A deep learning approach to population structure inference in inbred lines of maize

López-Cortés, Xaviera A.; Matamala, Felipe; Maldonado, Carlos; Mora-Poblete, Freddy; Scapim, Carlos A.

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dc.contributor.author	López-Cortés, Xaviera A.
dc.contributor.author	Matamala, Felipe
dc.contributor.author	Maldonado, Carlos
dc.contributor.author	Mora-Poblete, Freddy
dc.contributor.author	Scapim, Carlos A.
dc.date.accessioned	2020-12-28T19:31:53Z
dc.date.available	2020-12-28T19:31:53Z
dc.date.issued	2020
dc.identifier.uri	http://repositorio.ucm.cl/handle/ucm/3378
dc.description.abstract	Analysis of population genetic variation and structure is a common practice for genome-wide studies, including association mapping, ecology, and evolution studies in several crop species. In this study, machine learning (ML) clustering methods, K-means (KM), and hierarchical clustering (HC), in combination with non-linear and linear dimensionality reduction techniques, deep autoencoder (DeepAE) and principal component analysis (PCA), were used to infer population structure and individual assignment of maize inbred lines, i.e., dent field corn (n = 97) and popcorn (n = 86). The results revealed that the HC method in combination with DeepAE-based data preprocessing (DeepAE-HC) was the most effective method to assign individuals to clusters (with 96% of correct individual assignments), whereas DeepAE-KM, PCA-HC, and PCA-KM were assigned correctly 92, 89, and 81% of the lines, respectively. These findings were consistent with both Silhouette Coefficient (SC) and Davies–Bouldin validation indexes. Notably, DeepAE-HC also had better accuracy than the Bayesian clustering method implemented in InStruct. The results of this study showed that deep learning (DL)-based dimensional reduction combined with ML clustering methods is a useful tool to determine genetically differentiated groups and to assign individuals into subpopulations in genome-wide studies without having to consider previous genetic assumptions.	es_CL
dc.language.iso	en	es_CL
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 Chile	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/cl/	*
dc.source	Frontiers in genetics, 11, 543459	es_CL
dc.subject	Deep learning	es_CL
dc.subject	Genome-wide studies	es_CL
dc.subject	Machine learning	es_CL
dc.subject	Single-nucleotide polymorphisms	es_CL
dc.subject	Dimensionality reduction	es_CL
dc.title	A deep learning approach to population structure inference in inbred lines of maize	es_CL
dc.type	Article	es_CL
dc.ucm.facultad	Facultad de Ciencias de la Ingeniería	es_CL
dc.ucm.indexacion	Scopus	es_CL
dc.ucm.indexacion	Isi	es_CL
dc.ucm.doi	doi.org/10.3389/fgene.2020.543459	es_CL