Hybrid performance and heterosis in spring bread wheat, and their relations to SSR-based genetic distances and coefficients of parentage

Development of hybrids is considered to be a promising avenue to enhance the yield potential of crops. We investigated (i) the amount of heterosis observed in hybrid progeny, (ii) relative importance of general (GCA) versus specific (SCA) combining ability, and (iii) the relationship between heterosis and genetic distance measures in four agronomic traits of spring bread wheat. Eight male and 14 female lines, as well as 112 hybrids produced in a factorial design were grown in replicated trials at two environments in Mexico. Principal coordinate analysis based on Rogers’ distance (RD) estimates calculated from 113 SSRs revealed three different groups of parents. Mid-parent heterosis (MPH) for grain yield averaged 0.02 t ha−1 (0.5%) and varied from−15.33% to 14.13%. MPH and hybrid performance (F1P) were higher for intra-group hybrids than for inter-group hybrids, with low values observed in inter-group crosses involving two non-adapted Chinese parents. Combined analyses of variance revealed significant differences among parents and among hybrids. Estimates of GCA variances were more important than SCA variances for all traits. Tight correlations of GCA with line per se performance, and mid-parent value with F1P were observed for all traits. In contrast, correlations of MPH with RD and coefficient of parentage were not significant. It was concluded that the level of heterosis in spring wheat was too low to warrant a commercial exploitation in hybrids. SSRs proved to be a powerful tool for the identification of divergent groups in advanced wheat breeding materials

Haplotypes-based genetic analysis: benefits and challenges

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

SSR and Pedigree Analyses of Genetic Diversity among CIMMYT Wheat Lines Targeted to Different Megaenvironments

Improved bread wheat (Triticum aestivum L.) cultivars for diverse agroecological environments are important for success in the effort to increase food production. In the 1980s, CIMMYT introduced the megaenvironment (ME) concept to breed wheats specifically adapted to different areas. Our objective was to analyze the genetic diversity among 68 advanced CIMMYT wheat lines targeted to different MEs by using 99 simple sequence repeats (SSRs) and the coefficient of parentage (COP). The average number of alleles detected was higher for the 47 genomic SSRs (5.4) than for the 52 SSRs derived from expressed sequence tags (EST) (3.3), but gene diversity between MEs was similar for both types of markers. No significant differences among the five MEs were observed for the means of SSR-based genetic similarities (GS), calculated as 1 − Rogers' distance, and COP values. Both measures showed a low correlation (r = 0.43). High levels of genetic diversity were found within the germplasm targeted to each ME. However, principle coordinate analysis based on modified Rogers' distances did not separate the genotypes according to their targeted MEs. We conclude that presence of a single core germplasm can reflect large phenotypic differences. A sufficient number of diverse breeding lines for each ME is required because MEs generally combine various production areas. SSRs represent a powerful tool to quantify genetic diversity in wheat, but genotypic differentiation for adaptation to specific MEs in the CIMMYT program could not be proven

Genetic Diversity and Genome-Wide Association Study for the Phenology Response of Winter Wheats of North America, Western Asia, and Europe

Wheat is a staple food in many areas around the World. In the 20th century, breeders and scientists were able to boost wheat yield considerably. However, a yield plateau has become a concern and is threatening food security. Investments in cutting-edge technologies, including genomics and precision phenology measurements, can provide valuable tools to drive crop improvement. The objectives of this study were to (i) investigate the genetic diversity in a set of winter wheat lines, (ii) characterize their phenological response under different vernalization and photoperiod conditions, and (iii) identify effective markers associated with the phenological traits. A total of 249 adapted genotypes of different geographical origin were genotyped using the 35K Axiom® Wheat Breeder’s Array. A total of 11,476 SNPs were used for genetic analysis. The set showed an average polymorphism information content of 0.37 and a genetic diversity of 0.43. A population structure analysis revealed three distinct subpopulations mainly related to their geographical origin (Europe, North America, and Western Asia). The lines of CGIAR origin showed the largest diversity and the lowest genetic distance to all other subpopulations. The phenology of the set was studied under controlled conditions using four combinations of long (19 h light) and short photoperiod (13 h light) and long vernalization (49 days at 5 °C) and no vernalization. With this, phenological traits such as earliness per se (Eps), relative response to vernalization (RRV), and relative response to photoperiod (RRP) were calculated. The phenotypic variation of growing degree days was significant in all phenology combinations. RRV ranged from 0 to 0.56, while RRP was higher with an overall average of 0.25. The GWAS analysis detected 30 marker-trait associations linked to five phenological traits. The highest significant marker was detected on chromosome 2D with a value of −log10(p) = 11.69. Only four loci known to regulate flowering exceeded the Bonferroni correction threshold of −log10(p) > 5.1. These results outline a solid foundation to address global food security and offer tremendous opportunities for advancing crop improvement strategies.This research was funded by a European Union project entitled “Addressing the Challenges of Climate Change for Sustainable Food Security in Turkey, Iran and Morocco, through the creation and dissemination of an international database to promote the use of wheat genetic resources and increase genetic gains.” CFP 2014/2015-W3B-PR-18-Turkey.info:eu-repo/semantics/publishedVersio

