Protein–protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis

Uffelmann E, Huang QQ, Munung NS, de Vries J, Okada Y, Martin AR, et al. Genome-wide association studies. Nat Rev Methods Prim. 2021;1(1):59.

Article  CAS  Google Scholar 

Nica AC, Dermitzakis ET. Expression quantitative trait loci: present and future. Philos Trans R Soc B Biol Sci. 2013;368(1620):20120362.

Article  Google Scholar 

Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47(D1):D1005–12.

Article  CAS  PubMed  Google Scholar 

Akiyama M. Multi-omics study for interpretation of genome-wide association study. J Hum Genet. 2021;66(1):3–10.

Article  PubMed  Google Scholar 

Yan J, Risacher SL, Shen L, Saykin AJ. Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data. Brief Bioinform. 2017;19(6):1370–81.

PubMed Central  Google Scholar 

Yang X. Multitissue Multiomics systems biology to dissect complex diseases. Trends Mol Med. 2020;26(8):718–28.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Barabási AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12(1):56–68.

Article  PubMed  PubMed Central  Google Scholar 

Chimusa ER, Dalvie S, Dandara C, Wonkam A, Mazandu GK. Post genome-wide association analysis: dissecting computational pathway/network-based approaches. Brief Bioinform. 2019;20(2):690–700.

Article  CAS  PubMed  Google Scholar 

Wu S, Chen D, Snyder MP. Network biology bridges the gaps between quantitative genetics and multi-omics to map complex diseases. Curr Opin Chem Biol. 2022;66:102101.

Article  CAS  PubMed  Google Scholar 

Klapa MI, Tsafou K, Theodoridis E, Tsakalidis A, Moschonas NK. Reconstruction of the experimentally supported human protein interactome: what can we learn? BMC Syst Biol. 2013;7:96.

Article  PubMed  PubMed Central  Google Scholar 

Dimitrakopoulos GN, Klapa MI, Moschonas NK. How far are we from the completion of the human protein interactome reconstruction? Biomolecules. 2022;12(1):140.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabási AL. The human disease network. Proc Natl Acad Sci U S A. 2007;104:8685–90.

Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

Ratnakumar A, Weinhold N, Mar JC, Riaz N. Protein-Protein interactions uncover candidate ‘core genes’ within omnigenic disease networks. Liu X, editor. PLOS Genet. 2020;16(7):e1008903.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Guo X, Song Y, Liu S, Gao M, Qi Y, Shang X. Linking genotype to phenotype in multi-omics data of small sample. BMC Genomics. 2021;22(1):537.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jia P, Zhao Z. Network-assisted analysis to prioritize GWAS results: principles, methods and perspectives. Hum Genet. 2014;133(2):125–38.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Raj MR, Sreeja A. Analysis of Computational gene prioritization approaches. Proc Comput Sci. 2018;143:395–410.

Article  Google Scholar 

Kim Y, Park JH, Cho YR. Network-based approaches for disease-gene association prediction using protein–protein interaction networks. Int J Mol Sci. 2022;23(13):7411.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Oliver S. Guilt-by-association goes global. Nature. 2000;403(6770):601–2.

Article  ADS  CAS  PubMed  Google Scholar 

Oti M, Brunner H. The modular nature of genetic diseases. Clin Genet. 2006;71(1):1–11.

Article  Google Scholar 

Kjeldsen SE. Hypertension and cardiovascular risk: general aspects. Pharmacol Res. 2018;129:95–9.

Article  PubMed  Google Scholar 

Ehret GB, Ferreira T, Chasman DI, Jackson AU, Schmidt EM, Johnson T, et al. The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals. Nat Genet. 2016;48(10):1171–84.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Surendran P, Drenos F, Young R, Warren H, Cook JP, Manning AK, et al. Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nat Genet. 2016;48(10):1151–61.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Feitosa MF, Kraja AT, Chasman DI, Sung YJ, Winkler TW, Ntalla I, et al. Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570K individuals across multiple ancestries. Kuivaniemi H, editor. PLoS ONE. 2018;13(6):e0198166.

Article  PubMed  PubMed Central  Google Scholar 

Giri A, Hellwege JN, Keaton JM, Park J, Qiu C, Warren HR, et al. Trans-ethnic association study of blood pressure determinants in over 750,000 individuals. Nat Genet. 2019;51(1):51–62.

Article  CAS  PubMed  Google Scholar 

Huan T, Meng Q, Saleh MA, Norlander AE, Joehanes R, Zhu J, et al. Integrative network analysis reveals molecular mechanisms of blood pressure regulation. Mol Syst Biol. 2015;11(4):799–799.

Article  PubMed  PubMed Central  Google Scholar 

Botzer A, Grossman E, Moult J, Unger R. A system view and analysis of essential hypertension. J Hypertens. 2018;36(5):1094–103.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhao Y, Blencowe M, Shi X, Shu L, Levian C, Ahn IS, et al. Integrative genomics analysis unravels tissue-specific pathways, networks, and key regulators of blood pressure regulation. Front Cardiovasc Med. 2019;6:21.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Turner SD. qqman: an R package for visualizing GWAS results using Q–Q and manhattan plots. J Open Source Softw. 2018;3(25):731.

Article  ADS  Google Scholar 

Cunningham F, Allen JE, Allen J, Alvarez-Jarreta J, Amode MR, Armean IM, et al. Ensembl 2022. Nucleic Acids Res. 2022;50(D1):D988–95.

Article  CAS  PubMed  Google Scholar 

Eilbeck K, Lewis SE, Mungall CJ, Yandell M, Stein L, Durbin R, et al. The sequence ontology: a tool for the unification of genome annotations. Genome Biol. 2005;6(5):R44.

Article  PubMed  PubMed Central  Google Scholar 

Wolfe D, Dudek S, Ritchie MD, Pendergrass SA. Visualizing genomic information across chromosomes with PhenoGram. BioData Min. 2013;6(1):18.

Article  PubMed  PubMed Central  Google Scholar 

GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science (80-). 2020;69(6509):1318–30.

Article  Google Scholar 

Lonsdale J, Thomas J, Salvatore M, Phillips R, Lo E, Shad S, et al. The genotype-tissue expression (GTEx) project. Nat Genet. 2013;45(6):580–5.

Article  CAS  Google Scholar 

Gioutlakis A, Klapa MI, Moschonas NK. PICKLE 2.0: a human protein-protein interaction meta-database employing data integration via genetic information ontology. Oliva B, editor. PLoS ONE. 2017;12(10):e0186039.

Article  PubMed  PubMed Central  Google Scholar 

Dimitrakopoulos GN, Klapa MI, Moschonas NK. PICKLE 3.0: enriching the human meta-database with the mouse protein interactome extended via mouse–human orthology. Bioinformatics. 2021;37(1):145–6.

Article  CAS  PubMed  Google Scholar 

Bateman A, Martin MJ, Orchard S, Magrane M, Agivetova R, Ahmad S, et al. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2021;49(D1):D480–9.

Article  Google Scholar 

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software Environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Salavaty A, Ramialison M, Currie PD. Integrated value of influence: an integrative method for the identification of the most influential nodes within networks. Patterns. 2020;1(5):100052.

Article 

留言 (0)

沒有登入
gif