Goats (Capra hircus) are among the world’s most important livestock, providing milk, meat, and fiber across diverse agro-ecological zones. Traditional breeding relying on pedigree-based estimated breeding values (EBVs) has driven steady genetic progress but is constrained by long generation intervals and limited accuracy for sex-limited or difficult-to-measure traits. High-throughput single nucleotide polymorphism (SNP) chips and genomic selection (GS) have transformed goat breeding by enabling early, accurate selection independent of phenotypic records. This review synthesizes the development of goat SNP chip platforms from the foundational 52 K GoatSNP50 BeadChip through high-density solid-phase arrays and low-cost liquid-phase capture panels, with emphasis on their relative performance, cost-effectiveness, imputation potential, and suitability for different breeding systems. In addition to genomic selection (GS), genome-wide association studies (GWAS), and genetic diversity assessment, we also discuss candidate-gene selection and marker-assisted selection (MAS) as practical intermediate approaches that remain relevant in many goat breeding programs. GS has achieved genomic estimated breeding value (GEBV) prediction accuracies of 0.35–0.79 for key production traits across multiple countries and breeds. GWAS has identified candidate genes for milk composition (DGAT1, CSN1S1), growth (PLAG1, HMGA2), reproduction (BMPR1B, GDF9), and fiber quality (KRT, KRTAP families). We compare GS with traditional BLUP-based approaches, assess economic benefits, and discuss key challenges including reference population construction, genotype imputation, inbreeding management via Optimum Contribution Selection (OCS), and multi-omics integration. Future directions include customized chip design, AI-assisted genomic prediction, climate adaptation breeding, and CRISPR/Cas9 gene editing for precision improvement.
Genomic selection (GS) has transformed modern animal breeding by enabling the prediction of genetic merit using dense genome-wide molecular markers rather than relying solely on pedigree and phenotypic information. Since its conceptual introduction in 2001, GS has become a cornerstone of genetic improvement programs in livestock species, particularly dairy cattle, and has subsequently expanded to beef cattle, sheep, goats, swine, poultry, and aquaculture. The integration of high-density single nucleotide polymorphism (SNP) genotyping with advanced statistical prediction models has substantially increased the accuracy of breeding value estimation, shortened generation intervals, and accelerated rates of genetic gain. Compared with conventional best linear unbiased prediction (BLUP) and marker-assisted selection (MAS), genomic selection captures the combined effects of thousands of loci distributed throughout the genome, making it highly effective for complex quantitative traits governed by many genes of small effect. Recent advances, including single-step genomic BLUP, Bayesian prediction methods, whole-genome sequence analysis, functional genomics, multi-omics integration, artificial intelligence, and precision livestock farming technologies, have further enhanced the scope and efficiency of genomic prediction. These innovations are facilitating simultaneous improvement in productivity, fertility, feed efficiency, disease resistance, animal welfare, and environmental sustainability. Moreover, genomic information is increasingly being integrated with genome editing technologies such as CRISPR to support precision breeding strategies. This review summarizes the historical evolution, fundamental principles, methodological developments, and practical applications of genomic selection in livestock breeding while highlighting emerging innovations and future research directions that are expected to shape next-generation animal improvement programs.
S. Pathak, Amit Kumar, Vaishali Sah· International Journal of Env...· 0 citations
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, 3 SNPs were for IC, 2 SNPs were for DO, 3 SNPs were for NS, and 4 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.
W. A. Lombebo, Mingxin Du, G. M. Tarekegn et al.· Journal of Animal Science· 0 citations
Faba bean breeding and genomics have seen steady progress in recent years, supported by genome sequences and high-density genotyping platforms. These tools have been valuable for trait mapping, diversity assessment, and genomic research, but they have limited routine use in breeding programs due to their relatively high cost. Recent progress in establishing an optimized, cost-efficient genotyping-by-sequencing protocol tailored to the large and complex faba bean genome has created the foundation for a more accessible genotyping solution.
Using this approach, we explored the genetic diversity of faba bean germplasm from various panels, providing a comprehensive representation of the crop’s genetic landscape. From this dataset, we identified and selected a high-quality set of informative SNP markers that are evenly distributed across the genome. Building on these resources, we designed a breeder-friendly 10K SNP chip.
