A general framework for estimating the relative pathogenicity of human genetic variants.
A general framework for estimating the relative pathogenicity of human genetic variants.
复制标题
DOI：
10.1038/ng.2892
复制
发表时间：
2014-03
期刊：
影响因子：
30.8
通讯作者：
Shendure, Jay
中科院分区：
文献类型：
--
作者：
Kircher, Martin;Witten, Daniela M.;Jain, Preti;O'Roak, Brian J.;Cooper, Gregory M.;Shendure, Jay
我们对人类基因组测序的能力已经超过了我们解释遗传变异的能力。目前的基因组注释倾向于利用单一的信息类型（例如保守性）和/或在范围上受到限制（例如错义变化）。在这里，我们描述了组合注释相关消耗（CADD），一个框架，客观地将许多不同的注释集成到一个单一的，定量的分数。我们将CADD作为一种支持向量机来训练，以区分1470万个来自人类的高频等位基因和1470万个模拟变体。我们为所有86亿种可能的人类单核苷酸变体预先计算“C-评分”，并对短插入/缺失进行评分。C-分数与等位基因多样性、功能性注释、致病性、疾病严重程度、实验测量的调节效应和复杂性状关联相关，并且对个体基因组内的已知致病性变体进行高度排名。CADD在许多功能类别、效应大小和遗传结构中对功能性、有害性和致病性变体进行优先排序的能力是任何当前注释都无法比拟的。
Our capacity to sequence human genomes has exceeded our ability to interpret genetic variation. Current genomic annotations tend to exploit a single information type (e.g. conservation) and/or are restricted in scope (e.g. to missense changes). Here, we describe Combined Annotation Dependent Depletion (CADD), a framework that objectively integrates many diverse annotations into a single, quantitative score. We implement CADD as a support vector machine trained to differentiate 14.7 million high-frequency human derived alleles from 14.7 million simulated variants. We pre-compute “C-scores” for all 8.6 billion possible human single nucleotide variants and enable scoring of short insertions/deletions. C-scores correlate with allelic diversity, annotations of functionality, pathogenicity, disease severity, experimentally measured regulatory effects, and complex trait associations, and highly rank known pathogenic variants within individual genomes. The ability of CADD to prioritize functional, deleterious, and pathogenic variants across many functional categories, effect sizes and genetic architectures is unmatched by any current annotation.
登录
查看更多内容
影响因子：
16.2
作者：
Iossifov I;Ronemus M;Levy D;Wang Z;Hakker I;Rosenbaum J;Yamrom B;Lee YH;Narzisi G;Leotta A;Kendall J;Grabowska E;Ma B;Marks S;Rodgers L;Stepansky A;Troge J;Andrews P;Bekritsky M;Pradhan K;Ghiban E;Kramer M;Parla J;Demeter R;Fulton LL;Fulton RS;Magrini VJ;Ye K;Darnell JC;Darnell RB;Mardis ER;Wilson RK;Schatz MC;McCombie WR;Wigler M
通讯作者：
Wigler M
来自1,092个人基因组的遗传变异的综合图。
影响因子：
64.8
作者：
通讯作者：
--
DOI：
10.1126/science.1215040
发表时间：
2012-02-17
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
MacArthur DG;Balasubramanian S;Frankish A;Huang N;Morris J;Walter K;Jostins L;Habegger L;Pickrell JK;Montgomery SB;Albers CA;Zhang ZD;Conrad DF;Lunter G;Zheng H;Ayub Q;DePristo MA;Banks E;Hu M;Handsaker RE;Rosenfeld JA;Fromer M;Jin M;Mu XJ;Khurana E;Ye K;Kay M;Saunders GI;Suner MM;Hunt T;Barnes IH;Amid C;Carvalho-Silva DR;Bignell AH;Snow C;Yngvadottir B;Bumpstead S;Cooper DN;Xue Y;Romero IG;1000 Genomes Project Consortium;Wang J;Li Y;Gibbs RA;McCarroll SA;Dermitzakis ET;Pritchard JK;Barrett JC;Harrow J;Hurles ME;Gerstein MB;Tyler-Smith C
通讯作者：
Tyler-Smith C
影响因子：
9.5
作者：
Hubisz, Melissa J.;Pollard, Katherine S.;Siepel, Adam
通讯作者：
Siepel, Adam
影响因子：
48
作者：
Hoffman, Michael M.;Buske, Orion J.;Wang, Jie;Weng, Zhiping;Bilmes, Jeff A.;Noble, William Stafford
通讯作者：
Noble, William Stafford