Fast-forward breeding for a food-secure world
Authors: Rajeev K. Varshney, Abhishek Bohra, Manish Roorkiwal, Rutwik Barmukh, Wallace A. Cowling, Annapurna Chitikineni, Hon-Ming Lam, Lee T. Hickey, Janine S. Croser, Philipp E. Bayer, David Edwards, José Crossa, Wolfram Weckwerth, Harvey Millar, Arvind Kumar, Michael W. Bevan, Kadambot H.M. Siddique
Journal: Trends in Genetics (2021)

The rapid advances in plant genome sequencing and phenotyping have enhanced trait mapping and gene discovery in crops.Increasing adoption of machine learning algorithms is crucial to derive meaningful inferences from complex multidimensional phenotyping data.Emerging breeding approaches like optimal contribution selection, alone or in combination with genomic selection, will enhance the genetic base of breeding programs while accelerating genetic gain.Integrating speed breeding with new-age genomic breeding technologies holds promise to relieve the long-standing bottleneck of lengthy crop breeding cycles.Haplotype-based breeding, genomic prediction, and genome editing will hasten targeted assembly of superior alleles in future cultivars for sustainable agricultural development and long-term food security. Crop production systems need to expand their outputs sustainably to feed a burgeoning human population. Advances in genome sequencing technologies combined with efficient trait mapping procedures accelerate the availability of beneficial alleles for breeding and research. Enhanced interoperability between different omics and phenotyping platforms, leveraged by evolving machine learning tools, will help provide mechanistic explanations for complex plant traits. Targeted and rapid assembly of beneficial alleles using optimized breeding strategies and precise genome editing techniques could deliver ideal crops for the future. Realizing desired productivity gains in the field is imperative for securing an adequate future food supply for 10 billion people. Crop production systems need to expand their outputs sustainably to feed a burgeoning human population. Advances in genome sequencing technologies combined with efficient trait mapping procedures accelerate the availability of beneficial alleles for breeding and research. Enhanced interoperability between different omics and phenotyping platforms, leveraged by evolving machine learning tools, will help provide mechanistic explanations for complex plant traits. Targeted and rapid assembly of beneficial alleles using optimized breeding strategies and precise genome editing techniques could deliver ideal crops for the future. Realizing desired productivity gains in the field is imperative for securing an adequate future food supply for 10 billion people. Safeguarding a person's right to adequate and nutritious food requires intensive research efforts and innovative solutions to breed nutritious crops with improved productivity and resilience [1.Siddique K.H.M. et al.Re-discovering Asia's forgotten crops to fight chronic and hidden hunger.Nat. Plants. 2021; 7: 116-122Crossref PubMed Scopus (10) Google Scholar]. However, a major challenge is the uneven distribution of resources, resulting in a huge gap in supply and demand for food. Crop productivity and harvest are improved by access to modern infrastructure and technologies, including breeding for improved varieties, agronomic practices, and machinery for farm preparation, harvest, processing, and marketing. Regions with high populations and low crop production should be studied to address these uneven distribution challenges and provide equitable opportunities. Lessons learned from the pandemic highlight the need for self-sustainability, with less dependence on imports, especially for agriculture. For instance, a vast portion of the entire global population resides in low-income food deficit countries (32.23%), least developed countries (12%), and net food-importing developing countries (20.15%)i,ii. Therefore, enhancing crop productivity and addressing the worldwide zero hunger and nutrition food security challenges through modern breeding technologies, infrastructure, agronomic practices, and soil improvement remains essential. A high-quality reference genome (see Glossary) is a prerequisite for genomics studies in a given crop to attain accurate and precise results on crop performance [2.Varshney R.K. et al.5Gs for crop genetic improvement.Curr. Opin. Plant Biol. 2020; 56: 190-196Crossref PubMed Scopus (46) Google Scholar]. High-confidence variant calling facilitated by the availability of a high-quality reference genome, is crucial for genetic studies such as gene discovery and manipulation. 