Harnessing High-throughput Phenotyping and Genotyping for Enhanced Drought Tolerance in Crop Plants

Journal Pre-proof Harnessing High-throughput Phenotyping and Genotyping for Enhanced Drought Tolerance in Crop Plants Javaid Akhter Bhat, Rupesh Deshmukh, Tuanjie Zhao, Gunvant Patil, Amit Deokar, Suhas Shinde, Juhi Chaudhary

PII:

S0168-1656(20)30311-4

DOI:

https://doi.org/10.1016/j.jbiotec.2020.11.010

Reference:

BIOTEC 8788

To appear in:

Journal of Biotechnology

Received Date:

28 April 2020

Revised Date:

28 September 2020

Accepted Date:

8 November 2020

Please cite this article as: Bhat JA, Deshmukh R, Zhao T, Patil G, Deokar A, Shinde S, Chaudhary J, Harnessing High-throughput Phenotyping and Genotyping for Enhanced Drought Tolerance in Crop Plants, Journal of Biotechnology (2020), doi: https://doi.org/10.1016/j.jbiotec.2020.11.010

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Harnessing High-throughput Phenotyping and Genotyping for Enhanced Drought Tolerance in Crop Plants

Javaid Akhter Bhat1†, Rupesh Deshmukh2†, Tuanjie Zhao1, Gunvant Patil3, Amit Deokar4, Suhas

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Shinde5*, Juhi Chaudhary6*

Soybean Research Institute, Nanjing Agricultural University, Nanjing, Jiangsu, People’s Republic of China

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National Agri-Food Biotechnology Institute, Mohali, India

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Institute of Genomics for Crop Abiotic Stress Tolerance, Texas Tech University, Department of Plant and Soil

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Science, Box 42122, Lubbock, TX 79409, USA

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Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK, Canada;

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Department of Biology and Gus R. Douglass Institute, West Virginia State University, Institute, WV, 25112, USA

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Department of Biology, Oberlin College, Oberlin, OH 44074, USA

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Correspondance: [email protected]; [email protected] Authors contributed equally

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†

Highlights

Due to its complex nature, drought stress is a severe challenge in crop improvement Conventional phenotyping methods are time-consuming, destructive, and error-prone The accurate genotyping and phenotyping are vital for genomics-assisted breeding High-throughput genotyping and phenotyping are becoming pivotal in precision breeding Precision breeding methods improve our understanding of drought tolerance mechanisms Precision breeding methods accelerate crop-improvement programs

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     

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Abstract Development of drought-tolerant cultivars is one of the challenging tasks for the plant breeders due to its complex inheritance and polygenic regulation. Evaluating genetic material for drought tolerance is a complex process due to its spatiotemporal interactions with environmental factors. The conventional breeding approaches are costly, lengthy, and inefficient to achieve the expected gain in drought tolerance. In this regard, genomics-assisted breeding (GAB) offers promise to

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develop cultivars with improved drought tolerance in a more efficient, quicker, and cost-effective

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manner. The success of GAB depends upon the precision in marker-trait association and

estimation of genomic estimated breeding values (GEBVs), which mostly depends on coverage

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and precision of genotyping and phenotyping. A wide gap between the discovery and practical

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use of quantitative trait loci (QTL) for crop improvement has been observed for many important agronomical traits. Such a limitation could be due to the low accuracy in QTL detection, mainly

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resulting from low marker density and manually collected phenotypes of complex agronomic traits. Increasing marker density using the high-throughput genotyping (HTG), and accurate and

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precise phenotyping using high-throughput digital phenotyping (HTP) platforms can improve the precision and power of QTL detection. Therefore, both HTG and HTP can enhance the practical utility of GAB along with a faster characterization of germplasm and breeding material. In the present review, we discussed how the recent innovations in HTG and HTP would assist in the

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breeding of improved drought-tolerant varieties. We have also discussed strategies, tools, and analytical advances made on the HTG and HTP along with their pros and cons.

Keywords: Water-stress, Genomics-assisted breeding, Phenomics, Genomics, Genomic selection, Stress tolerance.

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Abbreviations: genomic selection

GAB

genomics-assisted breeding

MAS

marker-assisted selection

QTL

quantitative trait loci

GWAS

genome-wide association studies

GE

genotype x environment

GEBV

genomic estimated breeding value

BP

breeding population

GP

genotype-to-phenotype

SWIR

shortwave infrared

SRIs

shortwave infrared-based spectral reflectance indices

NIR

near-infrared

RSA

root system architecture

CT

computed tomography

NGS

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association mapping

next-generation sequencing whole-genome sequencing

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WGS

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AM

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GS

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Introduction Plants being sessile are exposed continuously to several biotic and abiotic stresses, including drought, which causes significant yield losses worldwide each year (Lambers et al., 2008; Farooq et al., 2011). Plants are subjected to drought stress when the external water supply does not meet its water demand, and the resulting water deficit is high enough to induce damage to the plant (Manivannan et al., 2008). Drought stress adversely restricts the growth and development of the

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plant with significant reductions in crop yield (Sharma et al., 2020). Over 70% of crop yield loss

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reported due to water stress across the globe (Jha et al., 2014). Physiological changes caused due to limited availability of water predominantly includes a reduced rate of cell division and

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expansion, stem elongation, root proliferation, leaf size, disturbed stomatal oscillations, and

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reduced nutrient uptake with decreased water use efficiency (WUE) (Farooq et al., 2012; Sharma and Zheng, 2019). Moreover, photosynthesis is considered a universally necessary process in

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plants for regulating growth and yield in plants; however, this process is highly sensitive to drought stress (Sharma et al. 2020).

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Furthermore, under the ongoing global climate changes, the climate models have predicted increased frequency and severity of drought (IPCC 2007, 2018; Farooq et al., 2012). Humaninduced warming reached approximately 1oC (likely between 0.8 oC and 1.2 oC) above preindustrial levels in 2017 and is further increasing at 0.2 oC (probably between 0.1 oC and 0.3 oC)

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per decade, which leads to increased drought and flooding conditions in the future (IPCC, 2018). For example, by 2050, drought is anticipated to result in severe crop yield loss on more than 50% of the irrigated land (Naveed et al., 2014). The world population is vigorously growing and expected to rise by 50% until 2050, increasing demand for crop production by 70% (Tester and Langridge, 2010; Furbank and Tester, 2011). To ensure food supply, excellent crop varieties that

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can withstand these external stresses, as well as protected crop yield under stress conditions, is required. Although many strategies involving advanced techniques and omics approaches are being used to improve crop production under water deficit conditions, among those, the genetic gain looks more promising in terms of providing long-term benefits in a sustainable manner (Sinclair, 2011). Conventional breeding methods have proved its effectiveness over the last century by

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contributing numerous high yielding crop varieties. However, limited progress for improving

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stress tolerance in crop species has been achieved with conventional approaches (Athar and Ashraf, 2009), mostly due to the complex mechanism of drought stress tolerance and time

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consumable and expensive conventional methods employed for the selection of the desired plant.

