Soybean root system architecture trait study through genotypic, phenotypic, and shape-based clusters
2020
Falk, Kevin G. | Juberi, Talukder Zaki | O'Rourke, Jamie A. | Singh, Arti | Sarkar, Soumik | Singh, Asheesh | Singh, Asheesh K.
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.
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