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PubMed2026

Engineering cold stress resilience in capsicum annuum through functional genomics and precision breeding.

This review synthesizes the molecular mechanisms of cold tolerance in pepper, integrating multi-omics data,genome editing, and precision breeding strategies to accelerate the development of cold-resilient cultivars. Cold stress is a significant environmental factor that affects the growth, productivity, and fruit quality of Capsicum annuum by impairing membrane integrity photosynthesis and cellular redox homeostasis. Although pepper has several endogenous cold-responsive regulators such as CaNAC035 and CabHLH035, along with antioxidant defense systems, its cold tolerance remains limited due to low transcriptional activation of key regulators, functional redundancy among cold-responsive genes, and the polygenicity of cold tolerance. These complexities, combined with low genetic diversity and linkage drag, have hindered the improvement of cold-resistant cultivars through conventional breeding. This review brings together the recent progress in understanding the molecular mechanisms of cold stress perception, signal transduction, transcriptional regulation, metabolic reprogramming, and phytohormone interactions in pepper. Precision Breeding 2.0 is a new innovation that combines the integration of multi-omics-based target identification with next-generation genome-editing techniques, allowing precise and multiplex engineering of complex and interconnected regulatory networks instead of single genes. We cover new approaches such as engineering the DREB/CBF pathway, allele-specific editing and targeted disruption of negative regulators to enhance the pathway(s) involved in cold response. Moreover, we propose a roadmap for integration of transcriptomics, proteomics, metabolomics, high-throughput phenomics, and speed breeding to accelerate the identification, validation, and deployment of superior alleles to boost cold tolerance. This review provides a foundation for developing climate-resilient pepper cultivars by connecting functional genomics with precision genome engineering approaches to maintain productivity under variable environmental conditions.

Capsicum↗

PubMed2026

The current and future perspective of ChickenGTEx project and its applications in precision breeding.

The Chicken Genotype-Tissue Expression (ChickenGTEx) project was established to systematically characterize the regulatory landscape of the chicken genome and to accelerate the translation of functional genomics into precision breeding. By integrating whole-genome sequencing with multi-tissue transcriptomic profiling, ChickenGTEx provides a comprehensive atlas of gene expression regulation across diverse tissues and physiological systems. Current findings demonstrate that complex production traits are governed by coordinated regulatory networks rather than isolated loci, with substantial contributions from tissue-specific gene expression, structural variation, and genotype-by-sex interactions. Sex-dependent regulatory effects further refine the genetic architecture of metabolic, immune, and reproductive traits, highlighting the importance of incorporating sex as a biological variable in genomic analyses. Application of integrative omics frameworks within elite layer populations has revealed multilayer regulatory mechanisms underlying extended laying performance, feed efficiency, metabolic health, and eggshell quality. By partitioning phenotypic variance into genetic, regulatory, and host-microbiome components, these approaches move beyond association-based mapping toward causal inference and biological interpretation. Importantly, validated regulatory loci identified through ChickenGTEx and related analyses provide actionable markers for genomic selection and rational targets for precision genome modification. Looking forward, continued expansion of regulatory atlases, incorporation of single-cell and longitudinal data in diverse environmental conditions, and integration of functional annotation into breeding pipelines will further enhance prediction accuracy and sustainable genetic improvement. The ChickenGTEx project thus represents a foundational platform bridging functional genomics and practical poultry breeding.

Animals↗

PubMed2026

From feasibility to predictability: prime editing redefines precision breeding in plants.

Originally developed in mammalian systems as a genome editing strategy without double-strand breaks, prime editing (PE) has been adapted for precise genome modifications. However, its deployment revealed key limitations, including reduced efficiency, strong locus dependency, low germline transmission, and somatic chimerism. Consequently, diverse PE variants have emerged, resulting in fragmented landscape of architectures with context-dependent and inconsistent performance. This review consolidates these advances and outlines emerging design principles behind plant PE systems. It evaluates optimization strategies at multiple levels, discusses their applications in monocots and eudicots, and highlights persistent bottlenecks and future directions, including AI-guided protein engineering and improved delivery strategies. These advances position PE as a rapidly evolving platform toward enabling precision breeding in plants.

cis-regulatory engineering↗

PubMed2026

Advances in genomics-driven genetic decoding and genomic design breeding in tomato.

