Shared Genetic Control of Root System Architecture between Zea mays and Sorghum bicolor

Nonphotochemical quenching kinetics GWAS in sorghum identifies genes that may play conserved roles in maize and Arabidopsis thaliana photoprotection

Photosynthetic organisms must cope with rapid fluctuations in light intensity. Nonphotochemical quenching (NPQ) enables the dissipation of excess light energy as heat under high light conditions, whereas its relaxation under low light maximizes photosynthetic productivity. We quantified variation in NPQ kinetics across a large sorghum (Sorghum bicolor) association panel in four environments, uncovering significant genetic control for NPQ. A genome-wide association study (GWAS) confidently identified three unique regions in the sorghum genome associated with NPQ and suggestive associations in an additional 61 regions. We detected strong signals from the sorghum ortholog of Arabidopsis thaliana Suppressor Of Variegation 3 (SVR3) involved in plastid-nucleus signaling. By integrating GWAS results for NPQ across maize (Zea mays) and sorghum-association panels, we identified a second gene, Non-yellowing 1 (NYE1), originally studied by Gregor Mendel in pea (Pisum sativum) and involved in the degradation of photosynthetic pigments in light-harvesting complexes. Analysis of nye1 insertion alleles in A. thaliana confirmed the effect of this gene on NPQ kinetics in eudicots. We extended our comparative genomics GWAS framework across the entire maize and sorghum genomes, identifying four additional loci involved in NPQ kinetics. These results provide a baseline for increasing the accuracy and speed of candidate gene identification for GWAS in species with high linkage disequilibrium.

Integrating GWAS with a gene co-expression network better prioritizes candidate genes associated with root metaxylem phenes in maize

Root metaxylems are phenotypically diverse structures whose function is particularly important under drought stress. Significant research has dissected the genetic machinery underlying metaxylem phenotypes in dicots, but that of monocots are relatively underexplored. In maize (Zea mays), a robust pipeline integrated a genome-wide association study (GWAS) of root metaxylem phenes under well-watered and water-stress conditions with a gene co-expression network to prioritize the strongest gene candidates. We identified 244 candidate genes by GWAS, of which 103 reside in gene co-expression modules most relevant to xylem development. Several candidate genes may be involved in biosynthetic processes related to the cell wall, hormone signaling, oxidative stress responses, and drought responses. Of those, six gene candidates were detected in multiple root metaxylem phenes in both well-watered and water-stress conditions. We posit that candidate genes that are more essential to network function based on gene co-expression (i.e., hubs or bottlenecks) should be prioritized and classify 33 essential genes for further investigation. Our study demonstrates a new strategy for identifying promising gene candidates and presents several gene candidates that may enhance our understanding of vascular development and responses to drought in cereals.

TWAS facilitates gene-scale trait genetic dissection through gene expression, structural variations, and alternative splicing in soybean

A genome-wide association study (GWAS) identifies trait-associated loci, but identifying the causal genes can be a bottleneck, due in part to slow decay of linkage disequilibrium (LD). A transcriptome-wide association study (TWAS) addresses this issue by identifying gene expression-phenotype associations or integrating gene expression quantitative trait loci with GWAS results. Here, we used self-pollinated soybean (Glycine max [L.] Merr.) as a model to evaluate the application of TWAS to the genetic dissection of traits in plant species with slow LD decay. We generated RNA sequencing data for a soybean diversity panel and identified the genetic expression regulation of 29 286 soybean genes. Different TWAS solutions were less affected by LD and were robust to the source of expression, identifing known genes related to traits from different tissues and developmental stages. The novel pod-color gene L2 was identified via TWAS and functionally validated by genome editing. By introducing a new exon proportion feature, we significantly improved the detection of expression variations that resulted from structural variations and alternative splicing. As a result, the genes identified through our TWAS approach exhibited a diverse range of causal variations, including SNPs, insertions or deletions, gene fusion, copy number variations, and alternative splicing. Using this approach, we identified genes associated with flowering time, including both previously known genes and novel genes that had not previously been linked to this trait, providing insights complementary to those from GWAS. In summary, this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.

Genomic basis determining root system architecture in maize

KEY MESSAGE

A total of 389 and 344 QTLs were identified by GWAS and QTL mapping explaining accumulatively 32.2-65.0% and 23.7-63.4% of phenotypic variation for 14 shoot-borne root traits using more than 1300 individuals across multiple field trails. Efficient nutrient and water acquisition from soils depends on the root system architecture (RSA). However, the genetic determinants underlying RSA in maize remain largely unexplored. In this study, we conducted a comprehensive genetic analysis for 14 shoot-borne root traits using 513 inbred lines and 800 individuals from four recombinant inbred line (RIL) populations at the mature stage across multiple field trails. Our analysis revealed substantial phenotypic variation for these 14 root traits, with a total of 389 and 344 QTLs identified through genome-wide association analysis (GWAS) and linkage analysis, respectively. These QTLs collectively explained 32.2-65.0% and 23.7-63.4% of the trait variation within each population. Several a priori candidate genes involved in auxin and cytokinin signaling pathways, such as IAA26, ARF2, LBD37 and CKX3, were found to co-localize with these loci. In addition, a total of 69 transcription factors (TFs) from 27 TF families (MYB, NAC, bZIP, bHLH and WRKY) were found for shoot-borne root traits. A total of 19 genes including PIN3, LBD15, IAA32, IAA38 and ARR12 and 19 GWAS signals were overlapped with selective sweeps. Further, significant additive effects were found for root traits, and pyramiding the favorable alleles could enhance maize root development. These findings could contribute to understand the genetic basis of root development and evolution, and provided an important genetic resource for the genetic improvement of root traits in maize.

