Updated guidelines for gene nomenclature in wheat.

KEY MESSAGE: Here, we provide an updated set of guidelines for naming genes in wheat that has been endorsed by the wheat research community. The last decade has seen a proliferation in genomic resources for wheat, including reference- and pan-genome assemblies with gene annotations, which provide new opportunities to detect, characterise, and describe genes that influence traits of interest. The expansion of genetic information has supported growth of the wheat research community and catalysed strong interest in the genes that control agronomically important traits, such as yield, pathogen resistance, grain quality, and abiotic stress tolerance. To accommodate these developments, we present an updated set of guidelines for gene nomenclature in wheat. These guidelines can be used to describe loci identified based on morphological or phenotypic features or to name genes based on sequence information, such as similarity to genes characterised in other species or the biochemical properties of the encoded protein. The updated guidelines provide a flexible system that is not overly prescriptive but provides structure and a common framework for naming genes in wheat, which may be extended to related cereal species. We propose these guidelines be used henceforth by the wheat research community to facilitate integration of data from independent studies and allow broader and more efficient use of text and data mining approaches, which will ultimately help further accelerate wheat research and breeding.

Tracking the adoption of bread wheat varieties in Afghanistan using DNA fingerprinting.

BACKGROUND: Wheat is the most important staple crop in Afghanistan and accounts for the main part of cereal production. However, wheat production has been unstable during the last decades and the country depends on seed imports. Wheat research in Afghanistan has emphasized releases of new, high-yielding and disease resistant varieties but rates of adoption of improved varieties are uncertain. We applied DNA fingerprinting to assess wheat varieties grown in farmers' fields in four Afghan provinces. RESULTS: Of 560 samples collected from farmers' fields during the 2015-16 cropping season, 74% were identified as varieties released after 2000, which was more than the number reported by farmers and indicates the general prevalence of use of improved varieties, albeit unknowingly. At the same time, we found that local varieties and landraces have been replaced and were grown by 4% fewer farmers than previously reported. In 309 cases (58.5%), farmers correctly identified the variety they were growing, while in 219 cases (41.5%) farmers did not. We also established a reference library of released varieties, elite breeding lines, and Afghan landraces, which confirms the greater genetic diversity of the landraces and their potential importance as a genetic resource. CONCLUSIONS: Our study is the first in wheat to apply DNA fingerprinting at scale for an accurate assessment of wheat varietal adoption and our findings point up the importance of DNA fingerprinting for accuracy in varietal adoption studies.

Genetic characterization of the wheat association mapping initiative (WAMI) panel for dissection of complex traits in spring wheat.

KEY MESSAGE: The wheat association mapping initiative is appropriate for gene discovery without the confounding effects of phenology and plant height. The wheat association mapping initiative (WAMI) population is a set of 287 diverse advanced wheat lines with a narrow range of variation for days to heading (DH) and plant height (PH). This study aimed to characterize the WAMI and showed that this diverse panel has a favorable genetic background in which stress adaptive traits and their alleles contributing to final yield can be identified with reduced confounding major gene effects through genome-wide association studies (GWAS). Using single nucleotide polymorphism (SNP) markers, we observed lower gene diversity on the D genome, compared with the other genomes. Population structure was primarily related to the distribution of the 1B.1R rye translocation. The narrow range of variation for DH and PH in the WAMI population still entailed segregation for a few markers associated with the former traits, while Rht genes were associated with grain yield (GY). Genotype by environment (G × E) interaction for GY was primarily explained by Rht-B1, Vrn-A1 and markers on chromosomes 2D and 3A when running GWAS with genotype scores from the G × E biplot. The use of PC scores from the G × E biplot seems a promising tool to determine genes and markers associated with complex interactions across environments. The WAMI panel lends itself to GWAS for complex trait dissection by avoiding the confounding effects of DH and PH which were reduced to a minimum (using Rht-B1 and Vrn-A1 scores as covariables), with significant associations with GY on chromosomes 2D, 3A and 3B.

Genomic prediction in CIMMYT maize and wheat breeding programs.

Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.

Genomic-enabled prediction with classification algorithms.

