Source: KANSAS STATE UNIV submitted to NRP
EMPOWERING WHEAT BREEDING USING THE GENOMIC SIGNALS OF ECO-GEOGRAPHIC ADAPTATION
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1008356
Grant No.
2016-67013-24473
Cumulative Award Amt.
$301,463.00
Proposal No.
2015-05831
Multistate No.
(N/A)
Project Start Date
Nov 1, 2015
Project End Date
Oct 31, 2019
Grant Year
2016
Program Code
[A1141]- Plant Health and Production and Plant Products: Plant Breeding for Agricultural Production
Recipient Organization
KANSAS STATE UNIV
(N/A)
MANHATTAN,KS 66506
Performing Department
Plant Pathology
Non Technical Summary
Genetic improvement of crops remains one of the critical elements of strategies aimed at reducing the effect of climate change on crop production and doubling agricultural outputs by 2050. A crop's ability to adapt to changing environments depends on the availability of variants of genes increasing plant's resilience to climatic extremes. Owing to their broad geographic distribution, crops and their wild relatives are a rich model system for investigating the genetic mechanisms of local adaptation, providing a valuable source of new diversity, of which only a small fraction so far has been used for improving crops' adaptive potential.An effective strategy for developing climate resilient varieties is to use variants of genes from the wild relatives of wheat. Previous studies showed wild relatives from stressed environments are a valuable source of new diversity for improving the drought and heat adaptive potential of wheat.With the advent of next-generation sequencing (NGS) technologies it has become possible to generate whole genome variation data for large geographically diverse populations of plants. These data combined with climatic data collected from the sites of sample collection were successfully used to identify variation in DNA sequence that helps plants to adapt to local environments. The main rationale behind these approaches referred to as genome-wide environmental scans (GWES), is that variation contributing to local adaptation can be identified based on an unusually high correlation between their frequencies in local populations and local climatic variables. Importantly, studies on a model plant species demonstrated that the presence of genetic variation correlating with climate in a specific plant line could serve as a good predictor of its performance in a specific environment. The proposed project will develop a strategy based on the NGS of wild goatgrass, one of the wild relatives of wheat, collected from regions along the environmental gradients to identify DNA sequence variation contributing to climatic adaptation. A genome-wide variation dataset will be tested for correlation with seasonal variation in temperature, precipitation, solar radiation, season lengths, and aridity.We will perform genetic crosses of wheat lines adapted to Kansas environment with multiple goatgrass accessions to develop populations that will carry the segments of wild chromosomes. These populations will be tested for the presence of genetic variation strongly associated with climatic variables, and will be planted at several locations across the state of Kansas showing significant seasonal differences in the amount of precipitation and temperature. Performance of wheat lines will be assessed by collecting phenotyping data reflecting their ability to cope with variation in temperature and water availability. These data will be used to test the utility of climate-associated variation to predict performance of wheat lines in Kansas environments. The information collected will be valuable for developing a new generation of genomic prediction models that will more effectively handle the uncertainty of phenotype predictions in diverse environments, and increase the overall effectiveness of crop breeding for targeted climatic conditions. The project efforts will also lead to the development of novel germplasm that will be used to support variety development and will be shared with the wheat breeding community. The long-term goal of the proposed project is to empower the US and international wheat breeding programs by establishing genomic and statistical tools that will leverage information about the genomic signals of eco-geographic adaptation to prioritize global genetic diversity in germplasm repositories for deployment in breeding programs.
Animal Health Component
30%
Research Effort Categories
Basic
70%
Applied
30%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
2011549108070%
2031540108130%
Knowledge Area
203 - Plant Biological Efficiency and Abiotic Stresses Affecting Plants; 201 - Plant Genome, Genetics, and Genetic Mechanisms;

Subject Of Investigation
1549 - Wheat, general/other; 1540 - Hard red winter wheat;

