Genetic variation among 481 diverse soybean accessions, inferred from genomic re-sequencing

Valliyodan, Babu; Brown, Anne V.; Wang, Juexin; Patil, Gunvant; Liu, Yang; Otyama, Paul I.; Nelson, Rex T.; Vuong, Tri D.; Song, Qijian; Musket, Theresa A.; Wagner, R; Marri, Pradeep Reddy; Reddy, Sam; Sessions, Allen; Wu, Xiaolei; Grant, David; Bayer, Philipp E.; Roorkiwal, Manish; Varshney, Rajeev K.; Liu, Xin; Edwards, David; Xu, Dong; Joshi, Trupti; Cannon, Steven B.; Nguyen, Henry T.

doi:10.1038/s41597-021-00834-w

Cited by 44 publications

(47 citation statements)

References 45 publications

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“…The first step is to select a genotype collection. For soybean, we select the “USB 481” set, from Valliyodan et al (2020). This has good genome‐wide SNP density, and contains a good mix of G. soja and G. max accessions and elite and landrace lines.…”

Section: Resultsmentioning

confidence: 99%

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The legume information system and associated online genomic resources

et al. 2021

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The Legume Information System (LIS; https://legumeinfo.org) houses genetic and genomic data, integrated in various online tools to allow comparative genomic analyses. The website and database maintain data for more than two dozen species, particularly focusing on crop and model species and holding data for other diverse species of taxonomic interest. Major analysis features include genome browsers, sequence‐search tools, legume‐focused gene families and a phylogenetic tree viewer, a gene annotation service (which places a submitted gene into a gene family and phylogenetic tree), an interactive microsynteny and pan‐genome viewer, a novel viewer of genetic variant data, genetic maps and viewers, a Data Store for data sets such as assemblies and annotations, InterMine instances for querying genetic and genomic data, and a tool for viewing geographic distributions of germplasm accessions. LIS also integrates with several other legume data resources and tools, including PeanutBase (https://peanutbase.org), SoyBase (https://soybase.org), Medicago Hapmap (https://medicagohapmap2.org), Alfalfa Breeder's Toolbox (https://alfalfatoolbox.org), and the Legume Federation (https://legumefederation.org).

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Section: Resultsmentioning

confidence: 99%

“…Genotype Comparative Visualization Tool (GCViT) view of selected soybean accessions from Valliyodan et al (2020). On the left of each chromosome, single nucleotide polymorphism (SNP) density is shown (as a gray histogram), for reference accession PI597458, from Glycine soja .…”

Section: Resultsmentioning

confidence: 99%

The legume information system and associated online genomic resources

et al. 2021

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“…Subsequent analysis of an expanded soybean resequencing dataset of 775 accessions distributed between 110 G. soja and 665 G. max revealed a total of 15 G. soja and 2 G. max accessions with e1la :K82E alleles [ 23 , 25 ]. In our original analysis of the 302 soybean dataset, no variant alleles of the E1Lb gene were identified.…”

Section: Resultsmentioning

confidence: 99%

“…The ten G. soja accessions also contained a synonymous variant (G/T) at Gm04:28,294,356 that was present in an additional 14 G. soja accessions. The soybean allele catalog ( http://soykb.org/GenescapeAnalysis/search.php ) was used to assess the distribution of alleles of E1La and E1Lb in our curated data set of 775 whole genome re-sequenced soybean accessions [ 23 , 25 ]. A protein blast at NCBI, using the Williams 82 reference peptide sequence of E1La , was used to obtain the orthologous sequences from 23 different legume species with the highest percent identity (Supplemental Table 2 ).…”

Section: Methodsmentioning

confidence: 99%

Geographic distribution of the E1 family of genes and their effects on reproductive timing in soybean

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Background Soybean is an economically important crop which flowers predominantly in response to photoperiod. Several major loci controlling the quantitative trait for reproductive timing have been identified, of which allelic combinations at three of these loci, E1, E2, and E3, are the dominant factors driving time to flower and reproductive period. However, functional genomics studies have identified additional loci which affect reproductive timing, many of which are less understood. A better characterization of these genes will enable fine-tuning of adaptation to various production environments. Two such genes, E1La and E1Lb, have been implicated in flowering by previous studies, but their effects have yet to be assessed under natural photoperiod regimes. Results Natural and induced variants of E1La and E1Lb were identified and introgressed into lines harboring either E1 or its early flowering variant, e1-as. Lines were evaluated for days to flower and maturity in a Maturity Group (MG) III production environment. These results revealed that variation in E1La and E1Lb promoted earlier flowering and maturity, with stronger effects in e1-as background than in an E1 background. The geographic distribution of E1La alleles among wild and cultivated soybean revealed that natural variation in E1La likely contributed to northern expansion of wild soybean, while breeding programs in North America exploited e1-as to develop cultivars adapted to northern latitudes. Conclusion This research identified novel alleles of the E1 paralogues, E1La and E1Lb, which promote flowering and maturity under natural photoperiods. These loci represent sources of genetic variation which have been under-utilized in North American breeding programs to control reproductive timing, and which can be valuable additions to a breeder’s molecular toolbox.

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“…Very recently, Valliyodan et al. (2021) analyzed genetic diversity and structure from the resequencing of 481 diverse soybean accessions, comprising 52 wild selections and 429 cultivated varieties (landraces and elites). This study identified evidence of distinct, mostly independent selection of lineages by particular geographic location.…”

Section: Rapidly Evolving Omics Approachesmentioning

confidence: 99%

Systems biology for crop improvement

et al. 2021

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In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species.Most of these datasets in different crop species, however, were studied independently and as a result, full insight could not be gained on the molecular basis of complex traits and biological networks. A systems biology approach involving integration of multiple omics data, modeling, and prediction of the cellular functions is required to understand the flow of biological information that underlies complex traits. In this context, systems biology with multiomics data integration is crucial and allows a holistic understanding of the dynamic system with the different levels of biological organization interacting with external environment for a phenotypic expression. Here, we present recent progress made in the area of various omics studies-integrative and systems biology approaches with a special focus on application to crop improvement. We have also discussed the challenges and opportunities in multiomics data integration, modeling, and understanding of the biology of complex traits underpinning yield and stress tolerance in major cereals and legumes.

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Genetic variation among 481 diverse soybean accessions, inferred from genomic re-sequencing

Cited by 44 publications

References 45 publications

The legume information system and associated online genomic resources

The legume information system and associated online genomic resources

Geographic distribution of the E1 family of genes and their effects on reproductive timing in soybean

Systems biology for crop improvement

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