Tomatoes are highly nutritious and represent one of the important vegetable fruits worldwide. Both historically and moving forward, genetic decoding and precision breeding remain fundamental to tomato improvement. Here, we summarize pivotal advances in decoding tomato genomes across domestication, improvement and evolution processes and provide a perspective on future breeding through precision design. In-depth population genetic studies have revealed how artificial selection systematically prioritized yield-related alleles at the cost of narrowing genetic diversity, especially at flavor-related loci-highlighting the urgent need to reconcile these trade-offs. Comparative genomics across species, viewed through an evolutionary lens, has uncovered critical insights into functional genes, deepening our understanding of the genetic architecture and regulatory mechanisms underlying key traits. Collectively, these advances have enabled precise identification and functional characterization of key genetic elements, paving the way for systematic redomestication of tomato through precision genomic design. Looking ahead, more efficient and precise breeding strategies will be required to accelerate genetic gains in tomato in the coming decades. The integration of recent genomic advances, coupled with genomic selection and artificial intelligence, into genomic design breeding offers a transformative framework, unlocking unprecedented opportunities for developing highly flavorful and consumer-customized tomato varieties.

Journal Article↗

PubMed2026

Bioinformatics in crop research: using genomic data for crop improvement.

Sustainable crop development aims to maintain or increase yields while reducing environmental impact and managing the challenges imposed by climate change. As the global population grows and arable land becomes scarcer, the integration of molecular breeding with bioinformatics has emerged as an effective strategy for long-term crop improvement. Bioinformatics enables researchers to analyze and interpret the vast quantities of genetic data generated by high-throughput sequencing, making it possible to identify molecular markers, candidate genes, and regulatory networks linked to specific agronomic traits, which breeders then translate into focused, ecologically sustainable breeding programs. This approach has enabled major progress across several fronts: the identification of genes conferring resistance to biotic stressors (pests, pathogens) and abiotic stressors (drought, salinity, heat); the development of nutrient-efficient, low-input crop varieties; the improvement of agronomic performance and nutritional quality through identification of yield- and quality-related genes; and the conservation and deployment of genetic diversity to safeguard long-term breeding sustainability. By combining genomic data with precision breeding techniques, researchers are developing crops that are better adapted to a growing population and a changing climate, positioning the integration of molecular breeding and bioinformatics as a central pillar of future global food security.

bioinformatics↗

PubMed2025

Landscape genomics analysis reveals the genetic basis underlying cashmere goats and dairy goats adaptation to frigid environments.

Understanding the genetic mechanism of cold adaptation in cashmere goats and dairy goats is very important to improve their production performance. The purpose of this study was to comprehensively analyze the genetic basis of goat adaptation to cold environments, clarify the impact of environmental factors on genome diversity, and lay the foundation for breeding goat breeds to adapt to climate change. A total of 240 dairy goats were subjected to genome resequencing, and the whole genome sequencing data of 57 individuals from 6 published breeds were incorporated. By integrating multiple approaches such as phylogenetic analysis, population structure analysis, gene flow and population history exploration, selection signal analysis, and genome-environment association analysis, an in-depth investigation was carried out. Phylogenetic analysis unraveled the genetic relationships and differentiation patterns among dairy goats and other goat breeds. Through signal analysis (θπ, FST, XP-CLR), we identified numerous candidate genes associated with cold adaptation in dairy goats (STRIP1, ALX3, HTR4, NTRK2, MRPL11, PELI3, DPP3, BBS1) and cashmere goats (MED12L, MARC2, MARC1, DSG3, C6H4orf22, CHD7, MYPN, KIAA0825, MITF). Genome-environment association (GEA) analysis confirmed the link between these genes and environmental factors. Moreover, a detailed analysis of the critical genes C6H4orf22 and STRIP1 demonstrated their significant roles in the geographical variations of cold adaptation and allele frequency differences among different breeds. This study contributes to understanding the genetic basis of cold adaptation, providing crucial theoretical support for precision breeding programs aimed at improving production performance in cold regions by leveraging adaptive alleles, thereby ensuring sustainable animal husbandry.

