Genetic Analysis of Platform-Phenotyped Root System Architecture of Bread and Durum Wheat in Relation to Agronomic Traits

Modeling plant phenotypic plasticity and its underlying genetic architecture: a comparative study

Phenotypic plasticity can contribute to crop adaptation to challenging environments. Plasticity indices are potentially useful to identify the genetic basis of crop phenotypic plasticity. Numerous methods exist to measure phenotypic plasticity, but their ability to capture quantitative trait loci with environmental effects remains elusive. Here, we analysed a published multi-trial maize phenotyping dataset that examined the water stress response of leaf area, shoot biomass, and water use efficiency, calculating phenotypic plasticity for these traits using seven different plasticity indices. A comprehensive genetic analysis of phenotypic plasticity for these traits was further performed and the ability of methods to detect genetic regions capturing variance due to genotype-by-environment interaction was evaluated. Our results suggest that not all plasticity indices are amenable to identifying genomic regions associated with phenotypic plasticity. We observed that plasticity indices based on calculation of a ratio between environments or the slope of the Finlay-Wilkinson model were particularly useful in uncovering the genetic architecture underlying phenotypic plasticity when studying responses to treatments within and across trials. Ultimately, a deeper understanding of phenotypic plasticity should provide opportunities for breeding plants better able to adapt to climate uncertainty.

Enhancing drought resilience in durum wheat: effect of root architecture and genotypic performance in semi-arid rainfed regions

Background

Developing drought-adapted genotypes is a primary goal for achieving resilient agriculture in the Mediterranean region. Durum wheat, a widely grown crop in the drylands of the Mediterranean basin, would significantly benefit from increased drought resistance.

Methods

We investigated a diverse set of 30 durum wheat varieties, including both local landraces and modern cultivars that have proven successful in Algeria. These varieties were evaluated in field trials over two consecutive years with contrasting rainfall patterns (one very dry, the other quite wet). Grain yield (PGY), yield components, and flag leaf characteristics such as area, canopy temperature, or rolling index were evaluated. Data from previous studies of root traits recorded on the same set of genotypes at seedling and adult growth stages were used to search for possible associations with grain yield and other agronomic traits measured in the current work.

Results

Genotypic variation was found for all traits measured under both conditions. Grain yield and aerial biomass were reduced by 76% (from 5.28 to 1.97 Mg ha-1) and 66% (from 15.94 to 3.80 Mg ha-1), respectively in the dry year, whereas the harvest index increased by 32%. The breeding history of the germplasm (cultivar vs. landrace) had a significant effect on the traits studied. Landraces showed higher biomass only under drought (4.27 vs. 3.63 Mg ha-1), whereas modern cultivars out-yielded landraces only under non-drought conditions (5.56 vs. 4.49 Mg ha-1). Promising associations were found between root and agronomic traits, especially with grain yield, indicating that a profuse (large root length) and shallow (wide root angle) root system was related to increased yield of modern cultivars only in the dry year, without penalizing yield in the wet year.

Conclusion

Breeding programs could improve grain yield under Algerian, semi-arid conditions, by making crosses between selected landraces with good growth potential under drought and modern cultivars, with high efficiency of biomass conversion into grain, and searching for lines with acceptable agronomic performance, which combine these desirable traits from landraces and modern cultivars, with the presence of shallow and profuse root systems.

Genetic Basis of Seedling Root Traits in Common Wheat (Triticum aestivum L.) Identified by Genome-Wide Linkage Mapping

