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)

Alexei V. Polonikov
Elena Yu. Klyosova
Iuliia E. Azarova

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.

Keywords: DNA polymorphisms, genome-wide association studies, multifactorial diseases, bioinformatics tools.

Number of views: 1599 (view statistics)

Количество скачиваний: 3875

Full text (HTML)Full text (PDF)To articles list

Information for citation:

Polonikov AV, Klyosova EYu, Azarova IE. Bioinformatic tools and internet resources for functional annotation of polymorphic loci detected by genome wide association studies of multifactorial diseases (review). Research Results in Biomedicine. 2021;7(1):15-31. Russian.
DOI: 10.18413/2658-6533-2020-7-1-0-2

User comments
Reference lists
Thanks

While nobody left any comments to this publication.
You can be first.

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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Hagège 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
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
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
Matys V, Fricke E, Geffers R, et al. TRANSFAC®: transcriptional regulation, from patterns to profiles. Nucleic acids research. 2003;31(1):374-378. DOI: https://doi.org/10.1093/nar/gkg108
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
Newburger DE, Bulyk ML. UniPROBE: an online database of protein binding microarray data on protein–DNA interactions. Nucleic acids research. 2008;37(1):D77-D82. DOI: https://doi.org/10.1093/nar/gkn660
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Strahl BD, Allis CD. The language of covalent histone modifications. Nature. 2000;403(6765):41. DOI: https://doi.org/10.1038/47412
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
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
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
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
Boyle A, Hong EL, Hariharan M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome research. 2012;22(9):1790-1797.
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
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
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
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
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
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
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
Rizvi NA, Hellmann MD, Snyder A, et al. Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer. Science. 2015;348(6230):124-128.
Zhou J. Troyanskaya OG, Predicting effects of noncoding variants with deep learning–based sequence model. Nature methods. 2015;12(10):931-934. DOI: https://doi.org/10.1038/nmeth.3547
Maher B. ENCODE: The human encyclopaedia. Nature News. 2012;489(7414):46-48. DOI: https://doi.org/10.1038/489046a
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
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
Schübeler D. Epigenetic islands in a genetic ocean. Science. 2012;338(6108):756-757. DOI: 10.1126/science.1227243
Flicek P, Ahmed I, Amode MR, et al. Ensembl 2013. Nucleic acids research. 2013;41(D1):D48-D55 DOI: 10.1093/nar/gks1236
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
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

All journals

Send article

Research Results in Biomedicine is included in the scientific database of the RINTs (license agreement No. 765-12/2014 dated 08.12.2014).

The journal is included in the list of peer-reviewed scientific publications recommended by the Higher Attestation Commission

The journal is indexed by the following scientific databases and platforms

Research Results in Biomedicine (ISSN 2658-6533)

The journal materials and website are licensed under Creative Commons «Attribution» 4.0 International.

The Founder: Federal State Autonomous Educational Institution of Higher Education "Belgorod National Research University"The Founder’s address: 85 Pobedy Street, Belgorod, the Belgorod region, 308015, Russia

The Publisher: Federal State Autonomous Educational Institution of HigherEducation "Belgorod National Research University" The Founder’s address:85 Pobedy Street, Belgorod, the Belgorod region, 308015, Russia

Editors Office: chief editor Mikhail I. Churnosov, e-mail: RR_MedPharm@bsu.edu.ru, phone: +7 (4722) 30-14-03.

Regulations on the Journal

Certificate

Have questions?
You can write to us:

✉ Content manager

✉ Site administration

✉ Executive Secretary