Identifying patients and assessing variant pathogenicity for an autosomal dominant disease-driving gene.
Identifying patients and assessing variant pathogenicity for an autosomal dominant disease-driving gene.
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常染色体显性遗传疾病驱动基因的患者识别和变异致病性评估。
DOI：
10.1016/j.xpro.2022.101150
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发表时间：
2022-03-18
期刊：
影响因子：
--
通讯作者：
Gennarino VA
中科院分区：
文献类型：
--
作者：
Lee W;de Prisco N;Gennarino VA
识别疾病基因并确定其在患者中的因果关系可能具有挑战性。在这里，我们提出了一种预测常染色体显性基因缺失和错义变异的致病性的方法。我们提供在线资源，用于识别患者和确定约束指标，以在包含在共享缺失区域的几个候选基因中分离出因果基因。我们还提供了优化函数注释程序的说明，否则计算方法的非专业或新手可能无法访问这些程序。有关使用和执行本协议的完整详情，请参阅。招募具有候选基因变异的受影响患者在跨越多个基因座的大基因组缺失中识别单个致病基因用多个致病性预测评分注释遗传变异评估普通人群中单个错义变异的致病性范围识别疾病基因并确定其在患者中的因果关系可能具有挑战性。在这里，我们提出了一种预测常染色体显性基因缺失和错义变异的致病性的方法。我们提供在线资源，用于识别患者和确定约束指标，以在包含在共享缺失区域的几个候选基因中分离出因果基因。我们还提供了优化函数注释程序的说明，否则计算方法的非专业或新手可能无法访问这些程序。
Identifying a disease gene and determining its causality in patients can be challenging. Here, we present an approach to predicting the pathogenicity of deletions and missense variants for an autosomal dominant gene. We provide online resources for identifying patients and determining constraint metrics to isolate the causal gene among several candidates encompassed in a shared region of deletion. We also provide instructions for optimizing functional annotation programs that may be otherwise inaccessible to a nonexpert or novice in computational approaches. For complete details on the use and execution of this protocol, please refer to. Recruit affected patients harboring variation in a candidate gene of interest Identify a single causal gene within a large genomic deletion spanning multiple loci Annotate genetic variants with multiple pathogenicity prediction scores Assess pathogenicity range of singleton missense variants from the general population Identifying a disease gene and determining its causality in patients can be challenging. Here, we present an approach to predicting the pathogenicity of deletions and missense variants for an autosomal dominant gene. We provide online resources for identifying patients and determining constraint metrics to isolate the causal gene among several candidates encompassed in a shared region of deletion. We also provide instructions for optimizing functional annotation programs that may be otherwise inaccessible to a nonexpert or novice in computational approaches.
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影响因子：
14.9
作者：
Piñero J;Bravo À;Queralt-Rosinach N;Gutiérrez-Sacristán A;Deu-Pons J;Centeno E;García-García J;Sanz F;Furlong LI
通讯作者：
Furlong LI
影响因子：
64.8
作者：
Karczewski, Konrad J;Francioli, Laurent C;MacArthur, Daniel G
通讯作者：
MacArthur, Daniel G
影响因子：
4.5
作者：
Petrovski S;Wang Q;Heinzen EL;Allen AS;Goldstein DB
通讯作者：
Goldstein DB
影响因子：
4.5
作者：
Petrovski S;Gussow AB;Wang Q;Halvorsen M;Han Y;Weir WH;Allen AS;Goldstein DB
通讯作者：
Goldstein DB
DOI：
10.1097/gim.0b013e31822c79f9
发表时间：
2011-09
期刊：
Genetics in medicine : official journal of the American College of Medical Genetics
影响因子：
--
作者：
Kaminsky EB;Kaul V;Paschall J;Church DM;Bunke B;Kunig D;Moreno-De-Luca D;Moreno-De-Luca A;Mulle JG;Warren ST;Richard G;Compton JG;Fuller AE;Gliem TJ;Huang S;Collinson MN;Beal SJ;Ackley T;Pickering DL;Golden DM;Aston E;Whitby H;Shetty S;Rossi MR;Rudd MK;South ST;Brothman AR;Sanger WG;Iyer RK;Crolla JA;Thorland EC;Aradhya S;Ledbetter DH;Martin CL
通讯作者：
Martin CL