Solving unsolved rare neurological diseases-a Solve-RD viewpoint.
Solving unsolved rare neurological diseases-a Solve-RD viewpoint.
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DOI：
10.1038/s41431-021-00901-1
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发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
Solve-RD Consortium
中科院分区：
文献类型：
--
作者：
Schüle R;Timmann D;Erasmus CE;Reichbauer J;Wayand M;Solve-RD-DITF-RND;van de Warrenburg B;Schöls L;Wilke C;Bevot A;Zuchner S;Beltran S;Laurie S;Matalonga L;Graessner H;Synofzik M;Solve-RD Consortium
罕见遗传性神经疾病(RND；ORPHA：71859)是一组不同的疾病，由1700个不同的遗传性疾病实体组成。然而，基因发现尚未转化为诊断产量的显著增加，事实上，在NGS模式和RND表型中，分子遗传学诊断率一直停留在约30%-50%[1，2]。通常引用未知疾病基因的存在以及常用的NGS技术和分析管道在检测某些变异类型方面的缺陷来解释低诊断率。为了提高RND的诊断产率-Solve-RD中的四个焦点疾病组之一-我们遵循两个主要方法，我们将在这里展示和举例：(I)对大量未解决的全外显子/基因组测序(WES/WGS)RND数据集的系统最新水平的重新分析；以及(Ii)新的组学方法。基于Solve-RD系统地组织研究人员和专业知识来引导这一方法的方式[3]，欧洲罕见神经疾病参考网络(ERN-RND)在Solve-RD内建立了自己的数据解释工作组(DITF)，该工作组目前由来自15个欧洲国家29个地点的临床和遗传学专家组成。
Rare genetic neurological disorders (RND; ORPHA: 71859) are a heterogeneous group of disorders comprising> 1700 distinct genetic disease entities. However, genetic discoveries have not yet translated into dramatic increases of diagnostic yield and indeed rates of molecular genetic diagnoses have been stuck at about 30–50% across NGS modalities and RND phenotypes [1, 2]. Existence of yet unknown disease genes as well as shortcomings of commonly employed NGS technologies and analysis pipelines in detecting certain variant types are typically cited to explain the low diagnosis rates.To increase the diagnostic yield in RNDs-one of the four focus disease groups in Solve-RD-we follow two major approaches, that we will here present and exemplify:(i) systematic state-of the art re-analysis of large cohorts of unsolved whole-exome/genome sequencing (WES/WGS) RND datasets; and (ii) novel-omics approaches. Based on the way Solve-RD systematically organizes researchers, expertise to channel this approach [3], the European Reference Network for Rare Neurological Diseases (ERN-RND) has established its own Data Interpretation Task Force (DITF) within SOLVE-RD, which is currently composed of clinical and genetic experts from 29 sites in 15 European countries.
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影响因子：
14.5
作者：
Minnerop, Martina;Kurzwelly, Delia;Schuele, Rebecca
通讯作者：
Schuele, Rebecca
影响因子：
14.9
作者：
Köhler S;Gargano M;Matentzoglu N;Carmody LC;Lewis-Smith D;Vasilevsky NA;Danis D;Balagura G;Baynam G;Brower AM;Callahan TJ;Chute CG;Est JL;Galer PD;Ganesan S;Griese M;Haimel M;Pazmandi J;Hanauer M;Harris NL;Hartnett MJ;Hastreiter M;Hauck F;He Y;Jeske T;Kearney H;Kindle G;Klein C;Knoflach K;Krause R;Lagorce D;McMurry JA;Miller JA;Munoz-Torres MC;Peters RL;Rapp CK;Rath AM;Rind SA;Rosenberg AZ;Segal MM;Seidel MG;Smedley D;Talmy T;Thomas Y;Wiafe SA;Xian J;Yüksel Z;Helbig I;Mungall CJ;Haendel MA;Robinson PN
通讯作者：
Robinson PN
影响因子：
3.4
作者：
Reinhard C;Bachoud-Lévi AC;Bäumer T;Bertini E;Brunelle A;Buizer AI;Federico A;Gasser T;Groeschel S;Hermanns S;Klockgether T;Krägeloh-Mann I;Landwehrmeyer GB;Leber I;Macaya A;Mariotti C;Meissner WG;Molnar MJ;Nonnekes J;Ortigoza Escobar JD;Pérez Dueñas B;Renna Linton L;Schöls L;Schuele R;Tijssen MAJ;Vandenberghe R;Volkmer A;Wolf NI;Graessner H
通讯作者：
Graessner H
影响因子：
2
作者：
Matos, Claudia Marques;Alonso, Isabel;Leao, Miguel
通讯作者：
Leao, Miguel
影响因子：
2.2
作者：
Bardakjian, Tanya M.;Helbig, Ingo;Gonzalez-Alegre, Pedro
通讯作者：
Gonzalez-Alegre, Pedro