Root phenotyping: from component trait in the lab to breeding.

Journal of Experimental Botany Advance Access published June 12, 2015 Journal of Experimental Botany doi:10.1093/jxb/erv239

REVIEW PAPER

Root phenotyping: from component trait in the lab to breeding René C.P. Kuijken1,2, Fred. A. van Eeuwijk3, Leo F.M. Marcelis4 and Harro J. Bouwmeester2,* 1

Wageningen UR, Greenhouse Horticulture, Wageningen, 6708 PB, The Netherlands Wageningen UR, Laboratory of Plant Physiology, Wageningen, 6708 PB, The Netherlands 3 Wageningen UR, Biometris, Wageningen, 6708 PB, The Netherlands 4 Wageningen UR, Horticulture and Product Physiology, Wageningen, 6708 PB, The Netherlands 2

Received 20 January 2015; Revised 3 April 2015; Accepted 10 April 2015 Editor: Roland Pieruschka

Abstract In the last decade cheaper and faster sequencing methods have resulted in an enormous increase in genomic data. High throughput genotyping, genotyping by sequencing and genomic breeding are becoming a standard in plant breeding. As a result, the collection of phenotypic data is increasingly becoming a limiting factor in plant breeding. Genetic studies on root traits are being hampered by the complexity of these traits and the inaccessibility of the rhizosphere. With an increasing interest in phenotyping, breeders and scientists try to overcome these limitations, resulting in impressive developments in automated phenotyping platforms. Recently, many such platforms have been thoroughly described, yet their efficiency to increase genetic gain often remains undiscussed. This efficiency depends on the heritability of the phenotyped traits as well as the correlation of these traits with agronomically relevant breeding targets. This review provides an overview of the latest developments in root phenotyping and describes the environmental and genetic factors influencing root phenotype and heritability. It also intends to give direction to future phenotyping and breeding strategies for optimizing root system functioning. A quantitative framework to determine the efficiency of phenotyping platforms for genetic gain is described. By increasing heritability, managing effects caused by interactions between genotype and environment and by quantifying the genetic relation between traits phenotyped in platforms and ultimate breeding targets, phenotyping platforms can be utilized to their maximum potential. Key words: Genomic selection, heritability, phenotyping, rhizosphere, root exudation, root system architecture (RSA).

Introduction World food production needs to double by 2050 to feed the predicted population by that time (Tilman et al., 2011). At the same time, worldwide salinization and erosion of agricultural land and decline in phosphate fertilizer reserves pose a threat to global yield increases. At this moment worldwide yields per hectare are far below their potential and intensification of agriculture by technology and knowledge transfer are often preferred over conversion of ecologically valuable land. Current yield improvements by technological

measures will not be sufficient to reach the production aims (Ray et al., 2013), creating the need for more rapid genetic improvement of crop varieties that are also better adapted to future climate and agricultural management conditions. To achieve an increased rate of genetic gain, new breeding tools and approaches are required. The introduction of new highthroughput sequencing technologies provides such a tool. In the last decade, the costs of sequencing have dropped dramatically from $800.00 per Mb of DNA sequence in 2008 to

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Page 2 of 13 | Kuijken et al. to grow a panel of individual plants, to acquire data and to further process these data into quantitative morphological or physiological traits. The second goal is to emphasize the strong and complex influence of environmental and genetic factors on root phenotype and heritability. We suggest how these factors should be taken into consideration when developing phenotyping strategies. The third goal is to offer a quantitative framework to assess the efficiency of a phenotyping platform for genetic improvement.

Phenotyping platforms for root system architecture Optimizing root system architecture (RSA) can facilitate higher crop yields (Wasson et al., 2012), especially in regions of low-input agriculture or drought. The term RSA comprises a broad range of root morphological parameters such as root length, root density, root branching and total root surface. The optimal phenotyping platform to quantify such parameters is often a compromise between a range of desirable platform properties. The priorities given to these compromised properties are best discussed in relation to three processes that constitute phenotyping: plant cultivation, data acquisition and data processing. For these three processes general desirable properties are (i) low developing and operating costs and (ii) the possibility to measure large numbers of individual replicates, genotypes or treatments with little effort. Process-specific properties that may need to be compromised are discussed below. Specific desirable properties for a suitable cultivation system are agronomic relevance, the ability to grow plants in several developmental stages, the extent to which experimental noise can be reduced and the possibility and effort to acquire good quality data. When agronomic relevance of the trait of interest is the main priority, soil is the preferred cultivation medium. Root systems grown in hydroponics, gellan gum or soil can differ considerably (Hargreaves et al., 2009; Wojciechowski et al., 2009; Clark et al., 2011) proving that artificial growth mediums are not always representative of field conditions. Strong nutrient, water, temperature and soil strength gradients under field conditions complicate the mimicking of natural soil conditions or abiotic stresses under controlled conditions, even in pots and rhizotrons (Whitmore and Whalley, 2009; Cairns et al., 2011). Nevertheless Hund et al. (2011) found consistent Quantitative Trait Locus (QTL) clusters for RSA across controlled and field environments. The most common practice of growing plants for root phenotyping in soil is the use of soil-filled tubes (Nagel et al., 2009; Ytting et al., 2014), flat cartridges (Nagel et al., 2012; Dresboll et al., 2013) or cultivation in regular soil (Burton et al., 2012; Bucksch et al., 2014). A disadvantage of soilbased cultivation methods is soil heterogeneity, which augments environmental noise. Indoor cultivation systems in artificial medium can reduce environmental noise by allowing a higher throughput and a more standardized micro-environment. The most commonly used method to reduce environmental noise and to easily observe the roots is to grow


