Source: UNIVERSITY OF MISSOURI submitted to NRP
ACCELERATING MARKER-TRAIT ASSOCIATION STUDIES IN CHESTNUT (CASTANEA SPP. AND HYBRIDS)
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
ACTIVE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1027966
Grant No.
2022-67013-36287
Cumulative Award Amt.
$563,626.00
Proposal No.
2021-07675
Multistate No.
(N/A)
Project Start Date
Dec 1, 2021
Project End Date
Nov 30, 2025
Grant Year
2022
Program Code
[A1141]- Plant Health and Production and Plant Products: Plant Breeding for Agricultural Production
Recipient Organization
UNIVERSITY OF MISSOURI
(N/A)
COLUMBIA,MO 65211
Performing Department
School of Natural Resources
Non Technical Summary
Before the introduction of a devastating fungal pathogen, chestnut (Castanea) species were ecologically keystone and provided high-value timber and food products in eastern North America. Considerable advancements in genetic resources over the last 15 years have brought us close to the broad realization of chestnut across this landscape. As improvement programs continue advanced breeding selections and populations, increased capacity is needed to reveal predictive genomic regions and markers. Chestnut improvement, whether for nut production, restoration, or timber, is based on multivariate selection criteria and environments, where exceptionally large breeding generations are developed and decentralized across regions. Robust marker-assisted selection and genomic prediction will accelerate program efficiency and precision.Our overall goal for this proposal is to accelerate marker-trait association studies in chestnut. With growing genomic resources, and declining Illumina sequencing costs, the bottlenecks to these studies are now in sample preparation. Robotic technology adapted to chestnuts can accelerate the rate of DNA extraction and library preparation for sequencing, enabling greater marker and putative gene discovery. This project focuses on three objectives: (1) implement robotic technology to accelerate DNA extraction and genotyping-by-sequencing library preparation, 2) identify quantitative trait loci and develop genomic prediction in Chinese chestnut and hybrid bi-parental families for (a) phenological traits, and (b) gall wasp and phytophthora root rot resistance, and 3) target conservation of climate-adaptive genetic diversity by comparing American backcross populations to range-wide wild type American chestnuts.This project supports extensive chestnut breeding efforts that seek to release improved plant materials broadly throughout the East and Midwest US. These efforts are challenged by complex selection criteria and the need for exceedingly large populations with a large physical footprint and a long juvenile period. Nevertheless, dedicated institutions and stakeholders have progressed populations that can now be exploited by modern technology and genetics. Broader impacts include engaging students in breeding and genetics disciplines, sharing discoveries with stakeholder groups, connecting researchers from multiple breeding programs, and implementing selection tools that directly lead to the adoption of improved germplasm.
Animal Health Component
34%
Research Effort Categories
Basic
33%
Applied
34%
Developmental
33%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
2031219108011%
2111219108012%
2121219108011%
2011219108033%
2021219108033%
Knowledge Area
212 - Pathogens and Nematodes Affecting Plants; 211 - Insects, Mites, and Other Arthropods Affecting Plants; 203 - Plant Biological Efficiency and Abiotic Stresses Affecting Plants; 202 - Plant Genetic Resources; 201 - Plant Genome, Genetics, and Genetic Mechanisms;

Subject Of Investigation
1219 - Edible tree nuts, general/other;