Advancements and Prospects of Genome-Wide Association Studies (GWAS) in Maize

Genome-wide association studies (GWAS) have emerged as a powerful tool for unraveling intricate genotype-phenotype association across various species. Maize (Zea mays L.), renowned for its extensive genetic diversity and rapid linkage disequilibrium (LD), stands as an exemplary candidate for GWAS. In maize, GWAS has made significant advancements by pinpointing numerous genetic loci and potential genes associated with complex traits, including responses to both abiotic and biotic stress. These discoveries hold the promise of enhancing adaptability and yield through effective breeding strategies. Nevertheless, the impact of environmental stress on crop growth and yield is evident in various agronomic traits. Therefore, understanding the complex genetic basis of these traits becomes paramount. This review delves into current and future prospectives aimed at yield, quality, and environmental stress resilience in maize and also addresses the challenges encountered during genomic selection and molecular breeding, all facilitated by the utilization of GWAS. Furthermore, the integration of omics, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics has enriched our understanding of intricate traits in maize, thereby enhancing environmental stress tolerance and boosting maize production. Collectively, these insights not only advance our understanding of the genetic mechanism regulating complex traits but also propel the utilization of marker-assisted selection in maize molecular breeding programs, where GWAS plays a pivotal role. Therefore, GWAS provides robust support for delving into the genetic mechanism underlying complex traits in maize and enhancing breeding strategies.

Root responses to abiotic stress: a comparative look at root system architecture in maize and sorghum

Under all environments, roots are important for plant anchorage and acquiring water and nutrients. However, there is a knowledge gap regarding how root architecture contributes to stress tolerance in a changing climate. Two closely related plant species, maize and sorghum, have distinct root system architectures and different levels of stress tolerance, making comparative analysis between these two species an ideal approach to resolve this knowledge gap. However, current research has focused on shared aspects of the root system that are advantageous under abiotic stress conditions rather than on differences. Here we summarize the current state of knowledge comparing the root system architecture relative to plant performance under water deficit, salt stress, and low phosphorus in maize and sorghum. Under water deficit, steeper root angles and deeper root systems are proposed to be advantageous for both species. In saline soils, a reduction in root length and root number has been described as advantageous, but this work is limited. Under low phosphorus, root systems that are shallow and wider are beneficial for topsoil foraging. Future work investigating the differences between these species will be critical for understanding the role of root system architecture in optimizing plant production for a changing global climate.

Pilot-scale genome-wide association mapping in diverse sorghum germplasms identified novel genetic loci linked to major agronomic, root and stomatal traits

This genome-wide association studies (GWAS) used a subset of 96 diverse sorghum accessions, constructed from a large collection of 219 accessions for mining novel genetic loci linked to major agronomic, root morphological and physiological traits. The subset yielded 43,452 high quality single nucleotide polymorphic (SNP) markers exhibiting high allelic diversity. Population stratification showed distinct separation between caudatum and durra races. Linkage disequilibrium (LD) decay was rapidly declining with increasing physical distance across all chromosomes. The initial 50% LD decay was ~ 5 Kb and background level was within ~ 80 Kb. This study detected 42 significant quantitative trait nucleotide (QTNs) for different traits evaluated using FarmCPU, SUPER and 3VmrMLM which were in proximity with candidate genes related and were co-localized in already reported quantitative trait loci (QTL) and phenotypic variance (R2) of these QTNs ranged from 3 to 20%. Haplotype validation of the candidate genes from this study resulted nine genes showing significant phenotypic difference between different haplotypes. Three novel candidate genes associated with agronomic traits were validated including Sobic.001G499000, a potassium channel tetramerization domain protein for plant height, Sobic.010G186600, a nucleoporin-related gene for dry biomass, and Sobic.002G022600 encoding AP2-like ethylene-responsive transcription factor for plant yield. Several other candidate genes were validated and associated with different root and physiological traits including Sobic.005G104100, peroxidase 13-related gene with root length, Sobic.010G043300, homologous to Traes_5BL_8D494D60C, encoding inhibitor of apoptosis with iWUE, and Sobic.010G125500, encoding zinc finger, C3HC4 type domain with Abaxial stomatal density. In this study, 3VmrMLM was more powerful than FarmCPU and SUPER for detecting QTNs and having more breeding value indicating its reliable output for validation. This study justified that the constructed subset of diverse sorghums can be used as a panel for mapping other key traits to accelerate molecular breeding in sorghum.