$0.08 in 2014 (Wetterstrand, 2014). This decline in price and increase in throughput have resulted in tremendous amounts of genomic data, creating many new opportunities, but at the same time making phenotyping the major bottleneck in plant breeding and fundamental plant science. Currently large public, private and academic efforts are aimed at increasing phenotyping capacity to keep up with the developments in genotyping and sequencing. Rapid developments in robotics, information technology and automation are reinforcing these efforts. In the past the majority of phenotyping efforts was focused on shoot traits such as yield, shoot vigour, product quality and disease resistance, whereas root phenotyping received less attention. The main reason for this negligence is the technical difficulty in accessing the soil when phenotyping root traits, especially by non-destructive methods. The development of root phenotyping accelerated only recently, although the root system is crucial for plant functioning. This crucial role is illustrated by the 20–50% of total fixed carbon that plants may translocate to their root system (Lynch and Whipps, 1990; Kuzyakov and Domanski, 2000). In addition, resistance to soil pathogens and pests, yield of root crops, and nutrient and water uptake are root traits of great agronomic importance. These root traits are still relatively easy to incorporate into breeding programmes. Phenotyping for resistance against soil-borne pathogens is often done through in vivo studies as is the case with Verticillium dahliae (Bolek et al., 2005) Aphanomyces cochlioides, beet necrotic yellow vein virus, Rhizoctonia solani and Pythium ultimum (Luterbacher et al., 2005). In vitro tests exist for resistance against Colletotrichum graminicola (Planchamp et al., 2013), Pseudomonas syringae (Ishiga et al., 2011) and nematodes (Williams et al., 2002; Haase et al., 2007b). Nutrient uptake is a trait that clearly affects the whole plant and can be phenotyped by measuring shoot traits such as nutrient use efficiency (NUE) (Moll et al., 1982; Good et al., 2004), relative growth rate, and chlorophyll content (SPAD). Drought tolerance can be phenotyped by analysing yield under arid conditions (Chapuis et al., 2012), turgor and a range of shoot imaging techniques (Berger et al., 2010) as proxy. Yield of root crops can be quantified destructively. In conclusion, for these traits development of automated root phenotyping platforms is not the main challenge as they can be measured relatively easily. For root system architecture (RSA) and root exudation traits, however, large-scale phenotyping has only just started to emerge. The study of RSA traits, such as lateral root number and length, is complicated by the inaccessibility of the soil matrix. Phenotyping of root exudation is hampered by binding of exudates to the same soil matrix (Jones and Darrah, 1994; Jones and Edwards, 1998; Kirk et al., 1999; Radersma and Grierson, 2004) and by degradation of exudates by the microbial community around the root system (Kuijken et al., 2015). The first goal of this review is to give an overview of the rapidly expanding number of root phenotyping platforms and to pinpoint the challenges and common denominators in the approaches used. The definition of phenotyping platform employed here is the combination of hard and software

Root phenotyping: from component trait to breeding | Page 3 of 13 (Mooney et al., 2012), creating a possible additional noise factor. An alternative imaging strategy is magnetic resonance imaging (MRI) (Rascher et al., 2011). Combining MRI with positron emission tomography (PET) allows monitoring of the distribution of photosynthetically fixed carbon in the root system (Jahnke et al., 2009; Nagel et al., 2009). However these methods face the same shortcomings as X-ray CT. These shortcomings combined make the current tomography approaches less suitable for breeding purposes. A promising method under development is biospeckle imaging, which reveals changes in heterogeneity of root tissue (Ribeiro et al., 2014). Ground-penetrating radar (GPR) and electrical resistivity tomography (ERT) (Zenone et al., 2008) are at this moment suitable techniques for imaging of the rhizosphere but need further development for a broad application in plant physiological and genetic studies. After acquisition of one or several images of the morphological state of a root system, the challenge is to process these images into quantitative data. One of the things that complicates extraction of a priori uncomplicated traits such as lateral root length and number is distinguishing overlapping roots in 2D images in an automated way. This problem manifests itself typically with adult plants grown in 2D or 3D growth systems. In contrast, traits that are complex to quantify by the human eye such as total root area and length can be imaged and processed fairly easily without manual intervention since they do not require a distinction between root zones and types. Data processing and extraction is preferably complete, fast, without errors and without the need for human intervention. In the last decade a broad range of software applications has been developed that meets these demands to a greater or lesser extent. The major focus points of these packages are hierarchical ordering of roots based on morphology, physiological function, topology, root growth rate and anatomy. Image J (Schneider et al., 2012) was one of the first open source image analysis packages that allowed measurement of many of these traits. It offers full control over data extraction but requires manual intervention. In the last five years, this program has been followed by an impressive number of (semi-) automated software packages (Table 1) (Lobet et al., 2013). Although many of these software packages are able to process images in a fully automated way, manual correction of the image analysis and a check on processing quality is often necessary. Many of the listed software packages feature also ready to go plug-ins or the ability to adapt or extend the software as the source code has been made available. The current wide range of customized RSA phenotyping systems offers great choice and flexibility, yet also reveals plenty of overlap in research efforts. A combination of highthroughput in vitro screens and verification of these screens using in situ 3D-MRI or tomography technology could combine the best of both worlds in the medium term, i.e. in a few years. In the long run, the ultimate phenotyping systems should be flexible, fully automated and should integrate cultivation, image acquisition and data processing. Although developments on integrated shoot phenotyping platforms are ahead of root phenotyping (Araus and Cairns, 2014),


