<?xml version='1.0' encoding='utf-8'?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN" "http://jats.nlm.nih.gov/publishing/1.2/JATS-journalpublishing1.dtd">
<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>Research Results in Biomedicine</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>Genetics</subject></subj-group></article-categories><title-group><article-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;</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>Polonikov</surname><given-names>Alexei V.</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>Klyosova</surname><given-names>Elena Yu.</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>Azarova</surname><given-names>Iuliia E.</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>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></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>DNA polymorphisms</kwd><kwd>genome-wide association studies</kwd><kwd>multifactorial diseases</kwd><kwd>bioinformatics tools</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>The study was supported by the Russian Science Foundation (№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. DOI: 10.1016/j.ajhg.2017.06.005</mixed-citation></ref><ref id="B2"><mixed-citation>Ermann J, Glimcher LH. After GWAS: mice to the rescue? Current opinion in immunology. 2012;24(5):564-570. DOI: 10.1016/j.coi.2012.09.005</mixed-citation></ref><ref id="B3"><mixed-citation>Edwards SL, Beesley J, French JD, et al. Beyond GWASs: illuminating the dark road from association to function. The American Journal of Human Genetics. 2013;93(5):779-797. DOI: https://doi.org/10.1016/j.ajhg.2013.10.012</mixed-citation></ref><ref id="B4"><mixed-citation>Wu J, Yu Z, Chen G. PD-1/PD-Ls: A New Target for Regulating Immunopathogenesis in Central Nervous System Disorders. Current drug delivery. 2017;14(6):791-796. DOI: https://doi.org/10.2174/1567201814666161123152311</mixed-citation></ref><ref id="B5"><mixed-citation>Zarrei M, MacDonald J, Merico D, et al. A copy number variation map of the human genome. Nature reviews genetics. 2015;16(3):172-183. DOI: https://doi.org/10.1038/nrg3871</mixed-citation></ref><ref id="B6"><mixed-citation>Grimm JB, English BP, Chen J, et al. A general method to improve fluorophores for live-cell and single-molecule microscopy. Nature methods. 2015;12(3):244-250. DOI: https://doi.org/10.1038/nmeth.3256</mixed-citation></ref><ref id="B7"><mixed-citation>Butkiewicz M, Bush WS. In silico functional annotation of genomic variation. Current protocols in human genetics. 2016;88(1):6.15.1-6.15.17. DOI: https://doi.org/10.1002/0471142905.hg0615s88</mixed-citation></ref><ref id="B8"><mixed-citation>Lower KM, Hughes JR, De Gobbi M, et al. Adventitious changes in long-range gene expression caused by polymorphic structural variation and promoter competition. Proceedings of the National Academy of Sciences. 2009;106(51):21771-21776. DOI: https://doi.org/10.1073/pnas.0909331106</mixed-citation></ref><ref id="B9"><mixed-citation>Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proceedings of the national academy of sciences. 1977;74(12):5463-5467. DOI: 10.1073/pnas.74.12.5463</mixed-citation></ref><ref id="B10"><mixed-citation>Shihab HA, Rogers MF, Gough J, et al. An integrative approach to predicting the functional effects of non-coding and coding sequence variation. Bioinformatics. 2015;31(10):1536-1543. DOI: https://doi.org/10.1093/bioinformatics/btv009</mixed-citation></ref><ref id="B11"><mixed-citation>Brodie A, Azaria JR, Ofran Y. How far from the SNP may the causative genes be? Nucleic acids research. 2016;44(13):6046-6054. DOI: https://doi.org/10.1093/nar/gkw500</mixed-citation></ref><ref id="B12"><mixed-citation>Chen G, Yu D, Chen J, et al. Re-annotation of presumed noncoding disease/trait-associated genetic variants by integrative analyses. Scientific reports. 