Application of Genetic and Genomic Tools in Wheat for Developing Countries

CHAPTER 12

Application of Genetic and Genomic Tools in Wheat for Developing Countries Susanne Dreisigacker, Deepmala Sehgal, Ravi P. Singh, Carolina Sansaloni, Hans-Joachim Braun

Centro Internacional de Mejoramiento de Maiz y Trigo (CIMMYT), El Batan, Texcoco, Mexico

Contents 12.1 Introduction 12.2 Key Breeding Priorities for Wheat Improvement in Developing Countries 12.3 Application of Genetic and Genomic Tools in the CIMMYT Wheat Breeding Pipeline 12.4 Gene Discovery and Genomic Prediction 12.4.1 Progress in Genetic Mapping and Cloning of Disease Resistance Genes 12.4.2 Genome-Wide Association Analyses for Biotic and Abiotic Stress Resistance/Tolerance and Quality Traits 12.4.3 New Genetic Loci and Alleles for Physiological Traits Including Flowering Time 12.4.4 Progress in Genomic Prediction 12.5 Deployment of Genomics-Assisted Breeding Strategies 12.6 Exploring Genetic Resources 12.7 Tracking Crop Varieties 12.8 Wheat Case Study 12.9 Evidence From Other Crops References

251 252 255 256 256 257 258 258 259 262 265 266 267 268

12.1 INTRODUCTION Wheat is a key staple food that provides around 20% of protein and calories consumed worldwide. The demand for wheat is projected to continue to grow over the coming decades, particularly in the developing world to feed an increasing population, and with wheat being a preferred food, it continues to account for a substantial share of human energy needs by 2050 (Wageningen, 2016). Based on recent trends, an increasing number of poor consumers in low- and middle-income countries, who benefit from Applications of Genetic and Genomic Research in Cereals https://doi.org/10.1016/B978-0-08-102163-7.00012-0

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e­ conomic d­ evelopments, will want to eat wheat-based food at an affordable price as populations and economies grow, women and men seek employment in cities, and dietary habits change. Just over half of the global wheat production comes from developing countries, characterized by smallholdings. CIMMYT’s wheat germplasm has always been selected for high yield and yield stability across a wide range of diverse environments.Wide performance adaptation is essential to respond to global climate change, to the vagaries of spatial heterogeneity within farmers’ fields and their production input management efficacies, and from unpredictable temporal climatic seasonal variability. Wide adaptation is often associated with geography, but is becoming increasingly important to cope with weather extremes at one farmer field over years. Many wheat farmers cultivate <1 ha and usually do not grow several varieties with, for example, varying maturity to mitigate risks. These farmers’ risks are best addressed by growing wheat varieties that can cope with annual environmental fluctuation (heat, drought, high rainfall, and diseases). To identify the widely adapted wheat lines with high and stable grain yields, multienvironment testing of lines selected under the shuttle scheme is paramount. Every year, new elite wheat lines are sent to around 200 cooperators in >70 countries, who evaluate the material and share the results with the international wheat community. Without this International Wheat Improvement Network (IWIN), in which basically every major wheat program worldwide participates, and which is based on germplasm and information exchange between CIMMYT and cooperators—the International Centre for Agriculture in Dry Areas using a similar system—it is unlikely that wheat developed in Mexico would have had a global impact on wheat improvement. The information on the performance of the wheat lines in international nurseries obtained through IWIN is paramount for the crossing plan at CIMMYT. Using parents that performed well across a wide range of environments allowed increasing the frequency of desirable alleles in CIMMYT germplasm and is the basis for the high and stable yield (Braun et al., 2010; Reynolds et al., 2017).

12.2  KEY BREEDING PRIORITIES FOR WHEAT IMPROVEMENT IN DEVELOPING COUNTRIES The CIMMYT spring wheat breeding program currently focuses on four of the six spring wheat mega-environments (Rajaram et  al., 1994). Table 12.1 lists the most relevant traits for enhancing productivity in each of the four mega-environments, which together total to about 60 million hectares. The key breeding objectives of the program overall are grain yield



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Table 12.1  Targeted wheat mega-environments (ME) and key traits for breeding spring wheat at CIMMYT Moisture % ME regime Temperature areaa Main target traits

ME1

Irrigated

Temperate

36.1

ME2

High rainfall (>500 mm)

Temperate

8.5

ME4

Low rainfall (<500 mm)

Temperate or hot

14.6

ME5

Irrigated or high rainfall

Warmer

7.1

a

Percent of developing country wheat area.