seedlings on agar plates (Nagel et al., 2009; Yazdanbakhsh and Fisahn, 2009; Slovak et al., 2014). Alternative 2D rhizotron systems are growth pouches (Hund et al., 2009; Adu et al., 2014), growth between paper (Le Marie et al., 2014) or between fabric cloths in bins (Chen et al., 2011). The forced 2D growth conformation and unnatural chemical and physical properties of the medium come at the cost of agronomic relevance, making this the major shortcoming of these methods. 3D cultivation in agar or gellan gum (Fang et al., 2009; Iyer-Pascuzzi et al., 2010; Clark et al., 2011; Topp et al., 2013; Ribeiro et al., 2014) or aqueous solution (Clark et al., 2013; Pace et al., 2014) offers a hybrid solution between soil and 2D strategies. The use of transparent soil is a promising and elegant method that combines good visibility with a mechanical resistance resembling sand (Downie et al., 2012). So far, most of the techniques developed for RSA phenotyping involve the of use seedlings. Although there are examples in which the early stage root phenotype has some predictive value for later developmental stages (Tuberosa et al., 2002a), the seedling root phenotype may not always be representative of the mature plant (Watt et al., 2013). A cultivation system flexible enough to allow growing plants in several developmental stages will increase agronomic relevance. Specific properties that may require a compromise when developing the optimal data acquisition method are: resolution, processability of data, match with the desired cultivation system and possibility of measuring all desired root parameters. Many of the newly developed high-throughput phenotyping platforms use 2D imaging with cameras (Nagel et al., 2009; Clark et al., 2013; Le Marie et al., 2014) or flatbed scanners (Hund et al., 2009; Chen et al., 2011; Shi et al., 2013; Adu et al., 2014; Slovak et al., 2014). The main advantage of 2D growth and image acquisition is the very high throughput. An alternative but low-throughput 2D imaging strategy is 2D neutron radiography and tomography (Oswald et al., 2008; Moradi et al., 2009; Leitner et al., 2014). One of the major difficulties in root data acquisition is imaging overlapping roots in complex and branched root systems in such a way that they can be distinguished easily. Untangling roots and organizing them into 2D conformation is tedious and often causes root damage. To solve this problem, root systems can be grown in 3D in gel followed by 2D optical imaging with cameras and reconstruction of 2D images into 3D models (Clark et al., 2011; Topp et al., 2013). Through the use of laser scanners, 3D-grown roots in gel can be imaged in 3D (Fang et al., 2009). Several 3D-imaging methods allow in situ visualization of root systems grown in soil without compromising temperature gradients, porosity, biochemical properties and mechanical resistance of natural soil. Most reported methods for data acquisition of 3D-grown roots in soil are based on X-ray computed tomography (CT), which has been extensively reviewed by Mooney et al. (2012). Disadvantages of this method are the potential loss of information due to a somewhat lower resolution and the complex 3D reconstruction of root systems (Mairhofer et al., 2013), the low throughput, and the immobile and expensive equipment. Moreover the automated 3D reconstruction of the root system from tomography frames is mostly based on modelling approaches

Page 4 of 13 | Kuijken et al. Table 1. Overview of available data processing software. Table adapted from http://www.plant-image-analysis.org (Lobet et al., 2013) Software package

Main root traits

2D root images Aria ARTT BRAT DART DIRT

CellSeT Cell geometry, cell number RootScan Cell number per tissue, tissue area, vessel area Software dedicated to 3D root systems iRoCS Toolbox 3D reconstruction, diameter, localisation, volume RootReader3D

RooTrak

Convex-hull, depth, insertion-angle, length, number of branches, orientation surface, volume, width 3D reconstruction

efforts to create an RSA phenotyping platform that integrates cultivation, data acquisition and processing do exist (Yazdanbakhsh and Fisahn, 2009). As we will describe later, the ideal phenotyping platform causes little environmental noise between genotypes, treatments or individual replicates and results in high heritability for the measured traits, which are preferably agronomically relevant.

