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

Abstract

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.

Introduction

There is an increasing demand, particularly in Western cultures, for plant-based protein sources, including analogs of meat, egg, and dairy (Wild et al., 2014; Joshi and Kumar, 2016; Niva et al., 2017; Aschemann-Witzel et al., 2020). Numerous factors influence this change, including social, political, environmental, ethical, health-focused, technological, and economical (Vinnari, 2008; Markiewicz, 2010). Pulses such as lentils (Lens culinaris), horse beans (Dolichos spp.), lupins (Lupinus albus L.), common beans (Phaseolus vulgaris), chickpea (Cicer arietinum), field peas (Pisum sativum), cowpeas (Vigna unguiculata), fava beans (Vicia faba), mung bean [Vigna radiata (L.) Wilczek], urd beans (Vigna mungo), and food-grade soybeans (Glycine max) have consistently been used as protein sources in the global south (Niva et al., 2017). The plant protein demand has been fueled by the sustainable production of pulses coupled with their health benefits and the production of meat analogs (Wild et al., 2014; Niva et al., 2017). Residents of sub-Saharan Africa, the Caribbeans, and South America consume more than 10 kg/capita/year of pulses, compared to 3 kg/capita/year among Western cultures (Akibode and Maredia, 2012).

Mung bean (V. radiata L. Wilczek), initially domesticated in India, is now cultivated in over 7 million hectares worldwide (Nair et al., 2019; Aski et al., 2021). Mung bean is a short-duration crop, usually between 60 and 90 days from planting to harvest (Sandhu and Singh, 2021). Mung beans, being relatively heat- and drought-tolerant, may be helpful in the agricultural adaptation to climate change (Pataczek et al., 2018; Wang et al., 2018). Mung bean is easily digestible, with seed composition of 22–28% protein, 1–1.5% fat, and 60–65% carbohydrates, as well as minerals, vitamins, and antioxidants (Jahan et al., 2020; Aski et al., 2021; Sandhu and Singh, 2021). Mung beans are consumed as whole grain, sprouted gram, split dhal, and mung bean flour in Indian dishes (Nair et al., 2019; Aski et al., 2021). The complementation of mung beans with cereals provides a balanced intake of required nutrients. In Western cultures, mung beans are consumed mostly as sprouts and, more recently, as processed products such as meat substitutes, egg substitutes, chips, no-nut butter, and pasta (Sandhu and Singh, 2021). The demand for plant-based protein in the United States has led to enhancing existing and establishment of breeding programs at United States institutions, including at Iowa State University (Sandhu and Singh, 2021). However, due to limited breeding efforts in North America, there is a knowledge gap in agronomic trait diversity including root traits that are emerging as important area of research and breeding efforts (Lynch, 2007; White et al., 2013; Burridge et al., 2017).

Root system architecture (RSA) can be defined as the morphology of the root system at many scales, both global (i.e., the entire root system) and local (i.e., primary and lateral root levels), as well as the spatial variability of the morphology (Hodge et al., 2009; Rogers and Benfey, 2015; Lobet et al., 2019; Aski et al., 2021). The morphometric traits include the number, length, volume, mass, shape, angle, depth, etc. The spatio-temporal variation seen in RSA of different plants reflects the phenotypic plasticity and the genotype × environment interaction (Rogers and Benfey, 2015; Lobet et al., 2019). Roots have a great impact on yield and plant fitness by providing plants with the structural stability, nutrient foraging, plant-microbe interactions, preventing soil erosion, aeration, and water extraction (Hodge et al., 2009; Rogers and Benfey, 2015).

The desired root phenotypes by plant breeders will be ones that enhances plant adaptation to the edaphic stress while maintaining or increasing yields, for example, deeper and proliferating roots are desired during water-deficient stresses in the changing climate (Gaur et al., 2008; Aski et al., 2021). Lynch and Brown (2001) coined the term “topsoil foraging ideotype,” which is characterized by proliferation of lateral roots, long root hairs, association with mycorrhizal fungi, and suited to uptake of the immobile phosphorus mineral from the topsoil stratum (White et al., 2013). The “steep, cheap, and deep” ideotype (Lynch, 2013) optimizes on the uptake of water and the soluble nitrogen in the soil minimizing leaching. The “steep, cheap, deep” is characterized by thick and long primary roots, high affinity for N by epidermal cells, and the high concentration of cortical aerenchyma cells (White et al., 2013). Falk et al. (2020b) used the term “informative root” (iRoot) category to capture the biological significance of the captured root traits as would simulate field conditions. They reported that the topsoil foraging had a faster total root length-growth rate (TRL_GR), wider (WID), and a large TRL upper root ratio (TRL_Upper). The steep, cheap, and deep ideotype contained a deeper primary root length (PRL), faster TRL_GR, steep lateral root angles (LRA), and lower solidity traits (SOL2). These works have been possible due to the use of computer vision and machine learning in extraction of complex traits.

Advances in computer vision, machine learning, and high-throughput phenotyping (HTP) technologies, coupled with efficient statistical methods and collaborative research, have opened the way for more research to be carried out in plants as reviewed in Singh et al. (2016, 2018), Atkinson et al. (2019), Ghosal et al. (2019), Parmley et al. (2019), and Singh A. et al. (2021). The use of these technologies has been implemented in the collection of agronomic and yield estimation traits (Riera et al., 2021), detection of abiotic and biotic stress (Naik et al., 2017; Zhang et al., 2017; Nagasubramanian et al., 2018, 2019), and monitoring plant health (Ghosal et al., 2018). However, as shown in Falk et al. (2020a), computer vision and machine learning-based methods are essential to advance the root phenotyping and large-scale studies (Singh et al., 2016, 2018; Singh D. P. et al., 2021). Root phenotyping is classified depending on where it is carried out, i.e., in controlled environments or in the field, destructive or nondestructive, and whether the HTP uses 2-dimensional (2D) or 3-dimensional (3D) to capture the traits of interest (see reviews, Atkinson et al., 2019; Singh A. K. et al., 2021).

Previous methods developed for extracting roots in the field include destructive methods such as “shovelomics” (Trachsel et al., 2011), the use of soil cores (Wasson et al., 2016), and nondestructive methods such as electrical resistance tomography (Srayeddin and Doussan, 2009), electromagnetic inductance (Shanahan et al., 2015), and ground penetrating radar (Liu et al., 2018). Soil opacity is still a limiting factor to access roots in most field experiments. Controlled environmental methods include the use of rhizotrons, which utilize soil (Rellán-Álvarez et al., 2015), nonsoil methods such as hydroponics (Aski et al., 2021), transparent artificial growth media (Ma et al., 2019), and growth pouches (Tan and Nopamornbodi, 1979). The high-throughput nature of acquiring 2D root images from controlled environments necessitated the development of the image analysis software to extract the traits (Atkinson et al., 2019). Commercially available software includes WinRhizo (Regent Instruments, Quebec, Canada). The open-source software available for use includes SmartRoot (Lobet et al., 2011), RootNav (Pound et al., 2013), GiaRoots (Galkovskyi et al., 2012), DART (Le Bot et al., 2010), Ez-Rhizo (Armengaud, 2009), DIRT (Das et al., 2015), ARIA (Pace et al., 2014; Falk et al., 2020a), RhizoVision (Seethepalli et al., 2020), MyRoot (Betegón-Putze et al., 2019), and IJ_Rhizo (Pierret et al., 2013). A combination of the methods above has been used to study the roots of a variety of plants under various conditions. Species of plant roots studied include common bean (Bonser et al., 1996), maize (Hund et al., 2009; Zheng et al., 2020), wheat (Atkinson et al., 2015), pearl millet (Passot et al., 2016), soybean (Falk et al., 2020a), and canola (Gioia et al., 2016). In a recent study, Aski et al. (2021) utilized modified hydroponics to study the RSA phenotypic diversity of the mung bean mini-core collection at the World Vegetable Center (formerly known as Asian Vegetable Development and Research Center [AVRDC]) (Schafleitner et al., 2015). As this software is capable of generating the useful data on multiple phenotypic root traits, these also lend themselves to genetic studies.

Genome-wide association studies (GWAS) is a statistical tool that uses historical recombination events to uncover the significant genotypic variation associated with the phenotypic variation for the trait of interest (Huang and Han, 2014; Tibbs Cortes et al., 2021). GWAS has been extensively used to investigate important agronomic traits such as plant height, days to flower, yield, nutrient content, flood and drought tolerance, and insect and pest resistance in maize (Yang et al., 2014), soybean (Zhang et al., 2015; Fang et al., 2017), common bean (Kamfwa et al., 2015), mung bean (Sandhu and Singh, 2021), and rice (Huang et al., 2010) among others.

The current study was conducted with the objectives to (1) study the diversity of the RSA trait in the Iowa (IA) mung bean panel, (2) contextualize these RSA traits with root-based ideotypes, and (3) conduct GWAS on RSA traits and identify candidate genes for these associations.