Dissecting the genetic architecture of root-related traits in a grafted wild Vitis berlandieri population for grapevine rootstock breeding

In woody perennial plants, quantitative genetics and association studies remain scarce for root-related traits, due to the time required to obtain mature plants and the complexity of phenotyping. In grapevine, a grafted cultivated plant, most of the rootstocks used are hybrids between American Vitis species (V. rupestris, V. riparia, and V. berlandieri). In this study, we used a wild population of an American Vitis species (V. berlandieri) to analyze the genetic architecture of the root-related traits of rootstocks in a grafted context. We studied a population consisting of 211 genotypes, with one to five replicates each (n = 846 individuals), plus four commercial rootstocks as control genotypes (110R, 5BB, Börner, and SO4). After two independent years of experimentation, the best linear unbiased estimates method revealed root-related traits with a moderate-to-high heritability (0.36-0.82) and coefficient of genetic variation (0.15-0.45). A genome-wide association study was performed with the BLINK model, leading to the detection of 11 QTL associated with four root-related traits (one QTL was associated with the total number of roots, four were associated with the number of small roots (< 1 mm in diameter), two were associated with the number of medium-sized roots (1 mm < diameter < 2 mm), and four were associated with mean diameter) accounting for up to 25.1% of the variance. Three genotypes were found to have better root-related trait performances than the commercial rootstocks and therefore constitute possible new candidates for use in grapevine rootstock breeding programs.

Amand P, Diggle AJ, Rengel Z. Genetic characterization of root architectural traits in barley (Hordeum vulgare L.) using SNP markers

Increasing attention is paid to providing new tools to breeders for targeted breeding for specific root traits that are beneficial in low-fertility, drying soils; however, such information is not available for barley (Hordeum vulgare L.). A panel of 191 barley accessions (originating from Australia, Europe, and Africa) was phenotyped for 26 root and shoot traits using the semi-hydroponic system and genotyped using 21 062 high-quality single nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing (GBS). The population structure analysis of the barley panel identified six distinct groups. We detected 1199 significant (P<0.001) marker-trait associations (MTAs) with r2 values up to 0.41. The strongest MTAs were found for root diameter in the top 20 cm and the longest root length. Based on the physical locations of these MTAs in the barley reference genome, we identified 37 putative QTLs for the root traits, and three QTLs for shoot traits, with nine QTLs located in the same physical regions. The genomic region 640-653 Mb on chromosome 7H was significant for five root length-related traits, where 440 annotated genes were located. The putative QTLs for various root traits identified in this study may be useful for genetic improvement regarding the adaptation of new barley cultivars to suboptimal environments and abiotic stresses.

Transcriptome-wide association study coupled with eQTL analysis reveals the genetic connection between gene expression and flowering time in Arabidopsis

Genome-wide association study (GWAS) has improved our understanding of complex traits, but challenges exist in distinguishing causation versus association caused by linkage disequilibrium. Instead, transcriptome-wide association studies (TWAS) detect direct associations between expression levels and phenotypic variations, providing an opportunity to better prioritize candidate genes. To assess the feasibility of TWAS, we investigated the association between transcriptomes, genomes, and various traits in Arabidopsis, including flowering time. The associated genes formerly known to regulate growth allometry or metabolite production were first identified by TWAS. Next, for flowering time, six TWAS-newly identified genes were functionally validated. Analysis of the expression quantitative trait locus (eQTL) further revealed a trans-regulatory hotspot affecting the expression of several TWAS-identified genes. The hotspot covers the FRIGIDA (FRI) gene body, which possesses multiple haplotypes differentially affecting the expression of downstream genes, such as FLOWERING LOCUS C (FLC) and SUPPRESSOR OF OVEREXPRESSION OF CO 1 (SOC1). We also revealed multiple independent paths towards the loss of function of FRI in natural accessions. Altogether, this study demonstrates the potential of combining TWAS with eQTL analysis to identify important regulatory modules of FRI-FLC-SOC1 for quantitative traits in natural populations.

Genetic dissection of root architecture in Ethiopian sorghum landraces

KEY MESSAGE

This study quantified genetic variation in root system architecture (root number, angle, length and dry mass) within a diversity panel of 1771 Ethiopian sorghum landraces and identified 22 genomic regions associated with the root variations. The root system architecture (RSA) of crop plants influences adaptation to water-limited conditions and determines the capacity of a plant to access soil water and nutrients. Four key root traits (number, angle, length and dry mass) were evaluated in a diversity panel of 1771 Ethiopian sorghum landraces using purpose-built root chambers. Significant genetic variation was observed in all studied root traits, with nodal root angle ranging from 16.4° to 26.6°, with a high repeatability of 78.9%. Genome wide association studies identified a total of 22 genomic regions associated with root traits which were distributed on all chromosomes except chromosome SBI-10. Among the 22 root genomic regions, 15 co-located with RSA trait QTL previously identified in sorghum, with the remaining seven representing novel RSA QTL. The majority (85.7%) of identified root angle QTL also co-localized with QTL previously identified for stay-green in sorghum. This suggests that the stay-green phenotype might be associated with root architecture that enhances water extraction during water stress conditions. The results open avenues for manipulating root phenotypes to improve productivity in abiotic stress environments via marker-assisted selection.

