Comparative assessment of genetic diversity matrices and clustering methods in white Guinea yam (Dioscorea rotundata) based on morphological and molecular markers.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
06 08 2020
06 08 2020
Historique:
received:
29
11
2019
accepted:
16
07
2020
entrez:
9
8
2020
pubmed:
9
8
2020
medline:
17
12
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32764649
doi: 10.1038/s41598-020-69925-9
pii: 10.1038/s41598-020-69925-9
pmc: PMC7413250
doi:
Substances chimiques
Genetic Markers
0
Types de publication
Comparative Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
13191Références
FAOSTAT. Food and Agriculture Organization of the United Nations Statistics Database, FAOSTAT. https://www.fao.org/faostat/en/#data/QC (2017).
Lebot, V. Tropical Root and Tuber Crops: cassava, Sweet Potato, Yams and Aroids (Crop Production Science in Horticulture, Wallingford, 2009).
Tostain, S. et al. Genetic diversity analysis of yam cultivars (Dioscorea rotundata Poir.) in Benin using simple sequence repeat (SSR) markers. Plant Genet. Resour. C.5, 71–81 (2007).
Hassan, N. et al. Identification and quantitative analyses of medicinal plants in Shahgram valley, district Swat, Pakistan. Acta Ecol. Sinica40, 44–51. https://doi.org/10.1016/j.chnaes.2019.05.002 (2020).
doi: 10.1016/j.chnaes.2019.05.002
Mustafa, A., Ahmad, A., Tantray, A. H. & Parry, P. A. Ethnopharmacological potential and medicinal uses of miracle herb Dioscorea spp. J. Ayu. Her. Med.4, 79–85 (2018).
Obidiegwu, J. E. & Akpabio, E. M. The geography of yam cultivation in southern Nigeria: Exploring its social meanings and cultural functions. J. Ethn. Foods4, 28–35 (2017).
Bhandari, H. R., Bhanu, A. N., Srivastava, K., Singh, M. N. & Shreya, I. Assessment of genetic diversity in crop plants—An overview. Adv. Plants Agric. Res.7, 279–286 (2017).
Norman, P. E., Tongoona, P. & Shanahan, P. E. Diversity of the morphological traits of yam (Dioscorea spp.) genotypes from Sierra Leone. J. Appl. Biosci.45, 3045–3058 (2011).
Loko, Y. L., Adjatin, A., Dansi, A., Vodouhè, R. & Sanni, A. Participatory evaluation of Guinea yam (Dioscorea cayenensis Lam.–D. rotundata Poir complex) landraces from Benin and agro-morphological characterization of cultivars tolerant to drought, high soil moisture and chips storage insects. Genet. Resour. Crop. Evol.62, 1181–1192 (2015).
Dansi, A. et al. Using isozyme polymorphism to assess genetic variation within cultivated yams (Dioscorea cayenensis/Dioscorea rotundata complex) of the Republic of Benin. Genet. Resour. Crop. Evol.47, 371–383 (2000).
Efisue, A. A. Genetic diversity Study of Dioscoreas using morphological traits and isozyme markers analyses. Niger. J. Biotechnol.30, 7–17 (2015).
Mignouna, H. D., Abang, M. M. & Fagbemi, S. A. A comparative assessment of molecular marker assays (AFLP, RAPD and SSR) for white yam (Dioscorea rotundata) germplasm characterization. Ann. Appl. Biol.142, 269–276 (2003).
Loko, Y. L. et al. Genetic diversity and relationship of Guinea yam (Dioscorea cayenensis Lam.–D. rotundata Poir complex) germplasm in Benin (West Africa) using microsatellite markers. Genet. Resour. Crop. Evol.64, 1205–1219 (2017).
Arnau, G., Némorin, A., Maledon, E. & Abraham, K. Revision of ploidy status of Dioscorea alata L. (Dioscoreaceae) by cytogenetic and microsatellite segregation analysis. Theor. Appl. Genet.118, 1239–1249 (2009).
pubmed: 19253018
Dansi, A. et al. Identification of some Benin Republic’s Guinea yam (Dioscorea cayenensis/Dioscorea rotundata complex) cultivars using randomly amplified polymorphic DNA. Genet. Resour. Crop. Evol.47, 619–625 (2000).