Materials and Methods

Plant Materials

A total of 376 accessions were used in this study. A total of 372 Plant Introductions (PI) were filtered from the 482 IA mung bean panel (Sandhu and Singh, 2021) using the identity-by-state method in SNPRelate and genetic distance of Nei (Zheng et al., 2012). PIs that were common among the two methods were dropped. The 482 PIs were a part of the over 3,000 mung bean accessions obtained from the United States Department of Agriculture-Germplasm Resources Information Network (USDA-GRIN), in Griffin Georgia that were able to flower and form pods in IA conditions. Three Asian Vegetable mung bean (AVMU) lines, namely, AVMU001, AVMU0201, and AVMU9701, were included as checks, since they are improved cultivars from the WVC, formerly AVRDC (Fernandez and Shanmugasundaram, 1988).

Experimental Design and Germination Protocol

This study used a randomized incomplete block design, with each growth chamber serving as a replicate, for a total of eight replications for the experiment. Two growth chambers were used for an increased throughput. Each chamber had four blocks. Each block contained six complete and two incomplete sub-blocks. Each complete sub-block held twelve genotypes, while each incomplete sub-block held eleven genotypes. The genotypes were randomized within each block and sub-blocks. Randomization was generated using the R package blocksdesign (Edmondson and Edmondson, 2021). The procedures described in Falk et al. (2020a) were followed with little modification in the experimental design (Figure 1). First, ten seeds of each genotype were equally spaced near the top (∼1″) of a 9″ × 12″ germination paper. The paper was rolled into germination rolls. All the germination rolls for each sub-block were rubber banded and labeled with a tag. Once all the 376 were planted, water was filled halfway in the rectangular bucket, and the rolls transferred to a Conviron growth chamber (Controlled Environments Ltd., Winnipeg, Canada) set at 25°C for 16 h of light and 20°C for 8 h darkness. The lighting was set to 276–280 μmol/s/m² and constantly monitored using the LI-250A photometer (Li-Cor Biosci-sciences, Lincoln, NE, United States). On the 5th day of germination, a representative sample for each genotype was picked and carefully placed onto the 12″ × 18″ blue germination paper (Anchor Paper, Minneapolis, MN, United States). Labeled bar-coded tags are stapled onto the 1″ folded top of the blue paper. A 12″ × 16″ brown blotting paper (Anchor Paper, Minneapolis, MN, United States) was carefully placed on top of the blue paper. Two full blue papers are clipped together using binder clips and placed in the rack on the plot number in the chamber. Chamber conditions were monitored daily.

FIGURE 1

Imaging, Image Processing, and Trait Extraction

A high-throughput imaging station was set up similar to the one reported by Falk et al. (2020a). Images were captured using a Canon T5i digital SLR camera (lens: EF-S 18–55 mm f/3.5–5.6 IS II) (Canon USA, Inc., Melville, NY, United States). The setup allowed for the automated renaming of images captured using the SmartShooter software (Hart, 2021; Figure 1E). The seedlings were imaged on days 12, 15, and 18 without moving roots. Exceptions to moving were on day 12 where some secondary roots did not emerge from the fold and on day 18, when some of the roots of the genotypes were overgrowing the length of the blue paper. The days to image and the spacing were determined by a preliminary study. On the 18th day, the seedlings were cut at the junction between the shoot and camera-visible root section. The root and shoot of each genotype were placed in small brown bags. The wet-cut seedlings were dried in growth chambers set at 34°C/24 h for 2 days, with the light set to 276–280 μmol/s/m² and stored for weighing. Each root and shoot of the genotype were measured using the Ohaus portable weighing balance (Ohaus Corporation, NJ, United States).

More than 9,000 (376 genotypes × 8 reps × 3 time points) images were collected from the whole experiment. The images were first rotated manually to portrait orientation to enable consistent preprocessing. Images with no germinated seed, herein referred to as blank, were excluded from processing. JPEGCrops (2021), an open-source software, was used to auto crop all the images in a batch by cutting off the top part with the labeled bar-coded tag. The images were then converted into black/white images by thresholding (heuristically using red, green, and blue, LAB, or Hue, Saturation, and Value color spaces). This was carried out using the image processing step and followed by the trait extraction step within the improved Automatic Root Image Analysis (ARIA) 2.0 tool (Falk et al., 2020a). Different color spaces were used due to the variations in the images caused by unequal lightning and water spots. The ARIA 2.0 tool runs on Matlab (2020a). Traits extracted in ARIA are shown in the Supplementary Materials (Supplementary Table 1).

Statistical Analysis

All the analyses were carried out using the R statistical software (R Core Team, 2021). A separate code was written for the extraction of median LRA. Outliers were filtered out using the Tukey’s box plot method (Hoaglin, 2003). A soybean genotype previously included was dropped due to clear visual differences with mung beans. The preprocessing steps above left 8,611 observations represent 375 genotypes for the analysis. Most of the reported analysis is from day 15 data with references and comparisons to days 12 and 18. Day 15 was chosen as a good representation of root growth between day 12 and day 18. A subset of ARIA traits was used for the analysis (Table 1). They were informed by traits used in a similar study by Aski et al. (2021) and traits important for the iRoot categories: topsoil foraging, and steep, cheap, and deep ideotypes described by Falk et al. (2020b). A mixed linear model (Eq. 1) was used to extract the best linear unbiased predictors (BLUPs) for each trait per genotype. All model variables were considered a random effect except chamber, which was a fixed effect. The model was run within the H2Cal function from the inti package (Lozano-Isla, 2021), which utilized the unbalanced data (Cullis et al., 2006; Piepho and Möhring, 2007; Schmidt et al., 2019). Broad sense heritability (H) was calculated using Eq. 2 (Cullis et al., 2006). Pearson correlations were used to draw correlations among the root traits.

Where μ is the overall population mean, Y_ijkl is the phenotypic trait, Chamber_i is the fixed effect of the ith growth chamber (1| Chamber:Block)_ij is the random interaction effect between the ith chamber and the jth block (1| Chamber:Block:Sub-block)_ijk is the three way random interaction effect between the ith chamber, jth block and kth sub-block (1| Genotype)_l is the random effect of the lth genotype, and e_ijkl is the random error term following the N(0,q²e).

Where is the genotypic variance and is the mean variance of the difference of two genotypic BLUPs for the genotypic effect (Schmidt et al., 2019).

Root Ideotypes, Phenotypic, and Genotypic Diversity

The iRoots were formed by first ranking the genotypes under each trait, getting the sum of the ranks and then ranking the sums for each category. For topsoil foraging, the genotypes were ranked individually under the TRL_GR, WID, and TRL_Upper. The sum of the ranks was ranked, and this yielded to the final ranking of each genotype. A similar approach was used for the “steep, cheap, and deep” ideotype using the TRL_GR, steep LRA, and SOL2.

The principal component analysis (PCA) and hierarchical clustering were used in both the phenotypic and genotypic clustering of the genotypes using the Euclidean distance matrix. The base R function hclust with methods “complete” and “prcomp” was used. The package factoextra (Kassambara and Mundt, 2020) was used to determine the optimum number of clusters to be used by comparing 30 different indices. The clusters were related to the country of origin. Heat maps were developed according to the trait performance and iRoot category ranking using the Complex Heatmap package (Gu et al., 2016). Phenotypic and genotypic dendrograms were made using the dendextend (Galili, 2015) and circlize (Gu et al., 2014) packages. The pairwise fixation index (Fst) was calculated between the two genotypic clusters using the function genet.dist (method = “WC84”) within the ade4 package (Dray Stéphane, 2007). Fst is an indication of the amount of differentiation within subpopulations, with low Fst indicating high gene flow (low genetic diversity) (Wright, 1965).

Genome-Wide Association Analysis

In total, 26,550 SNPs (marker data) were obtained using genotype-by-sequencing and preprocessed earlier by Sandhu and Singh (2021). Sites with >15% missing data and minor allele frequency > 0.01 were filtered out. GWAS was carried out using BLUPs on all the trait data. Associations were conducted using the Trait Analysis by aSSociation, Evolution, and Linkage (TASSEL, Bradbury et al., 2007) software using a linear mixed model (Yu et al., 2006). Both the kinship matrix and PCA were generated in TASSEL controlling for population structure. Bonferroni correction with p-value = 0.05 was used to control for false positives and declare significant associations (Kuo, 2017). Manhattan plots for visualizing the associations were carried out in R using the CMplot library in the rMVP package (Yin et al., 2021). Authors also used a newly developed computational framework, selection of variables with embedded screening (SVEN), a Bayesian based model to run GWAS (Li et al., 2020). The identification of candidate genes was carried out by locating the significant SNP on the sequenced and annotated mung bean genome using the “genome data viewer” tool at the National Center for Biotechnology Information (NCBI; Kang et al., 2014).

Results

Descriptive Statistics, Correlation, and Heritabilities

We observed the significant phenotypic variability for root traits. The coefficient of variation (CV) ranged from 2 to 19% and standard deviation (SD) from 0.01 to 628.67 (different units of measurements for traits). Most of the traits had low SD, i.e., <10 except for TRL, VOL, TRL_Upper, and CVA that had SD < 100, while RHZO had an SD > 500. TRL, TRL_Upper, CVA, WID, NWA, RHZO, and TRL_GR had 10% < CV < 20%, while the rest of the traits had CV < 10% (Table 2). Day 12 CV ranged from 0 to 22% while for day 18 was 0–28%. Dry matter weight measurements, including shoot dry weight (SDW), root dry weight (RDW), and total dry weight (TDW) had CV 24, 28, and 26%, respectively, at day 18 (Supplementary Table 2).