Field Of Science
1081 - Breeding; 1080 - Genetics;
Goals / Objectives
Recent studies of the yield trends in major crops has shown that in 24-39% of global crop growing areas yields never improve or sometimes even decline. Most variation (>60%) in yield in the major crop producing regions was explained by climatic variation in the amount of precipitation and temperature. Modeling showed that warming has already begun slowing yield gains in the majority of wheat growing regions, with production expected to fall by 6% for each degree of temperature increase. In the face of global environmental changes, and the reduction of arable land and available water, these trends are expected to worsen, and coupled with population growth, will increase pressure on agricultural production.A plant's ability to adapt to changing environments depends on the availability of alleles linked to the maximal expression of adaptive traits. Owing to their broad geographic distribution, wheat and its wild relatives provide a rich system for investigating the basis of adaptation, providing a valuable source of new allelic diversity, of which only a small fraction was used for improving wheat's adaptive potential. Prioritization of this allelic diversity for deployment in the breeding programs remains critical for increasing their effectiveness and improving their ability to assemble allelic complexes in high-yielding varieties resilient to future climates.With the advent of next-generation sequencing (NGS) it has become possible to generate genome-wide variation data for large geographically diverse populations. These datasets combined with eco-geographic variables were successfully used to identify alleles associated with adaptive phenotypes. The main rationale behind these approaches referred to as genome-wide environmental scans (GWES), is that loci involved in local adaptation can be identified based on an unusually high correlation between allele frequencies and important eco-geographic variables. In some studies, loci associated with climate variation were shown can predict the relative fitness of plant accessions in a specific environment.Here, we propose to use the recently developed methods of genome-wide environmental scans to identify adaptive alleles based on their correlation with important climatic variables in the global populations of wheat and its wild relatives. The long-term goal of the proposed project is to empower the US and international wheat breeding programs by establishing genomic and statistical tools that will leverage information about the genomic signals of eco-geographic adaptation to prioritize global genetic diversity for deployment in breeding programs, and to predict the phenotypic performance from its genotype.Objective 1: Catalog genome-wide DNA sequence variation in wheat and its wild relatives. A cost-effective next-generation sequencing of population pools will be performed using the wheat exome capture assay in populations of Ae. tauschii selected from regions showing strong differences in the amount of precipitation and temperature. These datasets will be combined with previously generated data for geographically diverse lines of tetraploid and hexaploid wheat to construct high-density variation maps. The SNPs discovered in our study along with their adaptive values inferred based on environmental association analyses (Objective 2) and phenotypic data (Objective 3) will be deposited to the public T3 database (triticeaetoolbox.org) for advancing wheat genetic studies and informing breeding decisions.Objective 2: Identify adaptive alleles through the analysis of eco-geographic patterns of genomic variation. The distribution of genomic variation across diverse environmental gradients and geographic areas will be studied using the genome-wide environmental scans for seasonal variation in temperature, precipitation, photosynthetically active radiation, relative humidity, season lengths, and aridity.Objective 3: Field-based phenotyping of drought and heat adaptive traits. Field testing of adaptive Ae. tauschii alleles will be performed at three locations using populations developed by backcrossing "adapted cultivar"-Ae. tauschii amphiploids to parental lines, which are adapted to Kansas or Great Plains' environemnts and show good yield potential, tolerance to drought or race nonspecific resistance to fungal pathogens. These adapted lines are crossed with geographically diverse accessions of Ae. tauschii to develop amphiploids. To assess plant's performance, physiological measurements including canopy temperature depression, NDVI, spectral reflectance, and chlorophyll content will be collected along with phenological data and yield-related traits.Objective 4: Prediction of phenotypic performance using climate-associated alleles. The ability of alleles correlating with seasonal variation in precipitation, temperature, and aridity to predict phenotypic performance in Kansas environmental conditions will be tested by comparing with field-based phenotyping data and the results of genomic predictions based on whole-genome variation data.
Project Methods
Recent advances in the development of genomic resources for wheat and its wild relatives make the whole genome analysis of variation possible even in the complex genomes of these species. Here, we will apply two re-sequencing approaches wheat exome capture (WEC) and genotyping by sequencing (GBS), each differing in the extent and depth of genome coverage. Compared to GBS, the WEC approach generates much richer and more detailed description of variation in the low copy fraction of the wheat genome. However, the small overlap of GBS-based SNPs with the WEC dataset suggests that by combining both approaches we can achieve a more comprehensive characterization of variation across the genome, than by using the WEC alone. We will call SNPs and small indels in 350 accessions of Ae. tauschii, the ancestor of the wheat D genome. DNAs of samples originating from the same geographic regions within the same climatic window will be pooled (40-50 samples/pool) and re-sequenced using the WEC assay. The NGS of population pools was shown to be an effective strategy for cost-effective assessment of allele frequencies in the populations. Recently developed methods of variation analyses in the NGS datasets of pooled samples, make this approach very attractive for detecting adaptive alleles We will combine the variation dataset generated in this project with the one that we have generated for hexaploid and tetraploid wheat lines within the scopes of the Triticeae CAP and collaborative projects in the PD Akhunov lab.We will scan for alleles associated with environmental variables estimated from the collection location for each accession. The following climatic parameters will be obtained, extrapolated or calculated from public databases: the mean, extremes, and variability in temperature, precipitation, relative humidity, and aridity on monthly and annual scales, growing season length, mean levels of solar radiation, and vapour pressure deficit. GEWEX Surface Radiation Budget Project data will be used to assess variation in photosynthetically active radiation. The Bayesian methods will be used to perform environmental scans using the SNP calls obtained by NGS of population pools.The following phenotypic measurements will be obtained for Ae. tauschii-wheat introgression populations: normalized difference vegetative index, canopy temperature depression, canopy spectral reflectance index and chlorophyll content. The physiological data will be collected along with phenological measurements, yield traits and climatic data to assess the performance of Ae. tauschii-wheat introgression lines across environments in Kansas. Mixed model variance component analysis of mean phenotypic values will be carried out to partition variation of quantitative trait phenotypes into genotype (G, random), environment (E, fixed), year (T, fixed), two-way and three-way interactions (random), replicates (R, random), and error variance. Best linear unbiased predictions (BLUP) from the full model or reduced models will be used in GWAS to identify genetic variants underlying the observed trait variation. The top performing outlier lines will be selected to make available to breeding community.With the advent of next-generation sequencing, prediction of breeding values based on genotype (genomic selection or GS) became a powerful tool of modern breeding. GS relies on genotypic and phenotypic data to develop models that can use genotypes to predict genomic estimated breeding values (GEBVs). Due to the non-linear responses of genotype to environment, GEBV cannot always be expanded to different locations. Here we will test the ability of SNPs correlating with such environmental variables as precipitation, temperature, and aridity to predict phenotypic performance in Kansas environments. We will use a set of environment-associated markers (EAM) for testing the accuracy of prediction as compared to the observed field-based phenotypic values. First, based on weather data for each Kansas location we will detect which EAM should be favored at each location by identifying the set of Ae. tauschii accessions that share a similar climate. We will test if the presence of favorable alleles in the population of Ae. tauschii-wheat introgression lines correlates with the phenotypic measurements. The significance of the correlation will be assessed by permutation using a random set of SNPs. Second, we will use two genomic selection models (with and without environmental covariates), genotyping datasets, and phenotypes collected for Ae. tauschii-wheat introgression lines to develop predictive models for genomic selection, to estimate the marker effects and use them for calculating GEBVs. Both cross-validation and Pearson correlation coefficients between the GEBVs and the observed phenotypes will be used to assess prediction accuracy.The data and prediction models developed in the project will be integrated into the T3 database along with the detailed explanation of how this information could be utilized in breeding programs. The T3 is one of the central databases supporting national and international breeding activities by providing tools to access and analyze genomic and phenotypic data for breeding applications. Our project will also provide training to 2 graduate students in application of population and quantitative genomics in breeding.

