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

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

Submitted to: bioRxiv
Publication Type: Pre-print Publication
Publication Acceptance Date: 11/3/2020
Publication Date: 11/3/2020
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., Spalding, E.P., Leakey, A. 2020. Machine learning enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions. bioRxiv. https://doi.org/10.1101/2020.11.02.365213.
DOI: https://doi.org/10.1101/2020.11.02.365213

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 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.