Biological methylation: redefining the link between genotype and phenotype

The central dogma of molecular biology is responsible for the crucial flow of genetic information from DNA to protein through the transcription and translation process. Although the sequence of DNA is constant in all organs, the difference in protein and variation in the phenotype is mainly due to the quality and quantity of tissue-specific gene expression and methylation pattern. The term methylation has been defined and redefined by various scientists in the last fifty years. There is always huge excitement around this field because the inheritance of something is beyond its DNA sequence. Advanced gene methylation studies have redefined molecular genetics and these tools are considered de novo in alleviating challenges of animal disease and production. Recent emerging evidence has shown that the impact of DNA, RNA, and protein methylation is crucial for embryonic development, cell proliferation, cell differentiation, and phenotype production. Currently, many researchers are focusing their work on methylation to understand its significant role in expression, disease-resistant traits, productivity, and longevity. The main aim of the present review is to provide an overview of DNA, RNA, and protein methylation, current research output from different sources, methodologies, factors responsible for methylation of genes, and future prospects in animal genetics.

Signatures of selection in riverine buffalo populations revealed by genome-wide SNP data

The detection of selection signatures assists in understanding domestication, evolution, and the identification of genomic regions related to adaptation and production traits in buffaloes. The emergence of high-throughput technologies like Next Generation Sequencing and SNP genotyping had expanded our ability to detect these signatures of selection. In this study, we sought to identify signatures of selection in five buffalo populations (Brazilian Murrah, Bulgarian Murrah, Indian Murrah, Nili-Ravi, and Kundi) using Axiom Buffalo 90 K Genotyping Array data. Using seven different methodologies (Tajima's D, CLR, ROH, iHS, FST, FLK and hapFLK), we identified selection signatures in 374 genomic regions, spanning a total of 381 genes and 350 quantitative trait loci (QTLs). Among these, several candidate genes were associated with QTLs for milk production, reproduction, growth and carcass traits. The genes and QTLs reported in this study provide insight into selection signals shaping the genome of buffalo breeds. Our findings can aid in further genomic association studies, genomic prediction, and the implementation of breeding programmes in Indian buffaloes.

Progress and future perspectives of livestock genomics in India: a mini review

The field of genetics has evolved a lot after the emergence of molecular and advanced genomic technologies. The advent of Next Generation Sequencing, SNP genotyping platforms and simultaneous reduction in the cost of sequencing had opened the door to genomic research in farm animals. There are various applications of genomics in livestock, such as the use of genomic data: (i) to investigate genetic diversity and breed composition/population structure (ii) to identify genetic variants and QTLs related to economically important and ecological traits, genome-wide association studies (GWAS) and genomic signatures of selection; (iii) to enhance breeding programs by genomic selection. Compared to traditional methods, genomic selection is expected to improve selection response by increasing selection accuracy and reducing the generation interval due to early selection. Genomic selection (GS) in developed countries has led to rapid genetic gains, especially in dairy cattle, due to a well-established genetic evaluation system. Indian livestock system is still lagging behind developed nations in adopting these technologies. This review discusses the current status, challenges, and future perspectives of livestock genomics in India.

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.

Role of genomics in combating COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) quickly swept over the world, becoming one of the most devastating outbreaks in human history. Being the first pandemic in the post-genomic era, advancements in genomics contributed significantly to scientific understanding and public health response to COVID-19. Genomic technologies have been employed by researchers all over the world to better understand the biology of SARS-CoV-2 and its origin, genomic diversity, and evolution. Worldwide genomic resources have greatly aided in the investigation of the COVID-19 pandemic. The pandemic has ushered in a new era of genomic surveillance, wherein scientists are tracking the changes of the SARS-CoV-2 genome in real-time at the international and national levels. Availability of genomic and proteomic information enables the rapid development of molecular diagnostics and therapeutics. The advent of high-throughput sequencing and genome editing technologies led to the development of modern vaccines. We briefly discuss the impact of genomics in the ongoing COVID-19 pandemic in this review.

