003cp\u003eAfter SNP calling and initial filtering, a set of 23,243 SNPs were produced. Application of the more stringent filtering criteria (Table 1) generated a final set of 17,807 SNPs. Individuals were assessed for read depth. Mean read depth over all individuals was 91.5 and means ranged from 30–150 (Figure 2).\u003c/p\u003e\n\u003ch2\u003ePrincipal Component Analysis\u003c/h2\u003e\n\u003cp\u003eFor major clusters (labeled as right, lower, upper, and middle) were identified in the PCA based on 17,807 SNPs (Figure 3A). Diverging substantially from all other groups, the cluster on the right contained trees from different geographic locations, including Houghton, Kensington, Oakwoods, and Willow. Furthermore, all the individuals in this cluster were classified as tolerant to \u003cem\u003eA. planipennis\u003c/em\u003e attack. Clustering within the PCA did not appear to be influenced by tree species, with white and green ash occurring together in two of the four clusters (Figure 3C).\u003c/p\u003e\n\u003ch2\u003eOutlier SNPs\u003c/h2\u003e\n\u003cp\u003eOutlier detection based on the four PCA clusters identified 32 outlier SNPs within 28 different loci (Table 2). Outlier detection based on vigor rating identified 17 outlier SNPs with 13 different loci (Table 3). All outlier \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e values were skewed to the right and outliers were relatively high, suggesting directional selection (Figure 4). There were no outliers detected by analysis when trees were grouped based on tolerance and susceptibility. Out of all 41 outlier loci, only one matched to a known sequence from the NCBI nucleotide database (locus 30133_137; top blast hit: XM_011468015.1). This locus mapped to a PTI1-like tyrosine-protein kinase receptor.\u003c/p\u003e\n\u003cp\u003eOf the outliers detected between the PCA clusters, ten had a clear pattern of the polymorphic nucleotide being predominantly present in the five right cluster individuals (Figure 5). These patterns were slightly offset by similar genetic trends between the middle and right clusters, however, the right cluster clearly had the highest occurrence of these polymorphisms. Interestingly, one set of outlier SNPs, all occurring at the locus 16669_22, was present in all trees except the five in the right group and two trees from the middle group (Figure 6). These trees retained the reference nucleotide in this case, not the polymorphic nucleotide. For the outlier SNPs present at this locus, each individual either had all four polymorphic nucleotides or retained all four reference nucleotides.\u003c/p\u003e\n\u003cp\u003eOf the outliers detected between groups based on vigor rating, four had a clear pattern of the polymorphic nucleotide being present exclusively in trees with high vigor (Figure 7). Three of these SNPs were present at one locus (4467_128). One outlier SNP at locus 10225_13 displayed a pattern of the reference nucleotide occurring more frequently in trees with high vigor, whereas, trees with low vigor all had the polymorphic nucleotide (Figure 8).\u003c/p\u003e\n\u003ch2\u003ePhenotypic Analysis\u003c/h2\u003e\n\u003cp\u003ePCA based on all phenotypic data resulted in no distinct clustering between geographic locations or species (Figure 3B). Tolerant and susceptible trees did separate in this PCA, however, this is due to the categorization being defined by the same phenotypic data used to calculate the PCA. The PC1 axes of both the SNP and phenotype PCA analyses were not correlated (r = –0.16, P-value = 0.270). There was no correlation between the SNP PC1 axis and vigor (r = –0.22, P-value = 0.132). Likewise, there was no correlation between the SNP PC1 axis and dieback (r = –0.16, P-value = 0.271). Additionally, there was overlap between green and white ash (Figure 3D).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study identified polymorphic loci in \u003cem\u003eFraxinus \u003c/em\u003espp. using RAD-sequencing genotyping-by-sequencing. The filter settings were selected to ensure high quality nucleotide data with sufficient coverage across individuals [31,32]. The resulting SNPs were used to highlight insights into a potential genetic basis for host tolerance to \u003cem\u003eA. planipennis.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003ePatterns of Genetic Variation\u003c/h2\u003e\n\u003cp\u003ePCA plots provided visual representation of genetic divergence among individuals. There was no clear relationship between the PCA clusters and geographic distribution, as three of the four clusters contained trees from multiple locations.