Phenotyping platforms for root exudation As discussed above, plants translocate 20–50% of the produced assimilates to their root system. 10–18% of this large carbon investment is released into the soil by root exudation

Availability

Publication

Automated Automated Automated Manual Automated

Freeware On-demand Open-source, freeware Freeware Open source, freeware

(Pace et al., 2014) (Russino et al., 2013) (Slovak et al., 2014) (Le Bot et al., 2010) (Bucksch et al., 2014)

Automated Semi-automated

Freeware Freeware

(Benoit et al., 2014) (Armengaud et al., 2009)

Automated

Freeware

(Galkovskyi et al., 2012)

Automated Semi-automated Automated Automated Automated Automated Automated Automated Automated Automated Manual Semi-automated

On-demand On demand On-demand Open Source Freeware On-demand Freeware Freeware Freeware Freeware Open source, freeware Open source

(Walter et al., 2002) (Nagel et al., 2009) (Basu and Pal, 2012) (Pierret et al., 2013) (Basu et al., 2007) (Roberts et al., 2010) (Remmler et al., 2014) (Leitner et al., 2014) (Kumar et al., 2014) (van der Weele et al., 2003) (Zeng et al., 2008) (Pound et al., 2013)

Semi-automated Semi-automated Semi-automated Automated Semi-automated

Freeware Freeware Freeware Open source, freeware Freeware

(Clark et al., 2013) (Ristova et al., 2013) (Geng et al., 2013) (French et al., 2009) (Lobet et al., 2011)

Manual, semi-automated Automated Automated

Open source, freeware

(French et al., 2012)

Freeware Freeware

(Pound et al., 2012) (Burton et al., 2012)

Automated, semi-automated Automated

Open source, freeware

(Schmidt et al., 2014)

On-demand

(Clark et al., 2011)

Semi-automated

Freeware

(Mairhofer et al., 2012)

(Kuzyakov and Domanski, 2000), although lower (Jones et al., 2004) and higher (Lynch and Whipps, 1990) shares have been reported. In any case, exudation represents a significant factor in the total carbon balance of the plant. Under highly controlled conditions, root exudation could be considered a loss whereas under field conditions exudation could be considered an opportunity. Quantity and quality of root exudates play a significant role in nutrient acquisition in marginal soils (Radersma and Grierson, 2004; Hoffland et al., 2006; Richardson et al., 2009; Oburger et al., 2011), phytoremediation (Wenzel, 2009), avoidance of aluminium toxicity (Kochian et al., 2005; Ryan et al., 2009), generation of electricity (Strik et al., 2011), production of proteins or


Convex-hull, count length, shape, Localisation, orientation, velocity-profile Diameter, length, orientation Insertion, length, topology, Branching angle, density, diameter, distribution, length ElonSim Length EZ-Rhizo Insertion, insertion-angle, length, nr. of branches, topology GiA Roots Convex-hull, depth, length, nr. of branches, perimeter, surface, volume GROW Map-Root Velocity profile GrowScreen-Root Insertion-angle, length, nr. of branches Growth Explorer Velocity-profile IJ-Rhizo Diameter, length KineRoot Gravitropism, growth PlantVis Velocity RNQS Count, length, nodules, Root System Analyzer Count, diameter, insertion-angle, insertion, length Root Tip Detection Count, diameter RootFlowRT Growth, velocity-profile RootFly Colour, diameter, length, RootNav Convex-hull, count, insertion, insertion-angle, length RootReader2D Depth, length, number of branches, topology, RootScape Shape RootTip Trace Length RootTrace Curvature, length, number of branches SmartRoot Diameter, insertion, insertion-angle, length, number of branches, orientation, topology Anatomy of cross-sections Cell-O-Tape Cell number, cell-size

Automated

Root phenotyping: from component trait to breeding | Page 5 of 13 Alternative practices are the use of ion exchange resin (Shane et al., 2008) or filter paper (Haase et al., 2007a). Aeroponics is a useful technique to collect root exudates under high oxygen availability (Kohlen et al., 2012). The choice of data acquisition platform depends on whether as many metabolites as possible in the root exudates need to be analysed or just a certain compound or chemical class. In both cases taking an aqueous sample of the rhizosphere is most convenient and low cost, making this approach by far the most popular (Neumann et al., 2009). Exudate concentrations in such aqueous samples tend to be low. Therefore, depending on the target trait and the analytical platform used, samples have to be concentrated or purified. High-performance liquid chromatography (HPLC) and capillary electrophoresis (CE) are fast, low cost and low tech platforms to quantify concentrations of organic acids and carbohydrates. The limited number of compounds that can be analysed in a targeted way and the relatively low resolution are disadvantages of these platforms. Platforms for high resolution chemical analysis usually combine chromatography with mass spectrometry, as with liquid chromatography coupled to mass spectrometry (LC-MS) (Strehmel et al., 2014) and gas chromatography coupled to mass spectrometry (GC-MS). Nuclear magnetic resonance (NMR) is another option for high-throughput untargeted root exudate analysis (Fan et al., 1997) although it has lower sensitivity than the MS-based methods. Mass spectrometry is also used for targeted analysis, especially of low abundant compounds such as signalling molecules. For this purpose, triple-quad mass spectrometry coupled to HPLC, or preferably UPLC, is superior. This is illustrated by the detection of ultralow concentrations of strigolactones in root exudates (Sato et al., 2005; Lopez-Raez et al., 2008). An alternative analytical approach to quantify total rhizodeposition is the use of carbon isotopes (Kuzyakov and Domanski, 2000). The ability to phenotype exudation from intact root systems in a more natural rhizosphere is the major advantage of this approach. However, carbon isotope studies are not high-throughput, are limited to untargeted exudate analysis and have low resolution. Root exudate data acquired by targeted approaches such as HPLC or CE does not require complicated data analysis, in contrast to data from untargeted approaches. In untargeted approaches metabolite identification is usually done only after statistical analysis of the data (Hall, 2011). But even then identification is sometimes cumbersome, making metabolite identification the most important challenge in the analysis of root exudates. Unfortunately, the LC-MS technology, which is most suitable for the untargeted analysis of root exudates, suffers from low reproducibility between machines and labs, and does not produce highly informative mass data in contrast to GC-MS. Besides, the large diversity of chemical structures and concentrations within a crude exudate sample does not allow for capture of the entire exudate metabolome with a single purification procedure and only one standardized setting of the analytical platform. These issues discourage the development of databases that can be used for analysis on every machine. Nevertheless, public databases are being developed such as the MoTo database of semi-polar tomato


