Jorge Franco

Association analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure

Linkage disequilibrium can be used for identifying associations between traits of interest and genetic markers. This study used mapped diversity array technology (DArT) markers to find associations with resistance to stem rust, leaf rust, yellow rust, and powdery mildew, plus grain yield in five historical wheat international multienvironment trials from the International Maize and Wheat Improvement Center (CIMMYT). Two linear mixed models were used to assess marker–trait associations incorporating information on population structure and covariance between relatives. An integrated map containing 813 DArT markers and 831 other markers was constructed. Several linkage disequilibrium clusters bearing multiple host plant resistance genes were found. Most of the associated markers were found in genomic regions where previous reports had found genes or quantitative trait loci (QTL) influencing the same traits, providing an independent validation of this approach. In addition, many new chromosome regions for disease resistance and grain yield were identified in the wheat genome. Phenotyping across up to 60 environments and years allowed modeling of genotype × environment interaction, thereby making possible the identification of markers contributing to both additive and additive × additive interaction effects of traits.

A method for combining molecular markers and phenotypic attributes for classifying plant genotypes

Abstract  Classifying genotypes into clusters based on DNA fingerprinting, and/or agronomic attributes, for studying genetic and phenotypic diversity is a common practice. Researchers are interested in knowing the minimum number of fragments (and markers) needed for finding the underlying structural patterns of diversity in a population of interest, and using this information in conjunction with the phenotypic attributes to obtain more precise clusters of genotypes. The objectives of this study are to present: (1) a retrospective method of analysis for selecting a minimum number of fragments (and markers) from a study needed to produce the same classification of genotypes as that obtained using all the fragments (and markers), and (2) a classification strategy for genotypes that allows the combination of the minimum set of fragments with available phenotypic attributes. Results obtained on seven experimental data sets made up of different plant species, number of individuals per species’ and number of markers, showed that the retrospective analysis did indeed find few relevant fragments (and markers) that best discriminated the genotypes. In two data sets, the classification strategy of combining the information on the relevant minimum fragments with the available morpho-agronomic attributes produced compact and well-differentiated groups of genotypes.

Bringing wild relatives back into the family: recovering genetic diversity in CIMMYT improved wheat germplasm

SummaryThe dangers of a narrow genetic base of the worlds major domesticated food crops have become a great global concern in recent decades. The efforts of the International Maize and Wheat Improvement Center (CIMMYT) to breed common wheat cultivars for resource poor farmers in the developing world (known as the Green Revolution wheats) has met with notable success in terms of improved yield, yield stability, increased disease resistance and utilization efficiency of agricultural inputs. However, much of the success was bought at the cost of an overall reduction in genetic diversity in the species; average Modified Rogers distances (MRD) within groups of germplasm fell from 0.64 in the landraces to a low of 0.58 in the improved lines in the 1980s. Recent efforts by CIMMYT breeders to expand the genetic base of common wheat has included the use of landraces, materials from other breeding programs, and synthetic wheats derived from wild species in the pedigrees of new advanced materials. The result, measured using SSR molecular markers, is a highly significant increase in the latent genetic diversity of recently developed CIMMYT breeding lines and cultivars compared to the original Green Revolution wheats (average MRD of the latest materials (0.63) is not significantly different from that of the landraces, as tested using confidence intervals). At the same time, yield and resistance to biotic and abiotic stresses, and end-use quality continue to increase, indicating that the Green Revolution continues to this day.

Statistical methods for classifying genotypes

In genetic resource conservation and plant breeding, multivariate data on continuous and categorical traits are collected with the objective of selecting genotypes and accessions that best represent the entire population or gene collection with the minimum loss of genetic diversity. Therefore, the best numerical classification strategy is the one that produces the most compact and well-separated groups, that is, minimum variability within each group and maximum variability among groups. In this study, we review geometric classification techniques as well as statistical models based on mixed distribution models. The two-stage sequential clustering strategy uses all variables, continuous and categorical, and it tends to form more homogeneous groups of individuals than other clustering strategies. The sequential clustering strategy can be applied to three-way data comprising genotypes × environments × attributes. This approach groups genotypes with consistent responses for most of the continuous and categorical traits across environments.

Genetic mapping of maize streak virus resistance from the Mascarene source. I. Resistance in line D211 and stability against different virus clones

Abstract Maize streak virus (MSV) disease may cause significant grain yield reductions in maize in Africa. Réunion island maize germplasm is a proven source of strong resistance. Its genetic control was investigated using 123 RFLP markers in an F2 population of D211 (resistant) × B73 (susceptible). This population of 165 F2:3 families was carefully evaluated in Harare (Zimbabwe) and in Réunion. Artificial infestation was done with viruliferous leafhoppers. Each plant was rated weekly six times after infestation on a 1–9 scale previously adjusted by image analysis. QTL analyses were conducted for each scoring date, and for the areas under the disease, incidence and severity progress curves. The composite interval mapping method used allowed the estimation of the additive and dominance effects and QTL × environment interactions. Heritabilities ranged from 73% to 98%, increasing with time after infestation. Resistance to streak virus in D211 was provided by one region on chromosome 1, with a major effect, and four other regions on chromosomes 2, 3 (two regions) and 10, with moderate or minor effects. Overall, they explained 48–62% of the phenotypic variation for the different variables. On chromosome 3, one of the two regions seemed to be more involved in early resistance, whereas the second was detected at the latest scoring date. Other QTLs were found to be stable over time and across environments. Mild QTL × environment interactions were detected. Global gene action appeared to be partially dominant, in favor of resistance, except at the earliest scoring dates, where it was additive. From this population, 32 families were chosen, representing the whole range of susceptibility to MSV. They were tested in Réunion against three MSV clones, along with a co-inoculation of two of them. Virulence differences between clones were significant. There were genotype × clone interactions, and these were more marked for disease incidence than for severity. Although these interactions were not significant for the mean disease scores, it is suggested that breeders should select for completely resistant genotypes.