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In this regard, advances in genomics and phenomics provide an opportunity to perform precision breeding in a time-efficient manner (Cobb et al., 2012). For example, genomics-assisted breeding

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(GAB) offers the opportunity to achieve significant improvement in the complex trait in relatively less time and with a cost-effective manner but requires genomic resources and

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molecular understanding of the trait (Deshmukh et al., 2014; Chaudhary et al., 2015). The most promising approaches available for the GAB includes marker-assisted selection (MAS) and genomic selection (GS). However, MAS depends upon the availability of markers associated with the trait of interest, which can be identified either through quantitative trait loci (QTL)

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mapping or genome-wide association studies (GWAS). The successful utilization of MAS has been demonstrated in several crop plants to incorporate major genes and large-effect QTLs controlling drought stress (Gupta et al., 2017). Still, many small-effect genes governing drought tolerance have never been considered because of the limitations of MAS (Spindel et al., 2015). Besides, genotype x environment (G x E) and epistatic interactions and genetic background

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effects of different plant genotypes, make molecular breeding even more complicated. Thus, MAS is not a practical option for the improvement of drought tolerance. In this context, Meuwissen et al. (2001) proposed GS as an alternative to traditional MAS. The GS approach enhances the breeding efficiency by achieving higher genetic gain per selection-cycle in a breeding program per unit time. In contrast to traditional MAS, genotyping in GS is not limited to selected markers

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tagged to a few putative genes. Still, rather genome-wide marker profile is used to predict the

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genomic estimated breeding value (GEBV) for each marker and avoid the loss of loci with minor effect (Spindel et al., 2015). The GS approach considers the entire set of markers that make it

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possible to track and select minor effect loci in addition to the major effect genes/QTL.

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However, the accuracy in the identification of marker-trait associations as well as estimation of GEBV depends upon precise genotyping and phenotyping analysis, which in turn determine the

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success of GAB. The manual low-throughput phenotyping and genotyping often led to false positive or negatives selection (Tuberosa, 2012). In this regard, high-throughput genotyping and

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phenotyping allows effective MAS and GS and offers faster and focused improvement of breeding populations (Khan et al., 2016). However, before introducing these techniques, only limited success stories are available for marker-assisted breeding related to drought tolerance in crop improvement (Tuberosa, 2012). However, with the availability of these high-throughput

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techniques, the gene discovery and GS became faster and feasible in both model and non-model crop species (Bhat et al., 2016). To this effect, phenomics and genomics are equally critical for precise gene discovery and deriving GS model to estimate the GEBV of the breeding population (BP). Therefore, combining these approaches with appropriate genetic diversity, soil and weather

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data, analytical tools, and databases would result in new varieties development with enhanced water stress tolerance at a fast pace. In the present review, recent advances in high-throughput phenomics and genomics were discussed to address the genotype-to-phenotype gap (GP gap), and how they can be effective to understand the genetic architecture of drought stress tolerance for developing next-generation

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Global food security challenges under frequent drought stress

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crop improvement strategies.

Drought stress is a severe global problem and a significant challenge to the future of crop

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production, imposing a substantial threat to global food security at present conditions. Drought

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will be more severe shortly since climate change with high temperatures and limited precipitation have been projected in many parts of the world (IPCC 2007, 2018). The percentage

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of land areas affected worldwide by drought has increased two-folds from the 1970s to 2000s (Isendahl and Schmidt, 2006). Estimation suggests that ~15 million km2 of the land surface is

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under crop cultivation, and out of which only ~16% area is equipped with irrigation (McLaughlin and Kinzelbach, 2015). Moreover, about one-third part of the arable land in the world being exposed to water stress, which badly disturbs the crop yield (Kramer, 1980). Also, about 5.2 billion hectares (70%) across the globe constitute the drylands that are used for crop

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cultivation with low productivity, where crop yield is ascertained by the mode of drought (UNEP, 1997). The International Maize and Wheat Improvement Centre (CIMMYT) studied abiotic stresses and reported that low fertility and drought conditions are the critical cause for a lower yield in farmer’s fields (Edmeades and Deutsch, 1994). In the United States, the crop losses of ~67% over the last 50 years was due to water stress (Comas et al. 2013). Over the past

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decade, the worldwide annual yield loss due to drought is around 17% that can also sometimes increase to over 70% per year in some countries (Sharif, 2017). Estimation suggests that 50% of the world’s rice production is affected more or less by drought (Serraj et al., 2011). Globally, 20 to 25% of the maize cultivated areas are affected by drought stress (Golbashy et al., 2010). About 65 million ha of the wheat area is subjected to drought stress (FAO, 2013), leading to a reduction in grain yield by 17% to 70% (Joshi et al., 2007; Nouri-Ganbalani et al., 2009).

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Drought is also a significant yield-limiting factor in other drought-tolerant crops, such as

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chickpea (Cicer arietinum L.), which is mainly grown under rainfed conditions (Deokar et al., 2011). By the year 2025, up to 30% of global crop production losses are projected due to water

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shortages (Zhang, 2011). An unexpected situation would occur in the water-stressed areas around

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the world by 2030, which may affect 50 countries, harboring almost three billion people (Graham and Vance, 2003).

