Ritik Kumar Singh

Metagenomics, the study of genetic material directly extracted from environmental samples, has revolutionised microbial research by enabling culture-independent investigation of microbial diversity, community structure, and potential. It has become a potent tool in livestock production systems for addressing major challenges related to animal health, productivity, environmental sustainability, antimicrobial resistance, and greenhouse gas emissions. Metagenomic approaches have provided critical insights into ruminal microbial ecology, host–microbiome interactions, feed efficiency, milk production, heat stress resilience, metabolic disorders, and methane emissions, in addition to facilitating the identification of microbial biomarkers and functional pathways associated with economically important traits. Furthermore, metagenomics has improved One Health surveillance through characterization of resistomes, mobile genetic elements, zoonotic pathogens, and microbial reservoirs of antimicrobial resistance. Applications also extend to uterine and faecal microbiome research, viral detection, novel enzyme discovery, therapeutic development, and biodegradation. The use of metagenomics in precision nutrition, microbiome-informed breeding, disease monitoring, and sustainable livestock management has been greatly increased by recent developments in high-throughput sequencing, bioinformatics, and multi-omics integration. This review highlights the revolutionary potential of metagenomics in livestock production systems by examining its methodological advancements, historical background, and diverse applications.

The Karan Fries (KF), a newly developed dairy cattle (Holstein Friesian × Tharparkar), after nine generations of inter se mating (mating among crossbreds) by maintaining a stable, composite breed structure, is expected to preserve some heterosis or hybrid vigour through the retention of heterozygosity and selective pressure for performance traits. Although a decline in productivity is generally seen from F1 to later inter se generations, the rate of loss of heterosis slows down, allowing the population to reach a new equilibrium. This seems to be an interesting proposition. Therefore, to assess the retained heterosis and to evaluate the current status of the KF population, the present study was conducted, which included a sizeable phenotyped and genotyped animals (n = 355). The analysis identified a stabilized genomic architecture with a mean exotic inheritance of 64.34 ± 0.74% (predominantly Holstein Friesian) and an indigenous contribution of 35.66 ± 0.95% (primarily Tharparkar). Genomic indicators of heterosis revealed a mean Genomic Retained Heterosis (RH_g) of 59% which accounts for 15.82% of the total observed KF average yield (4153.14 ± 167.32 kg). Genomic Retained Heterozygosity (RHET_g) of 0.44%. Notably, a near-perfect correlation (r = 0.98) was observed between these two metrics, indicating that RHET_g can be used interchangeably with RH_g to assess heterosis and its effects in instances where parental genotypic records were absent. Linear regression analysis demonstrated that RH_g was a primary driver of productivity, with Total Milk Yield (TMY) increasing by 1113.81 ± 301.91 kg per unit increase in RH_g (p < 0.001). The impact on TMY was significantly larger than on 305-day milk yield (339.08 ± 282.92 kg), suggesting that genomic heterosis specifically enhances lactation persistence and environmental robustness. A joint-model GWAS identified 182 SNPs with significant additive effects and 117 SNPs with significant dominance effects for TMY. Functional annotation revels candidate genes for milk synthesis (SLC25A1, PIP4K2A, LATS2), heat stress resilience (DNAJB5, DNAJC1), and immunological defence (ARHGAP15, LPAR3). Protein network analysis identified the Mitochondrial Ribosomal Protein (MRP) family, specifically hub gene MRPL22, as the central driving gene for both additive and dominance components. These findings confirm that KF cattle effectively leverage retained heterosis to integrate high production potential with robust tropical climatic adaptability.

Background: This study evaluates machine learning (ML) algorithms as a robust alternative to traditional linear models, which often overlook complex biological patterns for predicting lactation performance in Karan Fries (KF) cattle. Using phenotypic records from 473 lactating animals, the analysis focuses on predicting 305-day milk yield (305 DMY) and total milk yield (TMY). The population exhibited an average peak yield of 13.28±0.25 kg and a persistent average fat concentration of 4.14%. Methods: This study was done by integrating Wood’s incomplete gamma function to model lactation curves, compressing longitudinal test-day data into mathematically significant parameters. These parameters, alongside fixed environmental factors, served as predictors for three models: Multiple linear regression (MLR), random forest (RF) and extreme gradient boosting (XGBoost). Result: Results revealed that the RF model significantly outperformed both MLR and XGBoost. For 305DMY, RF achieved a high coefficient of determination (R2 = 0.88) compared to the poor performance of the MLR baseline (R2 = 0.21). A similar hierarchy persisted for TMY, where RF attained an R2 of 0.87, effectively reducing the root mean square error (RMSE) by over 50% compared to MLR. The study concludes that integrating Wood’s parameters with ensemble-based ML models offers a transformative tool for dairy breeding by facilitating early selection and optimized herd management.