Soybean Root System Architecture Trait Study through Genotypic,Phenotypic,and Shape-Based Clusters

查看全文 作　　者：Kevin [1]G.Falk;Talukder Zaki [2]Jubery;Jamie A.O’[1,3]Rourke;Arti [1]Singh;Soumik [2]Sarkar;Baskar [2]Ganapathysubramanian;Asheesh [1]K.Singh

机构地区：[1]Department of Agronomy,Iowa State University,Ames,Iowa,USA;[2]Department of Mechanical Engineering,Iowa State University,Ames,Iowa,USA;[3]USDA-Agricultural Research Service,Corn Insects and Crop Genetics Research Unit,Ames,Iowa,USA

出　　处：《Plant Phenomics》索引2020年第2卷第1期,共23页

基　　金：The authors sincerely appreciate the funding support from the Iowa Soybean Research Center at Iowa State University,RF Baker Center for Plant Breeding at Iowa State University,Plant Sciences Institute at Iowa State University,Iowa Soybean Association,Monsanto Chair in Soybean Breeding at Iowa State University,and USDA CRIS project(IOW04314)to AKS and AS and 5030-21220-005-00D to JAO. 摘　　要：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 ideotypebased 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. 关键词：neural BREEDING ROOT

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Soybean Root System Architecture Trait Study through Genotypic,Phenotypic,and Shape-Based Clusters

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