Field Of Science
1080 - Genetics;
Goals / Objectives
Our primary goal in the proposed project is to build breeding program capacity to discover genomic regions and markers that are predictive of abiotic and biotic adaptations and to introduce beneficial loci into desired genetic backgrounds, defined by ancestry, endemic region, or commercial attributes. Understanding donor trait genetic control is crucial to selecting desired genotypes accurately. DNA markers linked to resistance/adaptive loci could reduce the size of breeding populations before field establishment and curtail arduous multi-trait screening requirements. Recent progress in genome sequencing and re-sequencing of prominent chestnut breeding parents enables genome-wide haplotype phasing for marker-trait association studies. This advancement creates the ability to track donor and recurrent parent genomic regions in offspring, deepen the power of association studies, and increase the potential to facilitate progeny selection. The ability to realize these benefits with regularity, for many traits in large and diverse populations, requires addressing workflow bottlenecks. By addressing these bottlenecks, greater opportunities in both ecological restoration and high-value nut production can be realized.Our central rationale for the proposal is that improving DNA extraction and library preparation throughput will eliminate bottlenecks to discovering marker-traits associations and enhance output rates. Supporting Objectives aim to expedite the rate of sending samples to high-throughput sequencing and demonstrate application in robust, high-quality QTL/allele discovery through the following:Obj. 1 Implement high throughput robotic technology to accelerate DNA extraction and genotyping-by-sequencing library preparationWe will test two protocols for gDNA extraction: (1) aMagnetic particle-based method and (2) apublished custom method that does not require magnetic beads(Anderson et al., 2018).We will develop two approaches to high-throughput library preparation: (1) genotyping-by-sequencing and (2) whole-genome sequencing.We will first design custom adapters that will allow as many as 768 samples to be multiplexed in an Illumina NovaSeq S4 flow cell lane.To design adapters that enable this deep level of multiplexing, which in turn allows us to take advantage of the favorable cost per base-pair of the S4 flow cell, we will follow the approach of Bayona-Vasquez et al. (2019).We will optimize whole-genome library preparation using the NEBNext Ultra FS II kit. This method will be straightforward to automate on the EpMotion, and our primary objective will be to test downscaling the volume to extend the number of samples that can be prepped per kit.Obj. 2 Develop genomic prediction and identify QTLs for phenological traits, gall wasp resistance, and PRR resistance in Chinese chestnut bi-parental mapping populationsDetect QTLs in multi-parent populations segregating, for each of the three respectivetraits listed. A hidden markov model (e.g. the RABBIT algorithm; Zheng et al. 2015) will be applied to the genotypes of the phenotyped progeny to infer local identity by descent (IBD) probabilities. Then, markers will be pruned based on linkage disequilibrium in SNPRelate to a density that represents local ancestry haplotypes (Zheng et al. 2012). Best linear unbiased predictions of breeding values will be estimated with a mixed model in ASReml-R 4.1 (Butler et al. 2018) from the pedigree of phenotyped trees. QTLs will be detected by regressing parental allelic dosages against the breeding values in r/qtl2 (Broman et al. 2019). Population structure will be accounted for with kinship matrices using the "leave one chromosome out" method (Yang et al. 2014; Broman et al. 2019).Genomic prediction for PRR resistance, phenology, and gall wasp resistance will be performed with the single step best linear unbiased predictions method (Legerra et al. 2009).Model the potential to reduce genotyping costs for genomic prediction: test genomic prediction accuracy by re-running prediction with 80% of the training populationand comparing the estimated allelic effects that underlieQTLs to the effects estimated in thefull training population using cross validation.Obj 3. Target conservation of climate-adaptive genetic diversity through comparison of American chestnut backcross populations to range-wide wild type American chestnutsCompare allele frequencies at loci putatively underlying local adaptation in a range-wide sample of wild-type American chestnuts to allele frequencies at these same loci in American chestnut backcross hybrids that founded TACF's breeding program.
Project Methods
Obj 1. Implemented high throughput robotic technology to accelerate DNA extraction and library preparation.Effort: We will test two protocols for gDNA extraction: one magnetic particle-based and one published custom method that does not require magnetic beads. For the former, we will test the MagAttract 96 Plant DNA Core Kit manufactured by Qiagen. The principle behind this method is to first disrupt tissue in deep well plates and steel beads in a mixer mill, and subsequently resuspend in lysis buffer, centrifuge, and transfer to custom microplates containing the magnetic particles, which selectively bind gDNA. The advantage of this method is its simplicity, the disadvantage is cost (~$2/sample). The second method (Anderson et al., 2018) requires more intervention by the technician but is significantly more cost effective ($0.62/sample). This method involves homogenization with sodium chloride buffer, binding with guanidinium chloride/TE buffer, wash, and precipitation. Evaluation: Metrics to compare the success of each method will include 260/280 and 260/230 ratios and their variance as measured on a spectrophotometer, gDNA concentration and its variance, and visual inspection on a 1% agarose gel (consistent high molecular weight).Effort: We will develop two approaches to high-throughput library preparation: genotyping-by-sequencing and whole-genome sequencing. Evaluation: For the former, we will first design custom adapters that will allow as many as 768 samples to be multiplexed in an Illumina NovaSeq S4 flow cell lane. We currently sequence 96 GBS libraries in an SP lane, at a cost of ~$2,200. The S4 lane