pharmaceuticals (Borisjuk et al., 1999; Gontier et al., 2002) and the interaction with parasites (Bouwmeester et al., 2003), pathogens (Hartmann et al., 2009; Baetz and Martinoia, 2014), predators of these pathogens (van Tol et al., 2001) and growth promoting micro-organisms (Hartmann et al., 2009). Many of these roles are specific to a single exudate or a chemical subgroup of exudates, which indicates the need for exudate identification and targeted quantification to exploit their potential for breeding. Elucidating the genetic background of exudate composition and efflux rate allows the creation of elite lines with favourable exudation profiles. However, phenotyping for root exudate composition has shown to be very difficult so far. Despite these difficulties, claims of genetic variation in exudation have been made (Aulakh et al., 2001; Hoekenga et al., 2003; Yan et al., 2004; Hoffland et al., 2006; Gao et al., 2009; Jamil et al., 2011, 2012). Often used meas2 ures for genetic variation are genetic variance (σG ), coefficient of genetic variation (CVg), coefficient of additive genetic variation (CVA) or the phenotypic range of the measured trait. Of the mentioned studies only Yan et al. (2004) use such numeric measures to quantify the genetic variation in exudation. They report phenotypic ranges for total acid exudation (TAE, µmol plant-1) and acid exudation rate (AER, μmol g-1 h-1) of 3.60–20.60 and 1.65–64.00, respectively for Phaseolus vulgaris. Although research on phenotyping of root exudation has been intensified, automated phenotyping platforms for exudation have yet to be described. Parallel to phenotyping for RSA, phenotyping for exudation is best discussed by three processes: plant cultivation, data acquisition and data processing, all having their specific challenges. Finding the optimal cultivation method for phenotyping for exudation entails almost the same compromise between desirable cultivation and data acquisition properties as for RSA phenotyping. Two additional difficulties complicate plant cultivation for exudation phenotyping and have conflicting solutions. First of all, catabolism of exudates in nonsterile rhizospheres (Kuijken et al., 2015) and the dynamic adhesion of exudates to the soil (Radersma and Grierson, 2004) obscure net exudation quantity and quality. Secondly, the combined effects of the root environment on RSA (Clark et al., 2011) and exudation (Veneklaas et al., 2003; Mimmo et al., 2011) and the effect of RSA on exudation (Lambers et al., 2006) result in a complex three-way interaction making exudation a complex and plastic process. Soil is therefore a commonly used medium because it represents a growth medium that approximates natural growth environments or field conditions and allows for the cultivation of large numbers of plants at a low price. Hydroponics is often used (Veneklaas et al., 2003; Hoffland et al., 2006; Pearse et al., 2007) because it allows easy sample collection, reduces the micro-environmental variation caused by micro-organisms and the soil matrix, facilitates the concomitant study of RSA and approximates hydroponic production methods in glasshouse horticulture. Soil and hydroponics are occasionally combined with sterilization agents (Imas et al., 1997; Gherardi and Rengel, 2004; Gent et al., 2005), sterile cultivation (Paterson et al., 2005; Sandnes et al., 2005; Kuijken et al., 2015) or microsuction cups (Dessureault-Rompré et al., 2006).

Page 6 of 13 | Kuijken et al. fruit metabolites (Moco et al., 2006), which offer a starting point for exudate identification. Meanwhile, studies that phenotype the root exudate metabolome are scarce but do exist (Strehmel et al., 2014). Identifying differentially exuded compounds between genotypes and elucidating the biosynthetic pathways behind these compounds will help to bridge the gap between phenotypic exudation data and genetics.

Environmental and genetic factors influencing root phenotype and heritability

Assessing usability of a phenotyping platform for genetic improvement There are two main motivations for measuring root traits on a phenotyping platform instead of in the target environment, i.e. the future growing conditions. The first motivation is a higher efficiency or increased heritability when the trait of interest can be measured directly on the phenotyping platform. Higher efficiency can be achieved when a root trait is hard to measure under future growing conditions. The phenotyping platform may then be cheaper and less time- and resource-consuming. A higher efficiency is also achieved when a phenotyping platform allows measurement of root traits at an earlier developmental stage, which shortens the selection cycle and speeds up genetic improvement. A phenotyping platform may increase heritabilities by reducing noise levels. This is particularly beneficial when the future growing conditions are such that genotypic differences are hard to detect. The second motivation for using a phenotyping platform is when the trait of interest is not the trait as measured at the platform itself, but a breeding target in the field, such as yield. Such breeding targets may show low heritability under field conditions because they are composed of many small genetic factors that interact with many environmental factors. In the case of low heritability, measurements on a phenotyping platform of one or several component traits that contribute to the breeding target might be a solution. Requirements for this approach to be successful are a substantial heritability of the component trait and a high correlation with the breeding target in the target environment. A set of exudates or a certain root morphology that contributes to yield under a stress condition can be an example of such an approach.


