Genomic Selection for Milk Production Traits in Xinjiang Brown Cattle

One-step genomic selection is a method for improving the reliability of the breeding value estimation. This study aimed to compare the reliability of pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP), single-trait and multitrait models, and the restricted maximum likelihood (REML) and Bayesian methods. Data were collected from the production performance records of 2207 Xinjiang Brown cattle in Xinjiang from 1983 to 2018. A cross test was designed to calculate the genetic parameters and reliability of the breeding value of 305 daily milk yield (305 dMY), milk fat yield (MFY), milk protein yield (MPY), and somatic cell score (SCS) of Xinjiang Brown cattle. The heritability of 305 dMY, MFY, MPY, and SCS estimated using the REML and Bayesian multitrait models was approximately 0.39 (0.02), 0.40 (0.03), 0.49 (0.02), and 0.07 (0.02), respectively. The heritability and estimated breeding value (EBV) and the reliability of milk production traits of these cattle calculated based on PBLUP and ssGBLUP using the multitrait model REML and Bayesian methods were higher than those of the single-trait model REML method; the ssGBLUP method was significantly better than the PBLUP method. The reliability of the estimated breeding value can be improved from 0.9% to 3.6%, and the reliability of the genomic estimated breeding value (GEBV) for the genotyped population can reach 83%. Therefore, the genetic evaluation of the multitrait model is better than that of the single-trait model. Thus, genomic selection can be applied to small population varieties such as Xinjiang Brown cattle, in improving the reliability of the genomic estimated breeding value.

Bayesian Genomic Linear Regression

The Bayesian paradigm for parameter estimation is introduced and linked to the main problem of genomic-enabled prediction to predict the trait of interest of the non-phenotyped individuals from genotypic information, environment variables, or other information (covariates). In this situation, a convenient practice is to include the individuals to be predicted in the posterior distribution to be sampled. We explained how the Bayesian Ridge regression method is derived and exemplified with data from plant breeding genomic selection. Other Bayesian methods (Bayes A, Bayes B, Bayes C, and Bayesian Lasso) were also described and exemplified for genome-based prediction. The chapter presented several examples that were implemented in the Bayesian generalized linear regression (BGLR) library for continuous response variables. The predictor under all these Bayesian methods includes main effects (of environments and genotypes) as well as interaction terms related to genotype × environment interaction.

Molecular phylogeny of Lytorhynchus diadema (Reptilia, Colubridae) populations in Saudi Arabia

This study presents the molecular phylogenetic relationships among Lytorhynchus diadema (Duméril, Bibron & Duméril, 1854) populations in Saudi Arabia relative to populations from Africa and Asia. This phylogenetic analysis was based on mitochondrial 16S and 12S rRNA partial gene fragments using Neighbor-joining, Maximum Parsimony, and Bayesian methods. The results strongly support the monophyly of Lytorhynchus based on two concatenated genes and the 12S rRNA gene separately. Also, a significant separation is observed between the Arabian samples from Saudi Arabia, Yemen, and Oman, and the African populations from Egypt, Tunisia, and Morocco.

Calcatedessa gen.n. a new genus sister to Grammedessa Correia & Fernandes (Heteroptera, Pentatomidae, Edessinae) based on a cladistic analysis

Grammedessa Correia & Fernandes, 2016 is a genus raised to include some species of Edessa Fabricius, 1803 that is a very common group of stink bugs inhabiting only the Neotropical region. Grammedessa was proposed excluding a few species that were morphologically similar but not completely fitting in the diagnostic requirements of the genus. Grammedessa was also proposed without considering a phylogenetic context. In this work, the monophyly of Grammedessa was tested using a cladistic analysis, including all species that were originally excluded, under both Maximum Parsimony and Bayesian methods. As a result, six new species are now included in Grammedessa, which will be described in a forthcoming paper; Edessa botocudo Kirkaldy, 1909 was considered an unnecessary new name for Edessa hamata Walker, 1868 that was transferred to Grammedessa, resulting in G. hamata (Walker, 1868) comb.n. Calcatedessa gen.n., a new genus sister to Grammedessa, is here proposed to include four new species: C. anthomorpha sp.n., C. clarimarginata sp.n., C. germana sp.n. and C. temnomarginata sp.n. The Calcatedessa–Grammedessa clade and both genera were recovered as monophyletic by Maximum Parsimony and Bayesian methods. An identification key to the species of Calcatedessa gen.n. is provided. The new genus is distributed in Guyana, Suriname, French Guyana, and Brazil.

Comparing the topology of phylogenetic network generators

Phylogenetic networks represent evolutionary history of species and can record natural reticulate evolutionary processes such as horizontal gene transfer and gene recombination. This makes phylogenetic networks a more comprehensive representation of evolutionary history compared to phylogenetic trees. Stochastic processes for generating random trees or networks are important tools in evolutionary analysis, especially in phylogeny reconstruction where they can be utilized for validation or serve as priors for Bayesian methods. However, as more network generators are developed, there is a lack of discussion or comparison for different generators. To bridge this gap, we compare a set of phylogenetic network generators by profiling topological summary statistics of the generated networks over the number of reticulations and comparing the topological profiles.