Hybrid performance evaluation and genome-wide association analysis of root system architecture in a maize association population

KEY MESSAGE

The genetic architecture of RSA traits was dissected by GWAS and coexpression networks analysis in a maize association population. Root system architecture (RSA) is a crucial determinant of water and nutrient uptake efficiency in crops. However, the maize genetic architecture of RSA is still poorly understood due to the challenges in quantifying root traits and the lack of dense molecular markers. Here, an association mapping panel including 356 inbred lines were crossed with a common tester, Zheng58, and the test crosses were phenotyped for 12 RSA traits in three locations. We observed a 1.3 ~ sixfold phenotypic variation for measured RSA in the association panel. The association panel consisted of four subpopulations, non-stiff stalk (NSS) lines, stiff stalk (SS), tropical/subtropical (TST), and mixed. Zheng58 × TST has a 2.1% higher crown root number (CRN) and 8.6% less brace root number (BRN) than Zheng58 × NSS and Zheng58 × SS, respectively. Using a genome-wide association study (GWAS) with 1.25 million SNPs and correction for population structure, 191 significant SNPs were identified for root traits. Ninety (47%) of the significant SNPs showed positive allelic effects, and 101 (53%) showed negative effects. Each locus could explain 0.39% to 11.8% of phenotypic variation. By integrating GWAS results and comparing coexpression networks, 26 high-priority candidate genes were identified. Gene GRMZM2G377215, which belongs to the COBRA-like gene family, affected root growth and development. Gene GRMZM2G468657 encodes the aspartic proteinase nepenthesin-1, related to root development and N-deficient response. Collectively, our research provides progress in the genetic dissection of root system architecture. These findings present the further possibility for the genetic improvement of root traits in maize.

Natural variation of maize root hydraulic architecture underlies highly diverse water uptake capacities

Plant water uptake is determined by the root system architecture and its hydraulic capacity, which together define the root hydraulic architecture. The current research aims at understanding the water uptake capacities of maize (Zea mays), a model organism and major crop. We explored the genetic variations within a collection of 224 maize inbred Dent lines and successively defined core genotype subsets to access multiple architectural, anatomical, and hydraulic parameters in the primary root (PR) and seminal roots (SR) of hydroponically grown seedlings. We found 9-, 3.5-, and 12.4-fold genotypic differences for root hydraulics (Lpr), PR size, and lateral root size, respectively, that shaped wide and independent variations of root structure and function. Within genotypes, PR and SR showed similarities in hydraulics and, to a lesser extent, in anatomy. They had comparable aquaporin activity profiles that, however, could not be explained by aquaporin expression levels. Genotypic variations in the size and number of late meta xylem vessels were positively correlated with Lpr. Inverse modeling further revealed dramatic genotypic differences in the xylem conductance profile. Thus, tremendous natural variation of maize root hydraulic architecture underlies a high diversity of water uptake strategies and paves the way to quantitative genetic dissection of its elementary traits.

Convergently selected NPF2.12 coordinates root growth and nitrogen use efficiency in wheat and barley

Understanding the genetic and molecular function of nitrate sensing and acquisition across crop species will accelerate breeding of cultivars with improved nitrogen use efficiency (NUE). Here, we performed a genome-wide scan using wheat and barley accessions characterized under low and high N inputs that uncovered the NPF2.12 gene, encoding a homolog of the Arabidopsis nitrate transceptor NRT1.6 and other low-affinity nitrate transporters that belong to the MAJOR FACILITATOR SUPERFAMILY. Next, it is shown that variations in the NPF2.12 promoter correlated with altered NPF2.12 transcript levels where decreased gene expression was measured under low nitrate availability. Multiple field trials revealed a significantly enhanced N content in leaves and grains and NUE in the presence of the elite allele TaNPF2.12^TT grown under low N conditions. Furthermore, the nitrate reductase encoding gene NIA1 was up-regulated in npf2.12 mutant upon low nitrate concentrations, thereby resulting in elevated levels of nitric oxide (NO) production. This increase in NO correlated with the higher root growth, nitrate uptake, and N translocation observed in the mutant when compared to wild-type. The presented data indicate that the elite haplotype alleles of NPF2.12 are convergently selected in wheat and barley that by inactivation indirectly contribute to root growth and NUE by activating NO signaling under low nitrate conditions.

Genomic insight into changes of root architecture under drought stress in maize

Drought stress is a central environmental factor that severely limits maize production worldwide. Root architecture plays an important role in drought tolerance and can be targeted in breeding programmes. Here, we conducted phenotyping of root architecture under different water treatments for 373 maize inbred lines, representative germplasm from both China and the United States in different breeding eras. We found that seminal root length in response to drought stress experienced convergent increase during breeding in both countries. Using a genome-wide association study, we identified a total of 221 associated loci underlying 13 root traits under well-watered and water-stressed conditions. These loci harboured many reported root- and abiotic stress-related genes. Furthermore, a total of 75 strong candidate genes were prioritised by integrating candidate genes associated with seminal root length and differentially expressed genes in seminal root. One of high-confidence candidate genes, ZmCIPK3 was functionally characterised and probably plays a role in enhancing drought tolerance through regulating seminal root growth. This study provides valuable information for genetic improvement of root architecture and drought tolerance in maize.