The correlation between the root traits varied. TRL_Upper was highly correlated with WID, CVA, and TRL_GR. NWA was highly correlated with WID, CVA, TRL_GR, TRL_Upper, RHIZO, and TRL. LRA had the lowest correlation with the other traits. There was no correlation between LRA and VOL, and DIA (Figure 2). Correlation on day 12 was high. Negative correlations were observed at day 18 with SOL2 being negatively correlated to most traits and LRA negatively correlated with root shoot ratio (RSR) (image not shown).

FIGURE 2

Broad sense H ranged from 0.22 to 0.73. LRA and WID had the lowest and highest H at 0.22 and 0.73, respectively. DIA, VOL, surface area, LRB, and LRA had H < 0.5, while TRL, PRL, LED, TRL_Upper, CVA, DEP, WID, NWA, RHZO, SOL2, and TRL_GR had H > 0.5 (Table 2 and Figure 3). H was high at days 15 and 18 and low on days 12 for most of the traits. Day 12 H ranged from 0.24 to 0.51, with TRL_Upper having the highest H. Day 18 H ranged from 0.23 to 0.87, with dry weight traits (i.e., SDW, RDW, and TDW) showing higher levels at 0.84, 0.87, and 087, respectively (Supplementary Table 2).

FIGURE 3

Root Ideotypes

We described two root ideotypes, namely, topsoil foraging and “steep, cheap, and deep.” PI425425 (India), PI425045 (Philippines), PI425551 (Korea), PI264686 (Philippines), and PI425085 (Sri Lanka) emerged as top five in the topsoil foraging category. PI425594 (unknown origin), PI425599 (Thailand), PI425610 (Afghanistan), PI425485 (India), and AVMU0201 (Taiwan) were top five in the “steep, cheap, and deep” ideotype (Table 3 and Figure 4). For day 18, the PI425551 (Korea), PI264686 (Philippines), PI426026 (Thailand), PI425085 (Sri Lanka), and PI426042 (Australia) were the top five in the topsoil foraging category. In the “steep, cheap, and deep” ideotype, PI264686 (Philippines), PI425551 (Korea), PI363514 (India), and PI425599 (Thailand) were the top four (Supplementary Table 3). No iRoot categories were created on day 12 since TRL_GR could not be calculated.

FIGURE 4

Phenotypic and Genotypic Clusters

Three distinct phenotypic clusters were observed using the root trait data, while two clusters were observed from the SNP data of the genotypes (Figure 5). Phenotypic clusters 1, 2, and 3 had 69, 163, and 135 genotypes, respectively. Genotypic clusters 1 and 2 had 48 and 319 genotypes, respectively. India had the highest number of genotypes in both genotypic clusters 1 (37) and 2 (197). The United Kingdom had 13 genotypes in genotypic cluster 2, while the rest of the countries had less than 10 genotypes in each cluster. The United Kingdom and United States had no genotypes in genotypic cluster 1 (Supplementary Figure 1). Similarly in the phenotypic clusters 1, 2, and 3, India led with 17, 94, and 123 genotypes. The rest of the countries had less than ten genotypes (Supplementary Table 3). On day 18, there were two phenotypic clusters and two genotypic clusters. Phenotypic clusters 1 and 2 had 250 and 117 genotypes, respectively. Genotypic clusters had similar composition as from day 15. In the phenotypic cluster, India had 132 and 102 genotypes in clusters 1 and 2. The rest had less than 10 genotypes (Supplementary Figure 2 and Supplementary Table 4).

FIGURE 5

PC1 and PC2 explained 7.6 and 3.9% of the total genotypic variation in the IA mung bean GWAS panel (Figure 6). The PCs were not able to discern any distinct subpopulations. Superimposition of iRoot ranking on the PCs showed that genotypes from India dominated both in the “steep, cheap, and deep” and topsoil foraging (Figures 6C,D). For day 18, the top genotypes in the “steep, cheap, and deep” category are mostly from India, while in the topsoil foraging, they are mostly from the other countries, Australia, the United Kingdom, and others with few from India (Supplementary Figure 3). The complex heat map showed the patterns and correlations among the genotypic clusters, iRoot type rank, and root trait performance used in clustering (Figure 7). Most of the traits in genotypic cluster 2 had a better ranking in the topsoil foraging, while cluster 1 contained mostly the worst ranked in the same category. Genotypes were evenly distributed in ranking among the genotypic clusters 1 and 2 in the “steep, cheap, and deep” iRoot category. Some of the best genotypes for the traits, including TRL_Upper, RHZO, NWA, WID, and CVA, were in genotypic cluster 2, while cluster 1 was dominated by low values in the above traits. LRA, SOL2, LED, LRB, PRL, and DEP looked evenly distributed within genotypic clusters 1 and 2 (Figure 7). The pairwise Fst was 0.05.

FIGURE 6

FIGURE 7

Genome-Wide Association Studies and Candidate Genes

Association studies revealed significant SNPs for traits on different days. Day 12 LRA had seven significant SNPs. Day 15 LED had one significant SNP. On day 18, TRL_GR, TDW, and volume each had one significant SNP, while LED had two significant SNPs (Figure 8). Out of the seven SNPs for day 12 LRA, the first three had no mapping on the mung bean genome with no gene ID, genomic context, and gene description. On day 18, significant marker 8_10447903 for LED is an uncharacterized gene. Significant SNPs were found for the same trait LED, for days 15 and 18, marker 8_11481602 and marker 8_10447903, respectively. A summary of the significant SNP associations from the TASSEL software is presented in Table 4.

FIGURE 8

Day 12 SNP markers 2_2226549, 2_19972687, 7_19972687, and 11_7608353 were associated with LRA. Marker 2_2226549 is located within an exon for a gene described as lignin forming anionic peroxidase (LOC106755829). Marker 2_19972687 is located within an exon encoding a gene (-)-germacrene D synthase-like (LOC106753988). Marker 7_19972687 is located within an exon of the beta-galactosidase 3 gene (LOC106768494). Marker 11_7608353 associated with LRA also located within an exon for a gene described as protein FAR1-RELATED SEQUENCE 5 (LOC106776541). The same significant SNP marker 8_11481602 associated with LED from days 15 and 18 was found within the exon of a monodehydroascorbate reductase gene (LOC106772343). Day 18 SNP marker 8_10447903 was found within an intron for an uncharacterized gene but close to the LOC106772343 gene. Day 18 SNP marker 3_10004492 associated with TRL_GR is located within the exon for a gene coding for mannose-1-phosphate guanylyltransferase 1 (LOC106757974). Day 18 SNP maker 5_35265704 associated with TDW is located in the exon for a gene described as putative dehydration-responsive element-binding protein 2H (LOC106760865).

Selection of variables with embedded screening resulted in several significant markers for most of the traits across the 3 days (Figure 9, Supplementary Figures 4–6, and Supplementary Table 5). Two markers for LED (8_44518003) and TDW (5_35265704) from day 18 did overlap with TASSEL results. Marker 8_44518003 is an exon within the gene encoding monodehydroascorbate reductase (LOC106772343), while marker 5_35265704 was found within the gene encoding putative dehydration-responsive element-binding protein 2H (LOC106760865). Day 18 marker for TRL_Upper (2_22583526) was found within an exon in the gene encoding coilin (LOC106756657), while marker for DEP (5_23119832) was found in an exon within the gene encoding expansin-A11 (LOC106761944).

FIGURE 9

Discussion

Controlled environments have been successfully used to study organisms out of their in situ environments (Crop Science Controlled Environment Research Guidelines, 2021). Plants in controlled environments may be exposed to similar conditions as would be in the field to help better achieve the objectives under study (Tibbitts and Langhans, 1993). There have been successful results for measuring various above-ground phenotypes in controlled environments, but below-ground phenotypes pose additional challenges (White et al., 2013). While studies in controlled environments do not imitate what in situ root environments look like, they are helpful in a priori screening of genotypes to minimize the heavy below-ground phenotyping work required in the field (Lynch and Brown, 2012; Li R. et al., 2015; Ye et al., 2018).

Mung beans are mostly grown on residual moisture after primary crops in most of Southeast Asia (Poehlman and Milton, 1991; Aski et al., 2021). In the Western world, mung beans planted in the summers depend highly on the moisture residue, often following a wet, cold spring. In IA, mung beans are planted around the first week of June, capitalizing on the intense solar radiation for rapid growth (Sandhu and Singh, 2021). This would explain why mung beans, like other legume species, would be ideal with the “steep, cheap, and deep” root ideotypes, to chase the water and the soluble nitrogen before the establishment of root nodules for atmospheric nitrogen fixation. Schneider et al. (2021) showed that steep root angles improved nitrogen uptake in silico in maize. Using the OpenSimRoot model, an 11% increase in nitrogen uptake and a 4% increase in plant biomass were predicted at 40 days of growth (Schneider et al., 2021).

