Genetic diversity of root system architecture in response to drought stress in grain legumes

Genetic diversity of root system architecture in response to drought stress in grain legumes Abstract Climate change has increased the occurrence of extreme weather patterns globally, causing significant reductions in crop production, and hence threatening food security. In order to meet the food demand of the growing world population, a faster rate of genetic gains leading to productivity enhancement for major crops is required. Grain legumes are an essential commodity in optimal human diets and animal feed because of their unique nutritional composition. Currently, limited water is a major constraint in grain legume production. Root system architecture (RSA) is an important developmental and agronomic trait, which plays vital roles in plant adaptation and productivity under water-limited environments. A deep and proliferative root system helps extract sufficient water and nutrients under these stress conditions. The integrated genetics and genomics approach to dissect molecular processes from genome to phenome is key to achieve increased water capture and use efficiency through developing better root systems. Success in crop improvement under drought depends on discovery and utilization of genetic variations existing in the germplasm. In this review, we summarize current progress in the genetic diversity in major legume crops, quantitative trait loci (QTLs) associated with RSA, and the importance and applications of recent discoveries associated with the beneficial root traits towards better RSA for enhanced drought tolerance and yield. Climate change, drought tolerance, genomics, genomics-assisted breeding, legumes, natural variation Introduction Climate change has increased the frequency of extreme weather patterns including irregular precipitation, which can cause drought stress and adversely affect crop production (Lesk et al., 2016). Yield losses of major crops under such fluctuating weather patterns are increasing, despite the progressive efforts in yield improvement through breeding and management practices since the 1960s (Boyer, 1983; Lobell et al., 2014; Rosenzweig et al., 2014). Estimates show that crop yields would decline 30–82% by the end of the 21st century under the current climate change, despite increasing concentrations of CO2 that could benefit photosynthesis (Hatfield et al., 2011). Legumes form a major part of the human diet and animal feed, and are used for biofuel due to their oil content. Besides food and nutritional security, legumes act as a major cash crop, generating US$31 billion per annum to a large number of smallholders (>700 million) in developing nations (Abate et al., 2012). Legumes are known not only as an affordable protein-rich source, but also for their environmental and economic value. Inclusion of legumes in crop rotation programs leads to a positive environmental effect by improving the soil nutritional profile via symbiotic nitrogen fixation; it aids in sustainable agricultural production by promoting the yield of subsequent crops as well. It has been reported that when grown in rotation with cereals, in addition to improving the soil nutritional profile, they prevent soil erosion, and further lead to reduced incidences of soil pathogens (Daryanto et al., 2015). However, only a limited yield increase of 0–2% per year is occurring in legume crops (Foyer et al., 2016). Abruptly changing climatic conditions are having a negative impact on legume production. For example, soybean yields in the USA declined by 2–4% for every degree rise in temperature during the growing season, resulting a loss of US$11 billion (Mourtzinis et al., 2015). Rising global temperatures were reported to reduce the areas suitable for bean production due to increased drought stress (Beebe et al., 2011). Yields will decrease in developing countries in the tropics and subtropics such as India, Pakistan, and Ethiopia due to the increased occurrence of drought stress (Andrews and Hodge, 2010). A large yield gap for legume crops exists in Africa of >300% due to water-limiting issues. Moreover, as an integral part of Indian agriculture, legume crop production has remained unstable, with a yield gap for soybean ranging from 850–1320 kg ha–1, for groundnut from 1180–2010 kg ha–1, for pigeon pea from 550–770 kg ha–1, and for chickpea from 610–1150 kg ha–1 (Bhatia et al., 2006; Anderson et al., 2016). Plants have evolved stress adaptation mechanisms under natural selection. Exotic and wild relatives tend to have better stress-resistant characteristics and maintain natural variations favorable for stress resistance (Mickelbart et al., 2015). Incorporation of these favorable natural variations into current elite germplasm is essential to improve the performance of cultivars under stress conditions. This can be achieved through the identification of resistant genetic resources, discovery of favorable natural variations, and finally the incorporation of these natural variations into the elite cultivars. These natural variations can be identified through quantitative trait locus (QTL) mapping and genome-wide association studies (GWAS) (Takeda and Matsuoka, 2008; Varshney et al., 2015). With the assistance of genomic strategies, these natural genetic variations can be identified and expeditiously incorporated into the cultivars by marker-assisted selection (MAS) and genomic selection strategies. Characterization of the candidate genes underlying these natural variations will further elucidate biological mechanisms, which consequently will be translated to crop improvement through genetic modifications. Drought tolerance refers to the ability of plants to survive, grow, and reproduce under water deficit conditions (Levitt, 1972; Turner et al., 1978). In crops, drought tolerance actually suggests the ability of plants to yield under stress conditions (Turner, 2000; Fleury et al., 2010). Crop yield was determined by three major components: water use, water use efficiency, and harvest index (yield=water use×water use efficiency×harvest index) (Passioura, 1977, 1983; Turner, 2000; Turner et al., 2001). Studies clearly show a positive correlation between the amount of water reduction and yield reduction, whereas the degree of this correlation varies within legume species and the developmental stages at which plant encountered the drought stress (Daryanto et al., 2015). Acquisition of more water resource in plants is a key solution to improve drought tolerance in crops (Passioura, 1977, 1983). Thus, the development and distribution of the root system can be regarded as key factors for more efficient water uptake and thereby for managing the performance of legumes under drought stress (Newman and Moser, 1988; Gaur et al., 2008). Root traits are usually described as root system architecture (RSA), referring to the shape and physical space of the roots. A deeper and proliferative root system is able to avoid drought stress by its ability to acquire more water resource. RSA is also highly plastic to environmental changes, including water deficit stress. When plants perceive water deficit stress, roots tend to keep growing and penetrate into deeper soil layers (Hoogenboom et al., 1987; Sponchiado et al., 1989; Creelman et al., 1990; Wu et al., 1994). Root system architecture in drought tolerance Fifty decades ago, Kramer (1969) stated one of the essential characteristics of drought tolerance: ‘deep, wide-spreading, much-branched root system’. In order to access enhanced available soil moisture, plant adapts to greater rooting depth and root biomass (Blum, 2011; Fenta et al., 2014; Fig. 1). The benefit of a deep and proliferative root system for drought tolerance has been reported in various crops, including rice (Nguyen et al., 1997; Babu et al., 2003; Kato et al., 2006; MacMillan et al., 2006; Steele et al., 2006; Bernier et al., 2009; Uga et al., 2013), maize (Tuberosa et al., 2002; Giuliani et al., 2005; Hammer et al., 2009; Landi et al., 2010; Hund et al., 2011), barley (Forster et al., 2005), wheat (Manschadi et al., 2006; Wasson et al., 2012), chickpea (Varshney et al., 2011; Chen et al., 2012), and soybean (Hudak and Patterson, 1995; Pantalone et al., 1996; Sadok and Sinclair, 2011). The diameter and distribution of the metaxylem vessels that govern root conductivity are also known to provide drought tolerance in legumes (Purushothaman et al., 2013). Fig. 1. View largeDownload slide Roles of root system architecture in the improvement of drought tolerance. Fig. 1. View largeDownload slide Roles of root system architecture in the improvement of drought tolerance. Deep rooting is a complex trait affected by growth angle and root length (Araki et al., 2002; Fig. 1). Root angle determines the direction of horizontal and vertical distribution of roots in the soil, and is recognized as an essential trait for drought avoidance in sorghum (Mace et al., 2012), wheat (Christopher et al., 2013), and rice (Uga et al., 2013). Root angle was correlated with rooting depth in rice (Kato et al., 2006), chickpea (Sayar et al., 2007; Kashiwagi et al., 2015), and sorghum (Singh et al., 2011). Wider root angles could reduce the energy inputs to penetrate in deeper horizons to access water during limited rainfall (Wasson et al., 2012; Meister et al., 2014). A natural genetic variation in root angle was cloned in rice as DEEPER ROOTING 1 (DRO1) (Uga et al., 2013). Dro1 doubled the yield under moderate and severe drought stress due to its ability to develop a deep rooting system (Uga et al., 2013). Another deep rooting determinant is root diameter/thickness and penetrability (Fig. 1). Thicker roots tend to penetrate deeper through soil layers for deep rooting (Yu et al., 1995; Zheng et al., 2000). Proliferative rooting is mainly determined by lateral root initiation and elongation, which usually refers to lateral root number, root length density, and root surface area and volume (Fig. 1). Proliferative roots have relatively high water uptake efficiency in water deficit soils. Chickpea lines with higher root length density have shown clearly a better performance in yield and drought tolerance-related traits under water deficit environments (Jaganathan et al., 2015). In addition to deep and proliferative rooting, drought stress also induces the plasticity responses of root systems by increasing the number of fibrous roots, and decreasing lateral root diameter and fluctuations in root biomass (Nielsen et al., 1997; Osmont et al., 2007; Meister et al., 2014; Salazar-Henao et al., 2016). Drought-tolerant lines tend to elongate their rooting depth significantly more than sensitive lines