produces approximately 7-10X as much data as SP lane, the cost of S4 lanes at present is about twice that of SP. We will follow the approach of Bayona-Vasquez et al. (2019) to design adapters, in which they developed a GBS workflow ("Adapterama") suitable for automation, with adapter/index designs enabling up to quadruple barcoding. Thus, in combination, their barcoding scheme allows for multiplexing many thousands of samples if desired. With the liquid handler suitably programmed, this approach can be carried out with minimal intervention from the technician. We will also optimize whole-genome library preparation using the NEBNext Ultra FS II kit. This method will be automated on the EpMotion to test downscaling of the reagent volume to extend the number of samples that can be prepped per kit. Evaluation: We have completed preliminary tests with this kit and find that using ¼ reagents volumes from this kit yields suitable sequencing libraries, which reduces consumable costs from ~$40 to ~$10/sample.Obj 2. Develop genomic prediction models and identify QTLs for late leafing, gall wasp resistance, and PRR resistance in Chinese chestnut multi-parent bi-parental populations.Effort: Detect QTLs for the three traits listed in multi-parent populations. Given that the QTL discovery populations for PRR, phenology, and gall wasp resistance were derived from crosses among multiple parents, we will use a multipopulation approach (Scott et al. 2020). First, a hidden markov model (the RABBIT algorithm; Zheng et al. 2015) will be applied to the genotypes of the phenotyped progeny to infer local identity by descent (IBD) probabilities, defined as the probability that an allele at a genomic position was inherited from a specific parent. Prior to QTL analysis, markers will be pruned based on linkage disequilibrium in SNPRelate to a density that represents the mosaic of local ancestry haplotypes (Zheng et al. 2012). To maximize power to detect QTL, best linear unbiased predictions (BLUPs) of breeding values, which reflect the additive genetic component of the phenotypes, will be estimated with a mixed model in ASReml-R 4.1 (Butler et al. 2018) from the pedigree of phenotyped trees after accounting for experimental design effects. Evaluation: QTLs will be detected by regressing parental allelic dosages against the breeding values in r/qtl2 (Broman et al. 2019). Population structure will be accounted for with kinship matrices using the "leave one chromosome out" method (Yang et al. 2014; Broman et al. 2019).Effort: Train genomic prediction models for PRR resistance, late leafing, and gall wasp resistance in the multi-parent populations using the single step best linear unbiased predictions (ssBLUP) method (Legerra et al. 2009). Genomic prediction will be performed with the single step BLUP (ssBLUP) method (Legerra et al. 2009). In the ssBLUP method, breeding values for non-phenotyped individuals are predicted from a blend of pedigree and genomic relationships with phenotyped relatives. The ssBLUP method has the advantage of estimating breeding values for both genotyped and non-genotyped individuals with a single model. Evaluation: Phenotypic prediction accuracy of the ssBLUP method will be assessed over 25 replicates of five fold cross validation as the average correlation between the observed phenotypes and genomic estimated breeding values.Effort: Model the potential to reduce genotyping costs for genomic prediction. To determine if genotyping costs could potentially be reduced by genotyping subsets of markers within QTL regions, genomic prediction will also be performed by first performing QTL discovery in 80% of the training population. Allelic effects will be estimated for markers within QTL intervals with rrBLUP (Endelman et al. 2011), then QTL estimated breeding values will be obtained by multiplying QTL allele effects by marker genotypes for the remaining 20% of the training population. Evaluation: Average phenotypic prediction accuracy of ssBLUP and rrBLUP with markers in QTL will be compared cross validation framework.Oj 3. Target conservation of climate-adaptive genetic diversity through comparison of American chestnut backcross populations to range-wide wild type American chestnutsEffort: Compare allele frequencies at loci putatively underlying local adaptation in a range-wide sample of wild-type American chestnuts to allele frequencies at these same loci in American chestnut backcross hybrids that founded TACF's breeding program. We will classify SNPs with significant genotype-environment associations in the wild as putatively adaptive by performing single marker (e.g., BayEnv2) and multivariate (e.g., redundancy analysis or RDA) genome environment association (GEA) analyses to discover loci underlying local adaptation, with which we will use regression trees to divide the range into adaptive provenances that are relatively homogeneous with respect to their genome-wide adaptive allelic content. To compare adaptive allele frequencies, we will divide the wild-type American chestnut founders of the TACF breeding program into the same groups according to their geographic origin. We will categorize the environment-associated alleles as 'positive effect alleles' (PEA) that have a positive association with significant RDA environmental axes (MacLachlan et al. 2020). For example, we expect RDA will show that mean annual temperature is correlated with geography and that a subset of SNPs will be significantly associated with temperature. The PEA for these SNPs will be the allele that increases in frequency as temperature decreases. Evaluation: We will compare cognate geographic groups (wild-type and backcross founders) for their distributions and means of positive effect alleles and test for differences using a Wilcoxon rank-sum test. One possible result is that the breeding program has captured most (>95%) of the adaptive diversity from the wild population - existing orchards can diversify the GE population without additional germplasm conservation from wild populations. Another possibility is that the breeding program has under-sampled adaptive alleles in particular portions of the American chestnut range - helping us strategically target our germplasm conservation efforts to regions with under-sampled adaptive diversity.

