Machine learning enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions

Abstract

Sorghum is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is also studied as a feedstock for biofuel and forage. Mechanistic modelling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping for discovery of genotype to phenotype associations remain bottlenecks in efforts to understand the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a novel machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were then the subject of genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) across 869 field-grown biomass sorghum accessions. SD was correlated with plant height and biomass production. Plasticity in SD and SLA were interrelated with each other, and productivity, across wet versus dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population supported identification of associations between DNA sequence variation, or RNA transcript abundance, and trait variation. 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose orthologs in Arabidopsis have functions related to stomatal or leaf development and leaf gas exchange. These advances in methodology and knowledge will aid efforts to improve the WUE of C4 crops.

Bioenergy sorghum maintains photosynthetic capacity in elevated ozone concentrations

“…We further examined photosynthetic response to elevated O 3 in four genotypes which were used in both 2018 and 2019 in a growth chamber experiment that minimized variation in other environmental parameters. The genotypes were chosen from a panel of 229 diverse biomass sorghum lines with significant variation in agronomic and physiological traits (Ferguson et al, 2020; Valluru et al, 2019). Given that sorghum may show a similar O 3 sensitivity as maize due to their close phylogenetic relationship (Lawrence & Walbot, 2007), we tested the hypotheses that elevated O 3 would: (a) reduce photosynthetic traits and capacity, such as net CO 2 assimilation, stomatal conductance, the maximum carboxylation capacity of phosphoenolpyruvate and CO 2 saturated photosynthetic capacity; (b) alter the relationship between stomatal conductance and photosynthesis as predicted by the BWB and MED model; and (c) decrease biomass and nutrient composition in sorghum lines.…”

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

“…Gene candidates putatively associated with genetic variation in stomatal closure in sorghum were identified using GWAS and TWAS integrated with FCT, followed by GO enrichment analysis. This approach has identified known causal variants more efficiently than GWAS and TWAS alone (Kremling et al 2019), while also increasing the consistency in results observed when testing was repeated across different conditions (Ferguson et al 2020). The present study reinforced these prior reports, with an order of magnitude more genes being consistently identified by FCT versus TWAS across the two independent tissue sampling strategies used (Table S5).…”

Genetic variation associated with plastic and homeostatic growth responses to drought in Arabidopsis

“…We show here how the integrated application of GWAS, TWAS, and eGWAS yields insights into the genetic basis of responses to resource availability in Arabidopsis. A similarly integrative approach has identified water use efficiency traits in sorghum (Ferguson et al, 2020). Results arising from diverse genome-wide analyses in both model and crop species (Schindele et al, 2020) can now be used to inform powerful new CRISPR-based approaches for gene editing and modulation of transcript abundance toward crop improvement (Abudayyeh et al, 2017).…”