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

Abstract Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and the need to study increasingly complex evolutionary scenarios. With likelihood and Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, and in particular deep learning, algorithms are emerging as popular techniques for population genetic inferences. These approaches rely on algorithms that learn non-linear relationships between the input data and the model parameters being estimated through representation learning from training data sets. Deep learning algorithms currently employed in the field comprise discriminative and generative models with fully connected, convolutional, or recurrent layers. Additionally, a wide range of powerful simulators to generate training data under complex scenarios are now available. The application of deep learning to empirical data sets mostly replicates previous findings of demography reconstruction and signals of natural selection in model organisms. To showcase the feasibility of deep learning to tackle new challenges, we designed a branched architecture to detect signals of recent balancing selection from temporal haplotypic data, which exhibited good predictive performance on simulated data. Investigations on the interpretability of neural networks, their robustness to uncertain training data, and creative representation of population genetic data, will provide further opportunities for technological advancements in the field.

Deep Learning in Population Genetics

ABSTRACT The present broad range of sheep phenotypic variation is the consequence of selection for various production characteristics such as wool, milk, meat, and natural adaptability to diverse situations. In the current study, the genomic data of 79 animals from three Indian sheep breeds (Changthangi, Deccani, and Garole) were explored to decipher regions under positive selection for various productive traits using various statistical tools. We found 61, 102 regions using Tajima’s D, CLR and 58, 38, and 71 regions using FST, FLK, and hapFLK, respectively, under selective sweep. Significant candidate genes related to various important traits, such as KLHL for fibre diameter and the OMA1 gene for feed-induced thermogenesis in Changthangi, were identified. In addition, the SYCP2 and NAPAS3 genes were found to be associated with seasonal reproductivity, and BMPR1B for fertilization capacity in Garole. In a previous study, the HSPA6 gene (Garole) was found to be associated with thermo-tolerance in the Angus sheep breed. The outcomes discussed here provide a basis for detecting selective sweeps that underlie genetic variations of traits that have functional importance.

Revealing Genomic Footprints of Selection for Fiber and Production Traits in Three Indian Sheep Breeds

Microbial ester synthases are regarded as valuable catalysts in the food industry. Here, one strain of Acinetobacter venetianus with ester synthase-production capacity, SCSMX-3, was isolated from traditional fermented golden pomfret. It exhibited good growth in mesophilic, low salt, and slightly alkaline environments. The ester synthase produced by SCSMX-3 displayed maximum activity at pH 8.0 and 35 °C. Genome sequencing revealed that the strain contains one circular chromosome of 336313 bp and two circular plasmids (plasmid A-14424 bp and plasmid B-11249 bp). Six CRISPR structures enhance the genomic stability of SCSMX-3 and provide the opportunity to create new functional strains. Gene function analysis indicated that SCSMX-3 produces the necessary enzymes for survival under different conditions and for flavor substance synthesis. Furthermore, 49 genes encoding enzymes associated with lipid metabolism, including three triacylglycerol lipases and two esterases, were identified through the NCBI Non-Redundant Protein Database. The lipase encoded by gene0302 belongs to the GX group and the abH15.02 (Burkholderia cepacia lipase) homolog of the abH15 superfamily. Our results shed light on the genomic diversity of and lipid metabolism in A. venetianus isolated from fermented golden pomfret, laying a foundation for the exploration of new ester synthases to improve the flavor of fermented fish products.

/pdf/whole-genome-sequencing-of-a-potential-ester-synthesizing-396u9ztn.pdf

Whole-Genome Sequencing of a Potential Ester-Synthesizing Bacterium Isolated from Fermented Golden Pomfret and Identification of Its Lipase Encoding Genes

Heat stress caused by climatic changes is one of the most significant stresses on livestock in hot and dry areas. It has particularly adverse effects on the ability of the breed to maintain homeothermy. Developing countries are advised to protect and prepare their animal resources in the face of potential threats such as climate change. The current study was conducted in Egypt’s three hot and dry agro-ecological zones. Three local sheep breeds (Saidi, Wahati, and Barki) were studied with a total of 206 ewes. The animals were exercised under natural heat stress. The heat tolerance index of the animals was calculated to identify animals with high and low heat tolerance based on their response to meteorological and physiological parameters. Genomic variation in these breeds was assessed using 64,756 single nucleotide polymorphic markers (SNPs). From the perspective of comparative adaptability to harsh conditions, our objective was to investigate the genomic structure that might control the adaptability of local sheep breeds to environmental stress under hot and dry conditions. In addition, indices of population structure and diversity of local breeds were examined. Measures of genetic diversity showed a significant influence of breed and location on populations. The standardized index of association (rbarD) ranged from 0.0012 (Dakhla) to 0.026 (Assuit), while for the breed, they ranged from 0.004 (Wahati) to 0.0103 (Saidi). The index of association analysis (Ia) ranged from 1.42 (Dakhla) to 35.88 (Assuit) by location and from 6.58 (Wahati) to 15.36 (Saidi) by breed. The most significant SNPs associated with heat tolerance were found in the MYO5A, PRKG1, GSTCD, and RTN1 genes (p ≤ 0.0001). MYO5A produces a protein widely distributed in the melanin-producing neural crest of the skin. Genetic association between genetic and phenotypic variations showed that OAR1_18300122.1, located in ST3GAL3, had the greatest positive effect on heat tolerance. Genome-wide association analysis identified SNPs associated with heat tolerance in the PLCB1, STEAP3, KSR2, UNC13C, PEBP4, and GPAT2 genes.

/pdf/genome-wide-analysis-identified-candidate-variants-and-genes-t9vsa2sf.pdf

Genome-wide analysis identified candidate variants and genes associated with heat stress adaptation in Egyptian sheep breeds

Identification of important genomic footprints using eight different selection signature statistics in domestic cattle breeds

https://doi.org/10.1016/j.gene.2022.146387

Harshit Kumar

Papers

Identification of important genomic footprints using eight different selection signature statistics in domestic cattle breeds

Role of genomics in combating COVID-19 pandemic

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

Revealing Genomic Footprints of Selection for Fiber and Production Traits in Three Indian Sheep Breeds

Unraveling genetic admixture in the Indian crossbred cattle by different approaches using Bovine 50K BeadChip