Genetic dissection of root architecture in Ethiopian sorghum landraces

Genetic dissection of root architecture in Ethiopian sorghum landraces | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic dissection of root architecture in Ethiopian sorghum landraces Kassahun Bantte, Temesgen Matiwos Menamo, Andrew K. Borrell, Emma Mace, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2159601/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Sep, 2023 Read the published version in Theoretical and Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract The root system architecture (RSA) of crop plants influences adaptation to water-limited conditions and determines the capacity of a plant to access soil water and nutrients. Four key root traits (number, angle, length and dry mass) were evaluated in a diversity panel of 1,171 Ethiopian sorghum landraces using purpose-built root chambers. Significant genetic variation was observed in all studied root traits, with nodal root angle ranging from 16.4° to 26.6°, with a high repeatability of 78.9%. Genome wide association studies (GWAS) identified a total of 22 genomic regions associated with root traits which were distributed on all chromosomes except chromosome SBI-10. Among the 22 root genomic regions, 15 co-located with RSA trait QTL previously identified in sorghum, with the remaining seven representing novel RSA QTL. The majority (85.7%) of identified root angle QTL also co-localized with QTL previously identified for stay-green in sorghum. This suggests that the stay-green phenotype might be associated with root architecture that enhances water extraction during water stress conditions. The results open avenues for manipulating root phenotypes to improve productivity in abiotic stress environments via marker-assisted selection. Ethiopia Sorghum Genome-Wide Association Study Root Angle Root System Architecture Stay-green Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Key Message This study quantified genetic variation in root system architecture (root number, angle, length and dry mass) within a diversity panel of 1,171 Ethiopian sorghum landraces and identified 22 genomic regions associated with the root variations. Introduction Sorghum is a staple food crop for millions of resource-poor farmers in the semi-arid tropics, particularly in Sub-Saharan Africa, including Ethiopia. This is due, in part, to the higher levels of abiotic stress tolerance sorghum displays in comparison to other cereals, particularly in terms of water stress (Borrell et al. 2020 ). Sorghum has a very broad environmental adaptation and is grown in both temperate and tropical climates, high and low altitudes, and in both water-logged and drought-stressed conditions (Borrell et al. 2021 ). However, in the arid and semi-arid regions where sorghum is pre-dominantly grown, it is often affected by water stress at either the vegetative and/or reproductive stages of crop development, particularly during the post-flowering stage (Kebede et al. 2001 ; Ali et al. 2009 ; Jordan et al. 2012 ). Sorghum has a range of phenotypic characteristics that contribute to the crop’s enhanced adaptability to such harsh environments, both above ground and below ground (Borrell et al. 2020 ; Borrell et al. 2021 ). In particular, the root system architecture (RSA) has been shown to play a critical role in productivity under such water-limited conditions, with narrow root angles increasing access to water from deep soil in cereal crops, including sorghum (Singh et al. 2012 ; Uga et al. 2013 ). A putative association between root angle and grain yield has also been reported in sorghum (Mace et al. 2012 ). Therefore, characterization and deployment of selection for RSA traits could accelerate the development of drought adapted sorghum varieties. Significant progress has been made in the past decade on RSA as a selection criterion for stress adaptation in maize (Trachsel et al. 2011 ; Lobell et al. 2014 ), rice (Uga et al. 2013 ), wheat (Richard et al. 2018 ; Voss-Fels et al. 2018 ), barley (Robinson et al. 2018 ; Voss-Fels et al. 2018 ) and sorghum (Singh et al. 2008 ; Mace et al. 2012 ) following the development of a range of root phenotyping methods. Previous studies in sorghum have identified that significant variation for RSA exists, suggesting opportunities for selection and genetic improvement for water stress response breeding (Blum et al. 1977 ; Singh et al. 2011 ; Joshi et al. 2017 ; Zheng et al. 2020 ). In addition to characterization of RSA variation, linkage analysis and genome-wide association study (GWAS) approaches have been used to dissect genetic control of root traits in sorghum, with 11 recent studies identifying 392 QTLs for sorghum root traits (Mace et al. 2012 ; Fakrudin et al. 2013 ; Bekele et al. 2014 ; Hufnagel et al. 2014 ; Li et al. 2014 ; Wang et al. 2014 ; Chopra et al. 2017 ; Parra-Londono et al. 2018 ; Bernardino et al. 2019 ; Moghimi et al. 2019 ; Zheng et al. 2020 ). Among these QTL, 248 were repeatedly detected, while the remaining 144 were unique to each specific study. For example, Mace et al ( 2012 ) identified four QTL for nodal root angle in a bi-parental recombinant inbred line (RIL) population, which were subsequently also identified in other root trait GWAS studies (Bekele et al ( 2014 ); Parra-Londono et al ( 2018 ); Bernardino et al ( 2019 ); Moghimi et al ( 2019 ); Zheng et al ( 2020 )). These four root angle QTLs also co-localized with previously identified QTL for stay-green (Mace et al. 2012 ). Crops with stay-green exhibit a distinct green leaf phenotype during grain filling under terminal drought, resulting in enhanced grain yield under post-anthesis drought stress (Borrell et al. 2000a b ; Jordan et al. 2012 ; Borrell et al. 2014a b; Borrell et al., 2022 ). Stay-green loci also modify root architecture (Manschadi et al. 2006 ; Christopher et al., 2008 ), which is likely linked to the increased water accessibility displayed by stay-green genotypes during grain filling under end-of-season drought (Borrell et al. 2014b ). In general, RSA is highly plastic to environmental changes and heritability varies from low to high (Singh et al. 2011 ; Mace et al. 2012 ; Joshi et al. 2017 ). Despite the discovery of heritable natural variation in RSA, only limited genetic-based and co-location studies with stay-green QTLs of sorghum roots have been identified via high-throughput phenotyping. Recent studies have found that a stay-green phenotype involves modifications in root architecture through PIN genes (Borrell et al. 2022 ). Furthermore in barley, stay-green and greater yield have been associated with deeper roots and delayed root senescence (Williams et al. 2022 ). Hence, a study in genetic analysis of RSA and co-location with stay-green is needed to further understand its regulatory mechanisms. Ethiopian sorghum landraces are particularly rich in genetic diversity and thus are very valuable to dissect the genetic basis governing the variability of RSA traits. Around 80% of sorghum in Ethiopia is cultivated in the dry and hot lowlands where abiotic factors such as inadequate and erratic rainfall are among the major production constraints (Hulluka and Esele 1992 ; EIAR, 2014 ). Still there is no single root system trait or even set of traits that will confer drought adaptation in this environment. The Ethiopian Biodiversity Institute (EBI) has a collection of more than 10,000 sorghum accessions from across the country. However, the RSA of this germplasm resource has not yet been characterised. This study aims to: a) characterise the RSA of a subset of the collection of diverse Ethiopian sorghum landraces, and b) undertake GWAS to identify genomic regions associated with RSA to inform molecular breeding strategies in the future. Materials And Methods Site For Root Phenotyping Platform The experiment was conducted in a polyhouse at Jimma University, College of Agriculture and Veterinary Medicine (JUCAVM), Jimma, Ethiopia (7 o 33’N latitude, 36 o 57’E longitude). The mean daily air temperature during the growing season in the polyhouse ranged from a minimum of 7.8°C to a maximum of 40.8°C. The polyhouse allowed transmission of 60% of incident photosynthetically active radiation. Genetic materials In total, 1,771 Ethiopian landraces were selected for root phenotyping (Online Resource 1). The landraces were selected systematically from the EBI (791 genotypes), Melkassa Agricultural Research Center (935 genotypes) and JUCAVM (45 genotypes) to include landraces from all sorghum producing regions and ecologies of the country (Figs. 1 A and B). An individual from each accession was selected and self-pollinated for one generation to reduce residual heterozygosity and to increase seed quantity. A subset of 940 landraces were genotyped and used for GWAS. This panel of 940 landraces has been described previously by Menamo et al ( 2021 ) and used for genetic diversity and genome environment association studies. Experimental Procedure And Design The experiment was conducted using a purpose-built high-throughput root phenotyping platform (Singh et al. 2011 ; Joshi et al. 2017 ) consisting of 500 pairs of perspex plates (40 cm high, 35 cm wide and 3 mm thick; Fig. 2 ). The centre of the pairs of perspex sheets were drilled and screwed together to maintain a 3 mm spacing and minimize bulging of the chambers during soil filling. The void between each pair of plates was filled with red soil (Online Resource 2A). Fifty root chambers were placed vertically into slots in each of 10 large metal tubs (Online Resource 2C). The tops of the chambers in each tub were covered with black polycarbonate covers to exclude light from the soil and roots, but leaving 5 cm long slits for the seedlings to emerge. Prior to planting, chambers were watered to field capacity and left until soil water saturation. Three seeds were planted in the centre of each chamber on one side of the Perspex with the embryo angled towards the base (Online Resource 2B), then thinned to one plant per chamber after emergence. The seeds were treated with ApronStar 42WS fungicide before sowing. Plants were watered one or two times per week, depending on the environmental conditions. Plant nutrients were applied in the form of diammonium phosphate (DAP, 1.5 kg) and urea (1 kg) across the 500 chambers i.e. equivalent to 100 kg/ha urea and 150kg DAP for a population of 50,000 plants per hectare. DAP was applied during sowing in a granular form and urea when the plants reached the 3rd leaf stage in liquid form, applied equally in each chamber. A total of 1,771 landraces were phenotyped across 11 independent experimental runs, with each run consisting of 500 chambers (10 tubs with 50 root chambers per tub). In each run, 160 genotypes were included, of which 140 genotypes were replicated three times and 20 check genotypes were replicated four times. The 20 check genotypes were common to all experimental runs enabling co-analysis of all entries across all runs in a single analysis. The experimental runs had two blocks of ten tubs (five tubs in each block) and arranged in a row column design; columns being represented by the tubs and rows by the root chamber position within each tub. Collection Of Phenotypic Data Images of the root structure were taken on both sides of each chamber using two digital cameras (CANON SX610 HS) remotely operated through Wi-Fi by tablets in order to synchronize the imaging on both sides. The images were used to determine the root angle (RA) relative to the vertical plane (Fig. 2 ). RA was taken from the first flush of nodal roots at a distance of 2 cm from the base of the plant (Singh et al. 2011 ) using Opengelphoto.tcl software ( www.activestate.com/activetcl ). The recorded root angle for each plant was the mean of four observations (left and right side of each plant for both sides of the chamber). After root imaging, each chamber was laid flat, the top perspex plate removed, and then root length (the longest 1st flush nodal root) was measured. Root numbers per plant were counted after soil was washed from the roots with tap water. Root samples were air dried on blotting paper, then oven-dried at 60° C for 3 days to determine root dry mass (g/plant). Additionally, shoot traits were included to provide important information regarding impact on plant size. Leaf length and width (cm) were measured on fully expanded leaves (2nd, 3rd, 4th and 5th leaves) when the plants exhibited five fully expanded leaves. Leaf area (cm 2 ) was determined by multiplying the maximum leaf length and width, and by including a shape factor of 0.69 (Lafarge et al. 2002). The shoot length (cm) was measured from the base of the plant (soil level) to the ligule of Leaf 5. The shoot diameter of the wider side was measured 2 cm above the top of the chamber (soil surface) using digital callipers. Just before imaging the roots, shoots were cut at the base of the plant and subsequently dried. Shoot dry mass (g/plant) was determined after oven drying at 60 O C for 3 days. Total genomic DNA extraction and genotyping Samples of four individual leaves were collected from 15-day-old plants grown in small pots in a greenhouse and stored at -20 o C in 96 well plates for 24 hours. The leaf samples were freeze-dried and sent to DArT ( www.diversityarrays.com ) for DNA extraction. The DNA samples were then genotyped using DArTseq as previously described in Tao et al. ( 2020 ). This involves complexity reduction of the genomic DNA to remove repetitive sequences using methylation sensitive restriction enzymes prior to sequencing on Next Generation sequencing platforms. The sequence data generated were then aligned to version v3.1.1 of the sorghum reference genome sequence (McCormick et al. 2018 ) to identify SNPs (Single Nucleotide Polymorphisms). Root Phenotype Data Analysis The single and combined data for all experimental runs were analysed using a restricted maximum likelihood (REML) mixed model allowing for spatial variation across the experiments using “asreml-R” R package (Gilmour et al., 2006 ): REML mixed model: \(y=X\tau +Zu+e\) ; where: y = the measured data for each trait and each run; τ = the fixed effects (genotypes) in the trial; X = the design matrix for the fixed effects (row and column); u = random effects for each analysis; Z = the design matrix for the random effects and e = residual error effects. Best linear unbiased estimates (BLUEs) were analysed for each genotype and trait using linear mixed model (REML) spatial analysis by asreml-R package in R software. Repeatability (H 2 ) was estimated as H 2 = 1- uBLUE/2 σ 2 g (Cullis et al. 2006 ), where uBLUE is the mean variance of a difference BLUE, and σ 2 g is the genotypic variance component. The BLUE for each landrace was used to perform the pair-wise Pearson correlation analysis among the traits. The analysis was conducted using R software’s corrplot package (Wei et al. 2017 ). Genome Wide Association Study And Co-location A total of 54,080 SNP markers were identified across the 940 landraces. The data set was further filtered to exclude SNPs with MAF 25%, leaving a final data set of 25,634 SNP markers (Menamo et al. 2021 ). The remaining missing values were inferred using the Beagle 5.0 software package (Browning et al. 2018 ). Best linear unbiased estimates (BLUEs) for all traits were used in the genome-wide association study (GWAS) analysis using the software BLINK (Huang et al. 2018 ). The SNP data set was initially filtered for minor allele frequency (MAF) of 0.01. Kinship was calculated as described by Van-Raden ( 2008 ). Q–Q plots of p-values were examined to determine how well the models accounted for population structure and familial relatedness. The genetic type I error calculator (GEC) was used to determine significance thresholds for trait-marker associations (Li et al. 2012 ). The QTL detected in this study for each RSA trait was compared with 392 RSA QTLs in 12 previously identified studies. Additionally, co-located QTLs were also identified between root angle and stay-green QTLs studied under post-flowering water-stress conditions using Sorghum QTL Atlas database (Mace et al. 2019 ). The data collection for stay-green or green leaf area was considered between the flowering and maturity stage only. A 1 cM window was used to identify QTL co-location based on the genetic distances from the sorghum consensus map (Mace et al. 2009). Enrichment analysis of QTL co-location was undertaken using chi-square test with R software (“chisq.test” procedure). Results Substantial Variation In Root System Architecture (RSA) Traits The correlation among the experimental runs for each trait was first analysed (Online Resource 3) using the 20 genotypes repeated across the experimental runs. In almost all cases, all traits were positively correlated across experimental runs (Online Resource 3), with root angle showing the highest correlations (r = 0.57 to 0.94) among all 11 experimental runs; and root to shoot ratio showing the largest range in correlation values among the experimental runs (r = 0.02 to 0.76). Significant variation was observed for all traits measured across all genotypes (Table 1 ) with an approximately normal distribution (Online Resource 4), indicating the quantitative nature of the traits. The nodal root angle varied from 16.6º (Fig. 3 A) to 26.4º (Fig. 3 B), with an average of 20.9º. Both root number and length varied considerably, with number varying from 8.0 to 12.0 (average = 9.8), and length varying from 33.3 to 56.0 cm (average = 45.0 cm). The repeatability ( H 2 ) was generally high across traits, with nodal root angle having the highest repeatability of 78%. The phenotypic best linear unbiased estimations (BLUE) of root traits are detailed in Online Resource 5. Table 1 Phenotypic variation of all traits based on best linear unbiased estimations (BLUE) (calculated as genotype performance across the experimental runs). Traits Unit Max Min Mean G. Var Err Var H 2 Sign. RA degree 26.4 16.6 20.9 5.5 13.2 78.9 ** RDM g 0.55 0.34 0.46 0.10 0.10 18.20 ** RN number 12.0 8.0 9.8 0.6 2.0 24.9 ** RL cm 56.0 33.3 45.0 9.1 15.7 33.7 ** RS ratio 0.38 0.25 0.31 0.1 0.1 24.3 ** SDM g 1.68 1.31 1.49 0.1 0.1 17.2 ** SL cm 16.9 9.6 12.9 2.1 2.0 49.7 ** SD mm 7.2 5.4 6.5 0.2 0.6 20.7 ** LA cm 2 38.8 10.42 22.5 20.5 17.5 52.3 ** RA = root angle, RDM = root dry mass, RN = root number, RL = root length, RS = root shoot ratio, SDW = shoot dry mass, SL = shoot length, SD = shoot diameter and LA = leaf area. **=significant level at P < 0.01 Correlation Among RSA And Shoot Traits Root angle (RA) was