Lynch and Brown (2012) showed that common bean genotypes with wide basal root angles were superior in phosphorus (P) acquisition, while the ones with narrow basal root angles were superior in water acquisition during drought conditions. A recent study looked at the P efficiency of mung bean root morphology traits in low and optimum conditions (Reddy et al., 2020), a trait associated with topsoil foraging. They found Indian improved cultivars would be better with regards to P foraging. We identified the top genotypes, including PI425425 (India), PI425045 (Philippines), PI425551 (Korea), PI264686 (Philippines), and PI425085 (Sri Lanka), in the topsoil foraging (Table 3). Our hypothesis is that this represents the improved germplasm developed in India or after migration from India, while some of the lower ranks are still landraces or wild relatives, but this will need to be evaluated further in field conditions. For example, AVMU0201 is from Taiwan, the World Vegetable Center (Brassica, 2014) (formerly AVRDC), which has been breeding mung beans since the 1970s. Accessions PI425045 and PI264686 are from the Philippines, which also hosts a duplicate mung bean collection at the University of the Philippines, Los Banos (Poehlman and Milton, 1991).

We reported a wide variability of the root trait phenotype in the IA mung bean panel during the early stages of development (Table 2). Indian genotypes represented 24, 56, and 89% in the phenotypic clusters 1, 2, and 3 with an overall presence of 67% (Supplementary Figure 4). The high H for traits on days 15 and 18 could be due to better capture of the traits by ARIA unlike day 12 as it might be too early for trait development and differentiation. The high correlation could also be explained by the fact that young plants are utilizing all nutrients for the vegetative growth. These conclusions cannot be assumed to represent the rest of the developmental stages of mung bean plants, prompting the need for further studies. Similar observations were made in soybean (Falk et al., 2020b).

Genetic variability is one of the most important factors in a breeder’s toolbox (Cobb et al., 2019). Indian genotypes were 76 and 67% in the genotypic clusters 1 and 2 with an overall presence of 63% (Supplementary Figure 4). The lack of clear subpopulations as indicated by the PCs shows the homogeneity within the mungbean accessions (Figure 6). Previous studies have shown similar results using simple sequence repeat markers in the Indonesian germplasm (Lestari et al., 2014) and the USDA germplasm (Wang et al., 2018). In other studies, the STRUCTURE (Pritchard et al., 2000) analysis showed between 3 and 6 subpopulations although no clear pattern was seen according to their geographical origins (Lestari et al., 2014; Wang et al., 2018; Sandhu and Singh, 2021). A similar observation, attributed to population admixture, was shown in common bean (Burle et al., 2010). The low Fst of 0.05 shows a high gene flow or low differentiation between the two genotypic clusters. The low Fst in this study confirms similar earlier reports within the USDA collections (Wang et al., 2018) and within the Indonesian germplasm (Lestari et al., 2014). Overall, results indicate a narrow genetic diversity in mung bean (Poehlman and Milton, 1991; Singh D. P. et al., 2021). Early breeders had to opt for mutation breeding to increase the genetic diversity. The narrow genetic base can be explained by the self-pollinated nature, very low cross-pollination frequencies, and poor hybridization of mung bean with other Vigna species (Poehlman and Milton, 1991; Singh D. P. et al., 2021). The narrow genetic diversity within the IA panel seems to reflect the fact that most of the accessions were collected on the Indian subcontinent, where mung bean was domesticated (Fuller, 2007). Our results support the idea that, in pulses, the lack of genetic diversity is due in part to the continuous use of a few genotypes as parents in the population development (Kumar et al., 2011). This shows the urgency of breeding efforts to diversify the genetic basis.

Adaptive roots to biotic and abiotic stresses will play a key role in bridging the yield gap in crop plants in the changing climate. A solid understanding of the genetic and environmental factors impacting the RSA will be important to the breeding of stable cultivars (see review, Lynch, 2007; Koevoets et al., 2016). RSA traits associated with response to abiotic stresses, including nutrient deficiency, drought tolerance, salinity, flooding, and temperature, and the underlying candidate genes have previously been studied (see review, Koevoets et al., 2016). Narrow LRA, high LED, and increased LRB were highly correlated to high P accumulation in Arabidopsis (Gruber et al., 2013), maize (Zhu et al., 2005), and common bean (Bonser et al., 1996). Auxins and strigolactones are key regulators in root and shoot development. An auxin receptor TRANSPORT INHIBITOR RESPONSE1 (TIR1) was shown to be responsible for the change in LRB as a response to low P levels (Pérez-Torres et al., 2008). Reduced LRB and increased PRL are characteristics of the “steep, cheap, and deep” ideotype, where the plant increases resource allocation to chase water and the mobile N in the deeper soil as evidenced in Arabidopsis and maize (Lynch, 2013). The nitrate transporters NRT1.1 and NRT2.1 were identified for the reduced LRB and increased PRL (Linkohr et al., 2002). The extended root system in Arabidopsis (Yu et al., 2008), rice, cotton, and poplar (Yu et al., 2013) was attributed to HD-ZIP transcription factor (HDG11) which promotes cell elongation by up-regulating cell wall loosening proteins hence important for drought tolerance.

In the current study, several putative candidate genes were identified for root traits associated with genes involved in the plant growth and development and stress tolerance response (Table 4). Lagrimini et al. (1997) proposed that anionic peroxidases, associated with LOC106755829 (day 12 LRA), play a role in plant host defense using a transformed tobacco (Nicotiana tabacum L.) plant. They have also been identified as major enzymes in cell wall lignification and found in large quantities in the xylem tissue (Sasaki et al., 2007). (-)- Germacrene D synthase, associated with LOC106753988 (day 12 LRA), is a member of sesquiterpene synthases family of plant proteins that have the capability of converting a precursor molecule farnesyl diphosphate into many sesquiterpene isoforms (Picaud et al., 2006). (-)- Germacrene D synthase catalyzes the formation (-)- Germacrene D, which is known to have strong effects on insects. Beta-galactosidase 3 is associated with Loci LOC106768494 (day 12 LRA), which has been implicated in adventitious root development via transcriptomic studies in mung bean (Li S.-W. et al., 2015). In rice, beta-galactosidase 1 and 2 were found to be highly expressed in the root and shoot seedlings, with less expression in flowers and immature seeds. Beta-galactosidases are important in the breakdown of molecular complexes (carbohydrates, glycolipids, and glycoproteins) that contain galactose (Chantarangsee et al., 2007). Beta-galactosidases would be important in the supply of the required energy from storage reserves during the rapid growth phase. The Far-related sequence (FRS) family, associated with LOC106776541 (day 12 LRA), is conserved among plants. Genes in this family are involved in multiple cellular processes (Lin Ma-FAR1). For example, Arabidopsis (Arabidopsis thaliana) mutants of fhy3 were less sensitive to both osmotic and salinity stress while also reducing the ABA-dependent inhibition of seedling root elongation, seedling greening, and germination (Tang et al., 2013; Ma and Li, 2018).

Transcripts of the gene encoding monodehydroascorbate reductase associated with LOC106772343 (day 15 and 18 LED), an antioxidant enzyme, were significantly reduced in the root elongation zone when roots for tall fescue (Festuca arundinaceaSchreb.cv. “K-31”) when exposed to water stress (Xu et al., 2015). Water stress is associated with high concentration of reactive oxygen species. A mutation in the Arabidopsis CYT1 gene encoding mannose-1-phosphate guanylyltransferase 1 associated with LOC106757974 (day 18 TRL_GR) showed deficiency in the cell wall after depletion of GDP mannose. The mutants exhibited radial swelling and accumulation of callose at the root tip. The functional analysis revealed mannose-1-phosphate guanylyltransferase 1 is involved in N-glycosylation during the cellulose synthesis (Lukowitz et al., 2001). An orthologous gene (DREB1A/CBF3 and DREB2A) associated with LOC106760865 (day 18 TDW), in Arabidopsis, encodes transcription factors that are involved in activating downstream genes involved in drought and cold stress (Sakuma et al., 2006). In another study, DREB2A proteins were found to increase the stress tolerance by modulating root architecture traits like the lateral root number and root length (Shukla et al., 2006; Agarwal et al., 2010).

Selection of variables with embedded screening loci associated with LOC106756657 (day 18 TRLUpper) and LOC106761944 (day 18 DEP) were associated with the adventitious root development in mung bean like the TASSEL results (Li S.-W. et al., 2015). Coilin is important in the formation of Cajal bodies, which are mostly associated with RNA processes. Kanno et al. (2016) suggest that coilin may be acting in multiple levels fine tuning expression of some genes important for environmental adaptation. Expansins are proteins involved with cell wall loosening and modification, partly mediated by the pH expansion of the cell wall during plant growth (Lee et al., 2001). In rice, Zhiming et al. (2011) identified a gene encoding EXPA17 that was important for the root hair growth, which requires intensive cell wall modification.