High yield potential, lodging tolerance Water and nutrient use efficiency Resistance to three rusts Large white grain with leavened and flat bread quality High grain Zn and Fe High yield potential and lodging tolerance Resistance to three rusts, septoria tritici blotch and fusarium head blight Large red grain and leavened bread quality Drought tolerance with responsiveness to water availability Better root and emergence characteristics Adaptation to conservation agriculture Resistance to three rusts, septoria tritici blotch, tan spot, root diseases and nematodes Large white grain with leavened and flat bread quality High yield with early maturity, lodging tolerance Heat tolerance Resistance to leaf and stem rusts, spot blotch, wheat blast and fusarium head blight Large white grain with leavened and flat bread quality High grain Zn and Fe

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e­ nhancement together with yield stability, resistance/tolerance to biotic and abiotic stresses, end-use and nutritional quality characteristics. Breeding in Mexico utilizes two crop seasons per year, which not only cuts breeding time by about half but also allows selecting for a range of traits in contrasting field sites that have distinct day-length and temperature regimes. The irrigated field site at Ciudad Obregon (28°N latitude, 39 masl) in northwestern Mexico is the main field site for trait selection as well as for testing grain yield performance. Due to the lack of significant rains during the crop season, this site provides an ideal test environment for high yield potential, extreme and moderate drought stress, heat stress at early growth stage and continuous heat stress, all achieved side by side by controlling the amount of irrigation or sowing time. Large-scale field phenotyping has allowed making simultaneous progress on grain yield potential, drought, and heat tolerance in the germplasm. Ciudad Obregon is also ideal for selecting and testing for resistance to leaf rust, black rust, and Karnal bunt. Two field sites at Toluca and El Batan, situated at about 18°N latitude and at 2640 and 2230 masl, respectively, in the central Mexican highlands, allow growing a normal crop during the summer, and are used for the selection of agronomic traits and resistance to leaf rust, stripe rust, septoria tritici blotch, tan spot, and fusarium head blight (FHB).The Agua Fria Research Station (latitude 20°N; elevation 100–110 masl) is a hot-spot site for spot blotch disease and is used for phenotyping and selection. Since 2006, a field site at Njoro, Kenya (0.37°S latitude, 2300 masl), managed in collaboration with the Kenyan Agricultural and Livestock Research Organization (KALRO), is being used to select for resistance to the Ug99 race group of the stem rust fungus by growing F3 and F4 segregating populations, and their subsequent generations, for two continuous seasons in a year. Phenotyping of all advanced lines is also conducted at Njoro to assess resistance to stem rust. At all the field sites, selection is conducted under high disease pressure achieved through artificial inoculation using the most important stem rust races that prevail in the region. Selecting for grain yield and heat and drought tolerance is highly complex because these traits are under quantitative genetic control involving an undetermined number of minor genes. This has led to slow genetic yield gains not only in wheat but also in other crops. Durable resistance to the rusts and other foliar and head diseases and blast is also controlled by multiple small-to-intermediate additive effect genes and good progress has been made in developing resistant varieties (Singh et al., 2016a).The same is true of quality and micronutrient traits. To continue achieving genetic progress for a combination of traits, our approach therefore includes: (1) carefully selecting



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high-value and relevant parents; (2) making an optimized number of crosses of various types: simple, three-way, and single-backcross; (3) using large population sizes in all segregating generations for selection; (4) large numbers of individual plant-derived advanced lines in the F5/F6 generations for selection, and (5) include a large number of advanced lines for grain yield phenotyping in replicated yield trials grown in diverse environments. The breeding populations/advanced lines are grown under high pressure of relevant diseases to enable selection of resistant lines before testing for grain yield. Unpredictable warmer temperatures at all growth stages have become common worldwide due to climate change. Warmer temperatures not only accelerate the growth rate of plants but also increase their water requirements. With receding water tables in vast areas where ground water is used for irrigation, there is a strong need to select varieties that possess combined tolerance to both heat and drought stresses. It should be noted that high yield potential has been combined with heat and drought stress tolerance through simultaneous selection in CIMMYT wheat germplasm (Mondal et al., 2015), though the frequency of such lines remains low. These types of wheat varieties with flexible and stable performance are the most desirable options for deployment under the climate change scenario. An annual yield gain of about 1% is being realized in current CIMMYT germplasm in all environments, and higher yielding, more productive varieties continue to be released in several countries resulting in enhanced productivity including in eastern India (Lantican et al., 2016; Mondal et al., 2016; CrespoHerrera et al., 2017). Achieving higher genetic gains in grain yield and abiotic stress tolerance continues to be a major research focus worldwide; selection based on physiological traits, marker-assisted incorporation of QTL, and genomic selection are being used as a result.The constraints when screening for physiological traits include the lack of rapid, accurate, easy-to-operate tools, repeatability, destructive, and cost-effective methods. However, canopy temperature depression and the normalized difference vegetation index are used routinely in the selection for drought and heat stress tolerance. Laboratory facilities are used to analyze quality and nutritional traits and apply molecular markers.

