Search

Key message Malt for craft “all-malt” brewing can have high quality, PHS resistance, and malted in normal timeframes. Canadian style adjunct malt is associated with PHS susceptibility. Abstract Expansion of malting barley production into non-traditional growing regions and erratic weather has increased the demand for preharvest sprouting (PHS) resistant, high quality malting barley cultivars. This is hindered by the relatively unknown relationships between PHS resistance and malting quality. Here we present a three-year study of malting quality and germination at different after-ripening durations post physiological maturity. Malting quality traits alpha amylase (AA) and free amino nitrogen (FAN) and germination rate at six days post PM shared a common association with a SNP in HvMKK3 on chromosome 5H in the Seed Dormancy 2 (SD2) region responsible for PHS susceptibility. Soluble protein (SP) and soluble over total protein (S/T) both shared a common association with a marker in the SD2 region. Significant genetic correlations between PHS resistance and the malting quality traits AA, FAN, SP, S/T were detected across and within HvMKK3 allele groups. High adjunct malt quality was related to PHS susceptibility. Selection for PHS resistance led to a correlated response in malting quality traits. Results strongly suggest pleiotropy of HvMKK3 on malting quality traits and that the classic “Canadian-style” malt is caused by a PHS susceptible allele of HvMKK3. PHS susceptibility appears to benefit the production of malt intended for adjunct brewing, while PHS resistance is compatible with all-malt brewing specifications. Here we present our analysis on the effect of combining complexly inherited and correlated traits with contrasting goals to inform breeding practice in malting barley, the general principles of which can be extended to other breeding programs.

KEY MESSAGE HvMKK3 alleles are temperature sensitive and are major contributors to environmental stability of preharvest sprouting in barley. Preharvest sprouting (PHS) can severely damage barley (Hordeum vulgare L.) malting quality, but PHS resistance is often negatively correlated with malting quality. Seed dormancy is closely related to PHS. Increased temperature during grain fill can decrease seed dormancy in barley, but genetic components of seed dormancy temperature sensitivity are poorly understood. Six years of PHS data were used to fit quantitative trait locus (QTL) x environment mixed models incorporating marker data from seed dormancy genes HvAlaAT1, HvGA20ox1, and HvMKK3 and weather covariates in spring and winter two-row malting barley. Variation in winter barley PHS was best modeled by average temperature range during grain fill and spring barley PHS by total precipitation during grain fill. Average high temperature during grain fill also accurately modeled PHS for both datasets. A highly non-dormant HvMKK3 allele determined baseline PHS susceptibility and HvAlaAT1 interactions with multiple HvMKK3 alleles conferred environmental sensitivity. Polygenic variation for PHS within haplotype was detected. Residual genotype and QTL by environment interaction variance indicated additional environmental and genetic factors involved in PHS. These models provide insight into genotype and environmental regulation of barley seed dormancy, a method for PHS forecasting, and a tool for breeders to improve PHS resistance.

Abstract There is an increased demand for food-grade grains grown sustainably. Hard red winter wheat has comparative advantages for organic farm rotations due to fall soil cover, weed competition, and grain yields. However, limitations of currently available cultivars such as poor disease resistance, winter hardiness, and baking quality, challenges its adoption and use. Our goal was to develop a participatory hard red winter wheat breeding program for the US Upper Midwest involving farmers, millers, and bakers. Specifically, our goals include (1) an evaluation of genotype-by-environment interaction (GEI) and genotypic stability for both agronomic and quality traits, and (2) the development of on-farm trials as well as baking and sensory evaluations of genotypes to include farmers, millers, and bakers’ perspectives in the breeding process. Selection in early generations for diseases and protein content was followed by multi-environment evaluations for agronomic, disease, and quality traits in three locations during five years, on-farm evaluations, baking trials, and sensory evaluations. GEI was substantial for most traits, but no repeatable environmental conditions were significant contributors to GEI making selection for stability a critical trait. Breeding lines had similar performance in on-station and on-farm trials compared to commercial checks, but some breeding lines were more stable than the checks for agronomic, quality traits, and baking performance. These results suggest that stable lines can be developed using a participatory breeding approach under organic management. Crop improvement explicitly targeting sustainable agriculture practices for selection with farm to table participatory perspectives are critical to achieve long-term sustainable crop production. Key message We describe a hard red winter wheat breeding program focused on developing genotypes adapted to organic systems in the US Upper Midwest for high-end artisan baking quality using participatory approaches.

Most oat grains destined for human consumption must possess the ability to pass through an industrial de-hulling process with minimal breakage and waste. Uniform grain size and a high groat to hull ratio are desirable traits related to milling performance. The purpose of this study was to characterize the genetic architecture of traits related to milling quality by identifying quantitative trait loci (QTL) contributing to variation among a diverse collection of elite and foundational spring oat lines important to North American oat breeding programs. A total of 501 lines from the Collaborative Oat Research Enterprise (CORE) panel were evaluated for genome-wide association with 6 key milling traits. Traits were evaluated in 13 location years. Associations for 36,315 markers were evaluated for trait means across and within location years, as well as trait variance across location years, which was used to assess trait stability. Fifty-seven QTL influencing one or more of the milling quality related traits were identified, with fourteen QTL mapped influencing mean and variance across location years. The most prominent QTL was Qkernel.CORE.4D on chromosome 4D at approximately 212 cM, which influenced the mean levels of all traits. QTL were identified that influenced trait variance but not mean, trait mean only and both.

Genomic selection (GS) models have been validated for many quantitative traits in wheat (Triticum aestivum L.) breeding. However, those models are mostly constrained within the same growing cycle and the extension of GS to the case of across cycles has been a challenge, mainly due to the low predictive accuracy resulting from two factors: reduced genetic relationships between different families and augmented environmental variances between cycles. Using the data collected from diverse field conditions at the International Wheat and Maize Improvement Center, we evaluated GS for grain yield in three elite yield trials across three wheat growing cycles. The objective of this project was to employ the secondary traits, canopy temperature, and green normalized difference vegetation index, which are closely associated with grain yield from high-throughput phenotyping platforms, to improve prediction accuracy for grain yield. The ability to predict grain yield was evaluated reciprocally across three cycles with or without secondary traits. Our results indicate that prediction accuracy increased by an average of 146% for grain yield across cycles with secondary traits. In addition, our results suggest that secondary traits phenotyped during wheat heading and early grain filling stages were optimal for enhancing the prediction accuracy for grain yield.

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The Wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the Wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 92% imputation accuracy with 0.01× sequence coverage, which was slightly lower than the accuracy obtained using the 0.5× sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (P-value < 2 × 10−14) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2× GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequilibrium and proportion of identity-by-descent regions among accessions in the PHG database. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.