The high H among the dry weight measurements can be used in the selection of parents with the root to shoot ratio (RSR) previously used as a measure of the photosynthetic materials allocations (Figure 3). During a low supply of water, nitrogen, and phosphorus in the soil, more resources are allocated to roots relative to shoots (Xu et al., 2015; Lynch et al., 2021). Within legumes QTLs for fibrous rooting/surface area (Abdel-Haleem et al., 2011), root length (Prince et al., 2015), lateral root number, and root thickness (Manavalan et al., 2015; Prince et al., 2019) in soybean have been mapped. In cowpea, QTLs for basal root angle, root diameter, median width, and width accumulation were reported (Burridge et al., 2017). In pea, root length QTL (Fondevilla et al., 2010) and in common bean basal root angle QTL have been identified. Root length density, root surface area, RDW ratio, and root depth in chickpea have been mapped (Jaganathan et al., 2015). In cereals for, maize and sorghum associations with area, convex hull area, median width, maximum width, width-profile angle, and adjusted depth were identified (Zheng et al., 2020), deep root mass, and the number of deep roots in rice (Courtois et al., 2013) and PRL, RDW in wheat (Sanguineti et al., 2007).

Our study has elucidated the phenotypic and genotypic variability for the root traits in the 375 genotypes in the IA mung bean panel. We identified candidate genotypes that can now be advanced to the greenhouse or field for further testing, especially for the root ideotypes. If their trait response and expression can be confirmed, these can be utilized as parents in the breeding program. Using GWAS, we identified significant markers associated with several RSA traits. Taken together, the ideotypes after field evaluation and significant markers can be utilized as tools for marker-assisted selection and crop improvement in mung bean breeding programs.

Publisher’s Note

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References

• 1

  Abdel-HaleemH.LeeG.-J.BoermaR. H. (2011). Identification of QTL for increased fibrous roots in soybean.Theor. Appl. Genet.122935–946. 10.1007/s00122-010-1500-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 2

  AgarwalP.AgarwalP. K.JoshiA. J.SoporyS. K.ReddyM. K. (2010). Overexpression of PgDREB2A transcription factor enhances abiotic stress tolerance and activates downstream stress-responsive genes.Mol. Biol. Rep.371125–1135. 10.1007/s11033-009-9885-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 3

  AkibodeS.MarediaM. (2012). Global and Regional Trends in Production, Trade and Consumption of Food Legume Crops. Available online at: https://ageconsearch.umn.edu/record/136293(accessed October 11, 2021).

  • Google Scholar

• 4

  ArmengaudP. (2009). EZ-Rhizo software: the gateway to root architecture analysis.Plant Signal. Behav.4139–141. 10.4161/psb.4.2.7763

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 5

  Aschemann-WitzelJ.GantriisR. F.FragaP.Perez-CuetoF. J. A. (2020). Plant-based food and protein trend from a business perspective: markets, consumers, and the challenges and opportunities in the future.Crit. Rev. Food Sci. Nutr.613119–3128. 10.1080/10408398.2020.1793730

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 6

  AskiM. S.RaiN.ReddyV. R. P.Gayacharan, DikshitH. K.MishraG. P.et al (2021). Assessment of root phenotypes in mungbean mini-core collection (MMC) from the World Vegetable Center (AVRDC) Taiwan.PLoS One16:e0247810. 10.1371/journal.pone.0247810

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 7

  AtkinsonJ. A.PoundM. P.BennettM. J.WellsD. M. (2019). Uncovering the hidden half of plants using new advances in root phenotyping.Curr. Opin. Biotechnol.551–8. 10.1016/j.copbio.2018.06.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 8

  AtkinsonJ. A.WingenL. U.GriffithsM.PoundM. P.GajuO.FoulkesM. J.et al (2015). Phenotyping pipeline reveals major seedling root growth QTL in hexaploid wheat.J. Exp. Bot.662283–2292. 10.1093/jxb/erv006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 9

  Betegón-PutzeI.GonzálezA.SevillanoX.Blasco-EscámezD.Caño-DelgadoA. I. (2019). MyROOT: a method and software for the semiautomatic measurement of primary root length in Arabidopsis seedlings.Plant J.981145–1156. 10.1111/tpj.14297

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 10

  BonserA. M.LynchJ.SnappS. (1996). Effect of phosphorus deficiency on growth angle of basal roots in Phaseolus vulgaris.New Phytol.132281–288. 10.1111/j.1469-8137.1996.tb01847.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 11

  BradburyP. J.ZhangZ.KroonD. E.CasstevensT. M.RamdossY.BucklerE. S. (2007). TASSEL: software for association mapping of complex traits in diverse samples.Bioinformatics232633–2635. 10.1093/bioinformatics/btm308

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 12

  Brassica (2014). Home - World Vegetable Center. Available online at: https://avrdc.org/(accessed October 09, 2021).

  • Google Scholar

• 13

  BurleM. L.FonsecaJ. R.KamiJ. A.GeptsP. (2010). Microsatellite diversity and genetic structure among common bean (Phaseolus vulgaris L.) landraces in Brazil, a secondary center of diversity.Theor. Appl. Genet.121801–813. 10.1007/s00122-010-1350-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 14

  BurridgeJ. D.SchneiderH. M.HuynhB.-L.RobertsP. A.BuckschA.LynchJ. P. (2017). Genome-wide association mapping and agronomic impact of cowpea root architecture.Theor. Appl. Genet.130419–431. 10.1007/s00122-016-2823-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 15

  ChantarangseeM.TanthanuchW.FujimuraT.FryS. C.Ketudat CairnsJ. (2007). Molecular characterization of β-galactosidases from germinating rice (Oryza sativa).Plant Sci.173, 118–134. 10.1016/j.plantsci.2007.04.009

  • CrossRef
  • Google Scholar

• 16

  CobbJ. N.JumaR. U.BiswasP. S.ArbelaezJ. D.RutkoskiJ.AtlinG.et al (2019). Enhancing the rate of genetic gain in public-sector plant breeding programs: lessons from the breeder’s equation.Theor. Appl. Genet.132627–645. 10.1007/s00122-019-03317-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 17

  CourtoisB.AudebertA.DardouA.RoquesS.Ghneim-HerreraT.DrocG.et al (2013). Genome-wide association mapping of root traits in a japonica rice panel.PLoS One8:e78037. 10.1371/journal.pone.0078037

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 18

  Crop Science Controlled Environment Research Guidelines (2021). Available online at: https://www.crops.org/publications/journals/author-resources/cs-instructions/controlled-environment-research/(accessed October 6, 2021).

  • Google Scholar

• 19

  CullisB. R.SmithA. B.CoombesN. E. (2006). On the design of early generation variety trials with correlated data.J. Agric. Biol. Environ. Stat.11381–393.

  • Google Scholar

• 20

  DasA.SchneiderH.BurridgeJ.AscanioA. K. M.WojciechowskiT.ToppC. N.et al (2015). Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics.Plant Methods11:51. 10.1186/s13007-015-0093-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 21

  Dray StéphaneD. A.-B. (2007). The ade4 Package: implementing the duality diagram for ecologists.J. Stat. Softw.221–20.

  • Google Scholar

• 22

  EdmondsonR. N.EdmondsonM. R. (2021). Package ‘blocksdesign’. Available online at: https://cran.r-project.org(accessed October 27, 2021).

  • Google Scholar

• 23

  FalkK. G.JuberyT. Z.O’RourkeJ. A.SinghA.SarkarS.GanapathysubramanianB.et al (2020b). Soybean root system architecture trait study through genotypic, phenotypic, and shape-based clusters.Plant Phenomics2020:1925495. 10.34133/2020/1925495

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 24

  FalkK. G.JuberyT. Z.MirnezamiS. V.ParmleyK. A.SarkarS.SinghA.et al (2020a). Computer vision and machine learning enabled soybean root phenotyping pipeline.Plant Methods16:5. 10.1186/s13007-019-0550-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 25

  FangC.MaY.WuS.LiuZ.WangZ.YangR.et al (2017). Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean.Genome Biol.18:161. 10.1186/s13059-017-1289-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 26

  FernandezG. C. J.ShanmugasundaramS. (1988). “The AVRDC mungbean improvement program: the past, present and future,” in Proceedings of the 1988 Second International Symposium held at Bangkok, Bangkok, 58–70.

  • Google Scholar

• 27

  FondevillaS.Fernández-AparicioM.SatovicZ.EmeranA. A.TorresA. M.MorenoM. T.et al (2010). Identification of quantitative trait loci for specific mechanisms of resistance to Orobanche crenata Forsk. in pea (Pisum sativum L.).Mol. Breed.25259–272. 10.1007/s11032-009-9330-7

  • CrossRef
  • Google Scholar

• 28

  FullerD. Q. (2007). Contrasting patterns in crop domestication and domestication rates: recent archaeobotanical insights from the Old World.Ann. Bot.100903–924. 10.1093/aob/mcm048

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 29

  GaliliT. (2015). dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering.Bioinformatics313718–3720. 10.1093/bioinformatics/btv428

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 30

  GalkovskyiT.MileykoY.BuckschA.MooreB.SymonovaO.PriceC. A.et al (2012). GiA Roots: software for the high throughput analysis of plant root system architecture.BMC Plant Biol.12:116. 10.1186/1471-2229-12-116

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 31

  GaurP. M.KrishnamurthyL.KashiwagiJ. (2008). Improving Drought-Avoidance Root Traits in Chickpea (Cicer arietinum L.) -Current Status of Research at ICRISAT.Plant Prod. Sci.113–11.

