@article{e46f7ca5b15d496bbe299ba886413199,
title = "Application of an Improved 2-Dimensional High-Throughput Soybean Root Phenotyping Platform to Identify Novel Genetic Variants Regulating Root Architecture Traits",
abstract = "Nutrient-efficient root system architecture (RSA) is becoming an important breeding objective for generating crop varieties with improved nutrient and water acquisition efficiency. Genetic variants shaping soybean RSA is key in improving nutrient and water acquisition. Here, we report on the use of an improved 2-dimensional high-throughput root phenotyping platform that minimizes background noise by imaging pouch-grown root systems submerged in water. We also developed a background image cleaning Python pipeline that computationally removes images of small pieces of debris and filter paper fibers, which can be erroneously quantified as root tips. This platform was used to phenotype root traits in 286 soybean lines genotyped with 5.4 million single-nucleotide polymorphisms. There was a substantially higher correlation in manually counted number of root tips with computationally quantified root tips (95% correlation), when the background was cleaned of nonroot materials compared to root images without the background corrected (79%). Improvements in our RSA phenotyping pipeline significantly reduced overestimation of the root traits influenced by the number of root tips. Genome-wide association studies conducted on the root phenotypic data and quantitative gene expression analysis of candidate genes resulted in the identification of 3 putative positive regulators of root system depth, total root length and surface area, and root system volume and surface area of thicker roots (DOF1-like zinc finger transcription factor, protein of unknown function, and C2H2 zinc finger protein). We also identified a putative negative regulator (gibberellin 20 oxidase 3) of the total number of lateral roots.",
author = "Rahul Chandnani and Tongfei Qin and Heng Ye and Haifei Hu and Karim Panjvani and Mutsutomo Tokizawa and Macias, {Javier Mora} and Medina, {Alma Armenta} and Bernardino, {Karine C.} and Pradier, {Pierre Luc} and Pankaj Banik and Ashlyn Mooney and Magalhaes, {Jurandir V.} and Nguyen, {Henry T.} and Kochian, {Leon V.}",
note = "Funding Information: We thank all the research technicians and staff from the Plant Growth Facility of the Global Institute for Food Security (GIFS) for their excellent work helping on plant growth and maintenance and soybean seed increases. Funding: This study was funded by the Canada Excellence Research Chair (CERC) in Funding Information: Food Systems grant, funding from the Global Institute for Food Security funding (to L.V.K.) and National Council for Scientific and Technological Development—CNPq. Root genetics and genomics research in the H.TN. laboratory was funded by the United Soybean Board and the Missouri Agricultural Experiment Station. Author contributions: R.C., L.V.K., and H.T.N. conceived the project. R.C., L.V.K., H.T.N., and J.V.M. designed the project. R.C., T.Q., P.B., A.M., and P.-L.P. conducted the phenotyping. R.C. and K.P. performed image preprocessing and analysis. R.C., H.H., H.Y., and K.C.B. performed statistical and bioinformatic data analysis. M.T., A.A.M., J.M.M. and T.Q. assisted with laboratory experiments. R.C., L.V.K., and J.V.M. wrote the manuscript. Competing interests: The authors declare that they have no competing interests. Funding Information: We thank all the research technicians and staff from the Plant Growth Facility of the Global Institute for Food Security (GIFS) for their excellent work helping on plant growth and maintenance and soybean seed increases. Publisher Copyright: Copyright {\textcopyright} 2023 Rahul Chandnani et al.",
year = "2023",
doi = "10.34133/plantphenomics.0097",
language = "English",
volume = "5",
journal = "Plant Phenomics",
issn = "2643-6515",
publisher = "American Association for the Advancement of Science (AAAS)",
}