GWAS identified eight yield-related, peak starch type of waxy and wild-type starch and 21 starch pasting property-related traits (QTLs). Prediction ability of eight GS models resulted in low to high predictability, depending on trait, heritability, and genetic architecture. Cassava is both a food and an industrial crop in Africa, South America, and Asia, but knowledge of the genes that control yield and starch pasting properties remains limited. We carried out a genome-wide association study to clarify the molecular mechanisms underlying these traits and to explore marker-based breeding approaches. We estimated the predictive ability of genomic selection (GS) using parametric, semi-parametric, and nonparametric GS models with a panel of 276 cassava genotypes from Thai Tapioca Development Institute, International Center for Tropical Agriculture, International Institute of Tropical Agriculture, and other breeding programs. The cassava panel was genotyped via genotyping-by-sequencing, and 89,934 single-nucleotide polymorphism (SNP) markers were identified. A total of 31 SNPs associated with yield, starch type, and starch properties traits were detected by the fixed and random model circulating probability unification (FarmCPU), Bayesian-information and linkage-disequilibrium iteratively nested keyway and compressed mixed linear model, respectively. GS models were developed, and forward predictabilities using all the prediction methods resulted in values of − 0.001–0.71 for the four yield-related traits and 0.33–0.82 for the seven starch pasting property traits. This study provides additional insight into the genetic architecture of these important traits for the development of markers that could be used in cassava breeding programs.

Key message Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Abstract Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

Amylase/trypsin-inhibitors (ATIs) comprise about 2–4% of the total wheat grain proteins and may contribute to natural defense against pests and pathogens. However, they are currently among the most widely studied wheat components because of their proposed role in adverse reactions to wheat consumption in humans. ATIs have long been known to contribute to IgE-mediated allergy (notably Bakers' asthma), but interest has increased since 2012 when they were shown to be able to trigger the innate immune system, with attention focused on their role in coeliac disease which affects about 1% of the population and, more recently, in non-coeliac wheat sensitivity which may affect up to 10% of the population. This has led to studies of their structure, inhibitory properties, genetics, control of expression, behavior during processing, effects on human adverse reactions to wheat and, most recently, strategies to modify their expression in the plant using gene editing. We therefore present an integrated account of this range of research, identifying inconsistencies, and gaps in our knowledge and identifying future research needs.Note This paper is the outcome of an invited international ATI expert meeting held in Amsterdam, February 3-5 2020

Over the past decade, genomics-assisted breeding (GAB) has been instrumental in harnessing the potential of modern genome resources and characterizing and exploiting allelic variation for germplasm enhancement and cultivar development. Sustaining GAB in the future (GAB 2.0) will rely upon a suite of new approaches that fast-track targeted manipulation of allelic variation for creating novel diversity and facilitate their rapid and efficient incorporation in crop improvement programs. Genomic breeding strategies that optimize crop genomes with accumulation of beneficial alleles and purging of deleterious alleles will be indispensable for designing future crops. In coming decades, GAB 2.0 is expected to play a crucial role in breeding more climate-smart crop cultivars with higher nutritional value in a cost-effective and timely manner.

Key message Genome-wide association mapping in conjunction with population sequencing map and Ensembl plants was used to identify markers/candidate genes linked to leaf rust, stripe rust and tan spot resistance in wheat. Abstract Leaf rust (LR), stripe rust (YR) and tan spot (TS) are some of the important foliar diseases in wheat (Triticum aestivum L.). To identify candidate resistance genes for these diseases in CIMMYT’s (International Maize and Wheat Improvement Center) International bread wheat screening nurseries, we used genome-wide association studies (GWAS) in conjunction with information from the population sequencing map and Ensembl plants. Wheat entries were genotyped using genotyping-by-sequencing and phenotyped in replicated trials. Using a mixed linear model, we observed that seedling resistance to LR was associated with 12 markers on chromosomes 1DS, 2AS, 2BL, 3B, 4AL, 6AS and 6AL, and seedling resistance to TS was associated with 14 markers on chromosomes 1AS, 2AL, 2BL, 3AS, 3AL, 3B, 6AS and 6AL. Seedling and adult plant resistance (APR) to YR were associated with several markers at the distal end of chromosome 2AS. In addition, YR APR was also associated with markers on chromosomes 2DL, 3B and 7DS. The potential candidate genes for these diseases included several resistance genes, receptor-like serine/threonine-protein kinases and defense-related enzymes. However, extensive LD in wheat that decays at about 5 × 107 bps, poses a huge challenge for delineating candidate gene intervals and candidates should be further mapped, functionally characterized and validated. We also explored a segment on chromosome 2AS associated with multiple disease resistance and identified seventeen disease resistance linked genes. We conclude that identifying candidate genes linked to significant markers in GWAS is feasible in wheat, thus creating opportunities for accelerating molecular breeding. Electronic supplementary material The online version of this article (10.1007/s00122-018-3086-6) contains supplementary material, which is available to authorized users.

Greenbug and Hessian fly are important pests that decrease wheat production worldwide. We developed and validated breeder-friendly KASP markers for marker-assisted breeding to increase selection efficiency. Greenbug (Schizaphis graminum Rondani) and Hessian fly [Mayetiola destructor (Say)] are two major destructive insect pests of wheat (Triticum aestivum L.) throughout wheat production regions in the USA and worldwide. Greenbug and Hessian fly infestation can significantly reduce grain yield and quality. Breeding for resistance to these two pests using marker-assisted selection (MAS) is the most economical strategy to minimize losses. In this study, doubled haploid lines from the Synthetic W7984 × Opata M85 wheat reference population were used to construct linkage maps for the greenbug resistance gene Gb7 and the Hessian fly resistance gene H32 with genotyping-by-sequencing (GBS) and 90K array-based single nucleotide polymorphism (SNP) marker data. Flanking markers were closely linked to Gb7 and H32 and were located on chromosome 7DL and 3DL, respectively. Gb7-linked markers (synopGBS773 and synopGBS1141) and H32-linked markers (synopGBS901 and IWB65911) were converted into Kompetitive Allele Specific PCR (KASP) assays for MAS in wheat breeding. In addition, comparative mapping identified syntenic regions in Brachypodium distachyon, rice (Oryza sativa), and sorghum (Sorghum bicolor) for Gb7 and H32 that can be used for fine mapping and map-based cloning of the genes. The KASP markers developed in this study are the first set of SNPs tightly linked to Gb7 and H32 and will be very useful for MAS in wheat breeding programs and future genetic studies of greenbug and Hessian fly resistance.

Identifying varieties best suited to local food systems requires a comprehensive understanding of varietal performance from field to fork. After conducting four years of field trials to test which varieties of ancient, heritage, and modern wheat grow best on organically managed land, we screened a subset of varieties for bread, pastry, pasta, and cooked grain quality. The varieties evaluated were three lines of emmer (T. turgidum L. ssp. dicoccum Schrank ex Schubl) and eleven lines of common wheat (Triticum aestivum L.), including two modern soft wheat varieties, four soft heritage wheat varieties, four hard modern wheat varieties, and one hard heritage wheat variety. A diverse group of bakers, chefs, researchers, and consumers compared varieties for qualities of interest to regional markets. Participants assessed differences in sensory profiles, pasta making ability, and baking quality for sourdough, matzah crackers, yeast bread, and shortbread cookies. In addition to detecting significant differences among varieties for pasta, sourdough, and pastry quality, participants documented variation in texture and flavor for the evaluated products. By demonstrating which varieties perform best in the field, in the bakery, and on our taste buds, these results can support recommendations that strengthen the revival of local grain economies.

