A cross-disorder dosage sensitivity map of the human genome.
A cross-disorder dosage sensitivity map of the human genome.
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人类基因组的交叉疾病剂量敏感性图谱。
DOI：
10.1016/j.cell.2022.06.036
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发表时间：
2022-08-04
期刊：
影响因子：
64.5
通讯作者：
Talkowski, Michael E.
中科院分区：
文献类型：
--
作者：
Collins, Ryan L.;Glessner, Joseph T.;Porcu, Eleonora;Lepamets, Maarja;Brandon, Rhonda;Lauricella, Christopher;Han, Lide;Morley, Theodore;Niestroj, Lisa-Marie;Ulirsch, Jacob;Everett, Selin;Howrigan, Daniel P.;Boone, Philip M.;Fu, Jack;Karczewski, Konrad J.;Kellaris, Georgios;Lowther, Chelsea;Lucente, Diane;Mohajeri, Kiana;Noukas, Margit;Nuttle, Xander;Samocha, Kaitlin E.;Trinh, Mi;Ullah, Farid;Vosa, Urmo;Hurles, Matthew E.;Aradhya, Swaroop;Davis, Erica E.;Finucane, Hilary;Gusella, James F.;Janze, Aura;Katsanis, Nicholas;Matyakhina, Ludmila;Neale, Benjamin M.;Sanders, David;Warren, Stephanie;Hodge, Jennelle C.;Lal, Dennis;Ruderfer, Douglas M.;Meck, Jeanne;Magi, Reedik;Esko, Tonu;Reymond, Alexandre;Kutalik, Zoltan;Hakonarson, Hakon;Sunyaev, Shamil;Brand, Harrison;Talkowski, Michael E.
罕见拷贝数变异（rcnv）包括在全球人群中不常见的缺失和重复，并可能带来重大疾病风险。在这项研究中，我们旨在量化整个人类基因组的单倍不全（即缺失不耐受）和三倍敏感性（即重复不耐受）的特性。我们对来自近100万个体的rCNVs进行了协调和meta分析，构建了54种疾病的剂量敏感性全基因组目录，其中定义了163个与至少一种疾病相关的剂量敏感片段。这些片段通常是基因密集的，并且通常含有显性剂量敏感驱动基因，我们能够使用统计精细图谱对其进行优先排序。最后，我们设计了一个集成机器学习模型来预测所有常染色体基因的剂量敏感性概率（pHaplo和pTriplo），确定了2,987个单倍体不足基因和1,559个三倍体敏感基因，其中648个是唯一三倍体敏感基因。该剂量敏感性资源将为人类疾病研究和临床遗传学提供广泛的应用。协调来自近100万人的基因组数据，可以深入了解人类疾病中罕见拷贝数变异的特性，并预测所有常染色体蛋白质编码基因的剂量敏感性。
Rare copy-number variants (rCNVs) include deletions and duplications that occur infrequently in the global human population and can confer substantial risk for disease. In this study, we aimed to quantify the properties of haploinsufficiency (i.e., deletion intolerance) and triplosensitivity (i.e., duplication intolerance) throughout the human genome. We harmonized and meta-analyzed rCNVs from nearly one-million individuals to construct a genome-wide catalog of dosage sensitivity across 54 disorders, which defined 163 dosage sensitive segments associated with at least one disorder. These segments were typically gene-dense and often harbored dominant dosage sensitive driver genes, which we were able to prioritize using statistical fine-mapping. Finally, we designed an ensemble machine learning model to predict dosage sensitivity probabilities (pHaplo & pTriplo) for all autosomal genes, which identified 2,987 haploinsufficient and 1,559 triplosensitive genes, including 648 that were uniquely triplosensitive. This dosage sensitivity resource will provide broad utility for human disease research and clinical genetics. Harmonizing genomic data from nearly one-million individuals yields insights into the properties of rare copy number variants across human disorders and dosage sensitivity predictions for all autosomal protein-coding genes.
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影响因子：
9.8
作者：
通讯作者：
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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：
14.9
作者：
Frankish A;Diekhans M;Ferreira AM;Johnson R;Jungreis I;Loveland J;Mudge JM;Sisu C;Wright J;Armstrong J;Barnes I;Berry A;Bignell A;Carbonell Sala S;Chrast J;Cunningham F;Di Domenico T;Donaldson S;Fiddes IT;García Girón C;Gonzalez JM;Grego T;Hardy M;Hourlier T;Hunt T;Izuogu OG;Lagarde J;Martin FJ;Martínez L;Mohanan S;Muir P;Navarro FCP;Parker A;Pei B;Pozo F;Ruffier M;Schmitt BM;Stapleton E;Suner MM;Sycheva I;Uszczynska-Ratajczak B;Xu J;Yates A;Zerbino D;Zhang Y;Aken B;Choudhary JS;Gerstein M;Guigó R;Hubbard TJP;Kellis M;Paten B;Reymond A;Tress ML;Flicek P
通讯作者：
Flicek P
影响因子：
4
作者：
Ansari M;Poke G;Ferry Q;Williamson K;Aldridge R;Meynert AM;Bengani H;Chan CY;Kayserili H;Avci S;Hennekam RC;Lampe AK;Redeker E;Homfray T;Ross A;Falkenberg Smeland M;Mansour S;Parker MJ;Cook JA;Splitt M;Fisher RB;Fryer A;Magee AC;Wilkie A;Barnicoat A;Brady AF;Cooper NS;Mercer C;Deshpande C;Bennett CP;Pilz DT;Ruddy D;Cilliers D;Johnson DS;Josifova D;Rosser E;Thompson EM;Wakeling E;Kinning E;Stewart F;Flinter F;Girisha KM;Cox H;Firth HV;Kingston H;Wee JS;Hurst JA;Clayton-Smith J;Tolmie J;Vogt J;Tatton-Brown K;Chandler K;Prescott K;Wilson L;Behnam M;McEntagart M;Davidson R;Lynch SA;Sisodiya S;Mehta SG;McKee SA;Mohammed S;Holden S;Park SM;Holder SE;Harrison V;McConnell V;Lam WK;Green AJ;Donnai D;Bitner-Glindzicz M;Donnelly DE;Nellåker C;Taylor MS;FitzPatrick DR
通讯作者：
FitzPatrick DR
影响因子：
64.8
作者：
Cummings, Beryl B.;Karczewski, Konrad J.;MacArthur, Daniel G.
通讯作者：
MacArthur, Daniel G.