Focused Identification Of Germplasm Strategy Research Articles

Focused identification of germplasm strategy (FIGS): a strategic approach for trait-enhanced pre-breeding

Focused identification of germplasm strategy (FIGS): a strategic approach for trait-enhanced pre-breeding

Genome-wide superior alleles, haplotypes and candidate genes associated with tolerance on sodic-dispersive soils in wheat (Triticum aestivum L.)

Key messageThe pleiotropic SNPs/haplotypes, overlapping genes (metal ion binding, photosynthesis), and homozygous/biallelic SNPs and transcription factors (HTH myb-type and BHLH) hold great potential for improving wheat yield potential on sodic-dispersive soils.Sodic-dispersive soils have multiple subsoil constraints including poor soil structure, alkaline pH and subsoil toxic elemental ion concentration, affecting growth and development in wheat. Tolerance is required at all developmental stages to enhance wheat yield potential on such soils. An in-depth investigation of genome-wide associations was conducted using a field phenotypic data of 206 diverse Focused Identification of Germplasm Strategy (FIGS) wheat lines for two consecutive years from different sodic and non-sodic plots and the exome targeted genotyping by sequencing (tGBS) assay. A total of 39 quantitative trait SNPs (QTSs), including 18 haplotypes were identified on chromosome 1A, 1B, 1D, 2A, 2B, 2D, 3A, 3B, 5A, 5D, 6B, 7A, 7B, 7D for yield and yield-components tolerance. Among these, three QTSs had common associations for multiple traits, indicating pleiotropism and four QTSs had close associations for multiple traits, within 32.38 Mb. The overlapping metal ion binding (Mn, Ca, Zn and Al) and photosynthesis genes and transcription factors (PHD-, Dof-, HTH myb-, BHLH-, PDZ_6-domain) identified are known to be highly regulated during germination, maximum stem elongation, anthesis, and grain development stages. The homozygous/biallelic SNPs having allele frequency above 30% were identified for yield and crop establishment/plants m−2. These SNPs correspond to HTH myb-type and BHLH transcription factors, brassinosteroid signalling pathway, kinase activity, ATP and chitin binding activity. These resources are valuable in haplotype-based breeding and genome editing to improve yield potential on sodic-dispersive soils.

Mining and predictive characterization of resistance to leaf rust (Puccinia hordei Otth) using two subsets of barley genetic resources

Sustainable barley (Hordeum vulgare L.) production will require access to diverse ex-situ conserved collections to develop varieties with high yields and capable of overcoming the challenges imposed by major abiotic and biotic stresses. This study aimed at searching efficient approaches for the identification of new sources of resistance to barley leaf rust (Puccinia hordei Otth). Two subsets, Generation Challenge Program Reference set (GCP) with 188 accessions and leaf rust subset constructed using the filtering approach of the Focused Identification of Germplasm Strategy (FIGS) with 86 accessions, were evaluated for the seedling as well as the adult plant stage resistance (APR) using two barley leaf rust (LR) isolates (ISO-SAT and ISO-MRC) and in four environments in Morocco, respectively. Both subsets yielded a high percent of accessions with a moderately resistant (MR) reaction to the two LR isolates at the seedling stage. For APR, more than 50% of the accessions showed resistant reactions in SAT2018 and GCH2018, while this rate was less than 20% in SAT2017 and SAT2019. Statistical analysis using chi-square test of independence revealed the dependency of LR reaction on subsets at the seedling (ISO-MRC), as well as at the APR (SAT2017 and SAT2018) stage. At seedling stage, the test of goodness of fit showed that GCP subset yielded higher percentages of resistant accessions than FIGS-LR in case of ISO-MRC isolate but the two subsets did not differ for ISO-SAT. At APR, FIGS approach performed better than GCP in yielding higher percentages of accessions in case of SAT2017 and SAT2018. Although some of the tested machine learning models had moderate to high accuracies, none of them was able to find a strong and significant relationship between the reaction to LR and the environmental conditions showing the needs for more fine tuning of approaches for efficient mining of genetic resources using machine learning.