SummaryOat ranks sixth in world cereal production and has a higher content of health-promoting compounds compared to other cereals. However, there is neither a robust oat reference genome nor transcriptome. Using deeply sequenced full-length mRNA libraries of oat cultivar Ogle-C, a de novo high-quality and comprehensive oat seed transcriptome was assembled. With this reference transcriptome and QuantSeq 3’ mRNA sequencing, gene expression was quantified during seed development from 22 diverse lines across six time points. Transcript expression showed higher correlations between adjacent time points. Based on differentially expressed genes, we identified 22 major temporal co-expression (TCoE) patterns of gene expression and revealed enriched gene ontology biological processes. Within each TCoE set, highly correlated transcripts, putatively commonly affected by genetic background, were clustered, and termed genetic co-expression (GCoE) sets. 17 of the 22 TCoE sets had GCoE sets with median heritabilities higher than 0.50, and these heritability estimates were much higher than that estimated from permutation analysis, with no divergence observed in cluster sizes between permutation and non-permutation analyses. Linear regression between 634 metabolites from mature seeds and the PC1 score of each of the GCoE sets showed significantly lower p-values than permutation analysis. Temporal expression patterns of oat avenanthramides and lipid biosynthetic genes were concordant with previous studies of avenanthramide biosynthetic enzyme activity and lipid accumulation. This study expands our understanding of physiological processes that occur during oat seed maturation and provides plant breeders the means to change oat seed composition through targeted manipulation of key pathways.

Soilborne wheat mosaic virus (SBWMV) was detected in New York in 1998 for the first time and has been associated with yield loss where identified. We assessed 115 regionally adapted small grains genotypes for resistance to SBWMV over four growing seasons. Resistance to SBWMV reduces the percentage of plants that develop detectable viral titer and symptoms. Logistic regression was used to analyze disease incidence data and was compared with a general linear model for categorizing relative resistance to SBWMV. Logistic regression facilitated assessment of the effects of small sample size, low disease incidence, and nonuniform disease distribution. By increasing sample size from 20 to 30 stems per replicate, the number of resistance categories was increased through improved resolution of intermediate resistance classes. In environments with low disease incidence, the number of genotypes categorized as susceptible decreased while intermediate genotypes appeared to be resistant in the analysis. Inclusion of disease distribution data as covariates in a spatially balanced experiment did not increase the power of the logistic analysis. No genotype assessed in multiple years was immune to infection. However, 41 of the regionally adapted genotypes tested repeatedly expressed strong resistance to SBWMV, providing growers a choice of cultivars resistant to SBWMV.

Genomic prediction models have been trained for a large number of economically important traits in wheat, however to date there has only been one study that has reported realized gain from genomic selection. There have been several investigations that have modeled genotype by environment interaction and epistatic interactions. Genomic selection for traits that are difficult or expensive to evaluate such as milling and baking quality is especially promising. The development of climate resilient wheat varieties is of increasing importance and genomic selection is a valuable new tool that breeders can use to more efficiently identify genotypes that tolerate environmental extremes. Genomic selection provides the breeder with the opportunity to impose selection on any quantitative trait of interest at any stage of the breeding cycle, giving more power and freedom to today’s breeders.

The role of wheat, and particularly of gluten protein, in our diet has recently been scrutinized. This article provides a summary of the main pathologies related to wheat in the human body, including celiac disease, wheat allergy, nonceliac wheat sensitivity, fructose malabsorption, and irritable bowel syndrome. Differences in reactivity are discussed for ancient, heritage, and modern wheats. Due to large variability among species and genotypes, it might be feasible to select wheat varieties with lower amounts and fewer types of reactive prolamins and fructans. Einkorn is promising for producing fewer immunotoxic effects in a number of celiac research studies. Additionally, the impact of wheat processing methods on wheat sensitivity is reviewed. Research indicates that germination and fermentation technologies can effectively alter certain immunoreactive components. For individuals with wheat sensitivity, less-reactive wheat products can slow down disease development and improve quality of life. While research has not proven causation in the increase in wheat sensitivity over the last decades, modern wheat processing may have increased exposure to immunoreactive compounds. More research is necessary to understand the influence of modern wheat cultivars on epidemiological change.

Genomic selection (GS) has created a lot of excitement and expectations in the animal- and plant-breeding research communities. In this review, we briefly describe how genomic pre-diction can be integrated into breeding efforts and point out achievements and areas where more research is needed. Genomic selec-tion provides many opportunities to increase genetic gain in plant breeding per unit time and cost. Early empirical and simulation results are promising, but for GS to deliver genetic gains, careful consideration of the problem of optimal resource allocation is needed. Consideration of the cost-benefit balance of using markers for each trait and stage of the breeding cycle is needed, moving beyond only focusing on recur -rent selection with GS on a few complex traits, using prediction on unphenotyped individuals. With decreasing marker cost, phenotype data is quickly becoming the most valuable asset and marker-assisted selection strategies should focus on making the most of scarce and expen -sive phenotypes. It is important to realize that markers can also improve accuracy of selection for phenotyped individuals. Use of markers as an aid to phenotype analysis suggests a num-ber of new strategies in terms of experimental design and multi-trait models. GS also provides new ways to analyze and deal with genotype by environment interactions. Lastly, we point to some recent results showing that new models are needed to improve predictions particularly with respect to the use of distantly related indi-viduals in the training population.N. Heslot, J.-L. Jannink, and M.E. Sorrells, Cornell Univ., Dep. of Plant Breeding and Genetics, 240 Emerson Hall, Ithaca, NY 14853. J.-L. Jannink, USDA-ARS, R.W. Holley Center for Agriculture and Health, Cornell Univ., Ithaca, NY 14853. N. Heslot, Limagrain Europe, CS3911, Chappes, 63720 France. Received 27 Mar. 2014. *Corresponding author (mes12@cornell.edu).

High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat ( L.) grain yield across time. Incorporating such secondary traits in the multivariate pedigree and genomic prediction models would be desirable to improve indirect selection for grain yield. In this study, we evaluated three statistical models, simple repeatability (SR), multitrait (MT), and random regression (RR), for the longitudinal data of secondary traits and compared the impact of the proposed models for secondary traits on their predictive abilities for grain yield. Grain yield and secondary traits, canopy temperature (CT) and normalized difference vegetation index (NDVI), were collected in five diverse environments for 557 wheat lines with available pedigree and genomic information. A two-stage analysis was applied for pedigree and genomic selection (GS). First, secondary traits were fitted by SR, MT, or RR models, separately, within each environment. Then, best linear unbiased predictions (BLUPs) of secondary traits from the above models were used in the multivariate prediction models to compare predictive abilities for grain yield. Predictive ability was substantially improved by 70%, on average, from multivariate pedigree and genomic models when including secondary traits in both training and test populations. Additionally, (i) predictive abilities slightly varied for MT, RR, or SR models in this data set, (ii) results indicated that including BLUPs of secondary traits from the MT model was the best in severe drought, and (iii) the RR model was slightly better than SR and MT models under drought environment.

