Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.
Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.
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DOI：
10.1016/j.cell.2021.07.038
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发表时间：
2021-09-02
期刊：
影响因子：
64.5
通讯作者：
Zeggini E
中科院分区：
文献类型：
--
作者：
Boer CG;Hatzikotoulas K;Southam L;Stefánsdóttir L;Zhang Y;Coutinho de Almeida R;Wu TT;Zheng J;Hartley A;Teder-Laving M;Skogholt AH;Terao C;Zengini E;Alexiadis G;Barysenka A;Bjornsdottir G;Gabrielsen ME;Gilly A;Ingvarsson T;Johnsen MB;Jonsson H;Kloppenburg M;Luetge A;Lund SH;Mägi R;Mangino M;Nelissen RRGHH;Shivakumar M;Steinberg J;Takuwa H;Thomas LF;Tuerlings M;arcOGEN Consortium;HUNT All-In Pain;ARGO Consortium;Regeneron Genetics Center;Babis GC;Cheung JPY;Kang JH;Kraft P;Lietman SA;Samartzis D;Slagboom PE;Stefansson K;Thorsteinsdottir U;Tobias JH;Uitterlinden AG;Winsvold B;Zwart JA;Davey Smith G;Sham PC;Thorleifsson G;Gaunt TR;Morris AP;Valdes AM;Tsezou A;Cheah KSE;Ikegawa S;Hveem K;Esko T;Wilkinson JM;Meulenbelt I;Lee MTM;van Meurs JBJ;Styrkársdóttir U;Zeggini E
骨关节炎影响着全世界超过 3 亿人。在这里，我们对 826,690 名个体（177,517 名患有骨关节炎）进行了全基因组关联研究荟萃分析，并确定了 11 种骨关节炎表型的 100 个独立相关的风险变异，其中 52 种以前与该疾病无关。我们报告了拇指和脊柱骨关节炎的风险变异，并确定了负重关节和非负重关节之间遗传效应的差异。我们确定了性别特异性和早期发病年龄的骨关节炎风险位点。我们整合来自原发性患者组织（包括关节软骨、软骨下骨和骨赘软骨）的功能基因组数据，并鉴定出高可信度的效应基因。我们提供了与疼痛（主要疾病症状）相关表型的遗传相关性的证据，并确定了与神经元过程相关的可能致病基因。我们的结果提供了对疾病过程中关键分子参与者的见解，并突出了有吸引力的药物靶点以加速转化。一项多队列研究确定了 52 种以前未知的骨关节炎遗传风险变异 骨关节炎遗传风险的相似性和差异取决于关节部位 骨关节炎遗传成分与疼痛相关表型相关 高置信度效应基因突出了药物干预的潜在目标 骨关节炎的多队列全基因组关联荟萃分析强调了关节部位类型对遗传风险变异特征的影响以及骨关节炎遗传学与疼痛相关之间的联系表型，指出治疗干预的潜在目标。
Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation. A multicohort study identifies 52 previously unknown osteoarthritis genetic risk variants Similarities and differences in osteoarthritis genetic risk depend on joint sites Osteoarthritis genetic components are associated with pain-related phenotypes High-confidence effector genes highlight potential targets for drug intervention A multicohort genome-wide association meta-analysis of osteoarthritis highlights the impact of joint site types on the features of genetic risk variants and the link between osteoarthritis genetics and pain-related phenotypes, pointing toward potential targets for therapeutic intervention.
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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：
Das, Sayantan;Forer, Lukas;Schoenherr, Sebastian;Sidore, Carlo;Locke, Adam E.;Kwong, Alan;Vrieze, Scott I.;Chew, Emily Y.;Levy, Shawn;McGue, Matt;Schlessinger, David;Stambolian, Dwight;Loh, Po-Ru;Iacono, William G.;Swaroop, Anand;Scott, Laura J.;Cucca, Francesco;Kronenberg, Florian;Boehnke, Michael;Abecasis, Goncalo R.;Fuchsberger, Christian
通讯作者：
Fuchsberger, Christian
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：
Choi, Shing Wan;O'Reilly, Paul F.
通讯作者：
O'Reilly, Paul F.