References

vavilov

Вавиловский журнал генетики и селекции

Vavilov Journal of Genetics and Breeding

2500-3259

Institute of Cytology and Genetics of Siberian Branch of the RAS

10.18699/VJ21.003

vavilov-2912

Research Article

БИОИНФОРМАТИКА И СИСТЕМНАЯ КОМПЬЮТЕРНАЯ БИОЛОГИЯ

BIOINFORMATICS AND COMPUTATIONAL SYSTEMS BIOLOGY

Геномная изменчивость в регуляторных районах генов, ассоциированная с заболеваниями человека: механизмы влияния на транскрипцию генов и полногеномные информационные ресурсы, обеспечивающие исследование этих механизмов

Disease-associated genetic variants in the regulatory regions of human genes: mechanisms of action on transcription and genomic resources for dissecting these mechanisms

https://orcid.org/0000-0002-8588-6511

Игнатьева

Е. В.

Ignatieva

E. V.

Новосибирск

Novosibirsk

eignat@bionet.nsc.ru

Матросова

Е. А.

Matrosova

E. A.

Новосибирск

Novosibirsk

Федеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук; Новосибирский национальный исследовательский государственный университетРоссияInstitute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences; Novosibirsk State UniversityRussian Federation

2021

15032021

2511829

2021

Игнатьева Е.В., Матросова Е.А.

Ignatieva E.V., Matrosova E.A.

This work is licensed under a Creative Commons Attribution 4.0 License.

https://vavilov.elpub.ru/jour/article/view/2912

Полногеномные и полноэкзомные технологии секвенирования играют важную роль в исследованиях генетических аспектов патогенеза различных заболеваний. Широкое применение методов полногеномного и полноэкзомного анализа ассоциаций позволяет идентифицировать множество вариантов геномной изменчивости (ГИ), ассоциированных с заболеваниями. Эта информация накапливается в базах данных GWAS central, GWAS catalog, OMIM, ClinVar и др. Большинство вариантов, идентифицированных методикой полногеномного анализа ассоциаций, располагается в некодирующих областях генома человека. По данным проекта ENCODE, доля участков в геноме человека, потенциально задействованных в регуляции транскрипции, во много раз превышает долю кодирующих областей. Таким образом, геномная изменчивость в некодирующих областях генома может повышать предрасположенность к заболеваниям, нарушая функционирование различных регуляторных элементов (промоторов, энхансеров, участков, определяющих 3D структуру хроматина и т. д.). Однако идентификация механизмов влияния патогенных вариантов ГИ на риск развития заболеваний затруднена ввиду большого разнообразия регуляторных элементов. В обзоре рассмотрены молекулярно-генетические механизмы влияния патогенных вариантов ГИ на экспрессию генов. При этом внимание сосредоточено на транскрипционном уровне регуляции как ключевой стадии, запускающей последовательность этапов экспрессии любого гена. Пусковым событием, опосредующим влияние патогенного варианта ГИ на уровень экспрессии гена, может быть, например, изменение функциональной активности сайтов связывания транскрипционных факторов или уровня метилирования ДНК, что, в свою очередь, отражается на функциональной активности промоторов или энхансеров. Выявление регуляторных эффектов полиморфных локусов невозможно без тесной интеграции современных экспериментальных подходов с компьютерным анализом больших массивов генетических данных, получаемых на основе омиксных технологий. В обзоре кратко описаны наиболее известные открытые полногеномные информационные ресурсы, содержащие данные, полученные на основе омиксных технологий, в том числе: ресурсы, накапливающие сведения о состоянии хроматина и участках его связывания с транскрипционными факторами, выявленными с помощью технологии ChIP-seq; ресурсы по геномным локусам, для которых на основе данных ChIP-seq выявлено аллель-специфичное связывание с транскрипционными факторами; а также ресурсы, содержащие предсказанные in silico данные о потенциальном влиянии геномной изменчивости на сайты связывания транскрипционных факторов.

