Molecular and phylogenetic analysis of MHC class I exons 7-8 in a variety of cattle and buffalo breeds.

The present study was conducted on the MHC class I (BoLA-A/BuLA-A) gene in Sahiwal, Jersey, Hariana, and Tharparkar breeds of cattle and Murrah, Mehsana, and Bhadawari breeds of buffalo to study the polymorphism. Exons 7-8 of the MHC class I gene was first characterized for polymorphism study in buffalo and the results reveal that this gene has a higher level of nucleotide changes than the cattle. Genes were investigated for polymorphisms in 285 animals of cattle and buffalo breeds. Molecular characterization of the MHC class I (BoLa-A/Bula-A) gene reveals a higher degree of polymorphism at the nucleotide level in cattle and buffalo. Results revealed this region has a higher level of polymorphisms in buffalo as campared to the cattle. Alul restriction patterns were monomorphic except for three different patterns but it was able to illustrate the differences in buffalo and cattle. SSCP analysis of exons 7-8 showed remarkable differences in cattle and buffalo. Sequence analysis revealed more closeness of Murrah breed with crossbred and indigenous cattle than Holstein Friesian. Exon 8 had more deletion and stop codon as compared to exon 7. The investigation confirmed that MHC class I BoLa-A/Bula-A exons 7-8 is highly polymorphic in buffalo as compared to cattle.

Characterization of BoLA class II DQA and DQB by PCR-RFLP, cloning, and sequencing reveals sequence diversity in crossbred cattle.

The BoLA class II DQA and DQB genes in crossbred cattle were studied using PCR-RFLP, cloning, and sequencing techniques. Seventy-two crossbred cattle (Vrindavani) were used in the current study. HaeIII and XbaI restriction enzymes digested DQA exon 2-3, revealing seven (HaeIII-A-G) and three (XbaI A-C) motifs, respectively. The BoLA-DQB gene was analyzed using PCR-RFLP with PstI and TaqI restriction enzymes, yielding five restriction motifs for each restriction enzyme (PstI-A-E and TaqI-A-E). In crossbred cattle, addition, deletion, and substitutions were observed in distinct sequences, resulting in variations in overall gene length. Changes in nucleotides at positions 64-80, 110-200, and 207-264 were largely responsible for polymorphism in DQA exon 2. The phylogenetic analysis predicted a high degree of nucleotide and amino acid changes in DQA exon 2-3 and DQB exon 2. DQA genes had a nucleotide dissimilarity of 0.3-25.4 percent, while DQB genes had a nucleotide dissimilarity of 1.5-14.3 percent. We cloned and sequenced 20 genotypes based on PCR-RFLP of the DQA and DQB genes. The current study observed variation in the DQA and DQB genes and will serve as a foundation for future research on the BoLA DQA and DQB genes.

Trajectory of livestock genomics in South Asia: A comprehensive review.

Livestock plays a central role in sustaining human livelihood in South Asia. There are numerous and distinct livestock species in South Asian countries. Several of them have experienced genetic development in recent years due to the application of genomic technologies and effective breeding programs. This review discusses genomic studies on cattle, buffalo, sheep, goat, pig, horse, camel, yak, mithun, and poultry. The frontiers covered in this review are genetic diversity, admixture studies, selection signature research, QTL discovery, genome-wide association studies (GWAS), and genomic selection. The review concludes with recommendations for South Asian livestock systems to increasingly leverage genomic technologies, based on the lessons learned from the numerous case studies. This paper aims to present a comprehensive analysis of the dichotomy in the South Asian livestock sector and argues that a realistic approach to genomics in livestock can ensure long-term genetic advancements.

Machine-Learning Prospects for Detecting Selection Signatures Using Population Genomics Data.

Natural selection has been given a lot of attention because it relates to the adaptation of populations to their environments, both biotic and abiotic. An allele is selected when it is favored by natural selection. Consequently, the favored allele increases in frequency in the population and neighboring linked variation diminishes, causing so-called selective sweeps. A high-throughput genomic sequence allows one to disentangle the evolutionary forces at play in populations. With the development of high-throughput genome sequencing technologies, it has become easier to detect these selective sweeps/selection signatures. Various methods can be used to detect selective sweeps, from simple implementations using summary statistics to complex statistical approaches. One of the important problems of these statistical models is the potential to provide inaccurate results when their assumptions are violated. The use of machine learning (ML) in population genetics has been introduced as an alternative method of detecting selection by treating the problem of detecting selection signatures as a classification problem. Since the availability of population genomics data is increasing, researchers may incorporate ML into these statistical models to infer signatures of selection with higher predictive accuracy and better resolution. This article describes how ML can be used to aid in detecting and studying natural selection patterns using population genomic data.