12.3  APPLICATION OF GENETIC AND GENOMIC TOOLS IN THE CIMMYT WHEAT BREEDING PIPELINE Today, the concept and application of “genomics-assisted breeding” has become common in breeding programs worldwide (Poland et  al., 2012). Genomic-assisted breeding involves integrating large collections of

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molecular markers, high-throughput genotyping strategies, high-density genetic maps, and/or new experimental populations into the existing breeding methods. In CIMMYT’s Global Wheat Program (GWP), application of advanced genetics and genomics tools has increased over the years. They are used to dissect and map important traits and identify markers for forward breeding or marker-assisted backcross selection (MABC). Important disease resistance genes in wheat have been cloned in the past decade using CIMMYT lines as donors, and others are being investigated using advanced comparative genomics, mutagenesis, and transformation techniques (Moore et  al., 2015, Sánchez-Martín et  al., 2016). GWP breeders are utilizing high-throughput genotyping platforms, such as kompetitive allele specific PCR (KASP) and genotyping-by-sequencing (GBS), for marker-assisted selection (MAS) and genomic selection (GS).

12.4  GENE DISCOVERY AND GENOMIC PREDICTION 12.4.1  Progress in Genetic Mapping and Cloning of Disease Resistance Genes Rust research in the GWP has mapped and officially designated 12 rust genes in the past decade using biparental linkage mapping with SSR or DArT markers. These genes are Lr34/Yr18/Sr57/Pm38, Lr46/Yr29/Sr58/ Pm39, Lr67/Yr46/Sr55/Pm46, Lr61, Lr68, Lr72, Yr54, Yr60, Sr2/Yr30, Sr55, Sr57, and Sr58 (reviewed in Lan and Basnet, 2016). In addition, seven resistance genes have been temporarily designated: YrF, YrSuj, YrKK, SrND643, SrNini, SrSHA7/SrHaril, and SrBlouk. Among these, especially the three pleiotropic adult-plant resistance genes Lr34/Yr18/Sr57/Pm38, Lr46/Yr29/Sr58/Pm39, and Lr67/Yr46/Sr55/Pm46Lr34 are widely used in the spring bread wheat breeding program as a partial basis of resistance against the three rusts. In collaboration with Australian scientists at CSIRO, Lr34/Yr18/Sr57/Pm38 was cloned using a standard chromosome walking technique combined with bacterial artificial chromosome library screening (Krattinger et al., 2009). The candidate gene identified for Lr34/Yr18/ Sr57/Pm38 was a putative ABC transporter (Krattinger et al., 2009; Risk et al., 2012). The same group of scientists, with wider collaboration among CIMMYT, Mexican, Australian, Norwegian, and Australian scientists, also recently cloned Lr67/Yr46/Sr55/Pm46 (Moore et al., 2015). Comparative genomics, mutagenesis, and transformation approaches were used to isolate the Lr67/Yr46/Sr55/Pm46 gene. The Lr67/Yr46/Sr55/Pm46 gene was identified as being a hexose transporter with the resistance allele i­ncapable



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of glucose import and the susceptible allele to form a functional glucose transporter. Selection for the closely linked Rht-D1b semidwarf allele has most likely fixed the Lr67/Yr46/Sr55/Pm46 susceptible allele in modern CIMMYT wheat germplasm; the gene is hence newly introduced. Additional rust-resistance genes are being cloned using the advanced techniques of MutChromSeq, a complexity reduction approach based on flow sorting and sequencing of mutant chromosome lines (Sánchez-Martín et al., 2016). In addition to rust, genetic mapping conducted for FHB and septoria tritici blotch (STB) using biparental linkage mapping has led to the identification of major and consistent resistance loci across environments in the current germplasm (Dreisigacker et  al., 2015; Zhu et  al., 2016, He et  al., 2016). For the FHB QTL on chromosomes 2DL (associated with FHB resistance and DON content; He et al., 2016) and 3DL (exclusively associated with DON content; He et al., unpublished), KASP assays have recently been designed.