Whole genome and whole exome sequencing technologies play a very important role in the studies of the genetic aspects of the pathogenesis of various diseases. The ample use of genome-wide and exome-wide association study methodology (GWAS and EWAS) made it possible to identify a large number of genetic variants associated with diseases. This information is accumulated in the databases like GWAS central, GWAS catalog, OMIM, ClinVar, etc. Most of the variants identified by the GWAS technique are located in the noncoding regions of the human genome. According to the ENCODE project, the fraction of regions in the human genome potentially involved in transcriptional control is many times greater than the fraction of coding regions. Thus, genetic variation in noncoding regions of the genome can increase the susceptibility to diseases by disrupting various regulatory elements (promoters, enhancers, silencers, insulator regions, etc.). However, identification of the mechanisms of influence of pathogenic genetic variants on the diseases risk is difficult due to a wide variety of regulatory elements. The present review focuses on the molecular genetic mechanisms by which pathogenic genetic variants affect gene expression. At the same time, attention is concentrated on the transcriptional level of regulation as an initial step in the expression of any gene. A triggering event mediating the effect of a pathogenic genetic variant on the level of gene expression can be, for example, a change in the functional activity of transcription factor binding sites (TFBSs) or DNA methylation change, which, in turn, affects the functional activity of promoters or enhancers. Dissecting the regulatory roles of polymorphic loci have been impossible without close integration of modern experimental approaches with computer analysis of a growing wealth of genetic and biological data obtained using omics technologies. The review provides a brief description of a number of the most well-known public genomic information resources containing data obtained using omics technologies, including (1) resources that accumulate data on the chromatin states and the regions of transcription factor binding derived from ChIP-seq experiments; (2) resources containing data on genomic loci, for which allele-specific transcription factor binding was revealed based on ChIP-seq technology; (3) resources containing in silico predicted data on the potential impact of genetic variants on the transcription factor binding sites.

регуляция транскрипциигеномная изменчивостьпатогенные геномные вариантырайонырегулирующие транскрипциюсайты связывания транскрипционных факторовгеномные базы данных

transcription regulationgenetic variabilitypathogenic genetic variantstranscription regulatory regionstranscription factor binding sites (TFBSs)genomic databases

The study was supported from the funds of the budget project No. 0259-2021-0009.

References1

Angeloni A., Bogdanovic O. Enhancer DNA methylation: implications for gene regulation. Essays Biochem. 2019;63(6):707-715. DOI 10.1042/EBC20190030.

Beck T., Shorter T., Brookes A.J. GWAS Central: a comprehensive resource for the discovery and comparison of genotype and phenotype data from genome-wide association studies. Nucleic Acids Res. 2020;48(D1):D933-D940. DOI 10.1093/nar/gkz895.

Belokopytova P., Fishman V. Predicting genome architecture: challenges and solutions. Front. Genet. 2021. DOI 10.3389/fgene.2020.617202.

Belokopytova P.S., Nuriddinov M.A., Mozheiko E.A., Fishman D., Fishman V. Quantitative prediction of enhancer-promoter interactions. Genome Res. 2020;30(1):72-84. DOI 10.1101/gr.249367.119.

Benton M.C., Lea R.A., Macartney-Coxson D., Sutherland H.G., White N., Kennedy D., Mengersen K., Haupt L.M., Griffiths L.R. Genome-wide allele-specific methylation is enriched at gene regulatory regions in a multi-generation pedigree from the Norfolk Island isolate. Epigenetics Chromatin. 2019;12(1):60. DOI 10.1186/s13072-019-0304-7.

Cavalli M., Baltzer N., Umer H.M., Grau J., Lemnian I., Pan G., Wallerman O., Spalinskas R., Sahlén P., Grosse I., Komorowski J., Wadelius C. Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases. Sci. Rep. 2019;9(1):2695. DOI 10.1038/s41598-019-39633-0.

Cavalli M., Pan G., Nord H., Wallén Arzt E., Wallerman O., Wadelius C. Allele-specific transcription factor binding in liver and cervix cells unveils many likely drivers of GWAS signals. Genomics. 2016a;107(6):248-254. DOI 10.1016/j.ygeno.2016.04.006.

Cavalli M., Pan G., Nord H., Wallerman O., Wallén Arzt E., Berggren O., Elvers I., Eloranta M.L., Rönnblom L., Lindblad Toh K., Wadelius C. Allele-specific transcription factor binding to common and rare variants associated with disease and gene expression. Hum. Genet. 2016b;135(5):485-497. DOI 10.1007/s00439-016-1654-x.

Chen C.-Y., Chang I.-S., Hsiung C.A., Wasserman W.W. On the identification of potential regulatory variants within genome wide association candidate SNP sets. BMC Med. Genomics. 2014;7:34. DOI 10.1186/1755-8794-7-34.

Chen J., Rozowsky J., Galeev T.R., Harmanci A., Kitchen R., Bedford J., Abyzov A., Kong Y., Regan L., Gerstein M. A uniform survey of allele-specific binding and expression over 1000-GenomesProject individuals. Nat. Commun. 2016;18(7):11101. DOI 10.1038/ncomms11101.

