Bioinformatic approach to identifying causative missense polymorphisms in animal genomes

Abstract

Background: 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.

Results: 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.

Conclusions: 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.

Keywords: In silico analysis; Amino acid substitution; Animal genome; Bioinformatics; Causative missense polymorphisms; Rational selective breeding design; Sequence- and structure-oriented approaches.

Fig. 1

Interactions of pig PRKAG3 with AMP ligand. The protein with R250 (a) forms an additional salt bridge-type bond with AMP compared to the protein with Q250 (b). The length of the salt bridge is 4.79 Å. Salt bridges and attractive charge bonds are shown in orange, conventional hydrogen bonds in green, carbon-hydrogen bonds in light green, and pi-alkyl bonds in purple

Fig. 2

Changes in chemical interactions due to polymorphisms in proteins. a–b: pig PRKAG3 protein with R250 is shown in green, and with Q250 in cyan; c–d: pig MC4R protein with D298 is shown in green, and with N298 in cyan; e–f: cow ABCG2 protein with Y581 is shown in green, and with S581 in cyan. H-bonds are shown in yellow. Distances between atoms are indicated in Å