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The world population is expected to grow to 9 billion by 2050, and this will need a considerable increase in food production (Tilman et al., 2002). The major food grain crops such

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as maize, rice, and wheat production need to enhance up to 50% to fulfill the food demand for the projected population by 2050 (Godfray et al., 2010). Besides, there is a challenge that atmospheric CO2 will surpass 550 ppm in the next 30–80 years, and many food crops grown under 550 ppm have protein, iron, and zinc contents that are reduced by 3–17% compared with

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current conditions (Smith and Myers, 2018). The increasing population, climate change, decreasing water availability, and reducing agricultural land collectively makes the situation highly severe (Mittler and Blumwald 2010). Cropping systems with protected irrigation utilize about 80% of the world's usable water resources that impose constraints on water use for domestic, industrial, and municipal needs (Condon et al., 2004; Hamdy et al., 2003; Vorosmarty

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et al., 2000). Therefore, strategies are needed to achieve “more crop per drop” to meet the increasing food demands (Condon et al., 2004). Hence, sustainable crop productivity under drought conditions and protected irrigation are the two critical issues that need to be addressed immediately. Genetic improvement of crop plants for increased adaptation to limited water conditions can significantly help to fill this gap and support global food security. Thus, understanding the drought adaptation of crop species is crucial and a prerequisite for sustainable

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plant production. Therefore, it is contended that a further increase in crop productivity must be

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achieved through a genetic gain, particularly for the secure yield under drought. Conventional phenotyping and crop breeding

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Plant phenotyping is a detailed evaluation of crop traits such as growth, development, adaptation,

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quality, yield, resistance, tolerance, and architecture. The conventional phenotyping methods are based on visual observations and manual measurements of traits. Also, the processes are labor-

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intensive, time-consuming, low-throughput, less precise, and usually destructive to plants (Chen et al., 2014). Phenotyping drought-related traits such as root architecture involve a destructive

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harvest of plants and hence making it impossible to repeat the measurements on the same plant. Furthermore, measurement of parameters like length and width of plant parts, the number of hairs, branches, seeds, seedlings, pods, or flowers, and leaf area is time and labor-intensive. The visual scoring by different individuals generates biased data between different individuals and

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also with a replicated experiment (Li et al., 2014). Tanger et al. (2017) reported that manual phenotyping by two experienced professionals for a relatively simple trait like plant height in rice required multiple visits to collect data, and it takes about 45 hours per plots or approximately 200 hours for all plots. This is more severe for complex traits like drought tolerance, which have low heritability, governed by hundreds of minor genes, and have high environmental

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sensitiveness (Tuberosa, 2012). Moreover, the labor-intensive, slow, and expensive nature of manual phenotyping has limited many plant breeding programs. In such cases, a single data or reading of final yield is made for replicated plots in different environments over multiple seasons to avoid delay in breeding cycles and to save money (Mir et al., 2015). But such phenotyping with less statistical confidence and lower precision leads to the failure of variety at the releasing or adaption stage.

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Genomic approaches are being used for gene discovery as well as a selection of desirable

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genotypes. For gene discovery, the phenotyping needs to be done precisely to have to get an

accurate estimation of the allelic effect (D’Agostino and Tripodi, 2017). For selecting desirable

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lines, the evaluation of a larger population with replicated trails across multiple environments

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over a few seasons is required (Bhat et al., 2016). Also, traits like root architecture need destructive harvests for efficient evaluation, which is more complicated, therefore ignored

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mainly in breeding programs (Cobb et al., 2013; Manavalan et al., 2015). Due to these challenges, growth in crop improvement has dramatically restricted and led to the demand of a

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high-throughput, cost-effective, labor-free, and non-destructive phenotypic system to phenotype large-sized population precisely and accurately in a short duration of time. Therefore, the developments to overcome these challenges have led to the emergence of present-day HTP or phenomics.

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Screening for the drought tolerance in a high-throughput era Drought stress is a complex trait with low heritability, usually a nightmare to evaluate with precision and imposes several limitations for efficient breeding (Bhat et al., 2016). However, genomics and crop physiology have provided new tools and knowledge to accelerate breeding for drought tolerance (Tuberosa and Salvi, 2006). Genomics approaches like GAB look efficient

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but require genomic resources such as markers linked with the trait of interest, gene sequence, and annotation information and understanding of the molecular mechanism involved in trait development. The genome sequence information, advanced genomic tools, and HTG platforms are in place for major crop species where GAB is being employed for yield and biotic stress (Davey et al., 2011; Edwards et al., 2013). However, limited research has been done for drought tolerance, mostly because of the lack of high-throughput and large-scale phenotyping methods

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(Guimarães et al., 2017). Therefore, to harness the actual benefits of the genomic revolution,

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precise phenotyping is essential to understand trait genetics and its interaction with

environmental factors (Großkinsky et al., 2015) (Fig. 1 & 2). Several efforts have been made by

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identifying QTLs for drought tolerance related traits, but most of them are inconsistent and with

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minimal effects (Yadav and Sharma, 2016). Statistical methods for linkage mapping, association mapping, and genomic selection have been developed to estimate the environmental impact and

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correctly determine the allelic effect, but all these statistical tools depend on the phenotypic data (Zargar et al. 2015; Bhat et al. 2016). Phenotyping with less precision cannot be interfered to

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correctly identify associated genomic loci (Joshi et al., 2016). Besides having advancements in computational and statistical tools, efficient GAB for drought will be not possible until unless phenotyping methods improved.

In this context, the recent introduction of high-throughput techniques has promoted the

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plant phenomics area, where high-end technological advancements like robotics, spectroscopy, and imaging are being integrated. The phenomics platforms are also facilitating the highperformance computing system, which can efficiently analyze data obtained at a high-throughput level (Rahaman et al., 2015). For example, the LemnaTec Scanalyzer phenotyping system offers proximal remote-sensing and imaging of individual plants together with data acquisition (plant

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architecture, growth, health, and responses), to comprehend the genotype x environment (GxE) interactions with significant accuracy and precision (Petrozza et al., 2013; Ge et al., 2016; Malinowska et al., 2017). These advances have enabled plant breeders and scientists to do many phenotypic experiments for large-sized breeding populations under different environments (Eberius and Lima-Guerra, 2009). Furthermore, using the non-destructive imaging technologies, the complex response of drought, which requires dissection into a series of component traits, can

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be accurately and efficiently measured (Berger et al., 2010). For instance, Honsdorf et al. (2014)

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used the high-throughput phenotyping platform to evaluate a set of 47 barley introgression for drought tolerance. In this study, the biomass estimated with the image processing found to be

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highly correlated with the actual biomass. Recently, Tanger et al., (2017) efficiently and

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precisely detected QTLs by using a high-throughput phenotyping approach with effectively than the traditional labor-intensive measures of height, biomass, flowering time, harvest index,

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and grain yield. Furthermore, the root systems are important components of drought tolerance in crop genotypes, and conventional phenotyping of root traits is highly destructive, involving

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complete removal of plants. But the era of phenomics has provided non-destructive methods for the screening of root traits to improve drought tolerance. For example, Sharma and Carena (2016) reported a high throughput method (BRACE) for maize phenotyping under drought conditions. The BRACE system efficiently performs phenotyping of root traits in a non-

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destructive manner, and it is speedy, usually takes less than 2 min/plot. The use of BRACE has been found to be a reliable method for large-scale, high-throughput phenotyping. Similarly, thermography has been used for high-throughput phenotyping in a tropical maize population under water stress, where the method found reliable, fast, and much more efficient than the conventional approaches (Romano et al., 2011). Furthermore, it was demonstrated that a

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combination of shortwave infrared (SWIR)-based spectral reflectance indices (SRIs) and nearinfrared (NIR) is a convenient option for what to perform fast phenotyping and subsequent selection of desirable lines (El-Hendawy et al., 2017). Similarly, a ground-based highthroughput LiDAR sensor system was developed for measuring the traits related to canopy architecture in peanut, and this approach proved to be useful for high-throughput phenotyping of

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peanut germplasm for canopy architecture (Yuan et al. 2019). Some traits such as chlorophyll content, photosynthesis rate, and canopy temperature contributing to drought tolerance in plants,

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and the high-throughput techniques available to measure these traits are presented in Table 1.