Girma, G. et al. Next-generation sequencing based genotyping, cytometry and phenotyping for understanding diversity and evolution of guinea yams. Theor. Appl. Genet.127, 1783–1794 (2014).
pubmed: 24981608
Mulualem, T., Mekbib, F., Shimelis, H., Gebre, E. & Amelework, B. Genetic diversity of yam (Dioscorea spp.) landrace collections from Ethiopia using simple sequence repeat markers. Aust. J. Crop Sci.12, 1223–1230 (2018).
Andrade, E. K. et al. Genetic dissimilarity among sweet potato genotypes using morphological and molecular descriptors. Acta Sci. Agron.39, 447–455 (2017).
Alves, R. M., de Sousa, S. C. R., de Albuquerque, P. S. B. & dos Santos, V. S. Phenotypic and genotypic characterization and compatibility among genotypes to select elite clones of cupuassu. Acta Amaz.47, 175–184 (2017).
Sartie, A., Asiedu, R. & Franco, J. Genetic and phenotypic diversity in a germplasm working collection of cultivated tropical yams (Dioscorea spp.). Genet. Resour. Crop Evol.59, 1753–1765 (2012).
Cortese, L. M., Honig, J., Miller, C. & Bonos, S. A. Genetic diversity of twelve switchgrass populations using molecular and morphological markers. Bioenergy Res.3, 262–271 (2010).
Alves, A. A. et al. Joint analysis of phenotypic and molecular diversity provides new insights on the genetic variability of the Brazilian physic nut germplasm bank. Genet. Mol. Biol.36, 371–381 (2013).
pubmed: 24130445
pmcid: 3795176
Kosman, E. & Leonard, K. J. Similarity coefficients for molecular markers in studies of genetic relationships between individuals for haploid, diploid, and polyploid species. Mol. Ecol.14, 415–424 (2005).
pubmed: 15660934
Reif, J. C., Melchinger, A. E. & Frisch, M. Genetical and mathematical properties of similarity and dissimilarity coefficients applied in plant breeding and seed bank management. Crop Sci.45, 1–7 (2005).
Padilla, G., Cartea, M. E. & Ordás, A. Comparison of several clustering methods in grouping kale landraces. J. Am. Soc. Hortic. Sci.132, 387–395 (2007).
Mohammadi, S. A. & Prasanna, B. M. Review and interpretation analysis of genetic diversity in crop plants—Salient statistical tools. Crop Sci.43, 1235–1248 (2003).
Meyer, A. D. S., Garcia, A. A. F., Souza, A. P. D. & Souza, C. L. D. Jr. Comparison of similarity coefficients used for cluster analysis with dominant markers in maize (Zea mays L.). Genet. Mol. Biol.27, 83–91 (2004).
Sokal, R. R. & Rohlf, F. J. The comparison of dendrograms by objective methods. Taxon11, 33–40 (1962).
Saraçli, S., Doğan, N. & Doğan, İ. Comparison of hierarchical cluster analysis methods by cophenetic correlation. J. Inequal. Appl.203, 1–8 (2013).
Teodoro, P. E. et al. Comparison of clustering methods for study of genetic dissimilarity in soybean genotypes. Afr. J. Agric. Res.10, 1331–1337 (2015).
Balestre, M., Von Pinho, R. G., Souza, J. C. & Lima, J. L. Comparison of maize similarity and dissimilarity genetic coefficients based on microsatellite markers. Genet. Mol. Res.7, 695–705 (2008).
pubmed: 18752197
Krzanowski, W. J. Principles of Multivariate Analysis (Oxford University Press, New York, 2000).
Hussain, K., Nisar, M. F., Nawaz, K., Majeed, A. & Bhatti, K. H. Morphological traits vs Genetic diversity: Reliable basis for sugarcane varieties identification. BIOL EJ. Life Sci.1, 41–43 (2010).
Sujii, P. S. et al. Morphological and molecular characteristics do not confirm popular classification of the Brazil nut tree in Acre, Brazil. Genet. Mol. Res.12, 4018–4027 (2013).
pubmed: 24089091
Zannou, A., Struik, P., Richards, P. & Zoundjihékpon, J. Yam (Dioscorea spp.) responses to the environmental variability in the Guinea Sudan zone of Benin. Afr. J. Agric. Res.10, 4913–4925 (2015).
Nadeem, M. A. DNA molecular markers in plant breeding: Current status and recent advancements in genomic selection and genome editing. Biotechnol. Biotechnol. Equip.32, 261–285 (2018).
Scarcelli, N. et al. Yam genomics supports West Africa as a major cradle of crop domestication. Sci. Adv.5, 1–7 (2019).
