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

Ferguson, John N.; Fernandes, Samuel B.; Monier, Brandon; Miller, Nathan D.; Allen, Dylan; Dmitrieva, Anna; Schmuker, Peter; Lozano, Roberto; Valluru, Ravi; Buckler, Edward S.; Gore, Michael A.; Brown, Patrick J.; Spalding, Edgar P.; Leakey, Andrew D. B.

doi:10.1093/plphys/kiab346

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

Journal Article · Tue Jul 27 00:00:00 EDT 2021 · Plant Physiology (Bethesda)

DOI:https://doi.org/10.1093/plphys/kiab346· OSTI ID:1828986

Ferguson, John N. ^[1];

^[1];

^[2];

^[3]; Allen, Dylan ^[1];

^[1]; Schmuker, Peter ^[1]; Lozano, Roberto ^[4];

^[2];

^[5];

^[4]; Brown, Patrick J. ^[1];

^[3];

^[6]

Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA
Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA
Department of Botany, University of Wisconsin, Madison, Wisconsin 53706, USA
Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA, Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA
Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA, Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA, Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61901, USA

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 (g_s) 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 g_s 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, g_s, 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.

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Research Organization:: Univ. of Illinois at Urbana-Champaign, IL (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Grant/Contract Number:: DE-AR0000661; AR0000661

OSTI ID:: 1828986

Alternate ID(s):: OSTI ID: 1904829

Journal Information:: Plant Physiology (Bethesda), Journal Name: Plant Physiology (Bethesda) Vol. 187 Journal Issue: 3; ISSN 0032-0889

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United States

Language:: English

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