'Democratization' of sequencing technologies in concert with advanced informatics tools has improved the contiguity and completeness of existing and genome assemblies. Since a single reference genome cannot capture all genomic variations of a species, an increased number of gold- or platinum-standard reference genomes have become available for several crops. Long-read or linked-read sequencing platforms, such as PacBio, 10X Chromium, and Oxford Nanopore, supplemented by short reads from next-generation sequencing (NGS), allow the assembly of long contigs with high base-to-base precision (Figure 1). Hi-C sequencing [3.Belton J.M. et al.Hi-C: a comprehensive technique to capture the conformation of genomes.Methods. 2012; 58: 268-276Crossref PubMed Scopus (418) Google Scholar] and Bionano Genomics Optical Mapping [4.Pendleton M. et al.Assembly and diploid architecture of an individual human genome via single-molecule technologies.Nat. Methods. 2015; 12: 780-786Crossref PubMed Scopus (310) Google Scholar] have facilitated genome assemblies with greater contiguity by dramatically improving haplotype phasing and haplotype scaffolding, especially in polyploid genomes [5.Zhuang W. et al.The genome of cultivated peanut provides insight into legume karyotypes, polyploid evolution and crop domestication.Nat. Genet. 2019; 51: 865-876Crossref PubMed Scopus (174) Google Scholar]. Due to the reduction in sequencing costs, high-density genotyping is now affordable for assaying large samples with high precision [6.Huang B.E. et al.MAGIC populations in crops: current status and future prospects.Theor. Appl. Genet. 2015; 128: 999-1017Crossref PubMed Scopus (136) Google Scholar]. A large suite of genotyping platforms (e.g., Affymetrix Axiom, Affymetrix GeneChip, and Illumina Infinium BeadChip) with varying single nucleotide polymorphisms (SNPs) are now available for most crop species [7.Rasheed A. et al.Crop breeding chips and genotyping platforms: progress, challenges, and perspectives.Mol. Plant. 2017; 10: 1047-1064Abstract Full Text Full Text PDF PubMed Scopus (206) Google Scholar]. Several genome-wide genotyping approaches integrating deep sequencing with reduced representation methods, such as genotyping-by-sequencing, restriction site associated DNA sequencing, double-digest RAD, and restriction fragment sequencing, have led to major innovations in marker discovery in various plant species (Figure 1). Compared with array-based or NGS methods, whole genome resequencing can simultaneously detect known and uncatalogued SNPs and large structural variations (SVs), such as presence/absence variations (PAVs) and copy number variations (CNVs). Concurrent advances in plant phenotyping driven mainly by innovations in image and sensor technologies, with cost- and time-efficient acquisition of massive spatial and temporal data, have leveraged the field of predictive phenomics [8.Mir R.R. et al.Integrated genomics, physiology and breeding approaches for improving drought tolerance in crops.Theor. Appl. Genet. 2012; 125: 625-645Crossref PubMed Scopus (274) Google Scholar]. Automated platforms equipped with plant-to-sensor and sensor-to-plant modes can monitor the dynamic response of plant at the organ, whole plant, and field scales (Figure 1). For instance, 3D structural imaging applications, such as X-ray computed tomography, allow in situ phenotyping of root system architecture, alleviating underground phenotyping bottlenecks. The growing need to nondestructively monitor plant performance in the real world has led to fully automated field-based phenotyping facilities. In contrast, aerial platforms include unmanned aerial vehicles, manned aerial vehicles, and satellites with varying levels of payload capacity and image resolution (Figure 1) [9.Jin X. et al.High-throughput estimation of crop traits: a review of ground and aerial phenotyping platforms.IEEE Trans. Geosci. Remote Sens. 2020; 9: 200-231Crossref Scopus (54) Google Scholar]. Fewer than 20% of mechanized phenotyping platforms established worldwide are field-based infrastructures [10.Yang W. et al.Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives.Mol. Plant. 2020; 13: 187-214Abstract Full Text Full Text PDF PubMed Scopus (159) Google Scholar]. In African countries, instead of such specialized facilities, setting up field stations and surveying local pathogens with a regional, as opposed to an international foundation, needs to be considered. Fast-tracking mining of beneficial alleles in plant genetic resources (PGRs), such as wild relatives and landraces conserved in genebanks, is crucial for ensuring future food supplies. Current crops have accumulated a suite of traits suitable for modern agriculture and human consumption through human-mediated domestication and improvement of wild species. For trait dissection, a range of genetic populations, including biparental and multiple parental populations such as multi-parent advanced generation inter-cross (MAGIC) populations and nested association mapping populations, have been developed for many crops [6.Huang B.E. et al.MAGIC populations in crops: current status and future prospects.Theor. Appl. Genet. 2015; 128: 999-1017Crossref PubMed Scopus (136) Google Scholar,11.Scott M.F. et al.Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding.Heredity. 2020; 125: 396-416Crossref PubMed Scopus (38) Google Scholar]. With the availability of new sequencing/genotyping technologies, PGRs and genetic populations can be assayed with sequencing and genotyping technologies and evaluated for agronomic and nutrition traits in multiple environments and seasons [2.Varshney R.K. et al.5Gs for crop genetic improvement.Curr. Opin. Plant Biol. 2020; 56: 190-196Crossref PubMed Scopus (46) Google Scholar]. Superior genes/alleles for traits of interest can be identified through pangenomics (Box 1) or genome-wide association studies (GWAS) (Table S1 in the supplemental information online, Figure 1). Concerning modernizing agriculture in developing countries, local needs should be addressed to identify and conserve the germplasm of local crops and wild relatives and undertake genomic breeding programs for accelerating crop improvement.Box 1Pangenomics to bring genes back from the pastSequencing of multiple plant genomes has popularized the 'pangenome' or set of genes present within a species, including the core genes present in all individuals and dispensable genes absent from at least one individual. The growing literature on multiple genome sequencing and pangenomics has provided strong evidence for the contribution of large SVs, to evolutionary processes that have shaped adaptive diversity in plants. In plants, PAVs within a species range from 7.8% in rice [80.Schatz M.C. et al.Whole genome de novo assemblies of three divergent strains of rice, Oryza sativa, document novel gene space of aus and indica.Genome Biol. 2014; 15: 506PubMed Google Scholar] to up to 40% in wheat [81.Montenegro J.D. et al.The pangenome of hexaploid bread wheat.Plant J. 2017; 90: 1007-1013Crossref PubMed Scopus (170) Google Scholar], with many diploid plants having ~30–50% variable genes [82.Gao L. et al.The tomato pan-genome uncovers new genes and a rare allele regulating fruit flavor.Nat. Genet. 2019; 51: 1044-1051Crossref PubMed Scopus (190) Google Scholar]. De novo genome sequencing of wild species accessions has enabled the development of pangenome of different species, called 'super-pangenome' [83.Khan A.W. et al.Super-pangenome by integrating the wild side of a species for accelerated crop improvement.Trends Plant Sci. 2020; 25: 148-158Abstract Full Text Full Text PDF PubMed Scopus (70) Google Scholar].Gene PAVs are associated with environmental adaptation, domestication, and breeding [84.Tan S. et al.Variation of presence/absence genes among Arabidopsis populations.BMC Evol. Biol. 2012; 12: 86Crossref PubMed Scopus (31) Google Scholar], and annotations for variable genes in many pangenomes are often enriched for agronomic traits, such as biotic and abiotic stress. Many variable genes have been lost during domestication and breeding bottlenecks; identifying and characterizing these genes can support their targeted reintroduction into breeding programs. Calling PAVs across wild and domesticated lines helps to retrace the