Fine mapping by recombinant backcross populations revealed that a preharvest sprouting QTL on 2B contained two QTLs linked in coupling with different effects on the phenotype. Wheat preharvest sprouting (PHS) occurs when grain germinates on the plant before harvest, resulting in reduced grain quality. Previous mapping of quantitative trait locus (QTL) revealed a major PHS QTL, QPhs.cnl-2B.1, located on chromosome 2B significant in 16 environments that explained from 5 to 31 % of the phenotypic variation. The objective of this project was to fine map the QPhs.cnl-2B.1 interval. Fine mapping was carried out in recombinant backcross populations (BC1F4 and BC1F5) that were developed by backcrossing selected doubled haploids to a recurrent parent and self-pollinating the BC1F4 and BC1F5 generations. In each generation, three markers in the QPhs.cnl-2B.1 interval were used to screen for recombinants. Fine mapping revealed that the QPhs.cnl-2B.1 interval contained two PHS QTLs linked in coupling. The distal PHS QTL, located between Wmc453c and Barc55, contributed 8 % of the phenotypic variation and also co-located with a major seed dormancy QTL determined by germination index. The proximal PHS QTL, between Wmc474 and CNL415-rCDPK, contributed 16 % of the variation. Several candidate genes including Mg-chelatase H subunit family protein, GTP-binding protein and calmodulin/Ca2+-dependent protein kinase were linked to the PHS QTL. Although many recombinant lines were identified, the lack of polymorphism for markers in the QTL interval prevented the localization of the recombination breakpoints and identification of the gene underlying the phenotype.

Previous research on genomic selection (GS) has focused on predicting unphenotyped lines. Genomic selection can also improve the accuracy of phenotyped lines at low heritability, e.g., in a preliminary yield trial (PYT). Our �耀 rst objective was to estimate this effect within a biparental family, using multilocation yield data for barley (Hordeum vulgare L.) and maize (Zea mays L.). We found that accuracy increased with training population size and was higher with an unbalanced design spread across multiple locations than when testing all entries in one location. The latter phenomenon illustrates that when seed is limited, genome-wide markers enable broader sampling from the target population of environments. Our second objective was to explore the optimum allocation of resources at a �耀 xed budget. When PYT selections are advanced for further testing, we propose a new metric for optimizing genetic gain: R max , the expected maximum genotypic value of the selections. For budgets up to 250 yield plot equivalents per family, the optimal design did not involve genotyping more progeny than were phenotyped, even when the cost of creating and genotyping each line was only 0.25 the cost of one yield plot unit (YPU). At a genotyping cost of 0.25 YPU, GS offered up to a 5% increase in genetic gain compared with phenotypic selection. To increase genetic gains further, the training population must be expanded beyond the full-sib family under selection, using close relatives of the parents as a source of prediction accuracy.

Development of models to predict genotype by environment interactions, in unobserved environments, using environmental covariates, a crop model and genomic selection. Application to a large winter wheat dataset. Genotype by environment interaction (G*E) is one of the key issues when analyzing phenotypes. The use of environment data to model G*E has long been a subject of interest but is limited by the same problems as those addressed by genomic selection methods: a large number of correlated predictors each explaining a small amount of the total variance. In addition, non-linear responses of genotypes to stresses are expected to further complicate the analysis. Using a crop model to derive stress covariates from daily weather data for predicted crop development stages, we propose an extension of the factorial regression model to genomic selection. This model is further extended to the marker level, enabling the modeling of quantitative trait loci (QTL) by environment interaction (Q*E), on a genome-wide scale. A newly developed ensemble method, soft rule fit, was used to improve this model and capture non-linear responses of QTL to stresses. The method is tested using a large winter wheat dataset, representative of the type of data available in a large-scale commercial breeding program. Accuracy in predicting genotype performance in unobserved environments for which weather data were available increased by 11.1 % on average and the variability in prediction accuracy decreased by 10.8 %. By leveraging agronomic knowledge and the large historical datasets generated by breeding programs, this new model provides insight into the genetic architecture of genotype by environment interactions and could predict genotype performance based on past and future weather scenarios.

Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F2-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F2-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set.

Fusarium head blight (FHB) resistance is quantitative and diffi cult to evaluate. Genomic selection (GS) could accelerate FHB resistance breeding. We used U.S. cooperative FHB wheat nursery data to evaluate GS models for several FHB resistance traits including deoxynivalenol (DON) levels. For all traits we compared the models: ridge regression (RR), Bayesian LASSO (BL), reproducing kernel Hilbert spaces (RKHS) regression, random forest (RF) regression, and multiple linear regression (MLR) (fi xed effects). For DON, we evaluated additional prediction methods including bivariate RR models, phenotypes for correlated traits, and RF regression models combining markers and correlated phenotypes as predictors. Additionally, for all traits, we compared different marker sets including genomewide markers, FHB quantitative trait loci (QTL) targeted markers, and both sets combined. Genomic selection accuracies were always higher than MLR accuracies, RF and RKHS regression were often the most accurate methods, and for DON, marker plus trait RF regression was more accurate than all other methods. For all traits except DON, using QTL targeted markers alone led to lower accuracies than using genomewide markers. This study indicates that cooperative FHB nursery data can be useful for GS, and prior information about correlated traits and QTL could be used to improve accuracies in some cases.

Association mapping identified quantitative trait loci (QTLs) and the markers linked to pre-harvest sprouting (PHS) resistance in an elite association mapping panel of white winter wheat comprising 198 genotypes. A total of 1,166 marker loci including DArT and SSR markers representing all 21 chromosomes of wheat were used in the analysis. General and mixed linear models were used to analyze PHS data collected over 4 years. Association analysis identified eight QTLs linked with 13 markers mapped on seven chromosomes. A QTL was detected on each arm of chromosome 2B and one each on chromosome arms 1BS, 2DS, 4AL, 6DL, 7BS and 7DS. All except the QTL on 7BS are located in a location similar to previous reports and, if verified, the QTL on 7BS is likely to be novel. Principal components and the kinship matrix were used to account for the presence of population structure but had only a minor effect on the results. Although, none of the QTLs was highly significant across all environments, a QTL on the long arm of chromosome 4A was detected in three different environments and also using the best linear unbiased predictions over years. Although previous reports have identified this as a major QTL, its effects were minor in our biparental mapping populations. The results of this study highlight the benefits of association mapping and the value of using elite material in association mapping for plant breeding programs.