under drought stress in legumes (Garay and Wilhelm, 1983; Sponchiado et al., 1989). Alterations in root anatomy, such as aerenchyma formation in maize (Lynch, 2011; Burton et al., 2013) and soybean (Prince et al., 2017), saves the energy inputs to allow improved soil penetration and exploration to mitigate water deficit (Addington et al., 2006; Maseda and Fernández, 2006). The number of metaxylems was identified to be correlated with drought tolerance in soybean, and more metaxylems help in water transportation in the roots (Prince et al., 2017; Fig. 1). Root phenotyping and modeling RSA Root phenotyping in the laboratory, greenhouse, and field encompasses simple agar plates and germination papers, labor-intensive root digging in the field, an underground root observation facility, and sophisticated computer-assisted root imaging equipment (reviewed by Paez-Garcia et al., 2015). Primary RSA traits include primary root length, root length density, root angle, fibrous and lateral roots, root diameters, and root anatomy. In laboratory conditions, a larger numbers of samples under well-controlled conditions can be phenotyped; however, RSA may be affected by the containers and cannot reflect real growth stages and environmental conditions. Greenhouse evaluation is an intermediate system between the laboratory and field. However, it has space limitations and requires more labor input. Field evaluation reflects the real performance of roots, correlating plant physiology with RSA well; however, intensive investment in labor is needed. Currently, high-throughput phenotyping of RSA has focused on field-based approaches in crops such as maize (York and Lynch, 2015), rice (Wissuwa et al., 2016), and wheat (Richard et al., 2015). With a recent shift from studying vegetative stage root traits in the greenhouse (Manavalan et al., 2015; Prince et al., 2015) to more field-based reproductive stage research (Burridge et al., 2016) in legume species, knowledge of the genetic regulation of RSA has improved. The challenges of digging for a large number of plants using a ‘Shovelomics’ (Trachsel et al., 2011) approach have been offset by the development and application of high-throughput image analysis platforms such as digital imaging of root traits (DIRT) (Bucksch et al., 2014; Das et al., 2015). The adoption of digital image analysis platforms for field-grown plants is gaining prevalence in legumes. Mechanistic understanding of RSA and plasticity can be developed from functional–structural plant modeling systems. These modeling systems rely on accurate phenotyping techniques and integrate structural, physiological, and environmental information to simulate the overall understanding of how roots distribute in soil and interact with different environmental factors (Ndour et al., 2017). It requires involvement of various fields, including plant physiology, soil science, engineering, mathematics, computer science, and so on. Many of these models have been developed and used to simulate the development of the plant with water acquisition and transportation (reviewed by Ndour et al., 2017). These models aim to quantify RSA and set up descriptors of RSA, and eventually identify mechanisms of RSA and plasticity under given stress environments (reviewed by Ndour et al., 2017). Recently, some new RSA models have been developed for various purposes to predict RSA and interaction with environments more accurately. This includes a root hydraulics model to estimate plant-scale hydraulic parameters from RSA (Meunier et al., 2017), OPENSIMROOT for simulation of RSA, nutrient acquisition, and plant growth (Postma et al., 2017), and RootBox to simulate matured RSA from seedling RSA (Leitner et al., 2010; Zhao et al., 2017). Challenges exist in developing and improving RSA models, such as the bottleneck of root phenotyping techniques, difficulties under field conditions, and integration of the plant growing ecosytems (reviewed by Ndour et al., 2017). Overall, the future of RSA modeling relies on successful integration of all biological scales. Root system architecture in legumes and cereals Roots are one of the major organs for transmitting different resources from soil, and thereby control productivity (Lynch, 1995). Plants have the capacity to customize their root architecture in order to adapt to different environments, including stress, through integrating the genetic programs controlling root growth (Jovanovic et al., 2008). Plants with different genetic and nutritional conditions have shown extensive differences in root structure in terms of the number and distribution of root systems, which allows plants to recover efficiently when transiting from stress to normal conditions (López-Bucio et al., 2003). In the case of cereals, the root system is monocotyledonous, comprising primary root(s) generated from the seed embryo and axile nodal roots. The number of primary roots varies from one in warm-climate cereals to six or seven in cool-climate cereals. However, in the case of a dicotyledonous root system (Arabidopsis, Medicago, and legumes), a single primary root was generated from the embryo with successive orders of branch/lateral roots (Sorin et al., 2005; Rich and Watt, 2013). The number and size of lateral roots are determining factors for RSA in legumes (Dubrovsky et al., 2006). Comparative analysis of monocot (cereals and wheat) and dicot (legumes and lupin) root systems for length and water uptake suggested the existence of a longer root system in wheat compared with lupin. However, lupin had a higher water uptake per unit length as compared with wheat due to anatomical differences (Hamblin and Tennant, 1987; Bramley et al., 2009). RSA in legumes is also partially determined by the symbiotic interaction along with soil conditions. Roots in legumes have N-fixing nodules for symbiotic interactions with soil bacteria (Oldroyd and Downie, 2004). A common regulatory pathway between nodule formation and root development was identified among legumes (Penmetsa et al., 2003; Veereshlingam et al., 2004; Bright et al., 2005). RSA in symbiotic and non-symbiotic conditions is at least partially controlled by the same genes through an autoregulation mechanism controlling hypernodulating or supernodulating effects (Wopereis et al., 2000; Krusell et al., 2002). Genetic diversity and analysis of RSA in legumes Soybean As a major cash crop, soybean has been intensively studied for RSA. Improved RSA has been shown to alleviate drought stress by increasing exploration for water and nutrients in soybean (Hoogenboom et al., 1987). Natural variations in RSA were reported in soybean, and can be utilized for improvement of drought tolerance (Carter, 1989). In a field evaluation, a drought-tolerant landrace (PI 416937) showed more fibrous roots and exploration of a larger volume of soil (Hudak and Patterson, 1996). Recently several soybean accessions were identified showing promising RSA for extensive fibrous rooting, root length, or narrow root angle by screening of a core set (~400 lines) of the USDA Germplasm Collection. Genetic diversity in root anatomy of soybean is also reported, which affected water movement through root systems, as variations were observed in the number of metaxylems in soybean roots and the metaxylems were found to be induced during drought (Rincon et al., 2003; Prince et al., 2017). More metaxylems help in water transportation in the roots and protect yield under drought stress (Prince et al., 2017). QTL mapping has been conducted in soybean populations for RSA traits. Initially, five QTLs associated with fibrous rooting were mapped between the soybean lines Benning and PI 416937 under field conditions, with four out of the five QTLs donated by the drought-tolerant parent PI 416937 (Abdel-Haleem et al., 2011). In a bi-parental recombinant inbred population, one QTL cluster was mapped for both root length and lateral root number with the donor alleles from an older cultivar, Dunbar (Manavalan et al., 2015). This indicated the potential for some favorable alleles for RSA in some older soybean varieties and these genetic materials should be better candidates for breeding design (Table 1). Wild soybeans are valuable genetic resources for soybean improvement. One study identified four QTLs associated with surface area and root volume in a population of Glycine max×Glycine soja, and the favorable alleles of the QTLs were from the wild parent (PI 407162) (Prince et al., 2015). Recent results show that one rare allele can promote lateral root growth with the potential to improve drought tolerance. A GWAS of 252 phenotypically diverse soybean germplasms pinpointed this allele, and this single nucleotide polymorphism (SNP) can cause more proliferative lateral rooting in soybean (unpublished results). Further screening of the USDA Germplasm Collection found that the frequency of the favorable alleles at this locus is ~0.5% (unpublished results). As discussed above, several QTLs for RSA in soybean have been identified and validated (Table 1); however, the relationships between these QTLs and drought tolerance have not yet been evaluated. Therefore, further field evaluation of the effects of these QTLs on yield stability under drought conditions is needed. Table 1. Summary of QTLs associated with root system architecture and plasticity to drought in major legumes Species Trait QTL namea Donor parentb Chro.c Nearest markerd R2e Reference Soybean Fibrous rooting/ Surface area FR_Gm01 PI 416937 1 Satt383 7.5 Abdel-Haleem et al. (2011) FR_Gm03 PI 416937 3 Satt339 13.5 Abdel-Haleem et al. (2011) FR_Gm04 PI 416937 4 Satt713 7.3 Abdel-Haleem et al. (2011) FR_Gm08 PI 416937 8 Satt228 12.8 Abdel-Haleem et al. (2011) FR_Gm20 Benning 20 Sat_420 7.6 Abdel-Haleem et al. (2011) SA_Gm06 PI 407162 6 4222.1.S1_10 13 Prince et al. (2015) Root length TRL_Gm06 PI 407162 6 4222.1.S1_10 11 Prince et al. (2015) RDL3_Gm07 PI 407162 7 8398.1.S1_11 10 Prince et al. (2015) TRL_Gm08 Dunbar 8 Satt315 12.3 Manavalan et al. (2015) Lateral root number LRN_Gm08 Dunbar 8 Satt315 11 Manavalan et al. (2015) GRL1 PI 603713 16 Gm16_29679156 – Unpublished data Root thickness RDT2_Gm08 PI 407162 7 59884.1.S1_8 15 Prince et al. (2015) Cowpea Basal root angle Brg10 – 10 4510_497 – Burridge et al. (2017) Root diameter RD1 – 1 10905_418 – Burridge et al. (2017) Median width MW6 – 6 2227_693 – Burridge et al. (2017) Width accumulation WA10 – 10 4245_136 – Burridge et al. (2017) Pea Root length rl1 Messire VI OPZ17_1259 14 Fondevilla et al. (2010, 2011) rl2 Messire III OPB11_1003 17 Fondevilla et al. (2010, 2011) rl3 Messire II OPAI14_1353 11 Fondevilla et al. (2010, 2011) Common bean Basal root angle Brg1.1 DOR364 1 O20105G 14 