Allelic Variation at the Vernalization Response (Vrn-1) and Photoperiod Sensitivity (Ppd-1) Genes and Their Association With the Development of Durum Wheat Landraces and Modern Cultivars

Wheat adaptability to a wide range of environmental conditions is mostly determined by allelic diversity within genes controlling vernalization requirement (Vrn-1) and photoperiod sensitivity (Ppd-1). We characterized a panel of 151 durum wheat Mediterranean landraces and 20 representative locally adapted modern cultivars for their allelic composition at Vrn-1 and Ppd-1 gene using diagnostic molecular markers and studied their association with the time needed to reach six growth stages under field conditions over 6 years. Compared with the more diverse and representative landrace collection, the set of modern cultivars were characterized by a reduction of 50% in the number of allelic variants at the Vrn-A1 and Vrn-B1 genes, and the high frequency of mutant alleles conferring photoperiod insensitivity at Ppd-A1, which resulted on a shorter cycle length. Vrn-A1 played a greater role than Vrn-B1 in regulating crop development (Vrn-A1 > Vrn-B1). The results suggest that mutations in the Vrn-A1 gene may have been the most important in establishing the spring growth habit of Mediterranean landraces and modern durum cultivars. The allele Vrn-A1d, found in 10 landraces, delayed development. The relative effects of single Vrn-A1 alleles on delaying the development of the landraces were vrn-A1 = Vrn-A1d > Vrn-A1b > Vrn-A1c. Allele vrn-B1 was present in all except two landraces and in all modern cultivars. The null allele at Ppd-A1 (a deletion first observed in the French bread wheat cultivar ‘Capelle-Desprez’) was found for the first time in durum wheat in the present study that identified it in 30 landraces from 13 Mediterranean countries. Allele Ppd-A1a (GS105) was detected in both germplasm types, while the allele Ppd-A1a (GS100) was found only in modern North American and Spanish cultivars. The relative effect of single Ppd-A1 alleles on extending phenological development was Ppd-A1(DelCD) > Ppd-A1b > Ppd-A1a (GS105) > Ppd-A1a (GS100). Sixteen Vrn-1+Ppd-1 allelic combinations were found in landraces and six in modern cultivars, but only three were common to both panels. Differences in the number of days to reach anthesis were 10 days in landraces and 3 days in modern cultivars. Interactive effects between Vrn-1 and Ppd-1 genes were detected.info:eu-repo/semantics/publishedVersio

Multi-environment QTL analysis using an updated genetic map of a widely distributed Seri × Babax spring wheat population

Seri/Babax spring wheat RIL population was developed to minimize the confounding effect of phenology in the genetic dissection of abiotic stress traits. An existing linkage map (< 500 markers) was updated with 6470 polymorphic Illumina iSelect 90K array and DArTseq SNPs to a genetic map of 5576.5 cM with 1748 non-redundant markers (1165 90K SNPs, 207 DArTseq SNPs, 183 AFLP, 111 DArT array, and 82 SSR) assigned to 31 linkage groups. We conducted QTL mapping for yield and related traits phenotyped in several major wheat growing areas in Egypt, Sudan, Iran, India, and Mexico (nine environments: heat, drought, heat plus drought, and yield potential). QTL analysis identified 39 (LOD 2.5–23.6; PVE 4.8–21.3%), 36 (LOD 2.5–15.4; PVE 2.9–21.4%), 30 (LOD 2.5–13.1; PVE 3.6–26.8%), 39 (LOD 2.7–14.4; PVE 2.6–15.9%), and 22 (LOD 2.8–4.8; PVE 6.8–12.9%) QTLs for grain yield, thousand-grain weight, grain number, days to heading, and plant height, respectively. The present study confirmed QTLs from previous studies and identified novel QTLs. QTL analysis based on high-yielding and low-yielding environmental clusters identified 11 QTLs (LOD 2.6–14.9; PVE 2.7–19.7%). The updated map thereby provides a better genome coverage (3.5-fold) especially on the D genome (4-fold), higher density (1.1-fold), and a good collinearity with the IWGSC RefSeq v1.0 genome, and increased the number of detected QTLs (5-fold) compared with the earlier map. This map serves as a useful genomic resource for genetic analyses of important traits on this wheat population that was widely distributed around the world.info:eu-repo/semantics/acceptedVersio

