Goats (Capra hircus) are a widely distributed livestock known for their exceptional environmental adaptability. This review presents an integrated overview of recent advances in understanding the genetic and molecular mechanisms underlying goat adaptation to heat, cold, and high-altitude hypoxia. We first discuss the development of high-quality reference genomes, including recent telomere-to-telomere assemblies. We then examine major adaptive genes such as HSP70, ACTHR, EPAS1, SLC2A1, FGF12, and UCP1, and their roles in thermoregulation, oxygen metabolism, and stress resistance. Additionally, the review explores the synergistic role of immune signaling pathways in environmental adaptation, as well as the regulatory effects of epigenetic mechanisms such as DNA methylation, histone modification, and non-coding RNAs. Future efforts should focus on integrating multi-omics data to uncover the complex molecular networks involved in goat adaptation. This comprehensive synthesis offers valuable insights for precision breeding and long-term sustainability in the context of environmental challenges and climate change.

With the rapid development of omics technologies, metabolomics and lipidomics have become important tools for elucidating the molecular basis of reproductive performance in livestock. These approaches, which focus on small metabolites and lipid species, provide valuable insights into dynamic interactions between genes, proteins, and the environment, offering a high level of sensitivity. This review summarizes key methodological advances in metabolomics and lipidomics and their applications to gametogenesis, oocyte maturation, embryo development, and pregnancy maintenance. It highlights how metabolic and lipid pathways, particularly those involving energy metabolism, redox regulation, lipid remodeling, and cell–cell signaling, affect reproductive cell quality. This review further integrates biomarkers identified from semen, follicular fluid, oocytes, embryos, and uterine tissues, highlighting their potential roles in fertility assessment. Together, these insights enhance our understanding of metabolic regulation in reproduction and support the effective application of metabolomics and lipidomics in improving livestock fertility.

Pangenomics has become an important framework for representing genetic diversity beyond a single linear reference genome. In agricultural species, it improves access to structural variants (SVs), copy number variations (CNVs), presence/absence variations (PAVs), and non-reference regulatory or coding sequences that may contribute to domestication, adaptation, and breeding traits. This review summarizes recent progress in long-read sequencing, telomere-to-telomere (T2T) assembly, and graph-based genome analysis, with emphasis on both livestock and crop systems. We first define the conceptual boundary between pangenome representations and reference-based variant catalogs. We then compare three major technical routes: variant integration, reference-guided iterative graph construction, and reference-free graph construction. Their performance is evaluated in terms of accuracy, scalability, coordinate consistency, reference bias, computational demand, annotation transfer, and suitability for downstream breeding questions. We further discuss how pangenome resources support hidden variant discovery, QTL and GWAS interpretation, environmental adaptation analysis, and multi-omics-based candidate prioritization. Importantly, we highlight unresolved limitations, including graph complexity, pipeline-dependent SV calls, incomplete functional annotation, weak cross-study comparability, and the difficulty of distinguishing causal variants from linked or neutral variation. This review therefore treats pangenome studies as connected but non-equivalent evidence: resource-building studies establish representational breadth, method papers define technical feasibility, and trait-focused studies provide varying levels of biological support. Apparent inconsistencies among studies are interpreted as signals of differences in sampling, genome complexity, validation depth, and graph construction strategy rather than as simple disagreements.

As essential branches of systems biology, metabolomics and lipidomics systematically reveal the composition, dynamic changes, and biological functions of small-molecule metabolites and lipids using high-throughput analytical techniques. This review examines the application of these omics technologies in evaluating livestock and poultry meat and egg quality, focusing on their roles in elucidating the molecular mechanisms behind key traits such as flavor, tenderness, and nutritional value. By identifying key metabolic markers—including glutamic acid, inosine monophosphate, and specific triglycerides—the intrinsic links between these markers and intramuscular fat deposition, flavor precursor formation, and antioxidant capacity are highlighted. Furthermore, this paper emphasizes the transformative impact of integrating multi-omics data with artificial intelligence (AI). AI-driven analytical frameworks are overcoming the limitations of traditional high-dimensional data processing, enabling robust biomarker discovery, predictive modeling for product quality, and reverse design for genetic improvement. Ultimately, the synergistic application of metabolomics, lipidomics, and AI will drive the development of modern animal husbandry toward intelligent, predictable, and precision-based production.

The important economic traits of ruminants result from interactions between genetic background and environmental factors, but key traits such as reproductive performance, feed efficiency, disease resistance, and livestock product quality are often not fully explained by DNA sequence variations alone. Increasing evidence suggests that epigenetic regulation serves as a crucial molecular bridge connecting environmental stimuli with changes in gene expression, allowing organisms to exhibit stable and plastic phenotypic differences without altering the DNA sequence. This review provides a structured synthesis of recent research in the field of epigenetics in ruminants, elucidating how multiple layers of epigenetic mechanisms, including DNA methylation, histone modifications, non-coding RNAs, and higher-order chromatin structures, coordinate to regulate growth, development, reproductive performance, metabolic and immune homeostasis, and livestock product traits across different tissues and developmental stages. These epigenetic marks not only demonstrate high responsiveness to nutrition, management, and environmental stressors, but can exhibit context-dependent stability within the same tissue and physiological stage when environmental conditions are comparable, thereby contributing to the regulation of phenotypic plasticity and offering potential value as predictive biomarkers. Furthermore, epigenetic information can supplement our understanding of phenotypic variation in ways that traditional genomic selection methods are unable to capture, offering new data dimensions for the prediction and improvement of low heritability, environmentally sensitive traits. Overall, integrating epigenetic information with genomic selection strategies may improve the accuracy of ruminant trait prediction and enhance environmental adaptability. This integration also offers a conceptual basis and technical pathway for developing more precise and sustainable breeding systems.