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Agronomy
Special Issues
Multi-omic Integration for Applied Prediction Breeding
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Multi-omic Integration for Applied Prediction Breeding

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Crop Breeding and Genetics".

Deadline for manuscript submissions: closed (11 March 2025) | Viewed by 4104

Special Issue Editors

Prof. Dr. Moysés Nascimento

E-Mail Website
Guest Editor
Department of Statistics, Federal University of Viçosa, Viçosa 36570-260, MG, Brazil
Interests: genomic selection; association analysis; applied statistics; plant breeding; multivariate statistics; regression models; computational intelligence; adaptability and stability; mixed model
Special Issues, Collections and Topics in MDPI journals
Dr. Diego Jarquin

E-Mail Website
Guest Editor
Agronomy Department, University of Florida, Gainesville, FL 32611, USA
Interests: statistical learning methods; computational intelligence; plant breeding; artificial intelligence; multi-omics
Dr. Camila Ferreira Azevedo

E-Mail Website
Guest Editor
Department of Statistics, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
Interests: statistical learning methods; computational intelligence; plant breeding; artificial intelligence; multi-omics

Special Issue Information

Dear Colleagues,

One of the primary goals of humanity is food security. However, environmental variations, limitations of arable land, reduced water availability, and the growing population require research to support plant breeding implementations. To achieve this goal, the integration of large multi-omics datasets could be seen as a good strategy to circumvent these challenges. New approaches based on artificial intelligence methods and traditional parametric models can help introduce quantitative genetic data and biostatistical concepts, among other layers of information, to explain trait performance. More specifically, these new developments aim to find new ways to drive genetic improvement and gain biological insights by designing and optimizing selection methods for plant breeding. These methods leverage information from multiple facets of plant biology (genomics, transcriptomics, proteomics, metabolomics, ionomics, and high-throughput phenotyping), providing novel solutions to unraveling the biological basis of complex traits for plant breeding programs. In this Special Issue, we aim to exchange knowledge on any aspect related to multi-omic integration for applied prediction breeding in any crops. It will contain reviews, regular research papers, communications and short notes, and there is no restriction on the maximum length of papers.

Prof. Dr. Moysés Nascimento
Dr. Diego Jarquin
Dr. Camila Ferreira Azevedo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • crop improvement
  • artificial intelligence
  • machine learning
  • mixed models

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Published Papers (2 papers)

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Research

18 pages, 3943 KiB  
Article
Comparison of Single-Trait and Multi-Trait GBLUP Models for Genomic Prediction in Red Clover
by Johanna Osterman, Lucia Gutiérrez, Linda Öhlund, Rodomiro Ortiz, Cecilia Hammenhag, David Parsons and Mulatu Geleta
Agronomy 2024, 14(10), 2445; https://doi.org/10.3390/agronomy14102445 - 21 Oct 2024
Cited by 1 | Viewed by 2317
Abstract
Red clover (Trifolium pratense) is a perennial forage legume wildly used in temperate regions, including northern Europe. Its breeders are under increasing pressure to obtain rapid genetic gains to meet the high demand for improved forage yield and quality. One solution [...] Read more.
Red clover (Trifolium pratense) is a perennial forage legume wildly used in temperate regions, including northern Europe. Its breeders are under increasing pressure to obtain rapid genetic gains to meet the high demand for improved forage yield and quality. One solution to increase genetic gain by reducing time and increasing accuracy is genomic selection. Thus, efficient genomic prediction (GP) models need to be developed, which are unbiased to traits and harvest time points. This study aimed to develop and evaluate single-trait (ST) and multi-trait (MT) models that simultaneously target more than one trait or cut. The target traits were dry matter yield, crude protein content, net energy for lactation, and neutral detergent fiber. The MT models either combined dry matter yield with one forage quality trait, all traits at one cut, or one trait across all cuts. The results show an increase with MT models where the traits had a genetic correlation of 0.5 or above. This study indicates that non-additive genetic effects have significant but varying effects on the predictive ability and reliability of the models. The key conclusion of this study was that these non-additive genetic effects could be better described by incorporating genetically correlated traits or cuts. Full article
(This article belongs to the Special Issue Multi-omic Integration for Applied Prediction Breeding)
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15 pages, 2796 KiB  
Article
Multivariate Adaptive Regression Splines Enhance Genomic Prediction of Non-Additive Traits
by Maurício de Oliveira Celeri, Weverton Gomes da Costa, Ana Carolina Campana Nascimento, Camila Ferreira Azevedo, Cosme Damião Cruz, Vitor Seiti Sagae and Moysés Nascimento
Agronomy 2024, 14(10), 2234; https://doi.org/10.3390/agronomy14102234 - 27 Sep 2024
Cited by 1 | Viewed by 989
Abstract
The present work used Multivariate Adaptive Regression Splines (MARS) for genomic prediction and to study the non-additive fraction present in a trait. To this end, 12 scenarios for an F2 population were simulated by combining three levels of broad-sense heritability (h2 [...] Read more.
The present work used Multivariate Adaptive Regression Splines (MARS) for genomic prediction and to study the non-additive fraction present in a trait. To this end, 12 scenarios for an F2 population were simulated by combining three levels of broad-sense heritability (h2 = 0.3, 0.5, and 0.8) and four amounts of QTLs controlling the trait (8, 40, 80, and 120). All scenarios included non-additive effects due to dominance and additive–additive epistasis. The individuals’ genomic estimated breeding values (GEBV) were predicted via MARS and compared against the GBLUP method, whose models were additive, additive–dominant, and additive–epistatic. In addition, a linkage disequilibrium study between markers and QTL was performed. Linkage maps highlighted the QTL and molecular markers identified by the methodologies under study. MARS showed superior results to the GBLUP models regarding predictive ability for traits controlled by 8 loci, and results were similar for traits controlled by more than 40 loci. Moreover, the use of MARS, together with a linkage disequilibrium study of the trait, can help to elucidate the traits’ genetic architecture. Therefore, MARS showed potential to improve genomic prediction, especially for oligogenic traits or traits controlled by approximately 40 QTLs, while enabling the elucidation of the genetic architecture of traits. Full article
(This article belongs to the Special Issue Multi-omic Integration for Applied Prediction Breeding)
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