Assessment and modeling using machine learning of resistance to scald (Rhynchosporium commune) in two specific barley genetic resources subsets

Barley production worldwide is limited by several abiotic and biotic stresses and breeding of highly productive and adapted varieties is key to overcome these challenges. Leaf scald, caused by Rhynchosporium commune is a major disease of barley that requires the identification of novel sources of resistance. In this study two subsets of genebank accessions were used: one extracted from the Reference set developed within the Generation Challenge Program (GCP) with 191 accessions, and the other with 101 accessions selected using the filtering approach of the Focused Identification of Germplasm Strategy (FIGS). These subsets were evaluated for resistance to scald at the seedling stage under controlled conditions using two Moroccan isolates, and at the adult plant stage in Ethiopia and Morocco. The results showed that both GCP and FIGS subsets were able to identify sources of resistance to leaf scald at both plant growth stages. In addition, the test of independence and goodness of fit showed that FIGS filtering approach was able to capture higher percentages of resistant accessions compared to GCP subset at the seedling stage against two Moroccan scald isolates, and at the adult plant stage against four field populations of Morocco and Ethiopia, with the exception of Holetta nursery 2017. Furthermore, four machine learning models were tuned on training sets to predict scald reactions on the test sets based on diverse metrics (accuracy, specificity, and Kappa). All models efficiently identified resistant accessions with specificities higher than 0.88 but showed different performances between isolates at the seedling and to field populations at the adult plant stage. The findings of our study will help in fine-tuning FIGS approach using machine learning for the selection of best-bet subsets for resistance to scald disease from the large number of genebank accessions.

Focused Identification of Germplasm Strategy (FIGS): polishing a rough diamond.

Focused Identification of Germplasm Strategy (FIGS) has been advocated as an efficient approach to predict and harness variation in adaptive traits in genebanks or wild populations of plants. However, a weakness of the current FIGS approach is that it only utilizes a priori knowledge of one evolutionary factor: natural selection. Further optimization is needed to capture elusive traits, and this review shows that nonadaptive evolutionary processes (gene flow and genetic drift) should be incorporated to increase precision. Focusing on plant resistance to insect herbivores, we also note that historic selection pressures can be difficult to disentangle, and provide suggestions for successful mining based on eco-evolutionary theory. We conclude that with such refinement FIGS has high potential for enhancing breeding efforts and hence sustainable plant production.

Screening of 19,460 genotypes of wheat species for resistance to powdery mildew and identification of potential candidates using focused identification of germplasm strategy (FIGS)

AbstractPowdery mildew (PM) caused by Blumeria graminis (DC). E.U. Speer f.sp. tritici Em. Marchal (Syn. Erysiphe gramnis DC f.sp. tritici, Em. Marchal) is one of the important diseases of wheat worldwide. A comprehensive evaluation of wheat germplasm accessions (19,460) conserved in the National Genebank of ICAR–National Bureau of Plant Genetic Resources was conducted to identify sources of resistance to PM. Accessions belonging to the three wheat species—bread wheat (Triticum aestivum L. subsp. aestivum) (15,944), durum wheat (T. durum Desf.) (3,359), and emmer wheat (T. dicoccum Schrank ex Schübl.) (157)—were screened at Wellington, a hotspot location for PM, for two consecutive seasons. Screening results indicated that 7271 (45%) from bread wheat, 756 (22%) from durum wheat, and 22 (14%) from emmer were resistant. Out of 8094 PM‐resistant accessions, 60% were indigenous, while majority of the 40% exotic were from CIMMYT. Focused identification of germplasm strategy (FIGS), which identifies a set of similar plant genotypes with a greater possibility of containing specific target traits, was used to form a subset of 52 accessions (from 19,460) that have the potential to contain new PM resistance genes. Resistant accessions identified in the study have enriched the existing gene pool for PM resistance in wheat and will serve as a potential source for resistance in future.