12.4.2  Genome-Wide Association Analyses for Biotic and Abiotic Stress Resistance/Tolerance and Quality Traits In the past decade, CIMMYT has adopted various high-throughput whole genome genotyping platforms including the Illumina 15K, 20K, and 90K SNP chips, the Breeders’ 35K Axiom array (Affymetrix) and GBS. As a result, high-density marker data have been generated on different sets of germplasm. These datasets have been utilized in genome-wide association analyses for various priority traits including grain quality and resistance to a number of diseases (Crossa et al., 2007; Yu et al., 2011; Edae et al., 2014; Lopes et al., 2015; Sukumaran et al., 2015; Singh et al., 2016b; Sehgal et al., 2016, 2017;Valluru et al., 2017; Battenfield et al., 2016; Juliana et al., 2015). As a result, novel genetic loci for resistance to tan spot (6A and 7B), stem rust (2BL, 3BL, 4AL, 5B, 6B, and 7D) and stripe rust (1BL, 2DS, 3BL, 5BS, 5BL), and for yield under drought and heat stress conditions (2D, 3A, 3B, 4A, 5B, and 7B) became known. Genome-wide association analysis for leaf tip necrosis and pseudo-black chaff identified loci closely linked to leaf rust-resistance genes Lr34/Yr18/Sr57/Pm38 (7DS), Lr46/Yr29/Sr58/ Pm39 (1BL), Lr68 (7BL), and Sr2/Yr30 (3BS), respectively, in addition to three new loci on chromosomes 5B, 7BS, and 4BS (Juliana et al., 2015). In addition, candidate gene-based association analyses in the wheat association mapping initiative (WAMI) panel identified SNPs from five candidate genes (DREB1A, ERA1-B, ERA1-D, 1-FEH-A, and 1-FEH-B) as

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being associated with yield and yield components (Edae et  al., 2014). A metaanalysis was conducted for processing and end-use quality phenotypes in advanced breeding lines of the CIMMYT bread wheat breeding program generated from 2009 to 2014 (Battenfield et al., unpublished). The metaanalysis identified a hot-spot QTL for dough extensibility and loaf volume on chromosome 7A. The recently discovered wheat bread making (wbm) gene was found in LD with this 7A QTL.

12.4.3  New Genetic Loci and Alleles for Physiological Traits Including Flowering Time Of the traits that contribute to grain yield, none is probably as well understood at the genetic level as flowering time and phenology. They are critical elements that can contribute to a step change in grain yield by fitting crop growth into the best timeframe of a particular environment. Various allelic forms of Vrn-1 and Ppd-1 genes are known in wheat, derived from insertions or deletions within the promoter region and/or in intron 1. A novel allele Vrn-A1f was identified in a focused identification of germplasm strategy (FIGS)-derived landrace collection that promotes flowering by 6 to 7 days in landraces from Iraq, Iran, and Afghanistan (Sehgal et al., 2015). Furthermore, a major eps QTL was recently confirmed on chromosome 1DL in the WAMI panel (Sukumaran et al., 2016). The genetic basis of the more detailed aspects of flowering time and phenology, and how these interact with the environment to determine the proportion of above-ground biomass that is allocated to grains are currently being established. Recently, co-mapping of important physiological traits with yield/yield components such as spike photosynthesis was conducted, and important genomic regions were identified on chromosomes 7B, 3A, and 3B (Molero et  al., unpublished).

12.4.4  Progress in Genomic Prediction Genomic prediction combines marker data with phenotypic and pedigree data (when available) in an attempt to increase the accuracy of the prediction of breeding and genotypic values. At CIMMYT, genomic prediction and subsequent selection have been investigated mainly for predicting: (a) the genotypic values of individuals for potential release as cultivars and (b) the breeding value of candidates in rapid-cycle populations. New models have been developed and tested on various sets of germplasm that were evaluated for different diseases, end uses and nutritional quality and grain yield (Crossa et al., 2010, 2011, 2014, 2016; Pérez-Rodríguez et al., 2012;



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Burgueño et al., 2012; Rutkoski et al., 2014, 2016; Saint Pierre et al., 2016; Juliana et al., 2017a, b). Important outcomes of these studies were: (a) prediction accuracy between genetically unrelated populations (or families) was low, regardless of marker density; (b) combining marker and pedigree data in prediction models often resulted in higher prediction abilities; (c) no one prediction model fitted all situations. However, nonlinear models—for example, Reproducing Kernel Hilbert Spaces (RKHS) and Neural Network methods with a Radial Basis Function (RBFNN)— tended to increase the prediction ability for complex traits like grain yield, as compared to linear models (Bayesian LASSO, Bayesian Ridge Regression, Bayes A, and Bayes B models). In addition, multivariate and multienvironment models incorporating secondary traits and genotype × environment interaction (GE), respectively, have been investigated specifically for grain yield (Burgueño et  al., 2012; Pérez-Rodríguez et al., 2012; Rutkoski et al., 2016). Rutkoski et al. (2016) showed that the prediction accuracy of multivariate models that incorporated secondary traits such as canopy temperature and the normalized difference vegetation index was higher compared to univariate models, especially when these traits were measured on both the training and the test sets. With the upsurge in high-throughput phenotyping data, this approach seems useful and practical. Burgueño et al. (2012) were the first to evaluate the impact of modeling GE in wheat using CIMMYT historical lines evaluated in several mega-environments. Their results clearly indicated that compared to single-environment mixed models, the modeling of GE with a factor analytic structure increased the prediction accuracy by 21.8%. Since then, GS models integrating GE have proven useful (Crossa et  al., 2016; Lopez-Cruz et al., 2015; Pérez-Rodríguez et al., 2017). Genomic prediction has also been investigated for end-use quality traits; Battenfield et al. (2016) and Guzmán et  al. (2016) demonstrated that very expensive traits such as dough rheology and loaf volume can be predicted with a high degree of confidence. Overall, the many results generated by the GWP have shown that reasonably high prediction accuracy can be achieved in wheat.

