Spatial range expansion during population colonization is characterized by demographic events that may have significant effects on the efficiency of natural selection. Population genetics suggests that genetic drift brought by small effective population size (N e) may undermine the efficiency of selection, leading to a faster accumulation of nonsynonymous mutations. However, it is still unknown whether this effect might be balanced or even reversed by strong selective constraints. Here, we used wild boars and local domestic pigs from tropical (Vietnam) and subarctic region (Siberia) as animal model to evaluate the effects of functional constraints and genetic drift on shaping molecular evolution. The likelihood‐ratio test revealed that Siberian clade evolved significantly different from Vietnamese clades. Different datasets consistently showed that Siberian wild boars had lower Ka/Ks ratios than Vietnamese samples. The potential role of positive selection for branches with higher Ka/Ks was evaluated using branch‐site model comparison. No signal of positive selection was found for the higher Ka/Ks in Vietnamese clades, suggesting the interclade difference was mainly due to the reduction in Ka/Ks for Siberian samples. This conclusion was further confirmed by the result from a larger sample size, among which wild boars from northern Asia (subarctic and nearby region) had lower Ka/Ks than those from southern Asia (temperate and tropical region). The lower Ka/Ks might be due to either stronger functional constraints, which prevent nonsynonymous mutations from accumulating in subarctic wild boars, or larger N e in Siberian wild boars, which can boost the efficacy of purifying selection to remove functional mutations. The latter possibility was further ruled out by the Bayesian skyline plot analysis, which revealed that historical N e of Siberian wild boars was smaller than that of Vietnamese wild boars. Altogether, these results suggest stronger functional constraints acting on mitogenomes of subarctic wild boars, which may provide new insights into their local adaptation of cold resistance.

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The results of modeling the variability of the complex trait "body volume" by linear traits measured on a 10-point scale in accordance with the current instructions for cattle grading of dairy and dairy-beef breeds are presented. The object of research is the complex indicator "body volume" of Irmen type cattle. The exterior of the livestock was evaluated by experts on a collegial basis. The models obtained made it possible to identify a group of exterior features associated with the variability of the studied trait and to identify errors in the work of the evaluators. The tasks were solved using multiple linear, polynomial, power and logarithmic regression models. It was found that multiple linear regression models accurately describe the norm reaction of the body volume response. Residue distribution diagrams made it possible to control the quality of appraisers' assessment and adjust their further work. The logarithmic model was marked as closest to linear. The residues in most cases turned out to be close to zero, which was explained by the low level of variability of the traits used. It was revealed that the use of different levels of power orders in modeling the variability of the body volume in points can lead to the emergence of biologically inexplicable relationships with such linear features as the location of the front teats, the location of the rear teats, attachment of the anterior lobes and the position of the bottom of the udder. The construction of the scatter diagram revealed a high level of variation in the residues and led to the conclusion that it was inexpedient to introduce power series models into the practical work of livestock breeders. The insignificant contribution of the studied linear features to the variation of the complex feature under study is shown. High intra-group variance in the construction of second- and fourth-order polynomial models was reflected in the lowest values of the Fisher criterion.

The authors evaluated the significance of paratypic factors in fat variability in the article. The study looked at the role of fixed effects such as: “Calving Season”, “Calving Year”, “Starting Season”, “Starting Year” and their interacting factors: “Calving Season: Calving Year”, “Starting Season: Starting Year”, “Calving Season: Starting Season”, “Calving Year: Starting Year”. The authors used data from Irmen’s primary zootechnical census of black-and-white cattle (n = 319210) from 2000 to 2020. The role of genetic and paratypical factors was assessed using linear mixed regression models and appropriate statistical methods and criteria. The following were selected as random effects: father, age of fertile insemination and animal identification data. The influence of the fixed characteristics of the prospective mathematical model was evaluated using an analysis of variance. But beforehand, the authors identified different combinations with adjustment for the proportion of random contribution. The grant levels of the estimated factors to the variability of the dependent trait were determined. The authors note the high conjugate variability between predicted and actual milk yields (r = 0,905; p˂ 0,001). A relatively high coefficient of determination (R2 = 0,819) was observed for the test sample. In this case, only phenotypic data were considered in the example when constructing the model. Application of the resulting model to other subpopulations may require additional correction factors as part of regional or federal breeding value index programs.