impact of domestication and breeding on the pangenome; genes with negative effects can decrease in frequency during selection, while those with agronomic benefits increase (Figure 1). Due to low effective population size in many plant breeding programs and ineffective recombination, many PAVs between wild and cultivated lines may have been lost through genetic drift during breeding bottlenecks.Knowledge of pangenomes enables us to fine-tune breeding outcomes by constructing the optimal gene content for a crop variety. This requires building species-wide or even genus-wide super-pangenomes representing all genes and allelic variants for breeding the next generation of crops. Pangenomes teach us which haplotypes to combine to produce superior haplotype and gene combinations, enabling plant breeders to shift from useful individuals to useful haplotypes and useful genes when planning breeding programs. Several important agronomic traits have been associated with PAVs and the selective reintroduction of these genes into elite germplasm has led to improved varieties. This knowledge enables breeders to quickly breed cultivars with novel phenotypic attributes by simply backcrossing major genes into elite varieties. Alternatively, GE systems now provide precise molecular tools to modify key genes that have been drivers of crop domestication; for instance, de novo domestication of wild rice Oryza alta (CCDD) was achieved through gene editing of six agronomically important genes [85.Yu H. et al.A route to de novo domestication of wild allotetraploid rice.Cell. 2021; 184: 1156-1170Abstract Full Text Full Text PDF PubMed Scopus (65) Google Scholar]. Sequencing of multiple plant genomes has popularized the 'pangenome' or set of genes present within a species, including the core genes present in all individuals and dispensable genes absent from at least one individual. The growing literature on multiple genome sequencing and pangenomics has provided strong evidence for the contribution of large SVs, to evolutionary processes that have shaped adaptive diversity in plants. In plants, PAVs within a species range from 7.8% in rice [80.Schatz M.C. et al.Whole genome de novo assemblies of three divergent strains of rice, Oryza sativa, document novel gene space of aus and indica.Genome Biol. 2014; 15: 506PubMed Google Scholar] to up to 40% in wheat [81.Montenegro J.D. et al.The pangenome of hexaploid bread wheat.Plant J. 2017; 90: 1007-1013Crossref PubMed Scopus (170) Google Scholar], with many diploid plants having ~30–50% variable genes [82.Gao L. et al.The tomato pan-genome uncovers new genes and a rare allele regulating fruit flavor.Nat. Genet. 2019; 51: 1044-1051Crossref PubMed Scopus (190) Google Scholar]. De novo genome sequencing of wild species accessions has enabled the development of pangenome of different species, called 'super-pangenome' [83.Khan A.W. et al.Super-pangenome by integrating the wild side of a species for accelerated crop improvement.Trends Plant Sci. 2020; 25: 148-158Abstract Full Text Full Text PDF PubMed Scopus (70) Google Scholar]. Gene PAVs are associated with environmental adaptation, domestication, and breeding [84.Tan S. et al.Variation of presence/absence genes among Arabidopsis populations.BMC Evol. Biol. 2012; 12: 86Crossref PubMed Scopus (31) Google Scholar], and annotations for variable genes in many pangenomes are often enriched for agronomic traits, such as biotic and abiotic stress. Many variable genes have been lost during domestication and breeding bottlenecks; identifying and characterizing these genes can support their targeted reintroduction into breeding programs. Calling PAVs across wild and domesticated lines helps to retrace the impact of domestication and breeding on the pangenome; genes with negative effects can decrease in frequency during selection, while those with agronomic benefits increase (Figure 1). Due to low effective population size in many plant breeding programs and ineffective recombination, many PAVs between wild and cultivated lines may have been lost through genetic drift during breeding bottlenecks. Knowledge of pangenomes enables us to fine-tune breeding outcomes by constructing the optimal gene content for a crop variety. This requires building species-wide or even genus-wide super-pangenomes