The evolution of a new race of stem rust, generally referred to as Ug99, threatens global wheat production because it can overcome widely deployed resistance genes that had been effective for many years. To identify loci conferring resistance to Ug99 in wheat, a genome-wide association study was conducted using 232 winter wheat breeding lines from the International Winter Wheat Improvement Program. Breeding lines were genotyped with diversity array technology, simple sequence repeat and sequence-tagged site markers, and phenotyped at the adult plant stage for resistance to stem rust in the stem rust resistance screening nursery at Njoro, Kenya during 2009-2011. A mixed linear model was used for detecting marker-trait associations. Twelve loci associated with Ug99 resistance were identified including markers linked to known genes Sr2 and Lr34. Other markers were located in the chromosome regions where no Sr genes have been previously reported, including one each on chromosomes 1A, 2B, 4A and 7B, two on chromosome 5B and four on chromosome 6B. The same data were used for investigating epistatic interactions between markers with or without main effects. The marker csSr2 linked to Sr2 interacted with wPt4930 on 6BS and wPt729773 in an unknown location. Another marker, csLV34 linked to Lr34, also interacted with wPt4930 on 6BS and wPt4916 on 2BS. The frequent involvement of wPt4916 on 2BS and wPt4930 on 6BS in interactions with other significant loci on the same or different chromosomes suggested complex genetic control for adult plant resistance to Ug99 in winter wheat germplasm.

Tef, Eragrostis tef (Zucc.) Trotter, is the main Ethiopian cereal grown on 2.5 million ha annually, and serving as a staple food grain for more than 50 million people. The major constraints in tef husbandry are low productivity (average about 1 t ha21) and lodging. Scientific research on tef began in the late 1950s and over the years a number of improved varieties and management practices have been developed. However, the research outputs have, until recently, been little adopted by farmers. This paper gives an overview of new and impactful technological, institutional and partnership innovation undertaken by Debre Zeit Agricultural Research Centre with a new tef variety called Quncho. Quncho was developed from an intra-specific hybridization between two improved pure line selection varieties (DZ-01-974 and DZ-01-196). The variety DZ-01-974 is high yielding, but because of the seed colour (pale white) its preference by farmers was limited. On the other hand, the variety DZ-01-196 has been popular for its ...

Inheritance of stem rust (caused by Puccinia graminis f. sp. tritici) resistance in wheat can be either qualitative or quantitative. While quantitative disease resistance is believed to be more durable, it is more difficult to evaluate if it is expressed only in mature plants, i.e. adult plant resistance (APR). Marker-assisted selection (MAS) methods for APR would be useful; however, the multigenic nature of APR impedes the use of MAS efforts that aim to pyramid only a few target genes. A promising alternative is genomic selection (GS), which utilizes genome-wide marker coverage to predict genotypic values for quantitative traits. In turn, GS can reduce the selection cycle length of a breeding program for traits like APR that could take several seasons to generate reliable phenotypes. In this paper, we describe the GS process for use in crop improvement, both specifically for APR and in general. We also propose a GS–based wheat breeding scheme for quantitative resistance to stem rust that, when compared to current breeding schemes, can reduce cycle time by up to twofold and facilitates pyramiding of major genes with APR genes. Thus, GS could be an important tool for achieving the Borlaug Global Rust Initiative’s (BGRI) goal of developing durable stem rust resistance in wheat.

Advancements in Lorenz, USDA-ARS, R.W. Holley Center for Agriculture and Health, genotyping are rapidly decreasing Cornell Univ., Ithaca, NY 14853. Received 9 Nov. 2009. *Corremarker costs and increasing genome coverage. This is facilitating the use of marker-assisted selection (MAS) in plant breeding. Commonly employed MAS strategies, however, are not well suited for agronomically important complex traits, requiring extra time for !eld-based phenotyping to identify agro-nomically superior lines. Genomic selection (GS) is an emerging alternative to MAS that uses all marker information to calculate genomic esti-mated breeding values (GEBVs) for complex traits. Selections are made directly on GEBV without further phenotyping. We developed an analytical framework to (i) compare gains from MAS and GS for complex traits and (ii) provide a plant breeding context for interpreting results from studies on GEBV accuracy. We designed MAS and GS breeding strategies with equal budgets for a high-investment maize (Zea mays L.) program and a low-investment winter wheat (Triticum aestivum L.) program. Results indicate that GS can outperform MAS on a per-year basis even at low GEBV accuracies. Using a previ-ously reported GEBV accuracy of 0.53 for net merit in dairy cattle, expected annual gain from GS exceeded that of MAS by about threefold for maize and twofold for winter wheat. We con-clude that if moderate selection accuracies can be achieved, GS could dramatically accelerate genetic gain through its shorter breeding"cycle.E.L. He-ner and M.E. Sorrells, Cornell Univ., Dep. of Plant Breeding and Genetics, Emerson Hall, Ithaca, NY 14853; J.-L. Jannink and A.J. -sponding author (mes12@cornell.edu).

There have been many changes impacting wheat improvement since the 1996 International Maize and Wheat Improvement Center Wheat Yield Symposium. This review highlights a few of the technological advances and impacts of new knowledge on wheat improvement that have occurred in the past 10 years as well as on-going challenges. One of the most dramatic discoveries has been the revelation that the genomes of graminaceous crops are complex, rapidly evolving, and heterogeneous, even within species. The use of marker-assisted selection for improving complex traits is one of the challenges facing wheat breeders. Integration of association analysis into conventional breeding programs is proposed as a crop improvement strategy that has the potential to improve the efficiency of molecular breeding.

Two mapping approaches were use to identify and validate milling and baking quality QTL in soft wheat. Two LG were consistently found important for multiple traits and we recommend the use marker-assisted selection on specific markers reported here. Wheat-derived food products require a range of characteristics. Identification and understanding of the genetic components controlling end-use quality of wheat is important for crop improvement. We assessed the underlying genetics controlling specific milling and baking quality parameters of soft wheat including flour yield, softness equivalent, flour protein, sucrose, sodium carbonate, water absorption and lactic acid, solvent retention capacities in a diversity panel and five bi-parental mapping populations. The populations were genotyped with SSR and DArT markers, with markers specific for the 1BL.1RS translocation and sucrose synthase gene. Association analysis and composite interval mapping were performed to identify quantitative trait loci (QTL). High heritability was observed for each of the traits evaluated, trait correlations were consistent over populations, and transgressive segregants were common in all bi-parental populations. A total of 26 regions were identified as potential QTL in the diversity panel and 74 QTL were identified across all five bi-parental mapping populations. Collinearity of QTL from chromosomes 1B and 2B was observed across mapping populations and was consistent with results from the association analysis in the diversity panel. Multiple regression analysis showed the importance of the two 1B and 2B regions and marker-assisted selection for the favorable alleles at these regions should improve quality.