Core Hunter: an algorithm for sampling genetic resources based on multiple genetic measures

BackgroundExisting algorithms and methods for forming diverse core subsets currently address either allele representativeness (breeders preference) or allele richness (taxonomists preference). The main objective of this paper is to propose a powerful yet flexible algorithm capable of selecting core subsets that have high average genetic distance between accessions, or rich genetic diversity overall, or a combination of both.ResultsWe present Core Hunter, an advanced stochastic local search algorithm for selecting core subsets. Core Hunter is able to find core subsets having more genetic diversity and better average genetic distance than the current state-of-the-art algorithms for all genetic distance and diversity measures we evaluated. Furthermore, Core Hunter can attempt to optimize any number of genetic measures simultaneously, based on the preference of the user. Notably, Core Hunter is able to select significantly smaller core subsets, which retain all unique alleles from a reference collection, than state-of-the-art algorithms.ConclusionCore Hunter is a highly effective and flexible tool for sampling genetic resources and establishing core subsets. Our implementation, documentation, and source code for Core Hunter is available at http://corehunter.org

Genetic characterization of 218 elite CIMMYT maize inbred lines using RFLP markers

Characterization of genetic diversity among maize inbred lines can facilitate organization of germplasm and improve efficiency of breeding programs. A set of 218 phenotypically diverse inbred maize lines developed at CIMMYT for hybrid production was characterized using 32 RFLP markers to: (1) analyze the genetic diversity present; (2) define potential heterotic groups based on clusters formed with marker data; and (3) identify the most representative testers for each potential heterotic group. Lines were clustered using five different genetic distance measurements to find consensus non-hierarchical clusters. Dendrograms were produced to study hierarchical classification within smaller groups of lines. A very high average allelic diversity was seen in this germplasm. Lines did not cluster based on phenotype, environmental adaptation, grain color or type, maturity, or heterotic response (as determined based on hybrid performance with testers), but lines related by pedigree usually did cluster together. Previously defined testers from opposite heterotic groups were not genetically differentiated, and did not represent well their heterotic group. Discrete clusters were difficult to find; thus, potential heterotic groups will be difficult to suggest using RFLP markers alone. However, suggestions on how to use molecular markers and cross performance information to refine heterotic groups and select representative testers are presented.

Genetic Characterization of a Core Set of a Tropical Maize Race Tuxpeño for Further Use in Maize Improvement

The tropical maize race Tuxpeño is a well-known race of Mexican dent germplasm which has greatly contributed to the development of tropical and subtropical maize gene pools. In order to investigate how it could be exploited in future maize improvement, a panel of maize germplasm accessions was assembled and characterized using genome-wide Single Nucleotide Polymorphism (SNP) markers. This panel included 321 core accessions of Tuxpeño race from the International Maize and Wheat Improvement Center (CIMMYT) germplasm bank collection, 94 CIMMYT maize lines (CMLs) and 54 U.S. Germplasm Enhancement of Maize (GEM) lines. The panel also included other diverse sources of reference germplasm: 14 U.S. maize landrace accessions, 4 temperate inbred lines from the U.S. and China, and 11 CIMMYT populations (a total of 498 entries with 795 plants). Clustering analyses (CA) based on Modified Rogers Distance (MRD) clearly partitioned all 498 entries into their corresponding groups. No sub clusters were observed within the Tuxpeño core set. Various breeding strategies for using the Tuxpeño core set, based on grouping of the studied germplasm and genetic distance among them, were discussed. In order to facilitate sampling diversity within the Tuxpeño core, a minicore subset of 64 Tuxpeño accessions (20% of its usual size) representing the diversity of the core set was developed, using an approach combining phenotypic and molecular data. Untapped diversity represents further use of the Tuxpeño landrace for maize improvement through the core and/or minicore subset available to the maize community.

Out of America: tracing the genetic footprints of the global diffusion of maize

Maize was first domesticated in a restricted valley in south-central Mexico. It was diffused throughout the Americas over thousands of years, and following the discovery of the New World by Columbus, was introduced into Europe. Trade and colonization introduced it further into all parts of the world to which it could adapt. Repeated introductions, local selection and adaptation, a highly diverse gene pool and outcrossing nature, and global trade in maize led to difficulty understanding exactly where the diversity of many of the local maize landraces originated. This is particularly true in Africa and Asia, where historical accounts are scarce or contradictory. Knowledge of post-domestication movements of maize around the world would assist in germplasm conservation and plant breeding efforts. To this end, we used SSR markers to genotype multiple individuals from hundreds of representative landraces from around the world. Applying a multidisciplinary approach combining genetic, linguistic, and historical data, we reconstructed possible patterns of maize diffusion throughout the world from American “contribution” centers, which we propose reflect the origins of maize worldwide. These results shed new light on introductions of maize into Africa and Asia. By providing a first globally comprehensive genetic characterization of landraces using markers appropriate to this evolutionary time frame, we explore the post-domestication evolutionary history of maize and highlight original diversity sources that may be tapped for plant improvement in different regions of the world.

Genomic Prediction of Gene Bank Wheat Landraces

This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH), and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15–20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite materials.