Environmental adaptation↗

PubMed2026

Translating functional molecular knowledge into crop-breeding success.

Historical plant breeding, which optimizes phenotypes through selective crossing guided by phenotypic evaluation and molecular markers, is limited by evolutionary constraints that hinder rapid crop improvement. A new paradigm, precision breeding, circumvents these limitations by targeting genetic variants through functional molecular knowledge. To generate this knowledge at scale, sequence-based deep learning leverages high-quality genome sequence data to predict variant effects at base-pair resolution. When linked to agronomically important traits, these predictions enable breeders to prioritize variants for precision selection or editing. Although it is still in the early stages of development, we foresee three key applications for this approach: introgressing genes from distant breeding pools, purging deleterious mutations and designing new plant ideotypes. Looking ahead, refined computational models will facilitate targeted editing and the systematic redesign of complex physiological processes to address emerging breeding goals under shifting environmental conditions.

Crops, Agricultural↗

PubMed2026

Application of Omics Technologies for Cowpea Improvement.

Cowpea (Vigna unguiculata) is a vital crop for food security, nutrition, and climate resilience in sub-Saharan African and other semi-arid regions. However, its improvement is constrained by the complexity of polygenic traits such as drought tolerance, pest resistance, and seed quality. Conventional breeding, while foundational, remains insufficient to address these challenges at the required pace. Recent advances in multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, provide new opportunities to dissect complex traits, identify candidate genes, and accelerate the development of resilient, high-yielding cultivars. This review presents a critical synthesis of current applications of omics technologies in cowpea improvement, highlighting their contributions to stress adaptation, nutritional enhancement, and precision breeding. The review also examines key technical and institutional constraints limiting the adoption of omics-assisted breeding in cowpea, including inadequate research infrastructure, challenges in multi-omics data integration, and limited technical capacity across breeding programs in sub-Saharan Africa. It discusses strategies to address these barriers through regional collaboration, investment in bioinformatics capacity, and the integration of computational approaches into breeding pipelines. Overall, the review concludes that combining multi-omics technologies with artificial intelligence and machine learning has strong potential to improve genotype-phenotype prediction, accelerate breeding decisions, and support the development of climate-resilient and nutritionally enhanced cowpea cultivars.

cowpea↗

PubMed2026

Synergistic HMGN1 and VP64 Fusions Potentiate High-Precision and PAM-Flexible Base Editing.

RNA-guided CRISPR-derived base editors (BEs) have revolutionized genome editing by enabling targeted base substitutions. However, their application is frequently constrained by the stringent requirement for PAM sequences and low editing precision (bystander editing). Here, we present a robust strategy to overcome these limitations by coupling SpRY, a near-PAM-less Cas9 variant, with truncated CDA1 cytidine deaminases. While this combination enables precise editing of virtually any cytosine in the genome, it initially exhibited suboptimal efficiency. To address this, we systematically screened a diverse panel of candidate DNA-binding proteins and identified that the synergistic fusion of HMGN1 and VP64 substantially enhances editing activity without compromising precision. Importantly, this enhanced editing efficiency was achieved without markedly increasing off-target effects. Our new BEs demonstrated robust performance not only in yeast but also in rice, suggesting broad applicability in gene therapy, precision breeding, and fundamental research.

Gene Editing↗

PubMed2026

Multi-omics analysis identifies key genes and functional loci affecting teat number in American Large White and Landrace pigs and their application in optimizing genomic selection models.

