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Genetic diversity and population structure of soybean lines adapted to sub-Saharan Africa using Single Nucleotide Polymorphism (SNP) markers

dc.contributor.authorChander, S.
dc.contributor.authorGarcia-Oliveira, A.L.
dc.contributor.authorGedil, M.
dc.contributor.authorShah, T.
dc.contributor.authorOtusanya, G.O.
dc.contributor.authorAsiedu, R.
dc.contributor.authorChigeza, G.
dc.date.accessioned2021-07-07T09:57:25Z
dc.date.available2021-07-07T09:57:25Z
dc.date.issued2021
dc.identifier.citationChander, S., Garcia-Oliveira, A. L., Gedil, M., Shah, T., Otusanya, G. O., Asiedu, R., & Chigeza, G. (2021). Genetic diversity and population structure of soybean lines adapted to sub-Saharan Africa using Single Nucleotide Polymorphism (SNP) markers. Agronomy, 11(3), 604: 1-11.
dc.identifier.issn2073-4395
dc.identifier.urihttps://hdl.handle.net/20.500.12478/7158
dc.description.abstractSoybean productivity in sub-Saharan Africa (SSA) is less than half of the global average yield. To plug the productivity gap, further improvement in grain yield must be attained by enhancing the genetic potential of new cultivars that depends on the genetic diversity of the parents. Hence, our aim was to assess genetic diversity and population structure of elite soybean genotypes, mainly released cultivars and advanced selections in SSA. In this study, a set of 165 lines was genotyped with high-throughput single nucleotide polymorphism (SNP) markers covering the complete genome of soybean. The genetic diversity (0.414) was high considering the bi-allelic nature of SNP markers. The polymorphic information content (PIC) varied from 0.079 to 0.375, with an average of 0.324 and about 49% of the markers had a PIC value above 0.350. Cluster analysis grouped all the genotypes into three major clusters. The model-based STRUCTURE and discriminant analysis of principal components (DAPC) exhibited high consistency in the allocation of lines in subpopulations or groups. Nonetheless, they presented some discrepancy and identified the presence of six and five subpopulations or groups, respectively. Principal coordinate analysis revealed more consistency with subgroups suggested by DAPC analysis. Our results clearly revealed the broad genetic base of TGx (Tropical Glycine max) lines that soybean breeders may select parents for crossing, testing and selection of future cultivars with desirable traits for SSA.
dc.description.sponsorshipCGIAR Research Program on Grain Legumes and Dryland Cereals
dc.format.extent1-11
dc.language.isoen
dc.subjectGenetic Diversity
dc.subjectPopulation Structure
dc.subjectSingle Nucleotide Polymorphism
dc.subjectGenetic Markers
dc.subjectSoybeans
dc.subjectSubsaharan Africa
dc.titleGenetic diversity and population structure of soybean lines adapted to sub-Saharan Africa using Single Nucleotide Polymorphism (SNP) markers
dc.typeJournal Article
cg.contributor.crpMaize
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.crpPolicies, Institutions and Markets
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationCCS Haryana Agricultural University
cg.contributor.affiliationInternational Maize and Wheat Improvement Center
cg.contributor.affiliationFederal University of Agriculture, Abeokuta
cg.coverage.regionAfrica
cg.coverage.regionCentral Africa
cg.coverage.regionEast Africa
cg.coverage.regionSouthern Africa
cg.coverage.regionWest Africa
cg.coverage.countryBenin (Dahomey)
cg.coverage.countryBurundi
cg.coverage.countryCameroon
cg.coverage.countryCote d’Ivoire (Ivory Coast)
cg.coverage.countryEthiopia
cg.coverage.countryGhana
cg.coverage.countryKenya
cg.coverage.countryMalawi
cg.coverage.countryMozambique
cg.coverage.countryNigeria
cg.coverage.countrySierra Leone
cg.coverage.countryTogo
cg.coverage.countryUganda
cg.coverage.countryZambia
cg.coverage.hubSouthern Africa Hub
cg.coverage.hubHeadquarters and Western Africa Hub
cg.researchthemeBiometrics
cg.researchthemeBiotech and Plant Breeding
cg.researchthemePlant Production and Health
cg.identifier.bibtexciteidCHANDER:2021
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectAgronomy
cg.iitasubjectFood Security
cg.iitasubjectGenetic Improvement
cg.iitasubjectGrain Legumes
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Genetic Resources
cg.iitasubjectPlant Production
cg.iitasubjectSoybean
cg.journalAgronomy
cg.notesOpen Access Journal; Published online: 22 Mar 2021
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.identifier.doihttps://dx.doi.org/10.3390/agronomy11030604
cg.iitaauthor.identifierGarcia-Oliveira AL: 0000-0001-8561-4172
cg.iitaauthor.identifierMelaku Gedil: 0000-0002-6258-6014
cg.iitaauthor.identifierTrushar Shah: 0000-0002-0091-7981
cg.iitaauthor.identifierRobert Asiedu: 0000-0001-8943-2376
cg.iitaauthor.identifierGodfree Chigeza: 0000-0002-9235-0694
cg.futureupdate.requiredNo
cg.identifier.issue3: 604
cg.identifier.volume11
cg.contributor.acknowledgementsWe are grateful to Rodomiro Ortiz (Swedish University of Agricultural Sciences, Sweden) for enriching the revised manuscript. The authors appreciate and acknowledge Peter Oyelakin, Sunday Ojo, Ademola Ajayi, and Ilesanmi Yinka for their support in IITA soybean screenhouse and DNA extraction. We are also thankful to the four anonymous reviewers for discussions and their pertinent suggestions on the manuscript.


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