The elite variations in germplasms for soybean breeding

The genetic base of soybean cultivars (Glycine max (L.) Merr.) has been narrowed through selective domestication and specific breeding improvement, similar to other crops. This presents challenges in breeding new cultivars with improved yield and quality, reduced adaptability to climate change, and increased susceptibility to diseases. On the other hand, the vast collection of soybean germplasms offers a potential source of genetic variations to address those challenges, but it has yet to be fully leveraged. In recent decades, rapidly improved high-throughput genotyping technologies have accelerated the harness of elite variations in soybean germplasm and provided the important information for solving the problem of a narrowed genetic base in breeding. In this review, we will overview the situation of maintenance and utilization of soybean germplasms, various solutions provided for different needs in terms of the number of molecular markers, and the omics-based high-throughput strategies that have been used or can be used to identify elite alleles. We will also provide an overall genetic information generated from soybean germplasms in yield, quality traits, and pest resistance for molecular breeding.

A pan-grass transcriptome reveals patterns of cellular divergence in crops

Different plant species within the grasses were parallel targets of domestication, giving rise to crops with distinct evolutionary histories and traits1. Key traits that distinguish these species are mediated by specialized cell types2. Here we compare the transcriptomes of root cells in three grass species-Zea mays, Sorghum bicolor and Setaria viridis. We show that single-cell and single-nucleus RNA sequencing provide complementary readouts of cell identity in dicots and monocots, warranting a combined analysis. Cell types were mapped across species to identify robust, orthologous marker genes. The comparative cellular analysis shows that the transcriptomes of some cell types diverged more rapidly than those of others-driven, in part, by recruitment of gene modules from other cell types. The data also show that a recent whole-genome duplication provides a rich source of new, highly localized gene expression domains that favour fast-evolving cell types. Together, the cell-by-cell comparative analysis shows how fine-scale cellular profiling can extract conserved modules from a pan transcriptome and provide insight on the evolution of cells that mediate key functions in crops.

The 2020 derecho revealed limited overlap between maize genes associated with root lodging and root system architecture

Roots anchor plants in soil, and the failure of anchorage (i.e., root lodging) is a major cause of crop yield loss. Anchorage is often assumed to be driven by root system architecture. We made use of a natural experiment to measure the overlap between the genetic regulation of root system architecture and anchorage. After one of the most devastating derechos ever recorded in August 2020, we phenotyped root lodging in a maize (Zea mays) diversity panel consisting of 369 genotypes grown in six environments affected by the derecho. Genome-wide association studies and transcriptome-wide association studies identified 118 candidate genes associated with root lodging. Thirty-four percent (40/118) of these were homologs of genes from Arabidopsis (Arabidopsis thaliana) that affect traits such as root morphology and lignin content, expected to affect root lodging. Finally, Gene Ontology enrichment analysis of the candidate genes and their predicted interaction partners at the transcriptional and translational levels revealed the complex regulatory networks of physiological and biochemical pathways underlying root lodging in maize. Limited overlap between genes associated with lodging resistance and root system architecture in this diversity panel suggests that anchorage depends in part on factors other than gross characteristics of root system architecture.

An improved U-Net-based in situ root system phenotype segmentation method for plants

The condition of plant root systems plays an important role in plant growth and development. The Minirhizotron method is an important tool to detect the dynamic growth and development of plant root systems. Currently, most researchers use manual methods or software to segment the root system for analysis and study. This method is time-consuming and requires a high level of operation. The complex background and variable environment in soils make traditional automated root system segmentation methods difficult to implement. Inspired by deep learning in medical imaging, which is used to segment pathological regions to help determine diseases, we propose a deep learning method for the root segmentation task. U-Net is chosen as the basis, and the encoder layer is replaced by the ResNet Block, which can reduce the training volume of the model and improve the feature utilization capability; the PSA module is added to the up-sampling part of U-Net to improve the segmentation accuracy of the object through multi-scale features and attention fusion; a new loss function is used to avoid the extreme imbalance and data imbalance problems of backgrounds such as root system and soil. After experimental comparison and analysis, the improved network demonstrates better performance. In the test set of the peanut root segmentation task, a pixel accuracy of 0.9917 and Intersection Over Union of 0.9548 were achieved, with an F1-score of 95.10. Finally, we used the Transfer Learning approach to conduct segmentation experiments on the corn in situ root system dataset. The experiments show that the improved network has a good learning effect and transferability.

Genome-wide dissection of changes in maize root system architecture during modern breeding

Appropriate root system architecture (RSA) can improve maize yields in densely planted fields, but little is known about its genetic basis in maize. Here we performed root phenotyping of 14,301 field-grown plants from an association mapping panel to study the genetic architecture of maize RSA. A genome-wide association study identified 81 high-confidence RSA-associated candidate genes and revealed that 28 (24.3%) of known root-related genes were selected during maize domestication and improvement. We found that modern maize breeding has selected for a steeply angled root system. Favourable alleles related to steep root system angle have continuously accumulated over the course of modern breeding, and our data pinpoint the root-related genes that have been selected in different breeding eras. We confirm that two auxin-related genes, ZmRSA3.1 and ZmRSA3.2, contribute to the regulation of root angle and depth in maize. Our genome-wide identification of RSA-associated genes provides new strategies and genetic resources for breeding maize suitable for high-density planting.

