Bayesian Statistics: Concepts and Applications in Animal Breeding – A Review

Statistics uses two major approaches- conventional (or frequentist) and Bayesian approach. Bayesian approach provides a complete paradigm for both statistical inference and decision making under uncertainty. Bayesian methods solve many of the difficulties faced by conventional statistical methods, and extend the applicability of statistical methods. It exploits the use of probabilistic models to formulate scientific problems. To use Bayesian statistics, there is computational difficulty and secondly, Bayesian methods require specifying prior probability distributions. Markov Chain Monte-Carlo (MCMC) methods were applied to overcome the computational difficulty, and interest in Bayesian methods was renewed. In Bayesian statistics, Bayesian structural equation model (SEM) is used. It provides a powerful and flexible approach for studying quantitative traits for wide spectrum problems and thus it has no operational difficulties, with the exception of some complex cases. In this method, the problems are solved at ease, and the statisticians feel it comfortable with the particular way of expressing the results and employing the software available to analyze a large variety of problems

Bayesian Statistics: Concepts and Applications in Animal Breeding – A Review

Statistics uses two major approaches- conventional (or frequentist) and Bayesian approach. Bayesian approach provides a complete paradigm for both statistical inference and decision making under uncertainty. Bayesian methods solve many of the difficulties faced by conventional statistical methods, and extend the applicability of statistical methods. It exploits the use of probabilistic models to formulate scientific problems. To use Bayesian statistics, there is computational difficulty and secondly, Bayesian methods require specifying prior probability distributions. Markov Chain Monte-Carlo (MCMC) methods were applied to overcome the computational difficulty, and interest in Bayesian methods was renewed. In Bayesian statistics, Bayesian structural equation model (SEM) is used. It provides a powerful and flexible approach for studying quantitative traits for wide spectrum problems and thus it has no operational difficulties, with the exception of some complex cases. In this method, the problems are solved at ease, and the statisticians feel it comfortable with the particular way of expressing the results and employing the software available to analyze a large variety of problems

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Not AvailableThe DNA methylation events mark a major epigenetic change in the genome, reflecting non-genetic disease developments and varied phenotypes. The water buffalo is a dairy production animal with wide agro-climatic distribution in India. Breed-wise the coat color of water buffalo varies from ash-gray to jet black. A typical pigmentation pattern is found in one of the breeds of North India, Nili Ravi, with variedly distributed white patches. The DNA methylation pattern could potentially reveal the epigenetic factors responsible for the pigmentation patterns. To address this question, the DNA isolated from the skin tissues of Nili Ravi with varied white pigmentation and black Murrah buffaloes was subjected to reduced representation bisulfite sequencing. DNA methylation analysis revealed, 68.44%, 63.39%, and 47.94% of the promoter regions were hypermethylated in Nili Ravi over-white versus Murrah, Nili Ravi under-white versus Murrah, and Nili Ravi under-white versus Nili Ravi over-white, respectively. Major genes identified to be differentially methylated among over-white and under-white skin tissues in Nili Ravi included TBX2, SNAI2, HERC2, and CITED1. Overall the results have indicated differential methylation patterns to be potentially involved in hyper or hypopigmentation in Nili Ravi and Murrah buffaloes.Not Availabl