Genomics research is generating new tools, such as functional molecular markers and informatics, as well as new knowledge about statistics and inheritance phenomena that could increase the efficiency and precision of crop improvement. In particular, the elucidation of the fundamental mechanisms of heterosis and epigenetics, and their manipulation, has great potential. Eventually, knowledge of the relative values of alleles at all loci segregating in a population could allow the breeder to design a genotype in silico and to practice whole genome selection. High costs currently limit the implementation of genomics-assisted crop improvement, particularly for inbreeding and/or minor crops. Nevertheless, marker-assisted breeding and selection will gradually evolve into ‘genomics-assisted breeding’ for crop improvement.

Expressed sequence tag (EST) projects have generated a vast amount of publicly available sequence data from plant species; these data can be mined for simple sequence repeats (SSRs). These SSRs are useful as molecular markers because their development is inexpensive, they represent transcribed genes and a putative function can often be deduced by a homology search. Because they are derived from transcripts, they are useful for assaying the functional diversity in natural populations or germplasm collections. These markers are valuable because of their higher level of transferability to related species, and they can often be used as anchor markers for comparative mapping and evolutionary studies. They have been developed and mapped in several crop species and could prove useful for marker-assisted selection, especially when the markers reside in the genes responsible for a phenotypic trait. Applications and potential uses of EST-SSRs in plant genetics and breeding are discussed.

Drought limits cereal yields in several regions of the world and plant water status plays an important role in tolerance to drought. To investigate and understand the genetic and physiological basis of drought tolerance in barley, differentially expressed sequence tags (dESTs) and candidate genes for the drought response were mapped in a population of 167 F8 recombinant inbred lines derived from a cross between "Tadmor" (drought tolerant) and "Er/Apm" (adapted only to specific dry environments). One hundred sequenced probes from two cDNA libraries previously constructed from drought-stressed barley (Hordeum vulgare L., var. Tokak) plants and 12 candidate genes were surveyed for polymorphism, and 33 loci were added to a previously published map. Composite interval mapping was used to identify quantitative trait loci (QTL) associated with drought tolerance including leaf relative water content, leaf osmotic potential, osmotic potential at full turgor, water-soluble carbohydrate concentration, osmotic adjustment, and carbon isotope discrimination. A total of 68 QTLs with a limit of detection scoreor =2.5 were detected for the traits evaluated under two water treatments and the two traits calculated from both treatments. The number of QTLs identified for each trait varied from one to 12, indicating that the genome contains multiple genes affecting different traits. Two candidate genes and ten differentially expressed sequences were associated with QTLs for drought tolerance traits.

Because of the huge size of the common wheat (Triticum aestivum L., 2n = 6x = 42, AABBDD) genome of 17,300 Mb, sequencing and mapping of the expressed portion is a logical first step for gene discovery. Here we report mapping of 7104 expressed sequence tag (EST) unigenes by Southern hybridization into a chromosome bin map using a set of wheat aneuploids and deletion stocks. Each EST detected a mean of 4.8 restriction fragments and 2.8 loci. More loci were mapped in the B genome (5774) than in the A (5173) or D (5146) genomes. The EST density was significantly higher for the D genome than for the A or B. In general, EST density increased relative to the physical distance from the centromere. The majority of EST-dense regions are in the distal parts of chromosomes. Most of the agronomically important genes are located in EST-dense regions. The chromosome bin map of ESTs is a unique resource for SNP analysis, comparative mapping, structural and functional analysis, and polyploid evolution, as well as providing a framework for constructing a sequence-ready, BAC-contig map of the wheat genome.

A total of 1918 loci, detected by the hybridization of 938 expressed sequence tag unigenes (ESTs) from 26 Triticeae cDNA libraries, were mapped to wheat (Triticum aestivum L.) homoeologous group 4 chromosomes using a set of deletion, ditelosomic, and nulli-tetrasomic lines. The 1918 EST loci were not distributed uniformly among the three group 4 chromosomes; 41, 28, and 31% mapped to chromosomes 4A, 4B, and 4D, respectively. This pattern is in contrast to the cumulative results of EST mapping in all homoeologous groups, as reported elsewhere, that found the highest proportion of loci mapped to the B genome. Sixty-five percent of these 1918 loci mapped to the long arms of homoeologous group 4 chromosomes, while 35% mapped to the short arms. The distal regions of chromosome arms showed higher numbers of loci than the proximal regions, with the exception of 4DL. This study confirmed the complex structure of chromosome 4A that contains two reciprocal translocations and two inversions, previously identified. An additional inversion in the centromeric region of 4A was revealed. A consensus map for homoeologous group 4 was developed from 119 ESTs unique to group 4. Forty-nine percent of these ESTs were found to be homoologous to sequences on rice chromosome 3, 12% had matches with sequences on other rice chromosomes, and 39% had no matches with rice sequences at all. Limited homology (only 26 of the 119 consensus ESTs) was found between wheat ESTs on homoeologous group 4 and the Arabidopsis genome. Forty-two percent of the homoeologous group 4 ESTs could be classified into functional categories on the basis of blastX searches against all protein databases.

Molecular mapping of cultivated oats was conducted to update the previous reference map constructed using a recombinant inbred (RI) population derived from Avena byzantina C. Koch cv. Kanota × Avena sativa L. cv. Ogle. In the current work, 607 new markers were scored, many on a larger set of RI lines (133 vs. 71) than previously reported. A robust, updated framework map was developed to resolve linkage associations among 286 markers. The remaining 880 markers were placed individually within the most likely framework interval using χ2tests. This molecular framework incorporates and builds on previous studies, including physical mapping and linkage mapping in additional oat populations. The resulting map provides a common tool for use by oat researchers concerned with structural genomics, functional genomics, and molecular breeding.Key words: molecular marker, RFLP, linkage map, oat, Avena.

Genetic variation present in 64 durum wheat accessions was investigated by using three sources of microsatellite (SSR) markers: EST-derived SSRs (EST-SSRs) and two sources of SSRs isolated from total genomic DNA. Out of 245 SSR primer pairs screened, 22 EST-SSRs and 20 genomic-derived SSRs were polymorphic and used for genotyping. The EST-SSR primers produced high quality markers, but had the lowest level of polymorphism (25%) compared to the other two sources of genomic SSR markers (53%). The 42 SSR markers detected 189 polymorphic alleles with an average number of 4.5 alleles per locus. The coefficient of similarity ranged from 0.28 to 0.70 and the estimates of similarity varied when different sources of SSR markers were used to genotype the accessions. This study showed that EST-derived SSR markers developed in bread wheat are polymorphic in durum wheat when assaying loci of the A and B genomes. A minumum of ten EST-SSRs generated a very low probability of identity (0.36x10(-12)) indicating that these SSRs have a very high discriminatory power. EST-SSR markers directly sample variation in transcribed regions of the genome, which may enhance their value in marker-assisted selection, comparative genetic analysis and for exploiting wheat genetic resources by providing a more-direct estimate of functional diversity.