\u003c/p\u003e\n\u003cp\u003eWhiteand green ash did not separate out on the PCA as expected. This is not the first occurrence of genetic overlap of these two species. White ashis a polyploid species (2n = 46, 92, and 138) and hybridization appears to confound genetic results [33,34]. In some cases, white ash individuals group with green ashin phylogenetic analyses, a result that was attributed to the white ashsamples likely being a polyploid hybrid with green ash34]. Additionally, there is low genetic differentiation between white\u003cem\u003e, \u003c/em\u003evelvet (\u003cem\u003eF. velutina \u003c/em\u003eTorr.), and green ash [35]. Rapid radiation or recent exchange of genetic material could have led to these relationships [35]. The co-occurrence of white and green ash in all sampling locations presents the possibility of hybridization between the two species; therefore, low genetic differentiation could have resulted between individuals identified as the two different species based on phenotypic characters if hybridization occurred.\u003c/p\u003e\n\u003cp\u003eAcross the PCA, there was little separation based on tolerance and susceptibility categories applied by field assessment data. The exception to this pattern was the right cluster, which contained five tolerant individuals from various geographic locations in Michigan. Four of those individuals were in close proximity to the \u003cem\u003ede facto A. planipennis\u003c/em\u003e introduction epicenter [36], indicating they have been exposed to \u003cem\u003eA. planipennis\u003c/em\u003e for nearly 20 years and are still able to tolerate infestation. These five trees were located in Houghton County, Kensington, and Oakwoods (the one individual from Willow was grouped with Oakwoods for some analyses). For this reason, outlier SNPs were identified between the four clusters on the PCA to determine which SNPs were likely causing the variation in this group.\u003c/p\u003e\n\u003ch2\u003eSNP Candidates for Tolerance Selection\u003c/h2\u003e\n\u003cp\u003eAll outlier SNPs detected between the PCA clusters had high \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e \u003c/em\u003evalues and appeared to be responsible for the divergence of the right cluster. However, subsequent PCA on SNP variation with these outliers removed (results not shown) revealed that the five individuals in the right cluster still displayed the same pattern of divergence, indicating that these 28 loci are not the only source of variation within this group.\u003c/p\u003e\n\u003cp\u003eThroughout the outliers identified, there were similar genetic trends between trees in the middle and right clusters. The similarities between these two clusters are evident when looking at just the ten polymorphic loci that showed a pattern of almost exclusive presence in the right group. For seven of the ten loci, one to two trees from the middle cluster also had the polymorphic nucleotide. Two of these trees from the middle cluster were classified as susceptible; however, all of the trees from the middle group that had genotypic similarities with the five right group trees had no signs of \u003cem\u003eA. planipennis\u003c/em\u003e attack (i.e. lacking bark spits, exit holes, woodpecker activity, and sprouting), despite being located in areas where \u003cem\u003eA. planipennis\u003c/em\u003e is present.\u003c/p\u003e\n\u003cp\u003eThe outlier locus 30133_137 mapped to a PTI1-like tyrosine-protein kinase 2. This protein is known to be involved in growth and development, as well as defense responses [37,38]. PTI1 serine/threonine protein kinases were described to be key components of speck disease resistance in tomatoes by amplifying signaling pathways [39]. This gene may also play a role in defense against \u003cem\u003eA. planipennis\u003c/em\u003e by amplifying pathways necessary to tolerate infestation.\u003c/p\u003e\n\u003cp\u003eLocus 16669_22 is another potentially important gene for host tolerance. Four outlier SNPs at this locus had distinctive patterns in individuals with either all present as the polymorphism or all in their reference form. For the five individuals in the right cluster, the reference nucleotides were retained for all four SNPs at this locus. Unfortunately, BLAST analysis did not map this locus to any known genes. Additionally, two trees that clustered in the middle PCA group also had the reference nucleotides at this locus. These two trees were classified as susceptible, but interestingly, they were the only two susceptible trees with no signs of \u003cem\u003eA. planipennis\u003c/em\u003e attack. This exemplifies the coarseness of categorizing tolerance based on phenotypic characteristics of vigor and dieback. These two trees with poor vigor and high dieback may simply be displaying other disease manifestations not associated with \u003cem\u003eA. planipennis\u003c/em\u003e (i.e. Houghton County trees were along a highway and subject to salt spray).\u003c/p\u003e\n\u003cp\u003eOutliers detected between trees based on vigor rating resulted in an additional 13 loci being identified as potential candidates for host tolerance. None of these mapped to any known functional genes, however, the five outliers that did show a pattern of either the polymorphic or the reference nucleotide being present exclusively in high vigor trees are of particular interest. Future analyses on characterizing the functionality of the outlier loci detected in this study could expose the importance of these genes and the role they may play in tolerance.