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Remarkably the non-destructive methods like ultrasound, magnetic resonance imaging, computed tomography (CT), and X-rays developed for the study of root related traits look

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promising (Metzner et al., 2015; Clark et al., 2020; Li et al., 2020). Besides having several automated high-throughput phenotyping options available, relatively very less is being used for

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the root phenotyping. Furthermore, for the real-world phenotyping of above-ground traits, the ground-based and unmanned aerial high-throughput platforms have been developed such as

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breedvision, phenomobiles, phenocart, pheno-fields, blimps, pheno-towers, Image Harvest, and infrared imagery (Gupta et al., 2017; Knecht et al., 2016; Moghimi et al., 2020). Without a doubt, the increased throughput from the automated phenotyping platforms will generate numerous amount of data that will need computational resources for the systematic analysis of

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the data. Presently, analytical pipelines are being developed to facilitate hustle free analysis of high throughput phenotyping (Schadt et al., 2010; Knecht et al., 2016). Apart from the small semi-automated instruments focusing limited trait, the platforms fully automated and integrated for many traits are still extremely costly (cost $100,000). To reduce down the cost of such platforms, recently cheaper platforms such as “Phenocart” (cost $12,000) have been developed

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(White et al., 2012; Crain et al., 2016). Such an automated phenomics platform is expected to gain popularity soon (Rebetzke et al., 2016). Still, the challenge remains the same as having a versatile platform affordable to most researchers working worldwide. High-throughput genotyping facilitating understanding of drought tolerance in plants Over the last couple of decades, enormous efforts have been invested in dissecting the genetic basis of drought tolerance using QTL mapping in several crops such as rice (Tripathy et al.,

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2000; Price et al., 2002; Yue et al., 2006; Bernier et al., 2007; Venuprasad et al., 2009), maize

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(Tuberosa et al., 2002), sorghum (Kebede et al., 2001; Sanchez et al., 2002), barley (Talamé et al., 2004), tomato (Foolad et al., 2003), and wheat (Hao et al., 2003). However, most of these

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studies were primarily based on low-resolution genetic maps where low-throughput marker

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systems like simple sequence repeats (SSRs), restriction fragment length polymorphism (RFLP), and amplified fragment length polymorphism (AFLP) methods were employed. Such marker

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techniques have limitations for genomic distribution, less number of available, and difficult to cope up with automated platforms. Because of the low-resolution mapping or fewer numbers of

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markers, usually, the linkage between the associated marker and the genetic loci (QTL) is a week. This affects the efficient use of marker-QTL information in breeding. The major issues generally encountered during marker-assisted breeding are a high number of lines with the false positive selection, low phenotypic effect compared to the estimated effects, and more substantial

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unwanted genetic drag while selecting the desirable loci with markers (Podlich et al., 2004; Vargas et al., 2006). In this regard, next-generation sequencing and array-based SNP genotyping technologies provide several advantages to the high-resolution mapping of genetic loci governing the trait for identification of tightly linked markers and better estimation of phenotypic variation governed by the loci (Kole et al., 2015). These techniques have proved

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effective for the generation of ultra-high-density genetic maps and subsequent QTL/gene discovery as well as their functional cloning (Liu et al., 2017) (Fig. 1 & 2). The tightly linked marker(s) is helpful to monitor the linkage drag, and this is more efficient when several markers are high. In general, the SNP-based genotyping platform provides plenty of genome-wide DNA markers that are essential for constructing high-density genetic maps as well as fine mapping of QTLs related to drought stress tolerance (Table 2). For example, Varshney et al. (2014) have

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identified a “QTL-hotspot" region harboring 12 QTLs for 12 drought-related traits explaining up

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to 58.20% phenotypic variation using an intra-specific chickpea RIL (recombinant inbred line) population genotyped with 241 SSR markers. However, the QTL region was estimated to be 29

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cM on the genetic map and 7.74 Mb on the physical map making it difficult to explore for

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breeding as well as map-based cloning. In the subsequent study, they genotyped the same RIL population with a high-throughput genotyping-by-sequencing (GBS) approach, which helped to

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refine the QTL-hotspot to 14 cM, equivalent to 3 Mb (Jaganathan et al., 2014). However, the reduced interval found to contains thousands of genes. Therefore, efforts have been made to fine

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map the “QTL-hotspot” using the skim GBS and bin mapping approach, which further narrowdown the QTL from 3 Mb to two QTL regions adjacent to each other with about 139 Kb and 153 Kb in sizes (Kale et al. 2015). The narrowed QTLs further helped to identify a set of 12 candidate genes associated with drought. These three studies performed using the same genetic

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material, but a different type of markers provided great insight into the efficiency and limitations of marker systems. QTL mapping using bi-parental population is the most conventional approach used for

the QTL mapping in plants, but it has some severe limitations. For example, it deals with the allelic variation existing only among two parental lines, thereby represent a tiny portion of

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genetic variability present in the population. The development of a stable bi-parental population is also a time-consuming process. Also, because of the limited crossing-over events, bi-parental mapping lacks resolution. In this context, association mapping emerged as a robust approach to overcome the constraints of bi-parental QTL mapping. The association mapping approach involves a diverse set of the natural population taking advantage of historic linkage disequilibrium to establish an association between genomic loci with the phenotype (Pasam et

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al., 2012). The association mapping deals with the large-scale allelic variation present in a

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natural population and provides better chances to identify novel or superior alleles regulating the trait of interest (Zhu et al., 2008). For the efficient genome-wide association study (GWAS)

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higher number of markers distributed over the entire genome is the prerequisite (He et al., 2014;