Stem rust of wheat (Triticum aestivum L.) caused by Puccinia graminis f. sp. tritici Eriks. and E. Henn. is a globally important disease that can cause severe yield loss. Breeding for quantitative stem rust resistance (QSRR) is important for developing cultivars with durable resistance. Genomic selection (GS) could increase rates of genetic gain for quantitative traits, but few experiments comparing GS and phenotypic selection (PS) have been conducted. Our objectives were to (i) compare realized gain from GS based on markers only with that of PS for QSRR in spring wheat using equal selection intensities; (ii) determine if gains agree with theoretical expectations; and (iii) compare the impact of GS and PS on inbreeding, genetic variance, and correlated response for pseudo-black chaff (PBC), a correlated trait. Over 2 yr, two cycles of GS were performed in parallel with one cycle of PS, with each method replicated twice. For GS, markers were generated using genotyping-by-sequencing, the prediction model was initially trained using historical data, and the model was updated before the second GS cycle. Overall, GS and PS led to a 31 ± 11 and 42 ± 12% increase in QSRR and a 138 ± 22 and 180 ± 70% increase in PBC, respectively. Genetic gains were not significant but were in agreement with expectations. Per year, gains from GS and PS were equal, but GS led to significantly lower genetic variance. This shows that while GS and PS can lead to equal rates of short-term gains, GS can reduce genetic variance more rapidly. Further work to develop efficient GS implementation strategies in spring wheat is warranted.

Key message Population structure must be evaluated before optimization of the training set population. Maximizing the phenotypic variance captured by the training set is important for optimal performance. Abstract The optimization of the training set (TRS) in genomic selection has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the coefficient of determination (CDmean), mean of predictor error variance (PEVmean), stratified CDmean (StratCDmean) and random sampling, were evaluated for prediction accuracy in the presence of different levels of population structure. In the presence of population structure, the most phenotypic variation captured by a sampling method in the TRS is desirable. The wheat dataset showed mild population structure, and CDmean and stratified CDmean methods showed the highest accuracies for all the traits except for test weight and heading date. The rice dataset had strong population structure and the approach based on stratified sampling showed the highest accuracies for all traits. In general, CDmean minimized the relationship between genotypes in the TRS, maximizing the relationship between TRS and the test set. This makes it suitable as an optimization criterion for long-term selection. Our results indicated that the best selection criterion used to optimize the TRS seems to depend on the interaction of trait architecture and population structure. Electronic supplementary material The online version of this article (doi:10.1007/s00122-014-2418-4) contains supplementary material, which is available to authorized users.

Key message This consensus map of stem rust genes, QTLs, and molecular markers will facilitate the identification of new resistance genes and provide a resource of in formation for development of new markers for breeding wheat varieties resistant to Ug99. Abstract The global effort to identify new sources of resistance to wheat stem rust, caused by Pucciniagraminis f. sp. tritici race group Ug99 has resulted in numerous studies reporting both qualitative genes and quantitative trait loci. The purpose of our study was to assemble all available information on loci associated with stem rust resistance from 21 recent studies on Triticum aestivum L. (bread wheat) and Triticum turgidum subsp. durum desf. (durum wheat). The software LPmerge was used to construct a stem rust resistance loci consensus wheat map with 1,433 markers incorporating Single Nucleotide Polymorphism, Diversity Arrays Technology, Genotyping-by-Sequencing as well as Simple Sequence Repeat marker information. Most of the markers associated with stem rust resistance have been identified in more than one population. Several loci identified in these populations map to the same regions with known Sr genes including Sr2, SrND643, Sr25 and Sr57 (Lr34/Yr18/Pm38), while other significant markers were located in chromosome regions where no Sr genes have been previously reported. This consensus map provides a comprehensive source of information on 141 stem rust resistance loci conferring resistance to stem rust Ug99 as well as linked markers for use in marker-assisted selection. Electronic supplementary material The online version of this article (doi:10.1007/s00122-014-2326-7) contains supplementary material, which is available to authorized users.

Durum wheat (Triticum turgidum L. var. durum) is an economically and nutritionally important cereal crop in the Mediterranean region. To further our understanding of durum genome organization we constructed a durum linkage map using restriction fragment length polymorphisms (RFLPs), simple sequence repeats (SSRs) known as Gatersleben wheat microsatellites (GWMs), amplified fragment length polymorphisms (AFLPs), and seed storage proteins (SSPs: gliadins and glutenins). A population of 110 F9 recombinant inbred lines (RILs) was derived from an intraspecific cross between two durum cultivars, Jennah Khetifa and Cham 1. The two parents exhibit contrasting traits for resistance to biotic and abiotic stresses and for grain quality. In total, 306 markers have been placed on the linkage map – 138 RFLPs, 26 SSRs, 134 AFLPs, five SSPs, and three known genes (one pyruvate decarboxylase and two lipoxygenases). The map is 3598 cM long, with an average distance between markers of 11.8 cM, and 12.1% of the markers deviated significantly from the expected Mendelian ratio 1:1. The molecular markers were evenly distributed between the A and B genomes. The chromosome with the most markers is 1B (41 markers), followed by 3B and 7B, with 25 markers each. The chromosomes with the fewest markers are 2A (11 markers), 5A (12 markers), and 4B (15 markers). In general, there is a good agreement between the map obtained and the Triticeae linkage consensus maps. This intraspecific map provides a useful tool for marker-assisted selection and map-based breeding for resistance to biotic and abiotic stresses and for improvement of grain quality. L'articolo è disponibile sul sito dell'editore: http://www.springerlink.com

Physical attributes, including kernel morphology, are used to grade wheat, and indicate wheat milling and baking quality (MBQ). Using a recombinant inbred population derived from a soft by hard wheat cross, this study quantified kernel traits' sources of variation, studied their heritability, and relationships between morphological and MBQ traits. Transgressive segregation occurred for all traits. Thousand-kernel weight (TKW) and kernel texture (NIR-T) were primarily influenced by genotype and test weight (TW) mainly by year. NIR-T had the highest heritability. Low genetic correlation (GCOR) between kernel length (LEN) and width (WID) suggest independent inheritance. NIR-T and LEN, or WID, showed low GCOR. Thus, it is genetically feasible to produce cultivars with any kernel texture and LEN, or WID, combination. No GCOR was found between TW and flour milling yield (FY), TKW, NIR-T or kernel morphology. GCOR showed that harder wheats had greater FY. Traits' low correlations call for studies clarifying the efficacy of using kernel traits in wheat classification or end-use quality prediction.

Wheat spindle streak mosaic bymovirus (WSSMV) causes an economically important disease of winter wheat in Europe and North America. Artificial inoculation with this virus to identify resistant wheat genotypes is difficult. This study was conducted to identify restriction fragment length polymorphism (RFLP) markers associated with resistance to this disease. A population, consisting of 104 F5 recombinant inbred lines from a cross between hexaploid Triticum aestivum cultivars 'Geneva' (resistant) and 'Augusta' (susceptible), was evaluated for WSSMV symptoms under field conditions for four years. Two linked markers on the long arm of chromosome 2D, Xbcd1095 and Xcdo373, were determined to be associated with WSSMV resistance by bulked segregant analysis of the 10 most resistant and 10 most susceptible lines. Marker Xcdo373 accounted for 79% and Xbcd1095 for 73% of the phenotypic variation. Our results suggest that resistance to WSSMV in this population is qualitative in nature and is controlled by few genes. These markers should be useful in the development of wheat cultivars resistant to WSSMV and perhaps also to wheat yellow mosaic bymovirus (WYMV).Key words: wheat, wheat spindle streak mosaic virus (WSSMV), RFLP markers, resistance, bymovirus, wheat yellow mosaic virus, barley yellow mosaic virus.

