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.

Fusarium head blight (FHB) and stem rust (SR) threaten the sustainability of wheat production worldwide. Fhb1 and Sr2 confer partial durable resistance to FHB and SR, respectively. Despite resistant alleles of both genes are linked in repulsion, lines with Fhb1-Sr2 in coupling were developed at the University of Minnesota, USA. Marker-assisted backcrossing was used to incorporate the coupled Fhb1-Sr2 into four elite INIA-Uruguay spring wheat varieties lacking both genes and expressing different levels of FHB and SR resistance. In each case, the initial cross between the donor line and recurrent parent was backcrossed three times. Genotypes carrying Fhb1-Sr2 were selected using the molecular marker UMN10. In BC3F3 families, retention of Fhb1-Sr2 was further confirmed with the markers SNP3BS-8 and Sr2-ger9 for Fhb1 and Sr2, respectively. BC3F3 homozygous lines contrasting at UMN10, SNP3BS-8 and Sr2-ger9 were obtained to quantify the effect of Fhb1-Sr2 on the resistance to FHB under controlled conditions and to SR under field conditions. After 26 months period, successful introgression of Fhb1-Sr2 into the four cultivars was achieved, representing novel wheat genetic resources. Lines homozygous for the resistant alleles of Fhb1 were significantly more resistant to FHB as reflected by an 18% reduction of average FHB area under the disease progress curve. A significant effect of Sr2 on SR field resistance was observed in lines derived from the most susceptible cultivar ‘Génesis 2375’. The most resistant lines to both diseases are expected to be valuable genetic resources in breeding for durable resistance to FHB and SR.

Spot blotch (SB), caused by Bipolaris sorokiniana, is a major fungal disease of wheat in South Asia and South America. Two bi-parental mapping populations with 232 F2:7 progenies each were generated, with CIMMYT breeding lines CASCABEL and KATH as resistant parents and CIANO T79 as the common susceptible parent. The two populations were evaluated for field SB resistance in CIMMYT's Agua Fria station for three consecutive cropping seasons, with artificial inoculation. Genotyping was done with the DArTseq platform and approximately 1,500 high quality and non-redundant markers were used for QTL mapping. In both populations, a major QTL was found on chromosome 5A in the Vrn-A1 region, explaining phenotypic variations of 13.5-25.9%, which turned up to be less- or non-significant when days to heading and plant height were used as covariates in the analysis, implying a disease escape mechanism. Another major QTL was located on chromosome 5B in CASCABEL, accounting for 8.9-21.4% of phenotypic variation. Minor QTL were found on 4A and 4B in CASCABEL, 1B, 4B, and 4D in KATH, and 1B, 2B, and 4B in CIANO T79. Through an analysis of QTL projection onto the IWGSC Chinese Spring reference genome, the 5B QTL in CASCABEL was mapped in the Sb2 region, delimited by the SNP marker wsnp_Ku_c50354_55979952 and the SSR marker gwm213, with a physical distance of about 14 Mb to the Tsn1 locus.

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.

The increasing availability of Single Nucleotide Polymorphisms (SNPs) discovered by Next Generation Sequencing will enable a range of new genetic analyses in crops, which was not possible before. Concomitantly, researchers will face the challenge of handling large data sets at the whole-genome level. By grouping thousands of SNPs into a few hundred haplotype blocks, complexity of the data can be reduced with fewer statistical tests and a lower probability of spurious associations. Owing to the strong genome structure present in breeding lines of most crops, the deployment of haplotypes could be a powerful complement to improve efficiency of marker-assisted and genomic selection. This review describes in brief the commonly used approaches to construct haplotype blocks and some examples in animals and crops are cited where haplotype-based dissection of traits were proven beneficial. Some important considerations and facts while working with haplotypes in crops are reviewed at the end.

Since 2012, Australian support has resulted in the release of 23 wheat varieties by Afghanistan’s National Varietal Release Committee (NVRC). These varieties deliver both improved yield and disease tolerance. A 2015/16 farmer survey confirmed that farmers were indeed sharing seed with an average of three relatives/neighbours – although with no indication of how much seed was shared. This uncertainty regarding the adoption of new varieties has been intended to resolve by a recent DNA assessments of seed collected in 2015/16 that shows the general prevalence of the new varieties across 600 surveyed farms.