poorly correlated with the other root and shoot traits measured (Figs. 4 A and 4 B). In contrast, root number (RN), root dry mass (RDM) and root length (RL) grouped together (Fig. 4 B), indicating a positive correlation among them, with the strongest correlation between RN and RDM. Shoot dry mass (SDM) also showed a strong positive correlation with RDM and RN, but negative correlation with root to shoot ratio. Shoot diameter was moderately correlated with SDM, RDM and RN. Genome-wide association for RSA traits GWAS was conducted to identify trait-marker associations, where Q-Q plots showed that the observed p -values were close to the expected p -values for all traits with minimal deviation observed (Online Resource 6). A total of 22 SNP markers were significantly ( P-value ≤ 8.09E-05) associated with RSA traits (Table 2 & Fig. 5 ). Of the 22 significant SNPs for RSA traits, seven, seven, four and four were associated with root angle, root number, root length and root dry mass, respectively. Similar to the correlation result, none of the significant markers showed pleiotropy effects in root vs shoot traits. The significant markers were distributed over all chromosomes except chromosome SBI-10, with numbers varying from two on chromosomes SBI-04 and SBI-05 to ten on chromosome SBI-01. Of the 22 QTL associated with four RSA traits, 15 co-located with previously identified RSA QTL based on a 1 cM window, while the remaining seven were unique to this study (Online Resource 7). Table 2 Summary of significant single-nucleotide polymorphisms (SNPs) for the four RSA traits at seedling stage Traits QTLs Chr POSv3.01 P-values RA QERA3.1 SBI-03 70640280 6.5E-06 QERA4.2 SBI-04 52404340 4.85E-12 QERA5.3 SBI-05 7621214 1.57E-08 QERA6.4 SBI-06 47152373 4.48E-10 QERA6.5 SBI-06 49330161 1.4E-06 QERA7.6 SBI-07 53856839 6.06E-05 QERA8.7 SBI-08 47689441 1.22E-08 RN QERN1.1 SBI-01 76107179 3.35E-05 QERN2.2 SBI-02 19789147 3.21E-06 QERN2.3 SBI-02 60605417 7.76E-05 QERN2.4 SBI-02 73281144 3.91E-05 QERN6.5 SBI-06 54403226 9.5E-05 QERN8.6 SBI-08 53766348 2.62E-05 QERN9.7 SBI-09 49867350 1.54E-07 RL QERL1.1 SBI-01 66615979 9.22E-09 QERL3.2 SBI-03 2540858 1.96E-06 QERL3.3 SBI-03 10025278 3.64E-05 QERL5.4 SBI-05 53377135 9.78E-05 RDM QERDM1.1 SBI-01 27268290 1.24E-05 QERDM1.2 SBI-01 65740047 9.54E-10 QERDM3.3 SBI-03 59395700 1.02E-07 QERDM3.4 SBI-03 60530147 1.04E-09 RA = root angle, RN = root number, RL = root length, RDM = root dry mass, Chr = chromosome, POSv3.01 = marker/QTL position on chromosome Co-location Of Root Angle Qtls With Previously Identified Stay-green Qtls Six out of the seven root angle QTL (6/7 = 85.7%) co-located with previously identified QTL for drought resistance traits (Online Resource 8), including green leaf area after anthesis (Haussmann et al. 2002 ; Srinivas et al. 2009 ; Sabadin et al. 2012 ) and total number of green leaves after anthesis (Reddy et al., 2014 ), with a significant genome enrichment ( P-value = 0.011). Discussion Root system architecture screening in sorghum differs from other cereal crops Individual root growth patterns, including tropism, branching and elongation rates are determined by underlying genetics, contributing to the architecture of the overall system (Rich et al., 2020 ). The passage of roots through the soil is also constrained by soil physical and chemical conditions, with many biotic and abiotic factors contributing to the final architecture, including gravity, temperature, water, oxygen and nutrient availability, and soil biota (Rich and Watt, 2013 ). Changes to the mature (or near-mature) architecture will also be driven by the timing and initiation of lateral and adventitious roots under the control of nutrient and hormonal signals (Atkinson et al., 2014 ). The efficiency of soil water uptake is determined by the root system because roots are usually the site of the greatest resistance in the pathway for liquid phase movement of water through the soil-plant-atmosphere continuum (Kramer and Boyer, 1995 ). High-throughput phenotyping of RSA traits is of major importance to elucidate the underlying genetic control and to incorporate such traits in breeding programs through marker-assisted selection. Recently designed high-throughput root phenotyping platforms are an opportunity for further improvement of crop productivity, particularly in moisture-limited environments. Various high-throughput RSA phenotyping systems have been designed to screen seminal roots at the seedling stage i.e. clear pot and growth pouch methods in wheat (Richard et al., 2015) and clear-pot phenotyping method in barley (Robinson et al., 2016). However, these methods are less relevant for sorghum since seminal roots do not constitute the major proportion of the mature root system. Hence, Singh et al. (2010) developed a new root chamber-based method to screen nodal root angle in sorghum. While the assumption is that seedling screens for root architecture will be correlated with mature root systems, this is not always the case (Rich et al., 2020 ). However, it was reported that narrow primary root angle was found to be associated with deep field roots, providing some evidence that screening for narrow root angle at the seedling stage is worthwhile (Rich et al., 2020 ). Correlations between seedling and mature root traits in the field have been shown to be inconsistent from site to site and year to year in wheat (Rich et al., 2020 ). This could be due to the influence of soil physical, chemical, and edaphic factors on root traits. However, Singh et al. ( 2012 ) reported that root angle of young sorghum plants can be a useful selection criterion for specific drought adaptation. The Singh et al. ( 2012 ) study also reported that relationships between root angle screening and mature field traits were best achieved using soil rather than germination paper and agar in the initial screening. Hence, exploiting RSA variability and identifying genomic regions controlling the variability could open avenues for sorghum breeders to develop more drought adapted varieties through manipulating root architecture. In particular, the extent to which screening for root angle will help sorghum breeders to develop more drought adapted varieties will depend on the strength of the correlation between RA at seedling stage (6th fully expanded leaf stage) and the mature root system architecture. RSA may play a role in stay-green during post-flowering moisture-stress conditions. For example, narrow root angle increases access to water stored deep in the soil during the post-flowering period compared to wider root angles in wheat (Manschadi et al., 2006 ) and sorghum (Singh et al., 2012 ). Therefore, narrow root angle may contribute to a stay-green phenotype, although this will depend to some extent on genetics (e.g. canopy size), management (e.g. row spacing, fertilization) and environment (e.g. soil type, water availability). Genetic Variation For Diverse Rsa Traits Identified In Ethiopian Sorghum In this study, genetic materials were collected from all sorghum producing regions in Ethiopia to capture the genetic diversity. Hence, significant variability for RSA traits was identified. The study also revealed high repeatability and a broad range of variation for individual root traits, in particular, for root angle. Nodal root angle showed wider variation and higher repeatability than other root-related traits, suggesting that the variation observed in this trait was under genetic control. The high repeatability of root angle in this study (78%) is consistent with previous studies in sorghum (Mace et al. 2012 ; Joshi et al. 2017 ), which reported high repeatability in root angle measurements (73.7% and 96%, respectively), while Singh et al. ( 2011 ) also observed moderate repeatability (46.6%) in nodal root angle in sorghum. The presence of genetic variation with moderate to high repeatability suggests that nodal root angle could respond to selection in sorghum-improvement programs. Root Angle Is Poorly Associated With Other Traits Drought adaptation is of particular interest because it depends upon components associated with both water supply (e.g. root traits) and water demand (e.g. canopy traits). Root structural traits are important sub-component traits for extracting water and nutrients from soil. These traits might contribute positively or negatively to drought adaptation in association with other traits. Therefore, understanding the interaction between component traits could be helpful for breeders to decide whether to make direct or indirect selection for better yield under drought conditions. In this study, a poor association between root angle and other RSA traits was observed. Similar results have been observed previously in sorghum (Singh et al. 2011 ; Mace et al. 2012 ), wheat (Sanguineti et al. 2007 ) and barley (Robinson et al. 2018 ). The finding of weak correlations between root angle and plant size traits also agrees with the results reported by Singh et al. ( 2011 ), which found that traits associated with plant size (root mass, shoot mass, and leaf area) had a very low correlation with root angle in sorghum. These weak associations suggest that root angle and plant size traits might be controlled by different sets of genes. Furthermore, the incorporation of root angle screening into a sorghum breeding program would be helped if root angle was not associated with plant size because such an association might inadvertently result in selection for early vigour (Singh et al., 2011 ). Therefore, nodal root angle selection could be one strategy for selection in the improvement of drought adaptation. Genetic Control Of RSA Traits GWAS has proven to be a powerful tool to understand the genetic control of quantitative traits in sorghum and other species (Huang et al. 2012 ; Morris et al. 2013 ; Tao et al., 2020 ; Tao et al. 2021 ). Using this approach, this study identified 22 QTLs for key RSA traits (root angle, length, dry mass and number) in Ethiopian sorghum germplasm. Among RSA QTLs, 15 were co-located with RSA QTL reported in previous sorghum studies using different genetic materials. These QTL were repeatedly reported with different genetic backgrounds, possibly indicating hotspot genomic regions for root development. The seven unique QTLs potentially represent novel genetic loci for RSA from Ethiopian germplasm. The root traits investigated in this study have previously been associated with grain yield under water stressed conditions in different cereal crops (Mace et al. 2012 ; Ali et al. 2016 ; Robinson et al. 2018 ). Ali et al ( 2016 ) reported that total root mass and root length in maize were significantly positively correlated with grain yield under water deficit conditions in the field. A positive significant association between narrow root angle and grain yield was reported in sorghum by Mace et al ( 2012 ). Root Angle Qtls Co-located With Previously Reported Stay-green Qtls The majority of root angle QTLs (6/7) in this study co-located with previously reported stay-green QTLs under water-stressed conditions. For example, QERA7.6 co-located with a previously studied QTL (green leaf area 15 days after flowering) by Haussmann et al. 2002 who conducted the experiment during the post-rainy season at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT). These authors reported that the season was ideal for evaluating stay-green under terminal moisture-stress conditions and the crop was dependent almost entirely on stored soil moisture. Similarly, QERA6.4 co-located with a previously studied ‘green leaf area at maturity’ QTL under post-flowering moisture-stress conditions (Srinivas et al. 2009 ). Additionally, QERA6.5 and QERA8.7 co-located with previously studied green leaf area QTLs (QGLFA6.1 and QGLFA8.1, respectively) at 45 days after flowering under post-flowering moisture-stress conditions (Sabadin et al. 2012 ). The authors reported that one of these QTL (QGLFA8.1) explained the green leaf area variation by 14.46% and co-located with grain yield under water-stressed conditions. This is consistent with reports that stay-green QTL co-located with QTL for nodal root angle in sorghum (Mace et al. 2012 ) and seminal root angle in wheat (Christopher et al. 2018 ). These previous studies indicate that genotypes with narrow root angle can extract water from greater depth, thereby increasing access to water under drought, resulting in a stay-green phenotype. This suggests the importance of nodal root angle during post-flowering moisture-stress conditions. Stay-green is an important drought-adaptation feature in cereal crops, especially when they are subjected to moisture stress after flowering. Stay-green ( Stg ) loci reduce canopy growth at flowering by modifying tillering, leaf number, and leaf size (Borrell et al. 2014a b). The decreased canopy area at flowering reduces pre-anthesis water demand, moving it from pre- to post-anthesis and therefore boosting water availability during grain filling and thus grain production. However, the increased post-anthesis water use of Stg QTLs compared to RTx7000 was not solely a consequence of changes in temporal water-use patterns, as it more than compensated for the reduced pre-anthesis water use, resulting in increased total water use of 19 mm and 10 mm in the Stg1 and Stg3 NILs, respectively (Borrell et al., 2014a b). As water uptake under drought stress is supply-limited, these results indicate that the Stg QTLs could access more water than RTx7000, achieved either by better water extraction from the soil by roots or increasing the soil volume explored by the roots via deeper rooting or greater lateral spread. Importantly, the majority of root angle QTLs identified in this study were co-located with previously identified QTLs for stay-green. This suggests that the stay-green phenotype is a consequence of not only shifting water from pre- to post-anthesis (due to water savings from reduced pre-anthesis canopy size), but of increased water uptake throughout crop growth due to modification of root angle. This finding has significant implications for crop improvement programs because it highlights that root system architecture contributes to an enhanced stay-green phenotype in sorghum, presumably by impacting elements of water supply in water-limited scenarios. This aligns with the findings of Liedtke et al. ( 2020 ) which suggest that factors other than canopy size (e.g. roots) have an important role in the expression of a stay-green phenotype in grain sorghum. Conclusions Ethiopian sorghum genotypes have unexploited variation in root system architecture traits which could contribute to sorghum breeding programs for drought adaptation. Of the root system architecture traits included in this study, root angle likely has an important role in shaping root architecture and root distribution in deeper soil layers, thereby enhancing water uptake from depth. Several diverse landraces with narrow and wide root angles have been identified and these landraces could be used as a source of valuable RSA alleles for varietal development under drought tolerance. Root angle had high repeatability (H 2 ) and weak association with other traits. Furthermore, the poor correlation of RA with shoot traits suggests that selection for RA should not inadvertently result in selection for early vigour. Root angle QTLs also co-located with previously identified stay-green QTLs. Hence, further investigation is required to better understand the components of RSA that contribute to the water supply component of stay-green and genotype by management interaction strategies for target environments. The co-localization of the significant GWAS RSA traits in this study with RSA traits from previous studies (root angle and root length) could indicate the target regions for RSA. Introgressing these QTLs effectively into elite sorghum genotypes via molecular breeding strategies should enhance productivity of sorghum under moisture stress conditions. Furthermore, the root angle screening system highlighted in this paper is simple and inexpensive, indicating its utility for screening large numbers of sorghum genotypes, with potential for screening other cereal crops with further modification. Declarations Acknowledgments: We thank the Ethiopian Biodiversity Institute (EBI), and EIAR’s Melkassa Agricultural Research Center for providing us with the sorghum landraces and passport data. This study was supported by the Bill and Melinda Gates Foundation PEARL (Program for Emerging Agricultural Leaders) Program. Conflict of interest Authors have declared that no competing interests exist. Ethics approval Not applicable Consent to participate Not applicable Consent for publication Not applicable Availability of data and material All data are publicly accessible. Genotypic list with geographical information, SNP genotype and phenotypic data are available from Digital Repository and provided in Online Resource. Code availability Not applicable Author contributions: Conceived and designed the experiments: KB, AB, DJ & EM. Performed the experiments: TM & KB. Analysed the data: TM, EM, CH & YT. Wrote the paper and editing: TM, KB, DJ, EM, AB & YT. References Ali MA, Abbas A, Niaz S, Zulkiffal M, Ali S (2009) Morpho-physiological criteria for drought tolerance in sorghum (Sorghum bicolor) at seedling and post-anthesis stages. Int J Agric Biol 11(6):674–680 Ali ML, Luetchens J, Singh A, Shaver TM, Kruger GR, Lorenz AJ (2016) Greenhouse screening of maize genotypes for deep root mass and related root traits and their association with grain yield under water-deficit conditions in the field. Euphytica 207(1):79–94. https://doi.org/10.1007/s10681-015-1533-x Atkinson JA, Rasmussen A, Traini R, Voß U, Sturrock C, Mooney SJ, Wells DM, Bennett MJ (2014) Branching out in roots: uncovering form, function, and regulation. Plant Physiol 166:538–550. https://doi.org/10.1104/pp.114.245423 Bach L, Faure J-D (2010) Role of very-long-chain fatty acids in plant development, when chain length does matter. C R Biol 333(4):361–370. https://doi.org/10.1016/j.crvi.2010.01.014 Beaudoin F, Wu X, Li F, Haslam RP, Markham JE, Zheng H et al (2009) Functional characterization of the Arabidopsis β-ketoacyl-coenzyme A reductase candidates of the fatty acid elongase. Plant physiol 150(3):1174–1191. https://doi.org/10.1104/pp.109.137497 Bekele WA, Fiedler K, Shiringani A, Schnaubelt D, Windpassinger S, Uptmoor R et al (2014) Unravelling the genetic complexity of sorghum seedling development under low-temperature conditions. Plant cell environ 