\u003c/p\u003e\n\u003cp\u003eA clear link between genotypic and phenotypic data was not identified. Most likely, this was due to the coarseness of phenotype classification, which then failed to correlate with complex genetic diversity. Phenotypes were defined by tree assessments, which included categorical tree health observations and presence or absence of signs of \u003cem\u003eA. planipennis\u003c/em\u003e attack. A future study may have more success if these signs are quantified at finer scales (i.e. number of exit holes per square meter, area of phloem regrowth, and location of bark splits) as opposed to whole tree values. Detailed phenotypic data would allow for more robust analyses linking genotype and phenotype, such as a mixed-linear model. Finally, the power of genome-wide associations are affected by the genetic complexity and heritability of a trait [40]. As tolerance is expected to be a complex genetic trait, this increases the chance of false positive associations. To remedy this issue in future studies, as many genotypes as possible should be used along with high quality nucleotide data.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e\u003cem\u003eAgrilus planipennis\u003c/em\u003e has devastated populations of \u003cem\u003eFraxinus\u003c/em\u003e in North America, however, the survival of some individuals despite years of exposure to \u003cem\u003eA. planipennis\u003c/em\u003e is evidence of host tolerance. Understanding the mechanisms of host tolerance through genome-wide association has the potential to restore populations with cultivars that are able to persist in the presence of \u003cem\u003eA. planipennis.\u003c/em\u003e Despite the caveats presented above, this study was successful in using RAD-sequencing in order to identify SNPs that are potential candidates for tolerance to \u003cem\u003eA. planipennis.\u003c/em\u003e This was a first step toward uncovering the genetic basis for host tolerance to \u003cem\u003eA. planipennis.\u003c/em\u003e Future studies are needed to identify the functionality of the outlier loci detected in this study.\u003c/p\u003e"},{"header":"METHODS","content":"\u003ch2\u003eStudy Species and Sample Collection\u003c/h2\u003e\n\u003cp\u003eTrees were selected from \u003cem\u003eFraxinus \u003c/em\u003espp. individuals within Fort Wayne, Indiana USA (n = 3), Huron-Clinton Metroparks, Michigan USA (n = 39), and Houghton County, Michigan USA (n = 5) (Figure 1). Within most of these locations, green ashwas the dominant species with white ash being less common. However, in Houghton County, white ashdominated. Leaf, bud, and bark morphological characteristics were used to identify species (C. E. Hale and J. M. Marshall). Vouchers were not collected. Selection of trees was based on their occurrence along an apparent gradient from high tolerance to high susceptibility (i.e. low tolerance) to \u003cem\u003eA. planipennis \u003c/em\u003eattack. Apical buds were collected from trees and placed in liquid nitrogen immediately after collection. Fort Wayne and Huron-Clinton Metropark collections were made in July 2014. Houghton County collections were made in August 2016. Once returned to the lab, samples were stored at –80 °C.\u003c/p\u003e\n\u003ch2\u003eTree Assessment\u003c/h2\u003e\n\u003cp\u003eSelected trees were assessed on vigor (overall tree health: categorical 1–5 with 1 being high vigor [crown with relatively few dead twigs; normal foliage color and density] and 5 being low vigor [more than half of crown dead]), crown dieback (percent of dead branch tips: 5–100%), and signs of \u003cem\u003eA. planipennis\u003c/em\u003e attack (presence/absence: bark splits, exit holes, woodpecker damage, epicormic sprouts). Assessments followed those conducted in previous studies [26,41–44], which were derived from Millers et al. [45]. After assessment, 47 individuals were selected for analysis and given an overall categorization of tolerant or susceptible to \u003cem\u003eA. planipennis\u003c/em\u003e infestation. This tolerant-susceptible categorization was similar to [46]. Individuals with a vigor ≤ 3 and dieback of ≤ 30 were considered tolerant. Individuals with a vigor of ≥ 3 and dieback \u0026gt; 30 were considered susceptible. Chi-squared analysis was used to test the null hypothesis that tolerance categorization was independent of species.\u003c/p\u003e\n\u003ch2\u003eDNA Extraction and Quantification\u003c/h2\u003e\n\u003cp\u003eEntire bud samples (two to three buds) were homogenized using sterile ceramic mortars and pestles, which were first cooled with liquid nitrogen. DNeasy Plant Mini Kit (QIAGEN) was used to extract total genomic DNA following the manufacturer’s protocol. DNA from each sample was quantified using UV spectrophotometry (NanoDrop 1000) absorbance. All samples were subsequently diluted to a concentration of 25 ng/μl.