representing all genes and allelic variants for breeding the next generation of crops. Pangenomes teach us which haplotypes to combine to produce superior haplotype and gene combinations, enabling plant breeders to shift from useful individuals to useful haplotypes and useful genes when planning breeding programs. Several important agronomic traits have been associated with PAVs and the selective reintroduction of these genes into elite germplasm has led to improved varieties. This knowledge enables breeders to quickly breed cultivars with novel phenotypic attributes by simply backcrossing major genes into elite varieties. Alternatively, GE systems now provide precise molecular tools to modify key genes that have been drivers of crop domestication; for instance, de novo domestication of wild rice Oryza alta (CCDD) was achieved through gene editing of six agronomically important genes [85.Yu H. et al.A route to de novo domestication of wild allotetraploid rice.Cell. 2021; 184: 1156-1170Abstract Full Text Full Text PDF PubMed Scopus (65) Google Scholar]. Transcriptomics, proteomics, metabolomics, and epigenomics provide windows into molecular variation in breeding lines beyond the actual or interpretable genetic variation they contain [12.Weckwerth W. et al.PANOMICS meets germplasm.Plant Biotechnol. J. 2020; 18: 1507-1525Crossref PubMed Scopus (25) Google Scholar,13.Pazhamala L.T. et al.Systems biology for crop improvement: prospects and challenges.Plant Genome. 2021; 14e20098Crossref PubMed Scopus (12) Google Scholar]. These windows are closer to phenotype, narrow the genome to phenome divide, and provide independent sets of markers to complement genetic markers as breeding tools (Figure 2). Associative transcriptomics examines correlations of phenotypic variation with variations in both DNA sequence and transcript abundance [14.Harper A.L. et al.Associative transcriptomics of traits in the polyploid crop species Brassica napus.Nat. Biotechnol. 2012; 30: 798-802Crossref PubMed Scopus (219) Google Scholar]. In maize, cis expression QTLs (eQTLs) contribute to phenotypic diversity for several traits, including domestication and adaptation [15.Lemmon Z.H. et al.The role of cis regulatory evolution in maize domestication.PLoS Genet. 2014; 10e1004745Crossref PubMed Scopus (79) Google Scholar]. Expression read depth GWAS and transcriptome-wide association studies test associations of mRNA expression with phenotypic diversity [16.Lin H. et al.Substantial contribution of genetic variation in the expression of transcription factors to phenotypic variation revealed by eRD-GWAS.Genome Biol. 2017; 18: 192Crossref PubMed Scopus (27) Google Scholar,17.Kremling K.A.G. et al.Transcriptome-wide association supplements genome-wide association in Zea mays.G3 (Bethesda). 2019; 9: 3023-3033Crossref PubMed Scopus (29) Google Scholar]. Unlike genetic variants, the transcript levels are independent of linkage disequilibrium across the genome; these methods provide deep insights into the regulatory mechanisms of complex traits and enable better prioritization of causal candidate genes [17.Kremling K.A.G. et al.Transcriptome-wide association supplements genome-wide association in Zea mays.G3 (Bethesda). 2019; 9: 3023-3033Crossref PubMed Scopus (29) Google Scholar]. Proteomics approaches can also be used in multiple ways for refining the QTLs underlying complex traits (Box 2).Box 2Proteomics to refine QTLs for complex traitsProteomics, based on mass spectrometry identification of peptides and matching them to translated gene sequences, can be used for resolving QTLs for complex traits in various ways:1.Reference crop proteomes can be compared with actual or predicted proteomes of crops to evaluate species or genotype-specific protein differences via their orthology [86.Ghatak A. et al.Proteomics survey of Solanaceae family: current status and challenges ahead.J. Proteome. 