37(3):707–723. https://doi.org/10.1111/pce.12189 Bernardino KC, Pastina MM, Menezes CB, de Sousa SM, Maciel LS, Carvalho G Jr et al (2019) The genetic architecture of phosphorus efficiency in sorghum involves pleiotropic QTL for root morphology and grain yield under low phosphorus availability in the soil. BMC plant biol 19(1):87. https://doi.org/10.1186/s12870-019-1689-y Bibi A, Sadaqat H, Tahir M, Akram H (2012) Screening of sorghum (Sorghum bicolor var Moench) for drought tolerance at seedling stage in polyethylene glycol. J Anim Plant Sci 22(3):671–678 Blum A, Arkin G, Jordan W (1977) Sorghum root morphogenesis and growth. I. Effect of maturity genes. Crop sci 17(1):149–153. https://doi.org/10.2135/cropsci1977.0011183X001700010039x Borrell A, Oosterom E, George-Jaeggli B, Rodriguez D, Eyre J, Jordan D et al (2021) Sorghum. Chapter 5 in "Crop Physiology Case Histories for Major Crops". Academic Press, Elsevier. E. Sadras and Calderini, pp 197–214 Borrell A, Van Oosterom E, George-Jaeggli B, Vadez V, Singh V, Hammer G (2020) Physiology of growth, development and yield. Sorghum in the 21st Century: Food–Fodder–Feed–Fuel for a Rapidly Changing World, Springer: 127–155. https://doi.org/10.1007/978-981-15-8249-3_6 Borrell AK, Hammer GL, Douglas AC (2000a) Does maintaining green leaf area in sorghum improve yield under drought? I. Leaf growth and senescence. Crop sci 40(4):1026–1037. https://doi.org/10.2135/cropsci2000.4041026x Borrell AK, Hammer GL, Henzell RG (2000b) Does maintaining green leaf area in sorghum improve yield under drought? II. Dry matter production and yield. Crop sci 40(4):1037–1048. https://doi.org/10.2135/cropsci2000.4041037x Borrell AK, Oosterom EJ, Mullet JE, George-Jaeggli B, Jordan DR, Klein PE et al (2014a) Stay‐green alleles individually enhance grain yield in sorghum under drought by modifying canopy development and water uptake patterns. New Phytol 203(3):817–830. https://doi.org/10.1111/nph.12869 Borrell AK, Mullet JE, George-Jaeggli B, van Oosterom EJ, Hammer GL, Klein PE, Jordan DR (2014b) Drought adaptation of stay-green in sorghum associated with canopy development, leaf anatomy, root growth and water uptake. J Exp Bot 65(21):6251–6263. https://doi:.org/10.1093/jxb/eru232 Borrell AK, Wong A, George-Jaeggli B, van Oosterom EJ, Mace ES, Godwin ID et al (2022) Genetic modification of PIN genes induces causal mechanisms of stay-green drought adaptation phenotype. J Exp Bot. https://doi.org/10.1093/jxb/erac336 Browning BL, Zhou Y, Browning SR (2018) A one-penny imputed genome from next-generation reference panels. Am J Hum Genet 103(3):338–348. https://doi.org/10.1016/j.ajhg.2018.07.015 Burow G, Burke JJ, Xin Z, Franks CD (2011) Genetic dissection of early-season cold tolerance in sorghum (Sorghum bicolor (L.) Moench). Mol Breed 28(3):391–402. https://doi.org/10.1007/s11032-010-9491-4 Chen H, Xiong L (2005) Pyridoxine is required for post-embryonic root development and tolerance to osmotic and oxidative stresses. Plant J 44(3):396–408. https://doi.org/10.1111/j.1365-313X.2005.02538.x Chopra R, Burow G, Burke JJ, Gladman N, Xin Z (2017) Genome-wide association analysis of seedling traits in diverse Sorghum germplasm under thermal stress. BMC plant biol 17(1):12. https://doi.org/10.1186/s12870-016-0966-2 Christopher JT, Manschadi AM, Hammer GL, Borrell AK (2008) Developmental and physiological traits associated with high yield and stay-green phenotype in wheat. Aust J Agric Res 59:354–364 Christopher M, Chenu K, Jennings R, Fletcher S, Butler D, Borrell A et al (2018) QTL for stay-green traits in wheat in well-watered and water-limited environments. Field crops research 217:32–44. https://doi.org/10.1016/j.fcr.2017.11.003 Crasta O, Xu W, Rosenow D, Mullet J, Nguyen H (1999) Mapping of post-flowering drought resistance traits in grain sorghum: association between QTLs influencing premature senescence and maturity. Mol Gen Genet 262(3):579–588. https://doi.org/10.1007/s004380051120 Cullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. J Agric Biol Environ Stat 11(4):381. https://doi.org/10.1198/108571106X154443 Desnos T (2008) Root branching responses to phosphate and nitrate. Curr Opin Plant Biol 11(1):82–87. https://doi.org/10.1016/j.pbi.2007.10.003 Devaiah BN, Nagarajan VK, Raghothama KG (2007) Phosphate homeostasis and root development in Arabidopsis are synchronized by the zinc finger transcription factor ZAT6. Plant physiol 145(1):147–159. https://doi.org/10.1104/pp.107.101691 Du Y, Zhao Q, Chen L, Yao X, Zhang W, Zhang B et al (2020) Effect of drought stress on sugar metabolism in leaves and roots of soybean seedlings. Plant Physiol Biochem 146:1–12. https://doi.org/10.1016/j.plaphy.2019.11.003 EIAR (2014) Ethiopian strategy for sorghum Fakrudin B, Kavil S, Girma Y, Arun S, Dadakhalandar D, Gurusiddesh B et al (2013) Molecular mapping of genomic regions harbouring QTLs for root and yield traits in sorghum (Sorghum bicolor L. Moench). Physiol Mol Biol Plants 19(3):409–419. https://doi.org/10.1007/s12298-013-0188-0 Fiedler K, Bekele WA, Duensing R, Gründig S, Snowdon R, Stützel H et al (2014) Genetic dissection of temperature-dependent sorghum growth during juvenile development. Theor and appl genet 127(9):1935–1948. https://doi.org/10.1007/s00122-014-2350-7 Fiedler K, Bekele WA, Friedt W, Snowdon R, Stützel H, Zacharias A et al (2012) Genetic dissection of the temperature dependent emergence processes in sorghum using a cumulative emergence model and stability parameters. Theor appl genet 125(8):1647–1661. https://doi.org/10.1007/s00122-012-1941-4 Fiedler K, Bekele WA, Matschegewski C, Snowdon R, Wieckhorst S, Zacharias A et al (2016) Cold tolerance during juvenile development in sorghum: a comparative analysis by genomewide association and linkage mapping. Plant Breed 135(5):598–606. https://doi.org/10.1111/pbr.12394 Ghate T, Barvkar V, Deshpande S, Bhargava S (2019) Role of ABA signaling in regulation of stem sugar metabolism and transport under post-flowering drought stress in sweet sorghum. Plant Mol Biol Rep 37(4):303–313. https://doi.org/10.1007/s11105-019-01157-2 Gilmour A, Cullis B, Harding S, Thompson R (2006) ASReml Update: what’s new in Release 2.00. VSN International Ltd, Hemel Hempstead, UK Haussmann B, Mahalakshmi V, Reddy B, Seetharama N, Hash C, Geiger H (2002) QTL mapping of stay-green in two sorghum recombinant inbred populations. Theor appl genet 106(1):133–142. https://doi.org/10.1007/s00122-002-1012-3 Huang M, Liu X, Zhou Y, Summers RM, Zhang Z (2018) BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions. GigaScience 8(2):giy154. https://doi.org/10.1093/gigascience/giy154 Huang X, Zhao Y, Li C, Wang A, Zhao Q, Li W et al (2012) Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet 44(1):32–39 Hufnagel B, de Sousa SM, Assis L, Guimaraes CT, Leiser W, Azevedo GC et al (2014) Duplicate and conquer: multiple homologs of PHOSPHORUS-STARVATION TOLERANCE1 enhance phosphorus acquisition and sorghum performance on low-phosphorus soils. Plant physiol 166(2):659–677. https://doi.org/10.1104/pp.114.243949 Hulluka M, Esele J (1992) Sorghum diseases in eastern Africa. Sorghum and Millet Diseases: a Second. World Review, pp 36–39 Jordan D, Hunt C, Cruickshank A, Borrell A, Henzell R (2012) The relationship between the stay-green trait and grain yield in elite sorghum hybrids grown in a range of environments. Crop sci 52(3):1153–1161. https://doi.org/10.2135/cropsci2011.06.0326 Joshi DC, Singh V, Hunt C, Mace E, van Oosterom E, Sulman R et al (2017) Development of a phenotyping platform for high throughput screening of nodal root angle in sorghum. Plant Methods 13(1):56. https://doi.org/10.1186/s13007-017-0206-2 Kang Z, Qin T, Zhao Z (2019) Overexpression of the zinc finger protein gene OsZFP350 improves root development by increasing resistance to abiotic stress in rice. Acta Biochim Pol 66(2):183–190. https://doi.org/10.18388/abp.2018_2765 Kebede H, Subudhi P, Rosenow D, Nguyen H (2001) Quantitative trait loci influencing drought tolerance in grain sorghum (Sorghum bicolor L. Moench). Theor appl genet 103(2–3):266–276. https://doi.org/10.1007/s001220100541 Koch K (1996) Carbohydrate-modulated gene expression in plants. Annu Rev Plant Biol 47(1):509–540 Kramer PJ, Boyer JS (1995) Water relations of plants and soils. Academic press Lafarge T, Broad I, Hammer G (2002b) Tillering in grain sorghum over a wide range of population densities: identification of a common hierarchy for tiller emergence, leaf area development and fertility. Ann Bot 90(1):87–98. https://doi.org/10.1093/aob/mcf152 Li M-X, Yeung JM, Cherny SS, Sham PC (2012) Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum Genet 131(5):747–756. https://doi.org/10.1007/s00439-011-1118-2 Li L, Sheen J (2016) Dynamic and diverse sugar signaling. Curr Opin Plant Biol 33:116–125 Li R, Han Y, Lv P, Du R, Liu G (2014) Molecular mapping of the brace root traits in sorghum (Sorghum bicolor L. Moench). Breed sci 64(2):193–198. https://doi.org/10.1270/jsbbs.64.193 Liedtke JD, Hunt CH, George-Jaeggli B, Laws K, Watson J, Potgieter AB, Cruickshank A, Jordan DR (2020) High-Throughput Phenotyping of Dynamic Canopy Traits Associated with Stay-Green in Grain Sorghum. https://doi.org/10.34133/2020/4635153 . Plant Phenomics Ljung K, Nemhauser JL, Perata P (2015) New mechanistic links between sugar and hormone signalling networks. Curr Opin Plant Biol 25:130–137. https://doi.org/10.1016/j.pbi.2015.05.022 Lobell DB, Roberts MJ, Schlenker W, Braun N, Little BB, Rejesus RM et al (2014) Greater sensitivity to drought accompanies maize yield increase in the US Midwest. Science 344(6183):516–519. https://doi.org/10.1126/science.1251423 Lu B, Wen S, Zhu P, Cao H, Zhou Y, Bie Z et al (2020) Overexpression of Melon Tonoplast Sugar Transporter CmTST1 Improved Root Growth under High Sugar Content. Int J Mol Sci 21(10):3524. https://doi.org/10.3390/ijms21103524 Mace E, Innes D, Hunt C, Wang X, Tao Y, Baxter J et al (2019) The Sorghum QTL Atlas: a powerful tool for trait dissection, comparative genomics and crop improvement. Theor appl genet 132(3):751–766. https://doi.org/10.1007/s00122-018-3212-5 Mace E, Singh V, Van Oosterom E, Hammer G, Hunt C, Jordan D (2012) QTL for nodal root angle in sorghum (Sorghum bicolor L. Moench) co-locate with QTL for traits associated with drought adaptation. Theor appl genet 124(1):97–109. https://doi.org/10.1007/s00122-011-1690-9 Manschadi AM, Christopher J, deVoil P, Hammer GL (2006) The role of root architectural traits in adaptation of wheat to water-limited environments. Funct Plant Biol 33(9):823–837. https://doi.org/10.1071/FP06055 Massacci A, Battistelli A, Loreto F (1996) Effect of drought stress on photosynthetic characteristics, growth and sugar accumulation of field-grown sweet sorghum. Funct Plant Biol 23(3):331–340. https://doi.org/10.1071/PP9960331 McCormick RF, Truong SK, Sreedasyam A, Jenkins J, Shu S, Sims D et al (2018) The Sorghum bicolor reference genome: improved assembly, gene annotations, a transcriptome atlas, and signatures of genome organization. Plant J 93(2):338–354. https://doi.org/10.1111/tpj.13781 Menamo T, Kassahun B, Borrell A, Jordan D, Tao Y, Hunt C et al (2021) Genetic diversity of Ethiopian sorghum reveals signatures of climatic adaptation. Theor appl genet 134(2):731–742. https://doi.org/10.1007/s00122-020-03727-5 Mishra BS, Singh M, Aggrawal P, Laxmi A (2009) Glucose and auxin signaling interaction in controlling Arabidopsis thaliana seedlings root growth and development. PLoS ONE 4(2):e4502. https://doi.org/10.1371/journal.pone.0004502 Moghimi N, Desai JS, Bheemanahalli R, Impa SM, Vennapusa AR, Sebela D et al (2019) New candidate loci and marker genes on chromosome 7 for improved chilling tolerance in sorghum. J Exp Bot 50(12):3357–3371. https://doi.org/10.1093/jxb/erz143 Morris GP, Ramu P, Deshpande SP, Hash CT, Shah T, Upadhyaya HD et al(2013) Population genomic and genome-wide association studies of agroclimatic traits in sorghum. Proceedings of the National Academy of Sciences 110(2): 453–458 Nagel KA, Schurr U, Walter A (2006) Dynamics of root growth stimulation in Nicotiana tabacum in increasing light intensity. Plant Cell Environ 29(10):1936–1945. https://doi.org/10.1111/j.1365-3040.2006.01569.x Naser L, Kourosh V, Bahman K, Reza A (2010) Soluble sugars and proline accumulation play a role as effective indices for drought tolerance screening in Persian walnut (Juglans regia L.) during germination. Fruits 65(2):97–112. https://doi.org/10.1051/fruits/20010005 Ortiz D, Hu J, Salas Fernandez MG (2017) Genetic architecture of photosynthesis in Sorghum bicolor under non-stress and cold stress conditions. J Exp Bot 68(16):4545–4557. https://doi.org/10.1093/jxb/erx276 Parra-Londono S, Kavka M, Samans B, Snowdon R, Wieckhorst S, Uptmoor R (2018) Sorghum root-system classification in contrasting P environments reveals three main rooting types and root-architecture-related marker–trait associations. Ann Bot 121(2):267–280. https://doi.org/10.1093/aob/mcx157 Petrarulo M, Marone D, Ferragonio P, Cattivelli L, Rubiales D, De Vita P et al (2015) Genetic analysis of root morphological traits in wheat. Mol Genet Genom 290(3):785–806. https://doi.org/10.1007/s00438-014-0957-7 Reddy NRR, Ragimasalawada M, Sabbavarapu MM, Nadoor S, Patil JV (2014) Detection and validation of stay-green QTL in post-rainy sorghum involving widely adapted cultivar, M35-1 and a popular stay-green genotype B35. BMC Genomics 15(1):909. https://doi.org/10.1186/1471-2164-15-909 Richard C, Christopher JT, Chenu K, Borrell AK, Christopher MJ, Hickey L(2018) Selection in Early Generations to Shift Allele Frequency for Seminal Root Angle in Wheat The Plant Genome, https://doi.org/10.3835/plantgenome2017.08.0071 Rich SM, Christopher J, Richards R, Watt M (2020) Root phenotypes of young wheat plants grown in controlled environments show inconsistent correlation with mature root traits in the field. J Exp Bot 71(16):4751–4762 Rich SM, Watt M (2013) Soil conditions and cereal root system architecture: review and considerations for linking Darwin and Weaver. J Exp Bot 64(5):1193–1208 Robinson H, Kandic V, Christopher J, Fox G, Kelly A, Borrell A et al (2018) Root traits for yield improvement in barley. GRDC Update Papers Sabadin P, Malosetti M, Boer M, Tardin F, Santos F, Guimaraes C et al (2012) Studying the genetic basis of drought tolerance in sorghum by managed stress trials and adjustments for phenological and plant height differences. Theor Appl Genet 124(8):1389–1402. https://doi.org/10.1007/s00122-012-1795-9 Sanguineti M, Li S, Maccaferri M, Corneti S, Rotondo F, Chiari T et al (2007) Genetic dissection of seminal root architecture in elite durum wheat germplasm. Ann Appl Biol 151(3):291–305. https://doi.org/10.1111/j.1744-7348.2007.00198.x Singh V, Hammer G, Van Oosterom E(2008) Variability in structure and function of sorghum root systems. Global Issues, Paddock Action”. Proceedings of the 14th Australian Society of Agronomy Conference Singh V, van Oosterom EJ, Jordan DR, Hunt CH, Hammer GL (2011) Genetic variability and control of nodal root angle in sorghum. Crop Sci 51(5). https://doi.org/10.2135/cropsci2011.01.0038 Singh V, van Oosterom EJ, Jordan DR, Hammer GL (2012) Genetic control of nodal root angle in sorghum and its implications on water extraction. Eur J Agron 42:3–10. https://doi.org/10.1016/j.eja.2012.04.006 Slewinski TL (2012) Non-structural carbohydrate partitioning in grass stems: a target to increase yield stability, stress tolerance, and biofuel production. J Exp Bot 63(13):4647–4670. https://doi.org/10.1093/jxb/ers124 Srinivas G, Satish K, Madhusudhana R, Reddy RN, Mohan SM, Seetharama N (2009) Identification of quantitative trait loci for agronomically important traits and their association with genic-microsatellite markers in sorghum. Theor Appl Genet 118(8):1439–1454. https://doi.org/10.1007/s00122-009-0993-6 Subudhi P, Rosenow D, Nguyen H (2000) Quantitative trait loci for the stay green trait in sorghum (Sorghum bicolor L. Moench): consistency across genetic backgrounds and environments. Theor Appl Genet 101(5–6):733–741. https://doi.org/10.1007/s001220051538 Sukumaran S, Xiang W, Bean SR, Pedersen JF, Kresovich S, Tuinstra MR et al (2012) Association mapping for grain quality in a diverse sorghum collection. Plant genome 5(3):126–135. https://doi.org/10.3835/plantgenome2012.07.0016 Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J et al (2019) STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47(D1):D607–D613. https://doi.org/10.1093/nar/gky1131 Tao Y, Henzell R, Jordan D, Butler D, Kelly A, McIntyre C (2000) Identification of genomic regions associated with stay green in sorghum by testing RILs in multiple environments. Theor Appl Genet 100(8):1225–1232. https://doi.org/10.1007/s001220051428 Tao Y, Zhao X, Wang X, Hathorn A, Hunt C, Cruickshank AW et al (2020) Large-scale GWAS in sorghum reveals common genetic control of grain size among cereals. Plant Biotechnol J 18(4):1093–1105. https://onlinelibrary.wiley.com/doi/full/ 10.1111/pbi.13284 Tao Y, Trusov Y, Zhao X, Wang X, Cruickshank AW, Hunt C et al (2021) Manipulating assimilate availability provides insight into the genes controlling grain size in sorghum. Plant J. https://onlinelibrary.wiley.com/doi/full/ 10.1111/tpj.15437 Titiz O, Tambasco-Studart M, Warzych E, Apel K, Amrhein N, Laloi et al (2006) PDX1 is essential for vitamin B6 biosynthesis, development and stress tolerance in Arabidopsis. Plant J 48(6):933–946. https://doi.org/10.1111/j.1365-313X.2006.02928.x Toyofuku K, Kasahara M, Yamaguchi J (2000) Characterization and expression of monosaccharide transporters (OsMSTs) in rice. Plant and Cell Physiol 41(8):940–947. https://doi.org/10.1093/pcp/pcd016 Trachsel S, Kaeppler SM, Brown KM, Lynch JP (2011) Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field. Plant Soil 341(1–2):75–87. https://doi.org/10.1007/s11104-010-0623-8 Uga Y, Sugimoto K, Ogawa S, Rane J, Ishitani M, Hara N et al (2013) Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions. Nat genet 45(9):1097. https://doi.org/10.1038/ng.2725 Van-Raden PM (2008) Efficient methods to compute genomic predictions. J dairy sci 91(11):4414–4423. https://doi.org/10.3168/jds.2007-0980 Voss-Fels KP, Robinson H, Mudge SR, Richard C, Newman S, Wittkop B et al (2018) VERNALIZATION1 modulates root system architecture in wheat and barley. Mol Plant 11(1):226–229. https://doi.org/10.1016/j.molp.2017.10.005 Wang H, Chen G, Zhang H, Liu B, Yang Y, Qin L et al (2014) Identification of QTLs for salt tolerance at germination and seedling stage of Sorghum bicolor L. Moench. Euphytica 