2015;5:9453. DOI: https://doi.org/10.1038/srep09453</mixed-citation></ref><ref id="B13"><mixed-citation>Law MH, Bishop DT, Lee JE, et al. Genome-wide meta-analysis identifies five new susceptibility loci for cutaneous malignant melanoma. Nature Genetics. 2015;47(9):987-995. DOI: https://doi.org/10.1038/ng.3373</mixed-citation></ref><ref id="B14"><mixed-citation>Sidore C, Busonero F, Maschio A, et al. Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers. Nature genetics. 2015;47(11):1272-1281. DOI: https://doi.org/10.1038/ng.3368</mixed-citation></ref><ref id="B15"><mixed-citation>Lander ES, Linton LM, Birren B, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409(6822):860-921. DOI: 10.1038/35057062</mixed-citation></ref><ref id="B16"><mixed-citation>Hag&amp;egrave;ge H, Klous P, Braem, C, et al. Quantitative analysis of chromosome conformation capture assays (3C-qPCR). Nature protocols. 2007;2(7):1722-1733. DOI: https://doi.org/10.1038/nprot.2007.243</mixed-citation></ref><ref id="B17"><mixed-citation>Bulik-Sullivan B, Loh P, Finucane H, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature genetics. 2015;47(3):291-295. DOI: https://doi.org/10.1038/ng.3211</mixed-citation></ref><ref id="B18"><mixed-citation>Zhang F, Lupski JR. Non-coding genetic variants in human disease. Human molecular genetics. 2015;24(R1):R102-R110. DOI: https://doi.org/10.1093/hmg/ddv259</mixed-citation></ref><ref id="B19"><mixed-citation>Matys V, Fricke E, Geffers R, et al. TRANSFAC&amp;reg;: transcriptional regulation, from patterns to profiles. Nucleic acids research. 2003;31(1):374-378. DOI: https://doi.org/10.1093/nar/gkg108</mixed-citation></ref><ref id="B20"><mixed-citation>Sandelin A, Alkema W, Engstrom P, et al. JASPAR: an open‐access database for eukaryotic transcription factor binding profiles. Nucleic acids research. 2004;32(1):D91-D94. DOI: http://dx.doi.org/10.1093/nar/gkh012</mixed-citation></ref><ref id="B21"><mixed-citation>Newburger DE, Bulyk ML. UniPROBE: an online database of protein binding microarray data on protein&amp;ndash;DNA interactions. Nucleic acids research. 2008;37(1):D77-D82. DOI: https://doi.org/10.1093/nar/gkn660</mixed-citation></ref><ref id="B22"><mixed-citation>Bailey TL, Boden M, Buske FA, et al. MEME SUITE: tools for motif discovery and searching. Nucleic acids research. 2009;37(2):W202-W208. DOI: https://doi.org/10.1093/nar/gkp335</mixed-citation></ref><ref id="B23"><mixed-citation>Bryzgalov LO, Antontseva EV, Matveeva MYu, et al. Detection of regulatory SNPs in human genome using ChIP-seq ENCODE data. PLoS one. 2013;8(10):e78833. DOI: https://doi.org/10.1371/journal.pone.0078833</mixed-citation></ref><ref id="B24"><mixed-citation>Watanabe K, Taskesen E, van Bochoven A, et al. FUMA: Functional mapping and annotation of genetic associations. European Neuropsychopharmacology. 2019;29(3):S789-S790. DOI: https://doi.org/10.1016/j.euroneuro.2017.08.018</mixed-citation></ref><ref id="B25"><mixed-citation>Li MJ, Yan B, Sham PCh, et al. Exploring the function of genetic variants in the non-coding genomic regions: approaches for identifying human regulatory variants affecting gene expression. Briefings in bioinformatics. 2014;16(3):393-412. DOI: https://doi.org/10.1093/bib/bbu018</mixed-citation></ref><ref id="B26"><mixed-citation>Nishizaki SS, Boyle AP. Mining the unknown: assigning function to noncoding single nucleotide polymorphisms. Trends in Genetics. 2017;33(1):34-45. DOI: https://doi.org/10.1016/j.tig.2016.10.008</mixed-citation></ref><ref id="B27"><mixed-citation>Ameur A, Rada-Iglesias A, Komorowski J, et al. Identification of candidate regulatory SNPs by combination of transcription-factor-binding site prediction, SNP genotyping and haploChIP. Nucleic acids research. 