Screening the FIGS Set of Lentil (Lens culinaris Medikus) Germplasm for Tolerance to Terminal Heat and Combined Drought-Heat Stress

Lentil (Lens culinaris Medikus) is one of the most important cool season food legume crops grown in many countries. Seeds are typically rich in protein, fiber, prebiotic carbohydrates and minerals, such as iron and zinc. With changing climate and variability, the lentil crop faces frequent droughts and heat stress of varying intensity in its major production zones. In the present study, a set of 162 lentil accessions selected through the Focused Identification of Germplasm Strategy (FIGS) were screened for tolerance to heat stress and combined heat-drought stresses under field conditions at two contrasting locations, namely Marchouch and Tessaout in Morocco. The results showed a significant genotypic variation for heat tolerance and combined heat-drought tolerance among the accessions at both locations. Based on the heat tolerance index (HTI), accessions, namely ILL 7833, ILL 6338 and ILL 6104, were selected as potential sources of heat tolerance at Marchouch, and ILL 7814 and ILL 8029 at Tessaout. Using the stress tolerance index (STI), ILL 7835, ILL 6075 and ILL 6362 were identified as the most tolerant lines (STI > 1) at Marchouch, and ILL 7814, ILL 7835 and ILL 7804 (STI > 1) at Tessaout, under the combined heat-drought stress conditions. Accession ILL 7835 was identified as a good source of stable tolerance to heat stress and combined heat-drought stress at both locations.

Combining focused identification of germplasm and core collection strategies to identify genebank accessions for central European soybean breeding.

Environmental adaptation of crops is essential for reliable agricultural production and an important breeding objective. Genebanks provide genetic variation for the improvement of modern varieties, but the selection of suitable germplasm is frequently impeded by incomplete phenotypic data. We address this bottleneck by combining a Focused Identification of Germplasm Strategy (FIGS) with core collection methodology to select soybean (Glycine max) germplasm for Central European breeding from a collection of >17,000 accessions. By focussing on adaptation to high-latitude cold regions, we selected an "environmental precore" of 3,663 accessions using environmental data and compared the Donor opulation of Environments (DPE) in Asia and the Target Population of Environments (TPE) in Central Europe in the present and 2070. Using single nucleotide polymorphisms, we reduced the precore into two diverse core collections of 183 and 366 accessions to serve as diversity panels for evaluation in the TPE. Genetic differentiation between precore and non-precore accessions revealed genomic regions that control maturity, and novel candidate loci for environmental adaptation, demonstrating the potential of diversity panels for studying adaptation. Objective-driven core collections have the potential to increase germplasm utilization for abiotic adaptation by breeding for a rapidly changing climate, or de novo adaptation of crops to expand cultivation ranges.

Barley: Global challenges and perspectives under non-tropical dry areas

The International Center for Agricultural Research in Dry Areas (ICARDA) is one of the 15 CGIAR centers and has a global mandate for barley improvement, specifically for more than 16 million ha of barley is grown in non-tropical dry areas across the globe. ICARDA’s barley improvement program has been reorganized into spring barley for high and low input environments and winter barley programs to address the requirements of different agro-climatic conditions/regions. The spring barley program targets germplasm improvement for feed, food and malt purposes, targeting germplasm enhancement for global stressed environments in hulled and huskless barley. The winter barley program is basically focused to improve germplasm for colder regions in West and Central Asia where cold and frost are the major stresses along with drought. Each year, nearly 10000 advanced lines are evaluated for various agronomic, biotic and abiotic stress tolerances, and grain quality parameters in the breeding program. ICARDA’s gene bank holds more than 33,000 barley accessions of which 2042 accessions are wild relatives of barley. The Focused Identification of Germplasm Strategy (FIGS) approach is utilized to mine efficiently this collection by selecting manageable subsets with higher frequencies for finding the sought traits. ICARDA’s barley improvement program annually shares more than 330 sets of international trials and nurseries, with more than 60 collaborators in 35 to 40 countries. During 1977-2018, more than 269 barley varieties have been released across the globe by different countries, with direct introduction of germplasm from ICARDA’s barley breeding programs, out of which 53 are released during last 10 years. In recent year, ICARDA’s feed and food barley improvement program aims at improving nutritional qualities of barley, specifically Zn, Fe, and β-Glucan contents. Simultaneously, the malt barley improvement is targeting to identify better malting quality germplasm for developing countries in Africa and Asia with a possibility of additional income to farmers through premium price for better quality in East Africa and South Asia. Doi.org/10.25174/2249-4065/2018/85893