BACKGROUND: Teat number is a crucial economic trait in pigs. It directly affects the ability of sows to lactate, which in turn influences the survival and health of piglets. The teat number of French Large White pigs is close to 16, while the teat number of American Large White and Landrace pigs is about 14. In order to improve the teat number of American Landrace and Large White pigs through molecular approaches and precise breeding techniques, we genotyped 2,131 American Landrace and 4,564 American Large White with teat number phenotype using a 50 K SNP chip. Then, the SNP-chip data was imputed to the level of whole-genome sequencing (iWGS). Based on iWGS data, we conducted GWAS to identify novel, significant SNPs associated with teat number and to incorporate them into genomic selection. RESULTS: In Landrace pigs, significant SNPs for TTN mapped to SSC2, SSC7, SSC8, and SSC14; the SSC8 and SSC14 effects are novel. LTN mapped to SSC7, RTN to SSC7 and SSC8. The lead SSC7 SNP explained 2.60% of TTN phenotypic variance. In Large White pigs, significant SNPs were detected on SSC7 and SSC10 for TTN; SSC7, SSC10, and SSC12 for LTN; and SSC7 and SSC10 for RTN. The most significant locus on SSC7 accounted for 2.99% of the phenotypic variance in TTN. Additionally, a multi-population meta-analysis detected significant novel SNPs for LTN on SSC1 and SSC8. By utilizing Bayesian fine mapping, the most precise QTL confidence interval on SSC7 for both TTN and RTN in Large White pigs was reduced to 40 kb. By integrating functional gene annotation with RNA-seq and ATAC-seq data from Erhualian and Bamaxiang pigs mammary placodes at embryonic day 26, we prioritized PTPN13, TRPV3, ZDHHC13, and BRD2 as novel candidate genes for teat number. We then incorporated the significant SNPs to GBLUP and benchmarked genomic-selection accuracy. In both breeds, fitting the top SNP as fixed maximized prediction for TTN and RTN, whereas treating all significant loci as an additional random effect optimized LTN. CONCLUSIONS: Our findings provide a theoretical basis for dissecting new key genes affecting teat number and for advancing molecular breeding of teat number in pigs.

Animals↗

PubMed2026

Tobamoviruses: Advances in Molecular Biology, Host Interactions and Integrated Disease Management.

Tobamoviruses (viruses in the genus Tobamovirus, family Virgaviridae) lead to major yield losses in economically important crops around the world. In this review, we go beyond the canonical gene expression framework by integrating recent discoveries of reverse open reading frames (rORFs) on the negative-strand RNA. These rORFs have only been experimentally validated in cucumber green mottle mosaic virus (CGMMV), with predicted sequence-conserved homologs across a subset of the genus, including TMV, ToBRFV, and PMMoV. However, they are not universally present in all tobamoviruses. We systematically dissect the infection cycle-from disassembly and replication to cell-to-cell and systemic movement-with an emphasis on the host factors hijacked at each stage. We synthesize current understanding of plant antiviral immunity, focusing on RNA silencing and NLR receptor-mediated resistance as two pillars of defense, along with the transcription factors and microRNAs that orchestrate these responses. We critically evaluate the experimental evidence for both plant defenses and viral counter-strategies, noting that many mechanistic models derive from limited model systems. We further characterize host genetic resistance and susceptibility factors applicable to crop breeding. These resources include dominant NLR and non-NLR resistance, as well as recessive resistance derived from modified host susceptibility genes. We address how viral mutations, recombination and fitness trade-offs undermine resistance durability. We then evaluate their practical deployment through conventional breeding, the exploitation of quantitative resistance, and genome editing, and outline associated agronomic drawbacks and regulatory constraints. Using ToBRFV as a case study, we analyze its epidemiological traits and assess the current arsenal of surveillance tools, from field diagnostics to remote sensing. Finally, we survey management strategies across a spectrum of maturity. Some approaches, including sanitation protocols and conventionally bred resistant cultivars, have proven effective under field conditions. The first dsRNA-based biopesticide has recently been registered in China, while other biological control agents and low-risk chemical approaches remain largely at the experimental stage. We also discuss the bottlenecks that impede lab-to-field transition and highlight promising solutions such as precision breeding and evolution-oriented cultivar deployment. By bridging molecular virology, epidemiology, and integrated disease management, this review provides a critical, bench-to-field framework for the sustainable control of tobamoviruses.

