Location: Corn Insects and Crop Genetics ResearchTitle: Soybean root system architecture trait study through genotypic, phenotypic, and shape-based clusters
|FALK, KEVIN - Iowa State University|
|JUBERI, TALUKDER - Iowa State University|
|SINGH, ARTI - Iowa State University|
|SARKAR, SOUMIK - Iowa State University|
|GANAPATHYSUBRAMANIAN, BASKAR - Iowa State University|
|SINGH, ASHEESH - Iowa State University|
Submitted to: Plant Phenomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/16/2020
Publication Date: 6/9/2020
Publication URL: https://handle.nal.usda.gov/10113/7071198
Citation: Falk, K.G., Juberi, T.Z., O'Rourke, J.A., Singh, A., Sarkar, S., Ganapathysubramanian, B., Singh, A.K. 2020. Soybean root system architecture trait study through genotypic, phenotypic and shape-based clusters. Plant Phenomics. https://doi.org/10.34133/2020/1925495.
Interpretive Summary: The roots of a plant provide a subterranean support system for the above ground plant structures, while also acquiring and providing nutrients to the plant. Studying different root traits is difficult due to their below-ground location. This difficulty means there is not much information available for roots, especially soybean roots. We used a semi-automated system to document the root phenotypes of 292 diverse soybean lines. This identified genetic variability for root shape, length, number, mass, and root angle. We combined the phenotypic information with single nucleotide polymorphism (SNP) profiles for each genotype and used machine learning algorithms to develop eight root clusters. These clusters correlated with advantages in different environments. The clusters are an excellent method to catalog root traits for breeding and research projects. Integrating these clusters into breeding programs will facilitate more targeted breeding efforts to maximize future genetic gains in root traits of interest.
Technical Abstract: We report a root system architecture (RSA) trait examination of a large scale soybean accession set to study the genetic diversity of RSA present in the USDA soybean core collection. Suffering from the limitation of scale, scope, and susceptibility to measurement variation, RSA traits are tedious to phenotype. Combining 35,448 SNPs with a semi-automated phenotyping platform, 292 accessions (replications = 14) were examined for RSA traits to decipher the genetic diversity and explore informative root (iRoot) categories based on current literature for root shape categories. The RSA traits showed genetic variability for root shape, length, number, mass, and angle. Morphology parameters are used to classify roots into different categories and correlate with environmental advantages. Eight genotype- and phenotype-based clusters were found from the diverse accession set and displayed significant correlations. Genotype-based clusters (GBC) correlated with geographical origins. SNP profiles indicated that much of US origin genotypes lack genetic diversity for RSA traits. Through the integration of convolution neural net and Fourier transformation methods, we present methods to capture shape based clusters, a method of trait cataloging for breeding and research applications. This combination of genetic and phenotypic analyses results provides opportunities for targeted breeding efforts to maximize the beneficial genetic diversity for future genetic gains.