Genomic prediction in CIMMYT maize and wheat breeding programs

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

The effects of brief heat during early booting on reproductive, developmental and chlorophyll physiological performance in common wheat (Triticum aestivum L.)

Rising temperatures due to climate change threaten agricultural crop productivity. As a cool-season crop wheat is heat-sensitive, but often exposed to high temperatures during crop growing period. In the current study, a bread wheat panel of spring wheat genotypes, including putatively heat-tolerant Australian and CIMMYT genotypes, was exposed to a 5-day mild (34oC/28oC, day/night) or extreme (37oC/27oC) heat stress during the sensitive pollen developmental stage. Worsening effects on anther morphology were observed as heat stress increased from mild to extreme. Even under mild heat a significant decrease in pollen viability and number of grains per spike from primary spike was observed compared with the control (21oC/15oC), with Sunstar and two CIMMYT breeding lines performing well. A heat-specific positive correlation between the two traits indicates the important role of pollen fertility for grain setting. Interestingly, both mild and extreme heat induced development of new tillers after the heat stress, providing an alternative sink for accumulated photosynthates and significantly contributing to the final yield. Measurements of flag leaf maximum potential quantum efficiency of Photosystem II (Fv/Fm) showed an initial inhibition after the heat treatment, followed by a full recovery within a few days. Despite this, model fitting using chlorophyll SPAD measurements showed an earlier onset or faster senescence rate under heat stress. The data presented here provide interesting entry points for further research into pollen fertility, tillering dynamics and leaf senescence under heat. The identified heat-tolerant wheat genotypes can be used to dissect the underlying mechanisms and breed climate-resilient wheat

Bayesian multitrait kernel methods improve multienvironment genome-based prediction

When multitrait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this article, we explore Bayesian multitrait kernel methods for genomic prediction and we illustrate the power of these models with three-real datasets. The kernels under study were the linear, Gaussian, polynomial, and sigmoid kernels; they were compared with the conventional Ridge regression and GBLUP multitrait models. The results show that, in general, the Gaussian kernel method outperformed conventional Bayesian Ridge and GBLUP multitrait linear models by 2.2–17.45% (datasets 1–3) in terms of prediction performance based on the mean square error of prediction. This improvement in terms of prediction performance of the Bayesian multitrait kernel method can be attributed to the fact that the proposed model is able to capture nonlinear patterns more efficiently than linear multitrait models. However, not all kernels perform well in the datasets used for evaluation, which is why more than one kernel should be evaluated to be able to choose the best kernel

Diversity analysis of 80,000 wheat accessions reveals consequences and opportunities of selection footprints

Undomesticated wild species, crop wild relatives, and landraces represent sources of variation for wheat improvement to address challenges from climate change and the growing human population. Here, we study 56,342 domesticated hexaploid, 18,946 domesticated tetraploid and 3,903 crop wild relatives in a massive-scale genotyping and diversity analysis. Using DArTseqTM technology, we identify more than 300,000 high-quality SNPs and SilicoDArT markers and align them to three reference maps: the IWGSC RefSeq v1.0 genome assembly, the durum wheat genome assembly (cv. Svevo), and the DArT genetic map. On average, 72% of the markers are uniquely placed on these maps and 50% are linked to genes. The analysis reveals landraces with unexplored diversity and genetic footprints defined by regions under selection. This provides fertile ground to develop wheat varieties of the future by exploring specific gene or chromosome regions and identifying germplasm conserving allelic diversity missing in current breeding programs