Genomic Prediction Using Bayesian Regression Models With Global-Local Prior.

Bayesian regression models are widely used in genomic prediction for various species. By introducing the global parameter τ, which can shrink marker effects to zero, and the local parameter λ k , which can allow markers with large effects to escape from the shrinkage, we developed two novel Bayesian models, named BayesHP and BayesHE. The BayesHP model uses Horseshoe+ prior, whereas the BayesHE model assumes local parameter λ k , after a half-t distribution with an unknown degree of freedom. The performances of BayesHP and BayesHE models were compared with three classical prediction models, including GBLUP, BayesA, and BayesB, and BayesU, which also applied global-local prior (Horseshoe prior). To assess model performances for traits with various genetic architectures, simulated data and real data in cattle (milk production, health, and type traits) and mice (type and growth traits) were analyzed. The results of simulation data analysis indicated that models based on global-local priors, including BayesU, BayesHP, and BayesHE, performed better in traits with higher heritability and fewer quantitative trait locus. The results of real data analysis showed that BayesHE was optimal or suboptimal for all traits, whereas BayesHP was not superior to other classical models. For BayesHE, its flexibility to estimate hyperparameter automatically allows the model to be more adaptable to a wider range of traits. The BayesHP model, however, tended to be suitable for traits having major/large quantitative trait locus, given its nature of the "U" type-like shrinkage pattern. Our results suggested that auto-estimate the degree of freedom (e.g., BayesHE) would be a better choice other than increasing the local parameter layers (e.g., BayesHP). In this study, we introduced the global-local prior with unknown hyperparameter to Bayesian regression models for genomic prediction, which can trigger further investigations on model development.

Comparative Genomics, Evolutionary and Gene Regulatory Regions Analysis of Casein Gene Family in Bubalus bubalis.

Buffalo is a luxurious genetic resource with multiple utilities (as a dairy, draft, and meat animal) and economic significance in the tropical and subtropical regions of the globe. The excellent potential to survive and perform on marginal resources makes buffalo an important source for nutritious products, particularly milk and meat. This study was aimed to investigate the evolutionary relationship, physiochemical properties, and comparative genomic analysis of the casein gene family (CSN1S1, CSN2, CSN1S2, and CSN3) in river and swamp buffalo. Phylogenetic, gene structure, motif, and conserved domain analysis revealed the evolutionarily conserved nature of the casein genes in buffalo and other closely related species. Results indicated that casein proteins were unstable, hydrophilic, and thermostable, although αs1-CN, ß-CN, and κ-CN exhibited acidic properties except for αs2-CN, which behaved slightly basic. Comparative analysis of amino acid sequences revealed greater variation in the river buffalo breeds than the swamp buffalo indicating the possible role of these variations in the regulation of milk traits in buffalo. Furthermore, we identified lower transcription activators STATs and higher repressor site YY1 distribution in swamp buffalo, revealing its association with lower expression of casein genes that might subsequently affect milk production. The role of the main motifs in controlling the expression of casein genes necessitates the need for functional studies to evaluate the effect of these elements on the regulation of casein gene function in buffalo.