Common wheat production is significantly influenced by abiotic stresses. Identifying the genetic loci for seedling root traits and developing the available molecular markers are crucial for breeding high yielding and stable varieties. In this study, five wheat seedling root traits, including root length (RL), root surface area (RA), root volume (RV), number of root tips (RT), and root dry weight (RW), were measured in the Wp-072/Wp-119 recombinant inbred line (RIL) population. Genotyping was conducted for the RIL population and their parents using the wheat 90K single-nucleotide polymorphism (SNP) chip. In total, three quantitative trait loci (QTLs) for RL (QRL.gau-1DS, QRL.gau-1DL and QRL.gau-4AL), two QTLs for RA (QRA.gau-1D and QRA.gau-2DL), one locus for RV (QRV.gau-6AS), two loci for RW (QRW.gau-2DL and QRW.gau-2AS), and two loci for RT (QRT.gau-3AS and QRT.gau-6DL) were identified, with each explaining 4.5-8.4% of the phenotypic variances, respectively. Among these, QRT.gau-3AS, QRL.gau-4AL, and QRV.gau-6AS overlapped with the previous reports, whereas the other seven QTLs were novel. The favorable alleles of QRL.gau-1DS, QRL.gau-1DL, QRL.gau-4AL, QRA.gau-1D, QRW.gau-2AS, QRV.gau-6AS, QRT.gau-3AS, and QRT.gau-6DL were contributed by Wp-072, whereas the other two loci originated from Wp-119. Additionally, five kompetitive allele-specific PCR (KASP) markers, KASP-RL-1DL for RL, KASP-RA-1D and KASP-RA-2DL for RA, KASP-RW-2AS and KASP-RW-2DL for RW, were developed and validated successfully in 149 wheat accessions. Furthermore, seven candidate genes mainly for plant hormones were selected and validated by quantitative real-time PCR (qRT-PCR). This study provides new loci, new candidate genes, available KASP markers, and varieties for optimizing wheat root system architecture.

The value of early root development traits in breeding programs for biomass yield in perennial ryegrass (Lolium perenne L.)

KEY MESSAGE

Early root traits, particularly total root length, are heritable and show positive genetic correlations with biomass yield in perennial ryegrass; incorporating them into breeding programs can enhance genetic gain. Perennial ryegrass (Lolium perenne L.) is an important forage grass widely used in pastures and lawns, valued for its high nutritive value and environmental benefits. Despite its importance, genetic improvements in biomass yield have been slow, mainly due to its outbreeding nature and the challenges of improving multiple traits simultaneously. This study aims to assess the potential advantages of including early root traits in the perennial ryegrass breeding process. Root traits, including total root length (TRL) and root angle (RA) were phenotyped in a greenhouse using rhizoboxes, and genetic correlations with field yield were estimated across three European locations over two years. Bivariate models estimated significant genetic correlations of 0.40 (SE = 0.14) between TRL and field yield, and a weak but positive correlation to RA of 0.15 (SE = 0.14). Heritability estimates were 0.36 for TRL, 0.39 for RA, and 0.31 for field yield across locations. Incorporating root trait data into selection criteria can improve the efficiency of breeding programs, potentially increasing genetic gain by approximately 10%. This results highlight the potential of early root traits to refine selection criteria in perennial ryegrass breeding programs, contributing to higher yield and efficiency.

Applications of Artificial Intelligence in Wheat Breeding for Sustainable Food Security

In agriculture, especially in crop breeding, innovative approaches are required to address the urgent issues posed by climate change and global food security. Artificial intelligence (AI) is a revolutionary technology in wheat breeding that provides new approaches to improve the ability of crops to withstand and produce higher yields in response to changing climate circumstances. This review paper examines the incorporation of artificial intelligence (AI) into conventional wheat breeding methods, with a focus on the contribution of AI in tackling the intricacies of contemporary agriculture. This review aims to assess the influence of AI technologies on enhancing the efficiency, precision, and sustainability of wheat breeding projects. We conduct a thorough analysis of recent research to evaluate several applications of artificial intelligence, such as machine learning (ML), deep learning (DL), and genomic selection (GS). These technologies expedite the swift analysis and interpretation of extensive datasets, augmenting the process of selecting and breeding wheat varieties that are well-suited to a wide range of environmental circumstances. The findings from the examined research demonstrate notable progress in wheat breeding as a result of artificial intelligence. ML algorithms have enhanced the precision of predicting phenotypic traits, whereas genomic selection has reduced the duration of breeding cycles. Utilizing artificial intelligence, high-throughput phenotyping allows for meticulous examination of plant characteristics under different stress environments, facilitating the identification of robust varieties. Furthermore, AI-driven models have exhibited superior predicted accuracies for crop productivity and disease resistance in comparison to conventional methods. AI technologies play a crucial role in the modernization of wheat breeding, providing significant enhancements in crop performance and adaptability. This integration not only facilitates the growth of wheat cultivars that provide large yields and can withstand stressful conditions but also strengthens global food security in the context of climate change. Ongoing study and collaboration across several fields are crucial to improving and optimizing these AI applications, ultimately enhancing their influence on sustainable agriculture.