12.5  DEPLOYMENT OF GENOMICS-ASSISTED BREEDING STRATEGIES Genomics-assisted breeding strategies are applied in plant breeding programs to select individuals with superior performance. In MABC or MAS, individuals are selected based on QTL, which are detected through mapping

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approaches. Generally, MAS should be successful when (i) traits that are difficult to manage through conventional phenotypic selection because they are expensive or time consuming to measure have low penetrance or complex inheritance; (ii) trait selection depends on specific environments or host developmental stages; (iii) maintaining recessive alleles during backcrossing or for speeding up backcross breeding in general; and (iv) pyramiding multiple monogenic traits or several QTL for single disease resistance (Miedaner and Korzun, 2012). The number of markers per trait used in traditional MAS is, however, generally low. In GS, large numbers of genome-wide distributed markers are used for modeling the performance of an individual, regardless of the magnitude of their effect.The GS model should theoretically account for all QTL underlying the trait being studied regardless of the size of their effect (Goddard and Hayes, 2007). Therefore, for traits with complex inheritance, GS is expected to outperform MAS. The GWP is applying the MABC and MAS approaches to increase response to selection mainly for disease resistance, end-use quality, and nutritional quality. Targeted development of rust-resistance germplasm is one important example for which markers are adopted. The aim is to develop elite breeding lines that carry a combination of nonrace-specific adult-plant resistance genes and race-specific genes, to avoid applying extremely high selection pressure on the pathogen that might endanger the avirulence of individual genes in developing countries. Multiple nonrace-specific genes are present in the CIMMYT wheat germplasm pool. A large number of race-specific stem and yellow rust-resistance genes (e.g., Sr32, Sr50, Yr39, Yr41,Yr59, and others) not present in CIMMYT germplasm have recently been introgressed into a set of elite genetic backgrounds via MABC to build up different resistance gene pyramids. Furthermore, individual resistance genes are being successfully combined; MAS has resulted in high selection intensities and frequencies of resistance lines (Table 12.2). Marker-assisted selection is also being applied at CIMMYT to improve end-use and nutritional quality. Recently, expression of the wbm gene has been associated with good bread-making quality (Guzmán et al., 2016).The wbm allele has a significant effect on overall gluten quality, gluten strength, gluten extensibility, and bread-making quality and is present in CIMMYT germplasm at a relative low frequency of 14%. This frequency must be increased as CIMMYT lines serve as donors in many national breeding programs worldwide. Large-scale screening of available wheat genetic resources identified einkorn wheat, wild emmer, wheat, and wheat landraces with high amounts of Zn and Fe in the grain (Cakmak et  al., 2000;



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Table 12.2  Disease rating for stem rust in the 31st Semi-Arid Wheat Yield Screening Nursery Disease rating for stem rust Selection environmenta Total R R-MR MR MR-MS MS MSS S

Mexico— Conv Mexico— MAS Kenya— Conv All

No % No % No % No %

159 40 106 305

25 15.7 23 57.5 44 41.5 92 30.2

52 48.4 13 90.0 46 84.9 111 66.6

30 67.3 4 100.0 10 94.3 44 81.0

27 84.3 0

22 98.1 0

3 100.0 0

0

5 99.1 32 91.5

1 100.0 23 99.0

0

0

3 100.0

0

0

a

Lines were classified according the selection environment and using conventional selection (Conv) or marker-assisted selection (MAS).