Progress 11/01/15 to 10/31/19

Outputs
Target Audience:Project outputs were presented to the industry representatives and stakeholders at the meetings organized by the Kansas Association of Wheat Growers, Kansas Wheat Commission (KWC), and Heartland Plant Innovations Center (HPI) and KState Research and Extension. In addition, the project's objectives are aligned with the priorities of the Wheat CAP that includes an Industry Liaison Group, representatives in the National Wheat Improvement Committee, and breeders which coordinates research priorities with NAWG and Wheat Associates. Our project developed genetic tools and resources for wheat researchers and breeders from academia and industry that were delivered to this target audience through public databases and germplasm repositories, and presentations at the national and international meetings. The research outputs of the project were disseminated to the wheat community by publishing in the international peer-reviewed journals. Project closely collaborated with the national (Wheat CAP) and international (International Wheat Yield Partnership) groups to train the next generation of crop breeders. Project members were actively involved into the organization of workshops, symposia and meetings for graduate students and postdoctoral researchers to broaden training opportunties for graduate students. The KSU team also participated in the two-month REEU program organized by the KSU Department of Plant Pathology. Ths program hosts undergraduate students selected from the diverse pool of applicants. The KSU undergraduate students were actively involved into the project's research and breeding activities in the laboratories of the project directors. The project findings were disseminated to broader public though publications in public media (newspapers, KSU and KSRE websites, and Facebook and Twitter accounts) and developing video material. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The project technician (Dwight Davidson) has been trained in using three UAV-based platforms for collecting image data using hyperspectral, thermal and RGB cameras. He was also trained in initial image data processing procedures using specialized software. Two postdoctoral researchers have been trained in analyzing image data collected using the UAV-based platforms. One of the postdoctoral researchers (Huan Wang) received position in Indigo company to perform remote sensing data analyses (Boston, USA). A graduate student on the project was trained in novel methods of statistical data analyses during the Summer Institute in Statistical Genetics (Seattle, WA). The students and postdoctoral researchers were provided training in quantitative genetics and the genomic data processing and analysis though Plant Pathology journal clubs, and summer genomics workshops organized by the KSU Integrated Genomics Facility. Two graduate students and postdoctoral researchers involved in the project presented the results of their research at the following meetings: 1. Plant and Animal Genome (2016, 2017, 2018) 2. 1st International Wheat Congress. July 22-26, 2019, Saskatoon, Canada 3. 2019 NAPB Annual Meeting, August 25-29, 2019 at Pine Mountain, GA 4. "A New Era for the Green Revolution: Celebrating Women in Agriculture" symposium at Kansas State University, April 2019 5. K-State Graduate Research Forum, Manhattan, KS, October 31, 2019 6. Annual Meeting, American Society of Plant Biologists, Midwestern Section, March 16-17, 2019, West Virginia University, Morgantown, WV, USA 7. Nebraska Plant Breeding Symposium, University of Nebraska-Lincoln (March 13th, 2018) 8. WheatCAP workshop, University of California, Davis (Jan 8th-12th, 2018) 9. WheatCAP workshop, Kansas State University, Manhattan, KS (July 9th-13th, 2018) 10. KSU Plant Breeding and Genetics meeting (Manhattan, KS, 2017) How have the results been disseminated to communities of interest?The project outputs were presented to crop researchers, breeders and industry representatives at the international meetings, such as Plant and Animal Genome meetings (San Diego, CA, 2016-2018), International Wheat Congress (July 22-26, 2019) and Annual International Wheat Yield Partnership meetings in UK and Mexico, and national meetings, such as annual NAPB meeting (2017, 2019), and Plant Breeding Symposiums at the University of Nebraska-Lincoln (March 13th, 2018) and Kansas State University (April, 2017). The project SNP data was distributed through the public databases including Ensembl Plants, GrainGenes and T3. Germplasm was deposited to the USDA National Small Grains Collection repository. The results of the study were presented to wheat breeders, farmers, wheat workers, and industry representatives during the field days organized at the Kansas State University. The industry representatives and private partners have been updated on the status of the project during the meetings organized by Kansas Wheat Commission and Kansas Wheat, and at the yearly International Wheat Yield Partnership meetings in Mexico and UK. The results of the project were presented to the wheat community by publishing in the international peer-reviewed journals. The KSU Research and Extension developed video and on-line news stories highlighting the project accomplished (e.g. stories published in Science in 2017, and Nature Genetics in 2019) https://www.ksre.k-state.edu/news/stories/2017/07/wild-emmer-genome.html https://www.ksre.k-state.edu/news/stories/2019/05/wild-emmer-wheat-evolution-akhunov.html What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Impact To effectively select wild relatives for wheat breeding, we combine next-generation sequencing of a large number of accessions with historic environmental data from the accession's collection sites. Using these data, we identify genetic variation that helps wild relatives to adapt to environments with water-limiting and high temperature conditions. The accessions of wheat wild relatives that carry large amount of climate-adaptive genetic variation are top targets for breeding for drought and heat tolerance traits. These climate-adaptive genetic variation, once incorporated into genomic selection models can improve the accuracy phenotypic predictions in breeding programs that utilize wild relative-derived genetic material and, thereby, accelerate wheat improvement. In our project, we evaluated this strategy by crossing Aegilops tauschii with Kansas wheat breeding lines. The Ae. tauschiiderived wheat population was grown under irrigated and non-irrigated conditions and phenotyped using conventional and UAV-based methods for major agronomic and drought-adaptive traits reflecting plant's physiological response to water limiting conditions. We showed that by incorporating information about climate-adaptive genetic variation into genomic selection models we can improve the accuracy of yield prediction. This strategy can be expanded to wild relatives of any crop and have potential to substantially increase the efficiency of breeding drought and heat-resistant varieties. Genomic and genetic resources for mapping adaptive alleles in wheat We collaborated with the international consortium to sequence the genome of another wild relative of wheat, wild emmer (Avni et al., 2017). We used the wheat whole exome capture assay (Jordan et al., 2015) to re-sequence diverse population of wild and domesticated emmer resulting in 1,755,053 variants. We performed whole exome sequencing of 1,000 diverse wheat accessions (He et al., 2019). Seven million SNPs were discovered and used to identify SNPs targeted by breeders and responsible for adaptation to local climates. To assess the contribution of wild relatives to local adaptation and breeding, we compared SNPs from 1,000 wheat accessions with SNPs differentiating populations of wild and domesticated tetraploid emmer wheat (Avni et al., 2017). Our study published in Nature Genetics revealed that substantial fraction of climate-adaptive SNPs and breeding targets are located in the regions of wild relative introgressions (He et al. 2019). Alleles contributed by wild tetraploid wheat into bread wheat explained substantial proportion of trait variation in wheat. The spring wheat NAM population was developed, genotyped using 90K iSelect, GBS and exome capture assays and used to characterize the distribution of 102,000 recombination breakpoints across the wheat genome (Blake et al., 2019). The genotyping data and distribution of recombination breakpoints in NAM population were deposited to the T3 database. The population is distributed to the community through the National Small Grains Collection repository (Blake et al., 2019), and used for genetic analysis of complex traits in wheat. We used the 1st - generation haplotype map of wheat (Jordan et al., 2015) to impute SNPs in the winter wheat association mapping panel to demonstrate that increase in the SNP marker density substantially improves the precision of trait mapping and improves the accuracy of genomic prediction (Nyine et al., 2019). This study demonstrates the great value of developing a comprehensive haplotypic diversity resource for US breeding programs. Field-based assessment of drought and heat adaptive traits We developed 391 BC1F3:5 Ae. tauschii-derived lines in the background of five adapted Kansas wheat cultivars (Nyine et al., 2019). Population was evaluated in Colby (KS) for two field seasons in 2018 and 2019 under irrigated and non-irrigated conditions. Five parental cultivars (Larry, KS061406-LN~26, Danby, KanMark and KS061278M-4) and mix of three highperforming cultivars from western Kansas (Monument, Langin, and Joe) were used as checks. Phenotyping trait data collected for this experiment include yield component traits (grain weight and size, number of heads per unit area, number of spikelets per spike), biomass, heading date and plant height. In 2018 and 2019, three UAV-based phenotyping platforms have been used for collecting image data using hyperspectral, thermal and RGB cameras. The image data have been collected across the season resulting in more than 50,000 phenotyping plot-level measurements and used used to evaluate canopy temperature, spectral reflectance, NDVI, plant height, biomass and growth rate. The relative yield under irrigated and non-irrigated conditions of nearly half of the lines in the population was higher than that of the best-performing checks, suggesting that Ae. tauschii introgression can substantially improve yield stability under water limiting conditions. Under non-irrigated and irrigated conditions 87 and 79 Ae. tauschii-derived lines, respectively, had yield equal to or higher than KanMark, which has strong dryland performance and yield potential in western and central Kansas. Compared to cultivar mix, top 60 best-yielding Ae. tauschii-derived lines had 12.9% and 18.6% higher yield under irrigated and non-irrigated conditions, respectively. UAV-based phenotyping showed significant negative correlation (p < 0.001) between yield and canopy temperature indicating that the ability of drought stress-tolerant lines to maintain low leaf temperature under water-limiting conditions is associated with increase in yield potential. Our results suggest that alleles contributed by Ae. tauschii can beneficially affect yield potential under both irrigated and non-irrigated conditions. Prediction of phenotypic performance using SNPs contributed by Ae. tauschii. To evaluate the utility of introgressed alleles for phenotypic prediction, the Ae. tauschii-derived wheat population was genotyped at 118,933 SNP sites. In addition, a population of 158 geo-referenced Ae. tauschii accession was genotyped by sequencing at 83,335 SNP sites. The SNP datasets were combined and analyzed using Beagle program to identify the regions of Identity-by-Descent. On average, the size of genomic regions introgressed from Ae. tauschii was 17.7 Mb per line. Introgression covered most of the wheat D genome, except for low-recombining regions or regions controlling domestication traits (Nyine et al., 2019). Geo-referenced Ae. tauschii accessions were used to study relationship between climatic variables and genotypes using methods implemented in Bayenv and GAPIT. Ae. tauschii haplotypes carrying SNPs associated with variation in monthly precipitation and temperature were identified. These data was used to test the ability of climate-associated Ae. tauschiiderived alleles to predict yield and other drought-response related traits collected under irrigated and non-irrigated conditions. On average, the accuracy of genomic predictions (ranging from 0.13 to 0.64) for yield and yield component traits under nonirrigated conditions estimated using SNPs within Ae. tauschii introgression was 5.6% higher than the prediction accuracy estimated with SNPs located outside of the introgressed regions, indicating that Ae. tauschii contributed alleles beneficially affecting wheat productivity traits under stress conditions. Association mapping identified three loci on chromosomes 2D, 4D and 6D affecting the number of spikelets per spike, and two loci on chromosomes 1D and 2D affecting kernel size with the majority of beneficial alleles for both traits contributed by Ae. tauschii. In addition, two alleles from Ae. tauschii associated with significant decrease in canopy temperature and increase in vegetation index under drought conditions were identified on chromosomes 1D and 7D, respectively.

