Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes

Abstract

Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential for maintaining water-use efficiency (WUE). However, little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. Here, we used high-throughput thermal imaging to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum (Sorghum bicolor) accessions. Results from thermal imaging significantly correlated with photosynthetic gas-exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study (GWAS/TWAS) identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with greatest confidence, 77 were putative orthologs of Arabidopsis (Arabidopsis thaliana) genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production.

Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain

“…In an assessment of TWAS for the genetic dissection of tocochromanol and carotenoid maize grain traits in the Goodman-Buckler association panel, Kremling et al (2019) showed that the statistical power to detect previously identified causal genes could be increased through an ensemble approach combining GWAS and TWAS results with the Fisher’s combined test (FCT). Additionally, this approach was used to identify plausible causal genes associated with natural variation for water use efficiency traits in sorghum ( Ferguson et al 2021 ; Pignon et al 2021 ). The genetic markers used in GWAS could also be linked to mRNA abundance via expression quantitative trait locus (eQTL) mapping ( Majewski and Pastinen 2011 ), enabling the regulatory landscape of traits to be better explored as has been conducted for oil content and composition in maize grain ( Li et al 2013 ).…”

Plasticity in stomatal behaviour across a gradient of water supply is consistent among field‐grown maize inbred lines with varying stomatal patterning

“…It is also important to recognize that flexibility in structure-function relationships of the type observed here will set constraints and maybe create opportunities for efforts to engineer or select for improved crop WUE (Leakey et al, 2019). New high-throughput phenotyping and analytical techniques are providing unprecedented detail and depth of information about the suite of traits that underpin variation in WUE within C 4 species (Ferguson et al, 2021;Pignon et al, 2021aPignon et al, , 2021bXie et al, 2021).…”

Using breeding and quantitative genetics to understand the C4 pathway

“…GWAS has identified QTL associated with photosynthetic performance during chilling in maize ( Strigens et al , 2013 ) and sorghum ( Ortiz et al , 2017 ). More recently, a sorghum diversity panel of 756 African accessions was described ( Faye et al , 2021 ) and a diverse 869 line panel ( Valluru et al , 2019 ) was subjected to GWAS to identify genes controlling stomatal conductance and water use efficiency ( Ferguson et al , 2021 ; Pignon et al , 2021 ). The latter two studies used transcriptome data to allow transcriptome-wide association as well as GWAS (reviewed by Wainberg et al , 2019 ) to increase the likelihood of identifying candidate genes.…”