Sanjeev 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.

This study investigates the selection signatures of 11 Indigenous goat breeds from diverse eco-topographies of India, using whole-genome re-sequencing data from 103 individuals. We identified population-wide copy number variation regions (as well as selection signatures through a variance-stabilizing transformation approach for utility traits. A total of 32 711 polymorphic sites were analyzed, revealing 327 significant and 32 highly significant signatures under selection. Key genes identified in selection signatures include GHR, PLAG1, and MTOR, which play crucial roles in growth, development, and reproductive traits across different utility groups. Notable reproduction-related genes such as ITPR3, ESRRG, and SOX6 were found to be associated with fertility, hormone regulation, and reproductive system. Network analysis revealed ESR1 as a central hub gene forming significant interactions with RUNX2, HDAC2, and BCL2, indicating its vital role in muscle development and metabolism. The MTOR signaling pathway emerged as another crucial hub, connecting with DEPDC5 and SESN1, suggesting its importance in nutrient sensing and metabolic regulation for production traits. Gene ontology analysis of the selection signatures revealed pathways for functional categories between meat, milk, and fiber-producing breeds, reflecting the genetic architecture underlying their specialized phenotypes. Identified selection signatures and hub genes can be used in marker-assisted and genomic selection to improve growth, reproduction, and adaptability in indigenous goats, aiding precision breeding and conservation programs.

Identifying and classifying different cattle populations as per their breed and utility holds immense practical importance in effective breeding management

India is rich in the world’s largest and most diverse livestock with about 535.78 million animals. Cattle is a major livestock species in India comprising nearly 36% of the total livestock population. According to 20th livestock census, Maharashtra comprises 13.99 million cattle population, out of which 9.38 million are indigenous and 4.60 million are exotic and crossbred cattle. Maharashtra is divided into nine agro-climatic zones on the basis of annual rainfall, soil type, vegetation and cropping pattern. Due to this climatic diversity, different cattle populations have been developed according to the local demand. Out of total 53 registered cattle breeds, 7 breeds viz., Dangi, Gaolao, Khillar, Red Kandhari, Deoni, Konkan Kapila and Kathani are found in Maharashtra. These breeds are generally draft type and can be used for transport and agriculture. Other cattle populations (nondescript) consist of nearly 67% of the total cattle of the state and Maharashtra ranks 5th position in total nondescript cattle population of India. Indigenous livestock is adapted to the adverse conditions of our country and is resistant to various diseases. Hence, there is an urgent need for identification, characterization, evaluation and documentation of the nondescript populations. Further, admixture level of these populations should be studied for developing suitable breeding and conservation strategies for the breeds and/ populations.

India has 50 registered breeds of native cattle (Bos indicus) which are locally adapted to diverse environmental conditions. This study aimed to investigate the genomic basis of adaptation of native Indian cattle and to predict the impact of key SNPs on the amino acid changes that affect protein function. The Illumina 777 K BovineHD BeadChip was used to genotype 178 native cattle belonging to contrasting landscapes and agro-climatic conditions. The genotype-environment association was investigated with R. SamBada, using 5,74,382 QC passed SNPs and 11 predictor variables (10 multi-collinearity controlled environmental variables and 1 variable as “score of PCA” on ancestry coefficients of individuals). In total, 1,12,780 models were selected as significant (q < 0.05) based on G score. The pathway ontology of the annotated genes revealed many important pathways and genes having a direct and indirect role in cold and hot adaptation. Only ten SNP variants had a SIFT score of < 0.05 (deleterious), and only two of them, each lying in the genes CRYBA1 and USP18, were predicted to be deleterious with high confidence. RaptorX predicted the tertiary structures of proteins encoded by wild and mutant variants of these genes. The quality of the models was determined using Ramachandran plots and RaptorX parameters, indicating that they are accurate. RaptorX and I-Mutant 2.0 softwares revealed significant differences among wild and mutant proteins. Adaptive alleles identified in the present investigation might be responsible for the local adaptation of these cattle breeds.

Copy number variations (CNVs) include deletions, duplications, and insertions that are larger than 50 bp in size causing structural variation responsible for diversity, adaptation, and breed development. Indian cattle breeds are highly diverse from the taurine breeds. The pattern of CNVRs in 191 animals belonging to 39 cattle breeds (four Indicine and 35 Taurine) was studied based on Illumina 777K BovineHD chip data. The Indicine breeds revealed 2590 CNVs and 335 copy number variation regions (CNVRs) in autosomes. Out of the identified CNVs, 50 were found to be novel. Structure analysis revealed admixed nature of Siri. Neighbor joining tree from CNVR data showed that hot (Kankrej and Hallikar) and cold (Ladakhi and Siri) adapted cattle breeds clustered separately. CNVR of Indian and European breeds revealed that Balkan and Italian breeds of Podolian group are admixed with Indian cattle breeds corroborating indicine introgression (6.1–13.5%). CNVRs spanning the regions of olfactory receptors and immune system genes were identified. AMOVA revealed 9% variation among populations which is 2% greater than SNP based studies showing higher inclusion of variation by CNVR. Detailed analysis of CNVs/CNVRs in Indian cattle adapted to hot and cold climate, and their diversity among worldwide cattle is presented in this study.