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.

Association mapping across a multitude of traits collected in diverse environments in maize

Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data-18M markers-from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction.

Root Pulling Force Across Drought in Maize Reveals Genotype by Environment Interactions and Candidate Genes

High-throughput, field-based characterization of root systems for hundreds of genotypes in thousands of plots is necessary for breeding and identifying loci underlying variation in root traits and their plasticity. We designed a large-scale sampling of root pulling force, the vertical force required to extract the root system from the soil, in a maize diversity panel under differing irrigation levels for two growing seasons. We then characterized the root system architecture of the extracted root crowns. We found consistent patterns of phenotypic plasticity for root pulling force for a subset of genotypes under differential irrigation, suggesting that root plasticity is predictable. Using genome-wide association analysis, we identified 54 SNPs as statistically significant for six independent root pulling force measurements across two irrigation levels and four developmental timepoints. For every significant GWAS SNP for any trait in any treatment and timepoint we conducted post hoc tests for genotype-by-environment interaction, using a mixed model ANOVA. We found that 8 of the 54 SNPs showed significant GxE. Candidate genes underlying variation in root pulling force included those involved in nutrient transport. Although they are often treated separately, variation in the ability of plant roots to sense and respond to variation in environmental resources including water and nutrients may be linked by the genes and pathways underlying this variation. While functional validation of the identified genes is needed, our results expand the current knowledge of root phenotypic plasticity at the whole plant and gene levels, and further elucidate the complex genetic architecture of maize root systems.

Root traits for low input agroecosystems in Africa: Lessons from three case studies

In many regions across Africa, agriculture is largely based on low-input and small-holder farming systems that use little inorganic fertilisers and have limited access to irrigation and mechanisation. Improving agricultural practices and developing new cultivars adapted to these environments, where production already suffers from climate change, is a major priority for food security. Here, we illustrate how breeding for specific root traits could improve crop resilience in Africa using three case studies covering very contrasting low-input agroecosystems. We first review how greater basal root whorl number and longer and denser root hairs increased P acquisition efficiency and yield in common bean in South East Africa. We then discuss how water-saving strategies, root hair density and deep root growth could be targeted to improve sorghum and pearl millet yield in West Africa. Finally, we evaluate how breeding for denser root systems in the topsoil and interactions with arbuscular mycorrhizal fungi could be mobilised to optimise water-saving alternate wetting and drying practices in West African rice agroecosystems. We conclude with a discussion on how to evaluate the utility of root traits and how to make root trait selection feasible for breeders so that improved varieties can be made available to farmers through participatory approaches.

Root angle in maize influences nitrogen capture and is regulated by calcineurin B-like protein (CBL)-interacting serine/threonine-protein kinase 15 (ZmCIPK15)

Crops with reduced nutrient and water requirements are urgently needed in global agriculture. Root growth angle plays an important role in nutrient and water acquisition. A maize diversity panel of 481 genotypes was screened for variation in root angle employing a high-throughput field phenotyping platform. Genome-wide association mapping identified several single nucleotide polymorphisms (SNPs) associated with root angle, including one located in the root expressed CBL-interacting serine/threonine-protein kinase 15 (ZmCIPK15) gene (LOC100285495). Reverse genetic studies validated the functional importance of ZmCIPK15, causing a approximately 10° change in root angle in specific nodal positions. A steeper root growth angle improved nitrogen capture in silico and in the field. OpenSimRoot simulations predicted at 40 days of growth that this change in angle would improve nitrogen uptake by 11% and plant biomass by 4% in low nitrogen conditions. In field studies under suboptimal N availability, the cipk15 mutant with steeper growth angles had 18% greater shoot biomass and 29% greater shoot nitrogen accumulation compared to the wild type after 70 days of growth. We propose that a steeper root growth angle modulated by ZmCIPK15 will facilitate efforts to develop new crop varieties with optimal root architecture for improved performance under edaphic stress.

Dissecting the Root Phenotypic and Genotypic Variability of the Iowa Mung Bean Diversity Panel