SUMMARYUnderstanding the effect of genetic factors controlling flowering time is essential to fine-tune crop development to each target environment and to maximize yield. A set of 35 durum wheat genotypes of spring growth-habit involving different allelic combinations at Ppd-A1 and Ppd-B1 genes was grown for 2 years at four sites at latitudes ranging from 19°N to 41°N. The emergence-flowering period was reduced from north to south. The frequency in the collection of the insensitive allele GS-105 at Ppd-A1 was greater (34%) than that of allele GS-100 (20%). Genotypes that flowered earlier due to the presence of alleles causing photoperiod insensitivity extended their grain-filling period, but less than the shortening in flowering time. The effect of the allele conferring photoperiod sensitivity at Ppd-A1 was stronger than that at Ppd-B1 (Ppd-A1b > Ppd-B1b). The effect of photoperiod insensitivity alleles was classified as GS-100 > GS-105 > Ppd-B1a. The phenotypic expression of alleles conferring photoperiod insensitivity at Ppd-A1 increased at sites with average day length from emergence to flowering lower than 12 h. An interaction effect was found between Ppd-A1 and Ppd-B1. Differences between allelic combinations in flowering time accounted for c. 66% of the variability induced by the genotype effect, with the remaining 34% being explained by genes controlling earliness per se. The shortest flowering time across sites corresponded to the allelic combination GS-100/Ppd-B1a, which reduced flowering time by 11 days irrespective of the Ppd-A1b/Ppd-B1b combination. The current study marks a further step towards elucidation of the phenotypic expression of genes regulating photoperiod sensitivity and their interaction with the environment.

The wheat association mapping initiative is appropriate for gene discovery without the confounding effects of phenology and plant height.

Durum wheat (Triticum turgidum var. durum) is an important crop in the Mediterranean basin, and in southern Spain. One of the most important biotic constraints to durum wheat production is leaf rust. Breeder deployed resistant cultivars, but mutations in the pathogen create new virulent pathotypes that erode resistance. In this work, two collections of genotypes have been characterized for their leaf rust resistance both in field trials (in Spain and Mexico) and in the greenhouse (with an array of Spanish pathotypes). The first collection included 14 cultivars and lines, mostly from CIMMYT origin, plus six genotypes with known resistance genes. The second collection comprised 29 cultivars commercially available in Spain, plus 11 landraces from the Spanish germplasm bank. Besides, six resistant cultivars of particular importance were crossed with two cultivars with known effective genes (Lr14a and Lr27 + Lr31), and a susceptible one to infer the genetic basis of their resistance. F-2 populations were analyzed. At adult plant stage, 20 genotypes including four landraces were resistant in greenhouse to all Spanish pathotypes. In field trials, 24 genotypes were resistant in Spain, and 22 genotypes in Mexico. The presence of resistant genes Lr14a and the complementary genes Lr27+Lr31 has been deduced in some cultivars either by gene postulation or by genetic analysis. These results provide an opportunity for breeding programs in Spain and elsewhere to target their crossing and selection activities so they can yield lines with effective resistance in the Mediterranean region.

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.

Many ecological studies compare the genetic structure of populations undergoing natural or articial selection across dierent environments. High-throughput molecular markers are now commonly used for these comparisons and provide information on the adaptation of the populations to their environments. The genetic structure reflects the history of selection, mutation, migration, and the reproductive breeding system of the populations in their environments. This can be investigated by comparing the ordering of markers obtained from the population with that provided by a recombination or physical map. Inpopulations undergoing selection many genes (markers) have low or zero frequency and commonly used disequilibrium coecients become unstable under these conditions. A method is presented for ordering bi-allelic markers for populations of self-fertilizing plant species which consist of mixtures of related homozygous genotypes. This provides stablepair-wise marker similarity measures even when marker frequencies are low, identication of marker combinations that reect phenomena that cause dierentiation (such as selection and migration), and genetic information on the adaptation of the populations to the environments. The method is illustrated using data from a plant breeding program and inferences are made about accumulation of desirable genes (such as for disease resistance).

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.

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.

Many ecological studies compare the genetic structure of (natural or artificial) populations across different (spatial and/or temporal) environments. Genotyping, using modern high-throughput molecular maker technologies, provides abundant information on (large numbers of) markers and (large numbers of) individuals in populations. An ordering of the markers, based on the relationships among them, enhances the description of the genetic structure of each population. Comparisons of the genetic structures of populations across environments will provide information on the adaptation of the populations. A method is presented for ordering bi-allelic markers for populations of self-fertilising plant species which consist of mixtures of related homozygous genotypes. This method provides stable pair-wise marker similarity measures even when marker frequencies are low, identification of marker combinations that reflect phenomena that cause differentiation (such as selection and migration), and genetic information on the adaptation of the populations to the environments. The method is illustrated using data from a plant breeding program under artificial selection. Here inferences can be made about accumulation of desirable genes (such as for disease resistance), changes in selection objectives, and changes in germplasm in successive populations.