Progress 12/01/23 to 11/30/24

Outputs
Target Audience:Target audiences were reached through two peer-reviewed manuscripts, two conference presentation, and one grower workshop. The peer reviewed manuscripts in PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA andNATURE ECOLOGY & EVOLUTIONreached academic audiences in breeding, genetics, genomics, and abiotic stress tolerance. Outreach efforts (newsletters, social media, presentations) help deliver content from these manuscripts directly to chestnut researchers and stakeholders (e.g., growers, potential adopters proponents, the public) of American chestnut restoration and Chinese chestnut production. Presentations were given at the Translational Plant Science Center Annual Symposium,theUniversity of Missouri Center for Agroforestry Annual Review, and theAlley Cropping Workshopat the University of Missouri. Changes/Problems:As previously reported:We have had a couple setbacks that have delayed implementation of high throughput genotyping, and as a result have continued DNA isolations and library preparation using our standard procedures to meet the objectives of this project. Specifically, we experienced two mechanical issues with the high throughput tissue grinder purchased for this objective that required it be returned to the manufacturer for replacement. In addition, in late 2022 our research associate, who is responsible for all our genotyping efforts, suffered a serious wrist injury that caused her to be on leave for approximately two months, and due to the site of injury, required a slow transition back to work in 2023. In a pivot to throughput GBS library assembly, coPIs have developed a cost-effective and multifunctional DArTag array that is now available to Castanea scientists in use/testing. For the same reasons as listed above, there have been some delays in generating sequencing data for mapping populations, but data is now becoming available for QTL analysis (Obj 2) As previously reported:Our 2022 mapping population for phytophthora root rot became contaminated with an unintended root pathogen. The population was replaced in 2023 with newly developed seed and then screened in 2024; data analysis in progress. An additional population will be screening in 2025 What opportunities for training and professional development has the project provided?Over the past year, participants in the project at VA Tech have included Dr. Joanna Malukiewicz, led an effort to use comparative genomics to identify genes that govern blight resistance in Asian Castanea species, and research Associate Dr. Qian Zhang, who manages our laboratory, has assisted with optimizing our gDNA extraction protocols to meet the quantity/quality and developed the sequencing libraries. Participants at MU have included MS student Aubrey Teckam and research specialist Jericha Hervey have assisted with tissue resampling and record keeping of populations, DNA isolation, and lab preparation for high-throughput DNA extraction experiments. How have the results been disseminated to communities of interest?In addition to journal publications and conference presentations detailed above, our sequencing data have been deposited in the GenBank short read archive (PRJNA804196). What do you plan to do during the next reporting period to accomplish the goals?Objectives 1: Cost-benefit experiments for throughput 96-well-plate DNA extraction DArTag testing in different Castanea populations Objectives 2: Final year of vegetative phenology data collection on bud break and QTL analysis Gall wasp phenotyping and genotyping. Phytophthora root root: additional population screening and genotyping; QTL anaylsis