Ortiz-Monasterio et al., 2007); Zn and Fe are the key micronutrients that enhance wheat nutritional quality. Various studies have reported QTL for high grain Fe and Zn concentrations, including two novel large-effect QTL on chromosomes 2B and 3A identified in a recombinant inbred line (RIL) population developed from a cross between “PBW343” and “Kenya Swara” (Hao et al., 2014). Markers linked to the QTL were successfully converted into a usable form for subsequent high-throughput selection. During the 2014–2015 crop season, selected RILs that showed significantly enhanced Zn compared to either of the parental lines (PBW343 or Kenya Swara) were used to transfer the QTL of interest into elite genetic backgrounds via MABC. More examples of MABC and MAS in the GWP are provided in Dreisigacker et al. (2016). Similar to MAS, GS can be applied at various stages in a wheat breeding program to tackle different prediction problems. The greatest potential of GS should be at points in the breeding program where selection using traditional methods is too expensive, time consuming, or biologically or logistically not feasible. A large GBS dataset, combined with agronomic, grain yield, and high-throughput phenotyping data for about 40,000 breeding lines, has been generated in the CIMMYT spring wheat breeding program over the past 4 years. These metadata are being used for grain yield predictions, from high-throughput phenotyping and genomic prediction models to amend selection of the best lines being distributed via international nurseries (Lopez-Cruz et al., 2015; Rutkoski et al., 2016). GS is also being applied to predict the performance of wheat lines in environments in South Asia (India, Pakistan, and Bangladesh) using parents

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evaluated in Mexico and predicting progenies in each target environment (Pérez-Rodríguez et  al., 2017). Furthermore, recurrent GS is tested by inter-mating individuals within and between populations to exploit the full advantage of genomic prediction for increasing genetic gains per unit of time. In the near future, it might also be possible to first predict crosses that have a high probability of giving superior progenies and then make only high-value crosses (Lado et al., 2017). However, much more work is needed to make this a reality. Full exploitation of genetic information and the scaling up of genomicsassisted breeding approaches in the public sector are still hampered by high genotyping costs and insufficient data management support. As with the currently available and future genotyping platforms, the cost of genotyping is inversely proportional to quantity. Private sector organizations usually develop genotyping hubs and optimized pipelines across crops at a single site for full automation and throughput using the latest equipment. However, in the case of public sector organizations, genotyping is often carried out by the respective programs only; therefore, costs remain high, as the individual demand is too low to cause them to drop significantly. Recently, CGIAR centers have started to aggregate their demands to a single genotyping service provider who performs low-density genotyping at rates of US$ 1–5 per sample. Scaling up genomics-assisted breeding has accumulated hundreds of thousands of SNP marker data points on thousands of new breeding lines. This massive amount of data must be managed and analyzed, for example, for routinely implemented GS approaches.The Genomic and Open-source Breeding Informatics Initiative (GOBII), supported by the Bill & Melinda Gates Foundation, was launched to put in place the systems, databases, analytical pipelines, and decision support tools that will allow plant breeders and geneticists to apply genomic information in cultivar development (Varshney et al., 2015).

12.6  EXPLORING GENETIC RESOURCES Although several natural resources have been explored to improve crop production, little has been done in the evaluation of genetic resources, one of the most elementary and important biological resources to which we have access. In the case of wheat, undomesticated wild species, crop wild relatives, and landraces represent sources of new variation for cultivar improvement. These materials provide alternative mechanisms to face



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disadvantageous conditions, as they have survived extreme environmental challenges and continuous cycles of natural selection. However, their resilience and adaptive capacity mechanisms remain largely untapped and are poorly understood (McCouch et al., 2013). Indeed, >560,000 wheat accessions are maintained in nearly 40 genebanks globally, but breeding is restricted to a limited part of this genetic diversity. Accelerating the rate of genetic gains in wheat is a high priority that could benefit from more efficient exploitation of existing genetic variation through breeding (Tester and Langridge, 2010). Unlocking the untapped biodiversity of genetic resources can therefore be considered crucial to enrich wheat breeding strategies. New high-throughput genotyping technologies using next-generation sequencing offer a novel approach for the precise description of large numbers of accessions held in genebanks, leveraging the utilization of the untapped treasure of ex situ diversity (McCouch et  al., 2013, Uauy, 2017). GBS is an inexpensive and high-throughput whole-genome genotyping methodology that is used for genetic resource characterization because it minimizes ascertainment bias, a key feature when characterizing unexplored genepools.This methodology has been used extensively by the Seeds of Discovery (SeeD) initiative (http://seedsofdiscovery.org/), a project led by CIMMYT that aims to unlock and utilize wheat genetic diversity held in genebanks for the development of new varieties to meet the demands of a growing population in a changing climate. SeeD has generated >100,000 genetic profiles describing the diversity of the two biggest wheat genebanks in the world (68,000 from CIMMYT and 32,000 from ICARDA).To multiply the impacts of these results, a genetic resource utilization platform for breeders and researchers is being created, made up of publicly available data and software tools. Part of the data generated has been used for the classification of Mexican landraces, with developed core sets currently being regrown across the country for phenotypic evaluation and subsequent hybridization of the best accessions with modern germplasm (Vikram et al., 2016). In addition, prebreeding efforts in SeeD have developed diversity panels and a large number of advanced lines derived through a three-way top cross strategy (exotic × elite1 × elite2), which are now being extensively characterized at the genotypic and the phenotypic level. The new physical maps and re-sequencing of wheat cultivars will make it possible to translate these large-scale SNP datasets into haplotype maps that can greatly facilitate genome-wide association studies of complex traits and functional investigations of evolutionary changes when genebank