Pre-harvest sprouting (PHS) greatly reduces the quality and economic value of wheat grain. In this study, a total of 168 International Maize and Wheat Improvement Center (CIMMYT) wheat germplasm lines were examined to characterize the haplotypes of Vp-1A, Vp-1B and Vp-1D, which are located on the long arms of chromosomes 3A, 3B and 3D, respectively. Among them, five new alleles of Vp-1Aa (the wild allele) were identified on chromosome 3A, and designated as Vp-1Ab, Vp-1Ac, Vp-1Ad, Vp-1Ae and Vp-1Af, respectively. The main difference between Vp-1Aa and the newly identified alleles was in the numbers of CTT repeats located in the third intron, but Vp-1Af also had 6 and 2 bp deletions at positions 2860–2865 bp and 2880–2881 bp, and possessed five SNPs within the same intron region. In the Vp-1B locus, several alleles (Vp-1Ba, Vp-1Bb, Vp-1Bc, Vp-1Bd, Vp-1Be and Vp-1Bf) have already been identified. In the present material only two, the already known allele Vp-1Bc, and a new one, designated as Vp-1Bg, were detected. Compared with Vp-Ba, Vp-1Bg had additional insertion of TCC at position 2372 bp and a 9 bp change from CTGCATC AC to GCATCAGTG at 2417–2425 bp. However, no polymorphism was detected in Vp-1D. The frequencies of Vp-1Aa Vp-1Ab, Vp-1Ac, Vp-1Ad, Vp-1Ae, and Vp-1Af were 65, 10, 11, 4, 5 and 5%, respectively. For Vp-1B, 155 out of the 168 lines were Vp-1Bc; the remaining 13 were Vp-1Bg. Analyses of the germination index (GI) and abscisic acid (ABA) sensitivity showed that genotypes with Vp-1Ab or Vp-1Af showed higher PHS resistance than the ones with other alleles, suggesting that they might be valuable for CIMMYT breeding program or germplasm introduction. The results presented here will underpin the introduction of germplasm from CIMMYT and the improvement of PHS resistance, both in CIMMYT and elsewhere.

Long-term plant breeding programs generate large quantities of genealogical, genotypic, phenotypic and environment characterization data. Marker-trait association studies are starting to be used to integrate and represent such data, but the results are context dependent, as trait associated markers depend on the germplasm investigated, the environments in which they are studied, and the interaction of genotype and environment. The concept of a trait-associated marker block, defined as markers in a linkage disequilibrium block that show significant association with a trait, addresses non-independency of markers in association analysis. A Phenome Atlas is a collection of diagrammatic representations of chromosome regions that affect trait inheritance (phenome maps) to document the patterns of trait inheritance across the genome. This methodology is illustrated using the Wheat Phenome Atlas constructed from a genome wide association study of 20 economically important traits from the first 25 years of an international wheat breeding program. Three-way principal component analysis then provides information about which genotypes carry favourable trait-associated marker block combinations, which marker blocks discriminate among genotypes and which marker block combinations are available for any given combination of genotypes and traits. Context dependency is illustrated through different patterns of marker trait association profiles being observed when analysing the same genotypes for different marker blocks and trait combinations, and through data obtained from different combinations of environments. © 2006--111 Dixie W Publishing Corporation - All rights reserved

With 2 tables AbstractDifferences in photoperiod sensitivity are widely used in wheat breeding to provide adaptation to diverse agronomic environments. Two photoperiod insensitive (PI) mutations in the A genome (Ppd‐A1a alleles) were previously identified using near‐isogenic lines of tetraploid durum wheat. We show that these Ppd‐A1a alleles predominate in modern durum wheat but are absent from wild tetraploid wheat and from conventional hexaploid wheat, suggesting they were selected for improved adaptation during durum cultivation. To increase genetic diversity in hexaploid wheat, synthetic hexaploid wheat lines were developed at CIMMYT by hybridizing elite durum lines with Aegilops tauschii accessions. Ppd‐A1a alleles from durum wheat were found in 71.4% of 447 synthetic hexaploids and 9.6% of 115 advanced selections. Backcrosses to hexaploid wheat showed that the durum Ppd‐A1a alleles conferred a PI phenotype and that one allele was intermediate between known B and D genome mutations, providing a new source of flowering time variation in hexaploid wheat and the potential for novel combinations of PI alleles.

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.