\u003c/p\u003e\n\u003ch2\u003eLibrary Creation and SNP Discovery\u003c/h2\u003e\n\u003cp\u003eGenomic DNA was converted into nextRAD genotyping-by-sequencing libraries (SNPsaurus, LLC) as described by Russello et al. [47]. Briefly, genomic DNA was first fragmented with Nextera reagent (Illumina, Inc), which also ligates short adapter sequences to the ends of the fragments. The Nextera reaction was scaled for fragmenting 7 ng of genomic DNA, although 14 ng of genomic DNA was used for input to compensate for the amount of degraded DNA in the samples and to increase fragment sizes. Fragmented DNA was then amplified for 25 cycles at 75 °C, with one of the primers matching the adapter and extending eight nucleotides into the genomic DNA with the selective sequence TGCAGGAG. Thus, only fragments starting with a sequence that can be hybridized by the selective sequence of the primer will be efficiently amplified. The nextRAD libraries were sequenced on a HiSeq 4000 with one lane of 150 bp reads (University of Oregon).\u003c/p\u003e\n\u003cp\u003e The genotyping analysis used custom scripts (SNPsaurus, LLC) that trimmed the reads using bbduk (BBMap tools, \u003ca href=\"http://sourceforge.net/projects/bbmap/)\"\u003e\u003ca href=\"http://sourceforge.net/projects/bbmap/\"\u003ehttp://sourceforge.net/projects/bbmap/\u003c/a\u003e)\u003c/a\u003e. Command was as follows:\u003c/p\u003e\n\n\u003cp\u003ebash \u003ca href=\"http://bbmap/bbduk.sh\"\u003ebbmap/bbduk.sh\u003c/a\u003e in = $file out = $outfile ktrim = r k = 17 hdist = 1 mink = 8 ref = bbmap/resources/nextera.fa.gz minlen = 100 ow = t qtrim = r trimq = 10\u003c/p\u003e\n\n\u003cp\u003eNext, a \u003cem\u003ede novo\u003c/em\u003e reference genome was created by collecting 10 million reads in total, evenly from the samples, and excluding reads that had counts fewer than 7 or more than 700. The remaining loci were then aligned to each other to identify allelic loci and collapse allelic haplotypes to a single representative. All reads were mapped to the reference with an alignment identity threshold of 95% using bbmap (BBMap tools). In order to assess the proportion of sequence reads that originated from \u003cem\u003eFraxinus\u003c/em\u003e spp.versus other species, 1000 high-quality reads from each sample were subject to BLASTN analysis in the NCBI database.\u003c/p\u003e\n\u003cp\u003eGenotype calling was done using SAMtools and BCFtools [48]. Command was as follows:\u003c/p\u003e\n\n\u003cp\u003esamtools mpileup -gu -Q 12 -t DP, DPR -f ref.fasta -b samples.txt | bcftools call -cv - \u0026gt; genotypes.vcf\u003c/p\u003e\n\n\u003cp\u003eThe VCF file was filtered to remove alleles with a population frequency of less than 0.03. Loci were removed that were heterozygous in all samples or had more than two alleles in a sample (suggesting collapsed paralogs). The absence of artifacts was checked by counting SNPs at each read nucleotide position and determining that SNP number did not increase with reduced base quality at the end of the read. All polymorphic sequences retained were subject to BLASTN analysis in the NCBI database.\u003c/p\u003e\n\u003cp\u003eVCFtools [49] was used to further filter SNPs based on the following criteria: (1) Phred-quality score, (2) minor allele frequency, (3) maximum missing genotype, and (4) minimum mean read depth (Table 1). Loci that failed to meet the quantification threshold for any of the filtering criteria were removed and excluded from subsequent analyses. Samples were not filtered based on Hardy-Weinberg expectations because the goal of this study was to identify polymorphic loci under selection, which are expected to deviate from equilibrium. The VCF file was converted into file formats necessary for analysis using PGDSpider 2.1.1.3 [50].\u003c/p\u003e\n\u003ch2\u003ePrincipal Component Analysis\u003c/h2\u003e\n\u003cp\u003eThe packages \u003cem\u003evcfR \u003c/em\u003ev1.7.0 and \u003cem\u003eadegenet \u003c/em\u003ev2.1.1 in R v3.4.2 [51–53] were used to perform an individual-based principal component analysis (PCA) to characterize structure based on SNP variation. PCA was also used to visualize relationships between individuals based on phenotypic characteristics (vigor, dieback, and signs of \u003cem\u003eA. planipennis\u003c/em\u003e attack) using \u003cem\u003eprcomp \u003c/em\u003eR base function. Pearson’s correlation was used to test the hypothesis that the first principle component axis for SNP PCA and the first principle component axis for phenotypic PCA had a linear relationship. Similarly, linear correlations were tested between the first principle component axis for SNP PCA with vigor and dieback values.