Liao et al. (2004) Brg5.1 G19833 5 K124G 9.9 Liao et al. (2004) Brg5.2 G19833 5 GC13 15.9 Liao et al. (2004) Chickpea Root length density RLD4 ICC 4958 4 ICCM0249 12.1 Jaganathan et al. (2015) Root surface area RSA6 ICC 4958 6 H1F21 11 Jaganathan et al. (2015) Root dry weight ratio RDWR4 ICC 4958 4 ICCM0249 13.6 Jaganathan et al. (2015) Rooting depth/root length density QTL-hotspot ICC 4958 4 ICCM0249/ STMS11 11–47 Jaganathan et al. (2015) Species Trait QTL namea Donor parentb Chro.c Nearest markerd R2e Reference Soybean Fibrous rooting/ Surface area FR_Gm01 PI 416937 1 Satt383 7.5 Abdel-Haleem et al. (2011) FR_Gm03 PI 416937 3 Satt339 13.5 Abdel-Haleem et al. (2011) FR_Gm04 PI 416937 4 Satt713 7.3 Abdel-Haleem et al. (2011) FR_Gm08 PI 416937 8 Satt228 12.8 Abdel-Haleem et al. (2011) FR_Gm20 Benning 20 Sat_420 7.6 Abdel-Haleem et al. (2011) SA_Gm06 PI 407162 6 4222.1.S1_10 13 Prince et al. (2015) Root length TRL_Gm06 PI 407162 6 4222.1.S1_10 11 Prince et al. (2015) RDL3_Gm07 PI 407162 7 8398.1.S1_11 10 Prince et al. (2015) TRL_Gm08 Dunbar 8 Satt315 12.3 Manavalan et al. (2015) Lateral root number LRN_Gm08 Dunbar 8 Satt315 11 Manavalan et al. (2015) GRL1 PI 603713 16 Gm16_29679156 – Unpublished data Root thickness RDT2_Gm08 PI 407162 7 59884.1.S1_8 15 Prince et al. (2015) Cowpea Basal root angle Brg10 – 10 4510_497 – Burridge et al. (2017) Root diameter RD1 – 1 10905_418 – Burridge et al. (2017) Median width MW6 – 6 2227_693 – Burridge et al. (2017) Width accumulation WA10 – 10 4245_136 – Burridge et al. (2017) Pea Root length rl1 Messire VI OPZ17_1259 14 Fondevilla et al. (2010, 2011) rl2 Messire III OPB11_1003 17 Fondevilla et al. (2010, 2011) rl3 Messire II OPAI14_1353 11 Fondevilla et al. (2010, 2011) Common bean Basal root angle Brg1.1 DOR364 1 O20105G 14 Liao et al. (2004) Brg5.1 G19833 5 K124G 9.9 Liao et al. (2004) Brg5.2 G19833 5 GC13 15.9 Liao et al. (2004) Chickpea Root length density RLD4 ICC 4958 4 ICCM0249 12.1 Jaganathan et al. (2015) Root surface area RSA6 ICC 4958 6 H1F21 11 Jaganathan et al. (2015) Root dry weight ratio RDWR4 ICC 4958 4 ICCM0249 13.6 Jaganathan et al. (2015) Rooting depth/root length density QTL-hotspot ICC 4958 4 ICCM0249/ STMS11 11–47 Jaganathan et al. (2015) aName of the QTL that was generated in the corresponding reference or named afterwards in this review. bParental lines that contain the trait-favorable alleles. cChromosomes where the QTL are located. View Large Table 1. Summary of QTLs associated with root system architecture and plasticity to drought in major legumes Species Trait QTL namea Donor parentb Chro.c Nearest markerd R2e Reference Soybean Fibrous rooting/ Surface area FR_Gm01 PI 416937 1 Satt383 7.5 Abdel-Haleem et al. (2011) FR_Gm03 PI 416937 3 Satt339 13.5 Abdel-Haleem et al. (2011) FR_Gm04 PI 416937 4 Satt713 7.3 Abdel-Haleem et al. (2011) FR_Gm08 PI 416937 8 Satt228 12.8 Abdel-Haleem et al. (2011) FR_Gm20 Benning 20 Sat_420 7.6 Abdel-Haleem et al. (2011) SA_Gm06 PI 407162 6 4222.1.S1_10 13 Prince et al. (2015) Root length TRL_Gm06 PI 407162 6 4222.1.S1_10 11 Prince et al. (2015) RDL3_Gm07 PI 407162 7 8398.1.S1_11 10 Prince et al. (2015) TRL_Gm08 Dunbar 8 Satt315 12.3 Manavalan et al. (2015) Lateral root number LRN_Gm08 Dunbar 8 Satt315 11 Manavalan et al. (2015) GRL1 PI 603713 16 Gm16_29679156 – Unpublished data Root thickness RDT2_Gm08 PI 407162 7 59884.1.S1_8 15 Prince et al. (2015) Cowpea Basal root angle Brg10 – 10 4510_497 – Burridge et al. (2017) Root diameter RD1 – 1 10905_418 – Burridge et al. (2017) Median width MW6 – 6 2227_693 – Burridge et al. (2017) Width accumulation WA10 – 10 4245_136 – Burridge et al. (2017) Pea Root length rl1 Messire VI OPZ17_1259 14 Fondevilla et al. (2010, 2011) rl2 Messire III OPB11_1003 17 Fondevilla et al. (2010, 2011) rl3 Messire II OPAI14_1353 11 Fondevilla et al. (2010, 2011) Common bean Basal root angle Brg1.1 DOR364 1 O20105G 14 Liao et al. (2004) Brg5.1 G19833 5 K124G 9.9 Liao et al. (2004) Brg5.2 G19833 5 GC13 15.9 Liao et al. (2004) Chickpea Root length density RLD4 ICC 4958 4 ICCM0249 12.1 Jaganathan et al. (2015) Root surface area RSA6 ICC 4958 6 H1F21 11 Jaganathan et al. (2015) Root dry weight ratio RDWR4 ICC 4958 4 ICCM0249 13.6 Jaganathan et al. (2015) Rooting depth/root length density QTL-hotspot ICC 4958 4 ICCM0249/ STMS11 11–47 Jaganathan et al. (2015) Species Trait QTL namea Donor parentb Chro.c Nearest markerd R2e Reference Soybean Fibrous rooting/ Surface area FR_Gm01 PI 416937 1 Satt383 7.5 Abdel-Haleem et al. (2011) FR_Gm03 PI 416937 3 Satt339 13.5 Abdel-Haleem et al. (2011) FR_Gm04 PI 416937 4 Satt713 7.3 Abdel-Haleem et al. (2011) FR_Gm08 PI 416937 8 Satt228 12.8 Abdel-Haleem et al. (2011) FR_Gm20 Benning 20 Sat_420 7.6 Abdel-Haleem et al. (2011) SA_Gm06 PI 407162 6 4222.1.S1_10 13 Prince et al. (2015) Root length TRL_Gm06 PI 407162 6 4222.1.S1_10 11 Prince et al. (2015) RDL3_Gm07 PI 407162 7 8398.1.S1_11 10 Prince et al. (2015) TRL_Gm08 Dunbar 8 Satt315 12.3 Manavalan et al. (2015) Lateral root number LRN_Gm08 Dunbar 8 Satt315 11 Manavalan et al. (2015) GRL1 PI 603713 16 Gm16_29679156 – Unpublished data Root thickness RDT2_Gm08 PI 407162 7 59884.1.S1_8 15 Prince et al. (2015) Cowpea Basal root angle Brg10 – 10 4510_497 – Burridge et al. (2017) Root diameter RD1 – 1 10905_418 – Burridge et al. (2017) Median width MW6 – 6 2227_693 – Burridge et al. (2017) Width accumulation WA10 – 10 4245_136 – Burridge et al. (2017) Pea Root length rl1 Messire VI OPZ17_1259 14 Fondevilla et al. (2010, 2011) rl2 Messire III OPB11_1003 17 Fondevilla et al. (2010, 2011) rl3 Messire II OPAI14_1353 11 Fondevilla et al. (2010, 2011) Common bean Basal root angle Brg1.1 DOR364 1 O20105G 14 Liao et al. (2004) Brg5.1 G19833 5 K124G 9.9 Liao et al. (2004) Brg5.2 G19833 5 GC13 15.9 Liao et al. (2004) Chickpea Root length density RLD4 ICC 4958 4 ICCM0249 12.1 Jaganathan et al. (2015) Root surface area RSA6 ICC 4958 6 H1F21 11 Jaganathan et al. (2015) Root dry weight ratio RDWR4 ICC 4958 4 ICCM0249 13.6 Jaganathan et al. (2015) Rooting depth/root length density QTL-hotspot ICC 4958 4 ICCM0249/ STMS11 11–47 Jaganathan et al. (2015) aName of the QTL that was generated in the corresponding reference or named afterwards in this review. bParental lines that contain the trait-favorable alleles. cChromosomes where the QTL are located. View Large Cowpea Cowpea [Vigna unguiculata (L.) Walp] is a primary protein source and food security crop in Africa and South America. Cowpea plays a significant role in producing nitrogen in low-input systems (Singh et al., 1997, 2003; Huynh et al., 2013). Cowpea is also cultivated to adapt environments commonly experiencing drought stress (Yadav et al., 2015). Roots of 188 accession of cowpea were sampled from the field at the mature stage and analyzed for RSA (Bucksch et al., 2014). Large variations in RSA were identified in this diverse set, including root length, diameters, branching angles, density, and spatial root distribution estimations. The accessions, such as SuVita-2 and UCR 3310, with promising RSA could be used to breed drought-tolerant cowpea varieties. QTL mapping work for RSA has been reported in cowpea recently. GWAS on the same diverse set of 188 accessions identified 18 QTL clusters from manually and digitally scored RSA traits (Burridge et al., 2017). Subsequent comparisons of results from this root study with other field studies revealed four QTLs co-localized with seed weight per plant, pod number, or Striga (Striga gesnerioides) tolerance (listed in Table 1). These results suggest that selection for root phenotypes with MAS could be employed in breeding programs aiming to improve yield and yield stability in stress environments. Pea Pea (Pisum sativum L.) is the most widely grown legume crop in Europe and the fourth most widely grown worldwide (FAO 2010). Pea is traditionally produced in marginal areas and has received little attention from producers compared with other legume crops, such as soybean. Evaluation of RSA in pea started in the early 2000s. Significant variations in RSA were observed in a core collection of 330 pea accessions, and the total root length and root dry weight were positively correlated with seed size (McPhee, 2005). The top 10% of lines for total root production were suggested in this report for a breeding program with PI 261631; an accession from Spain produced the greatest total root length, largest root/shoot weight ratio, and the largest root volume (McPhee, 2005). Initial QTL mapping was conducted in a recombinant inbred line (RIL) population derived from a cross between P665 and Messire. Three QTLs associated with root length were mapped on chromosomes (Chr.) 3 and 4 where they co-localized with QTLs for weed and fungal resistance (Fondevilla et al., 2010, 2011; listed in Table 1). In the subsequent work, three QTLs were mapped for these drought tolerance-related traits and two of them were co-located with the previously reported root length QTL (Iglesias-García et al., 2015). These results further confirmed the two QTLs associated with the RSA and revealed their real effects in drought tolerance. Common bean Common bean (Phaseolus vulgaris L.) is the most important food legume, providing protein for a population of >500 million. It also provides necessary vitamins and micronutrients to over a billion people in developing countries (FAO, 2000). Common bean is usually grown in marginal areas, where soil is less fertile, and fertilizers and irrigation are not affordable. Therefore, improvement of stress resistance of common bean is urgent for these areas to maintain crop production. In an interspecific population, 86 RILs were derived from a cross between a deep-rooted genotype (DOR364) and a shallow-rooted genotype (G19833) (Liao et al., 2004). QTL mapping was conducted in this population and 16 QTLs were mapped for root gravitropic traits, including basal root angle, shallow root length, and relative shallow basal root length, in which three basal root angle QTLs were found to be associated with deep rooting (Liao et al., 2004; Table 1). The subsequent study identified that four QTLs associated with total root length in the field and one major QTL on Chr. B4 could explain 21% of phenotypic variation (Ochoa et al., 2006; Table 1). In this study, adventitious root was positively correlated with shoot mass, and two major QTLs associated with adventitious root were also identified, together explaining 61% of phenotypic variation (Ochoa et al., 2006). These results provide a potential genetic and molecular resource for MAS to develop new cultivars with improved RSA. Chickpea Chickpea (Cicer arietinum L.) is the second most important food legume after common bean worldwide (FAOSTAT, 