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DOI: 10.18413/2658-6533-2020-7-1-0-2

Bioinformatic tools and internet resources for functional annotation of polymorphic loci detected by genome wide association studies of multifactorial diseases (review)
 

Background: Genome-wide association studies (GWAS) are a type of genetic research whose purpose is to analyze the associations between genomic variants and phenotypic traits in a population. Over the past 12 years, more than 60000 associations have been established between three million single nucleotide variants (SNPs) and 829 diseases, however, despite the progress achieved, the pathogenetic interpretation of the data is a huge problem, since the vast majority of the loci are located in intergenic regions and non-coding sequences of the genome, or in genes that are not related to metabolic pathways involved in the development of a particular pathology. In this regard, the integrated usage of bioinformatic tools gives an opportunity to evaluate the possible effects of SNPs on certain molecular functions and biological processes related to disease pathogenesis. The aim of the study: To examine the capabilities of existing bioinformatics tools to evaluate possible phenotypic effects of SNPs on certain molecular functions and biological processes, as well as having pathogenetic significance for the development of multifactorial diseases. Materials and methods: The authors carried out an analysis of the Russian and foreign scientific literature on bioinformatic methods of analysis and Internet resources necessary for the assessment of the regulatory potential of polymorphic loci established in genome-wide associative studies of multifactorial diseases. Results: The review presents the main results of studying the spectrum of application of databases and Internet resources for assessing the effect of DNA variants on gene expression in various tissues, DNA methylation, and characteristics of the metabolomic profile. Conclusion: Genome-wide associative research has opened a new era in the history of genetic research on multifactorial diseases. In silico bioinformatics analysis provides a comprehensive assessment of the effects of SNPs and their role in the development of a phenotypic trait of disease.

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