Sivakumar Sukumaran

Phenotypic and genome‐wide association analysis of spike ethylene in diverse wheat genotypes under heat stress

The gaseous phytohormone ethylene plays an important role in spike development in wheat (Triticum aestivum). However, the genotypic variation and the genomic regions governing spike ethylene (SET) production in wheat under long-term heat stress remain unexplored. We investigated genotypic variation in the production of SET and its relationship with spike dry weight (SDW) in 130 diverse wheat elite lines and landraces under heat-stressed field conditions. We employed an Illumina iSelect 90K single nucleotide polymorphism (SNP) genotyping array to identify the genetic loci for SET and SDW through a genome-wide association study (GWAS) in a subset of the Wheat Association Mapping Initiative (WAMI) panel. The SET and SDW exhibited appreciable genotypic variation among wheat genotypes at the anthesis stage. There was a strong negative correlation between SET and SDW. The GWAS uncovered five and 32 significant SNPs for SET, and 22 and 142 significant SNPs for SDW, in glasshouse and field conditions, respectively. Some of these SNPs closely localized to the SNPs for plant height, suggesting close associations between plant height and spike-related traits. The phenotypic and genetic elucidation of SET and its relationship with SDW supports future efforts toward gene discovery and breeding wheat cultivars with reduced ethylene effects on yield under heat stress.

Association Mapping of Genetic Resources: Achievements and Future Perspectives

Association mapping studies in plants contribute to not only detecting the genetic basis of variation in physiological, developmental, and morphological traits (e.g., flowering time, plant height, grain quality, and nutrient content) but also bringing together researchers to assemble core collections and develop genetic platforms for genotyping, phenotyping, analysis, and interpretation. The establishment of the unified mixed model greatly facilitated association mapping studies in plants and further methodology work in general. Association mapping is well positioned to exploit the advances in next generation genomic technologies and high-throughput phenotyping. Genome-wide association studies (GWAS) are expected to increase dramatically once genome sequences of all major crop species are obtained. Moving forward, researchers in plant genetics and related disciplines need to develop improved genetic designs and computational tools to address several challenges such as missing heritability, new gene identification, genotyping-by-sequencing, and rare alleles. In this chapter, we describe major progress in understanding population structure, advancements in design and implementation of association mapping, and summarize examples of association mapping in maize, rice, Arabidopsis, wheat, barley, soybean, and sorghum. Finally, major opportunities with potential implications in plant genetics are discussed.

Genomic Prediction with Pedigree and Genotype × Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico

Developing genomic selection (GS) models is an important step in applying GS to accelerate the rate of genetic gain in grain yield in plant breeding. In this study, seven genomic prediction models under two cross-validation (CV) scenarios were tested on 287 advanced elite spring wheat lines phenotyped for grain yield (GY), thousand-grain weight (GW), grain number (GN), and thermal time for flowering (TTF) in 18 international environments (year-location combinations) in major wheat-producing countries in 2010 and 2011. Prediction models with genomic and pedigree information included main effects and interaction with environments. Two random CV schemes were applied to predict a subset of lines that were not observed in any of the 18 environments (CV1), and a subset of lines that were not observed in a set of the environments, but were observed in other environments (CV2). Genomic prediction models, including genotype × environment (G×E) interaction, had the highest average prediction ability under the CV1 scenario for GY (0.31), GN (0.32), GW (0.45), and TTF (0.27). For CV2, the average prediction ability of the model including the interaction terms was generally high for GY (0.38), GN (0.43), GW (0.63), and TTF (0.53). Wheat lines in site-year combinations in Mexico and India had relatively high prediction ability for GY and GW. Results indicated that prediction ability of lines not observed in certain environments could be relatively high for genomic selection when predicting G×E interaction in multi-environment trials.