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D’Agostino and Tripodi, 2017). Earlier, due to the low-throughput marker systems, the actual potential of GWAS has hardly been achieved. Recent advancement in the next-generation

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sequencing (NGS) provides an opportunity to perform GBS to get millions of informative markers distributed over the entire genome (D’Agostino and Tripodi, 2017). Several

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modifications providing customizable options for GBS have been developed (Sonah et al., 2013; Zarger et al., 2015). Besides, several arrays based techniques have also been developed, which provides genotyping with the known SNP markers placed on the array (Zarger et al., 2015). In recent years, association studies performed using GBS or array-based genotyping platforms have

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been extensively used to discover and validate QTLs/genes for important drought tolerance related traits and candidate genes mapping in many crop plants (Sonah et al., 2015; Bastien et al., 2014; Vuong et al., 2015). Also, GWAS based on SNP genotyping using HTG has been successfully used for drought tolerance related traits in several crop species (Table 2). Qin et al. (2016) reported GWAS for drought-tolerant related traits in wheat wild relative Aegilops tauschii

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using NGS-based genotyping and identified several candidate genes and associated informative markers useful for the breeding applications. The SNP markers and genes related to drought tolerance of the Aegilops tauschii can be potentially used for improving drought resistance of cultivated wheat. No doubt the bi-parental QTL mapping and GWAS approaches are useful in the identification of major genes/QTLs, but those have limitations for the identification of loci with a small effect and a complex trait like drought, many of such small effect loci are involved

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in the trait development (Xu and Crouch 2008). This limitation of the QTL mapping approach

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mostly affects the efficiency of MAS for drought-related traits. To this end, the concept of GS has been proposed as a new GAB approach to predict complex traits (Meuwissen et al., 2001).

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Even though GS was proposed in 2001, its application in plant breeding has been demonstrated

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in recent years. The delayed implementation of the GS approach is mostly because of the higher cost of HTG platforms, as genome-wide marker information is an essential requirement to

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predict breeding values in GS. However, the emergence of GBS and the utilization of NGS and SNP HTG platforms have resulted in implementing cost-effective SNP genotyping for GS in

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crop plants (Poland et al. 2012). The GS using genome-wide high-throughput SNP markers for drought tolerance has been found to have a prediction accuracy of 0.78 to 0.98, which has not been observed in low-throughput marker systems (SSR, RFLP, RAPD, etc.) (Shikha et al., 2017). Therefore, HTG has tremendously increased the scope of successful marker-assisted

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breeding in practical crop breeding.

Genomic selection for the enhancement of drought tolerance in plants The drought tolerance related traits have a varied pattern of inheritance from simple to very complex. Inheritance of simple traits is controlled by a few major genes/QTLs (Beyene et al., 2015) (Fig. 2). However, the majority of the drought-related traits have a complex inheritance,

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which is constrained by their low heritability and environmental noise phenotyping (Bhat et al., 2015). Amongst the several QTLs/genes reported for drought tolerance, only a few of them have been utilized to enhance stress tolerance through MAS. Since MAS can be performed efficiently with major gene/QTLs, the undetected minor QTLs that dominate the inheritance of drought tolerance were not included in the selection process. Hence the improvement of drought tolerance through MAS has not been so successful (Ceballos et al., 2015). Adverse results can be

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obtained when selection is made based on the marker estimated to have a less phenotypic effect

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(Castro et al., 2003; Xu and Crouch, 2008).

Furthermore, conventional phenotypic selection for drought tolerance breeding requires multi-

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environment and multi-year field testing, which is not possible every time due to the shortage of

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funds and labor. In this regard, GS has emerged as a powerful approach to apprehend the genome-wide effects of all the alleles for improving polygenic traits (Meuwissen et al., 2001).

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Genomic selection uses a genome-wide marker profile to generate a model for prediction of GEBVs that consider the effect of all the markers and ensure the selection of each of them

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affecting the trait regardless of the magnitude of the effect they govern (Bhat et al. 2016) (Fig. 2). The GS approach considers genotyping and phenotyping of training population (a small set of lines) to develop a statistical model that can be employed to get GEVB for breeding lines (large population) where only genotyping data is available (Meuwissen et al., 2001). Recent studies

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have demonstrated the GS models to be advantageous for improving drought tolerance in crop plants (Table 2). For example, GS has been attempted to investigate genome-wide prediction accuracies ranging from 0.28-0.92 in maize for drought tolerance using SNPs from HTG and suggested the immediate implementation of GS for the enhancement of drought tolerance in maize (Shikha et al., 2017). Similarly, Beyene et al. (2015) have shown the advantages of GS

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over the conventional pedigree-based phenotypic selection in a breeding program aiming enhancement in drought tolerance in tropical maize. Comparison of MAS, PS, and GS depicts their relative effectiveness for the improvement of drought tolerance in crop plants (Fig. 3). With the continued lowering of SNP genotyping cost and development of new, improved statistical methods of GS, more application of GS to improve drought tolerance would come in many more crops (Bhat et al., 2016). Furthermore, the most critical benefit of GS is the marker-profile of the

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seed or seedling can be used for drought-related traits that express late in the maturity (grain

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filling, seed shattering, etc.) without doing any extensive phenotyping evaluation environments. Consequently, GS will significantly accelerate the development of drought-tolerant varieties.

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Hence, in the coming era, genome-wide selection (GWS) will notably reduce the dependence of

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breeders and geneticists on the phenotypic selection and marker-assisted breeding protocols for drought tolerance.

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HTP and HTG: Implications in drought tolerance breeding During the last century, adequate progress has been achieved in developing drought-tolerant

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cultivars of some selected crops using conventional breeding strategies. However, the traditional breeding approach is laborious and highly time-consuming and unable to keep pace with a growing population and climate change (Gosal et al., 2009). In this context, GAB has the great potential to overcome these conventional breeding limitations because of the utilization of

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molecular markers for the indirect selection of desired traits (Collard and Mackill, 2008). However, the success of GAB for crop improvement largely depends on how precisely and accurately the marker-trait associations are identified in linkage mapping, and GWAS, as well as GEBV are estimated in GS. However, for the QTL mapping, the genetic material consists of a bi-parental mapping population, and in the case of GWAS, it is a core set that includes a diverse

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group of accessions representing the major diversity for the crop of interest (Fig. 1). Both linkage mapping and GWAS rely upon the primary data of marker genotyping and phenotyping used in gene discovery (Fig. 1 & 2). A large gap has been observed in discovering useful genes/QTLs and their deployment in the breeding program for crop improvement. To date, only a few examples are revealing the successful release of improved cultivars using MAS that showed a significant impact on farmer's fields (Thomson et al., 2014). One of the reasons behind

