Sheep (Ovis aries) were domesticated around 10,000 years ago and have since become an integral part of human agriculture, providing essential resources, such as wool, meat, and milk. Over the past century, advances in communication and agricultural productivity have driven the evolution of selective breeding practices, further enhancing the value of sheep in the global economy. Recently, the rapid development of whole-genome resequencing (WGR) technologies has significantly accelerated research in sheep molecular biology, facilitating the discovery of genetic underpinnings for critical traits. This review offers a comprehensive overview of the evolution of whole-genome resequencing and its application to sheep genetics. It explores the domestication and genetic origins of sheep, examines the genetic structure and differentiation of various sheep populations, and discusses the use of WGR in the development of genetic maps. In particular, the review highlights how WGR technology has advanced our understanding of key traits, such as wool production, lactation, reproductive performance, disease resistance, and environmental adaptability. The review also covers the use of WGR technology in the conservation and sustainable utilization of sheep genetic resources, offering valuable insights for future breeding programs aimed at enhancing the genetic diversity and resilience of sheep populations.

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.

Whole-genome resequencing (WGRS) is a critical branch of whole-genome sequencing (WGS), primarily targeting species with existing reference genomes. By aligning sequencing data to the reference genome, WGRS enables precise detection of genetic variations in individuals or populations. As a core technology in genomic research, WGS holds profound significance in ruminant studies. It not only reveals the intricate structure of ruminant genomes but also provides essential data for deciphering gene function, variation patterns, and evolutionary processes, thereby advancing the exploration of ruminant genetic mechanisms. However, WGS still faces several challenges, such as incomplete and inaccurate genome assembly, as well as the incomplete annotation of numerous unknown genes or gene functions. Although WGS can identify a vast number of genomic variations, the specific relationships between these variations and phenotypes often remain unclear, which limits its potential in functional studies and breeding applications. By performing WGRS on multiple samples, these assembly challenges can be effectively addressed, particularly in regions with high repeat content or complex structural variations. WGRS can accurately identify subtle variations among different individuals or populations and further elucidate their associations with specific traits, thereby overcoming the limitations of WGS and providing more precise genetic information for functional research and breeding applications. This review systematically summarizes the latest applications of WGRS in the analysis of ruminant genetic structures, genetic diversity, economic traits, and adaptive traits, while also discussing the challenges faced by this technology. It aims to provide a scientific foundation for the improvement and conservation of ruminant genetic resources.

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.

Multi-omics studies have multiplied associations, but many still lack causal resolution and a clear path to application. We present a practical roadmap built on four sequential steps: first, identify signals from genome-wide association studies; second, confirm these signals through regulatory colocalization and transcriptome-wide association analyses; third, integrate the evidence using network analyses and causal inference; and, fourth, test shortlisted candidates through functional and phenotypic validation. The roadmap is supported by three safeguards that make results reliable and reusable: containerized workflows that ensure end-to-end reproducibility, harmonization across batches with concise minimum-information records, and consistent identifier mapping with quality control across data layers. Across four classes of traits—growth and development, carcass and meat quality, reproduction, and environmental adaptation and resilience—we prioritize signals that remain robust across ancestries and environments, highlight modules with explicit regulatory support, and advance candidates that have already progressed to functional testing. Two application tracks follow from this process: integrating stable candidates into selection indices with context-dependent weighting, and recording and targeting mechanistic nodes for nutritional and management interventions. Taken together, this roadmap improves causal interpretability, strengthens cross-population robustness, and shortens the path from statistical association to genetic evaluation and industry uptake.