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<article article-type="research-article" dtd-version="1.2" xml:lang="ru" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><front><journal-meta><journal-id journal-id-type="issn">2658-6533</journal-id><journal-title-group><journal-title>Научные результаты биомедицинских исследований</journal-title></journal-title-group><issn pub-type="epub">2658-6533</issn></journal-meta><article-meta><article-id pub-id-type="doi">10.18413/2658-6533-2020-7-1-0-2</article-id><article-id pub-id-type="publisher-id">2279</article-id><article-categories><subj-group subj-group-type="heading"><subject>Генетика</subject></subj-group></article-categories><title-group><article-title>&lt;strong&gt;Биоинформатические инструменты и интернет-ресурсы для оценки регуляторного потенциала полиморфных локусов, установленных полногеномными ассоциативными исследованиями мультифакториальных заболеваний (обзор)&lt;/strong&gt;&lt;br /&gt;
&amp;nbsp;</article-title><trans-title-group xml:lang="en"><trans-title>&lt;strong&gt;Bioinformatic tools and internet resources for functional annotation of polymorphic loci detected by genome wide association studies of multifactorial diseases (review)&lt;/strong&gt;&lt;br /&gt;
&amp;nbsp;</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><name-alternatives><name xml:lang="ru"><surname>Полоников</surname><given-names>Алексей Валерьевич</given-names></name><name xml:lang="en"><surname>Polonikov</surname><given-names>Alexei V.</given-names></name></name-alternatives><email>polonikov@rambler.ru</email></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="ru"><surname>Клёсова</surname><given-names>Елена Юрьевна</given-names></name><name xml:lang="en"><surname>Klyosova</surname><given-names>Elena Yu.</given-names></name></name-alternatives><email>ecless@yandex.ru</email></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="ru"><surname>Азарова</surname><given-names>Юлия Эдуардовна</given-names></name><name xml:lang="en"><surname>Azarova</surname><given-names>Iuliia E.</given-names></name></name-alternatives><email>azzzzar@yandex.ru</email></contrib></contrib-group><pub-date pub-type="epub"><year>2021</year></pub-date><volume>7</volume><issue>1</issue><fpage>0</fpage><lpage>0</lpage><self-uri content-type="pdf" xlink:href="/media/medicine/2021/1/document-6-33-12-28.pdf" /><abstract xml:lang="ru"><p>Актуальность: Полногеномные ассоциативные исследования (genome-wide association studies, GWAS) представляют собой разновидность генетических исследований, целью которых является анализ ассоциаций между геномными вариантами и фенотипическими признаками в популяции. За последние 12 лет было установлено более 60 тысяч ассоциаций между тремя миллионами однонуклеотидных полиморфных вариантов (SNPs) и 829 заболеваниями. Тем не менее, несмотря на достигнутые успехи, большую проблему представляет вопрос патогенетической интерпретации полученных данных, поскольку абсолютное большинство локусов находятся в межгенных областях и некодирующих последовательностях генома. Цель исследования: Изучить возможности существующих биоинформатических инструментов, позволяющих оценить возможные фенотипические эффекты SNPs на определенные молекулярные функции и биологические процессы, а также имеющие патогенетическое значение для развития мультифакториальных заболеваний. Материалы и методы: Проведен анализ российской и зарубежной научной литературы по биоинформатическим методам анализа и интернет-ресурсам, необходимым для оценки регуляторного потенциала полиморфных локусов, установленных в полногеномных ассоциативных исследованиях мультифакториальных заболеваний. Результаты: В обзоре представлены основные итоги изучения спектра применения баз данных и интернет ресурсов для оценки влияния вариантов ДНК на экспрессию генов в различных тканях, метилирование ДНК, характеристики метаболомного профиля, рассмотрены алгоритмические подходы, систематизированы качественные и количественные online-инструменты, а также вычислительные методы. Заключение: Полногеномные ассоциативные исследования открыли новую эру в истории генетических исследований мультифакториальных заболеваний. Биоинформатический анализ in silico позволяет дать всестороннюю оценку эффектам SNPs и их роли в развитии того или иного фенотипического признака болезни.</p></abstract><trans-abstract xml:lang="en"><p>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.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>ДНК-полиморфизмы</kwd><kwd>полногеномные ассоциативные исследования</kwd><kwd>мультифакториальные заболевания</kwd><kwd>биоинформатические инструменты</kwd></kwd-group><kwd-group xml:lang="en"><kwd>DNA polymorphisms</kwd><kwd>genome-wide association studies</kwd><kwd>multifactorial diseases</kwd><kwd>bioinformatics tools</kwd></kwd-group></article-meta></front><back><ack><p>Работа выполнена при финансовой поддержке Российского научного фонда (проект № 20-15-00227)</p></ack><ref-list><title>Список литературы</title><ref id="B1"><mixed-citation>Visscher PM, Wray EM, Zhang Q, et al. 10 years of GWAS discovery: biology, function, and translation. The American Journal of Human Genetics. 2017;101(1):5-22. 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