\u003c/p\u003e\n\u003ch2\u003eDetection of Markers under Selection\u003c/h2\u003e\n\u003cp\u003eBAYESCAN v2.1 [57] was used to identify outlier loci based on populations defined by the PCA clusters, as well as populations defined by vigor rating. BAYESCAN uses a hierarchal-Bayesian method to estimate population-specific \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e coefficients, described by Beaumont and Balding [58]. A more conservative neutral model available in BAYESCAN (prior odds = 1000) was used to minimize the number of false positives. Prior odds or prior probability is the likelihood of the null hypothesis being true before the test is performed. This increase in prior odds corresponds to the selection model being 1000 times less likely than the neutral model, which was a more appropriate assumption given the number of SNPs included in this analysis [59]. After 100,000 iterations, SNPs with a posterior distribution over 0.95 were considered outliers. High \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e values (outliers) suggest that the locus has undergone directional selection (in contrast to balancing selection).\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ch3\u003e\u003cem\u003eBLASTN: \u003c/em\u003eStandard nucleotide basic local alignment search tool\u003c/h3\u003e\n\u003ch3\u003e\u003cem\u003eF\u003cem\u003e\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e: \u003c/em\u003eFixation index\u003c/h3\u003e\n\u003ch3\u003e\u003cem\u003ePCA: \u003c/em\u003ePrinciple component analysis\u003c/h3\u003e\n\u003ch3\u003e\u003cem\u003eRAD: \u003c/em\u003eRestriction site associated DNA\u003c/h3\u003e\n\u003ch3\u003e\u003cem\u003eSNP: \u003c/em\u003eSingle nucleotide polymorphism\u003c/h3\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank Nick White for field assistance and SNPsaurus, LLC for contributing to a portion of the methods. The authors would like to thank Kirk Broders for comments on an earlier version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eFinancial support for this work was partially provided by Purdue University Fort Wayne Institute for Research, Scholarship, and Creative Endeavors Collaborative Grant Program; funds were used for data acquisition.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the GenBank repository as BioProject ID: PRJNA561365, \u003ca href=\"https://www.ncbi.nlm.nih.gov/bioproject/561365\"\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/bioproject/561365\"\u003ehttps://www.ncbi.nlm.nih.gov/bioproject/561365\u003c/a\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003eAuthors’ contributions\u003c/h2\u003e\n\u003cp\u003eConceived and designed the experiments: AJS, VJN and JMM. Performed the experiments: CEH, VJN and JMM. Analyzed the data: CEH, MAJ, GB, VJN and JMM. Wrote the manuscript: CEH, VJN and JMM. All authors have read and approved the manuscript.\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent for Publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003eMcCullough DG, Katovich SA. Pest Alert. Emerald Ash Borer. NA-PR–02–04, U.S. Department of Agriculture, Forest Service. Northeastern Area, Newton Square, PA; 2004.\u003c/li\u003e\u003cli\u003eCappaert D, McCullough DG, Poland TM, Siegert NW. Emerald ash borer in Northern America: a research and regulatory challenge. 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Nat Rev Genet. 2011;12:443.\u003c/li\u003e\u003cli\u003eSchlesinger RC. Fraxinus americana L. white ash. In Silvics of North America. Volume 2: Hardwoods. Agriculture Handbook 654. US Department of Agriculture, US Forest Service, Washington, DC; 1990.\u003c/li\u003e\u003cli\u003eWallander E. Systematics of Fraxinus (Oleaceae) and evolution of dioecy. Plant System Evol. 2008;273:25–49.\u003c/li\u003e\u003cli\u003eHinsinger DD, Basak J, Gaudeul M, Cruaud C, Bertolino P, Frascaria-Lacoste N, Bousquet J. The phylogeny and biogeographic history of ashes (Fraxinus, Oleaceae) highlight the roles of migration and vicariance in the diversification of temperate trees. PLoS One. 2013;8:e80431.\u003c/li\u003e\u003cli\u003eSiegert NW, McCullough DG, Liebhold AM, Telewski FW. Dendrochronological reconstruction of the epicenter and early spread of emerald ash borer in North America. Div Distrib. 2014;20:847–858.\u003c/li\u003e\u003cli\u003eAnthony RG, Khan S, Costa J, Pais MS, Bögre L. The Arabidopsis protein kinase PTI1–2 is activated by convergent phosphatidic acid and oxidative stress signaling pathways downstream of PDK1 and OXI1. J Biol Chem. 2006;281:37536–37546.\u003c/li\u003e\u003cli\u003eFloriduz M, Terzi M, Filippini F. Comparative proteome bioinformatics: identification of a whole complement of putative protein tyrosine kinases in the model flowering plant Arabidopsis thaliana. Proteomics. 2002;2:1494–1503.\u003c/li\u003e\u003cli\u003eSessa G, D’Ascenzo M, Martin GB. The major site of the Pti1 kinase phosphorylated by the Pto kinase is located in the activation domain and is required for Pto–Pti1 physical interaction. FEBS J. 2000;267:171–178.