Chen L., Liang Y., Qiu J., Zhang L., Chen X., Luo X., Jiang J. Significance of rs1271572 in the estrogen receptor beta gene promoter and its correlation with breast cancer in a southwestern Chinese population. J. Biomed. Sci. 2013;20:32. DOI 10.1186/1423-0127-20-32.

Claussnitzer M., Dankel S.N., Kim K.-H., Quon G., Meuleman W., Haugen C., Glunk V., Sousa I.S., Beaudry J.L., Puviindran V., Abdennur N.A., Liu J., Svensson P.-A., Hsu Y.-H., Drucker D.J., Mellgren G., Hui C.-Ch., Hauner H., Kellis M. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 2015; 373:895-907. DOI 10.1056/NEJMoa1502214.

Cong Z., Li Q., Yang Y., Guo X., Cui L., You T. The SNP of rs6854845 suppresses transcription via the DNA looping structure alteration of super-enhancer in colon cells. Biochem. Biophys. Res. 2019;514: 734-741. DOI 10.1016/j.bbrc.2019.04.190.

ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489(7414):57-74. DOI 10.1038/nature11247.

Farh K.K.-H., Marson A., Zhu J., Kleinewietfeld M., Housley W.J., Beik S., Shoresh N., Whitton H., Ryan R.J.H., Shishkin A.A., Hatan M., Carrasco-Alfonso M.J., Mayer D., Luckey C.J., Patsopoulos N.A., De Jager P.L., Kuchroo V.K., Epstein C.B., Daly M.J., Hafler D.A., Bernstein B.E. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature. 2015;518(7539):337-343. DOI 10.1038/nature13835.

Fishman V.S., Salnikov P.A., Battulin N.R. Interpreting chromosomal rearrangements in the context of 3-dimentional genome organization: a practical guide for medical genetics. Biochemistry. 2018; 83(4):393-401. DOI 10.1134/S0006297918040107.

Gorbacheva A.M., Korneev K.V., Kuprash D.V., Mitkin N.A. The risk G allele of the single-nucleotide polymorphism rs928413 creates a CREB1-binding site that activates IL33 promoter in lung epithelial cells. Int. J. Mol. Sci. 2018;19(10):2911. DOI 10.3390/ijms19102911.

Guo L., Wang J. rSNPBase 3.0: an updated database of SNP-related regulatory elements, element-gene pairs and SNP-based gene regulatory networks. Nucleic Acids Res. 2018;46(D1):D1111-D1116. DOI 10.1093/nar/gkx1101.

Hansen A.S., Cattoglio C., Darzacq X., Tjian R. Recent evidence that TADs and chromatin loops are dynamic structures. Nucleus. 2018; 9(1):20-32. DOI 10.1080/19491034.2017.1389365.

Howard T.D., Mathias R.A., Seeds M.C., Herrington D.M., Hixson J.E., Shimmin L.C., Hawkins G.A., Sellers M., Ainsworth H.C., Sergeant S., Miller L.R., Chilton F.H. DNA methylation in an enhancer region of the FADS cluster is associated with FADS activity in human liver. PLoS One. 2014;9(5):e97510. DOI 10.1371/journal.pone.0097510.

Ibrahim D.M., Mundlos S. Three-dimensional chromatin in disease: what holds us together and what drives us apart? Curr. Opin. Cell Biol. 2020;64:1-9. DOI 10.1016/j.ceb.2020.01.003.

Izzi B., Pistoni M., Cludts K., Akkor P., Lambrechts D., Verfaillie C., Verhamme P., Freson K., Hoylaerts M.F. Allele-specific DNA methylation reinforces PEAR1 enhancer activity. Blood. 2016;128: 1003-1012. DOI 10.1182/blood-2015-11-682153.

Jones P.L., Veenstra G.J., Wade P.A., Vermaak D., Kass S.U., Landsberger N., Strouboulis J., Wolffe A.P. Methylated DNA and MeCP2 recruit histone deacetylase to repress transcription. Nat. Genet. 1998;19:187-191. DOI 10.1038/561.

