U.S. flag

An official website of the United States government

Dot gov

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Https

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

PubAg

Main content area

Soybean root system architecture trait study through genotypic, phenotypic, and shape-based clusters

Author:
Falk Kevin G., Juberi Talukder Zaki, O'Rourke Jamie A., Singh Arti, Sarkar Soumik, Ganapathysubramanian, Singh Asheesh K.
Source:
Plant phenomics 2020 v.2020 no.1925495 pp. -
ISSN:
2643-6515
Subject:
Glycine max, genetic analysis, genetic variation, genotype, genotype-phenotype correlation, phenotype, plant architecture, plant breeding, plant germplasm, provenance, root systems, roots, single nucleotide polymorphism, soybeans
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.
Agid:
7071198
Handle:
10113/7071198