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: He F, Pasam R, Shi F, Kant S, Keeble-Gagnere G, Kay P, Forrest K, Fritz A, Hucl P, Wiebe K, Knox R, Cuthbert R, Pozniak C, Akhunova A, Morrell PL, Davies JP, Webb SR, Spangenberg G, Hayes B, Daetwyler H, Tibbit J, Hayden M, Akhunov E. Exome sequencing highlights the role of historic wild relative introgression in broadening the adaptive potential of modern bread wheat. 1st International Wheat Congress. July 22-26, 2019, Saskatoon, Canada.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: He F, Pasam R, Shi F, Kant S, Keeble-Gagnere G, Kay P, Forrest K, Fritz A, Hucl P, Wiebe K, Knox R, Cuthbert R, Pozniak C, Akhunova A, Morrell PL, Davies JP, Webb SR, Spangenberg G, Hayes B, Daetwyler H, Tibbits J, Hayden M, Akhunov E. Exome sequencing highlights the role of historic wild relative introgression in broadening the adaptive potential of modern bread wheat. Annual Meeting, American Society of Plant Biologists, Midwestern Section. March 16-17, 2019, West Virginia University, Morgantown, WV, USA.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Elina Adhikari: Utilizing the signals of eco-geographic adaptation in wild relatives to improve wheat, Jan 8th-12th, 2018, WheatCAP workshop, University of California, Davis.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Jordan KW, Wang S, He F, Chao S, Lun Y, Paux E, Sourdille P, Sherman J, Akhunova A, Blake NK, Pumphrey MO, Glover K, Dubcovsky J, Talbert L, Akhunov ED. The genetic architecture of genome-wide recombination rate variation in allopolyploid wheat revealed by nested association mapping. Plant J. 2018, 95, 1039-1054.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Avni R, Nave M, Barad O, Baruch K, Twardziok SO, Gundlach H, Hale I, Mascher M, Spannagl M, Wiebe K, Jordan KW, Golan G, Deek J, Ben-Zvi B, Ben-Zvi G, Himmelbach A, MacLachlan RP, Sharpe AG, Fritz A, Ben-David R, Budak H, Fahima T, Korol A, Faris JD, Hernandez A, Mikel MA, Levy AA, Steffenson B, Maccaferri M, Tuberosa R, Cattivelli L, Faccioli P, Ceriotti A, Kashkush K, Pourkheirandish M, Komatsuda T, Eilam T, Sela H, Sharon A, Ohad N, Chamovitz DA, Mayer KFX, Stein N, Ronen G, Peleg Z, Pozniak CJ, Akhunov ED, Distelfeld A. Wild emmer genome architecture and diversity elucidate wheat evolution and domestication. Science. 2017 Jul 7;357(6346):93-97.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Nyine M, Adhikari E, Clinesmith M, Jordan K, Fritz AK, Akhunov ED. Understanding the introgression process from Aegilops tauschii into hexaploid wheat through identity by descent analysis and its effect on genetic diversity. BioRxiv, 2019. doi: https://doi.org/10.1101/855106.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Blake NK, Pumphrey M, Glover K, Chao S, Jordan K, Jannick J, Akhunov ED, Dubcovsky J, Bockelman H, Talbert LE. Registration of the Triticeae-CAP Spring Wheat Nested Association Mapping Population. J. Plant. Reg. 2019, 13, 294-297.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Gardiner LJ, Brabbs T, Akhunov A, Jordan K, Budak H, Richmond T, Singh S, Catchpole L, Akhunov E, Hall A. Integrating genomic resources to present full gene and putative promoter capture probe sets for bread wheat. GigaScience. 2019 Apr 1;8(4).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Adhikari E, Nyine M, Clinesmith M, Davidson D, Aiken R, Jordan K, Fritz AK, Akhunov E. Utilizing genomic signals of ecogeographic adaptation from wild relatives to improve wheat. 2019 NAPB Annual Meeting, August 25-29, 2019 at Pine Mountain, GA.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Adhikari E, Nyine M, Clinesmith M, Davidson D, Aiken R, Jordan K, Fritz AK, Akhunov E. A New Era for the Green Revolution: Celebrating Women in Agriculture symposium at Kansas State University, April 2019.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Adhikari E., Jordan K., Akhunova A, Steffenson B, Sela H, Akhunov E. Identifying the climate associated alleles in wild emmer and Aegilops taushchii. Pioneer Plant Breeding series, Training 21st century plant breeders in the omics era, University of Nebraska, Lincoln. March 12, 2019.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Adhikari E., Jordan K., Akhunova A, Steffenson B, Sela H, Akhunov E. Employing adaptation signals from ancestral wheat to improve drought tolerance in bread wheat, K-State Graduate Research Forum, Manhattan, KS, October 31, 2019.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Jordan K, Wang S, Chao S, Lun Y, Paux E, Sourdille P, Sherman J, Akhunova A, King R, Phillips AL, Uauy C, Dubcovsky C, Talbert L, Akhunov E. Genetic Architecture of Recombination Rate and Its Effects in Allopolyploid Wheat. Plant and Animal Genome meeting, Jan 13-17, 2018, San Diego, CA, USA.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: He F, Pasam R, Shi F, Kant S, Keeble-Gagnere G, Kay P, Forrest K, Fritz A, Hucl P, Wiebe K, Knox R, Cuthbert R, Pozniak C, Akhunova A, Morrell P, Davies J, Webb S, Spangenberg G, Hayes B, Daetwyler H, Tibbits J, Hayden M, Akhunov E. Exome sequencing highlights the role of wild relative introgression in shaping the adaptive landscape of the wheat genome. Nat Genet. 2019;51:896904.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Nyine M, Wang S, Kiani K, Jordan K, Liu S, Byrne P, Haley S, Baenziger PS, Chao S, Bowden R, Akhunov E, Genotype imputation in winter wheat using first-generation haplotype map SNPs improves genome-wide association mapping and genomic prediction of traits. G3, 2019, 9, 125-133.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Jordan K, Wang S, Chao S, Lun Y, Paux E, Sourdille P, Dubcovsky J, Sherman J, Akhunova A, Talbert L, Akhunov E. Nested Association Mapping Population Resource for Studying the Genetic Basis of Trait Variation in Wheat. Plant and Animal Genome meeting, Jan 14-18, San Diego, CA, USA.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Adhikari E, Jordan K, He F, Fritz AK, Chao S, Steffenson BJ, Sela H, Akhunov E. Identifying the Genetic Basis of Local Adaptation in Wild Emmer Wheat. Plant and Animal Genome meeting, Jan 14-18, San Diego, CA, USA.