2014). It is mostly grown in low-input conditions especially in semi-arid regions of sub-Saharan Africa and South Asia, as an important component of subsistence farming. Drought stress alone accounts for 40% of yield losses annually to global chickpea production (Jukanti et al., 2015). Strategic crop improvement programs rely on genetic improvement efforts aiming at incorporation of beneficial traits, such as better RSA, into elite cultivars (Subbarao et al., 1995; Varshney et al., 2009). Root biomass and root length density that aid in greater soil moisture extraction were identified as important root traits during terminal drought (Kashiwagi et al., 2006, 2007; Varshney et al., 2011). Extensive phenotyping and genotyping of two intraspecific RIL populations [ICC 4958×ICC 1882 (ICCRIL03) and ICC 283×ICC 8261 (ICCRIL04)] were conducted to map QTLs for various drought tolerance traits, including RSA in chickpea (Varshney et al., 2014). ICC 4958 and ICC 8261 were identified to be drought- tolerant genotypes with larger root systems (Saxena et al., 1993; Kashiwagi et al., 2005), and the other two contrasting parents, ICC 1882 and ICC 283, were landraces with shorter root systems (Kashiwagi et al., 2005). Three QTLs, one each for root length density (CaLG04), root surface area (CaLG06), and root dry weight/total plant dry weight ratio (CaLG04), have been identified (Jaganathan et al., 2015). This study also resulted in identification of an important genomic region, the ‘QTL hotspot’, harboring QTLs (M-QTL and E-QTL) for several RSA and drought tolerance traits (Jaganathan et al., 2015). Fine-mapping of this ‘QTL-hotspot’ delimited the candidate region from 29 cM to 14 cM on a genetic map, and several stress-related candidate genes were identified in this region (Jaganathan et al., 2015). This ‘QTL hotspot’ was introgressed from the donor parent ICC 4958 into JG 11 (a leading chickpea variety) using the marker-assisted backcrossing (MABC) strategy (Jaganathan et al., 2015). Twenty-nine BC3F2 plants, referred to as introgression lines (ILs), showed greater rooting depth in all 29 ILs as compared with both recurrent parents (Jaganathan et al., 2015). These ILs have shown better performance in comparison with the recurrent parents under rainfed as well as in irrigated environments (Jaganathan et al., 2015). More recently, another study identified 13 QTLs associated with root traits in the same two intraspecific populations through the development of an SNP genotyping array for chickpea (Roorkiwal et al., 2017). Lentil Lentil (Lens culinaris Medik.) is considered as a staple food in developing countries for its rich source of proteins and micronutrients. It is considered as an important component in crop rotation programs due to its ability to improve soil fertility by atmospheric nitrogen fixation. Large genetic variability and high heritability in root and shoot traits related to drought tolerance were observed in 133 RILs from a cross of ILL6002 (superior root and shoot traits and grain yield)×ILL5888 (pure line, short cultivar with prostrate growth habit) (Idrissi et al., 2015). Identification of QTLs or genes related to studied traits in this population would be a first step for starting MAS. Advances in breeding for drought tolerance Lines with a deep root system are able to extract water from the soil and enable the plants to survive in water-limited conditions. Despite the importance of the root system, there have been limited efforts directed towards developing breeding lines with an improved root system. In the case of chickpea, the role of the root system in the improvement of crop productivity under water-limited conditions has been well studied and established. Greater exploitation of subsoil water due to deeper root systems has been shown to improve chickpea yield under drought conditions (Kashiwagi et al., 2015). Similarly, in the case of soybean, the root system is important to protect yield under water-limited conditions. In order to determine the role of the root in crop productivity under water-limited conditions, different root traits including root length, root surface area, average root diameter, and average root volume were compared under normal and drought stress conditions (Zenis et al., 2016). This study confirmed the importance of a better root system in drought tolerance. Root traits have a direct impact on yield under drought stress among legumes (see table 2 in Farooq et al., 2016), suggesting the root as a significant target for further breeding. Although there have been many efforts to identify genetic diversity and QTLs/genes that control RSA, there have been limited efforts to utilize these genetic resources in breeding. In chickpea, efforts have been undertaken to introgress the ‘QTL-hotspot’ region from line CaLG04 into the elite chickpea line (JG11) by the MABC strategy. Introgression lines have shown improved performance and enhanced yield under drought stress (Varshney et al., 2013). Currently, efforts are being made to introgress this genomic region to several other elite chickpea lines (Pandey et al., 2016). To date, rich genetic resources for RSA have been identified in legume crops, but those resources are yet to be implemented in breeding for developing stress-tolerant lines. Genotypes with differential RSA patterns should be utilized for developing stress-tolerant lines with improved RSA for each specific condition. Conclusions and future perspectives The sustainability of crop production and food security is being threatened by the increasing unpredictability and severity of drought stress due to global climate changes. Genetic variations for stress tolerance in natural populations reflect the evolution of plants in diverse ecological conditions. Incorporation of these adapted natural genetic variations into breeding programs can enrich the current genetic diversity of stress tolerance and improve yield under stress. Screening germplasm for stress tolerance traits has provided donor resources with potential to improve yield under stress conditions. Identification of the genomic regions underlying these natural genetic variations and transferring the favorable alleles into elite germplasm with the assistance of genomic technologies will speed up the genetic improvement of stress tolerance in legumes. QTLs associated with RSA were identified in major legume crops, and associated DNA markers have been developed for MAS. Some of these QTLs or genes can be lost during domestication processes due to their linkage to negative loci or pleiotropic effects on yield. Subsequent fine-mapping is needed to determine whether the yield drag effects are due to pleiotropy or linkage to other negative loci. Screening a large population of segregants can break the tight linkage with the assistance of MAS. In addition, cloning and functional characterization of genes underlying the natural genetic variations can lead to understanding of the molecular and physiological pathways of stress adaptation, which will direct further improvement of stress tolerance in legumes. High-throughput and cost-effective phenotyping and genotyping technologies are foundations of genomics-assisted breeding programs. High-throughput phenotyping platforms that monitor RSA are essential to advance our knowledge of drought tolerance. High-throughput phenotyping of RSA has focused on field-based approaches in crops such as maize (Trachsel et al., 2011; York et al., 2015), rice (Wissuwa et al., 2016), and wheat (Richard et al., 2015), with a recent shift to legume species, such as soybean, common bean, and cowpea (Manavalan et al., 2015; Prince et al., 2015; Burridge et al., 2016). The adoption of high-throughput image analysis using DIRT will further improve the efficiency and accuracy of the phenotyping of RSA (Burridge et al., 2016). Until the last decade, legumes have fallen into the category of orphan crops with fewer genomic resources. Recent advances in sequencing technology have brought legumes into the category of genomic resource-rich crops. The availability of these resources resulted in the initiation of several crop improvement programs based on genomic-assisted breeding. MAS and genomic selection are the major genomic-assisted breeding approaches deployed in legume improvement programs (Pandey et al., 2016). MAS is mostly used to address the issues related to simple traits that are controlled by one or two genes. However, for complex traits, MAS has not been that successful. Recently, genomic selection has emerged. It has the ability to target many complex traits at once and holds the potential for genetic improvement at a faster pace (Crossa et al., 2017). High-precision phenotyping and genotyping have resulted in precise selection of genotypes with a reduced breeding cycle time by avoiding unnecessary rounds of phenotyping. The ability to capture both small and large effect genetic factors adds further advantages to the genomic selection strategy. Reduced cost of genotyping has resulted in establishment of a large range of genetic resources among legumes in the past decade. These resources are now providing required information for various genomic-assisted breeding approaches to enhance the genetic gains. Further establishment of high-throughput genotyping platforms in various legume crops has initiated the genomic selection efforts in legumes (Jain et al., 2013). Genetic modification has emerged as another important tool for crop improvement (Hussain, 2015). It provides an opportunity to edit targeted genes for specific needs. The targeted genome editing by the CRISPR/Cas9 (clustered regularly interspaced short palindromic repeat/CRISPR-associated protein 9) system (Jacobs et al., 2015; Sun et al., 2015) can enable further modification of RSA-related alleles to maximize the performance of the loci. In addition to soybean genome information, the release of reference genomes and continuous building up of resequencing information of various legume crops can strengthen the comparative legume genomics for discovery and characterization of key genes and gene families involved in stress tolerance. High-density genome variation information available for major legumes can facilitate haplotype mining of the traits of interest and can lead to the design of haplotype-assisted breeding. 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Quantitative trait loci for root-penetration ability and root thickness in rice: comparison of genetic backgrounds . Genome 43 , 53 – 61 . Google Scholar CrossRef Search ADS © The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Experimental Botany Oxford University Press

