ProbABEL package for genome-wide association analysis of imputed data.
ProbABEL package for genome-wide association analysis of imputed data.
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DOI：
10.1186/1471-2105-11-134
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发表时间：
2010-03-16
影响因子：
3
通讯作者：
van Duijn CM
中科院分区：
文献类型：
--
作者：
Aulchenko YS;Struchalin MV;van Duijn CM
在过去的几年中，全基因组关联（GWA）研究成为鉴定与复杂性状相关的基因座的首选工具。目前，估算的单核苷酸多态性 (SNP) 数据经常用于 GWA 分析。对估算数据的正确分析需要实施考虑基因型估算不确定性的特定方法。我们开发了 ProbABEL 软件包，用于分别在线性、逻辑和 Cox 比例风险模型下分析全基因组估算的 SNP 数据以及定量、二元和事件发生时间结果。对于数量性状，该软件包还实施了快速两步混合模型评分测试，以检测具有差异关系的样本之间的关联，从而促进基于家庭的研究、在人类遗传隔离群体和远交动物群体中进行的研究的分析。 ProbABEL 软件包提供了快速有效的方法来分析全基因组范围内的估算数据，并将有助于未来复杂性状基因座的识别。
Over the last few years, genome-wide association (GWA) studies became a tool of choice for the identification of loci associated with complex traits. Currently, imputed single nucleotide polymorphisms (SNP) data are frequently used in GWA analyzes. Correct analysis of imputed data calls for the implementation of specific methods which take genotype imputation uncertainty into account. We developed the ProbABEL software package for the analysis of genome-wide imputed SNP data and quantitative, binary, and time-till-event outcomes under linear, logistic, and Cox proportional hazards models, respectively. For quantitative traits, the package also implements a fast two-step mixed model-based score test for association in samples with differential relationships, facilitating analysis in family-based studies, studies performed in human genetically isolated populations and outbred animal populations. ProbABEL package provides fast efficient way to analyze imputed data in genome-wide context and will facilitate future identification of complex trait loci.
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影响因子：
30.8
作者：
Marchini, Jonathan;Howie, Bryan;Donnelly, Peter
通讯作者：
Donnelly, Peter
影响因子：
4.5
作者：
通讯作者：
--
影响因子：
4.5
作者：
Heard-Costa NL;Zillikens MC;Monda KL;Johansson A;Harris TB;Fu M;Haritunians T;Feitosa MF;Aspelund T;Eiriksdottir G;Garcia M;Launer LJ;Smith AV;Mitchell BD;McArdle PF;Shuldiner AR;Bielinski SJ;Boerwinkle E;Brancati F;Demerath EW;Pankow JS;Arnold AM;Chen YD;Glazer NL;McKnight B;Psaty BM;Rotter JI;Amin N;Campbell H;Gyllensten U;Pattaro C;Pramstaller PP;Rudan I;Struchalin M;Vitart V;Gao X;Kraja A;Province MA;Zhang Q;Atwood LD;Dupuis J;Hirschhorn JN;Jaquish CE;O'Donnell CJ;Vasan RS;White CC;Aulchenko YS;Estrada K;Hofman A;Rivadeneira F;Uitterlinden AG;Witteman JC;Oostra BA;Kaplan RC;Gudnason V;O'Connell JR;Borecki IB;van Duijn CM;Cupples LA;Fox CS;North KE
通讯作者：
North KE
影响因子：
9.8
作者：
Chen, Wei-Min;Abecasis, Goncalo R.
通讯作者：
Abecasis, Goncalo R.
影响因子：
30.8
作者：
Yu, JM;Pressoir, G;Buckler, ES
通讯作者：
Buckler, ES