Comparison of algorithms to infer genetic population structure from unlinked molecular markers

Peña-Malavera Andrea, Bruno Cecilia, Fernandez Elmer and Balzarini Monica ()
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Peña-Malavera Andrea: Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba and CONICET (National Council of Scientific and Technological Research), cc 509, 5000 Córdoba, Argentina
Bruno Cecilia: Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba and CONICET (National Council of Scientific and Technological Research), cc 509, 5000 Córdoba, Argentina
Fernandez Elmer: Facultad de Ingeniería, Universidad Católica de Córdoba and CONICET, Camino Alta Gracia Km 10, Cordoba, Argentina
Balzarini Monica: Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba and CONICET (National Council of Scientific and Technological Research), cc 509, 5000 Córdoba, Argentina

Statistical Applications in Genetics and Molecular Biology, 2014, vol. 13, issue 4, 391-402

Abstract: Identifying population genetic structure (PGS) is crucial for breeding and conservation. Several clustering algorithms are available to identify the underlying PGS to be used with genetic data of maize genotypes. In this work, six methods to identify PGS from unlinked molecular marker data were compared using simulated and experimental data consisting of multilocus-biallelic genotypes. Datasets were delineated under different biological scenarios characterized by three levels of genetic divergence among populations (low, medium, and high FST) and two numbers of sub-populations (K=3 and K=5). The relative performance of hierarchical and non-hierarchical clustering, as well as model-based clustering (STRUCTURE) and clustering from neural networks (SOM-RP-Q). We use the clustering error rate of genotypes into discrete sub-populations as comparison criterion. In scenarios with great level of divergence among genotype groups all methods performed well. With moderate level of genetic divergence (FST=0.2), the algorithms SOM-RP-Q and STRUCTURE performed better than hierarchical and non-hierarchical clustering. In all simulated scenarios with low genetic divergence and in the experimental SNP maize panel (largely unlinked), SOM-RP-Q achieved the lowest clustering error rate. The SOM algorithm used here is more effective than other evaluated methods for sparse unlinked genetic data.

Keywords: cluster analysis; multilocus-biallelic genotypes; plant breeding; self-organizing maps (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1515/sagmb-2013-0006

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