Genetic diversity of root system architecture in response to drought stress in grain legumes

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com
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0022-0957
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1460-2431
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10.1093/jxb/ery082
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Abstract

Abstract Climate change has increased the occurrence of extreme weather patterns globally, causing significant reductions in crop production, and hence threatening food security. In order to meet the food demand of the growing world population, a faster rate of genetic gains leading to productivity enhancement for major crops is required. Grain legumes are an essential commodity in optimal human diets and animal feed because of their unique nutritional composition. Currently, limited water is a major constraint in grain legume production. Root system architecture (RSA) is an important developmental and agronomic trait, which plays vital roles in plant adaptation and productivity under water-limited environments. A deep and proliferative root system helps extract sufficient water and nutrients under these stress conditions. The integrated genetics and genomics approach to dissect molecular processes from genome to phenome is key to achieve increased water capture and use efficiency through developing better root systems. Success in crop improvement under drought depends on discovery and utilization of genetic variations existing in the germplasm. In this review, we summarize current progress in the genetic diversity in major legume crops, quantitative trait loci (QTLs) associated with RSA, and the importance and applications of recent discoveries associated with the beneficial root traits towards better RSA for enhanced drought tolerance and yield. Climate change, drought tolerance, genomics, genomics-assisted breeding, legumes, natural variation Introduction Climate change has increased the frequency of extreme weather patterns including irregular precipitation, which can cause drought stress and adversely affect crop production (Lesk et al., 2016). Yield losses of major crops under such fluctuating weather patterns are increasing, despite the progressive efforts in yield improvement through breeding and management practices since the 1960s (Boyer, 1983; Lobell et al., 2014; Rosenzweig et al., 2014). Estimates show that crop yields would decline 30–82% by the end of the 21st century under the current climate change, despite increasing concentrations of CO2 that could benefit photosynthesis (Hatfield et al., 2011). Legumes form a major part of the human diet and animal feed, and are used for biofuel due to their oil content. Besides food and nutritional security, legumes act as a major cash crop, generating US$31 billion per annum to a large number of smallholders (>700 million) in developing nations (Abate et al., 2012). Legumes are known not only as an affordable protein-rich source, but also for their environmental and economic value. Inclusion of legumes in crop rotation programs leads to a positive environmental effect by improving the soil nutritional profile via symbiotic nitrogen fixation; it aids in sustainable agricultural production by promoting the yield of subsequent crops as well. It has been reported that when grown in rotation with cereals, in addition to improving the soil nutritional profile, they prevent soil erosion, and further lead to reduced incidences of soil pathogens (Daryanto et al., 2015). However, only a limited yield increase of 0–2% per year is occurring in legume crops (Foyer et al., 2016). Abruptly changing climatic conditions are having a negative impact on legume production. For example, soybean yields in the USA declined by 2–4% for every degree rise in temperature during the growing season, resulting a loss of US$11 billion (Mourtzinis et al., 2015). Rising global temperatures were reported to reduce the areas suitable for bean production due to increased drought stress (Beebe et al., 2011). Yields will decrease in developing countries in the tropics and subtropics such as India, Pakistan, and Ethiopia due to the increased occurrence of drought stress (Andrews and Hodge, 2010). A large yield gap for legume crops exists in Africa of >300% due to water-limiting issues. Moreover, as an integral part of Indian agriculture, legume crop production has remained unstable, with a yield gap for soybean ranging from 850–1320 kg ha–1, for groundnut from 1180–2010 kg ha–1, for pigeon pea from 550–770 kg ha–1, and for chickpea from 610–1150 kg ha–1 (Bhatia et al., 2006; Anderson et al., 2016). Plants have evolved stress adaptation mechanisms under natural selection. Exotic and wild relatives tend to have better stress-resistant characteristics and maintain natural variations favorable for stress resistance (Mickelbart et al., 2015). Incorporation of these favorable natural variations into current elite germplasm is essential to improve the performance of cultivars under stress conditions. This can be achieved through the identification of resistant genetic resources, discovery of favorable natural variations, and finally the incorporation of these natural variations into the elite cultivars. These natural variations can be identified through quantitative trait locus (QTL) mapping and genome-wide association studies (GWAS) (Takeda and Matsuoka, 2008; Varshney et al., 2015). With the assistance of genomic strategies, these natural genetic variations can be identified and expeditiously incorporated into the cultivars by marker-assisted selection (MAS) and genomic selection strategies. Characterization of the candidate genes underlying these natural variations will further elucidate biological mechanisms, which consequently will be translated to crop improvement through genetic modifications. Drought tolerance refers to the ability of plants to survive, grow, and reproduce under water deficit conditions (Levitt, 1972; Turner et al., 1978). In crops, drought tolerance actually suggests the ability of plants to yield under stress conditions (Turner, 2000; Fleury et al., 2010). Crop yield was determined by three major components: water use, water use efficiency, and harvest index (yield=water use×water use efficiency×harvest index) (Passioura, 1977, 1983; Turner, 2000; Turner et al., 2001). Studies clearly show a positive correlation between the amount of water reduction and yield reduction, whereas the degree of this correlation varies within legume species and the developmental stages at which plant encountered the drought stress (Daryanto et al., 2015). Acquisition of more water resource in plants is a key solution to improve drought tolerance in crops (Passioura, 1977, 1983). Thus, the development and distribution of the root system can be regarded as key factors for more efficient water uptake and thereby for managing the performance of legumes under drought stress (Newman and Moser, 1988; Gaur et al., 2008). Root traits are usually described as root system architecture (RSA), referring to the shape and physical space of the roots. A deeper and proliferative root system is able to avoid drought stress by its ability to acquire more water resource. RSA is also highly plastic to environmental changes, including water deficit stress. When plants perceive water deficit stress, roots tend to keep growing and penetrate into deeper soil layers (Hoogenboom et al., 1987; Sponchiado et al., 1989; Creelman et al., 1990; Wu et al., 1994). Root system architecture in drought tolerance Fifty decades ago, Kramer (1969) stated one of the essential characteristics of drought tolerance: ‘deep, wide-spreading, much-branched root system’. In order to access enhanced available soil moisture, plant adapts to greater rooting depth and root biomass (Blum, 2011; Fenta et al., 2014; Fig. 1). The benefit of a deep and proliferative root system for drought tolerance has been reported in various crops, including rice (Nguyen et al., 1997; Babu et al., 2003; Kato et al., 2006; MacMillan et al., 2006; Steele et al., 2006; Bernier et al., 2009; Uga et al., 2013), maize (Tuberosa et al., 2002; Giuliani et al., 2005; Hammer et al., 2009; Landi et al., 2010; Hund et al., 2011), barley (Forster et al., 2005), wheat (Manschadi et al., 2006; Wasson et al., 2012), chickpea (Varshney et al., 2011; Chen et al., 2012), and soybean (Hudak and Patterson, 1995; Pantalone et al., 1996; Sadok and Sinclair, 2011). The diameter and distribution of the metaxylem vessels that govern root conductivity are also known to provide drought tolerance in legumes (Purushothaman et al., 2013). Fig. 1. View largeDownload slide Roles of root system architecture in the improvement of drought tolerance. Fig. 1. View largeDownload slide Roles of root system architecture in the improvement of drought tolerance. Deep rooting is a complex trait affected by growth angle and root length (Araki et al., 2002; Fig. 1). Root angle determines the direction of horizontal and vertical distribution of roots in the soil, and is recognized as an essential trait for drought avoidance in sorghum (Mace et al., 2012), wheat (Christopher et al., 2013), and rice (Uga et al., 2013). Root angle was correlated with rooting depth in rice (Kato et al., 2006), chickpea (Sayar et al., 2007; Kashiwagi et al., 2015), and sorghum (Singh et al., 2011). Wider root angles could reduce the energy inputs to penetrate in deeper horizons to access water during limited rainfall (Wasson et al., 2012; Meister et al., 2014). A natural genetic variation in root angle was cloned in rice as DEEPER ROOTING 1 (DRO1) (Uga et al., 2013). Dro1 doubled the yield under moderate and severe drought stress due to its ability to develop a deep rooting system (Uga et al., 2013). Another deep rooting determinant is root diameter/thickness and penetrability (Fig. 1). Thicker roots tend to penetrate deeper through soil layers for deep rooting (Yu et al., 1995; Zheng et al., 2000). Proliferative rooting is mainly determined by lateral root initiation and elongation, which usually refers to lateral root number, root length density, and root surface area and volume (Fig. 1). Proliferative roots have relatively high water uptake efficiency in water deficit soils. Chickpea lines with higher root length density have shown clearly a better performance in yield and drought tolerance-related traits under water deficit environments (Jaganathan et al., 2015). In addition to deep and proliferative rooting, drought stress also induces the plasticity responses of root systems by increasing the number of fibrous roots, and decreasing lateral root diameter and fluctuations in root biomass (Nielsen