Progress 11/01/17 to 10/31/18

Outputs
Target Audience:The results of the study were presented to wheat breeders and farmers during the field days organized at the Kansas State University. The industry representatives and private partners have been updated on the status of the project during the meetings organized by Kansas Wheat Commission and Kansas Wheat, and at the yearly International Wheat Yield Partnership meeting in UK. The project results were presented to wheat researchers and breeders at the national and international meetings, such as Plant and Animal Genome (San Diego, CA), and yearly International Wheat Yield Partnership (Norwich, UK, 2018) meetings. The results of the project were presented to the international wheat community by publishing at the international peer-reviewed journals. The students were provided training in the genomic data though Plant Pathology journal club, and genomics workshop organized by the Integrated Genomics Facility. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The project technician has been trained in using three UAV-based platforms for collecting image data using hyperspectral, thermal and RGB cameras. He also was trained in initial image data processing procedures using specialized software. Two graduate students involved into the project presented the results of their research at the meetings: Nebraska Plant Breeding Symposium, University of Nebraska-Lincoln (March 13th, 2018), and WheatCAP workshop, University of California, Davis (Jan 8th-12th, 2018). Two postdoctoral researchers have been trained in analyzing image data collected using UAV-based platforms. How have the results been disseminated to communities of interest?The results of research were presented to crop researchers at the Plant and Animal Genome meeting (San Diego, CA, 2018), WheatCAP workshop, University of California, Davis (Jan 8th-12th, 2018) and Nebraska Plant Breeding Symposium, University of Nebraska-Lincoln (March 13th, 2018). The project SNP data was deposited to the public databases including GrainGenes and T3 databases. The wheat workers, breeders and industry representatives were briefed on project progress during the field days. What do you plan to do during the next reporting period to accomplish the goals?The entire phenotyping experiment will be repeated in Colby, KS under irrigated and non-irrigated conditions during the growth season of 2018-2019. The climate-associated alleles identified in Ae. tauschii-derived wheat lines will be incorporated into the genomic prediction models tested for ability to predict yield and drought-responsive traits.