2017; 169: 41-57Crossref PubMed Scopus (29) Google Scholar,87.Hooper C.M. et al.CropPAL for discovering protein subcellular location divergence in crops to support strategies for molecular crop breeding.Plant J. 2020; 104: 812-827Crossref PubMed Scopus (4) Google Scholar] and identify INDELS (missing or additional proteins) that are translated in genotypes.2.Quantitative proteomics approaches can be used in the same way as gene expression studies in breeding for single or multiple gene discoveries associated with specific traits [88.Hoehenwarter W. et al.MAPA distinguishes genotype-specific variability of highly similar regulatory protein isoforms in potato tuber.J. Proteome Res. 2011; 7: 2979-2991Crossref Scopus (27) Google Scholar]. Proteomics can identify genes of interest that have not been identified through other means because variation in protein abundance can be difficult to detect from gene structure or gene expression studies due to post-transcriptional modification processes [89.Millar A.H. et al.The scope, functions, and dynamics of posttranslational protein modifications.Annu. Rev. Plant Biol. 2019; 70: 119-151Crossref PubMed Scopus (76) Google Scholar].3.Major proteomics-based resources such as protein–protein interaction maps and protein coexpression maps can link gene products as functional units or responses [90.Duncan O. et al.Resource: mapping the Triticum aestivum proteome.Plant J. 2017; 89: 601-616Crossref PubMed Scopus (32) Google Scholar]. This can help reduce the solution space to resolve single mechanisms that underlie multiple QTL associations with a trait that may fail to reach statistical significance on its own [91.Weckwerth W. et al.Differential metabolic networks unravel the effects of silent plant phenotypes.Proc. Natl Acad. Sci. USA. 2004; 101: 7809-7814Crossref PubMed Scopus (306) Google Scholar].4.Specific traits that are traditionally poorly accessible to QTL analysis (e.g., postharvest physiology and seed germination traits) can be sought using proteomics because the accumulated proteome of plant tissues is critical for the timing of traits [92.Vanderschuren H. et al.Proteomics of model and crop plant species: status, current limitations and strategic advances for crop improvement.J. Proteome. 2013; 93: 5-19Crossref PubMed Scopus (56) Google Scholar].5.Traits involving post-translational processes, such as cascades of activation/deactivation by kinases/phosphatases or protein degradation studies, are usually resolved by gene loci studies alone, needing direct analysis of phosphopeptides [93.Chen Y. Weckwerth W. Mass spectrometry untangles plant membrane protein signaling networks.Trends Plant Sci. 2020; 25: 930-944Abstract Full Text Full Text PDF PubMed Scopus (14) Google Scholar] or protein turnover [94.Nelson C.J. Millar H. Protein turnover in plant biology.Nat. Plants. 2015; 1: 15017Crossref PubMed Scopus (49) Google Scholar]. Such analyses can provide insight into potential solutions for maintaining target levels (e.g., stabilizing target protein, overexpression of target protein) or altering signaling (phosphomimic alterations), which are important for gene editing in plant breeding.6.Proteomics now offers data-independent acquisition modes that allow targeted analysis of protein abundance in breeding populations of hundreds to thousands of lines [e.g., sequential window acquisition of all theoretical fragment ion spectra (SWATH) and multiple-reaction monitoring (MRM)] to directly assess protein markers in line selection during breeding cycles (e.g., Jacoby et al. [95.Jacoby R.P. et al.Application of selected reaction monitoring mass spectrometry to field-grown crop plants to allow dissection of the molecular mechanisms of abiotic stress tolerance.Front. Plant Sci. 2013; 4: 20Crossref PubMed Scopus (16) Google Scholar]). Proteomics, based on mass spectrometry identification of peptides and matching them to translated gene sequences, can be used for resolving QTLs for complex traits in various ways:1.Reference crop proteomes can be compared with actual or predicted proteomes of crops to evaluate species or genotype-specific protein differences via their orthology [86.Ghatak A. et al.Proteomics survey of Solanaceae family: current status and challenges ahead.J. Proteome. 