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Special Issue "Plant Population Genomics"

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Plant Sciences".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 641

Special Issue Editor

Special Issue Information

Dear Colleagues,

The field of plant population genomics is rapidly evolving in terms of its conceptual framework, its methodologies, and its applications, mostly due to advances in the sequencing technologies and computing capacity. For instance, the conceptual framework and practical tools have shifted from classical quantitative genetics and QTL genetic mapping to latest-generation GWAS models, genomic prediction, machine learning forecasting, and the omnigenic model. Similarly, this base knowledge has led to ecological genomic applications transcending the fields of speciation and adaptation into the frameworks of genomic islands of divergence, conservation genomics, genetic-assisted gene flow, and genomics for restoration. Plant breeding has also been boosted by developments in the population genomics field by shifting from Mendelian marker-assisted selection and marker-assisted backcrossing into genomic-enabled prediction, genomic-assisted introgression breeding, and enviromics. Therefore, this Special Issue aims to summarize, discuss, and recommend historical and modern developments that will continue enabling plant population genomics, its foundations, methodologies, and uses.  

Dr. Andrés J. Cortés
Guest Editor

Manuscript Submission Information

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Keywords

  • plant population genomics
  • GWAS models
  • genomic prediction
  • machine learning forecasting
  • the omnigenic model

Published Papers (1 paper)

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Research

Article
Multivariate Genomic Hybrid Prediction with Kernels and Parental Information
Int. J. Mol. Sci. 2023, 24(18), 13799; https://doi.org/10.3390/ijms241813799 - 07 Sep 2023
Viewed by 288
Abstract
Genomic selection (GS) plays a pivotal role in hybrid prediction. It can enhance the selection of parental lines, accurately predict hybrid performance, and harness hybrid vigor. Likewise, it can optimize breeding strategies by reducing field trial requirements, expediting hybrid development, facilitating targeted trait [...] Read more.
Genomic selection (GS) plays a pivotal role in hybrid prediction. It can enhance the selection of parental lines, accurately predict hybrid performance, and harness hybrid vigor. Likewise, it can optimize breeding strategies by reducing field trial requirements, expediting hybrid development, facilitating targeted trait improvement, and enhancing adaptability to diverse environments. Leveraging genomic information empowers breeders to make informed decisions and significantly improve the efficiency and success rate of hybrid breeding programs. In order to improve the genomic ability performance, we explored the incorporation of parental phenotypic information as covariates under a multi-trait framework. Approach 1, referred to as Pmean, directly utilized parental phenotypic information without any preprocessing. While approach 2, denoted as BV, replaced the direct use of phenotypic values of both parents with their respective breeding values. While an improvement in prediction performance was observed in both approaches, with a minimum 4.24% reduction in the normalized root mean square error (NRMSE), the direct incorporation of parental phenotypic information in the Pmean approach slightly outperformed the BV approach. We also compared these two approaches using linear and nonlinear kernels, but no relevant gain was observed. Finally, our results increase empirical evidence confirming that the integration of parental phenotypic information helps increase the prediction performance of hybrids. Full article
(This article belongs to the Special Issue Plant Population Genomics)
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