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the failure of MAS could be the use of a low-throughput marker system and manual

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phenotyping, which resulted in a large GP gap. However, the advances in high-throughput

phenotyping and genotyping are bridging this GP gap in gene discovery and crop selection

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(Cobb et al., 2012). For example, the combination of phenotypic and allelic data greatly

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facilitates the identification of genetic loci that are linked to key agronomic traits via GWAS (D’Agostino and Tripodi, 2017). Recent advancements in genotyping and phenotyping platforms

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and statistical methods and computational tools provided a basis for developing next-generation GAB. The combined use of these advanced technologies has precisely and accurately led the

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identification of genes/QTLs and their effective utilization in marker-assisted breeding to develop drought-tolerant varieties (Honsdorf et al., 2014; Tanger et al., 2017; Pauli et al., 2016). Although high-throughput SNP genotyping technologies have entirely changed the marker application in crop breeding, they have enabled research communities to use GWAS and GS as a

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routine work for crop improvement in both model and non-model crop species. However, to harness real benefits from genomic studies, these marker technologies could be combined with HTP to achieve valuable genetic gain from complex traits. A very few studies involving the combined use of both HTP and HTG have been published so far, aiming at the detection of significant genotype-phenotype associations for drought stress tolerance (Table 3). The limited

20

use of HTP in a breeding program is the high cost associated with large-scale field-based HTP. However, in the coming days, with the innovations in plant phenotyping platforms, the cost of HTP is expected to decrease at an affordable level, which will lead to the increasing use of these HTP platforms for routine phenotyping of drought tolerance related traits. This will expand the scale of germplasm evaluation for drought-related traits and will allow the rapid development of drought-tolerant crops. The declining cost of DNA sequencing allows whole-genome sequencing

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(WGS) based genotyping more feasible and cost-effective for breeding applications in the near

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future. However, for GS the cost of WGS is still high and is not a feasible approach in many

instances. Hence, the sequencing-based genotyping methods using genome-reduction techniques

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such as GBS and RAD-sequencing seems to be more cost-effective, particularly in the case of

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large size genome and un-decoded genomes. Conclusion

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Drought stress is a significant challenge in crop improvement leading to considerable annual yield losses in crop plants. The classical breeding has made a substantial contribution in

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developing stress-tolerant varieties; however, the overall progress and the process is tremendously slow in targeting drought tolerance due to its complex inheritance and low heritable nature. The recent advances in GAB have provided a great opportunity to produce drought-tolerant varieties in a short time and at a low cost. The precision and accuracy of the

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genotypic and phenotypic data determine the success of GAB and its outcome about the trait improvement. Low marker density and manually collected phenotypes for drought-related traits are the major factors resulting wider gap between the discovery and practical use of quantitative trait loci (QTLs) for drought tolerance improvement. In this context, recent developments in the HTG and HTP have paved the way for improving the precision and power of QTL detection.

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Hence, the use of HTG and HTP has been suggested to allow faster characterization of germplasm and breeding material and increase the practical utility of GAB. Furthermore, the HTG and HTP together will change the entire paradigm of plant breeding and lead to a significant increase in genetic gain for drought tolerance. Presently, the generation of highthroughput genotypic data is convenient and cost-effective; however, the facility available for the HTP is extended to only limited research centers. Therefore, to harness the actual benefits of

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HTP for drought tolerance, much more effort is needed in the future to commercialize the HTP

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technologies for the reach, even local breeding centers.

Author Contributions

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JAB, SS, and RD Conceptualized the review concept. JAB, RD, JC, GP, and SS wrote the manuscript. AD and TZ reviewed the manuscript. JAB, JC, and SS revised the manuscript. All authors read and approved the manuscript.

Compliance with Ethical Standards:

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Acknowledgments: We acknowledges the Department of Biotechnology, New Delhi, India, for the financial support to Rupesh Deshmukh in term of Ramalingaswami Fellowship.

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Conflict of Interest: The authors declare that they have no conflict of interest.

Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure Caption Fig. 1: Role of high-throughput phenotyping (HTP) and high-throughput genotyping (HTG) in identifying QTLs/genes and estimation of GEBVs for their effective use in markerassisted breeding of drought tolerance. Fig. 2: Schematic diagram showing the importance of phenotyping and genotyping as a key player in gene discovery and drought tolerance breeding.

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Fig. 3: Showing the comparative effectiveness and potential of phenotypic selection (PS), marker-assisted selection (MAS), and genomic selection (GS) for breeding droughttolerant crop varieties.

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Drought tolerance related traits

HTP platform/technique

Reference

Triticum aestivum

Biomass and leaf water status, photosynthetic activity

LemnaTec-Scanalyzer 3D system

Petrozza et al., 2013

2

Wheat

Triticum aestivum

Canopy height

LiDAR

Pérez-Ruiz et al., 2020

3

Wheat

Triticum aestivum

Leaf temperature, NDVI, SPAD, water stress indices

Thermography

Bayoumi et al., 2015

4

Wheat

Triticum aestivum

Drought tolerance

PhenoMobile and PhenoField

Deswarte et al., 2015

5

Wheat

Triticum aestivum

NDVI, leaf temperature, CT

Phenocart

Crain et al.. 2016

6

Wheat

Triticum aestivum

GreenSeeker RT101

7

Wheat

Triticum aestivum

Biomass fresh and dry weight; nitrogen content and uptake Biomass and leaf temperature, seed weight

Barmeier and Urs Schmidhalter, 2017 Fehér-Juhász et al., 2014

8

Triticale

Triticosecale rimpaui

Drought tolerance

9

Rice

Oryza sativa

CTD, CC, NDVI

10

Rice

Oryza sativa

Agronomical important traits

11

Maize

Zea mays

Root traits

12

Maize

(Zea mays)

13

Maize

14

-p

ro

Scientific name

1

Crop species Wheat

Self-construction, semi-automated

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S. No.

of

Table 1: List of studies using high-throughput phenotyping (HTP) to evaluate drought stress-related traits in different crop plants.

Busemeyer et al., 2013

Thermography

Romano et al., 2011

Image Harvest

Knecht et al., 2016

BRACE

Sharma and Carena, 2016

Shoot fresh weight and dry weight, leaf area, WUE, etc.