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accessions are compared to modern germplasm pools. These advances will also accelerate studies on crop designs via genomics-assisted breeding (Huang and Han, 2014). The wheat reference sequence with annotation of genes will eventually allow exploring linkages between SeeD GBS sequencing data and functional variations of major morphology and adaptation genes, to predict the initial performance of any genotyped genebank accession and to simplify the classification and exploration of the CIMMYT and ICARDA genebanks. Similar to SeeD, but at a lower scale, Kabbaj et al. (2017) recently investigated the genetic diversity within a global panel of durum wheat landraces and modern germplasm. With the SNP data derived from the 35K Axiom array, the authors were able to disentangle the history of durum wheat origins based on migration patterns observed within the landraces as well as breeding exchange and cross-hybridization among the modern germplasm. Population stratification in the panel also provided a better understanding of how many of the available alleles have been captured within a specific germplasm, and could have immediate practical impact on breeding. Complementing efforts on genetic characterization of accessions conserved ex situ in genebanks and the documentation, description, and phenotypic evaluation of genetic resources conserved in situ are of fundamental importance (Alsaleh et al., 2016). From 2009 to 2014, a nationwide effort was made to document, collect, conserve, and characterize wheat landraces grown in Turkey, which is considered part of the center of wheat origin. The long-term cultivation and exchange of wheat landraces by local farmers resulted in the continuous enhancement and adaptation of those landraces. Spike samples were collected from >1500 farmers from 59 provinces, planted as single-spike progenies, and classified into species, subspecies, and botanical varieties or morphotypes (Morgounov et  al., 2016). Diversity indices based on SNP markers and the number of morphotypes identified regions in Turkey with the highest conserved genetic diversity, and possible conservation efforts are being discussed. Developed core sets are being characterized in detail. Researchers are exploiting the potential of this germplasm by incorporating it into national wheat breeding programs and utilizing rapid selection and novel breeding methodologies to integrate elite traits without losing the desired landrace characteristics. The resulting locally adapted improved wheat varieties can benefit smallholder farmers in rural areas of the developing world, generally affected by harsh farming conditions.



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12.7  TRACKING CROP VARIETIES Crop germplasm improvement is a major activity of CGIAR centers and thousands of new varieties are developed to provide higher yields, better nutritional content, improved adaptation to fluctuating climates, and increased resistance to diseases and pests (Reynolds and Borlaug, 2006). Studying to what extent these crop varieties are adopted by farmers is crucial to evaluate the performance and to understand the impact of agricultural development programs. Rigorous impact assessment is also important for informed and evidence-based policy making, for instance, to develop appropriate support policy measures for improved targeting, access, and use of modern varieties (Shiferaw et al., 2014). However, measuring the dissemination of improved crop varieties is challenging.Various methodologies, such as seed sales inquiries, expert opinion estimates by breeders and extension services, as well as surveys at the household and plot levels are used, but the reliability of these approaches has never been verified and each approach has its own inherent limitations. For example, seed sales inquiries require specific surveys, which may not fit into the existing agricultural statistical systems. The estimates of expert panels are only as good as the experts’ knowledge and elicitation protocols, and household surveys are only as reliable as the farmer’s knowledge of the genotype she/he is sowing (Walker et al., 2014). In a major effort to quantify the adoption of improved varieties in Sub-Saharan Africa, DIIVA project 1 has shed light on the convergence of expert opinion estimates with household survey estimates (Walker et  al., 2014). Conclusions point toward the fact that expert opinion estimates are likely to overemphasize the uptake of specific varieties, while household surveys are likely to underestimate their importance. The study concluded that “probably neither surveys nor expert panels can do a good job in delivering accurate estimates of cultivar-specific adoption” (Walker, 2015). The study calls for the use of state-of-the-art technology to develop an improved monitoring system that could help track the diffusion of individual modern varieties more accurately and efficiently. Next-generation sequencing technologies have become increasingly affordable in crops, and costs per sample are projected to continue to decrease in the coming decade (Buckler et  al., 2016; Unamba et  al., 2015). As a survey instrument, DNA fingerprinting on the basis of next-generation sequencing provides the opportunity to conduct a survey validation exercise and assess the accuracy of existing methods of crop varietal identification (Maredia et al., 2016; Rabbi et al., 2012).