\u003c/li\u003e\u003cli\u003eBurghardt LT, Young ND, Tiffin P. A guide to genome‐wide association mapping in plants. Curr Protoc Plant Biol. 2017;2:22–38.\u003c/li\u003e\u003cli\u003eClark RE, Boyes KN, Morgan LE, Storer AJ, Marshall JM. Development and assessment of ash mortality models in relation to emerald ash borer infestation. Arbor Urban For. 2015;41:270–278.\u003c/li\u003e\u003cli\u003eMarshall JM, Storer AJ, Fraser I, Beachy JA, Mastro VC. Effectiveness of differing trap types for the detection of emerald ash borer (Coleoptera: Buprestidae). Environ Entomol. 2009;38:1226–1234.\u003c/li\u003e\u003cli\u003eMarshall JM, Storer AJ, Fraser I, Mastro VC. Efficacy of trap and lure types for detection of Agrilus planipennis (Col., Buprestidae) at low density. J Appl Entomol. 2010;134:296–302.\u003c/li\u003e\u003cli\u003eMarshall JM, Porter MJ, Storer AJ. Predicting emerald ash borer, Agrilus planipennis (Coleoptera: Buprestidae), landing behavior on unwounded ash. Great Lakes Entomol. 2012;45:29–39.\u003c/li\u003e\u003cli\u003eMillers I, Lachangce D, Burkman WG, Allen DC. North American sugar maple decline project: organization and field methods. USDA Forest Service Gen. Tech. Rep. NE–154; 1991. http://www.fs.fed.us/ne/newtown_square/publications/technical_reports/pdfs/scanned/gtr154.pdf.\u003c/li\u003e\u003cli\u003eHietala KG. Evaluation and monitoring of ash (Fraxinus spp.) tolerant to long-term emerald ash borer (Agrilus planipennis [Coleoptera: Buprestidae]) exposure. MS thesis. USA: Michigan Technological University; 2013\u003c/li\u003e\u003cli\u003eRussello MA, Waterhouse MD, Etter PD, Johnson EA. From promise to practice: pairing non-invasive sampling with genomics in conservation. PeerJ. 2015;3:e1106.\u003c/li\u003e\u003cli\u003eLi H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–2079.\u003c/li\u003e\u003cli\u003eDanecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G. The variant call format and VCFtools. Bioinformatics. 2011;27:2156–2158.\u003c/li\u003e\u003cli\u003eLischer HEL, Excoffier L. 2012. PGDSpider: An automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics. 2012;28:298–299.\u003c/li\u003e\u003cli\u003eKnaus BJ, Grünwald NJ. VCFR: a package to manipulate and visualize variant call format data in R. Mol Ecol Res. 2017;17:44–53.\u003c/li\u003e\u003cli\u003eJombart T. Adegenet: an R package for the multivariate analysis of genetic markers. Bioinformatics. 2008;24:1403–1405.\u003c/li\u003e\u003cli\u003eR Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2017 https://www.R-project.org/.\u003c/li\u003e\u003cli\u003ePritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155:945–959.\u003c/li\u003e\u003cli\u003eEarl DA. 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Mol Ecol. 2014;23:2178–2192.\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp style=\"margin: 0in; margin-bottom: .0001pt; line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003eTable 1. Filtering criteria for polymorphic loci. Loci failing to meet the quantification threshold for any of the criteria were excluded from subsequent analysis.\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"border-collapse: collapse; border: none;\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166.8pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"222\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e\u003cstrong\u003eFiltering Criteria\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.15pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"115\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e\u003cstrong\u003eQuantification\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166.8pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"222\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003eHeterozygous in all samples\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"115\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003eFalse\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166.8pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"222\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003eMore than 2 alleles in a sample\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"115\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003eFalse\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166.8pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"222\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003ePhred-like quality score\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"115\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e\u0026gt; 20\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166.8pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"222\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003eMinor allele frequency\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"115\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e\u0026gt; 0.1\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166.8pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"222\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003eMaximum