Leveraging genomic prediction to scan germplasm collection for crop improvement

dc.contributor.author Azevedo Peixoto, Leonardo de
dc.contributor.author Moellers, Tara
dc.contributor.author Singh, Asheesh
dc.contributor.author Zhang, Jiaoping
dc.contributor.author Lorenz, Aaron
dc.contributor.author Bhering, Leonardo
dc.contributor.author Beavis, William
dc.contributor.author Singh, Asheesh
dc.contributor.department Agronomy
dc.date 2018-02-19T07:31:18.000
dc.date.accessioned 2020-06-29T23:04:25Z
dc.date.available 2020-06-29T23:04:25Z
dc.date.copyright Sun Jan 01 00:00:00 UTC 2017
dc.date.issued 2017-06-09
dc.description.abstract <p>The objective of this study was to explore the potential of genomic prediction (GP) for soybean resistance against <em>Sclerotinia sclerotiorum</em> (Lib.) de Bary, the causal agent of white mold (WM). A diverse panel of 465 soybean plant introduction accessions was phenotyped for WM resistance in replicated field and greenhouse tests. All plant accessions were previously genotyped using the SoySNP50K BeadChip. The predictive ability of six GP models were compared, and the impact of marker density and training population size on the predictive ability was investigated. Cross-prediction among environments was tested to determine the effectiveness of the prediction models. GP models had similar prediction accuracies for all experiments. Predictive ability did not improve significantly by using more than 5k SNPs, or by increasing the training population size (from 50% to 90% of the total of individuals). The GP model effectively predicted WM resistance across field and greenhouse experiments when each was used as either the training or validation population. The GP model was able to identify WM-resistant accessions in the USDA soybean germplasm collection that had previously been reported and were not included in the study panel. This study demonstrated the applicability of GP to identify useful genetic sources of WM resistance for soybean breeding. Further research will confirm the applicability of the proposed approach to other complex disease resistance traits and in other crops.</p>
dc.description.comments <p>This article is published as de Azevedo Peixoto L, Moellers TC, Zhang J, Lorenz AJ, Bhering LL, Beavis WD, et al. (2017) Leveraging genomic prediction to scan germplasm collection for crop improvement. PLoS ONE 12(6): e0179191. doi: <a href="http://dx.doi.org/10.1371" target="_blank">10.1371/journal.pone.0179191</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/agron_pubs/339/
dc.identifier.articleid 1340
dc.identifier.contextkey 11371219
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath agron_pubs/339
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/4686
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/agron_pubs/339/2017_Singh_LeveragingGenomic.pdf|||Fri Jan 14 23:40:05 UTC 2022
dc.source.uri 10.1371/journal.pone.0179191
dc.subject.disciplines Agricultural Science
dc.subject.disciplines Agronomy and Crop Sciences
dc.subject.disciplines Genetics and Genomics
dc.subject.disciplines Plant Breeding and Genetics
dc.title Leveraging genomic prediction to scan germplasm collection for crop improvement
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication cdeb4dae-b065-4dd9-9831-9aa5ca394e25
relation.isOrgUnitOfPublication fdd5c06c-bdbe-469c-a38e-51e664fece7a
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