TMV↗

PubMed2026

Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.

Genomic prediction of multiple phenotypes is crucial in modern plant breeding; however, existing methods struggle with negative transfer and lack interpretability, particularly across high-dimensional small-sample data and diverse species. To address this, we propose Mul-PheG2P, a novel paradigm based on decoupled learning and predictive space fusion. It employs a two-stage design: first training phenotype-specific encoders using genetic data, then decoupling phenotype-specific learning from cross-phenotype aggregation via an interpretable prediction layer. Mul-PheG2P outperforms existing methods across diverse crop datasets, including maize (Zea mays), wheat (Triticum aestivum), and tomato (Solanum lycopersicum). It provides a multi-scale interpretability chain: at the macro level, it quantifies phenotypic contributions via attention-based weighting; at the micro level, Integrated Gradients reveal the genetic basis of predictions. Notably, the model successfully identified the CCT (CONSTANS, CO-like, and TOC) motif regulating photoperiodism and the SQUAMOSA (SQUAMOSA promoter binding protein) promoter for inflorescence development, confirming its ability to capture functional biological mechanisms. These results highlight the high performance and interpretability of Mul-PheG2P, showcasing its value for low-cost, large-scale screening to advance precision breeding.

Phenotype↗

PubMed2026

Impact of wheat GRF4-GIF1 morphogenic regulators on transformation and genome editing efficiency in elite barley cultivars.

INTRODUCTION: Efficient genetic transformation is essential for the delivery of the CRISPR/Cas9 genome editing system and thus represents an important technology for breeding-oriented research in barley (Hordeum vulgare L.). However, transformation and plant regeneration from tissue culture remain challenging in non-model barley genotypes. Previous studies demonstrated that expression of a chimeric fusion between two interacting transcription factors, GROWTH-REGULATING FACTOR 4 (GRF4) and GRF-INTERACTING FACTOR 1 (GIF1), enhances regeneration capacity in wheat and other species. METHODS: In this study, we evaluated the effect of the wheat-derived GRF4-GIF1 morphogenic regulators on biolistic transformation and genome editing efficiency in three commercial barley cultivars: Tselinniy 5, Aley, and G-23035. RESULTS: The JD633 construct carrying GRF4-GIF1 enabled recovery of stable transformants in all three genotypes, with efficiencies ranging from 2.5% to 5%, whereas the control construct lacking morphogenic regulators resulted in no transgenic events in any of the tested varieties. Among transformed T0 plantlets, genome editing efficiency reached 64.3%, with predominantly biallelic mutations that were stably inherited in the T1 generation. Molecular screening revealed the presence of plasmid-free edited plants in the T0 generation, likely arising from transient Cas9 expression, and provided evidence of tissue chimerism. DISCUSSION: These results demonstrate that the GRF-GIF system facilitates genome editing, providing a practical framework for accelerating precision breeding in barley.

CRISPR/Cas9↗

PubMed2026

Identification of candidate genes for reproductive traits in Chinese Holstein cattle using single-step genome-wide association study.