  • Google Scholar

• 32

  GhosalS.BlystoneD.SinghA. K.GanapathysubramanianB.SinghA.SarkarS. (2018). An explainable deep machine vision framework for plant stress phenotyping.Proc. Natl. Acad. Sci. U.S.A.1154613–4618. 10.1073/pnas.1716999115

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 33

  GhosalS.ZhengB.ChapmanS. C.PotgieterA. B.JordanD. R.WangX.et al (2019). A weakly supervised deep learning framework for sorghum head detection and counting.Plant Phenomics20191525874. 10.34133/2019/1525874

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34

  GioiaT.GalinskiA.LenzH.MüllerC.LentzJ.HeinzK.et al (2016). GrowScreen-PaGe, a non-invasive, high-throughput phenotyping system based on germination paper to quantify crop phenotypic diversity and plasticity of root traits under varying nutrient supply.Funct. Plant Biol.4476–93. 10.1071/FP16128

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 35

  GruberB. D.GiehlR. F. H.FriedelS.von WirénN. (2013). Plasticity of the Arabidopsis root system under nutrient deficiencies.Plant Physiol.163161–179. 10.1104/pp.113.218453

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 36

  GuZ.EilsR.SchlesnerM. (2016). Complex heatmaps reveal patterns and correlations in multidimensional genomic data.Bioinformatics322847–2849. 10.1093/bioinformatics/btw313

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 37

  GuZ.GuL.EilsR.SchlesnerM.BrorsB. (2014). circlize Implements and enhances circular visualization in R.Bioinformatics302811–2812. 10.1093/bioinformatics/btu393

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 38

  HartF. (2021). Smart Shooter Photography Software. Available on: https://kuvacode.com/smartshooter3(accessed September 13, 2021).

  • Google Scholar

• 39

  HoaglinD. C. (2003). John W. Tukey and data analysis. Stat. Sci.18311–318.

  • Google Scholar

• 40

  HodgeA.BertaG.DoussanC.MerchanF.CrespiM. (2009). Plant root growth, architecture and function.Plant Soil321153–187.

  • Google Scholar

• 41

  HuangX.HanB. (2014). Natural variations and genome-wide association studies in crop plants.Annu. Rev. Plant Biol.65531–551. 10.1146/annurev-arplant-050213-035715

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 42

  HuangX.WeiX.SangT.ZhaoQ.FengQ.ZhaoY.et al (2010). Genome-wide association studies of 14 agronomic traits in rice landraces.Nat. Genet.42961–967. 10.1038/ng.695

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 43

  HundA.TrachselS.StampP. (2009). Growth of axile and lateral roots of maize: I development of a phenotying platform.Plant Soil325335–349. 10.1007/s11104-009-9984-2

  • CrossRef
  • Google Scholar

• 44

  JaganathanD.ThudiM.KaleS.AzamS.RoorkiwalM.GaurP. M.et al (2015). Genotyping-by-sequencing based intra-specific genetic map refines a “QTL-hotspot” region for drought tolerance in chickpea.Mol. Genet. Genomics290559–571. 10.1007/s00438-014-0932-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 45

  JahanI.RahmanM. M.TuzzohoraM. F.HossainM. A.BegumS. N.BurrittD. J.et al (2020). Phenotyping of mungbean (Vigna radiata L.) genotypes against salt stress and assessment of variability for yield and yield attributing traits.J. Plant Stress Physiol.67–17.

  • Google Scholar

• 46

  JoshiV.KumarS. (2016). Meat analogues: plant based alternatives to meat products- a review.Int. J. Food Ferment. Technol.5107–119. 10.5958/2277-9396.2016.00001.5

  • CrossRef
  • Google Scholar

• 47

  JPEGCrops (2021). Available online at: http://ekot.dk/programmer/JPEGCrops/(accessed September 13, 2021).

  • Google Scholar

• 48

  KamfwaK.CichyK. A.KellyJ. D. (2015). Genome-wide association study of agronomic traits in common bean.Plant Genome8:elantgenome2014. 09.0059.

  • Google Scholar

• 49

  KannoT.LinW. D.FuJ. L.WuM. T.YangH. W.LinS. S.et al (2016). Identification of coilin mutants in a screen for enhanced expression of an alternatively spliced GFP reporter gene in Arabidopsis thaliana.Genetics203, 1709–1720. 10.1534/genetics.116.190751

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 50

  KassambaraA.MundtF. (2020). factoextra: Extract and Visualize the Results of Multivariate Data Analyses. Available online at: https://CRAN.R-project.org/package=factoextra(accessed September 27, 2021).

  • Google Scholar

• 51

  KangY. J.KimS. K.KimM. Y.LestariP.KimK. H.HaB.-K.et al (2014). Genome sequence of mungbean and insights into evolution within Vigna species.Nat. Commun.5:5443. 10.1038/ncomms6443

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 52

  KoevoetsI. T.VenemaJ. H.ElzengaJ. T. M.TesterinkC. (2016). Roots withstanding their environment: exploiting root system architecture responses to abiotic stress to improve crop tolerance.Front. Plant Sci.7:1335. 10.3389/fpls.2016.01335

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 53

  KumarJ.ChoudharyA. K.SolankiR. K.PratapA. (2011). Towards marker-assisted selection in pulses: a review.Plant Breed.130297–313. 10.1007/s00299-017-2127-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 54

  KuoK. H. M. (2017). Multiple testing in the context of gene discovery in sickle cell disease using genome-wide association studies.Genomics Insights10:1178631017721178. 10.1177/1178631017721178

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 55

  LagriminiL. M.GingasV.FingerF.RothsteinS.LiuT. (1997). Characterization of antisense transformed plants deficient in the tobacco anionic peroxidase. Plant Physiol.114, 1187–1196. 10.1104/pp.114.4.1187

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 56

  Le BotJ.SerraV.FabreJ.DrayeX.AdamowiczS.PagèsL. (2010). DART: a software to analyse root system architecture and development from captured images.Plant Soil326261–273.

  • Google Scholar

• 57

  LeeY.ChoiD.KendeH. (2001). Expansins: ever-expanding numbers and functions. Curr. Opin. Plant Biol.4, 527–532. 10.1016/s1369-5266(00)00211-9

  • CrossRef
  • Google Scholar

• 58

  LestariP.KimS. K.Reflinur, KangY. J.DewiN.LeeS.-H. (2014). Genetic diversity of mungbean (Vigna radiata L.) germplasm in Indonesia.Plant Genet. Resour. .12S91–S94.

  • Google Scholar

• 59

  LiD.DuttaS.RoyV. (2020). Model Based Screening Embedded Bayesian Variable Selection for Ultra-high Dimensional Settings.arXiv [Preprint]. Available online at: http://arxiv.org/abs/2006.07561(accessed September 27, 2021).

  • Google Scholar

• 60

  LiR.ZengY.XuJ.WangQ.WuF.CaoM.et al (2015). Genetic variation for maize root architecture in response to drought stress at the seedling stage.Breed. Sci.65298–307. 10.1270/jsbbs.65.298

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 61

  LiS.-W.ShiR.-F.LengY. (2015). De novo characterization of the mung bean transcriptome and transcriptomic analysis of adventitious rooting in seedlings using RNA-Seq.PLoS One10:e0132969. 10.1371/journal.pone.0132969

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 62

  LinkohrB. I.WilliamsonL. C.FitterA. H.LeyserH. M. O. (2002). Nitrate and phosphate availability and distribution have different effects on root system architecture of Arabidopsis.Plant J.29751–760. 10.1046/j.1365-313x.2002.01251.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 63

  LiuX.DongX.XueQ.LeskovarD. I.JifonJ.ButnorJ. R.et al (2018). Ground penetrating radar (GPR) detects fine roots of agricultural crops in the field.Plant Soil423517–531. 10.1007/s11104-017-3531-3

  • CrossRef
  • Google Scholar

• 64

  LobetG.Paez-GarciaA.SchneiderH.JunkerA.AtkinsonJ. A.TracyS. (2019). Demystifying roots: a need for clarification and extended concepts in root phenotyping.Plant Sci.28211–13. 10.1016/j.plantsci.2018.09.015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 65

  LobetG.PagèsL.DrayeX. (2011). A novel image-analysis toolbox enabling quantitative analysis of root system architecture.Plant Physiol.15729–39. 10.1104/pp.111.179895

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 66

  Lozano-IslaF. (2021). inti: Tools and Statistical Procedures in Plant Science. Available online at: https://cran.r-project.org/web/packages/inti/citation.html(accessed September 13, 2021).

  • Google Scholar

• 67

  LukowitzW.NickleT. C.MeinkeD. W.LastR. L.ConklinP. L.SomervilleC. R. (2001). Arabidopsis cyt1 mutants are deficient in a mannose-1-phosphate guanylyltransferase and point to a requirement of N-linked glycosylation for cellulose biosynthesis.Proc. Natl. Acad. Sci. U.S.A.98, 2262–2267. 10.1073/pnas.051625798

  • CrossRef
  • Google Scholar

• 68

  LynchJ. P. (2007). Roots of the second green revolution.Aust. J. Bot.55493–512.