Impacts
What was accomplished under these goals? Objective 1: As reported in previous periods, we've had a couple setbacks that have delayed implementation of high throughput genotyping, and as a result have continued DNA isolations and library preparation using our standard procedures to meet the objectives of this project. During the last period, DNA extraction was performed using standard procedures for 390 individuals of the gall wasp mapping population and 205 individuals of a phytophthora root rot population. During this period the Revord lab has established a throughput 96-well-plate DNA extraction workflow, using a Spex Minig 1600 machine, and has prepared to begin cost-benefit experiment on DNA extraction yield/quality. During the period, we extracted 672 Castanea samples were sequenced in service of the subsequent objectives of the University of Missouri Center for Agroforestry (Objective 2, led by PI Ron Revord) and The American Chestnut Foundation's core breeding program at Meadowview, VA (Objective 3, led by Co-PI Jared Westbrook). To support high-throughput genotyping, in addition to GBS sequencing, a 6k DArTag array was developed by coPI Jared Westbrook (The American Chestnut Foundation) comprised of a targeted panel of markers for genotyping that are suitable to a variety of chestnut breeding application: 1. Genomic prediction disease resistance and forest competitiveness in American chestnut (Castanea dentata) backcross hybrid populations. 2. Genomic prediction of culinary traits, cold hardiness, and other traits in Chinese chestnut (C. mollissima) and chestnut hybrid populations. 3. Assessment of local and global species ancestry in chestnut hybrids (primarily [C. mollissima x C. dentata] x C. dentata backcross hybrids. 4. Quantitative trait locus mapping in chestnut hybrids and pure species. 5. Prediction of origin location for C. dentata samples that have been planted outside of their native range. 6. Assessment of oxalate oxidase inheritance and zygosity in transgenic American chestnut populations. Markers were discovered for the 6k panel using the following steps: We had four unimputed whole genome datasets aligned by Westbrook to the C. dentata v 1.1 genome from which to filter SNPs: 1. Chestnut reference panel - 133 individuals representing all Castanea species sequenced to 15 x depth. 2. Dentata - 356 American chestnuts from throughout the species range sequenced to 18 x depth. 3. Revord - 94 accessions from Ron Revord including North American C. mollissima, assorted hybrids, and C. ozarkensis sequenced to 21x depth. 4. Backcross - 371 American chestnut backcross hybrids, wild type C. dentata accessions conserved in orchards, and large surviving American chestnuts sequenced to 9x depth. Markers were filtered for the 6k panel using the following steps: 1. Subset all variants in +/- 1 kb from transcription start and end sites of single copy orthologs between C. mollissima and C. dentata. 2. Mask repeat regions from the 1x orthologs. 3. Subset biallelic SNPs with QUAL > 30 and greater than 5 bp from an indel. 4. Drop individuals from each dataset that had high missing genotype calls. 5. Filter SNPs by allele depth > 0 for reference allele or > 1 for alternative allele. 6. Filter out SNPs with low and high overall read depth. 7. Retain SNPs with MAF > 1/2n & with zero missing genotype calls inRevord andchestnut ref and < 10%missingcalls in dentataand backcross8. Subset SNPs that pass these filteringcriteria and arepresent in all four datasets. 7. Merge filtered datasets. Markers were selected for the 6k panel using the following steps: 1. Select 2 markers per cM with Fst > 0.7 between C. mollissima and C. dentata. 2. Select a maximum of 10 markers per cM with MAF > 0.1 in C. mollissima and C. dentata. 3. Add addition markers with MAF > 0.1 in C. mollissima or C. dentata in 1 cM bins not represented by markers with MAF>0.1 in both species. 4. Select 195 dentata/mollissima MAF > 0.1 markers previously genotyped with GBS (max 1 per cM). 5. Select 82 markers that were associated with climatic variation in C. dentata (max 1 per cM). 6. Add in additional species ancestry informative markers (e.g. 312 mollissima v. crenata, 329 dentata v. pumila, 31 pumila v. ozarkensis, and 67 pumila v. ozarkensis markers) Objective 2: Bud break: A third year of phenotypic data was collected for the multiparent mapping population ('PQK') segregating for budbreak date. GBS sequencing data was processed (filtering and SNP calling) to a VCF file and is now available for QTL analysis. Gall wasp: DNA was isolated, lyophilized, and is awaiting genotyping on multi-species array; currently quality testing the array. PPR: A population of 205 individual was screened for phytophthora root rot and subsequently phenotyped. Leaf tissue from these individuals were collected and genotyped using the DArTag described above. Preliminary analyses (preceding QTL mapping) are ongoing by Jared Westbrook to discern segregating of ancestry among progeny: ancestry inference in 'ancestry HMM', DAPC + DART, and additional pedigree base imputation methods. Objectives 3: Analyses reported in the previous period were published: Sandercock A.M., Westbrook J.W., Zhang Q. & Holliday J.A. (2024). A genome- guided strategy for climate resilience in American chestnut restoration populations. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 121(30), 12 pages. doi:10.1073/pnas.2403505121

