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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 (20 March 2024) | Viewed by 3380

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Dr. Andrés J. Cortés

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AGROSAVIA (Corporación Colombiana de Investigación Agropecuaria), La Selva, Colombia
Interests: plant genomics; plant breeding; genetics of adaptation; population genomics
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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

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Keywords

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

Published Papers (3 papers)

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Research

28 pages, 3117 KiB

Open AccessArticle

Novel Alleles from Cicer reticulatum L. for Genetic Improvement of Cultivated Chickpeas Identified through Genome Wide Association Analysis

by Mohammad Waliur Rahman, Amit A. Deokar, Donna Lindsay and Bunyamin Tar’an

Int. J. Mol. Sci. 2024, 25(1), 648; https://doi.org/10.3390/ijms25010648 - 04 Jan 2024

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Abstract

The availability of wild chickpea (Cicer reticulatum L.) accessions has the potential to be used for the improvement of important traits in cultivated chickpeas. The main objectives of this study were to evaluate the phenotypic and genetic variations of chickpea progeny derived from interspecific crosses between C. arietinum and C. reticulatum, and to establish the association between single nucleotide polymorphism (SNP) markers and a series of important agronomic traits in chickpea. A total of 486 lines derived from interspecific crosses between C. arietinum (CDC Leader) and 20 accessions of C. reticulatum were evaluated at different locations in Saskatchewan, Canada in 2017 and 2018. Significant variations were observed for seed weight per plant, number of seeds per plant, thousand seed weight, and plant biomass. Path coefficient analysis showed significant positive direct effects of the number of seeds per plant, thousand seed weight, and biomass on the total seed weight. Cluster analysis based on the agronomic traits generated six groups that allowed the identification of potential heterotic groups within the interspecific lines for yield improvement and resistance to ascochyta blight disease. Genotyping of the 381 interspecific lines using a modified genotyping by sequencing (tGBS) generated a total of 14,591 SNPs. Neighbour-joining cluster analysis using the SNP data grouped the lines into 20 clusters. The genome wide association analysis identified 51 SNPs that had significant associations with different traits. Several candidate genes associated with early flowering and yield components were identified. The candidate genes and the significant SNP markers associated with different traits have a potential to aid the trait introgression in the breeding program. Full article

(This article belongs to the Special Issue Plant Population Genomics)

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29 pages, 2455 KiB

Open AccessArticle

QTLs and Candidate Loci Associated with Drought Tolerance Traits of Kaybonnet x ZHE733 Recombinant Inbred Lines Rice Population

by Yheni Dwiningsih, Julie Thomas, Anuj Kumar, Chirag Gupta, Navdeep Gill, Charles Ruiz, Jawaher Alkahtani, Niranjan Baisakh and Andy Pereira

Int. J. Mol. Sci. 2023, 24(20), 15167; https://doi.org/10.3390/ijms242015167 - 14 Oct 2023

Viewed by 1055

Abstract

Rice is the most important staple crop for the sustenance of the world’s population, and drought is a major factor limiting rice production. Quantitative trait locus (QTL) analysis of drought-resistance-related traits was conducted on a recombinant inbred line (RIL) population derived from the self-fed progeny of a cross between the drought-resistant tropical japonica U.S. adapted cultivar Kaybonnet and the drought-sensitive indica cultivar ZHE733. K/Z RIL population of 198 lines was screened in the field at Fayetteville (AR) for three consecutive years under controlled drought stress (DS) and well-watered (WW) treatment during the reproductive stage. The effects of DS were quantified by measuring morphological traits, grain yield components, and root architectural traits. A QTL analysis using a set of 4133 single nucleotide polymorphism (SNP) markers and the QTL IciMapping identified 41 QTLs and 184 candidate genes for drought-related traits within the DR-QTL regions. RT-qPCR in parental lines was used to confirm the putative candidate genes. The comparison between the drought-resistant parent (Kaybonnet) and the drought-sensitive parent (ZHE733) under DS conditions revealed that the gene expression of 15 candidate DR genes with known annotations and two candidate DR genes with unknown annotations within the DR-QTL regions was up-regulated in the drought-resistant parent (Kaybonnet). The outcomes of this research provide essential information that can be utilized in developing drought-resistant rice cultivars that have higher productivity when DS conditions are prevalent. Full article

(This article belongs to the Special Issue Plant Population Genomics)

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19 pages, 947 KiB

Open AccessArticle

Multivariate Genomic Hybrid Prediction with Kernels and Parental Information

by Osval A. Montesinos-López, José Crossa, Carolina Saint Pierre, Guillermo Gerard, Marco Alberto Valenzo-Jiménez, Paolo Vitale, Patricia Edwigis Valladares-Cellis, Raymundo Buenrostro-Mariscal, Abelardo Montesinos-López and Leonardo Crespo-Herrera

Int. J. Mol. Sci. 2023, 24(18), 13799; https://doi.org/10.3390/ijms241813799 - 07 Sep 2023

Viewed by 916

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 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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