2009;37(12):e85-e85. DOI: https://doi.org/10.1093/nar/gkp381</mixed-citation></ref><ref id="B28"><mixed-citation>Pabinger S, Dander A, Fischer M, et al. A survey of tools for variant analysis of next-generation genome sequencing data. Briefings in bioinformatics. 2014;15(2):256-278. DOI: https://doi.org/10.1093/bib/bbs086</mixed-citation></ref><ref id="B29"><mixed-citation>Duggal G, Wang H., Kingsford C. Higher-order chromatin domains link eQTLs with the expression of far-away genes. Nucleic acids research. 2013;42(1):87-96. DOI: https://doi.org/10.1093/nar/gkt857</mixed-citation></ref><ref id="B30"><mixed-citation>Pruitt KD, Tatusova T, Maglott DR. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic acids research. 2007;35(1):D61-D65. DOI: https://doi.org/10.1093/nar/gkl842</mixed-citation></ref><ref id="B31"><mixed-citation>Nishizaki SS, Boyle A. Mining the unknown: assigning function to noncoding single nucleotide polymorphisms. Trends in Genetics. 2017;33(1):34-45. DOI: https://doi.org/10.1016/j.tig.2016.10.008</mixed-citation></ref><ref id="B32"><mixed-citation>Gibbs JR, van der Brug MP, Hernandez DG, et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS genetics. 2010;6(5):e1000952. DOI: https://doi.org/10.1371/journal.pgen.1000952</mixed-citation></ref><ref id="B33"><mixed-citation>Gutierrez-Arcelus M, Lappalainen T, Montgomery SB, et al. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. Nelife. 2013;2:e00523. DOI: 10.7554/eLife.00523</mixed-citation></ref><ref id="B34"><mixed-citation>Kato N, Loh M, Takeuchi F, et al. Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation. Nature genetics. 2015;47(11):1282-1293. DOI: https://doi.org/10.1038/ng.3405</mixed-citation></ref><ref id="B35"><mixed-citation>Fisher S, Grice E, Vinton R, et al. Evaluating the biological relevance of putative enhancers using Tol2 transposon-mediated transgenesis in zebrafish. Nature protocols. 2006;1(3):1297-1305. DOI: https://doi.org/10.1038/nprot.2006.230</mixed-citation></ref><ref id="B36"><mixed-citation>Kheradpour P, Ernst J, Melnikov A, et al. Systematic dissection of regulatory motifs in 2000 predicted human enhancers using a massively parallel reporter assay. Genome research. 2013;23(5):800-811. DOI: 10.1101/gr.144899.112</mixed-citation></ref><ref id="B37"><mixed-citation>Arnold CD, Gerlach D, Stelzer Ch, et al. Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science. 2013;339(6123):1074-1077. DOI: 10.1126/science.1232542</mixed-citation></ref><ref id="B38"><mixed-citation>Strahl BD, Allis CD. The language of covalent histone modifications. Nature. 2000;403(6765):41. DOI: https://doi.org/10.1038/47412</mixed-citation></ref><ref id="B39"><mixed-citation>Klemm SL, Shipony Z, Greenleaf WJ. Chromatin accessibility and the regulatory epigenome. Nature Reviews Genetics. 2019;20:207-220. DOI: https://doi.org/10.1038/s41576-018-0089-8</mixed-citation></ref><ref id="B40"><mixed-citation>Harrow J, Frankish A, Gonzalez JM, et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome research. 2012;22(99):1760-1774. DOI: 10.1101/gr.135350.111</mixed-citation></ref><ref id="B41"><mixed-citation>Mungall CJ, Batchelor C, Eilbeck K. Evolution of the Sequence Ontology terms and relationships. Journal of biomedical informatics. 2011;44(1):87-93. DOI: https://doi.org/10.1016/j.jbi.2010.03.002</mixed-citation></ref><ref id="B42"><mixed-citation>McLaren W, Pritchard B, Rios D, et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics. 2010;26(16):2069-2070. DOI: https://doi.org/10.1093/bioinformatics/btq330</mixed-citation></ref><ref id="B43"><mixed-citation>Boyle A, Hong EL, Hariharan M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome research. 