In dairy farming, reproductive efficiency is vital to both profitability and sustainability. However, years of selective breeding for increased milk yield have adversely affected reproductive potential. This study aimed to pinpoint genomic regions and identify potential candidate genes associated with reproductive traits in Chinese Holstein cattle. In this study, a single-step genome-wide association study (ssGWAS) was conducted using 33,202 phenotypic records from 16,379 animals, 55,244 pedigree records, and genomic data from 1,698 cows. These data were integrated into the ssGWAS analysis, resulting in a total pedigree structure of 21,635 animals. A total of 12 significant markers were identified for calving interval (IC), days open (DO), number of services per conception (NS), and conception rate (CR). Among these significant SNPs, three SNPs were for IC, two SNPs were for DO, three SNPs were for NS, and four SNPs were for CR. Several promising candidate genes located near these SNPs have been identified, including SFXN4, B3GAT2, GRK5, PRDX3, and MTHFD1L, highlighting their potential involvement in fertility-related biological processes. Furthermore, functional enrichment analysis identified significant enrichment of pathways associated with cell adhesion and embryonic development, suggesting a potential mechanistic role for DSG family members (DSG1, DSG2, DSG3, and DSG4) in fertility regulation. Collectively, our findings enhance understanding of the complex genetic basis of reproductive traits in dairy cattle and may offer a valuable set of genomic targets for precision breeding of Chinese Holsteins. Integrating these markers into genomic selection programs may contribute to genetic improvements in reproductive efficiency and support the long-term sustainability of dairy production.

Animals↗

PubMed2026

Genome-wide identification and functional analysis of the BES1-like (VfBES1) gene family in Vernicia fordii reveals its role in floral development.

BACKGROUND: Vernicia fordii Hemsl (also known as Tung tree), an significant commercial oil-producing tree species, is a monoecious and diclinous species with male and female flowers on the same inflorescence; however, the molecular mechanisms governing its floral sex determination remain elusive, particularly the genetic basis underlying the skewed female-to-male flower ratio and the evolutionary dynamics of sex-related gene families, which severely restrict targeted breeding for yield enhancement. In the model plant Arabidopsis, the BRI1 EMS SUPPRESSOR 1 (BES1) transcription factor family plays a crucial role in Brassinosteroid (BR) signaling and reproductive development. However, its function remains largely unexplored in woody perennials. RESULTS: In this study, we introduce the genome-wide identification and functional characterization of the BES1-like (VfBES1) gene family in the Tung tree for the first time. Integrative multi-omics approaches reveal seven VfBES1 genes that are clustered into three phylogenetically distinct clades, each characterized by clade-specific motifs and structural simplicity. Segmental duplication events (VfBES1-1/VfBES1-5 and VfBES1-4/VfBES1-7) and promoter cis-element enrichment (hormone-responsive and abiotic stress-related motifs) highlight evolutionary innovation and functional diversification. Spatiotemporal expression profiling reveals VfBES1 genes' tissue- and stage-specific roles. VfBES1-1 predominantly expresses in female flowers and fruits, suggesting its possible roles in late-stage sex maintenance or ovule and fruit development. VfBES1-2 and VfBES1-6 exhibit male flower-specific and early floral developmental activation, respectively. Nuclear-localized VfBES1-6 displays co-expression with VfMYB35-1 gene, which is a regulator of male structure degeneration. CONCLUSIONS: Findings in this study shed light on the regulatory roles of VfBES1 genes in the floral development of the Tung tree, providing a reference for its precision breeding to enhance flowering synchrony and seed productivity. This study also provides a comparative framework for understanding the functional diversity of BES1-like genes in non-model woody plants.

Flowers↗

PubMed2025

Genomic insights into the population history of fat-tailed sheep and identification of two mutations that contribute to fat tail adipogenesis.