Feldberg, K., Váňa, J., Schulze, C., Bombosch, A. & Heinrichs, J. Morphologically similar but genetically distinct: On the differentiation of Syzygiella concreta and S. perfoliata (Adelanthaceae subfam. Jamesonielloideae). Bryologist.114, 686–695 (2011).
Geleta, N., Labuschagne, M. T. & Viljoen, C. D. Genetic diversity analysis in sorghum germplasm as estimated by AFLP, SSR and morpho-agronomical markers. Biodivers. Conserv.15, 3251–3265 (2006).
da Silva, M. J. et al. Phenotypic and molecular characterization of sweet sorghum accessions for bioenergy production. PLoS ONE12, e0183504 (2017).
pubmed: 28817696
pmcid: 5560702
RoldaÂn-Ruiz, I., Dendauw, J., Van Bockstaele, E., Depicker, A. & De Loose, M. AFLP markers reveal high polymorphic rates in ryegrasses (Lolium spp.). Mol. Breed.6, 125–134 (2000).
Hartings, H. Assessment of genetic diversity and relationships among maize (Zea mays L.) Italian landraces by morphological traits and AFLP profiling. Theor. Appl. Genet.117, 831–842 (2008).
pubmed: 18584146
Soriano, J. M. et al. Genetic structure of modern durum wheat cultivars and mediterranean landraces matches with their agronomic performance. PLoS ONE11, e0160983 (2016).
pubmed: 27513751
pmcid: 4981446
Najaphy, A., Parchin, R. A. & Farshadfar, E. Comparison of phenotypic and molecular characterizations of some important wheat cultivars and advanced breeding lines. Aust. J. Crop. Sci.6, 326–332 (2012).
Becelaere, G. V., Lubbers, E. L., Paterson, A. H. & Chee, P. W. Pedigree-vs DNA marker-based genetic similarity estimates in Cotton. Crop. Sci.45, 2281–2287 (2005).
Asfaw, A. Standard Operating Protocol for Yam Variety Performance Evaluation Trial (IITA, Ibadan, 2016).
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods12, 357 (2015).
pubmed: 25751142
pmcid: 4655817
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics25, 2078–2079 (2009).
pubmed: 19505943
pmcid: 2723002
McKenna, A. et al. The genome analysis toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res.20, 1297–1303 (2010).
pubmed: 20644199
pmcid: 2928508
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics27(15), 2156–2158. https://doi.org/10.1093/bioinformatics/btr330 (2011).
doi: 10.1093/bioinformatics/btr330
pubmed: 21653522
pmcid: 21653522
Purcell, S. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet.81, 559–575 (2007).
pubmed: 17701901
pmcid: 1950838
SAS. Statistical Analysis System Institute, SAS/STATS Users Guide (SAS Institute Inc., Cary, 2012).
Lê, S., Josse, J. & Husson, F. FactoMineR: An R package for multivariate analysis. J. Stat. Softw.25, 1–8 (2008).
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K. Cluster: cluster analysis basics and extensions. R package version 2.1.0. (2019).
Mahalanobis, P. C. On tests and measures of group divergence. J. Asiat. Soc. Bengal.26, 541–588 (1930).
D’Orazio, M. StatMatch: Statistical matching or data fusion. R package version 1.3.0. https://CRAN.R-project.org/package=StatMatch (2019).
Wessel, J. & Schork, N. J. Generalized genomic distance-based regression methodology for multilocus association analysis. Am. J. Hum. Genet.79, 792–806 (2006).
pubmed: 17033957
pmcid: 1698575
Bradbury, P. J. et al. TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics23, 2633–2635 (2007).
Nei, M. Genetic distance between populations. Am. Nat.106, 283–292 (1972).
Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ.2, e281 (2014).
pubmed: 24688859
pmcid: 3961149
Drost, H.-G. Package ‘Philentropy’, similarity and distance quantification between probability functions. https://github.com/HajkD/philentropy . Accessed 9 Apr 2019.
Rogers, J.S. Measures of Genetic Similarity and Genetic Distance (Stud. Genet. VII, Univ. Texas Publ. 7213, 145–153 ,1972).
Dray, S. & Dufour, A. The ade4 package: Implementing the duality diagram for ecologists. J. Stat. Softw.22(4), 1–20. https://doi.org/10.18637/jss.v022.i04 (2007).
doi: 10.18637/jss.v022.i04
Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-6. https://github.com/vegandevs/vegan (2019).