Identification of novel Ascochyta lentis resistance in a global lentil collection using a focused identification of germplasm strategy (FIGS)

The Australian lentil breeding program is historically genetically narrow and recent reports suggests a loss of resistance to Ascochyta lentis within resistant cultivars such as Northfield and Nipper. There is evidence that the pathogen population is becoming more virulent on other widely adopted cultivars, thus there is an urgent need to identify novel resistance sources that may be transferred into the cultigen. To reduce the substantial economic and time commitment in this search, a Focused Identification of Germplasm Strategy (FIGS) was applied. This entailed exploring potential association between collection site environment and climatic conditions favouring A. lentis development, to predict regions imposing favourable selection towards A. lentis resistance. Accordingly, a subset of 87 landraces (originating from 16 countries) with highest likelihood for A. lentis resistance was selected from 4576 accessions held by the International Centre for Agricultural Research in the Dry Areas (ICARDA), Morrocco collection. Significant variation for resistance was detected within the subset using completely randomized and replicated controlled climate bioassays with a highly virulent Australian A. lentis isolate, FT13037. Genotype IG 207 expressed the lowest percent area of symptomatic tissue and a further 12 genotypes demonstrated moderate resistance. Furthermore, IG 207 recorded lowest mean disease score against four other highly aggressive isolates and performed better than the currently used best resistance sources, ILL 7537 and Indianhead. In addition, delayed pre-penetration behaviour of isolate FT13038 on IG 207 leaflets indicated discovery through the FIGS technique of a novel and highly effective A. lentis resistance source.

Introgressiomics: a new approach for using crop wild relatives in breeding for adaptation to climate change

The need to boost agricultural production in the coming decades in a climate change scenario requires new approaches for the development of new crop varieties that are more resilient and more efficient in the use of resources. Crop wild relatives (CWRs) are a source of variation for many traits of interest in breeding, in particular tolerance to abiotic and biotic stresses. However, their potential in plant breeding has largely remained unexploited. CWRs can make an effective contribution to broadening the genetic base of crops and to introgressing traits of interest, but their direct use by breeders in breeding programs is usually not feasible due to the presence of undesirable traits in CWRs (linkage drag) and frequent breeding barriers with the crop. Here we call for a new approach, which we tentatively call ‘introgressiomics’, which consists of mass scale development of plant materials and populations with introgressions from CWRs into the genetic background of crops. Introgressiomics is a form of pre-emptive breeding and can be focused, when looking for specific phenotypes, or un-focused, when it is aimed at creating highly diverse introgressed populations. Exploring germplasm collections and identifying adequate species and accessions from different genepools encompassing a high diversity, using different strategies like the creation of germplasm diversity sets, Focused identification of germplasm strategy (FIGS) or gap analysis, is a first step in introgressiomics. Interspecific hybridization and backcrossing is often a major barrier for introgressiomics, but a number of techniques can be used to potentially overcome these and produce introgression populations. The generation of chromosome substitution lines (CSLs), introgression lines (ILs), or multi-parent advanced inter-cross (MAGIC) populations by means of marker-assisted selection allows not only the genetic analysis of traits present in CWRs, but also developing genetically characterized elite materials that can be easily incorporated in breeding programs. Genomic tools, in particular high-throughput molecular markers, facilitate the characterization and development of introgressiomics populations, while new plant breeding techniques (NPBTs) can enhance the introgression and use of genes from CWRs in the genetic background of crops. An efficient use of introgressiomics populations requires moving the materials into breeding pipelines. In this respect public–private partnerships (PPPs) can contribute to an increased use of introgressed materials by breeders. We hope that the introgressiomics approach will contribute to the development of a new generation of cultivars with dramatically improved yield and performance that may allow coping with the environmental changes caused by climate change while at the same time contributing to a more efficient and sustainable agriculture.