LemnaTec 3D Scanalyzer system

Ge et al., 2016

(Zea mays)

Traits related to drought tolerance

RAP platform

Zhang et al., 2017

Maize

(Zea mays)

Leaf photosynthetic and biochemical traits

Hyperspectral Reflectance

Yendrek et al., 2017

15

Maize

(Zea mays)

Canopy water mass and canopy temperature

Spectral and thermal radiance

Winterhalter et al., 2011

16

Barley

Hordeum vulgare

Drought tolerance related traits

Honsdorf et al., 2014

17

Barley

Hordeum vulgare

18

Barley

Hordeum vulgare

Biomass fresh and dry weight; nitrogen content and uptake Canopy temperature and grain yield under drought stress

The Plant Accelerator’’, Adelaide, Australia GreenSeeker RT100

19

Barley

Hordeum vulgare

20

Barley

Hordeum vulgare

21

Barley

Hordeum vulgare

22

Arabidopsis

23 24

ur na

lP

BreedVision

LemnaTec

Neumann et al., 2015

Self-construction, semi-automated

Cseri et al., 2013

LemnaTec

Chen et al., 2014

Arabidopsis thaliana

Biomass accumulation, grain yield, plant height, harvest index Green biomass production, CT, leaf temperature, WUE, yield, 1000-grain weight Biomass accumulation, plant growth, nutritional status, water content, chlorophyll content, Traits related to drought tolerance

WIWAM

Clauw et al., 2015

Arabidopsis

Arabidopsis thaliana

Traits related to drought tolerance

PHENOSCOPE

Tisné et al., 2013

Arabidopsis

Arabidopsis thaliana

Plant growth, leaf area, Plant senescence, and related traits

LemnaTec

Harshavardhan et al., 2014

Jo

Spectral reflectance sensors

Barmeier and Urs Schmidhalter, 2016 Elsayed et al., 2015

50

PHENOPSIS

Bresson et al., 2014

26

Arabidopsis

Arabidopsis thaliana

PHENOPSIS

Vasseur et al., 2014

27

Arabidopsis

Arabidopsis thaliana

28

Purple false brome

Brachypodium distachyon

29

Grapevine

Vitis vinifera

LemnaTec

Coupel-Ledru et al., 2014

Miscanthus sinensis

Fresh biomass, leaf area, transpiration rate, and related traits Biomass accumulation and WUE

30

Miscanthus

LemnaTec Scanalyzer 3D

Malinowska et al., 2017

31

Soybean

Glycine max

Plant growth and WUE

32

Tomato

Solanum lycopersicum

33

Peanut

Arachis hypogaea

PlantScreenTM Thermography

of

Photosynthetic efficiency, relative water content, leaf expansion, leaf number, and area Biomass, leaf morphology, stomata density, transpiration, net photosynthesis, WUE Growth, morphology, color, and photosynthetic performance CT and other traits related to drought stress

ro

Arabidopsis thaliana

-p

Arabidopsis

Awlia et al., 2016 Ruíz et al., 2015

GlyPh (self-construction)

Pereyra-Irujo et al., 2012

Plant water content, healthy green tissue, yellow chlorotic tissues, and brown necrotic tissue, plant height, photosynthetic apparatus

LemnaTec

Petrozza et al., 2014

canopy architecture

Ground-based LiDAR sensor

re

25

Yuan et al., 2019

Jo

ur na

lP

Abbreviations: NDVI: normalized difference vegetation index; CT: Canopy temperature; CTD: canopy temperature depression; CC: chlorophyll content; WUE: water use efficiency.

51

Crop species

Scientific name

HTG platform

Trait

Number of QTLs/marker trait associations or Prediction accuracy

References

1007 SNPs

164

Jaganathan et al., 2015

Population Size and type

Total SNP markers

264 RILs

ro

S.No

of

Table 2: List of studies involving the use of high-throughput SNP genotyping in QTL mapping, genome-wide association study, and genomic selection for drought stress tolerance in different crop species.

QTL/linkage Mapping Chickpea

Cicer arietinum

GBS

Drought tolerance-related traits

2

Barley

Hordeum vulgare

9k iSelect platform

83 S42ILs

7,864 SNPs

40

Honsdorf et al., 2017

3

Barley

Hordeum vulgare

SNP genotyping

72S42IL

1536-SNP

28

Naz et al., 2014

4

Rapeseed

Brassica napus

60K Infinium array

Grain yield, grain weight, grains per ear, grain filling duration, etc Root length, root dry weight, root volume, TIL and GH Flowering time, root mass, yield

225 DHs

1179 SNPs

20

Fletcher et al., 2015

5

Bread wheat

Triticum aestivum

GBS

Flag leaf traits

204 RILs

3641 SNPs

21

Hussain et al., 2017

6

Cotton

Gossypium hirsutum

GBS

97 RILs

481 GBS

165

Abdelraheem et al., 2018

7

Foxtail millet

Setaria italica

RAD-seq

chlorophyll content, transpiration, stomatal conductance, photosynthetic rate, leaf temperature, etc. Agronomic traits

124 F2

9,968 SNPs

11

Wang et al., 2017

8

Lentil

Lens culinaris

GBS

132 RILs

220 SNPs

18

Idrissi et al., 2016

9

Maize

125 RILs

2122 SNPs

14

Mukeshimana et al., 2014

10

Maize

root and shoot traits related to drought tolerance Agronomic traits related to drought tolerance Root traits and WUE

60 DHs

1257 SNPs

8

Pestsova et al., 2016

11

Maize

104 BCLs

955120 SNPs

7

Trachse et al., 2016

12

Maize

Early Vigor, stay-Green, flowering time, and other agronomic traits Agronomic traits related to DT

167 RILs

56,110 SNPs

988

Zhang et al., 2017

13

Potato

Agro-morphological and physiological traits Height, flowering, biomass, leaf area, greenness, and stomatal density

180 DMDD

1920 SNPs

45

Khan et al., 2015

70 RILs

403 SNPs/Indels

38

Kapanigowda et al., 2014

Drought resistance index; relative leaf water content

198 genotypes

26163 SNPs

34

Zhang et al., 2015

re

lP

ur na Zea mays

BARCBean6K_3 SNP array SNP genotyping

Zea mays

GBS

Zea mays

Illumina MaizeSNP50 BeadChip Illumina GoldenGate

Zea mays

Solanum tuberosum

Jo

14

-p

1

Sorghum

Sorghum bicolor

Alfalfa

Medicago sativa

Illumina GAII sequencer

Association mapping

15

GBS

52

Arabidopsis

Arabidopsis thaliana

215k SNP chip

Leaf area, fresh weight, etc.