196(1):117–127. https://doi.org/10.1007/s10681-013-1019-7 Wei T, Simko V, Levy M, Xie Y, Jin Y, Zemla J (2017) Package ‘corrplot’: Visualization of a Correlation Matrix (Version 0.84). Stat 56:316–324 Williams JL, Sherman JD, Lamb P, Cook J, Lachowiec JA, Bourgault M (2022) Relationships between roots, the stay-green phenotype, and agronomic performance in barley and wheat grown in semi‐arid conditions. The Plant Phenome Journal 5(1):e220050. https://doi.org/10.1002/ppj2.20050 Xu W, Rosenow D, Nguyen H (2000a) Stay green trait in grain sorghum: relationship between visual rating and leaf chlorophyll concentration. Plant Breed 119(4):365–367. https://doi.org/10.1046/j.1439-0523.2000.00506.x Xu W, Subudhi PK, Crasta OR, Rosenow DT, Mullet JE, Nguyen HT (2000b) Molecular mapping of QTLs conferring stay-green in grain sorghum (Sorghum bicolor L. Moench). Genome 43(3):461–469. https://doi.org/10.1139/g00-003 Zheng Z, Hey S, Jubery T, Liu H, Yang Y, Coffey L et al (2020) Shared genetic control of root system architecture between Zea mays and Sorghum bicolor. Plant physiol 182(2):977–991. https://doi.org/10.1104/pp.19.00752 Zhu J, Brown KM, Lynch JP (2010) Root cortical aerenchyma improves the drought tolerance of maize (Zea mays L.). Plant Cell Environ 33(5):740–749. https://doi.org/10.1111/j.1365-3040.2009.02099.x Supplementary Files OnlineResource1.xlsx OnlineResource2.pdf OnlineResource3.pdf OnlineResource4.pdf OnlineResource5.xlsx OnlineResource6.pdf OnlineResource7.pdf OnlineResource8.pdf Cite Share Download PDF Status: Published Journal Publication published 16 Sep, 2023 Read the published version in Theoretical and Applied Genetics → Version 1 posted Editorial decision: Major revisions 02 Apr, 2023 Reviewers agreed at journal 23 Oct, 2022 Reviewers invited by journal 22 Oct, 2022 Editor assigned by journal 14 Oct, 2022 First submitted to journal 13 Oct, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2159601","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":146329452,"identity":"b59b86bf-37c6-47f4-affc-81140b3326df","order_by":0,"name":"Kassahun Bantte","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACCRDB2MDAYMAOJBgMLEjRwnMApEWCFC0SCXA+fiDZfvzxh587DueZSz6/uuFHgQQDf3t3Al4t0jw5ZpK9Zw4XW87OKbvZA3SYxJmzG/BqkWPIYWPgbTucuOF2TtoNHqAWA4lcAlr4nz/++Bek5eaZtJt/iNEiLZFgIA225Qb7sdtE2SI5442ZtGxbeuLOnhy22zIGEjwE/SJxPv3xx7dt1onb2Y8/u/nmj40cf3svfi1IgMcATBKrHATYH5CiehSMglEwCkYQAADrmkrxeBCf1QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-4970-145X","institution":"Jimma University College of Agriculture and Veterinary Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Kassahun","middleName":"","lastName":"Bantte","suffix":""},{"id":146329453,"identity":"798b4ce2-8f29-49cf-93cf-155a219880b2","order_by":1,"name":"Temesgen Matiwos Menamo","email":"","orcid":"","institution":"Jimma University College of Agriculture and Veterinary Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Temesgen","middleName":"Matiwos","lastName":"Menamo","suffix":""},{"id":146329454,"identity":"8aa875d6-2b6f-4ac8-ba1b-9deb18e5a233","order_by":2,"name":"Andrew K. Borrell","email":"","orcid":"","institution":"Queensland Alliance for Agriculture and Food Innovation","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"K.","lastName":"Borrell","suffix":""},{"id":146329455,"identity":"088d30f1-a604-46f4-bd24-dcd09ff69ddc","order_by":3,"name":"Emma Mace","email":"","orcid":"","institution":"Queensland Alliance for Agriculture and Food Innovation","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Emma","middleName":"","lastName":"Mace","suffix":""},{"id":146329456,"identity":"e6fd711f-e787-4109-b43d-c1712af7cbcc","order_by":4,"name":"David R. Jordan","email":"","orcid":"","institution":"Queensland Alliance for Agriculture and Food Innovation","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"David","middleName":"R.","lastName":"Jordan","suffix":""},{"id":146329457,"identity":"5890b82e-0813-4669-b7e3-81f4686b513f","order_by":5,"name":"Yongfu Tao","email":"","orcid":"","institution":"Queensland Alliance for Agriculture and Food Innovation","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yongfu","middleName":"","lastName":"Tao","suffix":""},{"id":146329458,"identity":"01f275a0-1406-4a72-b25f-e83e32f4ac3a","order_by":6,"name":"Colleen Hunt","email":"","orcid":"","institution":"Queensland Department of Agriculture and Fisheries","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Colleen","middleName":"","lastName":"Hunt","suffix":""}],"badges":[],"createdAt":"2022-10-12 17:01:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2159601/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2159601/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00122-023-04457-0","type":"published","date":"2023-09-16T15:00:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":28247646,"identity":"ca178f8d-3d71-4e5e-a13c-28257bb09e90","added_by":"auto","created_at":"2022-10-25 20:25:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":270134,"visible":true,"origin":"","legend":"\u003cp\u003eEthiopian geographical map A) sorghum production areas from 1961 to 1990, and B) sorghum collected from Woredas/districts (dots represent the collection sites) that were used in the current study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2159601/v1/c3b05e29f6441fe32eb8922d.png"},{"id":28247645,"identity":"c5888036-f202-4a4e-a913-9acdc9da73f1","added_by":"auto","created_at":"2022-10-25 20:25:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":674214,"visible":true,"origin":"","legend":"\u003cp\u003eRoot angle was measured relative to the vertical line at the 5-leaf stage.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2159601/v1/3dfa25456dad4fe13085a2ac.png"},{"id":28247944,"identity":"8f936dc3-f281-4bd6-a6d2-e00fa6883de0","added_by":"auto","created_at":"2022-10-25 20:30:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":966734,"visible":true,"origin":"","legend":"\u003cp\u003eStructure of nodal roots visible on the glass surface of root chambers A) narrow root angle (JUS173657=16.6\u003csup\u003eo\u003c/sup\u003e) and B) wide root angle (JUS171368=26.4\u003csup\u003eo\u003c/sup\u003e).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2159601/v1/406743df7d96a3ff427f3c1e.png"},{"id":28247648,"identity":"eacc9502-0e4d-4268-8cfa-31bafe02d755","added_by":"auto","created_at":"2022-10-25 20:25:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":257820,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations of root system architecture traits with shoot traits A) Correlation plots among key root and shoot traits. The colour indicates the correlation values and size of the circle shows the level of significance (large circle highly significant and small/non circle non-significant). B) Bi-plot of root system architecture traits. RA=root angle, RDM=root dry mass, RN=root number, RL=root length, RS=root to shoot ratio, SDM=shoot dry mass, LA=leaf area, SD=shoot diameter and SL=Shoot length.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2159601/v1/5dca2e235bfa769ef6089b4a.png"},{"id":28247652,"identity":"67fa3211-4d18-4f85-9b73-b14af329770f","added_by":"auto","created_at":"2022-10-25 20:25:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":364252,"visible":true,"origin":"","legend":"\u003cp\u003eGenome wide association of four key root architecture traits using 940 diverse sorghum germplasm A) root angle, B) root length, C) root number, and D) root dry mass. Horizontal line indicates significance threshold (p = 8.09E-5).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2159601/v1/e29d1af5aecd5802b0088302.png"},{"id":43301201,"identity":"8c1d4696-9b32-442b-b6fe-00245c6da434","added_by":"auto","created_at":"2023-09-18 15:09:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2786947,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2159601/v1/43acc413-02a1-4deb-a980-7b5b7dc73d85.pdf"},{"id":28247945,"identity":"b49fb8d8-de72-4d93-8002-7ef033a952b7","added_by":"auto","created_at":"2022-10-25 20:30:46","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":100634,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2159601/v1/34ceb7291e129726183e9e14.xlsx"},{"id":28248102,"identity":"beeb54ff-2c6b-4ad6-b743-412fed2ad2b7","added_by":"auto","created_at":"2022-10-25 20:35:46","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":254111,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2159601/v1/b9d2f8ec8f8768ad52b33f3f.pdf"},{"id":28247657,"identity":"e208f5b1-0b42-48fe-b8a3-e4938c95860c","added_by":"auto","created_at":"2022-10-25 20:25:47","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":179595,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2159601/v1/d3ea92ca463f59e50f57b030.pdf"},{"id":28247947,"identity":"a4b9c981-f664-455b-b195-b055f7331054","added_by":"auto","created_at":"2022-10-25 20:30:46","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":203616,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2159601/v1/4f6ef4abe1b0ae15c21c36c7.pdf"},{"id":28247651,"identity":"ff70899a-f186-4e73-ae43-428ff2efe4f5","added_by":"auto","created_at":"2022-10-25 20:25:46","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":260307,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2159601/v1/bf906b88f283d4539bd1dcb8.xlsx"},{"id":28247655,"identity":"0185bcd4-8132-4bbf-bd38-6c83d7bb9c2b","added_by":"auto","created_at":"2022-10-25 20:25:46","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":172069,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2159601/v1/94c6e4cac96e4501dc037066.pdf"},{"id":28248103,"identity":"9063b7ec-e409-43d7-af1d-bde6b580ee02","added_by":"auto","created_at":"2022-10-25 20:35:46","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":119429,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2159601/v1/296341fa85b4363d8abba874.pdf"},{"id":28247656,"identity":"ebb4575a-2ef4-46cc-8a7b-c8aeb5b2347f","added_by":"auto","created_at":"2022-10-25 20:25:47","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":74054,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2159601/v1/a3bcc2d66773b3f4e180009f.pdf"}],"financialInterests":"","formattedTitle":"Genetic dissection of root architecture in Ethiopian sorghum landraces","fulltext":[{"header":"Key Message","content":"\u003cp\u003eThis study quantified genetic variation in root system architecture (root number, angle, length and dry mass) within a diversity panel of 1,171 Ethiopian sorghum landraces and identified 22 genomic regions associated with the root variations.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eSorghum is a staple food crop for millions of resource-poor farmers in the semi-arid tropics, particularly in Sub-Saharan Africa, including Ethiopia. This is due, in part, to the higher levels of abiotic stress tolerance sorghum displays in comparison to other cereals, particularly in terms of water stress (Borrell et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Sorghum has a very broad environmental adaptation and is grown in both temperate and tropical climates, high and low altitudes, and in both water-logged and drought-stressed conditions (Borrell et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, in the arid and semi-arid regions where sorghum is pre-dominantly grown, it is often affected by water stress at either the vegetative and/or reproductive stages of crop development, particularly during the post-flowering stage (Kebede et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ali et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Jordan et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Sorghum has a range of phenotypic characteristics that contribute to the crop\u0026rsquo;s enhanced adaptability to such harsh environments, both above ground and below ground (Borrell et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Borrell et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In particular, the root system architecture (RSA) has been shown to play a critical role in productivity under such water-limited conditions, with narrow root angles increasing access to water from deep soil in cereal crops, including sorghum (Singh et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Uga et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A putative association between root angle and grain yield has also been reported in sorghum (Mace et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, characterization and deployment of selection for RSA traits could accelerate the development of drought adapted sorghum varieties.\u003c/p\u003e \u003cp\u003eSignificant progress has been made in the past decade on RSA as a selection criterion for stress adaptation in maize (Trachsel et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lobell et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), rice (Uga et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), wheat (Richard et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Voss-Fels et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), barley (Robinson et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Voss-Fels et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and sorghum (Singh et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mace et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) following the development of a range of root phenotyping methods. Previous studies in sorghum have identified that significant variation for RSA exists, suggesting opportunities for selection and genetic improvement for water stress response breeding (Blum et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Joshi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to characterization of RSA variation, linkage analysis and genome-wide association study (GWAS) approaches have been used to dissect genetic control of root traits in sorghum, with 11 recent studies identifying 392 QTLs for sorghum root traits (Mace et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fakrudin et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bekele et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hufnagel et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Chopra et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Parra-Londono et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bernardino et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Moghimi et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Among these QTL, 248 were repeatedly detected, while the remaining 144 were unique to each specific study. For example, Mace et al (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) identified four QTL for nodal root angle in a bi-parental recombinant inbred line (RIL) population, which were subsequently also identified in other root trait GWAS studies (Bekele et al (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); Parra-Londono et al (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); Bernardino et al (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Moghimi et al (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Zheng et al (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)). These four root angle QTLs also co-localized with previously identified QTL for stay-green (Mace et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Crops with stay-green exhibit a distinct green leaf phenotype during grain filling under terminal drought, resulting in enhanced grain yield under post-anthesis drought stress (Borrell et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000a\u003c/span\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003eb\u003c/span\u003e; Jordan et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Borrell et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014a\u003c/span\u003eb; Borrell et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Stay-green loci also modify root architecture (Manschadi et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Christopher et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which is likely linked to the increased water accessibility displayed by stay-green genotypes during grain filling under end-of-season drought (Borrell et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn general, RSA is highly plastic to environmental changes and heritability varies from low to high (Singh et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mace et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Joshi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Despite the discovery of heritable natural variation in RSA, only limited genetic-based and co-location studies with stay-green QTLs of sorghum roots have been identified via high-throughput phenotyping. Recent studies have found that a stay-green phenotype involves modifications in root architecture through \u003cem\u003ePIN\u003c/em\u003e genes (Borrell et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore in barley, stay-green and greater yield have been associated with deeper roots and delayed root senescence (Williams et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hence, a study in genetic analysis of RSA and co-location with stay-green is needed to further understand its regulatory mechanisms. Ethiopian sorghum landraces are particularly rich in genetic diversity and thus are very valuable to dissect the genetic basis governing the variability of RSA traits. Around 80% of sorghum in Ethiopia is cultivated in the dry and hot lowlands where abiotic factors such as inadequate and erratic rainfall are among the major production constraints (Hulluka and Esele \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; EIAR, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Still there is no single root system trait or even set of traits that will confer drought adaptation in this environment. The Ethiopian Biodiversity Institute (EBI) has a collection of more than 10,000 sorghum accessions from across the country. However, the RSA of this germplasm resource has not yet been characterised. This study aims to: a) characterise the RSA of a subset of the collection of diverse Ethiopian sorghum landraces, and b) undertake GWAS to identify genomic regions associated with RSA to inform molecular breeding strategies in the future.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\n\u003ch3\u003eSite For Root Phenotyping Platform\u003c/h3\u003e\n\u003cp\u003eThe experiment was conducted in a polyhouse at Jimma University, College of Agriculture and Veterinary Medicine (JUCAVM), Jimma, Ethiopia (7\u003csup\u003eo\u003c/sup\u003e 33\u0026rsquo;N latitude, 36 \u003csup\u003eo\u003c/sup\u003e 57\u0026rsquo;E longitude). The mean daily air temperature during the growing season in the polyhouse ranged from a minimum of 7.8\u0026deg;C to a maximum of 40.8\u0026deg;C. The polyhouse allowed transmission of 60% of incident photosynthetically active radiation.