Kilpinen H., Waszak S.M., Gschwind A.R., Raghav S.K., Witwicki R.M., Orioli A., Migliavacca E., Wiederkehr M., Gutierrez-Arcelus M., Panousis N., Yurovsky A., Lappalainen T., Romano-Palumbo L., Planchon A., Bielser D., Bryois J., Padioleau I., Udin G., Thurnheer S., Hacker D., Core L.J., Lis J.T., Hernandez N., Reymond A., Deplancke B., Dermitzakis E.T. Coordinated effects of sequence variation on DNA binding, chromatin structure, and transcription. Science. 2013;342:744-747. DOI 10.1126/science.1242463.

Korneev K.V., Sviriaeva E.N., Mitkin N.A., Gorbacheva A.M., Uvarova A.N., Ustiugova A.S., Polanovsky O.L., Kulakovskiy I.V., Afanasyeva M.A., Schwartz A.M., Kuprash D.V. Minor C allele of the SNP rs7873784 associated with rheumatoid arthritis and type-2 diabetes mellitus binds PU.1 and enhances TLR4 expression. Biochim. Biophys. Acta Mol. Basis Dis. 2020;1866(3):165626. DOI 10.1016/j.bbadis.2019.165626.

Kulakovskiy I.V., Vorontsov I.E., Yevshin I.S., Sharipov R.N., Fedorova A.D., Rumynskiy E.I., Medvedeva Y.A., Magana-Mora A., Bajic V.B., Papatsenko D.A., Kolpakov F.A., Makeev V.J. HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis. Nucleic Acids Res. 2018;46(D1):D252-D259. DOI 10.1093/nar/gkx1106.

Kumar D., Puan K.J., Andiappan A.K., Lee B., Westerlaken G.H., Haase D., Melchiotti R., Li Z., Yusof N., Lum J., Koh G., Foo S., Yeong J., Alves A.C., Pekkanen J., Sun L.D., Irwanto A., Fairfax B.P., Naranbhai V., Common J.E., Tang M., Chuang C.K., Jarvelin M.R., Knight J.C., Zhang X., Chew F.T., Prabhakar S., Jianjun L., Wang Y., Zolezzi F., Poidinger M., Lane E.B., Meyaard L., Rötzschke O. A functional SNP associated with atopic dermatitis controls cell type-specific methylation of the VSTM1 gene locus. Genome Med. 2017;9(1):18. DOI 10.1186/s13073-017-0404-6.

Kumar S., Ambrosini G., Bucher P. SNP2TFBS – a database of regulatory SNPs affecting predicted transcription factor binding site affinity. Nucleic Acids Res. 2017;45(D1):D139-D144. DOI 10.1093/nar/gkw1064.

Lee C.M., Barber G.P., Casper J., Clawson H., Diekhans M., Gonzalez J.N., Hinrichs A.S., Lee B.T., Nassar L.R., Powell C.C., Raney B.J., Rosenbloom K.R., Schmelter D., Speir M.L., Zweig A.S., Haussler D., Haeussler M., Kuhn R.M., Kent W.J. UCSC Genome Browser enters 20th year. Nucleic Acids Res. 2020;48(D1):D756-D761. DOI 10.1093/nar/gkz1012.

Levitsky V.G., Kulakovskiy I.V., Ershov N.I., Oshchepkov D.Y., Makeev V.J., Hodgman T.C., Merkulova T.I. Application of experimentally verified transcription factor binding sites models for computational analysis of ChIP-Seq data. BMC Genom. 2014;15(1):80. DOI 10.1186/1471-2164-15-80.

Lewinsky R.H., Jensen T.G.K., Møller J., Stensballe A., Olsen J., Troelsen J.T. T –13910 DNA variant associated with lactase persistence interacts with Oct-1 and stimulates lactase promoter activity in vitro. Hum. Mol. Genet. 2005;14(24):3945-3953. DOI 10.1093/hmg/ddi418.

Li S., Li Y., Li X., Liu J., Huo Y., Wang J., Liu Z., Li M., Luo X.-J. Regulatory mechanisms of major depressive disorder risk variants. Mol. Psychiatry. 2020;25(9):1926-1945. DOI 10.1038/s41380-020-0715-7.

Lupiáñez D.G., Kraft K., Heinrich V., Krawitz P., Brancati F., Klopocki E., Horn D., Kayserili H., Opitz J.M., Laxova R., SantosSimarro F., Gilbert-Dussardier B., Wittler L., Borschiwer M., Haas S.A., Osterwalder M., Franke M., Timmermann B., Hecht J., Spielmann M., Visel A., Mundlos S. Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell. 2015;161(5):1012-1025. DOI 10.1016/j.cell.2015.04.004.