Impacts
What was accomplished under these goals? Impact To effectively select wild relatives for wheat breeding, we combine next-generation sequencing of a large number of accessions with historic environmental data from the accession's collection sites. Using these data, we identify genetic variation that helps wild relatives to adapt to environments with water-limiting and high temperature conditions. The accessions of wheat wild relatives that carry large amount of climate-adaptive genetic variation are top targets for breeding for drought and heat tolerance traits. These climate-adaptive genetic variation, once incorporated into genomic selection models, can accurately predict superior lines in breeding programs that utilize wild relative-derived genetic material and, thereby, substantially accelerate wheat improvement. In our project, we evaluate this strategy by crossing Aegilops tauschii and wild emmer, the wild relatives of wheat, with Kansas wheat breeding lines. The Ae. tauschii-derived wheat population was grown under irrigated and non-irrigated conditions and phenotyped for major agronomic (yield, heading date, and height) and drought-adaptive traits reflecting plant's physiological response to water limiting conditions. We combine this phenotyping data with information about climate-adaptive genetic variation to develop statistical prediction models that will help us to accurately select most drought tolerant wheat lines. This strategy can be expanded to wild relatives of any crop and have potential to substantially increase the efficiency of breeding drought and heat-resistant varieties. Identify adaptive alleles through the analysis of eco-geographic patterns of genomic variation Assessing the role of breeding selection, climatic adaptation, and historic introgressions from wild relatives in wheat adaptive potential: We have used exome capture assay targeting nearly 300 Mb of low-copy genic regions to re-sequence more than 1,000 accessions of wheat landraces and cultivars from diverse geographic regions. More 7 million high-quality SNPs have been discovered and used to identify regions of genome selected by breeders during the development of local wheat varieties. In addition, geographic distribution of SNPs was compared with distribution of 68 climatic and bioclimatic variables to detect SNPs that are responsible for adaptation of these climatic factors. Overlap among climate-adaptive SNPs and the signatures of breeding selection suggest that up to third of breeding targets are likely associated with local adaptation. To assess the contribution of wild relative diversity to local adaptation and breeding, we have compared exome capture SNPs from 1,000 wheat accessions with SNPs that we discovered using the same exome capture assay in the diverse populations of wild and domesticated emmer (Science 2017, 97, 93-97). Our study revealed that substantial fraction of climate-adaptive SNPs and breeding targets are located in the regions of wild relative introgressions (under consideration in Nature Genetics). Wheat NAM population: The spring wheat NAM population was developed, genotyped using 90K iSelect, GBS and exome capture assays and used to characterize the distribution of 102,000 recombination breakpoints across the wheat genome (Jordan et al., 2018). The genotyping data and distribution of recombination breakpoints in NAM population were deposited to the T3 database. This data will be used by the project for assessing the association of climate-adaptive alleles with agronomic traits (yield component traits, heading date, and yield). Exome capture genotype imputation: Akhunov lab used the first generation haplotype map of wheat (Jordan et al., 2015) to impute SNPs in the winter wheat association mapping panel to demonstrate that increase in the SNP marker density substantially improves the precision of trait mapping and improves the accuracy of genomic prediction (Nyine et al., 2018). This study demonstrates the great value of developing a comprehensive haplotypic diversity resource for US breeding programs. Field-based assessment of drought and heat adaptive traits A total 391 BC1F3:5 Ae. tauschii-derived lines in the background of five Kansas wheat breeding lines have been planted in Colby, KS under irrigated and non-irrigated conditions following the augmented design type 2. Five parental lines (Larry, KS061406-LN~26, Danby, KanMark and KS061278M-4) and mix of three best-performing cultivars from western Kansas (Monument, Langin, and Joe) were used as checks. The yield of many Ae. tauschii-derived lines under irrigated and non-irrigated conditions was equal to or higher than that of best checks in the experiment. For example, under non-irrigated and irrigated conditions 87 and 79 Ae. tauschii-derived lines showed yield potential equal to or higher than KanMark, which is a 2014 K-State release with very strong dryland performance and yield potential in western and central Kansas. About 60 of these high-performing accessions are included into the K-State breeding program and planned for germplasm release in 2019. Additional agronomic trait data collected for this experiment include yield component traits (seed weight and size, number of heads per plot), heading date and plant height. The moisture and protein contents, and test weights for both irrigated and dryland plots we assessed using a Perten Inframatic 9500 NIR machine. We are also in the process of measuring end use quality on the irrigated set of line through the SDS-SRC hybrid protocol. In spring 2018, three UAV-based phenotyping platforms have been used for collecting image data using hyperspectral, thermal and RGB cameras. The image data have been collected multiple times across the growing season resulting in more than 25,000 phenotyping plot-level measurements that are currently being analyzed to evaluate canopy temperature, spectral reflectance, NDVI, plant height, biomass and growth rate. The results of preliminary analyses of field-based phenotyping data suggest that Ae. tauschii-derived lines have high yield potential under both irrigated and non-irrigated conditions matching or even exceeding that of the best check varieties used in the experiment. Prediction of phenotypic performance using climate-associated alleles. To evaluate the ability of climate associated alleles to predict phenotypic performance of Ae. tauschii-derived lines, we have genotyped this population using GBS approach. A total of 118,933 have been identified in the population. To identify genomic regions introgressed from Ae. tauschii, we have mapped GBS data generated for geo-referenced Ae. tauschii accessions to the D genome of cultivar of Chinese Spring discovering 83,335 SNPs. The SNP datasets for Ae. tauschii and Ae. tauschii-derived wheat lines was combined and analyzed using Beagle program to identify Identity-by-Descent (IBD) regions. On average, the size of genomic regions introgressed from Ae. tauschii was 17.7 Mb per line. Introgressed regions covered most of the wheat D genome, except for regions controlling domestication and phenology traits, such as tenacious glume and dwarfism. The latter two traits have been selected against during the development of Ae. tauschii-derived lines due to their effect on phenology and seed threshability. The regions of Ae. tauschii-derived lines sharing IBD with Ae. tauschii are now being tested for association with climatic and bioclimatic variables from the WorldClim database focusing of variables associated with temperature and precipitation. These data will be used to test the ability of climate-associated Ae. tauschii-derived alleles to predict yield and other drought-response related traits collected under irrigated and non-irrigated conditions (see previous chapter).

Publications

  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Jordan KW, Wang S, He F, Chao S, Lun Y, Paux E, Sourdille P, Sherman J, Akhunova A, Blake NK, Pumphrey MO, Glover K, Dubcovsky J, Talbert L, Akhunov ED. The genetic architecture of genome-wide recombination rate variation in allopolyploid wheat revealed by nested association mapping. Plant J. 2018 Jun 27. doi: 10.1111/tpj.14009.
  • Type: Journal Articles Status: Under Review Year Published: 2018 Citation: Nyine M, Wang S, Kiani K, Jordan K, Liu S, Byrne P, Haley S, Baenziger PS, Chao S, Bowden R, Akhunov E, Genotype imputation in winter wheat using first-generation haplotype map SNPs improves genome-wide association mapping and genomic prediction of traits. 2018 (submitted to G3)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Quanli Pan: Application of CRISPR/Cas9 technology to analyzing gene function in the wheat genome and improving agronomic traits, March 13th, 2018/ Nebraska Plant Breeding Symposium 2018/ University of NebraskaLincoln
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Elina Adhikari: Utilizing the signals of eco-geographic adaptation in wild relatives to improve wheat, Jan 8th-12th, 2018, WheatCAP workshop, University of California, Davis.
  • Type: Journal Articles Status: Under Review Year Published: 2018 Citation: He F, Pasam R, Shi F, Kant S, Keeble-Gagnere G, Kay P, Forrest K, Fritz A, Hucl P, Wiebe K, Knox R, Cuthbert R, Pozniak C, Akhunova A, Morrell P, Davies J, Webb S, Spangenberg G, Hayes B, Daetwyler H, Tibbits J, Hayden M, Akhunov E, Exome sequencing reveals the adaptive landscape of the wheat genome. 2018 (submitted to Nature Genetics)