Traits measured with a phenotyping platform may display macro- and micro-environmental variation. Macroenvironmental variation concerns all variation between runs on a single platform for a particular genotype panel. Here a run is defined as a single measurement series on a single panel of individuals. Runs on a single platform under the same configurations that produce low correlations at the level of genotypic means reveal interaction between genotype and environment (G×E). In such a case, high G×E implies low reproducibility for the platform, which is an undesirable situation. In this case runs on the same platform, under the same configurations, intended to be exact replicates, should be considered as separate environments. Different platform configurations, plant nursing conditions or other deliberate treatments between runs constitute a different type of macroenvironmental variation. This type of variation does not corrupt reproducibility of a platform but can still cause G×E effects between genotypes across runs. Micro-environmental variation comprises differences between repeated samples of individual genotypes within a run on a platform. High microenvironmental variation will lead to low heritability, which is elaborated in the next section. Both macro- and micro-environmental factors can interact with a multitude of genetic factors, thereby undermining heritability of traits measured on a platform and obscuring the relation between traits measured on a phenotyping platform and the breeding targets in the field. Environmental factors known to influence RSA are temperature (Nagel et al., 2009), microbes (Lambers et al., 2009), water stress (Bengough et al., 2011), soil structure such as soil strength (Cairns et al., 2011; Acuna and Wade, 2012) and porosity (Bengough et al., 2011) and availability of nutrients such as nitrogen, phosphorus, iron and sulphate (LopezBucio et al., 2003; Manschadi et al., 2014). Nutrient enriched patches in the rhizosphere cause localized morphological responses of the root system (Giehl et al., 2014; Hodge, 2004; Forde and Walch-Liu, 2009). Environmental effects known to influence root exudation are soil type (Veneklaas et al., 2003; Mimmo et al., 2011), nutrient availability (Paterson et al., 2006; Pearse et al., 2006; Yoneyama et al., 2007; Lopez-Raez et al., 2008), aluminium toxicity (Ma, 2000; Kochian et al., 2005) and microbes (Meharg and Killham, 1991, 1995). As mentioned before, effects of environmental factors on RSA can lead to an indirect effect on exudation. Besides environmental cues, RSA and root exudation are controlled by various endogenous factors such as hormone balance (Fukaki and Tasaka, 2009), plant age (Aulakh et al.,

2001), gravitropic set-point angle (Malamy, 2005) responsiveness to the environment (Malamy, 2005) and a broad range of signal synthesis, signal transduction and signal sensitivity processes that involves many developmental genes (Giehl et al., 2014). These examples of endogenous factors result in a countless number of potential genotypic differences that obscure the effect of a single gene on a certain root phenotype. The complexity of endogenous root phenotype regulation is even further increased by pleiotropy and genetic interactions such as epistasis and crosstalk. An example of a possible pleiotropic effect is the Root-ABA1 QTL (Giuliani et al., 2005; Landi et al., 2007), causing resistance to root lodging, increased leaf ABA concentrations and lower yields. An example of crosstalk between signal transduction pathways is the interaction between transcript levels of P responsive genes and low P-induced regulation of root development (Sanchez-Calderon et al., 2006). Examples of epistasis are the various reports of interacting QTLs for root traits in rice, maize and Arabidopsis (Zhang et al., 2001; Zhu et al., 2006; Bouteillé et al., 2012). In conclusion, numerous interactions between genes, between environmental factors, and between genes and environments influence the very plastic root phenotype. These interactions obscure the contribution of individual genes to RSA and exudation phenotypes, making breeding for these traits difficult.

Root phenotyping: from component trait to breeding | Page 7 of 13 The efficiency and thus relevance of a phenotyping platform depends on two requirements. The first requirement is a sufficiently high heritability for the traits measured on the phenotyping platform. The second requirement is a sufficiently high correlation between a component trait analysed on the platform and the target trait in the target environment. The direct and indirect selection response theory (Falconer and Mackay, 1996) provides a convenient quantitative framework to establish if these requirements are met. According to this theory we need to evaluate the criterion in Eq. 1: 2 HC2 rCT > HT2 (1)

