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

Abstract

Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is 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 to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a 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 the subject of genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 (ci/ca) was genetically correlated with SD, SLA, gs and biomass production. Plasticity in SD and SLA were interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated 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 Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with non-synonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.

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

“…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 C4 species (Ferguson et al, 2021;Pignon et al, 2021a,b;Xie et al, 2021). This should then in turn allow additional studies of the type presented here to quantify gs-model parameters in other genotypes and provide the parameterization data needed to inform crop improvement effort with in silico analyses (Marshall-Colon et al, 2017).…”

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, 2021 ). 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 ( Supplemental Table S5 ).…”

Genetics as a key to improving crop photosynthesis

“… Jung and Niyogi, 2009 ; Yin et al , 2010 ; Lowry et al , 2013 ; Oakley et al , 2018 ; Feldman et al , 2018 ) seems equally successful in finding QTLs as GWAS (e.g. Chao et al , 2014 ; P. Wang et al , 2017 ; Ortiz et al , 2017 ; Van Rooijen et al , 2017 ; Rungrat et al , 2019 ; Prinzenberg et al , 2020 ; Joynson et al , 2021 ; Ferguson et al , 2021 ). Initially much of this work was performed in model species like Arabidopsis, for which suitable mapping populations are readily available, but these approaches are increasingly feasible in crop species.…”

“…Besides the already mentioned studies on linking genetic variation to photosynthetic variation, there is a growing body of literature on mapping studies in many plant species (e.g. Jung and Niyogi, 2009 ; Lowry et al , 2013 ; Chao et al , 2014 ; Ortiz et al , 2017 ; Feldman et al , 2018 ; Joynson et al , 2021 ; Ferguson et al , 2021 ). These QTLs may be selected in marker assisted breeding for improved photosynthesis, where information on the candidate genes is not essential to improve the trait.…”