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGenetic materials\u003c/h2\u003e \u003cp\u003eIn total, 1,771 Ethiopian landraces were selected for root phenotyping (Online Resource 1). The landraces were selected systematically from the EBI (791 genotypes), Melkassa Agricultural Research Center (935 genotypes) and JUCAVM (45 genotypes) to include landraces from all sorghum producing regions and ecologies of the country (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and B). An individual from each accession was selected and self-pollinated for one generation to reduce residual heterozygosity and to increase seed quantity. A subset of 940 landraces were genotyped and used for GWAS. This panel of 940 landraces has been described previously by Menamo et al (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and used for genetic diversity and genome environment association studies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperimental Procedure And Design\u003c/h3\u003e\n\u003cp\u003eThe experiment was conducted using a purpose-built high-throughput root phenotyping platform (Singh et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Joshi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) consisting of 500 pairs of perspex plates (40 cm high, 35 cm wide and 3 mm thick; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The centre of the pairs of perspex sheets were drilled and screwed together to maintain a 3 mm spacing and minimize bulging of the chambers during soil filling. The void between each pair of plates was filled with red soil (Online Resource 2A). Fifty root chambers were placed vertically into slots in each of 10 large metal tubs (Online Resource 2C). The tops of the chambers in each tub were covered with black polycarbonate covers to exclude light from the soil and roots, but leaving 5 cm long slits for the seedlings to emerge. Prior to planting, chambers were watered to field capacity and left until soil water saturation. Three seeds were planted in the centre of each chamber on one side of the Perspex with the embryo angled towards the base (Online Resource 2B), then thinned to one plant per chamber after emergence. The seeds were treated with ApronStar 42WS fungicide before sowing. Plants were watered one or two times per week, depending on the environmental conditions. Plant nutrients were applied in the form of diammonium phosphate (DAP, 1.5 kg) and urea (1 kg) across the 500 chambers i.e. equivalent to 100 kg/ha urea and 150kg DAP for a population of 50,000 plants per hectare. DAP was applied during sowing in a granular form and urea when the plants reached the 3rd leaf stage in liquid form, applied equally in each chamber.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 1,771 landraces were phenotyped across 11 independent experimental runs, with each run consisting of 500 chambers (10 tubs with 50 root chambers per tub). In each run, 160 genotypes were included, of which 140 genotypes were replicated three times and 20 check genotypes were replicated four times. The 20 check genotypes were common to all experimental runs enabling co-analysis of all entries across all runs in a single analysis. The experimental runs had two blocks of ten tubs (five tubs in each block) and arranged in a row column design; columns being represented by the tubs and rows by the root chamber position within each tub.\u003c/p\u003e\n\u003ch3\u003eCollection Of Phenotypic Data\u003c/h3\u003e\n\u003cp\u003eImages of the root structure were taken on both sides of each chamber using two digital cameras (CANON SX610 HS) remotely operated through Wi-Fi by tablets in order to synchronize the imaging on both sides. The images were used to determine the root angle (RA) relative to the vertical plane (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). RA was taken from the first flush of nodal roots at a distance of 2 cm from the base of the plant (Singh et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) using \u003cem\u003eOpengelphoto.tcl\u003c/em\u003e software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.activestate.com/activetcl\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.activestate.com/activetcl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The recorded root angle for each plant was the mean of four observations (left and right side of each plant for both sides of the chamber). After root imaging, each chamber was laid flat, the top perspex plate removed, and then root length (the longest 1st flush nodal root) was measured. Root numbers per plant were counted after soil was washed from the roots with tap water. Root samples were air dried on blotting paper, then oven-dried at 60\u0026deg; C for 3 days to determine root dry mass (g/plant). Additionally, shoot traits were included to provide important information regarding impact on plant size. Leaf length and width (cm) were measured on fully expanded leaves (2nd, 3rd, 4th and 5th leaves) when the plants exhibited five fully expanded leaves. Leaf area (cm\u003csup\u003e2\u003c/sup\u003e) was determined by multiplying the maximum leaf length and width, and by including a shape factor of 0.69 (Lafarge et al. 2002). The shoot length (cm) was measured from the base of the plant (soil level) to the ligule of Leaf 5. The shoot diameter of the wider side was measured 2 cm above the top of the chamber (soil surface) using digital callipers. Just before imaging the roots, shoots were cut at the base of the plant and subsequently dried. Shoot dry mass (g/plant) was determined after oven drying at 60\u003csup\u003eO\u003c/sup\u003eC for 3 days.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTotal genomic DNA extraction and genotyping\u003c/h2\u003e \u003cp\u003eSamples of four individual leaves were collected from 15-day-old plants grown in small pots in a greenhouse and stored at -20\u003csup\u003eo\u003c/sup\u003eC in 96 well plates for 24 hours. The leaf samples were freeze-dried and sent to DArT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.diversityarrays.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.diversityarrays.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for DNA extraction. The DNA samples were then genotyped using DArTseq as previously described in Tao et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This involves complexity reduction of the genomic DNA to remove repetitive sequences using methylation sensitive restriction enzymes prior to sequencing on Next Generation sequencing platforms. The sequence data generated were then aligned to version v3.1.1 of the sorghum reference genome sequence (McCormick et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) to identify SNPs (Single Nucleotide Polymorphisms).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRoot Phenotype Data Analysis\u003c/h3\u003e\n\u003cp\u003eThe single and combined data for all experimental runs were analysed using a restricted maximum likelihood (REML) mixed model allowing for spatial variation across the experiments using \u0026ldquo;asreml-R\u0026rdquo; R package (Gilmour et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e): REML mixed model: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(y=X\\tau +Zu+e\\)\u003c/span\u003e\u003c/span\u003e; where: y\u0026thinsp;=\u0026thinsp;the measured data for each trait and each run; τ\u0026thinsp;=\u0026thinsp;the fixed effects (genotypes) in the trial; X\u0026thinsp;=\u0026thinsp;the design matrix for the fixed effects (row and column); u\u0026thinsp;=\u0026thinsp;random effects for each analysis; Z\u0026thinsp;=\u0026thinsp;the design matrix for the random effects and e\u0026thinsp;=\u0026thinsp;residual error effects. Best linear unbiased estimates (BLUEs) were analysed for each genotype and trait using linear mixed model (REML) spatial analysis by asreml-R package in R software. Repeatability (H\u003csup\u003e2\u003c/sup\u003e) was estimated as H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1- uBLUE/2 σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e (Cullis et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), where uBLUE is the mean variance of a difference BLUE, and σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e is the genotypic variance component. The BLUE for each landrace was used to perform the pair-wise Pearson correlation analysis among the traits. The analysis was conducted using R software\u0026rsquo;s corrplot package (Wei et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eGenome Wide Association Study And Co-location\u003c/h3\u003e\n\u003cp\u003eA total of 54,080 SNP markers were identified across the 940 landraces. The data set was further filtered to exclude SNPs with MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.01 or missing values\u0026thinsp;\u0026gt;\u0026thinsp;25%, leaving a final data set of 25,634 SNP markers (Menamo et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The remaining missing values were inferred using the Beagle 5.0 software package (Browning et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Best linear unbiased estimates (BLUEs) for all traits were used in the genome-wide association study (GWAS) analysis using the software BLINK (Huang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The SNP data set was initially filtered for minor allele frequency (MAF) of 0.01. Kinship was calculated as described by Van-Raden (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Q\u0026ndash;Q plots of p-values were examined to determine how well the models accounted for population structure and familial relatedness. The genetic type I error calculator (GEC) was used to determine significance thresholds for trait-marker associations (Li et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe QTL detected in this study for each RSA trait was compared with 392 RSA QTLs in 12 previously identified studies. Additionally, co-located QTLs were also identified between root angle and stay-green QTLs studied under post-flowering water-stress conditions using Sorghum QTL Atlas database (Mace et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The data collection for stay-green or green leaf area was considered between the flowering and maturity stage only. A 1 cM window was used to identify QTL co-location based on the genetic distances from the sorghum consensus map (Mace et al. 2009). Enrichment analysis of QTL co-location was undertaken using chi-square test with R software (\u0026ldquo;chisq.test\u0026rdquo; procedure).\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003eSubstantial Variation In Root System Architecture (RSA) Traits\u003c/h3\u003e\n\u003cp\u003eThe correlation among the experimental runs for each trait was first analysed (Online Resource 3) using the 20 genotypes repeated across the experimental runs. In almost all cases, all traits were positively correlated across experimental runs (Online Resource 3), with root angle showing the highest correlations (r\u0026thinsp;=\u0026thinsp;0.57 to 0.94) among all 11 experimental runs; and root to shoot ratio showing the largest range in correlation values among the experimental runs (r\u0026thinsp;=\u0026thinsp;0.02 to 0.76).\u003c/p\u003e \u003cp\u003eSignificant variation was observed for all traits measured across all genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) with an approximately normal distribution (Online Resource 4), indicating the quantitative nature of the traits. The nodal root angle varied from 16.6\u0026ordm; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) to 26.4\u0026ordm; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), with an average of 20.9\u0026ordm;. Both root number and length varied considerably, with number varying from 8.0 to 12.0 (average\u0026thinsp;=\u0026thinsp;9.8), and length varying from 33.3 to 56.0 cm (average\u0026thinsp;=\u0026thinsp;45.0 cm). The repeatability (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) was generally high across traits, with nodal root angle having the highest repeatability of 78%. The phenotypic best linear unbiased estimations (BLUE) of root traits are detailed in Online Resource 5.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhenotypic variation of all traits based on best linear unbiased estimations (BLUE) (calculated as genotype performance across the experimental runs).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG. Var\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eErr Var\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSign.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e49.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e52.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eRA\u0026thinsp;=\u0026thinsp;root angle, RDM\u0026thinsp;=\u0026thinsp;root dry mass, RN\u0026thinsp;=\u0026thinsp;root number, RL\u0026thinsp;=\u0026thinsp;root length, RS\u0026thinsp;=\u0026thinsp;root shoot ratio, SDW\u0026thinsp;=\u0026thinsp;shoot dry mass, SL\u0026thinsp;=\u0026thinsp;shoot length, SD\u0026thinsp;=\u0026thinsp;shoot diameter and LA\u0026thinsp;=\u0026thinsp;leaf area. **=significant level at P\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCorrelation Among RSA And Shoot Traits\u003c/h3\u003e\n\u003cp\u003eRoot angle (RA) was poorly correlated with the other root and shoot traits measured (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In contrast, root number (RN), root dry mass (RDM) and root length (RL) grouped together (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), indicating a positive correlation among them, with the strongest correlation between RN and RDM. Shoot dry mass (SDM) also showed a strong positive correlation with RDM and RN, but negative correlation with root to shoot ratio. Shoot diameter was moderately correlated with SDM, RDM and RN.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide association for RSA traits\u003c/h2\u003e \u003cp\u003eGWAS was conducted to identify trait-marker associations, where Q-Q plots showed that the observed \u003cem\u003ep\u003c/em\u003e-values were close to the expected \u003cem\u003ep\u003c/em\u003e-values for all traits with minimal deviation observed (Online Resource 6). A total of 22 SNP markers were significantly (\u003cem\u003eP-value\u003c/em\u003e \u0026le; 8.09E-05) associated with RSA traits (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Of the 22 significant SNPs for RSA traits, seven, seven, four and four were associated with root angle, root number, root length and root dry mass, respectively. Similar to the correlation result, none of the significant markers showed pleiotropy effects in root vs shoot traits. The significant markers were distributed over all chromosomes except chromosome SBI-10, with numbers varying from two on chromosomes SBI-04 and SBI-05 to ten on chromosome SBI-01. Of the 22 QTL associated with four RSA traits, 15 co-located with previously identified RSA QTL based on a 1 cM window, while the remaining seven were unique to this study (Online Resource 7).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of significant single-nucleotide polymorphisms (SNPs) for the four RSA traits at seedling stage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQTLs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePOSv3.01\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERA3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70640280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.5E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERA4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52404340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.85E-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERA5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7621214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERA6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47152373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.48E-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERA6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49330161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERA7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53856839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.06E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERA8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47689441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERN1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76107179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.35E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERN2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19789147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.21E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERN2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60605417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.76E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERN2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73281144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.91E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERN6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54403226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.5E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERN8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53766348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.62E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERN9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49867350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.54E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERL1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66615979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.22E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERL3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2540858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.96E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERL3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10025278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.64E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERL5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53377135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.78E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERDM1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27268290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERDM1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65740047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.54E-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERDM3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59395700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQERDM3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBI-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60530147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eRA\u0026thinsp;=\u0026thinsp;root