et al., 1997; Osmont et al., 2007; Meister et al., 2014; Salazar-Henao et al., 2016). Drought-tolerant lines tend to elongate their rooting depth significantly more than sensitive lines under drought stress in legumes (Garay and Wilhelm, 1983; Sponchiado et al., 1989). Alterations in root anatomy, such as aerenchyma formation in maize (Lynch, 2011; Burton et al., 2013) and soybean (Prince et al., 2017), saves the energy inputs to allow improved soil penetration and exploration to mitigate water deficit (Addington et al., 2006; Maseda and Fernández, 2006). The number of metaxylems was identified to be correlated with drought tolerance in soybean, and more metaxylems help in water transportation in the roots (Prince et al., 2017; Fig. 1). Root phenotyping and modeling RSA Root phenotyping in the laboratory, greenhouse, and field encompasses simple agar plates and germination papers, labor-intensive root digging in the field, an underground root observation facility, and sophisticated computer-assisted root imaging equipment (reviewed by Paez-Garcia et al., 2015). Primary RSA traits include primary root length, root length density, root angle, fibrous and lateral roots, root diameters, and root anatomy. In laboratory conditions, a larger numbers of samples under well-controlled conditions can be phenotyped; however, RSA may be affected by the containers and cannot reflect real growth stages and environmental conditions. Greenhouse evaluation is an intermediate system between the laboratory and field. However, it has space limitations and requires more labor input. Field evaluation reflects the real performance of roots, correlating plant physiology with RSA well; however, intensive investment in labor is needed. Currently, high-throughput phenotyping of RSA has focused on field-based approaches in crops such as maize (York and Lynch, 2015), rice (Wissuwa et al., 2016), and wheat (Richard et al., 2015). With a recent shift from studying vegetative stage root traits in the greenhouse (Manavalan et al., 2015; Prince et al., 2015) to more field-based reproductive stage research (Burridge et al., 2016) in legume species, knowledge of the genetic regulation of RSA has improved. The challenges of digging for a large number of plants using a ‘Shovelomics’ (Trachsel et al., 2011) approach have been offset by the development and application of high-throughput image analysis platforms such as digital imaging of root traits (DIRT) (Bucksch et al., 2014; Das et al., 2015). The adoption of digital image analysis platforms for field-grown plants is gaining prevalence in legumes. Mechanistic understanding of RSA and plasticity can be developed from functional–structural plant modeling systems. These modeling systems rely on accurate phenotyping techniques and integrate structural, physiological, and environmental information to simulate the overall understanding of how roots distribute in soil and interact with different environmental factors (Ndour et al., 2017). It requires involvement of various fields, including plant physiology, soil science, engineering, mathematics, computer science, and so on. Many of these models have been developed and used to simulate the development of the plant with water acquisition and transportation (reviewed by Ndour et al., 2017). These models aim to quantify RSA and set up descriptors of RSA, and eventually identify mechanisms of RSA and plasticity under given stress environments (reviewed by Ndour et al., 2017). Recently, some new RSA models have been developed for various purposes to predict RSA and interaction with environments more accurately. This includes a root hydraulics model to estimate plant-scale hydraulic parameters from RSA (Meunier et al., 2017), OPENSIMROOT for simulation of RSA, nutrient acquisition, and plant growth (Postma et al., 2017), and RootBox to simulate matured RSA from seedling RSA (Leitner et al., 2010; Zhao et al., 2017). Challenges exist in developing and improving RSA models, such as the bottleneck of root phenotyping techniques, difficulties under field conditions, and integration of the plant growing ecosytems (reviewed by Ndour et al., 2017). Overall, the future of RSA modeling relies on successful integration of all biological scales. Root system architecture in legumes and cereals Roots are one of the major organs for transmitting different resources from soil, and thereby control productivity (Lynch, 1995). Plants have the capacity to customize their root architecture in order to adapt to different environments, including stress, through integrating the genetic programs controlling root growth (Jovanovic et al., 2008). Plants with different genetic and nutritional conditions have shown extensive differences in root structure in terms of the number and distribution of root systems, which allows plants to recover efficiently when transiting from stress to normal conditions (López-Bucio et al., 2003). In the case of cereals, the root system is monocotyledonous, comprising primary root(s) generated from the seed embryo and axile nodal roots. The number of primary roots varies from one in warm-climate cereals to six or seven in cool-climate cereals. However, in the case of a dicotyledonous root system (Arabidopsis, Medicago, and legumes), a single primary root was generated from the embryo with successive orders of branch/lateral roots (Sorin et al., 2005; Rich and Watt, 2013). The number and size of lateral roots are determining factors for RSA in legumes (Dubrovsky et al., 2006). Comparative analysis of monocot (cereals and wheat) and dicot (legumes and lupin) root systems for length and water uptake suggested the existence of a longer root system in wheat compared with lupin. However, lupin had a higher water uptake per unit length as compared with wheat due to anatomical differences (Hamblin and Tennant, 1987; Bramley et al., 2009). RSA in legumes is also partially determined by the symbiotic interaction along with soil conditions. Roots in legumes have N-fixing nodules for symbiotic interactions with soil bacteria (Oldroyd and Downie, 2004). A common regulatory pathway between nodule formation and root development was identified among legumes (Penmetsa et al., 2003; Veereshlingam et al., 2004; Bright et al., 2005). RSA in symbiotic and non-symbiotic conditions is at least partially controlled by the same genes through an autoregulation mechanism controlling hypernodulating or supernodulating effects (Wopereis et al., 2000; Krusell et al., 2002). Genetic diversity and analysis of RSA in legumes Soybean As a major cash crop, soybean has been intensively studied for RSA. Improved RSA has been shown to alleviate drought stress by increasing exploration for water and nutrients in soybean (Hoogenboom et al., 1987). Natural variations in RSA were reported in soybean, and can be utilized for improvement of drought tolerance (Carter, 1989). In a field evaluation, a drought-tolerant landrace (PI 416937) showed more fibrous roots and exploration of a larger volume of soil (Hudak and Patterson, 1996). Recently several soybean accessions were identified showing promising RSA for extensive fibrous rooting, root length, or narrow root angle by screening of a core set (~400 lines) of the USDA Germplasm Collection. Genetic diversity in root anatomy of soybean is also reported, which affected water movement through root systems, as variations were observed in the number of metaxylems in soybean roots and the metaxylems were found to be induced during drought (Rincon et al., 2003; Prince et al., 2017). More metaxylems help in water transportation in the roots and protect yield under drought stress (Prince et al., 2017). QTL mapping has been conducted in soybean populations for RSA traits. Initially, five QTLs associated with fibrous rooting were mapped between the soybean lines Benning and PI 416937 under field conditions, with four out of the five QTLs donated by the drought-tolerant parent PI 416937 (Abdel-Haleem et al., 2011). In a bi-parental recombinant inbred population, one QTL cluster was mapped for both root length and lateral root number with the donor alleles from an older cultivar, Dunbar (Manavalan et al., 2015). This indicated the potential for some favorable alleles for RSA in some older soybean varieties and these genetic materials should be better candidates for breeding design (Table 1). Wild soybeans are valuable genetic resources for soybean improvement. One study identified four QTLs associated with surface area and root volume in a population of Glycine max×Glycine soja, and the favorable alleles of the QTLs were from the wild parent (PI 407162) (Prince et al., 2015). Recent results show that one rare allele can promote lateral root growth with the potential to improve drought tolerance. A GWAS of 252 phenotypically diverse soybean germplasms pinpointed this allele, and this single nucleotide polymorphism (SNP) can cause more proliferative lateral rooting in soybean (unpublished results). Further screening of the USDA Germplasm Collection found that the frequency of the favorable alleles at this locus is ~0.5% (unpublished results). As discussed above, several QTLs for RSA in soybean have been identified and validated (Table 1); however, the relationships between these QTLs and drought tolerance have not yet been evaluated. Therefore, further field evaluation of the effects of these QTLs on yield stability under drought conditions is needed. Table 1. Summary of QTLs associated with root system architecture and plasticity to drought in major legumes Species Trait QTL namea Donor parentb Chro.c Nearest markerd R2e Reference Soybean Fibrous rooting/ Surface area FR_Gm01 PI 416937 1 Satt383 7.5 Abdel-Haleem et al. (2011) FR_Gm03 PI 416937 3 Satt339 13.5 Abdel-Haleem et al. (2011) FR_Gm04 PI 416937 4 Satt713 7.3 Abdel-Haleem et al. (2011) FR_Gm08 PI 416937 8 Satt228 12.8 Abdel-Haleem et al. (2011) FR_Gm20 Benning 20 Sat_420 7.6 Abdel-Haleem et al. (2011) SA_Gm06 PI 407162 6 4222.1.S1_10 13 Prince et al. (2015) Root length TRL_Gm06 PI 407162 6 4222.1.S1_10 11 Prince et al. (2015) RDL3_Gm07 PI 407162 7 8398.1.S1_11 10 Prince et al. (2015) TRL_Gm08 Dunbar 8 Satt315 12.3 Manavalan et al. (2015) Lateral root number LRN_Gm08 Dunbar 8 Satt315 11 Manavalan et al. (2015) GRL1 PI 603713 16 Gm16_29679156 – Unpublished data Root thickness RDT2_Gm08 PI 407162 7 59884.1.S1_8 15 Prince et al. (2015) Cowpea Basal root angle Brg10 – 10 4510_497 – Burridge et al. (2017) Root diameter RD1 – 1 10905_418 – Burridge et al. (2017) Median width MW6 – 6 2227_693 – Burridge et al. (2017) Width accumulation WA10 – 10 4245_136 – Burridge et al. (2017) Pea Root length rl1 Messire VI OPZ17_1259 14 Fondevilla et al. (2010, 