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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #390856

Research Project: Improving Crop Efficiency Using Genomic Diversity and Computational Modeling

Location: Plant, Soil and Nutrition Research

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

item FERGUSON, JOHN - University Of Illinois
item FERNANDES, SAMUEL - University Of Illinois
item MONIER, BRANDON - Cornell University
item MILLER, NATHAN - University Of Wisconsin
item ALLAN, DYLAN - University Of Illinois
item DMITRIEVA, ANNA - University Of Illinois
item SCHMUKER, PETER - University Of Illinois
item LOZANO, ROBERTO - Cornell University
item VALLURU, RAVI - Lincoln University - Pennsylvania
item Buckler, Edward - Ed
item GORE, MICHAEL - Cornell University
item BROWN, PATRICK - University Of Illinois
item SPALDING, EDGAR - University Of Wisconsin
item LEAKEY, ANDREW - University Of Illinois

Submitted to: Plant Physiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/29/2021
Publication Date: 7/27/2021
Citation: Ferguson, J.N., Fernandes, S.B., Monier, B., Miller, N.D., Allan, D., Dmitrieva, A., Schmuker, P., Lozano, R., Valluru, R., Buckler IV, E.S., Gore, M.A., Brown, P.J., Spalding, E.P., Leakey, A.D. 2021. Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions. Plant Physiology. 187(3):1481-1500.

Interpretive Summary: Due to global climate change, reduced precipitation events give rise to situations where more water is needed in crops and less is available. In order to breed for crops that are adapted for such changes, inherent understanding of water use efficiency (WUE) and similar traits are needed. However, our progress to efficiently link genetic and phenotypic WUE information in crop systems is limited by the current state of the art. In this study, novel machine learning and image processing methods, along with high-throughput phenotyping for WUE traits were developed for the analysis of 869 field-grown sorghum accessions. From this pipeline, we have shown (1) correlation between biomass and WUE traits, (2) heritability of WUE traits across the sorghum population, and (3) identification of genes possibly linked to WUE in related species. These advances in methodology and knowledge will aid in the improvement of crop selection for improved performance under water limited conditions, not only in sorghum, but other prominent commercial crops. Additionally, our novel pipeline can also be easily adapted for the high-throughput analysis of other important crop traits.

Technical 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 modeling 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 and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was 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. A total of 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 nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE