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Editorial

Molecular Genetics Enhances Plant Breeding

by
Andrés J. Cortés
1,*,† and
Hai Du
2,3,4,*
1
Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Km 7 vía Rionegro—Las Palmas, Rionegro 054048, Colombia
2
College of Agronomy and Biotechnology, Chongqing Engineering Research Center for Rapeseed, Southwest University, Chongqing 400000, China
3
Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, of Agronomy and Biotechnology, Southwest University, Chongqing 400000, China
4
Engineering Research Center, South Upland Agriculture, Ministry of Education, Chongqing 400000, China
*
Authors to whom correspondence should be addressed.
†
Current address: Facultad de Ciencias Agrarias—Departamento de Ciencias Forestales, Universidad Nacional de Colombia—Sede Medellín, Medellín 050034, Colombia.
Int. J. Mol. Sci. 2023, 24(12), 9977; https://doi.org/10.3390/ijms24129977
Submission received: 8 May 2023 / Accepted: 29 May 2023 / Published: 9 June 2023
(This article belongs to the Special Issue Molecular Genetics and Plant Breeding 2.0)
Versions Notes
Human-driven plant selection, a practice as ancient as agriculture itself, has laid the foundations of plant breeding and contemporary farming [1]. The principles of classical breeding still comprise the nucleus of modern crop science and agricultural production [2]. Recent unthinkable methodological achievements in molecular screening, genome sequencing, ‘omic’ technologies, trans-genesis, and computational power have advanced disciplinary boundaries in order to revolutionize the field of varietal development and its broader connections with related disciplines. These advances have opened up new trans-disciplinary arenas in which classical plant improvement intersects with genomics, molecular biology, biotechnology, and bioinformatics [3]. Yet, the factual potential of these interplays is often disregarded by prevailing atomized views and skepticism, both of which penalize the dawn of emerging properties and their ultimate deployment into framers’ fields [4]. Therefore, this second Special Issue of IJMS on “Molecular Genetics and Plant Breeding” exemplifies through 31 inter-disciplinary works (https://www.mdpi.com/journal/ijms/special_issues/gene_plant_breading_2nd, accessed on 6 June 2023) the ways in which disruptive molecular research opens up innovative pathways for the exploration, transformation, and utilization of crop biodiversity in order to improve essential breeding targets and replenish the genetic potential of the cultivated genepools in order to achieve greater yield and sustainability [5].

1. Marker-Guided Pre-Breeding

Natural genetic variation continues to constitute a major source of crop innovation, and its exploration remains a key milestone in molecular pre-breeding. This Special Issue provides some insights in this regard by presenting a series of molecular characterizations of diverse germplasm (Table 1). For instance, López-Hernández and Cortés [6] assessed the scale and determinants of somaclonal coding diversity in mint (Mentha spp.), introduced to the northern Andes, using RNA-seq on 29 clonally propagated accessions. The authors found a single genetic cluster for M. × piperita, and three clusters for M. spicata, suggesting two independent introductions of the latter.
Meanwhile, marker-assisted pre-breeding is underdoing a transformation from the method of classically screening crop genepools to the assessment of their associated antagonistic biotic agents. For example, Choi et al. [7] elucidated the molecular phylogenetic origin of seven strains of soybean mosaic virus (SMV) that had been sampled from 150 Glycine max L. accessions. The use of RNA-seq and 143 SMV available genomes enabled concluding that recombination and plant hosts drive the genetic diversity of SMV.
Table
Table 1. Compilation of 31 studies as part of this second Special Issue of IJMS on “Molecular Genetics and Plant Breeding”. Four of the contributions were modern literature reviews (first rows).
Table 1. Compilation of 31 studies as part of this second Special Issue of IJMS on “Molecular Genetics and Plant Breeding”. Four of the contributions were modern literature reviews (first rows).
SpeciesGoalSamplingGenotypingKey FindingReference
Reviews
Cruciferous crops and Plasmodiophora brassicae† Review clubroot control methods and breeding for resistant cultivarsCruciferous cropsResistance loci and R genesResistance loci offer feasible strategies for resistance breedingZhang et al. [8]
Various† Review functionality of MYB genes in plant rootsPlant rootsMYB genesMYB gene functionality spans responses from biotic to abiotic stressesChen et al. [9]
Various† Review applications of miR393 for plant development and stressesVariousmiR393 targeting TIR1 and AFB auxin receptorsmiR393 assists plant responses to biotic and abiotic stressesJiang et al. [10]
Various† Review the applications of AI in crop breedingVariousAI and “omics” toolsIntegration of AI into “omics” tools needed for crop-improvementKhan et al. [11]
Germplasm molecular characterization
Mint
(Mentha spp.)		º Assess somaclonal coding diversity in mint at the northern AndesA total of 29 clonally propagated mints from the northern Andes RNA-seqOne and three clusters found for M. × piperita and M. spicataLópez-Hernández and Cortés [6]
Soybean mosaic virus (SMV)º Elucidate the molecular phylogenetic origin of SMV strainsA total of 7 SMV from 150 different soybean germplasm RNA-seq and 143 SMV available genomesRecombination and plant hosts drive the genetic diversity of SMVChoi et al. [7]
Viromes of pepper
Capsicum annuum 		º Examine the viromes of 15 pepper cultivars through RNA-seqA total of 15 pepper (Capsicum annuum L.) cultivarsRNA-seq of pepper viromesFirst viromes in 15 major pepper cultivars through RNA-seqJo et al. [12]
Genetic mapping
Rice
(Oryza sativa)		‡ Infer the genomic bases (QTLs) of germination under cold conditions One hundred and twenty lines of the CNDH population778 SSR markersFour QTLs and 41 genes were recovered, and 25 were qRT-PCR testedKim et al. [13]
Sesame
(Sesamum indicum)		‡ Disclose the genomic basis (GWAS) for lignan lignin biosynthesis410 accessionsWGR:
5.38 M SNPs and 1.16 M InDels		SiNST1 is a target gene for the molecular breeding of lignans contentDossou et al. [14]
Soybean
(Glycine max)		‡ Reveal the genomic architecture (QTLs) for drought tolerance A total of 160 RILs drought-tolerant ‘Jindou21’ × control ‘Zhongdou33’WGR:
923,420 SNPs		Five QTLs may be useful for molecular marker-assisted selectionOuyang et al. [15]
Soybean
(Glycine max)		‡ Validate existing QTLs for MAS of pod-shattering toleranceA total of 2 RIL families (154 and 153) + 102 varieties and elite linesQTLs identified via WGR and
3 KASP markers		Recovered accuracy: 90.9% for RILs and 100% for varieties and lines Seo et al. [16]
Wheat
(Triticum aestivum)		‡ Reveal genomic basis (GWAS) for six traits in eight environmentsIn total, 509 European varieties (277) and breeding lines from the STH (232)Total 13,499 DArTseq-derived SNP markersA GWAS for heterosis revealed 1261 markers with significant effectsMokrzycka et al. [17]
Wheat
(Triticum aestivum)		‡ Reveal the genomic architecture (QTLs) of spike layer uniformityA total of 300 RILsWheat 55 K SNP array: 53,063 SNPs and one KASP-markerQSlu.sicau-2B-2 is a target for MAS of spike-layer uniformityZhou et al. [18]
Gene functional validation with expression analysis, RNA-seq, and/or other “omic” techniques
Arabidopsis thaliana+ Validate the overexpression effects of PIF4 for HSRColumbia-0 (Col) ecotypeRNA-seq, qRT-PCR, and ChIP-qPCROverexpression of PIF4 boosts basal thermotoleranceYang et al. [19]
Arabidopsis thaliana+ Characterize functionally AtEAU1-AtEAU2 via RT-PCR Columbia-0 (Col) ecotypeTwo ABA-responsive EAR-motif-containing genesAtEAU1 and AtEAU2 are novel repressors of ABA responses Zhang et al. [20]
Cabbage
(Brassica oleracea)		+ Explore the molecular mechanisms that enable ME via HSAccession ‘01-88’DNA methylation and miRNADNA methylation and miRNA interference regulate HS-induced MEKong et al. [21]
Chinese cabbage
(Brassica rapa)		+ Map and validate BrGOLDEN, a dominant gene for carotenoid contentA total of 151 tri-crossed hybrids between ‘1900264’ and ‘1900262’Full-length BrGOLDEN sequence and qRT-PCRBrGOLDEN gives insights into the regulation of carotenoid synthesis Zhang et al. [22]
Corn (Zea mays) and rice (Oryza sativa)+ Validate PHO-encoding genes phylogenetically derivedMo17 inbred line of Z. maysPHO1- and PHO2- encoding genes + qRT-PCR ABA could up-regulate the expression of both PHO1 and PHO2Yu et al. [23]
Rapeseed
(Brassica napus)		+ Identify and qRT-PCR test nitrate transporter 2 (NRT2) genesCultivars ‘Zhongshuang11’ and ‘Darmor-bzh’A total of 31 and 19 NRT2 genes respectively tested in ‘Zhongshuang11’ and ‘Darmor-bzh’ Candidates provided for functional BnaZSNRT2s studiesDu et al. [24]
Rapeseed
(Brassica napus)		+ Validate ARI gene family functionality for agronomic traitsVarietal ‘ZS 11’qRT-PCR of
39 ARI genes 		Eight BnARI genes identified as candidates for key traitsWahid et al. [25]
Rapeseed
(Brassica napus)		+ Explore the functionality of Trihelix (TH) genes across Brassica speciesSix Brassica species from the Brassica database (BRAD)A total of 455 Trihelix (TH) genes available sequences and qRT-PCRBnaTH genes are involved in response to drought, cold, and heatZhang et al. [26]
Ribes nigrum+ Evaluate the molecular mechanisms related to BRV toleranceCultivar AldoniaiRNA-seqNovel transcripts for breeding BRV-tolerance are providedMažeikienė et al. [27]
Sacha Inchi (Plukenetia volubilis)+ Recover transcriptomic and proteomic profiles of seeds at two stagesThree-year-old Peruvian trees introduced to South ChinaRNA-seq and iTRAQThe study enables further research and utilization of RIPsLiu et al. [28]
Sorghum
(Sorghum bicolor)		+ Validate DOF genes functionality for starch biosynthesisCultivar ‘BTx623’qRT-PCR of
30 DOF genes		SbDOF21 acts as a key master regulator for starch biosynthesisXiao et al. [29]
Wheat
(Triticum aestivum)		+ Characterize a new Q allele (Qc5) for compact spikes and good bread Cultivar “Roblin”Q gene and its new allele Qc5Qc5 boosts bread-making quality by repressing SPRGuo et al. [30]
Trans-genesis targeting inter-specific standing variation for validation purposes
Pitaya
(Hylocereus monacanthus)		§ Map and qRT-PCR validate NAC candidate genesCultivar ‘Hongguan No. 1’A total of 64 NAC TFs (from HuNAC1 to HuNAC64 genes)HuNAC20 and 25 from Pitaya confer cold tolerance in Arabidopsis Hu et al. [31]
Woody resurrection plant (Myrothamnus flabellifolia)§ Overexpressed MfWRKY40 in Arabidopsis for abiotic stress roles M. flabellifolia and A. thaliana Columbia-0 (Col) ecotypeWRKY TFs (i.e., early dehydration-induced gene MfWRKY40)MfWRKY40 confers tolerance to drought and salinity in an Arabidopsis Huang et al. [32]
Trans-genesis targeting intra-specific standing variation
Rapeseed
(Brassica napus)		λ Assess homozygous transcript overexpression lines for γ-TMT Cultivar ‘Zhongshuang11’Gene γ-Tocopherol methyltransferase (γ-TMT)Feasible to genetic engineer γ-TMT for salt tolerance breedingGuo et al. [33]
Soybean
(Glycine max)		λ Silence GmBIR1 by BPMV-VIGS and explore the phenotypesCultivar ‘Williams 82’BAK1-interacting receptor-like kinase (GmBIR1)GmBIR1 is a negative regulator of immunity in soybeanLiu et al. [34]
Mutagenesis targeting intra-specific de novo mutations
Wheat
(Triticum aestivum)		Ψ Characterize mutant Q alleles for grain yield and grain protein Wheat cultivar ‘Shumai482’ and its S-Cp1-1 mutantTwo new Q alleles (Qs1 and Qc1-N8) obtained via mutagenesisNew Q alleles offer novel germplasm relevant to wheat breedingChen et al. [35]
Introgression breeding targeting inter-specific standing variation
Rice
(Oryza sativa)		θ Breed for heat tolerance in rice via introgressed sorghum ancestryRice restorer line ‘R21’ and recipient restorer line Jin ‘Hui 1’WGRThe sorghum introgression in line ‘R21’ confers mobile heat tolerance Zhang et al. [36]
Table is arranged top-down by research goals, species, and citations. Studies are clustered by goals as follows: † for reviews; º for germplasm molecular characterization; ‡ for genetic mapping; + for gene functional validation with expression analysis, RNA-seq and/or other omic techniques; § for trans-genesis targeting inter-specific standing variation for validation purposes; λ for trans-genesis targeting intra-specific standing variation; Ψ for mutagenesis targeting intra-specific de novo mutations; and θ for introgression breeding targeting inter-specific standing variation. Abbreviations are as follows. ABA: abscisic acid; AFB: auxin signaling F-box; ARI: Ariadne proteins of ring-between-ring (RBR) finger protein subfamilies; BPMV-VIGS: bean-pod-mottle-virus-induced gene silencing; BRV: mite-transmitted blackcurrant reversion virus; CNDH: Cheongcheong Nagdong double haploid; DOF: C2-C2 zinc finger domain, EAR: ethylene-responsive element-binding-factor-associated amphiphilic repression; AI: artificial intelligence; GWAS: genome-wide association study; HS: heat shock; HSR: heat stress response; KASP: Kompetitive allele-specific PCR; MAS: marker-assisted selection; ME: microspore embryogenesis; PIF4: phytochrome interacting factor 4; PHO: starch phosphorylase; QTL: quantitative trait loci; RILs: recombinant inbreed lines; RIP: ribosome-inactivating protein; SNP: single-nucleotide polymorphism; SPR: storage protein repressor; SSR: simple sequence repeat; STH: Plant Breeding Strzelce, TIR1: transport inhibitor response1; TF: transcription factors; WGR: whole-genome re-sequencing.