Pearson's correlation coefficient (ρ) is the most commonly reported metric of the success of prediction in genomic selection (GS). However, in real breeding ρ may not be very useful for assessing the quality of the regression in the tails of the distribution, where individuals are chosen for selection. This research used 14 maize and 16 wheat data sets with different trait-environment combinations. Six different models were evaluated by means of a cross-validation scheme (50 random partitions each, with 90% of the individuals in the training set and 10% in the testing set). The predictive accuracy of these algorithms for selecting individuals belonging to the best α=10, 15, 20, 25, 30, 35, 40% of the distribution was estimated using Cohen's kappa coefficient (κ) and an ad hoc measure, which we call relative efficiency (RE), which indicates the expected genetic gain due to selection when individuals are selected based on GS exclusively. We put special emphasis on the analysis for α=15%, because it is a percentile commonly used in plant breeding programmes (for example, at CIMMYT). We also used ρ as a criterion for overall success. The algorithms used were: Bayesian LASSO (BL), Ridge Regression (RR), Reproducing Kernel Hilbert Spaces (RHKS), Random Forest Regression (RFR), and Support Vector Regression (SVR) with linear (lin) and Gaussian kernels (rbf). The performance of regression methods for selecting the best individuals was compared with that of three supervised classification algorithms: Random Forest Classification (RFC) and Support Vector Classification (SVC) with linear (lin) and Gaussian (rbf) kernels. Classification methods were evaluated using the same cross-validation scheme but with the response vector of the original training sets dichotomised using a given threshold. For α=15%, SVC-lin presented the highest κ coefficients in 13 of the 14 maize data sets, with best values ranging from 0.131 to 0.722 (statistically significant in 9 data sets) and the best RE in the same 13 data sets, with values ranging from 0.393 to 0.948 (statistically significant in 12 data sets). RR produced the best mean for both κ and RE in one data set (0.148 and 0.381, respectively). Regarding the wheat data sets, SVC-lin presented the best κ in 12 of the 16 data sets, with outcomes ranging from 0.280 to 0.580 (statistically significant in 4 data sets) and the best RE in 9 data sets ranging from 0.484 to 0.821 (statistically significant in 5 data sets). SVC-rbf (0.235), RR (0.265) and RHKS (0.422) gave the best κ in one data set each, while RHKS and BL tied for the last one (0.234). Finally, BL presented the best RE in two data sets (0.738 and 0.750), RFR (0.636) and SVC-rbf (0.617) in one and RHKS in the remaining three (0.502, 0.458 and 0.586). The difference between the performance of SVC-lin and that of the rest of the models was not so pronounced at higher percentiles of the distribution. The behaviour of regression and classification algorithms varied markedly when selection was done at different thresholds, that is, κ and RE for each algorithm depended strongly on the selection percentile. Based on the results, we propose classification method as a promising alternative for GS in plant breeding.

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.

An accurate DNA marker assay for stem rust resistance gene Sr2 in wheat.

The stem rust resistance gene Sr2 has provided broad-spectrum protection against stem rust (Puccinia graminis Pers. f. sp. tritici) since its wide spread deployment in wheat from the 1940s. Because Sr2 confers partial resistance which is difficult to select under field conditions, a DNA marker is desirable that accurately predicts Sr2 in diverse wheat germplasm. Using DNA sequence derived from the vicinity of the Sr2 locus, we developed a cleaved amplified polymorphic sequence (CAPS) marker that is associated with the presence or absence of the gene in 115 of 122 (95%) diverse wheat lines. The marker genotype predicted the absence of the gene in 100% of lines which were considered to lack Sr2. Discrepancies were observed in lines that were predicted to carry Sr2 but failed to show the CAPS marker. Given the high level of accuracy observed, the marker provides breeders with a selection tool for one of the most important disease resistance genes of wheat.

Wheat genetic diversity trends during domestication and breeding.

It has been claimed that plant breeding reduces genetic diversity in elite germplasm which could seriously jeopardize the continued ability to improve crops. The main objective of this study was to examine the loss of genetic diversity in spring bread wheat during (1) its domestication, (2) the change from traditional landrace cultivars (LCs) to modern breeding varieties, and (3) 50 years of international breeding. We studied 253 CIMMYT or CIMMYT-related modern wheat cultivars, LCs, and Triticum tauschii accessions, the D-genome donor of wheat, with 90 simple sequence repeat (SSR) markers dispersed across the wheat genome. A loss of genetic diversity was observed from T. tauschii to the LCs, and from the LCs to the elite breeding germplasm. Wheat's genetic diversity was narrowed from 1950 to 1989, but was enhanced from 1990 to 1997. Our results indicate that breeders averted the narrowing of the wheat germplasm base and subsequently increased the genetic diversity through the introgression of novel materials. The LCs and T. tauschii contain numerous unique alleles that were absent in modern spring bread wheat cultivars. Consequently, both the LCs and T. tauschii represent useful sources for broadening the genetic base of elite wheat breeding germplasm.