Integrating Image-Based Phenomics and Association Analysis to Dissect the Genetic Architecture of Temporal Salinity Responses in Rice

Salinity affects a significant portion of arable land and is particularly detrimental for irrigated agriculture, which provides one-third of the global food supply. Rice (Oryza sativa), the most important food crop, is salt sensitive. The genetic resources for salt tolerance in rice germplasm exist but are underutilized due to the difficulty in capturing the dynamic nature of physiological responses to salt stress. The genetic basis of these physiological responses is predicted to be polygenic. In an effort to address this challenge, we generated temporal imaging data from 378 diverse rice genotypes across 14 d of 90 mm NaCl stress and developed a statistical model to assess the genetic architecture of dynamic salinity-induced growth responses in rice germplasm. A genomic region on chromosome 3 was strongly associated with the early growth response and was captured using visible range imaging. Fluorescence imaging identified four genomic regions linked to salinity-induced fluorescence responses. A region on chromosome 1 regulates both the fluorescence shift indicative of the longer term ionic stress and the early growth rate decline during salinity stress. We present, to our knowledge, a new approach to capture the dynamic plant responses to its environment and elucidate the genetic basis of these responses using a longitudinal genome-wide association model.Nearly one-third of the 54 million ha of the highly saline soils in the world are located in South and Southeast Asia. Rice (Oryza sativa), which is the primary source of calories and protein for these two regions, is very sensitive to salinity stress, with even moderate salinity levels known to decrease yields by 50% (Zeng et al., 2002). Projected sea level rise due to climate change is expected to increase saltwater ingress in coastal rice-growing regions of South and Southeast Asia. Therefore, development of salt-tolerant rice cultivars is essential to maintain rice productivity in the salinity-affected regions globally.Salt tolerance, defined as the ability to maintain growth and productivity in saline conditions, is a complex polygenic trait that may be influenced by distinct physiological mechanisms (Munns et al., 1982; Munns and Termaat, 1986; Cheeseman, 1988; Munns and Tester, 2008; Horie et al., 2012; for a comprehensive review of genes involved in salinity tolerance in rice, see Negrão et al., 2011) At the cellular level, plants respond to saline conditions in two phases, namely an osmotic (shoot ion independent) and an ionic stress phase, which can occur in an overlapping manner with varying intensity during the course of salinity stress (Munns and Termaat, 1986; Munns, 2002; Munns and James, 2003; Munns and Tester, 2008; Horie et al., 2012). During the osmotic stress phase, which occurs soon after the onset of salinity, the reduction in external osmotic potential disrupts water uptake and impedes cell expansion, which, at the whole plant level, leads to reduced growth rate (Matsuda and Riazi, 1981; Munns and Passioura, 1984; Rawson and Munns, 1984; Azaizeh and Steudle, 1991; Fricke and Peters, 2002; Fricke, 2004; Boursiac et al., 2005). As salinity stress persists over several days and weeks, sodium ions (Na⁺) accumulate to toxic levels, resulting in cell death and precocious leaf senescence (Lutts and Bouharmont, 1996; Munns, 2002; Munns and James, 2003; Ghanem et al., 2008). This is typically observed during the ionic phase of the salinity response (Munns, 2002; Munns and James, 2003; Munns and Tester, 2008). Plants possess distinct mechanisms to adapt to these osmotic and ionic stresses that are controlled by a suite of genes (Apse et al., 1999; Carvajal et al., 1999; Halfter et al., 2000; Ishitani et al., 2000; Shi et al., 2000; Zeng and Shannon, 2000; Rus et al., 2001; Berthomieu et al., 2003; Martínez-Ballesta et al., 2003; Boursiac et al., 2005, 2008; Ren et al., 2005; Huang et al., 2006; Davenport et al., 2007; Obata et al., 2007; Székely et al., 2008; Horie et al., 2011; Rivandi et al., 2011; Asano et al., 2012; Munns et al., 2012; Latz et al., 2013; Schmidt et al., 2013; Campo et al., 2014; Choi et al., 2014; Liu et al., 2014). The genetic basis of temporal adaptive responses to salinity stress remains to be explored in rice and other crops. This is primarily due to