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Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes

Haipeng Yu, View ORCID ProfileMalachy T. Campbell, View ORCID ProfileQi Zhang, View ORCID ProfileHarkamal Walia, View ORCID ProfileGota Morota
doi: https://doi.org/10.1101/435792
Haipeng Yu
1Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061
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Malachy T. Campbell
2Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583
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Qi Zhang
3Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68583
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Harkamal Walia
2Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583
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Gota Morota
1Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061
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Abstract

With the advent of high-throughput phenotyping platforms, plant breeders have a means to assess many traits for large breeding populations. However, understanding the genetic interdependencies among high-dimensional traits in a statistically robust manner remains a major challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can better inform breeding decisions and accelerate the genetic improvement of plants. The objective of this study was to leverage confirmatory factor analysis and graphical modeling to elucidate the genetic interdependencies among a diverse agronomic traits in rice. We used a Bayesian network to depict conditional dependencies among phenotypes, which can not be obtained by standard multitrait analysis. We utilized Bayesian confirmatory factor analysis which hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology, yield, and morphological salt response. This was followed by studying the genetics of each latent variable, which is also known as factor, using single nucleotide polymorphisms. Bayesian network structures involving the genomic component of six latent variables were established by fitting four algorithms (i.e., Hill Climbing, Tabu, Max-Min Hill Climbing, and General 2-Phase Restricted Maximization algorithms). Physiological components influenced the flowering time and grain morphology, and morphology and grain morphology influenced yield. In summary, we show the Bayesian network coupled with factor analysis can provide an effective approach to understand the interdependence patterns among phenotypes and to predict the potential influence of external interventions or selection related to target traits in the interrelated complex traits systems.

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Posted March 04, 2019.
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Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes
Haipeng Yu, Malachy T. Campbell, Qi Zhang, Harkamal Walia, Gota Morota
bioRxiv 435792; doi: https://doi.org/10.1101/435792
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Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes
Haipeng Yu, Malachy T. Campbell, Qi Zhang, Harkamal Walia, Gota Morota
bioRxiv 435792; doi: https://doi.org/10.1101/435792

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