H2 =

σG2 σG2 = (2) σ 2P σG2 + ( σ e2 / n )

with σG2 = genotypic variance; σ 2P = phenotypic variance; σ 2e = error variance on a plot basis and n the number of independent measurements (replicates, plots) taken for a particular genotype. A phenotyping platform could be attractive because it allows for more precise measurements, thus lowering the micro-environmental variance σ 2e and simultaneously allowing for a larger number of replicates n to be measured. Together this results in increased heritability. In the case of multiple runs on a platform or multiple experiments for the breeding target in the target environment, we need to take into account G×E variation across different runs and/or experiments. An example of G×E for a root trait is rooting depth of wheat genotypes in soils with different soil strengths (Acuna and Wade, 2012, 2013). Examples of individual genes that show interaction with the environment are LPR1 (Camacho-Cristobal et al., 2008) causing a differential reaction to P stress, and GmEXPB2 responding to Fe, P and water stress (Guo et al., 2011). The question then is how to define the platform and target environment? A common solution is to take a weighted average across runs or experiments. Heritabilities can also differ across experiments as shown for depth penetration rates of roots in winter wheat (Ytting et al., 2014). Heritability for a component trait across multiple runs on the same platform, or for the breeding target across multiple experiments is described in Eq. 3 with σG2 = genotypic variance across runs or experiments; σ 2P = phenotypic 2 variance across runs or experiments; σGE = G×E variance; e 2 the number of runs or experiments σ e = error variance on a plot basis and n the number of independent measurements

H2 =

σG2 σG2 = 2 2 2 σ P σG + (σGE / e ) + (σ2e / e * n ) (3)

In the case of strong G×E effects, the heritability in a phenotyping platform can be increased by testing genotypes in an increased number of runs which reduces the influence of 2 the G×E variance σGE in the overall phenotypic variance (Eq. 3). The same is true for the breeding target in the target environment, for which the influence of the G×E interaction is reduced by running more experiments. With the simplified quantitative framework as described above, we can see that it is of no use to measure a platform trait with high precision, i.e. low noise level, when the (squared) 2 genetic correlation rCT between platform trait and target trait in the target environment is low (Eq. 1). The problem of meas2 uring a platform trait with high precision and low rCT will be more severe in the situation where the platform trait is a component trait than when it is a target trait. Although many studies on automated phenotyping platforms do not mention or quantify the genetic correlation between platform traits and target traits, many such relations have been described. Drought tolerance for example is an important breeding target for many crops in the world. Deeper rooting has been shown to be a component trait and thus predictive for this breeding target (Tuberosa, 2012). The DRO1 locus alters root angle and rooting depth and is predictive for yield performance during drought (Uga et al., 2013). Another important breeding target is yield in nutrient-poor soils. Shallower and more branching roots result in improved nutrient acquisition in the topsoil layer (Lynch and Brown, 2012). Also exudation of organic acids contributes to nutrient acquisition in poor soils (Dakora and Phillips, 2002; Oburger et al., 2011). Breeding for increased shoot height or biomass can lead to increased susceptibility to root lodging (van Delden et al., 2010). Root systems with high exploitation indices contribute to the breeding target root lodging tolerance (Giuliani et al., 2005). Mace et al. (2012) report associations between nodal root angle QTLs and the stay-green trait and grain yield. Low strigolactone exudation was shown to contribute to the breeding target Striga resistance (Jamil et al., 2011). Several studies report correlations between root QTLs and yield (Tuberosa et al., 2002b; Hund et al., 2011; Cai et al., 2012). The magni2 depends on the commonality tude of genetic correlation rCT in QTLs or underlying genes that drive phenotypic variation in component traits and breeding targets. It needs to be remarked that even when the genetic correlation between a component trait in the platform and the breeding target in the target environment is low, identifying component trait QTLs is not completely useless. As long as the direct contribution of component QTLs to the target trait can be shown they can be used in marker-assisted selection. Ideally, such direct contributions of the component QTLs to target traits prove to be based on causal relations instead of correlations. For example Hund et al. (2011) report about a


This equation describes the relation between the heritability for the trait measured at the platform HC2 (between 0 and 1); the heritability of the target trait in the target environment HT2 (between 0 and 1) and the squared genetic correlation 2 between component trait and target trait rCT (between −1 and 1). Meeting this criterion means that indirect selection for a component trait at the platform is more effective than direct selection for the target trait in the target environment. In the case of a single run on a phenotyping platform or a single experiment in the target environment, the heritability of component trait and breeding target is estimated by the ratio of genetic to phenotypic variance as described in Eq. 2:

(replicates, plots) taken for a particular genotype within a run or experiment.

Page 8 of 13 | Kuijken et al.

Prospects for root phenotyping So far QTL-mapping and genome-wide association studies (GWAS) have proved to be valuable tools to improve the root system by yielding a broad range of root QTLs. Some of these QTLs have been successfully introgressed in crops; examples are the QTLs for deep root mass (Shen et al., 2001) and root length (Steele et al., 2006) in near isogenic lines of rice. However, for many breeding programmes it is often not worth the effort to fine-map QTLs as their effects tend to be small and prone to G×E interactions. If these QTLs are connected to a component trait for yield, then their effects on the ultimate breeding target might be even smaller. When identifying small-effect QTLs is difficult, an alternative to QTL mapping or GWAS could be genomic selection (GS). GS is based on a genomic estimated breeding value (GEBV), modelled from genetic relations between individuals in training and breeding or selection populations (Crossa et al., 2011; Desta and Ortiz, 2014). Phenotype and genotype information of the training population is used to estimate marker effects. These marker effects are then used to select on the basis of markers alone in the breeding population, i.e. without phenotyping. GS allows simultaneous estimation of many large and small marker effects without any prior knowledge about their effect or function. In relation to phenotyping and breeding for root traits, the added values of GS are (i) better exploitation of minor genes, (ii) the ability to phenotype only a subset of a population and to predict the rest and (iii) the ability of pre-selection of a population prior to phenotyping in the target environment, which prevents phenotyping lines that are a priori known to be inferior. The strategies as mentioned in (ii) and (iii) reduce phenotyping costs and efforts, which is especially helpful when traits are difficult to quantify. With the advent of high density SNP panels and genotyping by sequencing, it may even be

