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Genetic Homogeneity of the Population of Male Rhesus Macaques by the Polymorphisms of Genes oprm1, npy, maoa, crh, 5-htt as Determined by Cluster Analysis of Blood Count Data

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Abstract

Genetic heterogeneity of the population used in the experimental studies with living organisms as model objects is one of the reasons for the poor replicability of the results. Nonhuman primates represent the phylogenetically closest model to humans and it is possible to use it in the studies of genetic basis of diseases with a hereditary predisposition. However, primates represent a population that is significantly limited in number when kept in captivity. We have studied the genetic homogeneity of the population of male rhesus macaques by polymorphisms of genes oprm1, npy, maoa, crh, and 5-htt. Cluster analysis (method of K-means) was used for the assessment considering data of genotyping, origination of the animals, blood count, and veterinary examination. Two significant clusters of animals were identified which differ in the values of blood count parameters within the reference range. The identified clusters are not associated with the origin, age, housing conditions, and the level of morbidity of animals. Clustering made it possible to identify a subpopulation of animals with frequency of carriers of maoa gene VNTR polymorphism with seven repeats 25% higher compared to the entire population. Thus, cluster analysis of nongenetic data may be of use to assess the homogeneity of the distribution of genetic variants in a population and to select a genetically more homogeneous subpopulation in order to reduce the problem of small samples in biomedical research.

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This work was carried out within the framework of the state assignment of the Research Institute of Medical Primatology for 2019–2021.

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Correspondence to L. E. Pavlova.

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Pavlova, L.E., Panchenko, A.V., Timina, M.F. et al. Genetic Homogeneity of the Population of Male Rhesus Macaques by the Polymorphisms of Genes oprm1, npy, maoa, crh, 5-htt as Determined by Cluster Analysis of Blood Count Data. Russ J Genet 58, 420–427 (2022). https://doi.org/10.1134/S1022795422030097

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