2011) rl2 Messire III OPB11_1003 17 Fondevilla et al. (2010, 2011) rl3 Messire II OPAI14_1353 11 Fondevilla et al. (2010, 2011) Common bean Basal root angle Brg1.1 DOR364 1 O20105G 14 Liao et al. (2004) Brg5.1 G19833 5 K124G 9.9 Liao et al. (2004) Brg5.2 G19833 5 GC13 15.9 Liao et al. (2004) Chickpea Root length density RLD4 ICC 4958 4 ICCM0249 12.1 Jaganathan et al. (2015) Root surface area RSA6 ICC 4958 6 H1F21 11 Jaganathan et al. (2015) Root dry weight ratio RDWR4 ICC 4958 4 ICCM0249 13.6 Jaganathan et al. (2015) Rooting depth/root length density QTL-hotspot ICC 4958 4 ICCM0249/ STMS11 11–47 Jaganathan et al. (2015) Species Trait QTL namea Donor parentb Chro.c Nearest markerd R2e Reference Soybean Fibrous rooting/ Surface area FR_Gm01 PI 416937 1 Satt383 7.5 Abdel-Haleem et al. (2011) FR_Gm03 PI 416937 3 Satt339 13.5 Abdel-Haleem et al. (2011) FR_Gm04 PI 416937 4 Satt713 7.3 Abdel-Haleem et al. (2011) FR_Gm08 PI 416937 8 Satt228 12.8 Abdel-Haleem et al. (2011) FR_Gm20 Benning 20 Sat_420 7.6 Abdel-Haleem et al. (2011) SA_Gm06 PI 407162 6 4222.1.S1_10 13 Prince et al. (2015) Root length TRL_Gm06 PI 407162 6 4222.1.S1_10 11 Prince et al. (2015) RDL3_Gm07 PI 407162 7 8398.1.S1_11 10 Prince et al. (2015) TRL_Gm08 Dunbar 8 Satt315 12.3 Manavalan et al. (2015) Lateral root number LRN_Gm08 Dunbar 8 Satt315 11 Manavalan et al. (2015) GRL1 PI 603713 16 Gm16_29679156 – Unpublished data Root thickness RDT2_Gm08 PI 407162 7 59884.1.S1_8 15 Prince et al. (2015) Cowpea Basal root angle Brg10 – 10 4510_497 – Burridge et al. (2017) Root diameter RD1 – 1 10905_418 – Burridge et al. (2017) Median width MW6 – 6 2227_693 – Burridge et al. (2017) Width accumulation WA10 – 10 4245_136 – Burridge et al. (2017) Pea Root length rl1 Messire VI OPZ17_1259 14 Fondevilla et al. (2010, 2011) rl2 Messire III OPB11_1003 17 Fondevilla et al. (2010, 2011) rl3 Messire II OPAI14_1353 11 Fondevilla et al. (2010, 2011) Common bean Basal root angle Brg1.1 DOR364 1 O20105G 14 Liao et al. (2004) Brg5.1 G19833 5 K124G 9.9 Liao et al. (2004) Brg5.2 G19833 5 GC13 15.9 Liao et al. (2004) Chickpea Root length density RLD4 ICC 4958 4 ICCM0249 12.1 Jaganathan et al. (2015) Root surface area RSA6 ICC 4958 6 H1F21 11 Jaganathan et al. (2015) Root dry weight ratio RDWR4 ICC 4958 4 ICCM0249 13.6 Jaganathan et al. (2015) Rooting depth/root length density QTL-hotspot ICC 4958 4 ICCM0249/ STMS11 11–47 Jaganathan et al. (2015) aName of the QTL that was generated in the corresponding reference or named afterwards in this review. bParental lines that contain the trait-favorable alleles. cChromosomes where the QTL are located. View Large Table 1. Summary of QTLs associated with root system architecture and plasticity to drought in major legumes Species Trait QTL namea Donor parentb Chro.c Nearest markerd R2e Reference Soybean Fibrous rooting/ Surface area FR_Gm01 PI 416937 1 Satt383 7.5 Abdel-Haleem et al. (2011) FR_Gm03 PI 416937 3 Satt339 13.5 Abdel-Haleem et al. (2011) FR_Gm04 PI 416937 4 Satt713 7.3 Abdel-Haleem et al. (2011) FR_Gm08 PI 416937 8 Satt228 12.8 Abdel-Haleem et al. (2011) FR_Gm20 Benning 20 Sat_420 7.6 Abdel-Haleem et al. (2011) SA_Gm06 PI 407162 6 4222.1.S1_10 13 Prince et al. (2015) Root length TRL_Gm06 PI 407162 6 4222.1.S1_10 11 Prince et al. (2015) RDL3_Gm07 PI 407162 7 8398.1.S1_11 10 Prince et al. (2015) TRL_Gm08 Dunbar 8 Satt315 12.3 Manavalan et al. (2015) Lateral root number LRN_Gm08 Dunbar 8 Satt315 11 Manavalan et al. (2015) GRL1 PI 603713 16 Gm16_29679156 – Unpublished data Root thickness RDT2_Gm08 PI 407162 7 59884.1.S1_8 15 Prince et al. (2015) Cowpea Basal root angle Brg10 – 10 4510_497 – Burridge et al. (2017) Root diameter RD1 – 1 10905_418 – Burridge et al. (2017) Median width MW6 – 6 2227_693 – Burridge et al. (2017) Width accumulation WA10 – 10 4245_136 – Burridge et al. (2017) Pea Root length rl1 Messire VI OPZ17_1259 14 Fondevilla et al. (2010, 2011) rl2 Messire III OPB11_1003 17 Fondevilla et al. (2010, 2011) rl3 Messire II OPAI14_1353 11 Fondevilla et al. (2010, 2011) Common bean Basal root angle Brg1.1 DOR364 1 O20105G 14 Liao et al. (2004) Brg5.1 G19833 5 K124G 9.9 Liao et al. (2004) Brg5.2 G19833 5 GC13 15.9 Liao et al. (2004) Chickpea Root length density RLD4 ICC 4958 4 ICCM0249 12.1 Jaganathan et al. (2015) Root surface area RSA6 ICC 4958 6 H1F21 11 Jaganathan et al. (2015) Root dry weight ratio RDWR4 ICC 4958 4 ICCM0249 13.6 Jaganathan et al. (2015) Rooting depth/root length density QTL-hotspot ICC 4958 4 ICCM0249/ STMS11 11–47 Jaganathan et al. (2015) Species Trait QTL namea Donor parentb Chro.c Nearest markerd R2e Reference Soybean Fibrous rooting/ Surface area FR_Gm01 PI 416937 1 Satt383 7.5 Abdel-Haleem et al. (2011) FR_Gm03 PI 416937 3 Satt339 13.5 Abdel-Haleem et al. (2011) FR_Gm04 PI 416937 4 Satt713 7.3 Abdel-Haleem et al. (2011) FR_Gm08 PI 416937 8 Satt228 12.8 Abdel-Haleem et al. (2011) FR_Gm20 Benning 20 Sat_420 7.6 Abdel-Haleem et al. (2011) SA_Gm06 PI 407162 6 4222.1.S1_10 13 Prince et al. (2015) Root length TRL_Gm06 PI 407162 6 4222.1.S1_10 11 Prince et al. (2015) RDL3_Gm07 PI 407162 7 8398.1.S1_11 10 Prince et al. (2015) TRL_Gm08 Dunbar 8 Satt315 12.3 Manavalan et al. (2015) Lateral root number LRN_Gm08 Dunbar 8 Satt315 11 Manavalan et al. (2015) GRL1 PI 603713 16 Gm16_29679156 – Unpublished data Root thickness RDT2_Gm08 PI 407162 7 59884.1.S1_8 15 Prince et al. (2015) Cowpea Basal root angle Brg10 – 10 4510_497 – Burridge et al. (2017) Root diameter RD1 – 1 10905_418 – Burridge et al. (2017) Median width MW6 – 6 2227_693 – Burridge et al. (2017) Width accumulation WA10 – 10 4245_136 – Burridge et al. (2017) Pea Root length rl1 Messire VI OPZ17_1259 14 Fondevilla et al. (2010, 2011) rl2 Messire III OPB11_1003 17 Fondevilla et al. (2010, 2011) rl3 Messire II OPAI14_1353 11 Fondevilla et al. (2010, 2011) Common bean Basal root angle Brg1.1 DOR364 1 O20105G 14 Liao et al. (2004) Brg5.1 G19833 5 K124G 9.9 Liao et al. (2004) Brg5.2 G19833 5 GC13 15.9 Liao et al. (2004) Chickpea Root length density RLD4 ICC 4958 4 ICCM0249 12.1 Jaganathan et al. (2015) Root surface area RSA6 ICC 4958 6 H1F21 11 Jaganathan et al. (2015) Root dry weight ratio RDWR4 ICC 4958 4 ICCM0249 13.6 Jaganathan et al. (2015) Rooting depth/root length density QTL-hotspot ICC 4958 4 ICCM0249/ STMS11 11–47 Jaganathan et al. (2015) aName of the QTL that was generated in the corresponding reference or named afterwards in this review. bParental lines that contain the trait-favorable alleles. cChromosomes where the QTL are located. View Large Cowpea Cowpea [Vigna unguiculata (L.) Walp] is a primary protein source and food security crop in Africa and South America. Cowpea plays a significant role in producing nitrogen in low-input systems (Singh et al., 1997, 2003; Huynh et al., 2013). Cowpea is also cultivated to adapt environments commonly experiencing drought stress (Yadav et al., 2015). Roots of 188 accession of cowpea were sampled from the field at the mature stage and analyzed for RSA (Bucksch et al., 2014). Large variations in RSA were identified in this diverse set, including root length, diameters, branching angles, density, and spatial root distribution estimations. The accessions, such as SuVita-2 and UCR 3310, with promising RSA could be used to breed drought-tolerant cowpea varieties. QTL mapping work for RSA has been reported in cowpea recently. GWAS on the same diverse set of 188 accessions identified 18 QTL clusters from manually and digitally scored RSA traits (Burridge et al., 2017). Subsequent comparisons of results from this root study with other field studies revealed four QTLs co-localized with seed weight per plant, pod number, or Striga (Striga gesnerioides) tolerance (listed in Table 1). These results suggest that selection for root phenotypes with MAS could be employed in breeding programs aiming to improve yield and yield stability in stress environments. Pea Pea (Pisum sativum L.) is the most widely grown legume crop in Europe and the fourth most widely grown worldwide (FAO 2010). Pea is traditionally produced in marginal areas and has received little attention from producers compared with other legume crops, such as soybean. Evaluation of RSA in pea started in the early 2000s. Significant variations in RSA were observed in a core collection of 330 pea accessions, and the total root length and root dry weight were positively correlated with seed size (McPhee, 2005). The top 10% of lines for total root production were suggested in this report for a breeding program with PI 261631; an accession from Spain produced the greatest total root length, largest root/shoot weight ratio, and the largest root volume (McPhee, 2005). Initial QTL mapping was conducted in a recombinant inbred line (RIL) population derived from a cross between P665 and Messire. Three QTLs associated with root length were mapped on chromosomes (Chr.) 3 and 4 where they co-localized with QTLs for weed and fungal resistance (Fondevilla et al., 2010, 2011; listed in Table 1). In the subsequent work, three QTLs were mapped for these drought tolerance-related traits and two of them were co-located with the previously reported root length QTL (Iglesias-García et al., 2015). These results further confirmed the two QTLs associated with the RSA and revealed their real effects in drought tolerance. Common bean Common bean (Phaseolus vulgaris L.) is the most important food legume, providing protein for a population of >500 million. It also provides necessary vitamins and micronutrients to over a billion people in developing countries (FAO, 2000). Common bean is usually grown in marginal areas, where soil is less fertile, and fertilizers and irrigation are not affordable. Therefore, improvement of stress resistance of common bean is urgent for these areas to maintain crop production. In an interspecific population, 86 RILs were derived from a cross between a deep-rooted genotype (DOR364) and a shallow-rooted genotype (G19833) (Liao et al., 2004). QTL mapping was conducted in this population and 16 QTLs were mapped for root gravitropic traits, including basal root angle, shallow root length, and relative shallow basal root length, in which three basal root angle QTLs were found to be associated with deep rooting (Liao et al., 2004; Table 1). The subsequent study identified that four QTLs associated with total root length in the field and one major QTL on Chr. B4 could explain 21% of phenotypic variation (Ochoa et al., 2006; Table 1). In this study, adventitious root was positively correlated with shoot mass, and two major QTLs associated with adventitious root were also identified, together explaining 61% of phenotypic variation (Ochoa et al., 2006). These results provide a potential genetic and