17

Barley

Hordeum vulgare

DArTseq & NGSbased SNP genotying

Drought-related traits

18

Barley

Hordeum vulgare

Illumina 9K iSelect SNP chip

Root and shoot traits

19

Canola

Brassica napus

GBS

Flowering time

20

Common bean

Phaseolus vulgaris

Drought-related traits

21

Maize

Zea mays

22

Maize

Zea mays

23

Maize

Zea mays

24

Maize

Zea mays

25

Maize

Zea mays

BARCBean6K_1 and BARCBean6K_2 BeadChips Infinium Maize SNP50 BeadChip Illumina GoldenGate Assay 50K Infinium HD Illumina array MaizeSNP50 BeadChip maize array GBS

26

Rice

Oryza sativa

GBS

Root traits

27

Soybean

Glycine max

Canopy wilting

28

Tomato

Illumina Infinium SoySNP50K iSelect SNP Beadchip Tomato Infinium Array

29

Maize

Zea mays

TaqMan assay & KASP assay

Grain yield under drought stress

30

Maize

Zea mays

Drought-related agronomic traits Drought-related traits Drought-related traits

15828 DArTseq markers & 7829 SNPs

6

Bac-Molenaar et al., 2016

58

Wójcik-Jagła et al., 2017

ro 179 different genotypes 182 accessions 96 accessions

5892 SNPs

17

Reinert et al., 2016

18 804 SNP

69

Raman et al., 2016

10,913 SNPs

27

Hoyos-Villegas et al., 2016

250 Inbred lines 318 inbred lines 244 maize hybrids 350 inbred lines

29 619 SNPs

-p

Agronomic traits

re

Drought-related traits

Grain yield and drought-related traits Plant architecture, Flowering time , Yield components

lP

ur na Solanum lycopersicum

324 natural accessions 109 spring barley genotypes

of

16

Drought-related traits

Drought-related traits

156,599 SNPs

123

Thirunavukkarasu et al., 2014 Zhang et al., 2016

515 081 SNPs

48

Millet et al., 2016

56110 SNPs

42

Xue et al., 2013

1972 CNNAM lines 180 rice varieties 373 maturity group 141 diverse accessions

333,577 SNPs

365

Li et al., 2016

>22,000 SNPs 31,260 SNPs

88

Phung et al., 2016

34

Kaler et al., 2017

6100 SNPs

44

Albert et al., 2016

148-300 biparental populations 240 inbred lines 3273 lines BP lines 10819

218 & 286 SNPs

Genomic selection

32

Maize

Zea mays

Wheat

Triticum aestivum

GBS

Jo

31

Maize SNP50 BeadChip GBS

Beyene et al., 2015

29619 SNPs

0.28-0.92

Shikha et al., 2017

48 662 to 78 005 SNPs 4000 SNPs

0.40-0.50

Zhang et al., 2015

0.18–0.65

Crossaetal., 2016

Abbreviations: RAD-seq: Restriction site Associated DNA Sequencing; DarT: Diversity arrays technology; GH: growth habit; TIL: tiller number per plant; BCLs: back cross lines; GBS: genotyping by sequencing; DH: double hybrid; SNP: single nucleotide polymorphism; RILs: recombinant inbred line; KASP: Kompetitive allele-specific PCR; NGS:next-generation sequencing; ILs: introgression lines; DMDD: diploid backcross mapping population; CN-NAM: Chinese (CN) maize nested association mapping;

53

of

ro

Table 3: Combined use of HTP and HTG for identification of marker-trait association and estimation of GEBVs for different traits in various crop species.

Crop species

Scientific name

Trait

HTG platform

Genomic approach QTL mapping

Reference

1

Barley

Hordeum vulgare

Drought stress

Illumina GoldenGate assay

2

Rice

Oryza sativa

GBS

3

Cotton

Gossypium hirsutum

Yield and component traits Drought stress

QTL mapping

Tanger et al., 2017

QTL mapping

Pauli et al., 2016

4

Maize

Zea mays

Agronomic traits

5

Rice

Oryza sativa

Agronomic traits

Illumina MaizeSNP50 BeadChip NGS based genotyping

RAP Platform

QTL mapping

Zhang et al., 2017

RAP and YTS Platform

GWAS

Yang et al., 2014

6

Sorghum

Sorghum bicolor

Plant height

RAD-seq

UAV remote sensing

GS

Watanabe et al., 2017

7

Wheat

Triticum aestivum

Drought stress

GBS

GS

Rutkoski et al., 2016

Salinity stress

SNP chip

Thermal and hyperspectral camera mounted to a manned aircraft Image-based phenomics platforms

8

Rice

Oryza sativa

GWAS

Campbell et al., 2015

-p

S.No

ur na

lP

re

GBS

HTP platform The Plant Accelerator, Adelaide, Australia Tractor-based HTP platform Field-based HTPP system

Honsdorf et al., 2014

Jo

Abbreviations: GBS: genotyping by sequencing; HTG: high-throughput genotyping; HTP: high-throughput phenotyping; HTPP: high-throughput plant phenotyping; RAP: rice automatic phenotyping; YTS: yield traits scorer; GS: genomic selection; GBS: genotyping by sequencing; NGS: next-generation sequencing; SNP: single nucleotide polymorphism; RAD-seq: Restriction site Associated DNA Sequencing; GWAS: genome-wide association mapping; GEBVs: genomic estimated breeding values.

54

Jo

ur na

lP

re

-p

ro

of

Fig. 1: Role of high-throughput phenotyping (HTP) and high-throughput genotyping (HTG) in identifying QTLs/genes and estimation of GEBVs (genomic estimated breeding values) for their effective use in marker-assisted breeding of drought tolerance. GWAS: genome-wide association mapping; RILs: recombinant inbred line; ILs: introgression lines; BILs: backcross inbred lines; MAGIC: Multi parent advanced generation intercross; NAM: nested association mapping; QTL: quantitative trait loci; MABC: marker-assisted backcrossing; MARS: marker-assisted recurrent selection; GS: genomic selection.

55

56

re

lP

ur na

Jo -p

ro

of

of ro -p re lP ur na Jo

Fig. 2: Schematic diagram showing the importance of phenotyping and genotyping as a key player in gene discovery and drought tolerance breeding.

57

of ro -p re lP ur na Jo

Fig. 3: Showing the comparative effectiveness and potential of phenotypic selection (PS), marker-assisted selection (MAS), and genomic selection (GS) for breeding drought-tolerant crop varieties.

58