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Comparative assessment of genetic diversity matrices and clustering methods in white Guinea yam (Dioscorea rotundata) based on morphological and molecular markers
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Date
2020Author
Darkwa, K.
Agre, P.
Olasanmi, B.
Iseki, K.
Matsumoto, R.
Powell, A.
Bauchet, G.,
De Koeyer, D.
Muranaka, S.
Adebola, P.
Asiedu, R.
Terauchi, R.
Asfaw, A.
Type
Review Status
Peer ReviewTarget Audience
Scientists
Metadata
Show full item recordAbstract/Description
Understanding the diversity and genetic relationships among and within crop germplasm is invaluable for genetic improvement. This study assessed genetic diversity in a panel of 173 D. rotundata accessions using joint analysis for 23 morphological traits and 136,429 SNP markers from the whole-genome resequencing platform. Various diversity matrices and clustering methods were evaluated for a comprehensive characterization of genetic diversity in white Guinea yam from West Africa at phenotypic and molecular levels. The translation of the different diversity matrices from the phenotypic and genomic information into distinct groups varied with the hierarchal clustering methods used. Gower distance matrix based on phenotypic data and identity by state (IBS) distance matrix based on SNP data with the UPGMA clustering method found the best fit to dissect the genetic relationship in current set materials. However, the grouping pattern was inconsistent (r = − 0.05) between the morphological and molecular distance matrices due to the non-overlapping information between the two data types. Joint analysis for the phenotypic and molecular information maximized a comprehensive estimate of the actual diversity in the evaluated materials. The results from our study provide valuable insights for measuring quantitative genetic variability for breeding and genetic studies in yam and other root and tuber crops.
https://dx.doi.org/10.1038/s41598-020-69925-9
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Permanent link to this item
https://hdl.handle.net/20.500.12478/6987IITA Authors ORCID
Paterne AGREhttps://orcid.org/0000-0003-1231-2530
Ryo Matsumotohttps://orcid.org/0000-0002-0106-6728
David De Koeyerhttps://orcid.org/0000-0001-8064-6538
Patrick Adebolahttps://orcid.org/0000-0002-5155-6194
Robert Asieduhttps://orcid.org/0000-0001-8943-2376
Asrat Asfawhttps://orcid.org/0000-0002-4859-0631
Digital Object Identifier (DOI)
https://dx.doi.org/10.1038/s41598-020-69925-9