Peka Mykyta, Balatsky Viktor
Institute of Pig Breeding and Agroindustrial Production, National Academy of Agrarian Sciences of Ukraine, 1 Shvedska Mohyla St, Poltava, 36013, Ukraine.
V. N. Karazin Kharkiv National University, 4 Svobody Sq, Kharkiv, 61022, Ukraine.
BMC Genomics. 2024 Dec 19;25(1):1226. doi: 10.1186/s12864-024-11126-z.
Trends in the development of genetic markers for the purposes of genomic and marker-assisted selection primarily focus on identifying causative polymorphisms. Using these polymorphisms as markers enables a more accurate association between genotype and phenotype. Bioinformatic analysis allows calculating the impact of missense polymorphisms on the structural and functional characteristics of proteins, which makes it promising for identifying causative polymorphisms. In this study, a bioinformatic approach is applied to evaluate and differentiate polymorphisms based on their causality in genes that affect the production traits of pigs and cows, which are two important livestock species.
The influence of both known causative and candidate missense polymorphisms in the MC4R, NR6A1, PRKAG3, RYR1, and SYNGR2 genes of pigs, as well as the ABCG2, DGAT1, GHR, and MSTN genes of cows, was assessed. The study included an evaluation of the effect of polymorphisms on protein functions, considering the evolutionary and physicochemical characteristics of amino acids at polymorphic sites. Additionally, it examined the impact of polymorphisms on the stability of tertiary protein structures, including changes in folding, binding of protein monomers, and interaction with ligands.
The comprehensive bioinformatic analysis used in this study enables the differentiation of polymorphisms into neutral, where both amino acids in the polymorphic site do not significantly affect the structure and function of the protein, and causative, where one of the amino acids significantly impacts the protein's properties. This approach can be employed in future research to screen extensive sets of polymorphisms in animal genomes, identifying the most promising polymorphisms for further investigation in association studies.
用于基因组和标记辅助选择的遗传标记的发展趋势主要集中在识别因果多态性。将这些多态性用作标记能够使基因型与表型之间建立更准确的关联。生物信息学分析可以计算错义多态性对蛋白质结构和功能特征的影响，这使其在识别因果多态性方面具有前景。在本研究中，应用一种生物信息学方法来评估和区分影响猪和牛（两种重要家畜物种）生产性状的基因中的多态性，并根据其因果关系进行区分。
评估了猪的MC4R、NR6A1、PRKAG3、RYR1和SYNGR2基因以及牛的ABCG2、DGAT1、GHR和MSTN基因中已知的因果错义多态性和候选错义多态性的影响。该研究包括评估多态性对蛋白质功能的影响，同时考虑多态性位点氨基酸的进化和物理化学特征。此外，还研究了多态性对蛋白质三级结构稳定性的影响，包括折叠变化、蛋白质单体的结合以及与配体的相互作用。
本研究中使用的全面生物信息学分析能够将多态性分为中性（多态性位点的两个氨基酸均不会显著影响蛋白质的结构和功能）和因果性（其中一个氨基酸会显著影响蛋白质的特性）。这种方法可用于未来的研究，以筛选动物基因组中的大量多态性，识别出最有前景的多态性，以便在关联研究中进行进一步研究。