Predicting resistance to stripe (yellow) rust (Puccinia striiformis)in wheat genetic resources using focused identification of germplasm strategy

SUMMARYStripe rust, caused byPuccinia striiformisf. sp.tritici(Pst), is a major wheat disease that can inflict yield losses of up to 70% on susceptible varieties under favourable environmental conditions. The timely identification of plant genetic resources likely to possess novel resistance to this disease would facilitate the rapid development of resistant wheat varieties. The focused identification of germplasm strategy (FIGS) approach was used to predict stripe rust resistance in a collection of wheat landraces conserved at ICARDA genebank. Long-term climate data for the collection sites, from which these accessions originated and stripe rust evaluation scores for one group of accessions were presented to three different non-linear models to explore the trait×collection site environment interactions. Patterns in the data detected by the models were used to predict stripe rust resistance in a second and different set of accessions. The results of the prediction were then tested against actual evaluation scores of the disease in the field. The study mimics the real scenario where requests are made to plant genetic resources curators to provide accessions that are likely to possess variation for specific traits such as disease resistance.The models used were able to identify stripe rust-resistant accessions with a high degree of accuracy. Values as high as 0·75 for area under the curve and 0·45 for Kappa statistics, which quantify the agreement between the models’ predictions and the curator's disease scores, were achieved. This demonstrates a strong environmental component in the geographic distribution of resistance genes and therefore supports the theoretical basis for FIGS. It is argued that FIGS will improve the rate of gene discovery and efficiency of mining genetic resource collections for adaptive traits by reducing the number of accessions that are normally required for evaluation to identify such variation.

Do faba bean (Vicia faba L.) accessions from environments with contrasting seasonal moisture availabilities differ in stomatal characteristics and related traits?

Drought is a major constraint to faba bean (Vicia faba L.) production, and there are many mechanisms by which leaves can regulate water loss. Our primary objective was to test if the origin of the faba bean accessions, from drought-prone and non-drought-prone environments, was associated with differences in measurable aspects of stomatal morphology and physiology related to water use. Two sets, each consisting of 201 faba bean accessions, were chosen from environments with contrasting seasonal moisture profiles following the focused identification of germplasm strategy (FIGS), and then screened under well watered conditions. From these, two subsets of 10 accessions each were chosen to test for differences in response to drought. Parameters related to stomatal function and water status were measured. The dry-adapted set had bigger stomata, higher leaf relative water content (LRWC) and cooler leaves under well watered conditions. Stomatal density and stomatal area per unit area of leaflet were negatively correlated with gas exchange parameters and positively correlated with intrinsic water use efficiency. Drought caused stomatal densities to increase in the dry set while stomatal length decreased in both sets. The moisture deficit was sufficient to decrease gas exchange in both sets to similar levels, but the dry-adapted set maintained warmer leaves and a higher LRWC that showed no significant correlations with leaf morphology or gas exchange, demonstrating more effective stomatal regulation. The results also support that collection site data from the environment where genetic resources are collected can be used as indicators of adaptive traits in an herbaceous annual species.

Sources of Resistance to Stem Rust (Ug99) in Bread Wheat and Durum Wheat Identified Using Focused Identification of Germplasm Strategy

ABSTRACTThe focused identification of germplasm strategy (FIGS) has been validated using predictive computer models in simulation studies to predict a priori known trait scores. This study was designed as a “blind” study where the person calculating the computer model did not know the actual trait scores. This study design provides a more realistic test of the predictive capacity of the FIGS approach compared to previous studies. Furthermore this study also explored the suitability of FIGS for the identification of resistance in bread wheat (Triticum aestivum L. subsp. aestivum) and durum wheat [Triticum turgidum L. subsp. durum (Desf.) Husn.] to Ug99—a strain of stem rust (Puccinia graminis Pers. f. sp. tritici Eriks. & Henn.) and typified to race TTKSK. The predictions were validated against a dataset with the screening of wheat accessions conducted in Yemen in 2008. Only a small training set representing 20% of the trait screening results was disclosed to the person conducting the data analysis for the calibration of the prediction model. The hit rate for identification of Ug99‐resistant accessions was more than two times higher when using the FIGS approach compared to a random selection of accessions. These results suggested that FIGS was well suited for the identification of samples with resistance to fungal pathogens. It is therefore recommended that FIGS approach be used as a complement to expert knowledge and experience when selecting accessions for plant breeding and crop research activities.