Similarly, Jo et al. [12] systematically examined the viromes of 15 pepper (Capsicum annuum L.) cultivars through RNA-seq, enabling a high-throughput identification of the principal viromes present in commercially important pepper genotypes. As a promising perspective, these initial characterizations of natural variation may unleash a further potential when coupled with explicit estimates of their genomic bases, i.e., the genetic determinants of trait variation.
As such, genetic mapping arises as a key step in the pre-breeding pipeline that enables the genetic architecture of key traits to be disclosed, and ultimately provides candidate markers and genes for further marker-guided applications, such as parental screening, marker-assisted selection [37], and gene editing [38]. In this regard, and as part of this Special Issue, Kim et al. [13] genotyped 120 double haploid rice lines with 778 SSRs in order to reconstruct the genetic basis of germination under cold conditions. The authors managed to retrieve 4 QTLs and 41 genes, 25 of which were validated via qRT-PCR. Similarly, Dossou et al. [14] characterized 410 sesame (S. indicum) accessions using WGR, and performed GWAS for lignan–lignin biosynthesis. The team found that SiNST1 is a major target gene for the molecular breeding of lignan content. In soybean, Ouyang et al. [15] and Seo et al. [16] performed WGR of RIL families with ca. 155 genotypes each. The teams, respectively, identified five QTLs for drought tolerance, and validated accuracies above 90% for existing QTLs for pod-shattering tolerance. Finally, Mokrzycka et al. [17] and Zhou et al. [18], respectively, genotyped 509 wheat accessions and 300 RILs with 13,499 DArT-SNPs and a 55 K SNP array. The authors discovered 1261 candidate markers for six agronomical traits, and the QSlu.sicau-2B-2 MAS target for spike layer uniformity. A common denominator across all genetic mapping exercises is the need for a further validation of the associated markers [39] before any downstream plant improvement application. After all, they are prone to displaying inflated rates of false positives due to multiple comparison testing, population stratification, and intrinsic redundancy from linkage disequilibrium (LD) [40].
Marker validation often requires that a battery of downstream analyses be performed. The first step required is narrow mapping of the causal variants using target genotyping across extended panels. A second, more experimental approach involves examining the expression profiles of flanking candidate genes in terms of congruency and stability. Several teams pursued this goal as part of the current Special Issue. For example, Yang et al. [19] and Zhang et al. [20] used the Columbia-0 (Col) A. thaliana ecotype to validate the functional roles of PIF4 and AtEAU (1 and 2) genes, respectively, through transcriptomic tools as boosters and repressors of basal thermotolerance and ABA response. Meanwhile, Kong et al. [21] and Zhang et al. [22], respectively, implemented epigenomics and qRT-PCR in cabbage (B. oleracea) and Chinese cabbage (B. rapa) to study the DNA methylation footprint during microspore embryogenesis induced by heat shock, and the functional role of BrGOLDEN in carotenoid biosynthesis. Interestingly, rapeseed (B. napus) was a widely considered study system by Du et al. [24], Wahid et al. [25], and Zhang et al. [26], who used qRT-PCR to corroborate the functionality of BnaZSNRT2s, BnARI, and BnaTH genes for nitrogen uptake, agronomical trait regulation, and abiotic stress tolerance, respectively. Other studies focused on key crops for food security. For example, while also considering the ABA pathway, Yu et al. [23] used qRT-PCR to demonstrate the up-regulation of PHO-encoding genes by ABA in maize and rice. On the other hand, Xiao et al. [29] validated SbDOF21 as a key regulator for starch biosynthesis in sorghum, and Guo et al. [30] characterized a new SPR-repressor Q allele (Qc5) for compact spikes and bread quality in wheat. The other studies which stand out are those by Mažeikienė et al. [27] and Liu et al. [28], who brought RNA-seq technology to the new promissory crops Ribes nigrum and Sacha Inchi (Plukenetia volubilis). Each team was able to provide transcriptomic resources to breed for tolerance to the BRV virus [27] and better comprehend the expression profiles of seeds at two different developmental stages [28].
Finally, the works by Hu et al. [31] and Huang et al. [32] utilized trans-genesis to move beyond the species boundary and validate in an Arabidopsis background candidate genes for cold (HuNAC20 and 25) and osmatic (MfWRKY40) stress tolerance, respectively, from two other exotic crops, namely, the cactus-fruit Pitaya (Hylocereus monacanthus) [31] and the woody resurrection plant (Myrothamnus flabellifolia) [32]. Despite these advances in germplasm characterization, genetic mapping, and functional validation, moving forward, elite cultivars are beginning to benefit from embracing genomic-enabled breeding.

