Diallel analysis and genetic differentiation of tropical and temperate maize inbred lines

Kulka, Vania Portes; Silva, Tereza Aparecida da; Contreras‐Soto, Rodrigo Iván; Maldonado, Carlos; Mora, Freddy; Scapim, Carlos Alberto

doi:10.1590/1984-70332018v18n1a5

Cited by 14 publications

(13 citation statements)

References 27 publications

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“…In addition, corn has many industrial applications such as the production of ethanol, oil and high-amylose starch. Consequently, several breeding programs have been undertaken to achieve genetic gains in several traits of interest (e.g., Kulka et al 2018). Studies of diversity and genetic structure allow plant breeders to investigate the population variability and thus provide basic information at the molecular level (Ballesta et al 2015, Contreras-Soto et al 2017); a key aspect in maize breeding programs (Saavedra et al 2013, Amaral et al 2016.…”

Section: Introductionmentioning

confidence: 99%

SSR-based genetic analysis of sweet corn inbred lines using artificial neural networks

Ferreira

Scapim

Maldonado

et al. 2018

Crop Breed. Appl. Biotechnol.

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Section: Introductionmentioning

confidence: 99%

SSR-based genetic analysis of sweet corn inbred lines using artificial neural networks

Ferreira

Scapim

Maldonado

et al. 2018

Crop Breed. Appl. Biotechnol.

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“…Artificial neural network models have been used before in order to genetically evaluate crop germplasm collections, such as maize (Ferreira et al, 2018;Kulka et al, 2018) and grapevine (Costa et al, 2020), in which the clustering analysis was based upon competitive learning-based neural networks. This alternative method was able to analyze population structure based on not only bi-allelic but also multi-allelic data (Peña-Malavera et al, 2014;Ferreira et al, 2018) and has been demonstrated to be computationally faster than MCMC methods (Nikolic et al, 2009) and does not consider the assumption of Hardy-Weinberg equilibrium in the population being studied (Ferreira et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Approach to Population Structure Inference in Inbred Lines of Maize

et al. 2020

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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.

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“…However, some NS lines were grouped with SS lines and vice versa, and some tropical lines were found with temperate lines. The mixture of some lines may be due to some common genetic background, or the ambiguous, or unknown pedigree of some ExPVP lines (Kulka et al., 2018; Nelson et al., 2008).…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have investigated the heterotic relationship between tropical and temperate lines using molecular data and estimate of the combining ability (Fan et al., 2008; Kulka et al., 2018; Mickelson, Cordova, Pixley, & Bjarnason, 2001; Richard, Osiru, Mwala, & Lubberstedt, 2016; Wu et al., 2019). These studies have always revealed the distinction between temperate and tropical germplasms but the heterotic pattern that might exit among the different heterotic groups is rarely determined.…”

Section: Discussionmentioning

confidence: 99%

Exploring the potential usefulness of U.S. maize expired Plant Variety Protection Act lines for maize breeding in sub‐Saharan Africa

et al. 2020

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Maize (Zea mays L.) inbred lines with expired Plant Variety Protection Act (ExPVP) certificates are publicly available and potentially represent a new germplasm resource for many public and private breeding programs. The use of these inbred lines for maize breeding in sub‐Saharan African (SSA) was little investigated. Hence, this study was conducted to explore their potential usefulness. Ninety‐six ExPVP lines, two temperate public lines, and 14 tropical lines were evaluated in five different trials from 2016 to 2018 in Burkina Faso to determine their phenotypic characteristics; resistance to drought, heat, and three diseases; and to identify elite ExPVP lines for local maize breeding programs. Cluster analysis based on phenotypic traits highlighted a clear distinction between the different groups (nonstiff stalk [NS] vs. Iowa Stiff Stalk Synthetic [SS] heterotic groups and temperate vs. tropical germplasms). The screening showed that 3, 28, and 68% of ExPVP lines were resistant, tolerant, and susceptible to maize leaf blight disease, respectively. However, the lines were either tolerant or resistant to Curvularia leaf spot and maize streak virus. Approximately 30% of ExPVP lines presented a tolerance to the three maize diseases tested and 8% of the lines were tolerant to drought. Heat stress was severe to both ExPVP and tropical lines. Yield potential of ExPVP lines varied from 1.68 to 2635.63 kg ha−1 vs. 798.76 to 1707.56 kg ha−1 for tropical lines. The ExPVP lines identified showing tolerance to stresses and a high‐yield performance can be integrated in inbred‐hybrid development program.

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Diallel analysis and genetic differentiation of tropical and temperate maize inbred lines

Cited by 14 publications

References 27 publications

SSR-based genetic analysis of sweet corn inbred lines using artificial neural networks

SSR-based genetic analysis of sweet corn inbred lines using artificial neural networks

A Deep Learning Approach to Population Structure Inference in Inbred Lines of Maize

Exploring the potential usefulness of U.S. maize expired Plant Variety Protection Act lines for maize breeding in sub‐Saharan Africa

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