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12.8  WHEAT CASE STUDY A number of studies have assessed the adoption and impact of improved wheat germplasm in developing countries (reviewed by Fisher and Erenstein, 2014). The studies used observational data and published varietal guides or cross-section analyses, and also included special analyses using remotely sensed data. Lantican et  al. (2016) documented the global use of improved wheat germplasm and the economic benefit of international collaboration in wheat improvement research by the CGIAR from 1994 to 2014.The study revealed an increase in the adoption of CGIAR-related varieties, covering about 106 million hectares (64%) in the studied countries. The benefits attributable specifically to wheat improvement research by the CGIAR ranged from 2.2 to 3.1 billion US dollars. Shiferaw et al. (2014) used the nationally representative data for Ethiopia to study the adoption and impact of improved wheat varieties, and found an adoption rate of 70%. An adoption analysis has shown that wheat prices, prices of competing crops, sources of information on new varieties, input costs, agroecology, and geographical location influenced the adoption of improved varieties. A survey of 1200 wheat farmers in five states in the Indo-Gangetic Plains of India indicated that the rate of wheat adoption and varietal turnover has slowed down (Ghimire et al., 2012). This study found that various socioeconomic factors influenced the choice of whether to grow new wheat varieties in India; however, the most important determinant was access to seed from different sources. Fisher and Erenstein (2014) noted that the quality data and empirical methods used varied considerably across the diverse impact studies. The authors concluded that to better enable the comparison across studies, it was necessary to apply a uniform set of core survey questions, collect longitudinal data, use existing high-quality datasets and conduct randomized control trials to increase the quality of impact assessment. The use of DNA fingerprinting methods was not suggested. Only one study deploying DNA fingerprinting to track the diffusion of wheat varieties has been published in wheat. Reported by Yirga et  al. (2016), DNA fingerprinting was explored among smallholder farmers in certain areas of Oromia, Ethiopia. Three complementary data collection methods were used. The estimates of varietal adoption based on farmer reports and DNA fingerprinting diverged considerably: 63% of the farmers were using improved wheat varieties according to survey respondents, while 96% of the farmers were using them according to DNA fingerprinting. This suggests that the household survey underestimated the economic importance of improved varieties. Besides its usefulness in assessing varietal



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adaptation, DNA fingerprinting was considered useful to evaluate more accurately the seed demand, resolve seed quality disputes, and to help the variety release mechanism.

12.9  EVIDENCE FROM OTHER CROPS The concept of essential derivation using DNA fingerprinting has mainly been used to protect breeders’ rights. However, the objective in developing countries is different and aims to help collect accurate variety-specific identification data that can be used to study adoption rates. DNA fingerprinting to address this objective in other crops is also limited to a few recent attempts, mostly pilot studies. Rabbi et al. (2012) used genotypingby-sequencing as an alternative method to track released cassava varieties in farmers’ fields. In total, 88% of the 917 cassava accessions were matched to specific released varieties or landraces in the reference library. Numerous admixtures within accessions were found; this was explained by the fact that cassava farmers grow more than one variety in their fields, allowing cross-breeding. There were many synonymous or homonymous clone names, which would make it difficult to track released varieties by relying on names only. Kosmowski et al. (2016) tested the effectiveness of three household-based survey methods of identifying sweet potato varietal adoptation against DNA fingerprinting. All methods were found to be less accurate than the DNA fingerprinting benchmark. Similar to the study in cassava, variety names given by farmers provided inconsistent varietal identities. Probably the most comprehensive comparison of the approaches used to collect variety-specific adoptation data was published by Maredia et al. (2016) for cassava and beans.The authors compared six different approaches including farmer and expert elicitation. Each method provided different estimates of adoption rates, and no method could be specifically recommended. All methods underestimated the adoption of improved varieties and misclassified improved and local varieties. The authors pointed out that DNA fingerprinting was the only credible method, but the method was only as good as the quality of the reference library. In conclusion, despite limited evidence, DNA fingerprinting seems a viable tool to estimate modern variety adoption for a range of crops, including wheat.The technique has shown additional benefits, for it has been used to resolve seed quality disputes, assess seed demands more accurately and investigate the functioning of the varietal development, release, and dissemination system.The informality of seed systems appears to be the main constraint for more accurate survey-based identification. DNA fingerprinting

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across large-scale household surveys may pose a substantial logistical challenge, but more and more countries are acquiring the technical capacity to extract DNA from field samples and carry out genotyping. In addition, as the cost of DNA fingerprinting declines further, the cost of conducting a survey will diminish. More evidence is needed to assess whether DNA fingerprinting can be used as a complementary part of crop varietal adoption.

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