2. Molecularly Enabled Breeding

Mobilizing germplasm’s allelic novelty, leveraging marker genetic mapping, and functionally corroborating candidate genes to customize crops requires the modernization of classical plant breeding with phenomic and genomic tools [2]. Perhaps one of the promptest strategies for bridging long breeding cycles is trans-genesis. When it targets standing variation, segregating within the same species genepool, it simply provides a shortcut to the time-consuming alternative of recurrent backcrossing [37]. However, it also allows customizability for de novo variation. This Special Issue compiles an exquisite repertoire in this regard. For instance, Guo et al. [33] illustrated, once again in rapeseed, that it is feasible to genetically engineer BnaC02.TMT.a, a γ-TMT paralogue, for salt tolerance breeding via the use of homozygous transgenic lines. Similarly, Liu et al. [34] silenced GmBIR1 with BPMV-VIGS to demonstrate that it is a negative regulator of immunity in soybean. Finally, Chen et al. [35] went one step further and explored mutagenesis as a reliable source of de novo allelic variation. The authors characterized mutant Q alleles (Qs1 and Qc1-N8) for grain yield and grain protein in the wheat cultivar ‘Shumai482’ and its S-Cp1-1 mutant [35], complementing the results achieved by Guo et al. [30] in terms of compact spikes and bread quality. In spite of these exciting results achieved for the development of trans-genesis, classical intercrossing has not lost validity as a breeding strategy, but rather it has been permeable to the latest genomic advances, such as marker-assisted backcrossing (MABC) [37], introgression breeding, and genomic-assisted recurrent selection [41].
Inter-specific introgression breeding, in particular, aims to break species boundaries to pyramid exotic variation from one species to elite commercial varieties from the other species [37]. However, inter-specific crossing conveys two main challenges, which are species incompatibility and polygenic trait variation. Therefore, coupling hybrid breeding with bridge genotypes and guiding molecular markers unleashes novel opportunities to improve the chances of success, pace, and precision of the target introgression. Such utility has been reinforced in rice by Zhang et al. [36], who bred for heat tolerance by introgressed sorghum ancestry. The authors utilized the rice restorer line ‘R21’ and the recipient restorer line Jin ‘Hui 1’, and were able to demonstrate with WGR that the sorghum introgression in line ‘R21’ confers mobile heat tolerance. Gene editing may speed up rice breeding for abiotic stresses [42], too. Bean breeders are also seeing a similar development by GBS-genotyping 87 advanced lines with inter-specific ancestries between common bean (P. vulgaris) and tepary bean (P. acutifolius) for heat and drought tolerance across four environments in coastal northern South America [43]. The authors found 47 associated loci and 90 flanking candidate genes for molecular-guided downstream selection. Meanwhile, the team also detected suitable allelic variation within the candidate genes of tepary bean (P. acutifolius) that transcends the adaptive genepool of common bean (P. vulgaris) [44]. These studies demonstrate that the integration of genomic- and gene-based strategies can leverage inter-specific adaptive variation via bridge genotypes in order to deliver candidate introgressed lines for heat tolerance [43]. In certain cases, grafting could also provide a fast track to harness such species diversity [45]. These success stories that intermingle modern genotyping technology with classical intercrossing exemplify, against any intellectual skepticism, the factual applicability of the molecular breeding paradigm. Still, there remains room for further developments.