  • Google Scholar

• 69

  LynchJ. P. (2013). Steep, cheap and deep: an ideotype to optimize water and N acquisition by maize root systems.Ann. Bot.112347–357. 10.1093/aob/mcs293

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 70

  LynchJ. P.BrownK. M. (2001). Topsoil foraging – an architectural adaptation of plants to low phosphorus availability.Plant Soil237225–237.

  • Google Scholar

• 71

  LynchJ. P.BrownK. M. (2012). New roots for agriculture: exploiting the root phenome.Philos. Trans. R. Soc. Lond. B Biol. Sci.3671598–1604. 10.1098/rstb.2011.0243

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 72

  LynchJ. P.StrockC. F.SchneiderH. M.SidhuJ. S.AjmeraI.Galindo-CastañedaT.et al (2021). Root anatomy and soil resource capture.Plant Soil46621–63.

  • Google Scholar

• 73

  MaL.LiG. (2018). FAR1-RELATED SEQUENCE (FRS) and FRS-RELATED FACTOR (FRF) family proteins in Arabidopsis growth and development.Front. Plant Sci.9:692. 10.3389/fpls.2018.00692

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 74

  MaL.ShiY.SiemianowskiO.YuanB.EgnerT. K.MirnezamiS. V.et al (2019). Hydrogel-based transparent soils for root phenotyping in vivo.Proc. Natl. Acad. Sci. U.S.A.11611063–11068. 10.1073/pnas.1820334116

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 75

  ManavalanL. P.PrinceS. J.MusketT. A.ChakyJ.DeshmukhR.VuongT. D.et al (2015). Identification of novel QTL governing root architectural traits in an interspecific soybean population.PLoS One10:e0120490. 10.1371/journal.pone.0120490

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 76

  MarkiewiczK. (2010). The Economics of Meeting Future Protein Demand.Wageningen: Wageningen University.

  • Google Scholar

• 77

  NagasubramanianK.JonesS.SarkarS.SinghA. K.SinghA.GanapathysubramanianB. (2018). Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems.Plant Methods14:86. 10.1186/s13007-018-0349-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 78

  NagasubramanianK.JonesS.SinghA. K.SarkarS.SinghA.GanapathysubramanianB. (2019). Plant disease identification using explainable 3D deep learning on hyperspectral images.Plant Methods15:98. 10.1186/s13007-019-0479-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 79

  NaikH. S.ZhangJ.LofquistA.AssefaT.SarkarS.AckermanD.et al (2017). A real-time phenotyping framework using machine learning for plant stress severity rating in soybean.Plant Methods13:23. 10.1186/s13007-017-0173-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 80

  NairR. M.PandeyA. K.WarA. R.HanumantharaoB.ShweT.AlamA.et al (2019). Biotic and abiotic constraints in mungbean production-progress in genetic improvement.Front. Plant Sci.10:1340. 10.3389/fpls.2019.01340

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 81

  NivaM.VainioA.JallinojaP. (2017). “10 - Barriers to increasing plant protein consumption in western populations,” in Vegetarian and Plant-Based Diets in Health and Disease Prevention (bll 157–171), ed.MariottiF. (Cambridge, MA: Academic Press).

  • Google Scholar

• 82

  PaceJ.LeeN.NaikH. S.GanapathysubramanianB.LübberstedtT. (2014). Analysis of maize (Zea mays L.) seedling roots with the high-throughput image analysis tool ARIA (Automatic Root Image Analysis).PLoS One9:e108255. 10.1371/journal.pone.0108255

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 83

  ParmleyK. A.HigginsR. H.GanapathysubramanianB.SarkarS.SinghA. K. (2019). Machine Learning Approach for Prescriptive Plant Breeding.Sci. Rep.9:17132. 10.1038/s41598-019-53451-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 84

  PassotS.GnackoF.MoukouangaD.LucasM.Guyomarc’hS.OrtegaB. M.et al (2016). Characterization of pearl millet root architecture and anatomy reveals three types of lateral roots.Front. Plant Sci.7:829. 10.3389/fpls.2016.00829

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 85

  PataczekL.ZahirZ. A.AhmadM.RaniS.NairR.SchafleitnerR.et al (2018). Beans with Benefits—The Role of Mungbean (Vigna radiate) in a Changing Environment.Am. J. Plant Sci.091577–1600. 10.4236/ajps.2018.97115

  • CrossRef
  • Google Scholar

• 86

  Pérez-TorresC.-A.López-BucioJ.Cruz-RamírezA.Ibarra-LacletteE.DharmasiriS.EstelleM.et al (2008). Phosphate availability alters lateral root development in Arabidopsis by modulating auxin sensitivity via a mechanism involving the TIR1 auxin receptor.Plant Cell203258–3272. 10.1105/tpc.108.058719

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 87

  PicaudS.OlssonM. E.BrodeliusM.BrodeliusP. E. (2006). Cloning, expression, purification and characterization of recombinant (+)-germacrene D synthase from Zingiber officinale.Arch. Biochem. Biophys.45217–28. 10.1016/j.abb.2006.06.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 88

  PiephoH.-P.MöhringJ. (2007). Computing heritability and selection response from unbalanced plant breeding trials.Genetics1771881–1888. 10.1534/genetics.107.074229

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 89

  PierretA.GonkhamdeeS.JourdanC.MaeghtJ.-L. (2013). IJ_Rhizo: an open-source software to measure scanned images of root samples.Plant Soil373531–539. 10.1093/aobpla/plab056

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 90

  PoehlmanMiltonJ. (1991). The Mungbean.New Delhi: Oxford & IBH Pub.

  • Google Scholar

• 91

  PoundM. P.FrenchA. P.AtkinsonJ. A.WellsD. M.BennettM. J.PridmoreT. (2013). RootNav: navigating images of complex root architectures.Plant Physiol.1621802–1814. 10.1104/pp.113.221531

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 92

  PrinceS. J.SongL.QiuD.Maldonado Dos, SantosJ. V.ChaiC.et al (2015). Genetic variants in root architecture-related genes in a Glycine soja accession, a potential resource to improve cultivated soybean.BMC Genomics16:132. 10.1186/s12864-015-1334-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 93

  PrinceS. J.ValliyodanB.YeH.YangM.TaiS.HuW.et al (2019). Understanding genetic control of root system architecture in soybean: insights into the genetic basis of lateral root number.Plant Cell Environ.42212–229. 10.1111/pce.13333

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 94

  PritchardJ. K.StephensM.DonnellyP. (2000). Inference of population structure using multilocus genotype data.Genetics155945–959. 10.1093/genetics/155.2.945

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 95

  R Core Team (2021). R: A Language and Environment for Statistical Computing.Vienna: R Foundation for Statistical Computing.

  • Google Scholar

• 96

  ReddyV. R. P.AskiM. S.MishraG. P.DikshitH. K.SinghA.PandeyR.et al (2020). Genetic variation for root architectural traits in response to phosphorus deficiency in mungbean at the seedling stage.PLoS One15:e0221008. 10.1371/journal.pone.0221008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 97

  Rellán-ÁlvarezR.LobetG.LindnerH.PradierP.-L.SebastianJ.YeeM.-C.et al (2015). GLO-Roots: an imaging platform enabling multidimensional characterization of soil-grown root systems.eLife4:e07597. 10.7554/eLife.07597

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 98

  RieraL. G.CarrollM. E.ZhangZ.ShookJ. M.GhosalS.GaoT.et al (2021). Deep multiview image fusion for soybean yield estimation in breeding applications.Plant Phenomics2021:9846470. 10.34133/2021/9846470

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 99

  RogersE. D.BenfeyP. N. (2015). Regulation of plant root system architecture: implications for crop advancement.Curr. Opin. Biotechnol.3293–98. 10.1016/j.copbio.2014.11.015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 100

  SakumaY.MaruyamaK.OsakabeY.QinF.SekiM.ShinozakiK.et al (2006). Functional analysis of an Arabidopsis transcription factor, DREB2A, involved in drought-responsive gene expression.Plant Cell18, 1292–1309. 10.1105/tpc.105.035881

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 101

  SandhuK.SinghA. (2021). Strategies for the utilization of the USDA mung bean germplasm collection for breeding outcomes.Crop Sci.61422–442. 10.1002/csc2.20322

  • CrossRef
  • Google Scholar

• 102

  SanguinetiM. C.LiS.MaccaferriM.CornetiS.RotondoF.ChiariT.et al (2007). Genetic dissection of seminal root architecture in elite durum wheat germplasm.Ann. Appl. Biol.151291–305. 10.1111/j.1744-7348.2007.00198.x

  • CrossRef
  • Google Scholar

• 103

  SasakiS.ShimizuM.WariishiH.TsutsumiY.KondoR. (2007). Transcriptional and translational analyses of poplar anionic peroxidase isoenzymes.J. Wood Sci.53427–435. 10.1007/s10086-007-0888-6

  • CrossRef
  • Google Scholar

• 104

  SchafleitnerR.NairR. M.RathoreA.WangY.-W.LinC.-Y.ChuS.-H.et al (2015). The AVRDC - The World Vegetable Center mungbean (Vigna radiata) core and mini core collections.BMC Genomics16:344. 10.1186/s12864-015-1556-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 105

  SchmidtP.HartungJ.RathJ.PiephoH.-P. (2019). Estimating broad-sense heritability with unbalanced data from agricultural cultivar trials.Crop Sci.59525–536.

