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Research Article
Cecilia Bruno
Universidad Nacional de Cordoba Facultad de Ciencias Agropecuarias
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3674-7128
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This work is licensed under a CC BY 4.0 License. Read Full License
A number of clustering algorithms are available to depict population genetic structure (PGS) with genomic data; however, there is no consensus on which methods are the best performing ones. We conducted a simulation study of three PGS scenarios with subpopulations k=2, 5 and 10, recreating several maize genomes as a model to: (i) compare three well-known clustering methods: UPGMA, k-means and, Bayesian method (BM), (ii) asses four internal validation indices: CH, Connectivity, Dunn and Silhouette, to determine the reliable number of groups defining a PGS, and (iii) estimate the misclassification rate for each validation index. Moreover, a publicly available maize dataset was used to illustrate the outcomes of our simulation. BM was the best method to classify individuals in all tested scenarios, without assignment errors. Conversely, UPGMA was the method with the highest misclassification rate. In scenarios with 5 and 10 subpopulations, CH and Connectivity indices had the maximum underestimation of group number for all cluster algorithms. Dunn and Silhouette indices showed the best performance with BM. Nevertheless, since Silhouette measures the degree of confidence in cluster assignment, and BM measures the probability of cluster membership, these results should be considered with caution. In this study we found that BM showed to be efficient to depict the PGS in both simulated and real maize datasets. This study offers a robust alternative to unveil the existing PGS, thereby facilitating population studies and breeding strategies in maize programs. Moreover, the present findings may have implications for other crop species.
Keywords
unsupervised learning, population genetic structure, multivariate technique, outcome misclassification, SNPs, maize
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CURRENT STATUS: Under Revision
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Posted 18 Jun, 2021
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Received 23 Jun, 2021
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This preprint is under consideration at Euphytica. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Cecilia Bruno
Universidad Nacional de Cordoba Facultad de Ciencias Agropecuarias
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3674-7128
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 18 Jun, 2021
No community comments so far
Editorial decision: Minor revisions
On 02 Aug, 2021
Reviews received
Received 23 Jun, 2021
Reviewers invited
Invitations sent on 14 Jun, 2021
Editor assigned
On 04 Jun, 2021
First submitted
On 03 Jun, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biomedical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
A number of clustering algorithms are available to depict population genetic structure (PGS) with genomic data; however, there is no consensus on which methods are the best performing ones. We conducted a simulation study of three PGS scenarios with subpopulations k=2, 5 and 10, recreating several maize genomes as a model to: (i) compare three well-known clustering methods: UPGMA, k-means and, Bayesian method (BM), (ii) asses four internal validation indices: CH, Connectivity, Dunn and Silhouette, to determine the reliable number of groups defining a PGS, and (iii) estimate the misclassification rate for each validation index. Moreover, a publicly available maize dataset was used to illustrate the outcomes of our simulation. BM was the best method to classify individuals in all tested scenarios, without assignment errors. Conversely, UPGMA was the method with the highest misclassification rate. In scenarios with 5 and 10 subpopulations, CH and Connectivity indices had the maximum underestimation of group number for all cluster algorithms. Dunn and Silhouette indices showed the best performance with BM. Nevertheless, since Silhouette measures the degree of confidence in cluster assignment, and BM measures the probability of cluster membership, these results should be considered with caution. In this study we found that BM showed to be efficient to depict the PGS in both simulated and real maize datasets. This study offers a robust alternative to unveil the existing PGS, thereby facilitating population studies and breeding strategies in maize programs. Moreover, the present findings may have implications for other crop species.
Figure 1
Figure 2
Figure 3
Figure 4
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