molecular resource for MAS to develop new cultivars with improved RSA. Chickpea Chickpea (Cicer arietinum L.) is the second most important food legume after common bean worldwide (FAOSTAT, 2014). It is mostly grown in low-input conditions especially in semi-arid regions of sub-Saharan Africa and South Asia, as an important component of subsistence farming. Drought stress alone accounts for 40% of yield losses annually to global chickpea production (Jukanti et al., 2015). Strategic crop improvement programs rely on genetic improvement efforts aiming at incorporation of beneficial traits, such as better RSA, into elite cultivars (Subbarao et al., 1995; Varshney et al., 2009). Root biomass and root length density that aid in greater soil moisture extraction were identified as important root traits during terminal drought (Kashiwagi et al., 2006, 2007; Varshney et al., 2011). Extensive phenotyping and genotyping of two intraspecific RIL populations [ICC 4958×ICC 1882 (ICCRIL03) and ICC 283×ICC 8261 (ICCRIL04)] were conducted to map QTLs for various drought tolerance traits, including RSA in chickpea (Varshney et al., 2014). ICC 4958 and ICC 8261 were identified to be drought- tolerant genotypes with larger root systems (Saxena et al., 1993; Kashiwagi et al., 2005), and the other two contrasting parents, ICC 1882 and ICC 283, were landraces with shorter root systems (Kashiwagi et al., 2005). Three QTLs, one each for root length density (CaLG04), root surface area (CaLG06), and root dry weight/total plant dry weight ratio (CaLG04), have been identified (Jaganathan et al., 2015). This study also resulted in identification of an important genomic region, the ‘QTL hotspot’, harboring QTLs (M-QTL and E-QTL) for several RSA and drought tolerance traits (Jaganathan et al., 2015). Fine-mapping of this ‘QTL-hotspot’ delimited the candidate region from 29 cM to 14 cM on a genetic map, and several stress-related candidate genes were identified in this region (Jaganathan et al., 2015). This ‘QTL hotspot’ was introgressed from the donor parent ICC 4958 into JG 11 (a leading chickpea variety) using the marker-assisted backcrossing (MABC) strategy (Jaganathan et al., 2015). Twenty-nine BC3F2 plants, referred to as introgression lines (ILs), showed greater rooting depth in all 29 ILs as compared with both recurrent parents (Jaganathan et al., 2015). These ILs have shown better performance in comparison with the recurrent parents under rainfed as well as in irrigated environments (Jaganathan et al., 2015). More recently, another study identified 13 QTLs associated with root traits in the same two intraspecific populations through the development of an SNP genotyping array for chickpea (Roorkiwal et al., 2017). Lentil Lentil (Lens culinaris Medik.) is considered as a staple food in developing countries for its rich source of proteins and micronutrients. It is considered as an important component in crop rotation programs due to its ability to improve soil fertility by atmospheric nitrogen fixation. Large genetic variability and high heritability in root and shoot traits related to drought tolerance were observed in 133 RILs from a cross of ILL6002 (superior root and shoot traits and grain yield)×ILL5888 (pure line, short cultivar with prostrate growth habit) (Idrissi et al., 2015). Identification of QTLs or genes related to studied traits in this population would be a first step for starting MAS. Advances in breeding for drought tolerance Lines with a deep root system are able to extract water from the soil and enable the plants to survive in water-limited conditions. Despite the importance of the root system, there have been limited efforts directed towards developing breeding lines with an improved root system. In the case of chickpea, the role of the root system in the improvement of crop productivity under water-limited conditions has been well studied and established. Greater exploitation of subsoil water due to deeper root systems has been shown to improve chickpea yield under drought conditions (Kashiwagi et al., 2015). Similarly, in the case of soybean, the root system is important to protect yield under water-limited conditions. In order to determine the role of the root in crop productivity under water-limited conditions, different root traits including root length, root surface area, average root diameter, and average root volume were compared under normal and drought stress conditions (Zenis et al., 2016). This study confirmed the importance of a better root system in drought tolerance. Root traits have a direct impact on yield under drought stress among legumes (see table 2 in Farooq et al., 2016), suggesting the root as a significant target for further breeding. Although there have been many efforts to identify genetic diversity and QTLs/genes that control RSA, there have been limited efforts to utilize these genetic resources in breeding. In chickpea, efforts have been undertaken to introgress the ‘QTL-hotspot’ region from line CaLG04 into the elite chickpea line (JG11) by the MABC strategy. Introgression lines have shown improved performance and enhanced yield under drought stress (Varshney et al., 2013). Currently, efforts are being made to introgress this genomic region to several other elite chickpea lines (Pandey et al., 2016). To date, rich genetic resources for RSA have been identified in legume crops, but those resources are yet to be implemented in breeding for developing stress-tolerant lines. Genotypes with differential RSA patterns should be utilized for developing stress-tolerant lines with improved RSA for each specific condition. Conclusions and future perspectives The sustainability of crop production and food security is being threatened by the increasing unpredictability and severity of drought stress due to global climate changes. Genetic variations for stress tolerance in natural populations reflect the evolution of plants in diverse ecological conditions. Incorporation of these adapted natural genetic variations into breeding programs can enrich the current genetic diversity of stress tolerance and improve yield under stress. Screening germplasm for stress tolerance traits has provided donor resources with potential to improve yield under stress conditions. Identification of the genomic regions underlying these natural genetic variations and transferring the favorable alleles into elite germplasm with the assistance of genomic technologies will speed up the genetic improvement of stress tolerance in legumes. QTLs associated with RSA were identified in major legume crops, and associated DNA markers have been developed for MAS. Some of these QTLs or genes can be lost during domestication processes due to their linkage to negative loci or pleiotropic effects on yield. Subsequent fine-mapping is needed to determine whether the yield drag effects are due to pleiotropy or linkage to other negative loci. Screening a large population of segregants can break the tight linkage with the assistance of MAS. In addition, cloning and functional characterization of genes underlying the natural genetic variations can lead to understanding of the molecular and physiological pathways of stress adaptation, which will direct further improvement of stress tolerance in legumes. High-throughput and cost-effective phenotyping and genotyping technologies are foundations of genomics-assisted breeding programs. High-throughput phenotyping platforms that monitor RSA are essential to advance our knowledge of drought tolerance. High-throughput phenotyping of RSA has focused on field-based approaches in crops such as maize (Trachsel et al., 2011; York et al., 2015), rice (Wissuwa et al., 2016), and wheat (Richard et al., 2015), with a recent shift to legume species, such as soybean, common bean, and cowpea (Manavalan et al., 2015; Prince et al., 2015; Burridge et al., 2016). The adoption of high-throughput image analysis using DIRT will further improve the efficiency and accuracy of the phenotyping of RSA (Burridge et al., 2016). Until the last decade, legumes have fallen into the category of orphan crops with fewer genomic resources. Recent advances in sequencing technology have brought legumes into the category of genomic resource-rich crops. The availability of these resources resulted in the initiation of several crop improvement programs based on genomic-assisted breeding. MAS and genomic selection are the major genomic-assisted breeding approaches deployed in legume improvement programs (Pandey et al., 2016). MAS is mostly used to address the issues related to simple traits that are controlled by one or two genes. However, for complex traits, MAS has not been that successful. Recently, genomic selection has emerged. It has the ability to target many complex traits at once and holds the potential for genetic improvement at a faster pace (Crossa et al., 2017). High-precision phenotyping and genotyping have resulted in precise selection of genotypes with a reduced breeding cycle time by avoiding unnecessary rounds of phenotyping. The ability to capture both small and large effect genetic factors adds further advantages to the genomic selection strategy. Reduced cost of genotyping has resulted in establishment of a large range of genetic resources among legumes in the past decade. These resources are now providing required information for various genomic-assisted breeding approaches to enhance the genetic gains. Further establishment of high-throughput genotyping platforms in various legume crops has initiated the genomic selection efforts in legumes (Jain et al., 2013). Genetic modification has emerged as another important tool for crop improvement (Hussain, 2015). It provides an opportunity to edit targeted genes for specific needs. The targeted genome editing by the CRISPR/Cas9 (clustered regularly interspaced short palindromic repeat/CRISPR-associated protein 9) system (Jacobs et al., 2015; Sun et al., 2015) can enable further modification of RSA-related alleles to maximize the performance of the loci. In addition to soybean genome information, the release of reference genomes and continuous building up of resequencing information of various legume crops can strengthen the comparative legume genomics for discovery and characterization of key genes and gene families involved in stress tolerance. High-density genome variation information available for major legumes can facilitate haplotype mining of the traits of interest and can lead to the design of haplotype-assisted breeding. 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Journal of Experimental BotanyOxford University Press

Published: Mar 7, 2018

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