Campus Units

Agronomy, Electrical and Computer Engineering, Mechanical Engineering, Plant Sciences Institute

Document Type

Article

Publication Version

Published Version

Publication Date

6-9-2020

Journal or Book Title

Plant Phenomics

Volume

2020

First Page

1925495

DOI

10.34133/2020/1925495

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.

Comments

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.

Rights

Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.

Language

en

File Format

application/pdf

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