Soybean Root System Architecture Trait Study through Genotypic, Phenotypic, and Shape-Based Clusters
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The Department of Agronomy seeks to teach the study of the farm-field, its crops, and its science and management. It originally consisted of three sub-departments to do this: Soils, Farm-Crops, and Agricultural Engineering (which became its own department in 1907). Today, the department teaches crop sciences and breeding, soil sciences, meteorology, agroecology, and biotechnology.
History
The Department of Agronomy was formed in 1902. From 1917 to 1935 it was known as the Department of Farm Crops and Soils.
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1902–present
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- Department of Farm Crops and Soils (1917–1935)
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- College of Agriculture and Life Sciences (parent college)
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Abstract
We report a root system architecture (RSA) traits examination of a larger scale soybean accession set to study trait genetic diversity. Suffering from the limitation of scale, scope, and susceptibility to measurement variation, RSA traits are tedious to phenotype. Combining 35,448 SNPs with an imaging phenotyping platform, 292 accessions (replications=14) were studied for RSA traits to decipher the genetic diversity. Based on literature search for root shape and morphology parameters, we used an ideotype-based approach to develop informative root (iRoot) categories using root traits. The RSA traits displayed genetic variability for root shape, length, number, mass, and angle. Soybean accessions clustered into eight genotype- and phenotype-based clusters and displayed similarity. Genotype-based clusters correlated with geographical origins. SNP profiles indicated that much of US origin genotypes lack genetic diversity for RSA traits, while diverse accession could infuse useful genetic variation for these traits. Shape-based clusters were created by integrating convolution neural net and Fourier transformation methods, enabling trait cataloging for breeding and research applications. The combination of genetic and phenotypic analyses in conjunction with machine learning and mathematical models provides opportunities for targeted root trait breeding efforts to maximize the beneficial genetic diversity for future genetic gains.
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This article is published as Falk, Kevin G., Talukder Zaki Jubery, Jamie A. O’Rourke, Arti Singh, Soumik Sarkar, Baskar Ganapathysubramanian, and Asheesh K. Singh. "Soybean Root System Architecture Trait Study through Genotypic, Phenotypic, and Shape-Based Clusters." 2020 Plant Phenomics (2020): 1925495. DOI: 10.34133/2020/1925495.