missing genotype\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"115\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e0.5\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166.8pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"222\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003eMinimum mean read depth\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"115\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e14\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva; color: #000000;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp style=\"text-align: left; line-height: 200%;\"\u003e\u003ca name=\"_Toc510696461\"\u003e\u003c/a\u003e\u003cspan style=\"line-height: 200%; color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003eTable 2. Summary of outlier loci detected between four clusters within PCA. Includes single nucleotide polymorphism (SNP) location within loci, reference/polymorphic nucleotide pairs, and \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e and q Values. False discovery rate of \u0026lt; 0.05 was used.\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"border-collapse: collapse; border: none;\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 13.95pt;\"\u003e\n\u003ctd style=\"width: 67.5pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.95pt;\" width=\"90\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e\u003cstrong\u003eLocus\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 85.85pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.95pt;\" width=\"114\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e\u003cstrong\u003eSNP Location\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 46.0pt; 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border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.95pt;\" width=\"74\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e0.022\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13.95pt;\"\u003e\n\u003ctd style=\"width: 67.5pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.95pt;\" width=\"90\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e67007_71\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 85.85pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.95pt;\" width=\"114\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e5\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 46.0pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.95pt;\" width=\"61\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003eC/A\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 56.7pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.95pt;\" width=\"76\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e0.461\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 55.8pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 13.95pt;\" width=\"74\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e0.017\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13.95pt;\"\u003e\n\u003ctd style=\"width: 67.5pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt; height: 13.95pt;\" width=\"90\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e88261_24\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 85.85pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt; height: 13.95pt;\" width=\"114\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e115\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 46.0pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt; height: 13.95pt;\" width=\"61\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003eT/C\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 56.7pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt; height: 13.95pt;\" width=\"76\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e0.51\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 55.8pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt; height: 13.95pt;\" width=\"74\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e0.004\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cspan style=\"font-size: 10pt; line-height: 200%; font-family: verdana, geneva; color: #000000;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp style=\"text-align: left; line-height: 200%;\"\u003e\u003ca name=\"_Toc510696462\"\u003e\u003c/a\u003e\u003cspan style=\"line-height: 200%; color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003eTable 3. Summary of outlier loci detected between populations based on vigor rating. Includes single nucleotide polymorphism (SNP) location within loci, reference/polymorphic nucleotide pairs, and \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e and q Values. False discovery rate of \u0026lt; 0.05 was used.