angle, RN\u0026thinsp;=\u0026thinsp;root number, RL\u0026thinsp;=\u0026thinsp;root length, RDM\u0026thinsp;=\u0026thinsp;root dry mass, Chr\u0026thinsp;=\u0026thinsp;chromosome, POSv3.01\u0026thinsp;=\u0026thinsp;marker/QTL position on chromosome\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCo-location Of Root Angle Qtls With Previously Identified Stay-green Qtls\u003c/h3\u003e\n\u003cp\u003eSix out of the seven root angle QTL (6/7\u0026thinsp;=\u0026thinsp;85.7%) co-located with previously identified QTL for drought resistance traits (Online Resource 8), including green leaf area after anthesis (Haussmann et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Srinivas et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Sabadin et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and total number of green leaves after anthesis (Reddy et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), with a significant genome enrichment (\u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRoot system architecture screening in sorghum differs from other cereal crops\u003c/h2\u003e \u003cp\u003eIndividual root growth patterns, including tropism, branching and elongation rates are determined by underlying genetics, contributing to the architecture of the overall system (Rich et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The passage of roots through the soil is also constrained by soil physical and chemical conditions, with many biotic and abiotic factors contributing to the final architecture, including gravity, temperature, water, oxygen and nutrient availability, and soil biota (Rich and Watt, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Changes to the mature (or near-mature) architecture will also be driven by the timing and initiation of lateral and adventitious roots under the control of nutrient and hormonal signals (Atkinson et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The efficiency of soil water uptake is determined by the root system because roots are usually the site of the greatest resistance in the pathway for liquid phase movement of water through the soil-plant-atmosphere continuum (Kramer and Boyer, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHigh-throughput phenotyping of RSA traits is of major importance to elucidate the underlying genetic control and to incorporate such traits in breeding programs through marker-assisted selection. Recently designed high-throughput root phenotyping platforms are an opportunity for further improvement of crop productivity, particularly in moisture-limited environments. Various high-throughput RSA phenotyping systems have been designed to screen seminal roots at the seedling stage i.e. clear pot and growth pouch methods in wheat (Richard et al., 2015) and clear-pot phenotyping method in barley (Robinson et al., 2016). However, these methods are less relevant for sorghum since seminal roots do not constitute the major proportion of the mature root system. Hence, Singh et al. (2010) developed a new root chamber-based method to screen nodal root angle in sorghum. While the assumption is that seedling screens for root architecture will be correlated with mature root systems, this is not always the case (Rich et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, it was reported that narrow primary root angle was found to be associated with deep field roots, providing some evidence that screening for narrow root angle at the seedling stage is worthwhile (Rich et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCorrelations between seedling and mature root traits in the field have been shown to be inconsistent from site to site and year to year in wheat (Rich et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This could be due to the influence of soil physical, chemical, and edaphic factors on root traits. However, Singh et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) reported that root angle of young sorghum plants can be a useful selection criterion for specific drought adaptation. The Singh et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) study also reported that relationships between root angle screening and mature field traits were best achieved using soil rather than germination paper and agar in the initial screening. Hence, exploiting RSA variability and identifying genomic regions controlling the variability could open avenues for sorghum breeders to develop more drought adapted varieties through manipulating root architecture. In particular, the extent to which screening for root angle will help sorghum breeders to develop more drought adapted varieties will depend on the strength of the correlation between RA at seedling stage (6th fully expanded leaf stage) and the mature root system architecture. RSA may play a role in stay-green during post-flowering moisture-stress conditions. For example, narrow root angle increases access to water stored deep in the soil during the post-flowering period compared to wider root angles in wheat (Manschadi et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and sorghum (Singh et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, narrow root angle may contribute to a stay-green phenotype, although this will depend to some extent on genetics (e.g. canopy size), management (e.g. row spacing, fertilization) and environment (e.g. soil type, water availability).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenetic Variation For Diverse Rsa Traits Identified In Ethiopian Sorghum\u003c/h3\u003e\n\u003cp\u003eIn this study, genetic materials were collected from all sorghum producing regions in Ethiopia to capture the genetic diversity. Hence, significant variability for RSA traits was identified. The study also revealed high repeatability and a broad range of variation for individual root traits, in particular, for root angle. Nodal root angle showed wider variation and higher repeatability than other root-related traits, suggesting that the variation observed in this trait was under genetic control. The high repeatability of root angle in this study (78%) is consistent with previous studies in sorghum (Mace et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Joshi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which reported high repeatability in root angle measurements (73.7% and 96%, respectively), while Singh et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) also observed moderate repeatability (46.6%) in nodal root angle in sorghum. The presence of genetic variation with moderate to high repeatability suggests that nodal root angle could respond to selection in sorghum-improvement programs.\u003c/p\u003e\n\u003ch3\u003eRoot Angle Is Poorly Associated With Other Traits\u003c/h3\u003e\n\u003cp\u003eDrought adaptation is of particular interest because it depends upon components associated with both water supply (e.g. root traits) and water demand (e.g. canopy traits). Root structural traits are important sub-component traits for extracting water and nutrients from soil. These traits might contribute positively or negatively to drought adaptation in association with other traits. Therefore, understanding the interaction between component traits could be helpful for breeders to decide whether to make direct or indirect selection for better yield under drought conditions.\u003c/p\u003e \u003cp\u003eIn this study, a poor association between root angle and other RSA traits was observed. Similar results have been observed previously in sorghum (Singh et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mace et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), wheat (Sanguineti et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and barley (Robinson et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The finding of weak correlations between root angle and plant size traits also agrees with the results reported by Singh et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which found that traits associated with plant size (root mass, shoot mass, and leaf area) had a very low correlation with root angle in sorghum. These weak associations suggest that root angle and plant size traits might be controlled by different sets of genes. Furthermore, the incorporation of root angle screening into a sorghum breeding program would be helped if root angle was not associated with plant size because such an association might inadvertently result in selection for early vigour (Singh et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, nodal root angle selection could be one strategy for selection in the improvement of drought adaptation.\u003c/p\u003e\n\u003ch3\u003eGenetic Control Of RSA Traits\u003c/h3\u003e\n\u003cp\u003eGWAS has proven to be a powerful tool to understand the genetic control of quantitative traits in sorghum and other species (Huang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Morris et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tao et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tao et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Using this approach, this study identified 22 QTLs for key RSA traits (root angle, length, dry mass and number) in Ethiopian sorghum germplasm. Among RSA QTLs, 15 were co-located with RSA QTL reported in previous sorghum studies using different genetic materials. These QTL were repeatedly reported with different genetic backgrounds, possibly indicating hotspot genomic regions for root development. The seven unique QTLs potentially represent novel genetic loci for RSA from Ethiopian germplasm.\u003c/p\u003e \u003cp\u003eThe root traits investigated in this study have previously been associated with grain yield under water stressed conditions in different cereal crops (Mace et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ali et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Robinson et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Ali et al (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported that total root mass and root length in maize were significantly positively correlated with grain yield under water deficit conditions in the field. A positive significant association between narrow root angle and grain yield was reported in sorghum by Mace et al (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eRoot Angle Qtls Co-located With Previously Reported Stay-green Qtls\u003c/h3\u003e\n\u003cp\u003eThe majority of root angle QTLs (6/7) in this study co-located with previously reported stay-green QTLs under water-stressed conditions. For example, QERA7.6 co-located with a previously studied QTL (green leaf area 15 days after flowering) by Haussmann et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e who conducted the experiment during the post-rainy season at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT). These authors reported that the season was ideal for evaluating stay-green under terminal moisture-stress conditions and the crop was dependent almost entirely on stored soil moisture. Similarly, QERA6.4 co-located with a previously studied \u0026lsquo;green leaf area at maturity\u0026rsquo; QTL under post-flowering moisture-stress conditions (Srinivas et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Additionally, QERA6.5 and QERA8.7 co-located with previously studied green leaf area QTLs (QGLFA6.1 and QGLFA8.1, respectively) at 45 days after flowering under post-flowering moisture-stress conditions (Sabadin et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The authors reported that one of these QTL (QGLFA8.1) explained the green leaf area variation by 14.46% and co-located with grain yield under water-stressed conditions. This is consistent with reports that stay-green QTL co-located with QTL for nodal root angle in sorghum (Mace et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and seminal root angle in wheat (Christopher et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These previous studies indicate that genotypes with narrow root angle can extract water from greater depth, thereby increasing access to water under drought, resulting in a stay-green phenotype. This suggests the importance of nodal root angle during post-flowering moisture-stress conditions.\u003c/p\u003e \u003cp\u003eStay-green is an important drought-adaptation feature in cereal crops, especially when they are subjected to moisture stress after flowering. Stay-green (\u003cem\u003eStg\u003c/em\u003e) loci reduce canopy growth at flowering by modifying tillering, leaf number, and leaf size (Borrell et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014a\u003c/span\u003eb). The decreased canopy area at flowering reduces pre-anthesis water demand, moving it from pre- to post-anthesis and therefore boosting water availability during grain filling and thus grain production. However, the increased post-anthesis water use of \u003cem\u003eStg\u003c/em\u003e QTLs compared to RTx7000 was not solely a consequence of changes in temporal water-use patterns, as it more than compensated for the reduced pre-anthesis water use, resulting in increased total water use of 19 mm and 10 mm in the \u003cem\u003eStg1\u003c/em\u003e and \u003cem\u003eStg3\u003c/em\u003e NILs, respectively (Borrell et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014a\u003c/span\u003eb). As water uptake under drought stress is supply-limited, these results indicate that the \u003cem\u003eStg\u003c/em\u003e QTLs could access more water than RTx7000, achieved either by better water extraction from the soil by roots or increasing the soil volume explored by the roots via deeper rooting or greater lateral spread.\u003c/p\u003e \u003cp\u003eImportantly, the majority of root angle QTLs identified in this study were co-located with previously identified QTLs for stay-green. This suggests that the stay-green phenotype is a consequence of not only shifting water from pre- to post-anthesis (due to water savings from reduced pre-anthesis canopy size), but of increased water uptake throughout crop growth due to modification of root angle. This finding has significant implications for crop improvement programs because it highlights that root system architecture contributes to an enhanced stay-green phenotype in sorghum, presumably by impacting elements of water supply in water-limited scenarios. This aligns with the findings of Liedtke et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) which suggest that factors other than canopy size (e.g. roots) have an important role in the expression of a stay-green phenotype in grain sorghum.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eEthiopian sorghum genotypes have unexploited variation in root system architecture traits which could contribute to sorghum breeding programs for drought adaptation. Of the root system architecture traits included in this study, root angle likely has an important role in shaping root architecture and root distribution in deeper soil layers, thereby enhancing water uptake from depth. Several diverse landraces with narrow and wide root angles have been identified and these landraces could be used as a source of valuable RSA alleles for varietal development under drought tolerance. Root angle had high repeatability (H\u003csup\u003e2\u003c/sup\u003e) and weak association with other traits. Furthermore, the poor correlation of RA with shoot traits suggests that selection for RA should not inadvertently result in selection for early vigour. Root angle QTLs also co-located with previously identified stay-green QTLs. Hence, further investigation is required to better understand the components of RSA that contribute to the water supply component of stay-green and genotype by management interaction strategies for target environments. The co-localization of the significant GWAS RSA traits in this study with RSA traits from previous studies (root angle and root length) could indicate the target regions for RSA. Introgressing these QTLs effectively into elite sorghum genotypes via molecular breeding strategies should enhance productivity of sorghum under moisture stress conditions. Furthermore, the root angle screening system highlighted in this paper is simple and inexpensive, indicating its utility for screening large numbers of sorghum genotypes, with potential for screening other cereal crops with further modification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eWe thank the Ethiopian Biodiversity Institute (EBI), and EIAR\u0026rsquo;s Melkassa Agricultural Research Center for providing us with the sorghum landraces and passport data. This study was supported by the\u0026nbsp;Bill and Melinda Gates Foundation PEARL (Program for Emerging Agricultural Leaders) Program.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eAuthors have declared that no competing interests exist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval Not applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate Not applicable \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication Not applicable \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e All data are publicly accessible. Genotypic list with geographical information, SNP genotype and phenotypic data are available from Digital Repository and provided in Online Resource.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability Not applicable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eConceived and designed the experiments: KB, AB, DJ \u0026amp; EM. Performed the experiments: TM \u0026amp; KB. Analysed the data: TM, EM, CH \u0026amp; YT. Wrote the paper and editing: TM, KB, DJ, EM, AB \u0026amp; YT.