Focused identification of germplasm strategy (FIGS) detects wheat stem rust resistance linked to environmental variables

Recent studies have shown that novel genetic variation for resistance to pests and diseases can be detected in plant genetic resources originating from locations with an environmental profile similar to the collection sites of a reference set of accessions with known resistance, based on the Focused Identification of Germplasm Strategy (FIGS) approach. FIGS combines both the development of a priori information based on the quantification of the trait-environment relationship and the use of this information to define a best bet subset of accessions with a higher probability of containing new variation for the sought after trait(s). The present study investigates the development strategy of the a priori information using different modeling techniques including learning-based techniques as a follow up to previous work where parametric approaches were used to quantify the stem rust resistance and climate variables relationship. The results show that the predictive power, derived from the accuracy parameters and cross-validation, varies depending on whether the models are based on linear or non-linear approaches. The prediction based on learning techniques are relatively higher indicating that the non-linear approaches, in particular support vector machine and neural networks, outperform both principal component logistic regression and generalized partial least squares. Overall there are indications that the trait distribution of resistance to stem rust is confined to certain environments or areas, whereas the susceptible types appear to be limited to other areas with some degree of overlapping of the two classes. The results also point to a number of issues to consider for improving the predictive performance of the models.

Predictive Association between Biotic Stress Traits and Eco‐Geographic Data for Wheat and Barley Landraces

Collections of crop genetic resources are a valuable source of new genetic variation for economically important traits, including resistance to crop diseases. New sources of useful crop traits are often identified through evaluation in field trials. The number of relevant accessions in genebank collections available to be evaluated is often substantially larger than the capacity of the evaluation project. The focused identification of germplasm strategy (FIGS) is an approach used to select subsets of germplasm from genetic resource collections in such a way as to maximize the likelihood of capturing a specific trait. This strategy uses a range of methods to link the expression of a specific trait (of a target crop) with the eco‐geographic parameters of the original collection site. This study contributes to the development of the approach by which a FIGS subset could be assembled for biotic traits. We have evaluated trait‐specific subset selection methods for two fungal crop diseases, namely stem rust (Puccinia graminis Pers.) in wheat (Triticum aestivum L. and Triticum turgidum L.) and net blotch (Pyrenophora teres Drechs.) in barley (Hordeum vulgare L.). The results indicate that the climate layers from freely available eco‐geographic databases are well suited to model and predict the reaction in these crops to biotic stress traits. This result has the potential to improve the efficiency of field screening trials to find novel sources of economically valuable crop traits.

Sources of resistance in bread wheat to Russian wheat aphid (Diuraphis noxia) in Syria identified using the Focused Identification of Germplasm Strategy (FIGS)

With 1 tableAbstractRussian wheat aphid, Diuraphis noxia (Kurdjumov), is an important pest of wheat and barley in North America, Africa and the Middle East. Host plant resistance is the most economical and practical means of controlling this insect. Field and greenhouse screening in Syria of bread wheat lines from the International Center for Agricultural Research in the Dry Areas’ (ICARDA) gene bank, using the Focused Identification of Germplasm Strategy (FIGS), identified 12 lines resistant to D. noxia. These sources of resistance will be used to develop germplasm resistant to D. noxia for the highland areas of North Africa, and West and Central Asia, where this insect causes economic damage. This study also showed the relevance of the FIGS approach in better targeting accessions held in gene banks for valuable traits.

Sources of wheat resistance to Sunn pest, Eurygaster integriceps Puton, in Syria

Sunn pest, Eurygaster integriceps Puton, is the most damaging insect pest of wheat in West and Central Asia and East Europe. Host plant resistance has been investigated as one component of a total integrated pest management program for the control of this pest. In Syria, field screening of artificially infested wheat accessions from the International Center for Agricultural Research in the Dry Areas (ICARDA) gene bank, selected using the Focused Identification of Germplasm Strategy (FIGS), identified one durum wheat and eight bread wheat accessions with good levels of resistance at the vegetative stage to overwintered Sunn pest adults. ICARDA is using these sources of resistance in wheat breeding programs to develop cultivars resistant to overwintered Sunn pest adults, which damage wheat at the vegetative stage (shoots and leaves). This study also demonstrated that the FIGS approach was effective in mining genetic resource collections for useful traits.