INTRODUCTION: Since their domestication, domestic sheep (Ovis aries) have been culturally and economically significant farming animals worldwide. Fat-tailed sheep serve as a unique genetic resource for understanding adipogenesis and adaptive evolution in livestock. OBJECTIVES: Several genomic analyses have been conducted on various sheep breeds to elucidate the genome and regulation mechanism of the fat tail trait, prior genomic studies have failed to reconcile conflicting evidence about the genetic basis of tail morphology, particularly regarding the roles of PDGFD and BMP2. METHODS: Here, we conducted whole-genome resequencing of 283 sheep, encompassing 66 domestic breeds and 5 wild ovine species, to investigate the domestication history and selection signatures of fat-tailed sheep. Additionally, we performed transcriptome sequencing on adipose tissue to identify differentially expressed genes and cellular assays to validate these results. RESULTS: Demographic analysis revealed that domestic sheep descended from Asiatic mouflon and fat-tailed sheep began to diverge from thin-tailed sheep approximately 4.4-7.5 thousand years ago in East Asia. Chinese indigenous sheep were classified into Mongolian, Kazakh, Tibetan, and Yunnan populations. The Yunnan population may have experienced more recent genetic introgression from wild species, rather than an independent domestication event. Moreover, many potential regions associated with the fat-tailed phenotype (DDI1, PDGFD, and BMP2) were identified by selective sweep and genome-wide association analyses. Additionally, a fine-scale analysis of fat-tailed and thin-tailed sheep revealed two novel mutations: a G/A missense variant of PDGFD (Chr15: 3900312) and a C/T missense variant of BMP2 (Chr13: 48462350), both of which were significantly associated with tail adiposity. Functional validation demonstrated that mutant A-PDGFD significantly activated PFGFD expression and reduced fat deposition compared to wildtype. The C-BMP2 mutant activated BMP2 expression and promoted preadipocyte fat deposition. CONCLUSION: Our study provides the first evidence that these genes jointly regulate fat tail development through complementary mechanisms: PDGFD promotes adipose expansion, whereas BMP2 modulates energy partitioning. These findings offer new insights into the evolutionary history of fat-tailed sheep and identify potential targets for precision breeding in small ruminants.

Animals↗

PubMed2026

Livestock Multi-Omics Integration: A Systematic Framework From Statistical Association to Causal Interpretation.

Livestock multi-omics integration is key to unraveling complex trait regulation, yet systematic, livestock-specific strategies remain scarce. This review traces the progression from single-omics accumulation to multi-dimensional integration, highlighting how large-scale genomic, epigenomic, and transcriptomic projects lay the foundation for functional dissection. We identify core impediments: extreme species diversity, marked data heterogeneity, limited sample sizes, and a pervasive reduction of multi-omics data to simplistic differential screens, resulting in low translational efficiency. We critically appraise four common pitfalls-overinterpreting correlation as causation, relegating proteomics to corroborating transcriptomics, incomplete microbiome-host integration lacking environmental context, and systematic neglect of metabolic fluxomics-and show how exposomics and fluxomics add necessary causal and dynamic dimensions. To address these, we propose a livestock-adapted three-tier analytical framework: (1) statistical association of cross-omics covariation patterns; (2) machine learning-driven feature mining and integrative modeling; and (3) causal interpretation encompassing Mendelian randomization, prior-knowledge-guided network inference, and physical causal evidence via fluxomics and metabolic control analysis. We further discuss how multimodal sequencing (single-cell, spatial, temporal) and generative AI can fundamentally mitigate heterogeneity and strengthen causal evidence. Finally, we outline future priorities in database standardization, livestock-specific benchmarking, and translational pipelines, charting a path from correlation-centric reporting to mechanistic causality and precision breeding.

Animals↗

PubMed2026

3D chromatin remodeling during domestication defines novel targets for crop improvement.

Three-dimensional (3D) genome folding shapes gene regulation, yet the genetic underpinnings linking 3D genome evolution to phenotypic innovation during domestication remain elusive. Using population-scale Hi-C profiling of 34 semi-wild and 267 cultivated allotetraploid cottons, we generated a pan-3D genome atlas capturing extensive diversity in topologically associating domains (TADs) and chromatin loops. Chromatin interactome-wide association studies identified 105 TAD reconfigurations and 58 loop rewirings that were established as the 3D chromatin basis of fiber quality, boosting heritability estimates for fiber strength by 16% and fiber length by 20%. We reveal that domestication selection within sequence-defined sweeps fixed 57% of 3D conformation signatures, thereby decoupling sequence-level from chromatin-level selection and shifting the subgenome expression balance of 39 homoeologs in cultivated cotton. Sequence-based modeling and mutational analyses identified the C2H2 zinc-finger protein YY1 as a conserved mediator of 3D genome organization. This study provides a resource for redefining precision-breeding paradigms by harnessing cryptic 3D chromatin targets.

3D genome↗