  • Google Scholar

• 106

  SchneiderH. M.LorV. S. N.HanlonM. T.PerkinsA.KaepplerS. M.BorkarA. N.et al (2021). Root angle in maize influences nitrogen capture and is regulated by calcineurin B-like protein (CBL)-interacting serine/threonine-protein kinase 15 (ZmCIPK15).Plant Cell Environ.10.1111/pce.14135[Epub ahead of print].

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 107

  SeethepalliA.GuoH.LiuX.GriffithsM.AlmtarfiH.LiZ.et al (2020). RhizoVision crown: an integrated hardware and software platform for root crown Phenotyping.Plant Phenomics2020:3074916. 10.34133/2020/3074916

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 108

  ShanahanP. W.BinleyA.WhalleyW. R.WattsC. W. (2015). The use of electromagnetic induction to monitor changes in soil moisture profiles beneath different wheat genotypes.Soil Sci. Soc. Am. J.79459–466. 10.2136/sssaj2014.09.0360

  • CrossRef
  • Google Scholar

• 109

  ShuklaR. K.RahaS.TripathiV.ChattopadhyayD. (2006). Expression of CAP2, an APETALA2-family transcription factor from chickpea, enhances growth and tolerance to dehydration and salt stress in transgenic tobacco.Plant Physiol.142113–123. 10.1104/pp.106.081752

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 110

  SinghA.GanapathysubramanianB.SinghA. K.SarkarS. (2016). Machine learning for high-throughput stress phenotyping in plants.Trends Plant Sci.21110–124. 10.1016/j.tplants.2015.10.015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 111

  SinghA.JonesS.GanapathysubramanianB.SarkarS.MuellerD.SandhuK.et al (2021). Challenges and opportunities in machine-augmented plant stress phenotyping.Trends Plant Sci.2653–69. 10.1016/j.tplants.2020.07.010

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 112

  SinghA. K.GanapathysubramanianB.SarkarS.SinghA. (2018). Deep learning for plant stress phenotyping: trends and future perspectives.Trends Plant Sci.23883–898. 10.1016/j.tplants.2018.07.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 113

  SinghA. K.SinghA.SarkarS.GanapathysubramanianB.SchapaughW.MiguezF. E.et al (2021). “High-Throughput Phenotyping in Soybean,” in High-Throughput Crop Phenotyping (bll 129–163), edsZhouJ.NguyenH. T. (Cham: Springer International Publishing).

  • Google Scholar

• 114

  SinghD. P.SinghA. K.SinghA. (2021). “Chapter 25 - Breeding of crop ideotypes,” in Plant Breeding and Cultivar Development (bll 497–516), edsSinghD. P.SinghA. K.SinghA. (Cambridge, MA: Academic Press).

  • Google Scholar

• 115

  SrayeddinI.DoussanC. (2009). Estimation of the spatial variability of root water uptake of maize and sorghum at the field scale by electrical resistivity tomography.Plant Soil319185–207.

  • Google Scholar

• 116

  TanK. H.NopamornbodiV. (1979). Effect of different levels of humic acids on nutrient content and growth of corn (Zea mays L.).Plant Soil51283–287.

  • Google Scholar

• 117

  TangW.JiQ.HuangY.JiangZ.BaoM.WangH.et al (2013). FAR-RED ELONGATED HYPOCOTYL3 and FAR-RED IMPAIRED RESPONSE1 transcription factors integrate light and abscisic acid signaling in Arabidopsis.Plant Physiol.163857–866. 10.1104/pp.113.224386

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 118

  TibbittsT. W.LanghansR. W. (1993). “Controlled-environment studies,” in Photosynthesis and Production in a Changing Environment: A Field and Laboratory Manual (bll 65–78), edsHallD. O.ScurlockJ. M. O.Bolhàr-NordenkampfH. R.LeegoodR. C.LongS. P. (Dordrecht: Springer).

  • Google Scholar

• 119

  Tibbs CortesL.ZhangZ.YuJ. (2021). Status and prospects of genome-wide association studies in plants.Plant Genome14:e20077. 10.1002/tpg2.20077

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 120

  TrachselS.KaepplerS. M.BrownK. M.LynchJ. P. (2011). Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field.Plant Soil34175–87.

  • Google Scholar

• 121

  VinnariM. (2008). The future of meat consumption — Expert views from Finland.Technol. Forecast. Soc. Change75893–904.

  • Google Scholar

• 122

  WangL.BaiP.YuanX.ChenH.WangS.ChenX.et al (2018). Genetic diversity assessment of a set of introduced mung bean accessions (Vigna radiata L.).Crop J.6207–213. 10.1016/j.cj.2017.08.004

  • CrossRef
  • Google Scholar

• 123

  WassonA.BischofL.ZwartA.WattM. (2016). A portable fluorescence spectroscopy imaging system for automated root phenotyping in soil cores in the field.J. Exp. Bot.671033–1043. 10.1093/jxb/erv570

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 124

  WhiteP. J.GeorgeT. S.GregoryP. J.BengoughA. G.HallettP. D.McKenzieB. M. (2013). Matching roots to their environment.Ann. Bot.112207–222. 10.1093/aob/mct123

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 125

  WildF.CzernyM.JanssenA. M.KoleA. P. W.DomigK. J. (2014). The evolution of a plant-based alternative to meat: from niche markets to widely accepted meat alternatives.Agro Food Ind. Hi Tech2545–49.

  • Google Scholar

• 126

  WrightS. (1965). The interpretation of population structure by f-statistics with special regard to systems of mating.Evolution19395–420.

  • Google Scholar

• 127

  XuW.CuiK.XuA.NieL.HuangJ.PengS. (2015). Drought stress condition increases root to shoot ratio via alteration of carbohydrate partitioning and enzymatic activity in rice seedlings.Acta Physiol. Plant.37:9.

  • Google Scholar

• 128

  YangN.LuY.YangX.HuangJ.ZhouY.AliF.et al (2014). Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel.PLoS Genet.10:e1004573. 10.1371/journal.pgen.1004573

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 129

  YeH.RoorkiwalM.ValliyodanB.ZhouL.ChenP.VarshneyR. K.et al (2018). Genetic diversity of root system architecture in response to drought stress in grain legumes.J. Exp. Bot.693267–3277. 10.1093/jxb/ery082

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 130

  YinL.ZhangH.TangZ.XuJ.YinD.ZhangZ.et al (2021). rMVP: a memory-efficient, visualization-enhanced, and parallel-accelerated tool for genome-wide association study.Genomics Proteomics Bioinformatics.10.1016/j.gpb.2020.10.007[Epub ahead of print].

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 131

  YuH.ChenX.HongY.-Y.WangY.XuP.KeS.-D.et al (2008). Activated expression of an Arabidopsis HD-START protein confers drought tolerance with improved root system and reduced stomatal density.Plant Cell201134–1151. 10.1105/tpc.108.058263

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 132

  YuJ.PressoirG.BriggsW. H.Vroh BiI.YamasakiM.DoebleyJ. F.et al (2006). A unified mixed-model method for association mapping that accounts for multiple levels of relatedness.Nat. Genet.38203–208. 10.1038/ng1702

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 133

  YuL.ChenX.WangZ.WangS.WangY.ZhuQ.et al (2013). Arabidopsis enhanced drought tolerance1/HOMEODOMAIN GLABROUS11 confers drought tolerance in transgenic rice without yield penalty.Plant Physiol.1621378–1391. 10.1104/pp.113.217596

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 134

  ZhangJ.NaikH. S.AssefaT.SarkarS.ReddyR. V. C.SinghA.et al (2017). Computer vision and machine learning for robust phenotyping in genome-wide studies.Sci. Rep.7:44048. 10.1038/srep44048

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 135

  ZhangJ.SongQ.CreganP. B.NelsonR. L.WangX.WuJ.et al (2015). Genome-wide association study for flowering time, maturity dates and plant height in early maturing soybean (Glycine max) germplasm.BMC Genomics16:217. 10.1186/s12864-015-1441-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 136

  ZhengX.LevineD.ShenJ.GogartenS. M.LaurieC.WeirB. S. (2012). A high-performance computing toolset for relatedness and principal component analysis of SNP data.Bioinformatics283326–3328. 10.1093/bioinformatics/bts606

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 137

  ZhengZ.HeyS.JuberyT.LiuH.YangY.CoffeyL.et al (2020). Shared Genetic Control of Root System Architecture between Zea mays and Sorghum bicolor.Plant Physiol.182977–991. 10.1104/pp.19.00752

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 138

  ZhimingY.BoK.XiaoweiH.ShaoleiL.YouhuangB.WonaD.et al (2011). Root hair-specific expansins modulate root hair elongation in rice.Plant J.66725–734. 10.1111/j.1365-313X.2011.04533.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 139

  ZhuJ.KaepplerS. M.LynchJ. P. (2005). Topsoil foraging and phosphorus acquisition efficiency in maize (Zea mays).Funct. Plant Biol.32749–762. 10.1071/FP05005

  • Pubmed Abstract
  • CrossRef
  • Google Scholar