challenges in capturing the dynamic physiological responses to salinity for a large number of genotypes in a nondestructive manner. Manual phenotyping to detect incremental changes in growth rate during the osmotic stress and slight shifts in leaf color due to ionic stress is difficult to quantify for a large number of genotypes.In rice, at least one major quantitative trait loci (QTL; saltol) for salinity tolerance has been characterized based on end point measurements of biomass, senescence/injury, and Na⁺ and K⁺ concentrations (Bonilla et al., 2002; Lin et al., 2004; Thomson et al., 2010). SHOOT K⁺ CONTENT1 (SKC1) is the causative gene underlying the saltol region. SKC1 encodes a Na⁺-selective high-affinity potassium transporter that regulates Na⁺/K⁺ homeostasis during salinity stress (Ren et al., 2005). High levels of Na⁺ displace cellular K⁺, an essential element for several enzymatic reactions and physiological processes (Gierth and Mäser, 2007). The ability to maintain cellular K⁺ levels during salinity through the action of Na⁺-selective potassium transporters or Na⁺/H⁺ antiporters is a well-characterized tolerance mechanism in cereals including rice (Ren et al., 2005; Sunarpi et al., 2005; Huang et al., 2006; Møller et al., 2009; Mian et al., 2011; Munns et al., 2012).Numerous studies have utilized conventional linkage mapping to identify QTL for morphological and physiological responses to salinity in rice using discrete end point measurements (Bonilla et al., 2002; Lin et al., 2004; Ren et al., 2005; Negrão et al., 2011; Wang et al., 2012). However, the physiological adaptation to saline conditions is a complex continuous process that develops over time. While some accessions will exhibit similar end point phenotypic values, the genetic and physiological mechanisms giving rise to the similar phenotypes may be very different and the growth trajectories throughout the experiment may be distinct. Although single time point studies have yielded important information regarding the genetic basis of salinity tolerance, such approaches are too simple to reveal the genetic architecture of stress adaptation. With the advent of high-throughput image-based phenotyping platforms, it is now feasible to quantify dynamic responses during the stress treatment for a large number of genotypes (Berger et al., 2010; Golzarian et al., 2011; Chen et al., 2014; Honsdorf et al., 2014).Image-based phenotyping has been combined with genome-wide association studies (GWAS) and linkage mapping to examine the genetic basis of complex developmental processes (Busemeyer et al., 2013; Moore et al., 2013; Topp et al., 2013; Slovak et al., 2014; Würschum et al., 2014; Yang et al., 2014; Bac-Molenaar et al., 2015). Moreover, the introduction of the time axis provides a better understanding of the physiological processes underlying complex stress and developmental responses compared with single end point measurements (Zhang et al., 2012; Moore et al., 2013; Brown et al., 2014; Chen et al., 2014; Slovak et al., 2014; Bac-Molenaar et al., 2015). However, to date, no studies have implemented an association mapping approach using image-derived phenotypes to address the genetic basis of dynamic stress responses in plants. Image-based phenotyping offers several advantages over conventional phenotyping: (1) quantitative measurements can be recorded over discrete time points to capture morphological and physiological responses in a nondestructive manner, and (2) the use of various types of spectral imaging address phenotypes that are not detectable to the human eye such as chlorophyll fluorescence and leaf water content. Integrating dynamic phenotypic data and association mapping has the potential to query genetic diversity across hundreds of accessions for complex traits and provides much higher resolution compared with conventional linkage mapping. Here, we explored the dynamic growth and chlorophyll responses to salinity of a diverse set of rice accessions using high-throughput visible and fluorescence imaging. To assess the genetic basis of plant growth in saline conditions, a logistic model was used to describe the temporal growth responses and was incorporated into the statistical framework necessary for association mapping. Coupled with temporal fluorescence imaging, we present, to our knowledge, new insights into the genetic architecture of osmotic and ionic responses during salinity stress in rice.