Publications

  • Type: Peer Reviewed Journal Articles Status: Published Year Published: 2024 Citation: Sandercock A.M., Westbrook J.W., Zhang Q. & Holliday J.A. (2024). A genome- guided strategy for climate resilience in American chestnut restoration populations. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 121(30), 12 pages. doi:10.1073/pnas.2403505121
  • Type: Peer Reviewed Journal Articles Status: Published Year Published: 2024 Citation: Whiting J.R., Booker T.R., Rougeux C., Lind B.M., Singh P., Lu M., Huang K., Whitlock M.C., Aitken S.N., Andrew R.L., Borevitz J.O., Bruhl J.J., Collins T.L., Fischer M.C., Hodgins K.A., Holliday J.A., Ingvarsson P.K., Janes J.K., Khandaker M., Koenig D., Kreiner J.M., Kremer A., Lascoux M., Leroy T., Milesi P., Murray K.D., Pyhajarvi T., Rellstab C., Rieseberg L.H., Roux F., Stinchcombe J.R., Telford I.R H., Todesco M., Tyrmi J.S., Wang B., Weigel D., Willi Y., Wright S.I., Zhou L. & Yeaman S. (2024). The genetic architecture of repeated local adaptation to climate in distantly related plants. NATURE ECOLOGY & EVOLUTION, 8(10), 26 pages. doi:10.1038/s41559-024-02514-5
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Holliday J., Sandercock A. & Westbrook J. (2024). Quantitative, functional, and comparative genomic tools for species restoration: the case of American chestnut. In Translational Plant Science Center Annual Symposium. Blacksburg, VA.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Revord, R.S., Sandercock A. & Holliday, J. (2024). Diversity and ancestry of on-farm chestnut selection across the eastern U.S. University of Missouri Annual Review. Columbia, MO.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Revord, R.S. (2024). Chestnut ancestry and genetic research updates. University of Missouri Chestnut Alley Cropping Workshop. New Franklin, MO.


Progress 12/01/22 to 11/30/23

Outputs
Target Audience:Target audiences were reached through one peer-reviewed manuscripts and five conference presentations. The peer reviewed manuscript in bioRxiv reached academic audiences in breeding, genetics, and plant pathology. Outreach efforts (newsletters, social media, presentations) help deliver content from these manuscripts directly to chestnut researchers and stakeholders (e.g., growers, potential adoptersproponents, the public) of American chestnut restoration and Chinese chestnut production. Presentations were given at the TACF Annual Meeting, Southern Forest Tree Improvement Conference, VII Encuentro Científico en Biología Vegetal y Biotecnología, de moléculas a ecosistemas,Department seminar at University of Concepcion, Concepcion, Chile, and the Chestnut Growers of America and Northern Nut Growers Association Joint-Annual Meeting. Changes/Problems:We have had a couple setbacks that have delayed implementation of high throughput genotyping, and as a result have continued DNA isolations and library preparation using our standard procedures to meet the objectives of this project. Specifically, we experienced two mechanical issues with the high throughput tissue grinder purchased for this objective that required it be returned to the manufacturer for replacement.In addition, in late 2022 our research associate, who is responsible for all our genotyping efforts, suffered a serious wrist injury that caused her to be on leave for approximately two months, and due to the site of injury, required a slow transition back to work in 2023. Our 2022mapping population for phytophthora root rot became contaminated with an unintended root pathogen. The population was replaced in 2023 with newly developed seed, which has already been delivered to the screening facility. What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest?The results or objective status have been communicated to communities of interest through the reported five conference presentations. What do you plan to do during the next reporting period to accomplish the goals?Project activities will continue according to the project plan, where the 2023 progress left off and include: Resume steps to implement/test throughput genotyping (obj 1), including testing of the new DArT array. Phenotyping each multi-parent mapping population (obj 2). Tissue grinding, DNA isolation, and GBS library development (or genotyping via the DArT array) for the Phytophthora and gall wasp mapping populations. Performing QTL analyses and genomic prediction on populations segregting for budbreak and Phytophthora root rot incidence