worthwhile to consider a phenotyping platform environment as a training situation for fitting a genomic prediction model, while the target trait in the target environment constitutes the selection environment. In other words, the marker effects as estimated on the platform may be used to predict the target trait in the target environment, under the condition that scaling issues are taken into account. Such predictions based on marker effects may also be applicable for standard multi-QTL models. Despite the fast development of selection strategies based on genomics, phenotyping will continue to play an important role in breeding. Although selection by GS is not directly based on phenotyping, GS still relies on a relationship between phenotype and genetic markers in the training population. Furthermore, GEBVs might become more accurate in the near future when identified genes with known effects are easier to include in GS prediction models. Although we can breed great crop varieties without cloning genes or annotating the genome, our fundamental understanding of plants will come to a stop if we stick with GS or comparable tools and neglect functional genomics through forward genetics. This further underlines the continued need for improved phenotyping facilities in plant science.

Conclusions The number and speed of developments in automated phenotyping are impressive, and as a consequence now a broad range of root phenotyping platforms is available. The costly and labour-intensive development of phenotyping platforms should not be a goal in itself. Instead, these platforms should support plant physiological research and breeding for agronomically relevant breeding targets. Numerous authors describe their root phenotyping platform aiming to make it a universal standard. In practice, such a standard is hard to establish since every research or breeding goal requires a different phenotyping approach, leading to many new phenotyping platforms. The large number of phenotyping platforms creates another issue: a lack of standardization in data storage. This deficiency prevents large-scale data exchange between root researchers and the comparison of phenotyping platforms. In an effort to overcome this issue the XML-based root system markup language (RSML) was developed (Lobet et al., 2015). Another omission in the use of root phenotyping platforms is that a description of G×E effects and reproducibility of results is often missing. G×E effects are very likely to occur, since the root system is very plastic and responds strongly to environmental influences. It is therefore important to quantify G×E effects when developing phenotyping platforms and breeding strategies for improved root traits. The choice for a phenotyping platform depends on the breeding target. This breeding target can be the same trait as measured on the platform, or the platform may measure a component trait of a breeding target in the field. The relation between platform traits and field traits, combined with their heritabilities, determines the efficiency of a phenotyping platform for breeding. Therefore we could reason that before we invest in the creation of yet another phenotyping platform,


consensus map of RSA QTLs from 17 QTL mapping studies in maize and grouped 161 QTLs in 24 clusters. These metadata showed that several QTLs with an effect on axile and lateral roots co-located, pointing towards QTLs for root system size instead of a specific root architecture. Such a meta-analysis demonstrates the possibility of a correlation based on the effects of yield on roots instead of roots on yield. In such case, selection for component traits will result in selection for pleiotropic genes or undesired physiological traits. Testing the correlations between root traits and plant size, as has been done for nodal root angle and root dry weight (Singh et al., 2011), could prevent some of such problems. The common practice of root phenotyping on seedlings outside the target environment represents another example in which the importance of a direct contribution of a component QTL needs to be emphasized. The combined environmental and plantdevelopmental gap between component and target traits represents a double risk of reduced relevance of a component trait. These examples demonstrate some of the pitfalls in component trait selection and show that insight in causal relations between component and target traits is required for successful improvement of the target trait.

Root phenotyping: from component trait to breeding | Page 9 of 13

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Root architecture simulation improves the inference from seedling root phenotyping towards mature root systems.

High-throughput phenotyping of seminal root traits in wheat.

Efficiency of direct selection on quantitative trait loci for a two-trait breeding objective.

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Mitochondrial diseases: From the lab bench to therapies.

A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding.

A portable fluorescence spectroscopy imaging system for automated root phenotyping in soil cores in the field.

Dynamic Precision Phenotyping Reveals Mechanism of Crop Tolerance to Root Herbivory.

Peptide biomarkers used for the selective breeding of a complex polygenic trait in honey bees.

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From phenotyping towards breeding strategies: using in vivo indicator traits and genetic markers to improve meat quality in an endangered pig breed.

Tissue engineering: Organs from the lab.

Pitfalls in Root Trait Calculations: How Ignoring Diameter Heterogeneity Can Lead to Overestimation of Functional Traits.

Breeding and preliminarily phenotyping of a congenic mouse model with alopecia areata.

Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries.

Development of pre-breeding technology for root system study and selection of Kihara Afghan wheat landraces (KAWLR) to enhance wheat breeding in the rain-fed region.

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Wild type p53 reactivation: from lab bench to clinic.

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Genome-wide association and high-resolution phenotyping link Oryza sativa panicle traits to numerous trait-specific QTL clusters.