\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"width: 325.5pt; border-collapse: collapse; border: none;\" width=\"434\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 24.0pt;\"\u003e\n\u003ctd style=\"width: 60.0pt; border-top: solid #7F7F7F 1.0pt; border-left: none; border-bottom: solid #7F7F7F 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 24.0pt;\" width=\"80\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e\u003cstrong\u003eLocus\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 1.25in; border-top: solid #7F7F7F 1.0pt; border-left: none; border-bottom: solid #7F7F7F 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 24.0pt;\" width=\"120\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e\u003cstrong\u003eSNP Location\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 40.5pt; border-top: solid #7F7F7F 1.0pt; border-left: none; border-bottom: solid #7F7F7F 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 24.0pt;\" width=\"54\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 76.5pt; border-top: solid #7F7F7F 1.0pt; border-left: none; border-bottom: solid #7F7F7F 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 24.0pt;\" width=\"102\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e\u003cstrong\u003e\u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 58.5pt; border-top: solid #7F7F7F 1.0pt; border-left: none; border-bottom: solid #7F7F7F 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt; height: 24.0pt;\" width=\"78\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e\u003cstrong\u003eq Value\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 24.0pt;\"\u003e\n\u003ctd style=\"width: 60.0pt; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 24.0pt;\" width=\"80\"\u003e\n\u003cp style=\"line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; font-size: 10pt;\"\u003e4467_128\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 1.25in; border: none; padding: 0in 5.4pt 0in 5.4pt; height: 24.0pt;\" width=\"120\"\u003e\n\u003cp style=\"text-align: center; line-height: 200%;\"\u003e\u003cspan style=\"color: #000000; font-family: verdana, geneva; 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Populations of ash in the United States and Canada have been decimated by the introduced pest, Agrilus planipennis (Coleoptera: Buprestidae; emerald ash borer), having both negative impacts on forest ecosystems and economic interests. The majority of trees succumb to attack by A. planipennis , but some trees have been found to be tolerant to infestation despite years of exposure. Restriction site-associated DNA (RAD) sequencing was used to sequence ash individuals, both tolerant and susceptible to A. planipennis attack, in order to identify SNP patterns related to tolerance and health declines.\u003c/p\u003e\u003cp\u003eResults A de novo reference genome was assembled and single nucleotide polymorphisms (SNPs) were called using SAMtools. After filtering criteria were implemented, a set of 17,807 SNPs were generated. Principle component analysis (PCA) of SNPs aligned individual trees into clusters related to geography, however, five tolerant trees clustered together despite geographic diversity. A subset of 32 outlier SNPs identified within this group, as well as a subset of 17 SNPs identified based on vigor rating, are candidates for selection on host tolerance.\u003c/p\u003e\u003cp\u003eConclusions Identifying genetic markers associated with host tolerance through genome-wide association has the potential to restore populations with cultivars that are able to withstand A. planipennis infestation. This study was successful in using RAD-sequencing in order to identify SNPs that are potential candidates to identify tolerance to A. planipennis . This was a first step toward uncovering the genetic basis for host tolerance to A. planipennis . Future studies are needed to identify the functionality of the loci where these SNPs occur and how they may be related to tolerance of A. planipennis attack.\u003c/p\u003e","manuscriptTitle":"Genome-wide SNP identification in Fraxinus linking genetic characteristics to tolerance of Agrilus planipennis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2019-09-07 22:08:15","doi":"10.21203/rs.2.14056/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6856bc40-28d7-430e-adca-201dd2be544a","owner":[],"postedDate":"September 7th, 2019","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":25040,"name":"Epigenetics \u0026 Genomics"}],"tags":[],"updatedAt":"2021-09-28T05:23:52+00:00","versionOfRecord":{"articleIdentity":"rs-4755","link":"https://doi.org/10.1002/ece3.8163","journal":{"identity":"ecology-and-evolution","isVorOnly":true,"title":"Ecology and Evolution"},"publishedOn":"2021-09-27 05:23:52","publishedOnDateReadable":"September 27th, 2021"},"versionCreatedAt":"2019-09-07 22:08:15","video":"","vorDoi":"10.1002/ece3.8163","vorDoiUrl":"https://doi.org/10.1002/ece3.8163","workflowStages":[]},"version":"v1","identity":"rs-4755","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"identity":"rs-4755","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}