Impacts
What was accomplished under these goals? Objective 1:We have had a couple setbacks that have delayed implementation of high throughput genotyping, and as a result have continued DNA isolations and library preparation using our standard procedures to meet the objectives of this project. Specifically, we experienced two mechanical issues with the high throughput tissue grinder purchased for this objective that required it be returned to the manufacturer for replacement. In addition, in late 2022 our research associate, who is responsible for all our genotyping efforts, suffered a serious wrist injury that caused her to be on leave for approximately two months, and due to the site of injury, required a slow transition back to work. Nevertheless, during 2023 we extracted gDNA from 1244 Castanea samples from a variety of breeding programs/sources. Major cohorts among these samples arise from the University of Missouri Center for Agroforestry (Objective 2, led by PI Ron Revord), The American Chestnut Foundation's core breeding program at Meadowview, VA (Objective 3, led by Co-PI Jared Westbrook), and a trial of hybrid Japanese, Chinese, and American chestnut germplasm at Lesesne State Forest in Virginia, managed by the Virginia Department of Forestry (Objective 3, led by Co-PI Jared Westbrook). Numerous additional small cohorts of samples were provided by various stakeholders, including TACF state chapter breeding programs, private horticulturalists developing and distributing putatively blight resistant chestnut germplasm, and samples from large surviving American chestnuts in the wild that are putatively blight resistant. In addition to GBS sequencing, which was completed for 366 of these samples and is in process for a subset of the remainder, we are developing a targeted panel of markers for DaRT genotyphing of chestnut backcross material for genomic prediction, which will be tested on approximately 450 of the samples noted above. Objective 2: A second year of phenotypic data was collected for the multiparent mapping population ('PQK') segregating for budbreak date and GBS libraries were prepared and sequenced. Sequencing data has been returned and is ready for SNP calling and analysis. An additional 83offsrping were added to the gall wasp multi-parent family (now n=623), and leaf tissue was collected and stored for DNA isolation from all offspring. The third multiparent familiy, destined for Phytophthora inoculation, had to be discarded due to contamination from an unknown root pathogen. The population was replaced through new seed developed in 2023 and delivered to a specialized screening facility in North Carolina for inoculation in winter 2024. Objective 3:To understand the extent to which wild diversity has been captured by the TACF backcross breeding program, we sent previously extracted gDNA for 384 of their blight and/or Phytophthora resistant selections for whole-genome sequencing, and combined these data with those of 384 wild American chestnut stump sprouts (Sandercock et al. 2022). With these data from wild re-sprouts, we previously characterized natural adaptive diversity across the historical American chestnut range, using genotype-environment analysis, and developed a strategy for capturing that diversity through grafting and pollen collection (Sandercock et al., 2024). An outcome of this analysis of wild trees was that rangewide adaptive diversity can be roughly partitioned into northern, central, and southern portions of the historical range, which we refer to respectively as Seed Zones 1-3. These findings indicate that the backcross breeding strategy effectively captures a significant portion of wild adaptive genomic diversity. However, two notable aspects require attention. Firstly, about 20% residual variance in adaptive allele frequency distribution exists between wild and backcross groups. This is influenced by C. mollissima ancestry and a bias towards medium-frequency alleles in the wild that are rare in the backcross group. The potential fixation of these low-frequency alleles needs consideration in future selections. Second, only approximately 8.8% of adaptive ancestry is associated with Seed Zone 3, located in the southern region of the historical chestnut range with high genomic diversity. Future efforts should focus on this underrepresented region, which holds a reservoir of historical diversity less affected by cyclical bottlenecks and likely comprises unique alleles crucial under climate change. Sandercock, A. M., Westbrook, J. W., Zhang, Q., Johnson, H. A., Saielli, T. M., Scrivani, J. A., Holliday, J. A. (2022). Frozen in time: Rangewide genomic diversity, structure, and demographic history of relict American chestnut populations. Molecular Ecology, 31(18), 4640-4655. doi:10.1111/mec.16629 Sandercock A., Westbrook J., Zhang Q. & Holliday J. (2023). The road to restoration: Identifying and conserving the adaptive legacy of American chestnut. bioRxiv. doi:10.1101/2023.05.30.542850

Publications

  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Sandercock A., Westbrook J., Zhang Q. & Holliday J. (2023). The road to restoration: Identifying and conserving the adaptive legacy of American chestnut. bioRxiv. doi:10.1101/2023.05.30.542850
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: 1. Holliday J., Westbrook J., Sandercock A. & Malukiewicz J. (2023). Quantitative, functional, and comparative genomic tools for species restoration: the case of American chestnut. In Southern Forest Tree Improvement Conference. Keynote Address. Knoxville, TN.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: 2. Holliday J., Westbrook J., Malukiewicz J. & Sandercock A. (2023, September 27). Quantitative, functional, and comparative genomic tools for species restoration: the case of American chestnut. In VII Encuentro Cient�fico en Biolog�a Vegetal y Biotecnolog�a, de mol�culas a ecosistemas. Keynote Address. University of Talca, Chile.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: 3. Holliday J., Malukiewicz J., Westbrook J. & Sandercock A. (2023). Quantitative, functional, and comparative genomic tools for species restoration: the case of American chestnut. Department seminar, University of Concepcion, Concepcion, Chile.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Holliday J., Sandercock A. & Westbrook J. (2023). Genomic tools for American chestnut restoration. In Forest Genetics 2023. Vernon, BC, Canada.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Revord, R.S., Meier, N.A., and M.A. Gold. Keynote: Chestnut research updated from the University of Missouri. 110th Northern Nut Growers Association Conference. Columbia, Missouri. July 2023.


