A global overview of genetically interpretable multimorbidities among common diseases in the UK Biobank.
A global overview of genetically interpretable multimorbidities among common diseases in the UK Biobank.
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英国生物银行常见疾病中可遗传解释的多重发病率的全球概述。
DOI：
10.1186/s13073-021-00927-6
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发表时间：
2021-07-05
期刊：
影响因子：
12.3
通讯作者：
Zhao XM
中科院分区：
文献类型：
--
作者：
Dong G;Feng J;Sun F;Chen J;Zhao XM
多病极大地增加了全球健康负担，但其遗传风险的景观尚未得到系统的研究。我们使用了英国生物银行中385，335名患者的住院数据，调查了439种常见疾病之间的多发病关系。进行GWAS后分析，以确定在基因组位点、网络以及整体遗传结构水平上的多突变共享遗传风险。我们对遗传学可解释的多病网络进行了网络分解，以检测枢纽疾病以及每个模块中涉及的分子和功能。在439种常见疾病中，共确定了11，285种多发病，其中46%在基因座，网络或整体遗传结构水平上可进行遗传解释。影响相同和不同生理系统的多发病表现出不同的共享遗传成分模式，前者更有可能共享基因座水平的遗传成分，而后者更有可能共享网络水平的遗传成分。此外，多发病所共有的基因座和网络水平的遗传成分都集中在细胞免疫、蛋白质代谢和基因沉默上。此外，我们发现，遗传可解释的多发病倾向于形成网络模块，介导的枢纽疾病和生理类别的特点。最后，我们展示了中枢疾病介导的多发病模块如何有助于为多发病的遗传贡献者提供有用的见解。我们的研究结果为理解多病的遗传易感性提供了系统的资源，并表明枢纽疾病和聚合分子和功能可能是治疗多病的关键。我们已经创建了一个在线数据库，便于研究人员和医生浏览、搜索或下载这些多病（https：multimorbidity.comp-sysbio.org）。在线版本包含补充材料，可通过10.1186/s13073-021-00927-6获得。
Multimorbidities greatly increase the global health burdens, but the landscapes of their genetic risks have not been systematically investigated. We used the hospital inpatient data of 385,335 patients in the UK Biobank to investigate the multimorbid relations among 439 common diseases. Post-GWAS analyses were performed to identify multimorbidity shared genetic risks at the genomic loci, network, as well as overall genetic architecture levels. We conducted network decomposition for the networks of genetically interpretable multimorbidities to detect the hub diseases and the involved molecules and functions in each module. In total, 11,285 multimorbidities among 439 common diseases were identified, and 46% of them were genetically interpretable at the loci, network, or overall genetic architecture levels. Multimorbidities affecting the same and different physiological systems displayed different patterns of the shared genetic components, with the former more likely to share loci-level genetic components while the latter more likely to share network-level genetic components. Moreover, both the loci- and network-level genetic components shared by multimorbidities converged on cell immunity, protein metabolism, and gene silencing. Furthermore, we found that the genetically interpretable multimorbidities tend to form network modules, mediated by hub diseases and featuring physiological categories. Finally, we showcased how hub diseases mediating the multimorbidity modules could help provide useful insights for the genetic contributors of multimorbidities. Our results provide a systematic resource for understanding the genetic predispositions of multimorbidities and indicate that hub diseases and converged molecules and functions may be the key for treating multimorbidities. We have created an online database that facilitates researchers and physicians to browse, search, or download these multimorbidities (https://multimorbidity.comp-sysbio.org). The online version contains supplementary material available at 10.1186/s13073-021-00927-6.
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影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
遗传对人体组织基因表达的影响。
影响因子：
64.8
作者：
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者：
Montgomery SB
影响因子：
30.8
作者：
Bulik-Sullivan, Brendan K.;Loh, Po-Ru;Finucane, Hilary K.;Ripke, Stephan;Yang, Jian;Patterson, Nick;Daly, Mark J.;Price, Alkes L.;Neale, Benjamin M.
通讯作者：
Neale, Benjamin M.
影响因子：
9.2
作者：
Chang CC;Chow CC;Tellier LC;Vattikuti S;Purcell SM;Lee JJ
通讯作者：
Lee JJ
影响因子：
30.8
作者：
Ellinghaus D;Jostins L;Spain SL;Cortes A;Bethune J;Han B;Park YR;Raychaudhuri S;Pouget JG;Hübenthal M;Folseraas T;Wang Y;Esko T;Metspalu A;Westra HJ;Franke L;Pers TH;Weersma RK;Collij V;D'Amato M;Halfvarson J;Jensen AB;Lieb W;Degenhardt F;Forstner AJ;Hofmann A;International IBD Genetics Consortium (IIBDGC);International Genetics of Ankylosing Spondylitis Consortium (IGAS);International PSC Study Group (IPSCSG);Genetic Analysis of Psoriasis Consortium (GAPC);Psoriasis Association Genetics Extension (PAGE);Schreiber S;Mrowietz U;Juran BD;Lazaridis KN;Brunak S;Dale AM;Trembath RC;Weidinger S;Weichenthal M;Ellinghaus E;Elder JT;Barker JN;Andreassen OA;McGovern DP;Karlsen TH;Barrett JC;Parkes M;Brown MA;Franke A
通讯作者：
Franke A