The 10K SNP chip delivers high accuracy, broad genomic coverage, and affordability. The chip was validated across diverse germplasm panels, demonstrating strong clustering performance, high reproducibility, and applicability to breeding-relevant germplasm.
This platform offers a cost-effective alternative to higher-density arrays, enabling its integration into genomic selection, marker-assisted breeding, and diversity monitoring, ultimately supporting accelerated genetic gain and the delivery of improved varieties to farmers.
Hyeonah Shim, Hailin Zhang, Thomas Groß et al.· Frontiers in Plant Science· 0 citations
Feed efficiency is a core economic trait in pig breeding, with feed costs accounting for 60%-70% of total production expenses. Deciphering its genetic basis is critical for precise molecular breeding and the development of grain-saving pig breeds. However, most existing studies rely on SNP arrays, which cannot capture insertion-deletion (InDel) variants, and systematic research on modern intensively selected Large White pig populations remains scarce. In this study, we conducted a genome-wide association study (GWAS) for three core traits-feed conversion ratio (FCR), average daily gain (ADG), and average daily feed intake (ADFI)-using whole-genome resequencing data from 299 purebred Large White pigs, with simultaneous detection of genome-wide single nucleotide polymorphisms (SNPs) and InDels. We identified multiple genomic regions significantly associated with feed efficiency traits. An intronic InDel in RASAL2 represented the strongest FCR association signal. As a specific negative regulator of the Ras signaling pathway, RASAL2 positively regulates preadipocyte differentiation and ameliorates high-fat diet-induced insulin resistance, suggesting it may be associated with feed conversion ratio through energy deposition and metabolic homeostasis. We further annotated 11 additional core candidate genes including TAFA5, TMCC3, BTG1, and PLCB1, which are mainly involved in adipogenesis, energy homeostasis, and skeletal muscle development. These findings enrich genetic resources for porcine feed efficiency research and support molecular marker development for genomic selection in Large White pigs.
Lin Chen, Bin Yang, Haoran Shi et al.· Animal Genetics· 0 citations
Genomic selection (GS) has become an important tool for accelerating genetic gain in wheat breeding by enabling the prediction of target traits using genome-wide molecular markers. However, the large-scale implementation of GS in public breeding programs remains constrained by the cost of high-density genotyping platforms. Medium-density targeted genotyping approaches provide a cost-effective alternative while maintaining prediction accuracy. In this study, we evaluated the performance of a public wheat mid-density genotyping platform (Wheat DArTag 3.9K EIB 2.0) for GS by comparing it with a previously deployed higher-density genotyping-by-sequencing (GBS) platform. The analyses were conducted using five consecutive years of CIMMYT Elite Yield Trials comprising more than 5000 elite spring wheat lines evaluated across multiple irrigated, drought, and heat-stressed environments. Trait predictability was assessed for agronomic, phenological, and disease resistance traits using the genomic best linear unbiased prediction (GBLUP) model under several cross-validation scenarios, including within-year and across-year predictions. After quality filtering, the DArTag platform retained approximately 1600–1800 SNPs, whereas the GBS platform retained approximately 6500–9600 SNPs. Across traits and years, no consistent superiority of GBS over DArTag was observed, and correlations between genomic estimated breeding values (GEBVs) obtained from both platforms were high, indicating that both genotyping systems would lead to highly similar selection decisions. The results suggest that, in elite wheat germplasm characterized by long-range linkage disequilibrium and strong realized genomic relationships, medium-density targeted genotyping platforms can retain most of the predictability achieved by higher-density systems. Overall, the public Wheat DArTag 3.9K EIB 2.0 platform represents a scalable and cost-effective solution for implementing GS in operational wheat breeding programs.
This narrative review synthesizes major advances in sheep genetic mapping, functional genomics, and GS relevant to economically important traits to provide researchers, breeders, and students with a balanced, evidence-based resource to guide future genomic improvements in sustainable sheep production.
Mostafa Ghaderi-Zefrehei, Effat Nasre Esfahani, Hassan Amini Pozveh et al.· Frontiers in Genetics· 0 citations