Quantitative Trait Locus Mapping Reveals Regions of the Maize Genome Controlling Root System Architecture

The quest to determine the genetic basis of root system architecture (RSA) has been greatly facilitated by recent developments in root phenotyping techniques. Methods that are accurate, high throughput, and control for environmental factors are especially attractive for quantitative trait locus mapping. Here, we describe the adaptation of a nondestructive in vivo gel-based root imaging platform for use in maize (Zea mays). We identify a large number of contrasting RSA traits among 25 founder lines of the maize nested association mapping population and locate 102 quantitative trait loci using the B73 (compact RSA) × Ki3 (exploratory RSA) mapping population. Our results suggest that a phenotypic tradeoff exists between small, compact RSA and large, exploratory RSA.Maize (Zea mays) serves a key role in food, feedstock, and biofuel production throughout the world. To date, maize improvement through breeding has kept pace with the increasing demand for this crop (faostat3.fao.org). This feat has been accomplished through the utilization of the tremendous genetic diversity in maize (Flint-Garcia et al., 2005; Jiao et al., 2012), but increasing environmental pressures and a growing global population will require unprecedented gains in yield in the coming years. In the last decade, researchers have begun to explore the possibility of yield improvements through the manipulation of root systems, for example through breeding for roots better able to cope with drought (Uga et al., 2013) and flooding (Jackson and Armstrong, 1999), the use of plant growth-promoting rhizobacteria (Silby et al., 2009), or increasing nutrient use efficiency (Garnett et al., 2009). The potential of belowground solutions to enhanced plant productivity has driven the development of numerous methodologies for phenotyping root system architecture (RSA), which is the spatial organization of the plant’s root system.Several methods ranging from techniques adapted from medical imaging, such as x-ray tomography (Hargreaves et al., 2008) and combined positron emission tomography-magnetic resonance imaging (Jahnke et al., 2009), to refined versions of classical methods, such as field excavations (Trachsel et al., 2010) and pouch systems (Le Marié et al., 2014), have been used in attempts to understand the phenotypic consequences of genetic and environmental variation on root traits. Each root-phenotyping method has its advantages and disadvantages. Although the medical imaging-based techniques can produce highly detailed representations of roots, they are also very time consuming and require specialized equipment. Excavations, although more easily scaled to higher throughput and not requiring special equipment, are destructive and offer only coarse measurements of RSA. An alternative method for root phenotyping based on an optically clear gel substrate strikes an effective balance between throughput and detail, using a simple digital camera while maintaining precise control over environmental conditions. This platform has been used to quantify and classify distinctive root architectures from 12 rice (Oryza sativa) genotypes (Iyer-Pascuzzi et al., 2010), conduct a quantitative trait locus (QTL) mapping study of rice root traits in three dimensions (Topp et al., 2013), study interspecific and intraspecific rice root interactions (Fang et al., 2013), and quantify contributions of different root types to overall RSA (Clark et al., 2011).Here, we describe the adaptation of this gel imaging platform for use with the large maize root system. We used the platform to quantify the phenotypic diversity of RSA among 25 of the 26 nested association mapping (NAM) founder lines, which encompass a wide spectrum of maize genetic diversity (Yu et al., 2008; McMullen et al., 2009). We found that these lines exhibit diverse RSAs, ranging from small and compact to large and exploratory, suggesting tradeoffs between different types of architectures. In order to identify genetic loci that control maize RSA traits, we characterized a subpopulation that best represented the contrast between the compact and exploratory RSAs. We phenotyped the B73 (compact) × Ki3 (exploratory) recombinant inbred line (RIL) NAM subpopulation for 19 RSA traits at three time points (Topp et al., 2013). These data were used to map 102 QTLs that localized to nine genomic clusters. We found high heritability and large-effect QTLs for most traits, in contrast to maize flowering time QTLs (Buckler et al., 2009). Additionally, several of our QTL clusters overlapped with meta-QTLs for yield traits (Tuberosa et al., 2003; Semagn et al., 2013) as well as novel and previously unreported loci, suggesting that this system can provide a time- and cost-effective means to identify genes controlling root architecture in maize.