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAli MA, Abbas A, Niaz S, Zulkiffal M, Ali S (2009) Morpho-physiological criteria for drought tolerance in sorghum (Sorghum bicolor) at seedling and post-anthesis stages. Int J Agric Biol 11(6):674\u0026ndash;680\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli ML, Luetchens J, Singh A, Shaver TM, Kruger GR, Lorenz AJ (2016) Greenhouse screening of maize genotypes for deep root mass and related root traits and their association with grain yield under water-deficit conditions in the field. Euphytica 207(1):79\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10681-015-1533-x\u003c/span\u003e\u003cspan address=\"10.1007/s10681-015-1533-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtkinson JA, Rasmussen A, Traini R, Vo\u0026szlig; U, Sturrock C, Mooney SJ, Wells DM, Bennett MJ (2014) Branching out in roots: uncovering form, function, and regulation. Plant Physiol 166:538\u0026ndash;550. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1104/pp.114.245423\u003c/span\u003e\u003cspan address=\"10.1104/pp.114.245423\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBach L, Faure J-D (2010) Role of very-long-chain fatty acids in plant development, when chain length does matter. C R Biol 333(4):361\u0026ndash;370. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.crvi.2010.01.014\u003c/span\u003e\u003cspan address=\"10.1016/j.crvi.2010.01.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeaudoin F, Wu X, Li F, Haslam RP, Markham JE, Zheng H et al (2009) Functional characterization of the Arabidopsis β-ketoacyl-coenzyme A reductase candidates of the fatty acid elongase. Plant physiol 150(3):1174\u0026ndash;1191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1104/pp.109.137497\u003c/span\u003e\u003cspan address=\"10.1104/pp.109.137497\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBekele WA, Fiedler K, Shiringani A, Schnaubelt D, Windpassinger S, Uptmoor R et al (2014) Unravelling the genetic complexity of sorghum seedling development under low-temperature conditions. Plant cell environ 37(3):707\u0026ndash;723. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/pce.12189\u003c/span\u003e\u003cspan address=\"10.1111/pce.12189\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBernardino KC, Pastina MM, Menezes CB, de Sousa SM, Maciel LS, Carvalho G Jr et al (2019) The genetic architecture of phosphorus efficiency in sorghum involves pleiotropic QTL for root morphology and grain yield under low phosphorus availability in the soil. BMC plant biol 19(1):87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12870-019-1689-y\u003c/span\u003e\u003cspan address=\"10.1186/s12870-019-1689-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBibi A, Sadaqat H, Tahir M, Akram H (2012) Screening of sorghum (Sorghum bicolor var Moench) for drought tolerance at seedling stage in polyethylene glycol. J Anim Plant Sci 22(3):671\u0026ndash;678\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlum A, Arkin G, Jordan W (1977) Sorghum root morphogenesis and growth. I. Effect of maturity genes. Crop sci 17(1):149\u0026ndash;153. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci1977.0011183X001700010039x\u003c/span\u003e\u003cspan address=\"10.2135/cropsci1977.0011183X001700010039x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorrell A, Oosterom E, George-Jaeggli B, Rodriguez D, Eyre J, Jordan D et al (2021) Sorghum. Chapter 5 in \"Crop Physiology Case Histories for Major Crops\". Academic Press, Elsevier. E. Sadras and Calderini, pp 197\u0026ndash;214\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorrell A, Van Oosterom E, George-Jaeggli B, Vadez V, Singh V, Hammer G (2020) Physiology of growth, development and yield. Sorghum in the 21st Century: Food\u0026ndash;Fodder\u0026ndash;Feed\u0026ndash;Fuel for a Rapidly Changing World, Springer: 127\u0026ndash;155. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-981-15-8249-3_6\u003c/span\u003e\u003cspan address=\"10.1007/978-981-15-8249-3_6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorrell AK, Hammer GL, Douglas AC (2000a) Does maintaining green leaf area in sorghum improve yield under drought? I. Leaf growth and senescence. Crop sci 40(4):1026\u0026ndash;1037. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2000.4041026x\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2000.4041026x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorrell AK, Hammer GL, Henzell RG (2000b) Does maintaining green leaf area in sorghum improve yield under drought? II. Dry matter production and yield. Crop sci 40(4):1037\u0026ndash;1048. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2000.4041037x\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2000.4041037x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorrell AK, Oosterom EJ, Mullet JE, George-Jaeggli B, Jordan DR, Klein PE et al (2014a) Stay‐green alleles individually enhance grain yield in sorghum under drought by modifying canopy development and water uptake patterns. New Phytol 203(3):817\u0026ndash;830. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.12869\u003c/span\u003e\u003cspan address=\"10.1111/nph.12869\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorrell AK, Mullet JE, George-Jaeggli B, van Oosterom EJ, Hammer GL, Klein PE, Jordan DR (2014b) Drought adaptation of stay-green in sorghum associated with canopy development, leaf anatomy, root growth and water uptake. J Exp Bot 65(21):6251\u0026ndash;6263. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:.org/10.1093/jxb/eru232\u003c/span\u003e\u003cspan address=\"https://doi:.10.1093/jxb/eru232\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorrell AK, Wong A, George-Jaeggli B, van Oosterom EJ, Mace ES, Godwin ID et al (2022) Genetic modification of \u003cem\u003ePIN\u003c/em\u003e genes induces causal mechanisms of stay-green drought adaptation phenotype. J Exp Bot. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jxb/erac336\u003c/span\u003e\u003cspan address=\"10.1093/jxb/erac336\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrowning BL, Zhou Y, Browning SR (2018) A one-penny imputed genome from next-generation reference panels. Am J Hum Genet 103(3):338\u0026ndash;348. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ajhg.2018.07.015\u003c/span\u003e\u003cspan address=\"10.1016/j.ajhg.2018.07.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurow G, Burke JJ, Xin Z, Franks CD (2011) Genetic dissection of early-season cold tolerance in sorghum (Sorghum bicolor (L.) Moench). Mol Breed 28(3):391\u0026ndash;402. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11032-010-9491-4\u003c/span\u003e\u003cspan address=\"10.1007/s11032-010-9491-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen H, Xiong L (2005) Pyridoxine is required for post-embryonic root development and tolerance to osmotic and oxidative stresses. Plant J 44(3):396\u0026ndash;408. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-313X.2005.02538.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-313X.2005.02538.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChopra R, Burow G, Burke JJ, Gladman N, Xin Z (2017) Genome-wide association analysis of seedling traits in diverse Sorghum germplasm under thermal stress. BMC plant biol 17(1):12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12870-016-0966-2\u003c/span\u003e\u003cspan address=\"10.1186/s12870-016-0966-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristopher JT, Manschadi AM, Hammer GL, Borrell AK (2008) Developmental and physiological traits associated with high yield and stay-green phenotype in wheat. Aust J Agric Res 59:354\u0026ndash;364\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristopher M, Chenu K, Jennings R, Fletcher S, Butler D, Borrell A et al (2018) QTL for stay-green traits in wheat in well-watered and water-limited environments. Field crops research 217:32\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fcr.2017.11.003\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2017.11.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrasta O, Xu W, Rosenow D, Mullet J, Nguyen H (1999) Mapping of post-flowering drought resistance traits in grain sorghum: association between QTLs influencing premature senescence and maturity. Mol Gen Genet 262(3):579\u0026ndash;588. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s004380051120\u003c/span\u003e\u003cspan address=\"10.1007/s004380051120\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. J Agric Biol Environ Stat 11(4):381. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1198/108571106X154443\u003c/span\u003e\u003cspan address=\"10.1198/108571106X154443\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesnos T (2008) Root branching responses to phosphate and nitrate. Curr Opin Plant Biol 11(1):82\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pbi.2007.10.003\u003c/span\u003e\u003cspan address=\"10.1016/j.pbi.2007.10.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevaiah BN, Nagarajan VK, Raghothama KG (2007) Phosphate homeostasis and root development in Arabidopsis are synchronized by the zinc finger transcription factor ZAT6. Plant physiol 145(1):147\u0026ndash;159. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1104/pp.107.101691\u003c/span\u003e\u003cspan address=\"10.1104/pp.107.101691\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu Y, Zhao Q, Chen L, Yao X, Zhang W, Zhang B et al (2020) Effect of drought stress on sugar metabolism in leaves and roots of soybean seedlings. Plant Physiol Biochem 146:1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.plaphy.2019.11.003\u003c/span\u003e\u003cspan address=\"10.1016/j.plaphy.2019.11.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEIAR (2014) Ethiopian strategy for sorghum\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFakrudin B, Kavil S, Girma Y, Arun S, Dadakhalandar D, Gurusiddesh B et al (2013) Molecular mapping of genomic regions harbouring QTLs for root and yield traits in sorghum (Sorghum bicolor L. Moench). Physiol Mol Biol Plants 19(3):409\u0026ndash;419. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12298-013-0188-0\u003c/span\u003e\u003cspan address=\"10.1007/s12298-013-0188-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiedler K, Bekele WA, Duensing R, Gr\u0026uuml;ndig S, Snowdon R, St\u0026uuml;tzel H et al (2014) Genetic dissection of temperature-dependent sorghum growth during juvenile development. Theor and appl genet 127(9):1935\u0026ndash;1948. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-014-2350-7\u003c/span\u003e\u003cspan address=\"10.1007/s00122-014-2350-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiedler K, Bekele WA, Friedt W, Snowdon R, St\u0026uuml;tzel H, Zacharias A et al (2012) Genetic dissection of the temperature dependent emergence processes in sorghum using a cumulative emergence model and stability parameters. Theor appl genet 125(8):1647\u0026ndash;1661. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-012-1941-4\u003c/span\u003e\u003cspan address=\"10.1007/s00122-012-1941-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiedler K, Bekele WA, Matschegewski C, Snowdon R, Wieckhorst S, Zacharias A et al (2016) Cold tolerance during juvenile development in sorghum: a comparative analysis by genomewide association and linkage mapping. Plant Breed 135(5):598\u0026ndash;606. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/pbr.12394\u003c/span\u003e\u003cspan address=\"10.1111/pbr.12394\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhate T, Barvkar V, Deshpande S, Bhargava S (2019) Role of ABA signaling in regulation of stem sugar metabolism and transport under post-flowering drought stress in sweet sorghum. Plant Mol Biol Rep 37(4):303\u0026ndash;313. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11105-019-01157-2\u003c/span\u003e\u003cspan address=\"10.1007/s11105-019-01157-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilmour A, Cullis B, Harding S, Thompson R (2006) ASReml Update: what\u0026rsquo;s new in Release 2.00. VSN International Ltd, Hemel Hempstead, UK\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaussmann B, Mahalakshmi V, Reddy B, Seetharama N, Hash C, Geiger H (2002) QTL mapping of stay-green in two sorghum recombinant inbred populations. Theor appl genet 106(1):133\u0026ndash;142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-002-1012-3\u003c/span\u003e\u003cspan address=\"10.1007/s00122-002-1012-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang M, Liu X, Zhou Y, Summers RM, Zhang Z (2018) BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions. GigaScience 8(2):giy154. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/gigascience/giy154\u003c/span\u003e\u003cspan address=\"10.1093/gigascience/giy154\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang X, Zhao Y, Li C, Wang A, Zhao Q, Li W et al (2012) Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet 44(1):32\u0026ndash;39\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHufnagel B, de Sousa SM, Assis L, Guimaraes CT, Leiser W, Azevedo GC et al (2014) Duplicate and conquer: multiple homologs of PHOSPHORUS-STARVATION TOLERANCE1 enhance phosphorus acquisition and sorghum performance on low-phosphorus soils. Plant physiol 166(2):659\u0026ndash;677. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1104/pp.114.243949\u003c/span\u003e\u003cspan address=\"10.1104/pp.114.243949\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHulluka M, Esele J (1992) Sorghum diseases in eastern Africa. Sorghum and Millet Diseases: a Second. World Review, pp 36\u0026ndash;39\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJordan D, Hunt C, Cruickshank A, Borrell A, Henzell R (2012) The relationship between the stay-green trait and grain yield in elite sorghum hybrids grown in a range of environments. Crop sci 52(3):1153\u0026ndash;1161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2011.06.0326\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2011.06.0326\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoshi DC, Singh V, Hunt C, Mace E, van Oosterom E, Sulman R et al (2017) Development of a phenotyping platform for high throughput screening of nodal root angle in sorghum. Plant Methods 13(1):56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13007-017-0206-2\u003c/span\u003e\u003cspan address=\"10.1186/s13007-017-0206-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang Z, Qin T, Zhao Z (2019) Overexpression of the zinc finger protein gene OsZFP350 improves root development by increasing resistance to abiotic stress in rice. Acta Biochim Pol 66(2):183\u0026ndash;190. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18388/abp.2018_2765\u003c/span\u003e\u003cspan address=\"10.18388/abp.2018_2765\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKebede H, Subudhi P, Rosenow D, Nguyen H (2001) Quantitative trait loci influencing drought tolerance in grain sorghum (Sorghum bicolor L. Moench). Theor appl genet 103(2\u0026ndash;3):266\u0026ndash;276. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s001220100541\u003c/span\u003e\u003cspan address=\"10.1007/s001220100541\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoch K (1996) Carbohydrate-modulated gene expression in plants. Annu Rev Plant Biol 47(1):509\u0026ndash;540\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKramer PJ, Boyer JS (1995) Water relations of plants and soils. Academic press\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLafarge T, Broad I, Hammer G (2002b) Tillering in grain sorghum over a wide range of population densities: identification of a common hierarchy for tiller emergence, leaf area development and fertility. Ann Bot 90(1):87\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aob/mcf152\u003c/span\u003e\u003cspan address=\"10.1093/aob/mcf152\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi M-X, Yeung JM, Cherny SS, Sham PC (2012) Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum Genet 131(5):747\u0026ndash;756. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00439-011-1118-2\u003c/span\u003e\u003cspan address=\"10.1007/s00439-011-1118-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Sheen J (2016) Dynamic and diverse sugar signaling. Curr Opin Plant Biol 33:116\u0026ndash;125\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi R, Han Y, Lv P, Du R, Liu G (2014) Molecular mapping of the brace root traits in sorghum (Sorghum bicolor L. Moench). Breed sci 64(2):193\u0026ndash;198. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1270/jsbbs.64.193\u003c/span\u003e\u003cspan address=\"10.1270/jsbbs.64.193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiedtke JD, Hunt CH, George-Jaeggli B, Laws K, Watson J, Potgieter AB, Cruickshank A, Jordan DR (2020) High-Throughput Phenotyping of Dynamic Canopy Traits Associated with Stay-Green in Grain Sorghum. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.34133/2020/4635153\u003c/span\u003e\u003cspan address=\"10.34133/2020/4635153\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Plant Phenomics\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLjung K, Nemhauser JL, Perata P (2015) New mechanistic links between sugar and hormone signalling networks. Curr Opin Plant Biol 25:130\u0026ndash;137. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pbi.2015.05.022\u003c/span\u003e\u003cspan address=\"10.1016/j.pbi.2015.05.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLobell DB, Roberts MJ, Schlenker W, Braun N, Little BB, Rejesus RM et al (2014) Greater sensitivity to drought accompanies maize yield increase in the US Midwest. Science 344(6183):516\u0026ndash;519. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.1251423\u003c/span\u003e\u003cspan address=\"10.1126/science.1251423\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu B, Wen S, Zhu P, Cao H, Zhou Y, Bie Z et al (2020) Overexpression of Melon Tonoplast Sugar Transporter CmTST1 Improved Root Growth under High Sugar Content. Int J Mol Sci 21(10):3524. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms21103524\u003c/span\u003e\u003cspan address=\"10.3390/ijms21103524\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMace E, Innes D, Hunt C, Wang X, Tao Y, Baxter J et al (2019) The Sorghum QTL Atlas: a powerful tool for trait dissection, comparative genomics and crop improvement. Theor appl genet 132(3):751\u0026ndash;766. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-018-3212-5\u003c/span\u003e\u003cspan address=\"10.1007/s00122-018-3212-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMace E, Singh V, Van Oosterom E, Hammer G, Hunt C, Jordan D (2012) QTL for nodal root angle in sorghum (Sorghum bicolor L. Moench) co-locate with QTL for traits associated with drought adaptation. Theor appl genet 124(1):97\u0026ndash;109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-011-1690-9\u003c/span\u003e\u003cspan address=\"10.1007/s00122-011-1690-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManschadi AM, Christopher J, deVoil P, Hammer GL (2006) The role of root architectural traits in adaptation of wheat to water-limited environments. Funct Plant Biol 33(9):823\u0026ndash;837. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/FP06055\u003c/span\u003e\u003cspan address=\"10.1071/FP06055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMassacci A, Battistelli A, Loreto F (1996) Effect of drought stress on photosynthetic characteristics, growth and sugar accumulation of field-grown sweet sorghum. Funct Plant Biol 23(3):331\u0026ndash;340. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/PP9960331\u003c/span\u003e\u003cspan address=\"10.1071/PP9960331\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCormick RF, Truong SK, Sreedasyam A, Jenkins J, Shu S, Sims D et al (2018) The Sorghum bicolor reference genome: improved assembly, gene annotations, a transcriptome atlas, and signatures of genome organization. Plant J 93(2):338\u0026ndash;354. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/tpj.13781\u003c/span\u003e\u003cspan address=\"10.1111/tpj.13781\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMenamo T, Kassahun B, Borrell A, Jordan D, Tao Y, Hunt C et al (2021) Genetic diversity of Ethiopian sorghum reveals signatures of climatic adaptation. Theor appl genet 134(2):731\u0026ndash;742. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-020-03727-5\u003c/span\u003e\u003cspan address=\"10.1007/s00122-020-03727-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra BS, Singh M, Aggrawal P, Laxmi A (2009) Glucose and auxin signaling interaction in controlling Arabidopsis thaliana seedlings root growth and development. PLoS ONE 4(2):e4502. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0004502\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0004502\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoghimi N, Desai JS, Bheemanahalli R, Impa SM, Vennapusa AR, Sebela D et al (2019) New candidate loci and marker genes on chromosome 7 for improved chilling tolerance in sorghum. J Exp Bot 50(12):3357\u0026ndash;3371. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jxb/erz143\u003c/span\u003e\u003cspan address=\"10.1093/jxb/erz143\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris GP, Ramu P, Deshpande SP, Hash CT, Shah T, Upadhyaya HD et al(2013) Population genomic and genome-wide association studies of agroclimatic traits in sorghum. Proceedings of the National Academy of Sciences 110(2): 453\u0026ndash;458\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagel KA, Schurr U, Walter A (2006) Dynamics of root growth stimulation in Nicotiana tabacum in increasing light intensity. Plant Cell Environ 29(10):1936\u0026ndash;1945. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-3040.2006.01569.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-3040.2006.01569.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaser L, Kourosh V, Bahman K, Reza A (2010) Soluble sugars and proline accumulation play a role as effective indices for drought tolerance screening in Persian walnut (Juglans regia L.) during germination. Fruits 65(2):97\u0026ndash;112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1051/fruits/20010005\u003c/span\u003e\u003cspan address=\"10.1051/fruits/20010005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtiz D, Hu J, Salas Fernandez MG (2017) Genetic architecture of photosynthesis in Sorghum bicolor under non-stress and cold stress conditions. J Exp Bot 68(16):4545\u0026ndash;4557. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jxb/erx276\u003c/span\u003e\u003cspan address=\"10.1093/jxb/erx276\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParra-Londono S, Kavka M, Samans B, Snowdon R, Wieckhorst S, Uptmoor R (2018) Sorghum root-system classification in contrasting P environments reveals three main rooting types and root-architecture-related marker\u0026ndash;trait associations. Ann Bot 121(2):267\u0026ndash;280. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aob/mcx157\u003c/span\u003e\u003cspan address=\"10.1093/aob/mcx157\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetrarulo M, Marone D, Ferragonio P, Cattivelli L, Rubiales D, De Vita P et al (2015) Genetic analysis of root morphological traits in wheat. Mol Genet Genom 290(3):785\u0026ndash;806. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00438-014-0957-7\u003c/span\u003e\u003cspan address=\"10.1007/s00438-014-0957-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReddy NRR, Ragimasalawada M, Sabbavarapu MM, Nadoor S, Patil JV (2014) Detection and validation of stay-green QTL in post-rainy sorghum involving widely adapted cultivar, M35-1 and a popular stay-green genotype B35. BMC Genomics 15(1):909. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2164-15-909\u003c/span\u003e\u003cspan address=\"10.1186/1471-2164-15-909\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichard C, Christopher JT, Chenu K, Borrell AK, Christopher MJ, Hickey L(2018) Selection in Early Generations to Shift Allele Frequency for Seminal Root Angle in Wheat The Plant Genome, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3835/plantgenome2017.08.0071\u003c/span\u003e\u003cspan address=\"10.3835/plantgenome2017.08.0071\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRich SM, Christopher J, Richards R, Watt M (2020) Root phenotypes of young wheat plants grown in controlled environments show inconsistent correlation with mature root traits in the field. J Exp Bot 71(16):4751\u0026ndash;4762\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRich SM, Watt M (2013) Soil conditions and cereal root system architecture: review and considerations for linking Darwin and Weaver. J Exp Bot 64(5):1193\u0026ndash;1208\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobinson H, Kandic V, Christopher J, Fox G, Kelly A, Borrell A et al (2018) Root traits for yield improvement in barley. GRDC Update Papers\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSabadin P, Malosetti M, Boer M, Tardin F, Santos F, Guimaraes C et al (2012) Studying the genetic basis of drought tolerance in sorghum by managed stress trials and adjustments for phenological and plant height differences. Theor Appl Genet 124(8):1389\u0026ndash;1402. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-012-1795-9\u003c/span\u003e\u003cspan address=\"10.1007/s00122-012-1795-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanguineti M, Li S, Maccaferri M, Corneti S, Rotondo F, Chiari T et al (2007) Genetic dissection of seminal root architecture in elite durum wheat germplasm. Ann Appl Biol 151(3):291\u0026ndash;305. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1744-7348.2007.00198.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1744-7348.2007.00198.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh V, Hammer G, Van Oosterom E(2008) Variability in structure and function of sorghum root systems. Global Issues, Paddock Action\u0026rdquo;. \u003cem\u003eProceedings of the 14th Australian Society of Agronomy Conference\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh V, van Oosterom EJ, Jordan DR, Hunt CH, Hammer GL (2011) Genetic variability and control of nodal root angle in sorghum. Crop Sci 51(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2011.01.0038\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2011.01.0038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh V, van Oosterom EJ, Jordan DR, Hammer GL (2012) Genetic control of nodal root angle in sorghum and its implications on water extraction. Eur J Agron 42:3\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eja.2012.04.006\u003c/span\u003e\u003cspan address=\"10.1016/j.eja.2012.04.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlewinski TL (2012) Non-structural carbohydrate partitioning in grass stems: a target to increase yield stability, stress tolerance, and biofuel production. J Exp Bot 63(13):4647\u0026ndash;4670. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jxb/ers124\u003c/span\u003e\u003cspan address=\"10.1093/jxb/ers124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSrinivas G, Satish K, Madhusudhana R, Reddy RN, Mohan SM, Seetharama N (2009) Identification of quantitative trait loci for agronomically important traits and their association with genic-microsatellite markers in sorghum. Theor Appl Genet 118(8):1439\u0026ndash;1454. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-009-0993-6\u003c/span\u003e\u003cspan address=\"10.1007/s00122-009-0993-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubudhi P, Rosenow D, Nguyen H (2000) Quantitative trait loci for the stay green trait in sorghum (Sorghum bicolor L. Moench): consistency across genetic backgrounds and environments. Theor Appl Genet 101(5\u0026ndash;6):733\u0026ndash;741. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s001220051538\u003c/span\u003e\u003cspan address=\"10.1007/s001220051538\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSukumaran S, Xiang W, Bean SR, Pedersen JF, Kresovich S, Tuinstra MR et al (2012) Association mapping for grain quality in a diverse sorghum collection. Plant genome 5(3):126\u0026ndash;135. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3835/plantgenome2012.07.0016\u003c/span\u003e\u003cspan address=\"10.3835/plantgenome2012.07.0016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J et al (2019) STRING v11: protein\u0026ndash;protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47(D1):D607\u0026ndash;D613. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gky1131\u003c/span\u003e\u003cspan address=\"10.1093/nar/gky1131\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao Y, Henzell R, Jordan D, Butler D, Kelly A, McIntyre C (2000) Identification of genomic regions associated with stay green in sorghum by testing RILs in multiple environments. Theor Appl Genet 100(8):1225\u0026ndash;1232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s001220051428\u003c/span\u003e\u003cspan address=\"10.1007/s001220051428\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao Y, Zhao X, Wang X, Hathorn A, Hunt C, Cruickshank AW et al (2020) Large-scale GWAS in sorghum reveals common genetic control of grain size among cereals. Plant Biotechnol J 18(4):1093\u0026ndash;1105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://onlinelibrary.wiley.com/doi/full/\u003c/span\u003e\u003cspan address=\"https://onlinelibrary.wiley.com/doi/full/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/pbi.13284\u003c/span\u003e\u003cspan address=\"10.1111/pbi.13284\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao Y, Trusov Y, Zhao X, Wang X, Cruickshank AW, Hunt C et al (2021) Manipulating assimilate availability provides insight into the genes controlling grain size in sorghum. Plant J. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://onlinelibrary.wiley.com/doi/full/\u003c/span\u003e\u003cspan address=\"https://onlinelibrary.wiley.com/doi/full/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/tpj.15437\u003c/span\u003e\u003cspan address=\"10.1111/tpj.15437\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTitiz O, Tambasco-Studart M, Warzych E, Apel K, Amrhein N, Laloi et al (2006) PDX1 is essential for vitamin B6 biosynthesis, development and stress tolerance in Arabidopsis. Plant J 48(6):933\u0026ndash;946. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-313X.2006.02928.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-313X.2006.02928.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToyofuku K, Kasahara M, Yamaguchi J (2000) Characterization and expression of monosaccharide transporters (OsMSTs) in rice. Plant and Cell Physiol 41(8):940\u0026ndash;947. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/pcp/pcd016\u003c/span\u003e\u003cspan address=\"10.1093/pcp/pcd016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrachsel S, Kaeppler SM, Brown KM, Lynch JP (2011) Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field. Plant Soil 341(1\u0026ndash;2):75\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-010-0623-8\u003c/span\u003e\u003cspan address=\"10.1007/s11104-010-0623-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUga Y, Sugimoto K, Ogawa S, Rane J, Ishitani M, Hara N et al (2013) Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions. Nat genet 45(9):1097. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ng.2725\u003c/span\u003e\u003cspan address=\"10.1038/ng.2725\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan-Raden PM (2008) Efficient methods to compute genomic predictions. J dairy sci 91(11):4414\u0026ndash;4423. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3168/jds.2007-0980\u003c/span\u003e\u003cspan address=\"10.3168/jds.2007-0980\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoss-Fels KP, Robinson H, Mudge SR, Richard C, Newman S, Wittkop B et al (2018) VERNALIZATION1 modulates root system architecture in wheat and barley. Mol Plant 11(1):226\u0026ndash;229. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.molp.2017.10.005\u003c/span\u003e\u003cspan address=\"10.1016/j.molp.2017.10.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Chen G, Zhang H, Liu B, Yang Y, Qin L et al (2014) Identification of QTLs for salt tolerance at germination and seedling stage of Sorghum bicolor L. Moench. Euphytica 196(1):117\u0026ndash;127. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10681-013-1019-7\u003c/span\u003e\u003cspan address=\"10.1007/s10681-013-1019-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei T, Simko V, Levy M, Xie Y, Jin Y, Zemla J (2017) Package \u0026lsquo;corrplot\u0026rsquo;: Visualization of a Correlation Matrix (Version 0.84). Stat 56:316\u0026ndash;324\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams JL, Sherman JD, Lamb P, Cook J, Lachowiec JA, Bourgault M (2022) Relationships between roots, the stay-green phenotype, and agronomic performance in barley and wheat grown in semi‐arid conditions. The Plant Phenome Journal 5(1):e220050. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ppj2.20050\u003c/span\u003e\u003cspan address=\"10.1002/ppj2.20050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu W, Rosenow D, Nguyen H (2000a) Stay green trait in grain sorghum: relationship between visual rating and leaf chlorophyll concentration. Plant Breed 119(4):365\u0026ndash;367. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1439-0523.2000.00506.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1439-0523.2000.00506.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu W, Subudhi PK, Crasta OR, Rosenow DT, Mullet JE, Nguyen HT (2000b) Molecular mapping of QTLs conferring stay-green in grain sorghum (Sorghum bicolor L. Moench). Genome 43(3):461\u0026ndash;469. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1139/g00-003\u003c/span\u003e\u003cspan address=\"10.1139/g00-003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng Z, Hey S, Jubery T, Liu H, Yang Y, Coffey L et al (2020) Shared genetic control of root system architecture between Zea mays and Sorghum bicolor. Plant physiol 182(2):977\u0026ndash;991. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1104/pp.19.00752\u003c/span\u003e\u003cspan address=\"10.1104/pp.19.00752\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu J, Brown KM, Lynch JP (2010) Root cortical aerenchyma improves the drought tolerance of maize (Zea mays L.). Plant Cell Environ 33(5):740\u0026ndash;749. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-3040.2009.02099.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-3040.2009.02099.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ethiopia, Sorghum, Genome-Wide Association Study, Root Angle, Root System Architecture, Stay-green ","lastPublishedDoi":"10.21203/rs.3.rs-2159601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2159601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe root system architecture (RSA) of crop plants influences adaptation to water-limited conditions and determines the capacity of a plant to access soil water and nutrients. Four key root traits (number, angle, length and dry mass) were evaluated in a diversity panel of 1,171 Ethiopian sorghum landraces using purpose-built root chambers. Significant genetic variation was observed in all studied root traits, with nodal root angle ranging from 16.4° to 26.6°, with a high repeatability of 78.9%. Genome wide association studies (GWAS) identified a total of 22 genomic regions associated with root traits which were distributed on all chromosomes except chromosome SBI-10. Among the 22 root genomic regions, 15 co-located with RSA trait QTL previously identified in sorghum, with the remaining seven representing novel RSA QTL. The majority (85.7%) of identified root angle QTL also co-localized with QTL previously identified for stay-green in sorghum. This suggests that the stay-green phenotype might be associated with root architecture that enhances water extraction during water stress conditions. The results open avenues for manipulating root phenotypes to improve productivity in abiotic stress environments via marker-assisted selection.\u003c/p\u003e","manuscriptTitle":"Genetic dissection of root architecture in Ethiopian sorghum landraces","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-25 20:25:44","doi":"10.21203/rs.3.rs-2159601/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2023-04-03T00:21:03+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-10-23T07:33:01+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-10-23T02:18:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-10-14T17:04:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2022-10-14T00:16:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"937082e1-c59e-42f2-aa9c-b652498d77c8","owner":[],"postedDate":"October 25th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-09-18T15:05:34+00:00","versionOfRecord":{"articleIdentity":"rs-2159601","link":"https://doi.org/10.1007/s00122-023-04457-0","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2023-09-16 15:00:49","publishedOnDateReadable":"September 16th, 2023"},"versionCreatedAt":"2022-10-25 20:25:44","video":"","vorDoi":"10.1007/s00122-023-04457-0","vorDoiUrl":"https://doi.org/10.1007/s00122-023-04457-0","workflowStages":[]},"version":"v1","identity":"rs-2159601","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2159601","identity":"rs-2159601","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}