Are high-throughput root phenotyping platforms suitable for informing root system architecture models with genotype-specific parameters? An evaluation based on the root model ArchiSimple and a small panel of wheat cultivars

Given the difficulties in accessing plant roots in situ, high-throughput root phenotyping (HTRP) platforms under controlled conditions have been developed to meet the growing demand for characterizing root system architecture (RSA) for genetic analyses. However, a proper evaluation of their capacity to provide the same estimates for strictly identical root traits across platforms has never been achieved. In this study, we performed such an evaluation based on six major parameters of the RSA model ArchiSimple, using a diversity panel of 14 bread wheat cultivars in two HTRP platforms that had different growth media and non-destructive imaging systems together with a conventional set-up that had a solid growth medium and destructive sampling. Significant effects of the experimental set-up were found for all the parameters and no significant correlations across the diversity panel among the three set-ups could be detected. Differences in temperature, irradiance, and/or the medium in which the plants were growing might partly explain both the differences in the parameter values across the experiments as well as the genotype × set-up interactions. Furthermore, the values and the rankings across genotypes of only a subset of parameters were conserved between contrasting growth stages. As the parameters chosen for our analysis are root traits that have strong impacts on RSA and are close to parameters used in a majority of RSA models, our results highlight the need to carefully consider both developmental and environmental drivers in root phenomics studies.

Exploitation of the Genetic Variability of Diverse Metric Traits of Durum Wheat (Triticum turgidum L. ssp. durum Desf.) Cultivars for Local Adaptation to Semi-Arid Regions of Algeria

Abiotic stresses pose significant challenges to wheat farming, yet exploiting the genetic variability within germplasm collections offers an opportunity to effectively address these challenges. In this study, we investigated the genetic diversity of key agronomic traits among twenty durum wheat cultivars, with the intention to pinpoint those better suited to semi-arid conditions. Field trials were conducted at the ITGC-FDPS Institute, Setif, Algeria, during the winter season of 2021/22. A completely randomized design was used with three replicates. Statistical analyses revealed significant variation among the genotypes for most of the studied traits, with some cultivars exhibiting a superior performance in a stressful environment. Notably, traits like the number of grains per spike (NGS) and the grain yield (GY) displayed high genotypic coefficients of variation (CVg). Except for membrane thermostability (MT) and biological yield (BY), the majority of the assessed traits exhibited moderate-to-high heritability estimates. Genotypic and phenotypic correlation studies have confirmed the importance of many yield-related traits in the expression of GY. The harvest index (HI) underscored the highest genotypic direct effect on GY, followed closely by spike number (SN), serving as consistent pathways through which most of the measured traits indirectly influenced GY. The cluster analysis categorized the durum wheat cultivars into seven distinct clusters. The largest inter-cluster distance was observed between clusters G3 and G4 (D2 = 6145.86), reflecting maximum dissimilarity between the individuals of these clusters. Hybridizing divergent clusters may benefit future breeding programs aiming to develop potential durum wheat varieties through cross combinations. This study's findings contribute to sustainable agriculture efforts by facilitating the selection of genotypes with enhanced resilience and productivity, particularly for cultivation in challenging semi-arid regions.