Strategic crossing of biomass and harvest index—source and sink—achieves genetic gains in wheat

To accelerate genetic gains in breeding, physiological trait (PT) characterization of candidate parents can help make more strategic crosses, increasing the probability of accumulating favorable alleles compared to crossing relatively uncharacterized lines. In this study, crosses were designed to complement “source” with “sink” traits, where at least one parent was selected for favorable expression of biomass and/or radiation use efficiency—source—and the other for sink-related traits like harvest-index, kernel weight and grains per spike. Female parents were selected from among genetic resources—including landraces and products of wide-crossing (i.e. synthetic wheat)—that had been evaluated in Mexico at high yield potential or under heat stress, while elite lines were used as males. Progeny of crosses were advanced to the F4 generation within Mexico, and F4-derived F5 and F6 generations were yield tested to populate four international nurseries, targeted to high yield environments (2nd and 3rd WYCYT) for yield potential, and heat stressed environments (2nd and 4th SATYN) for climate resilience, respectively. Each nursery was grown as multi-location yield trials. Genetic gains were achieved in both temperate and hot environments, with most new PT-derived lines expressing superior yield and biomass compared to local checks at almost all international sites. Furthermore, the tendency across all four nurseries indicated either the superiority of the best new PT lines compared with the CIMMYT elite checks, or the superiority of all new PT lines as a group compared with all checks, and in some cases, both. Results support—in a realistic breeding context—the hypothesis that yield and radiation use efficiency can be increased by improving source:sink balance, and validate the feasibility of incorporating exotic germplasm into mainstream breeding efforts to accelerate genetic gains for yield potential and climate resilience.

Molecular Marker-Based Selection Tools in Spring Bread Wheat Improvement: CIMMYT Experience and Prospects

Wheat is a staple food for the major part of the world’s population. For wheat and other crops, it is generally agreed that in order to meet future challenges in food production, multifaceted breeding approaches are needed, including the use of current available genomics resources. Since more than three decades, molecular markers have acted as a versatile genomics tool for fast and unambiguous genetic analysis of plant species of both diploid and polyploid origin. Together with decreasing marker assay costs and interconnected genotyping service facilities, the opportunity to apply marker-assisted selection (MAS) strategies is becoming accessible to more and more breeding programs. We describe the use of molecular markers in wheat breeding with emphasis on the status of MAS in the CIMMYT global wheat program and will share our experience on recently developed prediction methods using genome-wide markers to archive genetic gain for more complex traits.

Genome-Wide Association Analyses Identify QTL Hotspots for Yield and Component Traits in Durum Wheat Grown under Yield Potential, Drought, and Heat Stress Environments

Understanding the genetic bases of economically important traits is fundamentally important in enhancing genetic gains in durum wheat. In this study, a durum panel of 208 lines (comprised of elite materials and exotics from the International Maize and Wheat Improvement Center gene bank) were subjected to genome wide association study (GWAS) using 6,211 DArTseq single nucleotide polymorphisms (SNPs). The panel was phenotyped under yield potential (YP), drought stress (DT), and heat stress (HT) conditions for 2 years. Mean yield of the panel was reduced by 72% (to 1.64 t/ha) under HT and by 60% (to 2.33 t/ha) under DT, compared to YP (5.79 t/ha). Whereas, the mean yield of the panel under HT was 30% less than under DT. GWAS identified the largest number of significant marker-trait associations on chromosomes 2A and 2B with p-values 10−06 to 10−03 and the markers from the whole study explained 7–25% variation in the traits. Common markers were identified for stress tolerance indices: stress susceptibility index, stress tolerance, and stress tolerance index estimated for the traits under DT (82 cM on 2B) and HT (68 and 83 cM on 3B; 25 cM on 7A). GWAS of irrigated (YP and HT combined), stressed (DT and HT combined), combined analysis of three environments (YP + DT + HT), and its comparison with trait per se and stress indices identified QTL hotspots on chromosomes 2A (54–70 cM) and 2B (75–82 cM). This study enhances our knowledge about the molecular markers associated with grain yield and its components under different stress conditions. It identifies several marker-trait associations for further exploration and validation for marker-assisted breeding.

Genomic-enabled Prediction Accuracies Increased by Modeling Genotype × Environment Interaction in Durum Wheat

Genomic‐enabled prediction accuracy of G×E models was superior to the non‐G×E models. Forward prediction and sparse testing in durum wheat shows great promise. Genomic‐enabled prediction accuracy of yield and components traits were highly associated with heritability.

Correction to: Strategic crossing of biomass and harvest index—source and sink—achieves genetic gains in wheat

The original article was corrected. Author Muhammad Kundi should instead read: Muhammad Sohail.