3. Perspectives

As molecular and phenomic data continue to pile up, modern analytical techniques must be embraced. Predictive breeding, reached throughout genomic-enabled selection [41] and machine learning [11] at the interface of the plant breeding triangle [46], confers a primary way forward capable of bringing together molecular biology and plant genetic paradigms [47]. In this regard, Khan et al. [11] reviewed applications of crop breeding in this Special Issue, calling for a better integration of AI with “omics” tools. Still, effective crop mobilization networks and open access data must be assured in order to build sufficiently reliable training datasets without sampling bias or over-fitting prediction [48].
Meanwhile, biotic and abiotic stresses are becoming more common, jeopardizing global food production. In this sense, another review within this collection by Zhang et al. [8] was more concrete in envisioning gene targets for biotic pressures, specifically that clubroot control methods in cruciferous crops could be harnessed with allelic variation at the R genes, which are in turn susceptible to be gene-edited, eventually. Similarly, the reviews by Chen et al. [9] and Jiang et al. [10] prospected gene-enabled abiotic stress responses by looking at the MYB genes in plant roots and the miR393 during plant development, respectively.
The method of screening the genetic determinants of the abiotic susceptibility, and its potential gene editing, should be supplemented by explicit in situ eco-physiological indices targeting specific stresses, performing potential ecological niche modeling (ENM) under present and future scenarios as part of climate vulnerability assessments, undertaking gap analyses of the available variation for pre-breeding, exploring genomic selection signatures of historic adaptation, and unpicking the genome–environment associations (GEA) of current niche preferences [3]. After all, tackling the climate crisis and agrobiodiversity loss in the process of addressing food security [5] demands that we harness trans-disciplinary sustainable enterprises that promote integrative agendas among the otherwise disentangled fields of in situ and ex situ conversation, physiology, ecology, molecular genetics, plant breeding [2], conservation, seed delivery [4], food policy [49], and marketing.

Author Contributions

A.J.C. and H.D. conceived this Special Issue and jointly invited potential authors, handled manuscripts, recommended reviewers, and approved submissions. A.J.C. prepared a first draft, which was later on edited by H.D. All authors have read and agreed to the published version of the manuscript.

Funding

A.J.C. received support from Vetenskapsrådet (VR) and Kungliga Vetenskapsakademien (KVA) while conceiving and closing this Special Issue through grants 2016-00418/2022-04411 and BS2017-0036, respectively. A.J.C. also acknowledges Newton Fund’s 527023146 grant, executed during the time this Special Issue was being processed, as well as Fulbright U.S. Specialist Program for supporting synergistic discussion with M.W. Blair on molecular breeding of tropical species in Rionegro (Antioquia, Colombia) during the summer of 2019.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

For original datasets, please refer to the published articles [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36] within the second Special Issue of IJMS on “Molecular Genetics and Plant Breeding 2.0” (https://www.mdpi.com/journal/ijms/special_issues/gene_plant_breading_2nd, as accessed on 6 June 2023).

Acknowledgments

The Guest Editors acknowledge the time and effort contributed by all authors, reviewers, and editors who made possible this Special Issue on “Molecular Genetics and Plant Breeding 2.0”. Support from M.J. Torres-Urrego is appreciated. MDPI’s IJMS is thanked for inviting, encouraging, and assisting the Guest Editors to host this compilation. Section Managing Editor is also acknowledged for their continuous assistance.

Conflicts of Interest

The authors declare no conflict of interest.

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Cortés, A.J.; Du, H. Molecular Genetics Enhances Plant Breeding. Int. J. Mol. Sci. 2023, 24, 9977. https://doi.org/10.3390/ijms24129977

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Cortés AJ, Du H. Molecular Genetics Enhances Plant Breeding. International Journal of Molecular Sciences. 2023; 24(12):9977. https://doi.org/10.3390/ijms24129977

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Cortés, Andrés J., and Hai Du. 2023. "Molecular Genetics Enhances Plant Breeding" International Journal of Molecular Sciences 24, no. 12: 9977. https://doi.org/10.3390/ijms24129977

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