Genome-wide detection of copy number variations in Tharparkar cattle

Copy number variations (CNVs) are major forms of genetic variation with an increasing importance in animal genomics. This study used the Illumina BovineSNP 50 K BeadChip to detect the genome-wide CNVs in the Tharparkar cattle. With the aid of PennCNV software, we noticed a total of 447 copy number variation regions (CNVRs) across the autosomal genome, occupying nearly 2.17% of the bovine genome. The average size of detected CNVRs was found to be 122.2 kb, the smallest CNVR being 50.02 kb in size, to the largest being 1,232.87 Kb. Enrichment analyses of the genes in these CNVRs gave significant associations with molecular adaptation-related Gene Ontology (GO) terms. Most CNVR genes were significantly enriched for specific biological functions; signaling pathways, sensory responses to stimuli, and various cellular processes. In addition, QTL analysis of CNVRs described them to be linked with economically essential traits in cattle. The findings here provide crucial information for constructing a more comprehensive CNVR map for the indigenous cattle genome.

Genomic scans for selection signatures revealed candidate genes for adaptation and production traits in a variety of cattle breeds

Domestication and selection are the major driving forces responsible for the determinative genetic variability in livestock. These selection patterns create unique genetic signatures within the genome. BovineSNP50 chip data from 236 animals (seven indicine and five taurine cattle breeds) were analyzed in the present study. We implemented three complementary approaches viz. iHS (Integrated haplotype score), ROH (Runs of homozygosity), and F_ST, to detect selection signatures. A total of 179, 56, and 231 regions revealed 518, 277, and 267 candidate genes identified by iHS, ROH, and F_ST methods, respectively. We found several candidate genes (e.g., NCR3, ARID5A, HIST1H2BN, DEFB4, DEFB7, HSPA1L, HSPA1B, and DNAJB4) related to production traits and the adaptation of indigenous breeds to local environmental constraints such as heat stress and disease susceptibility. However, further studies are warranted to refine the findings using a larger sample size, whole-genome sequencing, and/or high density genotyping.

SNPs with intermediate minor allele frequencies facilitate accurate breed assignment of Indian Tharparkar cattle

Tharparkar cattle breed is widely known for its superior milch quality and hardiness attributes. This study aimed to develop an ultra-low density breed-specific single nucleotide polymorphism (SNP) genotype panel to accurately quantify Tharparkar populations in biological samples. In this study, we selected and genotyped 72 Tharparkar animals randomly from Cattle & Buffalo Farm of IVRI, India. This Bovine SNP50 BeadChip genotypic datum was merged with the online data from six indigenous cattle breeds and five taurine breeds. Here, we used a combination of pre-selection statistics and the MAF-LD method developed in our laboratory to analyze the genotypic data obtained from 317 individuals of 12 distinct breeds to identify breed-informative SNPs for the selection of Tharparkar cattle. This methodology identified 63 unique Tharparkar-specific SNPs near intermediate gene frequencies. We report several informative SNPs in genes/QTL regions affecting phenotypes or production traits that might differentiate the Tharparkar breed.

Genome-wide assessment of genetic diversity, linkage disequilibrium and haplotype block structure in Tharparkar cattle breed of India

Knowledge about genetic diversity is very essential for the management and sustainable utilization of livestock genetic resources. In this study, we presented a comprehensive genome-wide analysis of genetic diversity, ROH, inbreeding, linkage disequilibrium, effective population size and haplotype block structure in Tharparkar cattle of India. A total of 24 Tharparkar animals used in this study were genotyped with Illumina BovineSNP50 array. After quality control, 22,825 biallelic SNPs were retained, which were in HWE, MAF > 0.05 and genotyping rate >90%. The overall mean observed (H_O) and expected heterozygosity (H_E) were 0.339 ± 0.156 and 0.325 ± 0.129, respectively. The average minor allele frequency was 0.234 with a standard deviation of ± 0.131. We identified a total of 1832 ROH segments and the highest autosomal coverage of 13.87% was observed on chromosome 23. The genomic inbreeding coefficients estimates by F_ROH, F_HOM, F_GRM and F_UNI were 0.0589, 0.0215, 0.0532 and 0.0160 respectively. The overall mean linkage disequilibrium (LD) for a total of 133,532 pairwise SNPs measured by D' and r² was 0.6452 and 0.1339, respectively. In addition, we observed a gradual decline in effective population size over the past generations.