Mung bean [Vigna radiata (L.) Wilczek] is a drought-tolerant, short-duration crop, and a rich source of protein and other valuable minerals, vitamins, and antioxidants. The main objectives of this research were (1) to study the root traits related with the phenotypic and genetic diversity of 375 mung bean genotypes of the Iowa (IA) diversity panel and (2) to conduct genome-wide association studies of root-related traits using the Automated Root Image Analysis (ARIA) software. We collected over 9,000 digital images at three-time points (days 12, 15, and 18 after germination). A broad sense heritability for days 15 (0.22-0.73) and 18 (0.23-0.87) was higher than that for day 12 (0.24-0.51). We also reported root ideotype classification, i.e., PI425425 (India), PI425045 (Philippines), PI425551 (Korea), PI264686 (Philippines), and PI425085 (Sri Lanka) that emerged as the top five in the topsoil foraging category, while PI425594 (unknown origin), PI425599 (Thailand), PI425610 (Afghanistan), PI425485 (India), and AVMU0201 (Taiwan) were top five in the drought-tolerant and nutrient uptake "steep, cheap, and deep" ideotype. We identified promising genotypes that can help diversify the gene pool of mung bean breeding stocks and will be useful for further field testing. Using association studies, we identified markers showing significant associations with the lateral root angle (LRA) on chromosomes 2, 6, 7, and 11, length distribution (LED) on chromosome 8, and total root length-growth rate (TRL_GR), volume (VOL), and total dry weight (TDW) on chromosomes 3 and 5. We discussed genes that are potential candidates from these regions. We reported beta-galactosidase 3 associated with the LRA, which has previously been implicated in the adventitious root development via transcriptomic studies in mung bean. Results from this work on the phenotypic characterization, root-based ideotype categories, and significant molecular markers associated with important traits will be useful for the marker-assisted selection and mung bean improvement through breeding.

Maize root responses to drought stress depend on root class and axial position

In this study we tested the hypotheses that root classes would exhibit distinctive anatomical and architectural responses to drought stress, and that those responses would vary along the root axes. The root systems of four maize (Zea mays L.) sweet corn genotypes designated SC1, SC2, SC3 and SC4 were phenotyped under well-watered and drought treatments in greenhouse mesocosms, permitting increasing stratification of moisture availability as the drought progressed. Anatomical and architectural responses to drought were evaluated for each root class. Lignin distribution was assessed by image processing of UV-illuminated root cross-sections acquired by laser ablation tomography. The two cultivars with less biomass reduction under drought, SC3 and SC4, substantially enhanced lateral root development along the apical segments of axial roots when plants were grown with drought stress. These segments grew into the deeper part of the mesocosm where more moisture was available. Apical segments of the axial and large lateral roots from drought-stressed plants were thicker and had greater theoretical axial water conductance than basal segments, especially in SC3 and SC4. Basal segments of crown roots of SC3 and SC4 showed increased lignification of the stele under drought. Root anatomical and architectural responses to drought are complex and vary among cultivars and root classes, and along root axes. Drought-induced proliferation of lateral roots on apical segments of axial roots would be expected to enhance deep water acquisition, while lignification of axial roots could help preserve axial water transport.

Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review

Linking phenotypes and genotypes to identify genetic architectures that regulate important traits is crucial for plant breeding and the development of plant genomics. In recent years, genome-wide association studies (GWASs) have been applied extensively to interpret relationships between genes and traits. Successful GWAS application requires comprehensive genomic and phenotypic data from large populations. Although multiple high-throughput DNA sequencing approaches are available for the generation of genomics data, the capacity to generate high-quality phenotypic data is lagging far behind. Traditional methods for plant phenotyping mostly rely on manual measurements, which are laborious, inaccurate, and time-consuming, greatly impairing the acquisition of phenotypic data from large populations. In contrast, high-throughput phenotyping has unique advantages, facilitating rapid, non-destructive, and high-throughput detection, and, in turn, addressing the shortcomings of traditional methods. Aim of Review: This review summarizes the current status with regard to the integration of high-throughput phenotyping and GWAS in plants, in addition to discussing the inherent challenges and future prospects. Key Scientific Concepts of Review: High-throughput phenotyping, which facilitates non-contact and dynamic measurements, has the potential to offer high-quality trait data for GWAS and, in turn, to enhance the unraveling of genetic structures of complex plant traits. In conclusion, high-throughput phenotyping integration with GWAS could facilitate the revealing of coding information in plant genomes.

DIRT/3D: 3D root phenotyping for field-grown maize (Zea mays)

The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r2>0.84 and a high broad-sense heritability of Hmean2> 0.6 for all but one trait. The average values of the 18 traits and a developed descriptor to characterize complete root architecture distinguished all genotypes. DIRT/3D is a step toward automated quantification of highly occluded maize RCs. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.

TWAS results are complementary to and less affected by linkage disequilibrium than GWAS

A genome-wide association study (GWAS) is used to identify genetic markers associated with phenotypic variation. In contrast, a transcriptome-wide association study (TWAS) detects associations between gene expression levels and phenotypic variation. It has previously been shown that in the cross-pollinated species, maize (Zea mays), GWAS, and TWAS identify complementary sets of trait-associated genes, many of which exhibit characteristics of true positives. Here, we extend this conclusion to the self-pollinated species, Arabidopsis thaliana and soybean (Glycine max). Linkage disequilibrium (LD) can result in the identification, via GWAS, of false-positive associations. In all three analyzed plant species, most trait-associated genes identified via TWAS are well separated physically from other candidate genes. Hence, TWAS is less affected by LD than is GWAS, demonstrating that TWAS is particularly well suited for association studies in genomes with slow rates of LD decay, such as soybean. TWAS is reasonably robust to the plant organs/tissues used to determine expression levels. In summary, this study confirms that TWAS is a promising approach for accurate gene-level association mapping in plants that is complementary to GWAS, and established that TWAS can exhibit substantial advantages relative to GWAS in species with slow rates of LD decay.

Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges

Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.

Meta-Analysis Identifies Pleiotropic Loci Controlling Phenotypic Trade-offs in Sorghum

Community association populations are composed of phenotypically and genetically diverse accessions. Once these populations are genotyped, the resulting marker data can be reused by different groups investigating the genetic basis of different traits. Because the same genotypes are observed and scored for a wide range of traits in different environments, these populations represent a unique resource to investigate pleiotropy. Here we assembled a set of 234 separate trait datasets for the Sorghum Association Panel, a group of 406 sorghum genotypes widely employed by the sorghum genetics community. Comparison of genome wide association studies conducted with two independently generated marker sets for this population demonstrate that existing genetic marker sets do not saturate the genome and likely capture only 35-43% of potentially detectable loci controlling variation for traits scored in this population. While limited evidence for pleiotropy was apparent in cross-GWAS comparisons, a multivariate adaptive shrinkage approach recovered both known pleiotropic effects of existing loci and new pleiotropic effects, particularly significant impacts of known dwarfing genes on root architecture. In addition, we identified new loci with pleiotropic effects consistent with known trade-offs in sorghum development. These results demonstrate the potential for mining existing trait datasets from widely used community association populations to enable new discoveries from existing trait datasets as new, denser genetic marker datasets are generated for existing community association populations.

Rated-M for mesocosm: allowing the multimodal analysis of mature root systems in 3D

A plants' water and nutrients are primarily absorbed through roots, which in a natural setting is highly dependent on the 3-dimensional configuration of the root system, collectively known as root system architecture (RSA). RSA is difficult to study due to a variety of factors, accordingly, an arsenal of methods have been developed to address the challenges of both growing root systems for imaging, and the imaging methods themselves, although there is no 'best' method as each has its own spectrum of trade-offs. Here, we describe several methods for plant growth or imaging. Then, we introduce the adaptation and integration of three complementary methods, root mesocosms, photogrammetry, and electrical resistance tomography (ERT). Mesocosms can allow for unconstrained root growth, excavation and preservation of 3-dimensional RSA, and modularity that facilitates the use of a variety of sensors. The recovered root system can be digitally reconstructed through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. Lastly, we demonstrate how 3-dimensional water availability can be measured using ERT inside of root mesocosms.

Using Machine Learning to Develop a Fully Automated Soybean Nodule Acquisition Pipeline (SNAP)

Nodules form on plant roots through the symbiotic relationship between soybean (Glycine max L. Merr.) roots and bacteria (Bradyrhizobium japonicum) and are an important structure where atmospheric nitrogen (N₂) is fixed into bioavailable ammonia (NH₃) for plant growth and development. Nodule quantification on soybean roots is a laborious and tedious task; therefore, assessment is frequently done on a numerical scale that allows for rapid phenotyping, but is less informative and suffers from subjectivity. We report the Soybean Nodule Acquisition Pipeline (SNAP) for nodule quantification that combines RetinaNet and UNet deep learning architectures for object (i.e., nodule) detection and segmentation. SNAP was built using data from 691 unique roots from diverse soybean genotypes, vegetative growth stages, and field locations and has a good model fit (R ² = 0.99). SNAP reduces the human labor and inconsistencies of counting nodules, while acquiring quantifiable traits related to nodule growth, location, and distribution on roots. The ability of SNAP to phenotype nodules on soybean roots at a higher throughput enables researchers to assess the genetic and environmental factors, and their interactions on nodulation from an early development stage. The application of SNAP in research and breeding pipelines may lead to more nitrogen use efficiency for soybean and other legume species cultivars, as well as enhanced insight into the plant-Bradyrhizobium relationship.

Natural Variation and Domestication Selection of ZmCKX5 with Root Morphological Traits at the Seedling Stage in Maize

Root system architecture plays a crucial role in water and nutrient acquisition in maize. Cytokinins, which can be irreversibly degraded by the cytokinin oxidase/dehydrogenase (CKX), are important hormones that regulate root development in plants. In this study, ZmCKX5 was resequenced in 285 inbred lines, 68 landraces, and 32 teosintes to identify the significant variants associated with root traits in maize. Sequence polymorphisms and nucleotide diversity revealed that ZmCKX5 might be selected during domestication and improvement processes. Marker–trait association analysis in inbred lines identified 12 variants of ZmCKX5 that were significantly associated with six root traits, including seed root number (SRN), lateral root length (LRL), total root area (RA), root length in 0 to 0.5 mm diameter class (RL005), total root volume (RV), and total root length (TRL). SNP-1195 explained the most (6.01%) phenotypic variation of SRN, and the frequency of this allele G increased from 6.25% and 1.47% in teosintes and landraces, respectively, to 17.39% in inbred lines. Another significant variant, SNP-1406, with a pleiotropic effect, is strongly associated with five root traits, with the frequency of T allele increased from 25.00% and 23.73% in teosintes and landraces, respectively, to 35.00% in inbred lines. These results indicate that ZmCKX5 may be involved in the development of the maize root system and that the significant variants can be used to develop functional markers to accelerate the improvement in the maize root system.