Oat groats are unique among cereals for the high level and the embryo-plus-endosperm localization of lipids. Genetic manipulation of groat quality traits such as oil is desired for optimizing the value of oat in human and livestock diets. A locus having a major effect on oil content in oat groats was located on linkage group 11 by single-factor analysis of variance, simple interval mapping and simplified composite interval mapping. A partial oat cDNA clone for plastidic acetyl-CoA carboxylase (ACCase), which catalyzes the first committed step in de novo fatty acid synthesis, identified a polymorphism linked to this major QTL. Similar QTL and ACCase locus placements were obtained with two recombinant inbred populations, ‘Kanota’בOgle’ (KO) and ‘Kanota’בMarion’ (KM), containing 137 and 139 individual lines, respectively. By having a common parent these populations provide biological replication of the results in that significant genomic regions should be evident in analyses of multiple cross combinations. The KO population was mapped with 150 RFLP loci distributed over the genome and was grown in five diverse environments (locations and years) for measurement of groat oil content. The KM population was mapped with 60 RFLP loci and grown in three environments. The QTL linked to AccaseA on linkage group 11 accounted for up to 48% of the phenotypic variance for groat oil content. These results provide strong support for the hypothesis that ACCase has a major role in determining groat oil content. Other QTLs were identified in both populations which accounted for an additional 10–20% of the phenotypic variance.

Comparative mapping of cDNA clones provides an important foundation for examining structural conservation among the chromosomes of diverse genera and for establishing hypotheses about the relationship between gene structure and function in a wide range of organisms. In this study, “anchor probes” were selected from cDNA libraries developed from rice, oat, and barley that were informative for comparative mapping in the grass family. One thousand eight hundred probes were screened on garden blots containing DNA of rice, maize, sorghum, sugarcane, wheat, barley, and oat, and 152 of them were selected as “anchors” because (1) they hybridized to the majority of target grass species based on Southern analysis, (2) they appeared to be low or single copy in rice, and (3) they helped provide reasonably good genome coverage in all species. Probes were screened for polymorphism on mapping parents, and polymorphic markers were mapped onto existing species-specific linkage maps of rice, oat, maize, and wheat. In wheat, both polymorphic and monomorphic markers could be assigned to chromosomes or chromosome arms based on hybridization to nullitetrasomic and ditelosomic stocks. Linkage among anchored loci allowed the identification of homoeologous regions of these distantly related genomes. Anchor probes were sequenced from both ends, providing an average of 260 bp in each direction, and sequences were deposited in GenBank. BLAST was used to compare the sequences with each other and with a non-redundant protein sequence database maintained at the European Molecular Biology Laboratory (EMBL). Of the anchor probes identified in this study 78% showed significant similarity to protein sequences for known genes with BLASTX scores exceeding 100.

In “Corn genome pops out of the pack” (News & Comment,[27 June, p. 1960][1]), Jon Cohen summarizes the June 1997 National Academy of Sciences colloquium “Protecting Our Food Supply: The Value of Plant Genome Initiatives.” The National Corn Genome Initiative, proposing a $143-million, 5-year, federally funded research program, appears to emphasize basic biological research, but because corn is known largely as an agricultural crop, it is likely that the U.S. Department of Agriculture (USDA) would finance it. Even with successful legislation, it is unlikely that new money would become available; instead, it would likely be funded by diversions from other USDA programs, such as Hatch formula funds, the National Rice Institute competitive grants program, and the Agricultural Research Service (ARS) budget. We believe that there will be serious long-term consequences should a single-crop genome research initiative be supported at the expense of existing research infrastructure or mission-oriented research on many important U.S. food crops. We support genomic research on important cereal crop plants, especially wheat, corn, and rice, the three main crops on Earth, in that order. We intend no discredit to a strong corn genome program, but without concurrent research on other species, comparative genome structure cannot be resolved. Transfer of research results from corn to other cereals will not be automatic. Each crop has unique gene complexes and characteristics and specific agricultural research needs. We urge that a national multispecies cereal crop genome program be formulated and implemented. We suggest a tripartite program emphasizing basic genomic science in corn and other cereals, development of genetic resources and methodologies specific to each crop, and application of genomics to solve agricultural and consumer-based problems. With broad-based support, the development, financing, management, and implementation of such a program should reach beyond current modes. As genomic research is innovative, so also should be its facilitation. The Human Genome Project provides valuable lessons for a new plant genome effort. # {#article-title-2} *Co-authors: # {#article-title-3} Cohen's 27 June article discusses dissatisfaction about the rate of release of Arabidopsis genome data from Japanese and European sequencing groups. The European Union (EU) Arabidopsis Sequencing Project integrates its work carefully with the U.S. and Japanese groups (for example, by providing clones for sequencing on the short arm of chromosome 4 to U.S. labs and by integrating the sequence efforts of U.S., Japanese, and European labs with respect to the short arm of chromosome 5, with the aim of rapidly generating a contiguous sequence). The third deposit of a European sequence, ten 200-kilobase contiguous units, was made to the European Bioinformatics Institute on 20 June, before Cohen's article appeared. The preparation of this contiguous sequence from individual components took some time, because it was generated from about 60 cosmid and bacterial artificial chromosome (BAC) clones and contained some difficult regions in terms of sequence complexity. Gene modeling revealed potential frameshifts, which were all resequenced. Our goal of obtaining contiguous sequences may result in initial additional complexities, but it serves the important purpose of revealing larger-scale features that are needed for planning a sequencing strategy. We are now sequencing individual BACs that will not form a contiguous sequence until later this year, when another three will have been completed. The sequences of these individual BACs can be retrieved from the Martinsried Institute for Protein Sequences website while annotation is proceeding. This release policy is consistent with that described in a Memorandum of Understanding signed with our U.S. and Japanese partners last year. The agreement was put in place after the EU component of the Arabidopsis Genome Project began, and it has taken a little time to align our release policy more closely to that thought to be required by the Arabidopsis community. The EU sequencing group knows that, although the release of sequences is a central aspect of a successful genome project, many of the more interesting features of plant chromosomes are only revealed by tackling the construction of contiguous sequences and dealing with complex regions as they occur. # {#article-title-4} Response : My article reflected the criticisms of Bevan's U.S. collaborators, who had serious misgivings about what they contend are unnecessary delays in data release. [1]: /lookup/doi/10.1126/science.276.5321.1960