Mathelier A., Shi W., Wasserman W.W. Identification of altered cisregulatory elements in human disease. Trends Genet. 2015;31(2): 67-76. DOI 10.1016/j.tig.2014.12.003.

Maurano M.T., Humbert R., Rynes E., Thurman R.E., Haugen E., Wang H., Reynolds A.P., Sandstrom R., Qu H., Brody J., Shafer A., Neri F., Lee K., Kutyavin T., Stehling-Sun S., Johnson A.K., Canfield T.K., Giste E., Diegel M., Bates D., Hansen R.S., Neph S., Sabo P.J., Heimfeld S., Raubitschek A., Ziegler S., Cotsapas C., Sotoodehnia N., Glass I., Sunyaev S.R., Kaul R., Stamatoyannopoulos J.A. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337(6099):1190-1195. DOI 10.1126/science.1222794.

McVicker G., van de Geijn B., Degner J.F., Cain C.E., Banovich N.E., Raj A., Lewellen N., Myrthil M., Gilad Y., Pritchard J.K. Identification of genetic variants that affect histone modifications in human cells. Science. 2013;342:747-749. DOI 10.1126/science.1242429.

Meddens C., van der List A.C.J., Nieuwenhuis E.E.S., Mokry M. Noncoding DNA in IBD: from sequence variation in DNA regulatory elements to novel therapeutic potential. Gut. 2019;68(5):928-941. DOI 10.1136/gutjnl-2018-317516.

Mei S., Ke J., Tian J., Ying P., Yang N., Wang X., Zou D., Peng X., Yang Y., Zhu Y., Gong Y., Zhong R., Chang J., Miao X. A functional variant in the boundary of a topological association domain is associated with pancreatic cancer risk. Mol. Carcinog. 2019;58(10): 1855-1862. DOI 10.1002/mc.23077.

Merkulov V.M., Leberfarb E.Y., Merkulova T.I. Regulatory SNPs and their widespread effects on the transcriptome. J. Biosci. 2018;43(5): 1069-1075. DOI 10.1007/s12038-018-9817-7.

Nan X., Ng H.H., Johnson C.A., Laherty C.D., Turner B.M., Eisenman R.N., Bird A. Transcriptional repression by the methyl-CpGbinding protein MeCP2 involves a histone deacetylase complex. Nature. 1998;393:386-389. DOI 10.1038/30764.

Park C.-Y., Halevy T., Lee D.R., Sung J.J., Lee J.S., Yanuka O., Benvenisty N., Kim D.-W. Reversion of FMR1 methylation and silencing by editing the triplet repeats in fragile X iPSC-derived neurons. Cell. Rep. 2015;13(2):234-241. DOI 10.1016/j.celrep.2015.08.084.

Quenneville S., Verde G., Corsinotti A., Kapopoulou A., Jakobsson J., Offner S., Baglivo I., Pedone P.V., Grimaldi G., Riccio A., Trono D. In embryonic stem cells, ZFP57/KAP1 recognize a methylated hexanucleotide to affect chromatin and DNA methylation of imprinting control regions. Mol. Cell. 2011;44(3):361-372. DOI 10.1016/j.molcel.2011.08.032.

Rahbar E., Waits C.M.K., Kirby E.H., Jr., Miller L.R., Ainsworth H.C., Cui T., Sergeant S., Howard T.D., Langefeld C.D., Chilton F.H. Allele-specific methylation in the FADS genomic region in DNA from human saliva, CD4+ cells, and total leukocytes. Clin. Epigenetics. 2018;10:46. DOI 10.1186/s13148-018-0480-5.

Reddy T.E., Gertz J., Pauli F., Kucera K.S., Varley K.E., Newberry K.M., Marinov G.K., Mortazavi A., Williams B.A., Song L., Crawford G.E., Wold B., Willard H.F., Myers R.M. Effects of sequence variation on differential allelic transcription factor occupancy and gene expression. Genome Res. 2012;22(5):860-869. DOI 10.1101/gr.131201.111.

Roadmap Epigenomics Consortium, Kundaje A., Meuleman W., Ernst J., Bilenky M., Yen A., Heravi-Moussavi A., Kheradpour P., Zhang Z., Wang J., Ziller M.J., … Hirst M., Meissner A., Milosavljevic A., Ren B., Stamatoyannopoulos J.A., Wang T., Kellis M. Integrative analysis of 111 reference human epigenomes. Nature. 2015; 518(7539):317-330. DOI 10.1038/nature14248.