Progress 12/01/21 to 11/30/22

Outputs
Target Audience:Target audiences were reachedthrough three peer-reviewed manuscripts and four conference presentations. The peer-reviewed manuscripts in Frontiers in Plant Science,Molecular Ecology, and Plant Disease broadly reached academic audiences in breeding, genetics, and plant pathology. Outreach efforts (newsletters, social media, presentations) help deliver content from these manuscripts directly to chestnut researchers and stakeholders (e.g., growers, potential adopters, proponents, the public) of American chestnut restoration and Chinese chestnut production. Presentations were given at the TACF Annual Meeting,IUFRO Tree Biotechnology Conference,TACF Science and Technology Committee Annual Meeting, and the American Society for Horticulture Science Annual Conference. Changes/Problems:Research Associate Qian Zhang suffered an injury outside of work that meant an ~3 month absence in late 2022. This meant our sequencing library preparation is behindschedule, but she has now returned. What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Objective 1:The first step in developing a high-throughput GBS workflow was to automate tissue disruption in deep well plates, which we historically achieved by hand grinding. Because plant tissue in general and chestnut leaves in particular can be recalcitrant to DNA release and purification, as compared with animal tissue, Research Associate Qian Zhang investigated all available machines within our price range (~$10,000) to test their ability to sufficiently disrupt our samples. She located other labs on campus that had the following machines: Qiagen Tissuelyser, MP Bio FastPrep24, SPEX Genogrinder, and SPEX MiniG. The parameters we were interested in were (1) quality of tissue disruption, (2) ease of use (in particular the quality and ease of use of the clamp that holds the samples), (3) resulting DNA quality following downstream extraction steps (after grinding), (4) time required to grind, and (5) number of samples that can be processed simultaneously. Dr. Zhang found that the SPEX MiniG was the best compromise among these parameters, and we are currently working to optimize a plate-based extraction protocol. In parallel, we have continued DNA isolations and library preparation associated with objectives 2 and 3of this project. Objective 2: Leaf tissue was collected from a multi-parent mapping population (n=403) segregating for bud break time (late bud break donated by C. mollissima'Qing'), and year 1 data was collected.Tissue is ground, and DNA isolation+ GBS library development is in progress. The population wasincorporated into our germplasm database and barcode labeled. A second multi-parentmappingpopulation for gall wasp and leaf pest tolerance (n=540) was cycled through our nursery to field establishment under high pest incidence. A third multi-parent mapping population (n=403) segregating for Phytophthora root rot resistance was nursery grown in preparation for 2023 inoculation. Objective 3:Most notably, we identified and extracted DNA from 384 hybrid/backcross trees from among the families with highest chestnut blight resistance in The American Chestnut Foundation's breeding program, and sent these to the Duke University core facility for whole-genome library preparation followed by 10X sequencing. The goal of this effort is to understand the extent to which the TACF program has captured adaptive diversity through pollination over successive generations with pollen collected from rare, wild, flowering trees. To do this, we are leveraging a whole-genome dataset of 384 wild trees, which we previously used to characterize the genomic basis for local adaptation in American chestnut. Data for the backcross samples was recently returned and bioinformatic analyses are in progress.

Publications

  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Sandercock AM, Westbrook J, Zhang Q, Johnson H, Saielli T, Scrivani J, Fitzsimmons S, Collins K, Schmutz J, Grimwood J, Holliday JA (2022) Frozen in time: Rangewide genomic diversity, structure, and demographic history of relict American chestnut populations. Molecular Ecology 31 (18), 4640-4655.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Conn CE, Howie N, Lynch M, Lee S, Young E, Westbrook JW, Holliday JA, Zhang Q, Cipollini M (2022) Validation of an alternative small stem assay for blight resistance in backcross hybrid chestnuts (Castanea spp.) and recommendations for its expanded use. Plant Disease. 2022/11/16.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Revord RS, Miller G, Meier NA, Webber JB, Romero-Severson J, Gold MA, and Lovell ST (2022) A Roadmap for Participatory Chestnut Breeding for Nut Production in the Eastern United States. Frontiers in Plant Science 12 p.3057.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Holliday JA, Sandercock A, Westbrook J. Discovery of candidate genes for blight and root rot resistance in Castanea. TACF Annual Meeting (Invited). Sept 30-Oct , 2022.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Holliday JA, Sandercock A, Westbrook J. Genomic tools for species restoration: the case of American chestnut (Castanea dentata). IUFRO Tree Biotechnology Conference (Invited). July 6-8, 2022.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Sandercock, A., Holliday, J., & Westbrook, J. (2021) Landscape genomics of American chestnut. In TACF Science and Technology Committee Annual Meeting. Online.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Webber, J.B., Revord, R., Meier, N. and Miller, G. A Germplasm Management Database to Support on-Farm Conservation and Genetic Improvement of Chestnut for Nut Production in the Eastern US. In 2022 ASHS Annual Conference. ASHS. July 2022