2017; 169: 41-57Crossref PubMed Scopus (29) Google Scholar,87.Hooper C.M. et al.CropPAL for discovering protein subcellular location divergence in crops to support strategies for molecular crop breeding.Plant J. 2020; 104: 812-827Crossref PubMed Scopus (4) Google Scholar] and identify INDELS (missing or additional proteins) that are translated in genotypes.2.Quantitative proteomics approaches can be used in the same way as gene expression studies in breeding for single or multiple gene discoveries associated with specific traits [88.Hoehenwarter W. et al.MAPA distinguishes genotype-specific variability of highly similar regulatory protein isoforms in potato tuber.J. Proteome Res. 2011; 7: 2979-2991Crossref Scopus (27) Google Scholar]. Proteomics can identify genes of interest that have not been identified through other means because variation in protein abundance can be difficult to detect from gene structure or gene expression studies due to post-transcriptional modification processes [89.Millar A.H. et al.The scope, functions, and dynamics of posttranslational protein modifications.Annu. Rev. Plant Biol. 2019; 70: 119-151Crossref PubMed Scopus (76) Google Scholar].3.Major proteomics-based resources such as protein–protein interaction maps and protein coexpression maps can link gene products as functional units or responses [90.Duncan O. et al.Resource: mapping the Triticum aestivum proteome.Plant J. 2017; 89: 601-616Crossref PubMed Scopus (32) Google Scholar]. This can help reduce the solution space to resolve single mechanisms that underlie multiple QTL associations with a trait that may fail to reach statistical significance on its own [91.Weckwerth W. et al.Differential metabolic networks unravel the effects of silent plant phenotypes.Proc. Natl Acad. Sci. USA. 2004; 101: 7809-7814Crossref PubMed Scopus (306) Google Scholar].4.Specific traits that are traditionally poorly accessible to QTL analysis (e.g., postharvest physiology and seed germination traits) can be sought using proteomics because the accumulated proteome of plant tissues is critical for the timing of traits [92.Vanderschuren H. et al.Proteomics of model and crop plant species: status, current limitations and strategic advances for crop improvement.J. Proteome. 2013; 93: 5-19Crossref PubMed Scopus (56) Google Scholar].5.Traits involving post-translational processes, such as cascades of activation/deactivation by kinases/phosphatases or protein degradation studies, are usually resolved by gene loci studies alone, needing direct analysis of phosphopeptides [93.Chen Y. Weckwerth W. Mass spectrometry untangles plant membrane protein signaling networks.Trends Plant Sci. 2020; 25: 930-944Abstract Full Text Full Text PDF PubMed Scopus (14) Google Scholar] or protein turnover [94.Nelson C.J. Millar H. Protein turnover in plant biology.Nat. Plants. 2015; 1: 15017Crossref PubMed Scopus (49) Google Scholar]. Such analyses can provide insight into potential solutions for maintaining target levels (e.g., stabilizing target protein, overexpression of target protein) or altering signaling (phosphomimic alterations), which are important for gene editing in plant breeding.6.Proteomics now offers data-independent acquisition modes that allow targeted analysis of protein abundance in breeding populations of hundreds to thousands of lines [e.g., sequential window acquisition of all theoretical fragment ion spectra (SWATH) and multiple-reaction monitoring (MRM)] to directly assess protein markers in line selection during breeding cycles (e.g., Jacoby et al. [95.Jacoby R.P. et al.Application of selected reaction monitoring mass spectrometry to field-grown crop plants to allow dissection of the molecular mechanisms of abiotic stress tolerance.Front. Plant Sci. 2013; 4: 20Crossref PubMed Scopus (16) Google Scholar]). Alterations in gene expression can be attributed to heritable epigenetic changes that do not involve DNA sequences, including DNA methylation, histone modification, and noncoding RNAs [18.Hu Y. et al.Prediction of plant height in Arabidopsis thaliana using DNA methylation data.Genetics. 2015; 201: 779-793Crossref PubMed Scopus (40) Google Scholar]. Recent advances in NGS-based protocols, including methylated DNA immunoprecipitation and bisulfite sequencing, have facilitated large-scale analysis of methylation levels, the most common form of epigenetic polymorphisms. In plants, there is evidence for the role of epigenetic modifications on gene expression regulation. High-throughput analysis can screen many lines during breeding cycles, and proteomics and metabolomics have reached a technical standard for application in these studies. Specific metabolomic technologies, such as gas chromatography or liquid chromatography, coupled with mas