Rozowsky J., Abyzov A., Wang J., Alves P., Raha D., Harmanci A., Leng J., Bjornson R., Kong Y., Kitabayashi N., Bhardwaj N., Rubin M., Snyder M., Gerstein M. AlleleSeq: analysis of allele-specific expression and binding in a network framework. Mol. Syst. Biol. 2011;7:522. DOI 10.1038/msb.2011.54.

Schmitz R.J., Lewis Z.A., Goll M.G. DNA methylation: shared and divergent features across eukaryotes. Trends Genet. 2019;35(11): 818-827. DOI 10.1016/j.tig.2019.07.007.

Shi W., Fornes O., Mathelier A., Wasserman W.W. Evaluating the impact of single nucleotide variants on transcription factor binding. Nucleic Acids Res. 2016;44(21):10106-10116. DOI 10.1093/nar/gkw691.

Smith A.J.P., Deloukas P., Munroe P.B. Emerging applications of genome-editing technology to examine functionality of GWAS-associated variants for complex traits. Physiol. Genomics. 2018;50(7): 510-522. DOI 10.1152/physiolgenomics.00028.2018.

Sun J.H., Zhou L., Emerson D.J., Phyo S.A., Titus K.R., Gong W., Gilgenast T.G., Beagan J.A., Davidson B.L., Tassone F., PhillipsCremins J.E. Disease-associated short tandem repeats co-localize with chromatin domain boundaries. Cell. 2018;175(1):224-238. DOI 10.1016/j.cell.2018.08.005.

Visser M., Palstra R.J., Kayser M. Allele-specific transcriptional regulation of IRF4 in melanocytes is mediated by chromatin looping of the intronic rs12203592 enhancer to the IRF4 promoter. Hum. Mol. Genet. 2015;24(9):2649-2661. DOI 10.1093/hmg/ddv029.

Vohra M., Sharma A.R., Prabhu B.N., Rai P.S. SNPs in sites for DNA methylation, transcription factor binding, and miRNA targets leading to allele-specific gene expression and contributing to complex disease risk: a systematic review. Public Health Genomics. 2020;23: 1-16. DOI 10.1159/000510253.

Wang H., Lou D., Wang Z. Crosstalk of genetic variants, allele-specific DNA methylation, and environmental factors for complex disease risk. Front. Genet. 2019;9:695. DOI 10.3389/fgene.2018.00695.

Ward L.D., Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012;40(Database issue):D930-D934. DOI 10.1093/nar/gkr917.

Waszak S.M., Kilpinen H., Gschwind A.R., Orioli A., Raghav S.K., Witwicki R.M., Migliavacca E., Yurovsky A., Lappalainen T., Hernandez N., Reymond A., Dermitzakis E.T., Deplancke B. Identification and removal of low-complexity sites in allele-specific analysis of ChIP-seq data. Bioinformatics. 2014;30(2):165-171. DOI 10.1093/bioinformatics/btt667.

Wingender E., Schoeps T., Dönitz J. TFClass: an expandable hierarchical classification of human transcription factors. Nucleic Acids Res. 2013;41(D1):D165-D170. DOI 10.1093/nar/gks1123.

Yates A.D., Achuthan P., Akanni W., Allen J., Allen J., Alvarez-Jarreta J., Amode M.R., Armean I.M., Azov A.G., Bennett R., Bhai J., … Perry E., Ruffier M., Trevanion S.J., Cunningham F., Howe K.L., Zerbino D.R., Flicek P. Ensembl 2020. Nucleic Acids Res. 2020; 48(D1):D682-D688. DOI 10.1093/nar/gkz966.

Younesy H., Möller T., Heravi-Moussavi A., Cheng J.B., Costello J.F., Lorincz M.C., Karimi M.M., Jones S.J.M. ALEA: a toolbox for allele-specific epigenomics analysis. Bioinformatics. 2014;30(8): 1172-1174. DOI 10.1093/bioinformatics/btt744.

Zhang Y., Manjunath M., Zhang S., Chasman D., Roy S., Song J.S. Integrative genomic analysis predicts causative cis-regulatory mechanisms of the breast cancer-associated genetic variant rs4415084. Cancer Res. 2018;78(7):1579-1591. DOI 10.1158/0008-5472.CAN-17-3486.

Zhao T., Hu Y., Zang T., Wang Y. Integrate GWAS, eQTL, and mQTL data to identify Alzheimer’s disease-related genes. Front. Genet. 2019;10:1021. DOI 10.3389/fgene.2019.01021.

The authors declare that there are no conflicts of interest present.