2012;22(9):1790-1797.</mixed-citation></ref><ref id="B44"><mixed-citation>Coetzee SG, Rhie SK, Berman BP, et al. FunciSNP: an R/bioconductor tool integrating functional non-coding data sets with genetic association studies to identify candidate regulatory SNPs. Nucleic acids research. 2012;40(18):e139-e139. DOI: https://doi.org/10.1093/nar/gks542</mixed-citation></ref><ref id="B45"><mixed-citation>Zhang Z, Wang Y, Wang L, et al. The combined effects of amino acid substitutions and indels on the evolution of structure within protein families. PLoS One. 2010;5(12):e14316. DOI: https://doi.org/10.1371/journal.pone.0014316</mixed-citation></ref><ref id="B46"><mixed-citation>Ward LD, Kellis M. Interpreting noncoding genetic variation in complex traits and human disease. Nature biotechnology. 2012;30(11):1095-1106. DOI: https://doi.org/10.1038/nbt.2422</mixed-citation></ref><ref id="B47"><mixed-citation>Gulko B, Hubisz M, Gronau I, et al. A method for calculating probabilities of fitness consequences for point mutations across the human genome. Nature genetics.2015;47(3):276-283. https://doi.org/10.1038/ng.3196</mixed-citation></ref><ref id="B48"><mixed-citation>Ritchie GRS, Dunham I, Zeggini E, et al. Functional annotation of noncoding sequence variants. Nature methods. 2014;11(3):294-296. DOI: https://doi.org/10.1038/nmeth.2832</mixed-citation></ref><ref id="B49"><mixed-citation>Kircher M, Witten D, Jain P, et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nature genetics. 2014;46(3):310-315. DOI: https://doi.org/10.1038/ng.2892</mixed-citation></ref><ref id="B50"><mixed-citation>Quang D, Chen Y, Xie X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 2014;31(5):761-763. DOI: https://doi.org/10.1093/bioinformatics/btu703</mixed-citation></ref><ref id="B51"><mixed-citation>Rizvi NA, Hellmann MD, Snyder A, et al. Mutational landscape determines sensitivity to PD-1 blockade in non&amp;ndash;small cell lung cancer. Science. 2015;348(6230):124-128.</mixed-citation></ref><ref id="B52"><mixed-citation>Zhou J. Troyanskaya OG, Predicting effects of noncoding variants with deep learning&amp;ndash;based sequence model. Nature methods. 2015;12(10):931-934. DOI: https://doi.org/10.1038/nmeth.3547</mixed-citation></ref><ref id="B53"><mixed-citation>Maher B. ENCODE: The human encyclopaedia. Nature News. 2012;489(7414):46-48. DOI: https://doi.org/10.1038/489046a</mixed-citation></ref><ref id="B54"><mixed-citation>Li MJ, Sham PC, Wang J. FastPval: a fast and memory efficient program to calculate very low P-values from empirical distribution. Bioinformatics. 2010;26(22):2897-2899. DOI: https://doi.org/10.1093/bioinformatics/btq540</mixed-citation></ref><ref id="B55"><mixed-citation>Jones PA. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nature Reviews Genetics. 2012;13(7):484-492. DOI: https://doi.org/10.1038/nrg3230</mixed-citation></ref><ref id="B56"><mixed-citation>Sch&amp;uuml;beler D. Epigenetic islands in a genetic ocean. Science. 2012;338(6108):756-757. DOI: 10.1126/science.1227243</mixed-citation></ref><ref id="B57"><mixed-citation>Flicek P, Ahmed I, Amode MR, et al. Ensembl 2013. Nucleic acids research. 2013;41(D1):D48-D55 DOI: 10.1093/nar/gks1236</mixed-citation></ref><ref id="B58"><mixed-citation>McCarthy DJ, Humburg P, Kanapin A, et al. Choice of transcripts and software has a large effect on variant annotation. Genome medicine. 2014;6(3):26. DOI: https://doi.org/10.1186/gm543</mixed-citation></ref><ref id="B59"><mixed-citation>Morin RD, Mendez-Lago M, Mungall A, et al. Frequent mutation of histone-modifying genes in non-Hodgkin lymphoma. Nature. 2011;476(7360):298-303. DOI: https://doi.org/10.1038/nature10351</mixed-citation></ref></ref-list></back></article>