Progress 11/01/16 to 10/31/17

Outputs
Target Audience:The results of the study were presented to the wheat breeders and farmers during the field days organized at the Kansas State University. The industry representatives and private partners have been updated on the status of the project during the meetings organized by Kansas Wheat Commission and Kansas Wheat, and at the yearly International Wheat Yield Partnership meeting in Mexico. The project results were presented to wheat researchers and breeders at the national and international meetings, such as Plant and Animal Genome (San Diego, CA),National Association of Plant Breeders (UC Davis, 2017) and yearly International Wheat Yield Partnership (Obregon, Mexico, 2017) meetings. The results of the project were presented to the international wheat community by publishing at the international peer-reviewed journals. The students were provided training in the genomic data though Plant Pathology journal club, and genomics workshop organized by the Integrated Genomics Facility. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The project technician has been trained in operating a drone and become FAA certified drone pilot. The graduate student on the project was trained in novel methods of statistical data analyses during the Summer Institute in Statistical Genetics (Seattle, WA). The project students were provided opportunity to present and discuss the results of their research at the Plant and Animal Genome meeting (San Diego, CA, 2017), NAPB annual meeting (UC Davis, CA, 2017) and KSU Plant Breeding and Genetics meeting (Manhattan, KS, 2017). How have the results been disseminated to communities of interest?The results of research were presented to crop researchers at the Plant and Animal Genome meeting (San Diego, CA, 2017), NAPB annual meeting (UC Davis, CA, 2017) and KSU Plant Breeding and Genetics meeting (Manhattan, KS, 2017). The project SNP data was deposited to the public databases including GrainGenes and T3 databases. The wheat workers, breeders and industry representatives were briefed on project progress during the field days. The KSU Research and Extension developed video and on-line news stories highlighting the project accomplished (e.g. story published this year in Science). Project team published a story on characterizing the genetic diversity and sequencing the wild emmer genome in journal Science. Graduate students presented the results of their research at the number of national and international meetings. What do you plan to do during the next reporting period to accomplish the goals?In spring 2018, our group will start field-based phenotyping the wild-relative derived wheat lines planted in irrigated and non-irrigated plots at two locations in Kansas. The population of 397 wild-relative derived lines will be genotyped at SNP sites showing strong correlation with water-limiting and high temperature conditions. We will incorporate drone-based hyperspectral phenotyping into the phenotypic evaluation of wild-relative derived genetic material. The value of climate-associated alleles for predicting wheat performance in Kansas will be assessed in the end of 2018.

Impacts
What was accomplished under these goals? Impact To prioritize genetic variation in wheat and its wild relatives for inclusion into the breeding programs, we proposed to use the methods of genome-wide environmental scans. These methods identify genetic variation that helps plants to adapt to environmental conditions based on the correlation of genetic variants' frequency with the critical climatic variables. Our project performed the systematic analysis of genetic variation in wheat and its wild relatives using new genomics approaches and sequencing tools. These analyses helped us to identify variation that have the potential to improve adaptation of drought and heat. We have finalized the analysis of genetic variation in the diverse populations of wild relatives of wheat creating a diversity map of nearly 10 million genetic variants. This resource was used as a tool for assessing the potential value of genetic variation in adaption to specific climatic factors and prioritizing lines for inclusion into breeding programs. Our project developed multiple populations of Kansas-adapted wheat lines carrying introgressions from the Aegilops tauschii, the wild relative of wheat. These populations are currently being evaluated for ability to grow in water-limiting conditions to assess their value for breeding drought and heat-resistant wheat varieties. Catalog genome-wide DNA sequence variation in wheat and its wild relatives. Whole exome capture approach using the assay developed by PD Akhunov group (Jordan et al., Genome Biol. 2015) was used to re-sequence a population of 1,000 wheat lines including wheat cultivars and landraces. A high density genetic variation map including nearly 9 million SNPs was developed by mapping to a NRGene genome assembly of wheat (IWGC). PD Akhunov collaborated with the international team of scientists to sequence the genome of another wild relative of wheat, Triticum dicoccoides or wild emmer (published in Science, 2017). The study discovered 1,755,053 SNPs and used them to identified the regions of the wild emmer genome that carry genes selected by humans to develop modern wheat varieties. The set of exome-sequenced wild emmer lines was crossed by PD Fritz with the adapted wheat varieties from Kansas. A cost-effective NGS of Ae. tauschii accessions selected from the geographic regions with different climatic conditions was performed. The genetic variation correlating with the climatic conditions in Ae. tauschii populations was identified and used to prioritize the selection of lines for new round of crossing. Identify adaptive alleles through the analysis of eco-geographic patterns of genomic variation. Genomic scans performed in the population of 1,000 wheat lines using bioclimatic variables including temperature, precipitation, photosynthetically active radiation, relative humidity, season lengths, and aridity revealed a number of genomic regions showing strong correlation with climate. The value of these climate-associated alleles for predicting the phenotypic traits is being evaluated. The population genetic analyses were used to identify genomic regions in tetraploid wheat that carry genes affected by domestication. A total of 57 regions covering 134 Mb of genome were shown to show the evidence of domestication selection. Two genomic regions were shown to include the QTL contributing to two major domestication traits, brittle rachis and seed germination/size (published in Science). A total of 20 accessions of wild emmer re-sequenced by exome capture in this project were used by PD Fritz to develop wild emmer-derived lines in the backgrounds of Kansas-adapted wheat cultivars. We performed analyses of wild emmer (tetraploid wheat) and Ae. tauschii populations and identified SNPs correlating with the historic climatic variables including temperature and water availability. Top 100 alleles correlating with climatic variable have been selected for genotyping the wild-relative derived wheat populations to assess the value of these alleles for predicting wheat yield in Kansas. A graduate student presenting the results of this study at the PAG meeting (San Diego, 2017) and KSU Plant Breeding and Genetics Symposium (Manhattan, 2017) was awarded first place in poster competition. Field-based assessment of drought and heat adaptive traits. A total of 397 BC1F4 lines have been increased in the field in 2016-2017 season. These lines have been planted in fall of 2017 at two locations in Kansas: Colby and Hutchinson. In Colby, planting was performed following the un-replicated augmented design in two replications on irrigated and non-irrigated plots. One replication was planted in Hutchinson. In spring of 2018, all plots will be phenotypically evaluated for drought and heat adaptive traits, phenology, yield-related traits and yield. To improve the accuracy and throughput of phenotypic evaluation, our group established drone-based phenotypic platforms based on hyperspectral camera and RGB camera. A technician in the Akhunov's group received FAA-certified pilot license. Both technician and graduate students on the project are being trained in collecting phenotyping data using the UAV platforms and analyzing image data. The UAV-based systems will be used in 2018 phenotyping season.

Publications

  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Avni R, Nave M, Barad O, Baruch K, Twardziok SO, Gundlach H, Hale I, Mascher M, Spannagl M, Wiebe K, Jordan KW, Golan G, Deek J, Ben-Zvi B, Ben-Zvi G, Himmelbach A, MacLachlan RP, Sharpe AG, Fritz A, Ben-David R, Budak H, Fahima T, Korol A, Faris JD, Hernandez A, Mikel MA, Levy AA, Steffenson B, Maccaferri M, Tuberosa R, Cattivelli L, Faccioli P, Ceriotti A, Kashkush K, Pourkheirandish M, Komatsuda T, Eilam T, Sela H, Sharon A, Ohad N, Chamovitz DA, Mayer KFX, Stein N, Ronen G, Peleg Z, Pozniak CJ, Akhunov ED, Distelfeld A. Wild emmer genome architecture and diversity elucidate wheat evolution and domestication. Science. 2017 Jul 7;357(6346):93-97.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Adhikari E, Jordan K, He F, Fritz AK, Chao SC, Sela H, Steffenson BJ, Akhunov E. Identifying the Genetic Basis of Local Adaptation in Wild Emmer Wheat. January 13 - 18, 2017, Plant and Animal Genome meeting, San Diego, CA
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Adhikari E, Jordan K, He F, Fritz AK, Chao SC, Sela H, Steffenson BJ, Akhunov E. IDENTIFYING THE CLIMATE ASSOCIATED ALLELES IN WILD EMMER AND AEGILOPS TAUSHCHII. National Association of Plant Breeders meeting, 2017, UC Davis, CA
  • Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Adhikari E, Jordan K, He F, Fritz AK, Chao SC, Sela H, Steffenson BJ, Akhunov E. Identifying the Genetic Basis of Local Adaptation in Wild Emmer Wheat.April 5, 2017, KSU Plant Breeding, Genetics and Genomics Symposium, Manhattan, KS.


