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.
Highlights
Root system architecture (RSA) is essential for water and nutrient acquisition, microbe interaction, nutrient storage, and structural anchorage and impacts grain yield [1, 2]
Large variation was observed for a majority of traits evidenced through comparison of mean, median, and trait ranges for RSA traits
We explored informative root categories, built on a previous literature in different crops, leveraging data to identify specific trait measurements and statistical analyses to quantify iconic root shapes
Summary
Root system architecture (RSA) is essential for water and nutrient acquisition, microbe interaction, nutrient storage, and structural anchorage and impacts grain yield [1, 2]. Crop breeding programs including soybean rarely utilize RSA as selection criteria; RSA traits have developed indirectly in crop species [3]. Researchers are cognizant of the genetic and phenotypic complexity that is inherent at the organismal level and promote standardization in terminology and removal of redundancies for the measurement of every conceivable trait [4,5,6]. RSA studies have been hindered by trait, measurement, and environment complexity. The plethora of root traits identified through different studies and software further complicate the identification of opportunities to select the most informative and relevant suite of traits [5, 7,8,9,10,11]. Continual efforts are needed to utilize genomics and phenomics tools to study the RSA trait variation for application in crop breeding and research programs [18]
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