Understanding role of roots in plant response to drought: Way forward to climate-resilient crops

Drought stress leads to a significant amount of agricultural crop loss. Thus, with changing climatic conditions, it is important to develop resilience measures in agricultural systems against drought stress. Roots play a crucial role in regulating plant development under drought stress. In this review, we have summarized the studies on the role of roots and root-mediated plant responses. We have also discussed the importance of root system architecture (RSA) and the various structural and anatomical changes that it undergoes to increase survival and productivity under drought. Various genes, transcription factors, and quantitative trait loci involved in regulating root growth and development are also discussed. A summarization of various instruments and software that can be used for high-throughput phenotyping in the field is also provided in this review. More comprehensive studies are required to help build a detailed understanding of RSA and associated traits for breeding drought-resilient cultivars.

Chromosome groups 5, 6 and 7 harbor major quantitative trait loci controlling root traits in bread wheat (Triticum aestivum L.)

Identifying genomic regions for root traits in bread wheat can help breeders develop climate-resilient and high-yielding wheat varieties with desirable root traits. This study used the recombinant inbred line (RIL) population of Synthetic W7984 × Opata M85 to identify quantitative trait loci (QTL) for different root traits such as rooting depth (RD), root dry mass (RM), total root length (RL), root diameter (Rdia) and root surface areas (RSA1 for coarse roots and RSA2 for fine roots) under controlled conditions in a semi-hydroponic system. We detected 14 QTL for eight root traits on nine wheat chromosomes; we discovered three QTL each for RD and RSA1, two QTL each for RM and RSA2, and one QTL each for RL, Rdia, specific root length and nodal root number per plant. The detected QTL were concentrated on chromosome groups 5, 6 and 7. The QTL for shallow RD (Q.rd.uwa.7BL: Xbarc50) and high RM (Q.rm.uwa.6AS: Xgwm334) were validated in two independent F₂ populations of Synthetic W7984 × Chara and Opata M85 × Cascade, respectively. Genotypes containing negative alleles for Q.rd.uwa.7BL had 52% shallower RD than other Synthetic W7984 × Chara population lines. Genotypes with the positive alleles for Q.rm.uwa.6AS had 31.58% higher RM than other Opata M85 × Cascade population lines. Further, we identified 21 putative candidate genes for RD (Q.rd.uwa.7BL) and 13 for RM (Q.rm.uwa.6AS); TraesCS6A01G020400, TraesCS6A01G024400 and TraesCS6A01G021000 identified from Q.rm.uwa.6AS, and TraesCS7B01G404000, TraesCS7B01G254900 and TraesCS7B01G446200 identified from Q.rd.uwa.7BL encoded important proteins for root traits. We found germin-like protein encoding genes in both Q.rd.uwa.7BL and Q.rm.uwa.6AS regions. These genes may play an important role in RM and RD improvement. The identified QTL, especially the validated QTL and putative candidate genes are valuable genetic resources for future root trait improvement in wheat.

Integrating advancements in root phenotyping and genome-wide association studies to open the root genetics gateway

Plant adaptation to challenging environmental conditions around the world has made root growth and development an important research area for plant breeders and scientists. Targeted manipulation of root system architecture (RSA) to increase water and nutrient use efficiency can minimize the adverse effects of climate change on crop production. However, phenotyping of RSA is a major bottleneck since the roots are hidden in the soil. Recently the development of 2- and 3D root imaging techniques combined with the genome-wide association studies (GWASs) have opened up new research tools to identify the genetic basis of RSA. These approaches provide a comprehensive understanding of the RSA, by accelerating the identification and characterization of genes involved in root growth and development. This review summarizes the latest developments in phenotyping techniques and GWAS for RSA, which are used to map important genes regulating various aspects of RSA under varying environmental conditions. Furthermore, we discussed about the state-of-the-art image analysis tools integrated with various phenotyping platforms for investigating and quantifying root traits with the highest phenotypic plasticity in both artificial and natural environments which were used for large scale association mapping studies, leading to the identification of RSA phenotypes and their underlying genetics with the greatest potential for RSA improvement. In addition, challenges in root phenotyping and GWAS are also highlighted, along with future research directions employing machine learning and pan-genomics approaches.