Progress 11/01/15 to 10/31/16

Outputs
Target Audience: Nothing Reported Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Training activities: Two graduate students have been trained in the analysis of next-generation sequence data, genotype calling using 90K iSelect assay, performing genome-wide association mapping to identify marker-trait associations, and the development of advanced populations carrying chromosomal introgressions of wild relatives in the adapted wheat genetic background. Professional development: Two graduate students have attended the Plant Breeding Symposium, March 29, 2016 (Nebraska, Lincoln) and Donald Danforth Plant Science Center's 18th annual Fall Genetics and Genomics of Crop Improvement Symposium, September 28 - September 30, 2016 (Saint Louis, Missouri). 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? Impact To prioritize genetic variation in wheat and its wild relatives for inclusion into the breeding programs, we proposed to use the recently developed methods of genome-wide environmental scans. These methods identify genetic variation that helps plants to adapt to environmental conditions based on the correlation of genetic variants' frequency with the critical climatic variables. Our project started the systematic analysis of genetic variation in wheat and its wild relatives using new genomics approaches and sequencing tools. We have finalized the analysis of genetic variation in the global population of 1,000 hexaploid and 74 tetraploid wheat linescreating a diversity map of nearly 9 million and 1.7 million genetic variants, respectively. This resource provides valuable tool for assessing the potential value of genetic variation in adaption to specific climatic factors and prioritizing lines for inclusion into breeding programs. Our project developed multiple populations of Kansas-adapted wheat lines carrying the small chromosomal introgressions of Aegilops tauschii, the wild relative of wheat. These populations will be evaluated for ability to grow in water-limiting conditions and developed as a germplasm resource for breeding drought and heat-resistant wheat varieties. In addition, these germplasm resource will accelerate deployment of other useful alleles from Aegilops tauschii in the national breeding programs. Catalog genome-wide DNA sequence variation in wheat and its wild relatives. Whole exome capture approach using the assay developed by PI Akhunov group (Jordan et al., Genome Biol. 2015) was used to characterize a population of 1000 wheat lines including wheat cultivars and landraces. A high density genetic variation map including nearly 9 million genetic variants and based on ~1,000 geographically diverse wheat lines was developed. All 9 million genetic variants have been mapped to a new NRGene genome assembly of wheat (International Wheat Genome Sequencing Consortium). PD Akhunov collaborated with the international team of scientists to sequence the genome of another wild relative of wheat, Triticum dicoccoides or wild emmer (Avni et al., 2016, under review). In addition, PD Akhunov used the wheat whole exome capture assay (Jordan et al., 2015) to re-sequence low-copy genic regions in the geographically diverse population of tetraploid wheat lines including 34 lines of wild emmer, 32 lines of domesticated emmer, and 8 lines of durum wheat. The resulting dataset contained 1,755,053 variants including 1,596,132 SNPs and 158,921 indels. A cost-effective NGS of Ae. tauschii accessions selected from the geographic regions with different climatic conditions was performed. Currently, the identification of genetic variation correlating with the climatic conditions is underway. Identify adaptive alleles through the analysis of eco-geographic patterns of genomic variation. Analysis of eco-geographic patterns of SNP variation in 1000 wheat exomes dataset revealed that 28% of SNP distribution in wheat populations can be attributed to the combined effects of climate and geography with about 4% attributed to climate alone. Genomic scans performed using bioclimatic variables including temperature, precipitation, photosynthetically active radiation, relative humidity, season lengths, and aridity (http://www.worldclim.org/) revealed a number of genomic regions showing strong correlation with climate. The experimental testing of SNP variants contributing to climatic adaptation is currently underway. The population genetic analyses were used to identify genomic regions in tetraploid wheat that carry genes affected by domestication. A total of 57 regions covering 134 Mb of genome were shown to show the evidence of domestication selection. Two genomic regions were shown to include the QTL contributing to two major domestication traits, brittle rachis and seed germination/size (Avni et al., 2016, under review). A total of 20 accessions of wild emmer re-sequenced by exome capture in this project have been used by PD Fritz to develop introgression libraries carrying wild emmer chromosomal segments in the backgrounds of Kansas-adapted wheat cultivars. Field-based assessment of drought and heat adaptive traits. Testing of adaptive Ae. tauschii alleles will be performed in 35 populations that are developed by PD Fritz. These populations are developed by backcrossing "adapted cultivar"-Ae. tauschii amphiploids to adapted parental lines. As of mid-October, 150 BC1F3 plants for each population are currently in the greenhouse and are all between head emergence and mid-grain fill. Individual spikes will be harvested and two seed from each spike will be held back as remnant. The remaining seed, along with the recurrent parents, will be planted in 1 meter rows in the field at the Ashland Research Farm near Manhattan, KS. Anticipated planting date is the 20th of November. While later than optimal, it will still allow for adequate plant development for evaluation and will likely expose the material to greater heat stress.

Publications

  • Type: Journal Articles Status: Under Review Year Published: 2016 Citation: Raz Avni, Moran Nave, Omer Barad, Sven O. Twardziok, Heidrun Gundlach, Iago Hale, Martin Mascher, Manuel Spannagl, Krystalle Wiebe, Katherine W Jordan, Guy Golan, Jasline Deek, Batsheva Ben-Zvi, Kobi Baruch, Gil Ben-Zvi, Axel Himmelbach, Ron P. MacLachlan, Andrew G. Sharpe, Allan Fritz, Roi Ben-David, Hikmet Budak, Tzion Fahima, Abraham Korol, Justin D. Faris, Alvaro Hernandez, Mark A. Mikel, Avraham Levy, Brian Steffenson, Marco Maccaferri, Roberto Tuberosa, Luigi Cattivelli, Primetta Faccioli, Aldo Ceriotti, Khalil Kashkush, Mohammad Pourkheirandish, Takao Komatsuda, Tamar Eilam, Hanan Sela, Amir Sharon, Nir Ohad, Daniel A. Chamovitz, Klaus F.X. Mayer, Nils Stein, Gil Ronen, Zvi Peleg, Curtis J. Pozniak, Eduard D. Akhunov, Assaf Distelfeld. Wild emmer genome assembly provides insights into wheat evolution and domestication (submitted to Nature).