Identifying genetic interactions in genome-wide data using Bayesian networks.
Identifying genetic interactions in genome-wide data using Bayesian networks.
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DOI：
10.1002/gepi.20514
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发表时间：
2010-09
影响因子：
2.1
通讯作者：
Visweswaran, Shyam
中科院分区：
文献类型：
--
作者：
Jiang, Xia;Barmada, M. Michael;Visweswaran, Shyam
据信，基因之间的相互作用（上位性）可能在常见疾病的易感性中发挥重要作用。为了研究疾病的潜在遗传变异，同时分析数十万个 SNP 的全基因组关联研究 (GWAS) 被越来越多地使用。通常，这些研究的数据是用单基因座方法进行分析的。然而，使用单位点方法可能不容易检测到上位相互作用。因此，已经开发了参数和非参数多位点方法来检测这种相互作用。然而，使用高维全基因组数据有效分析上位性仍然是一个严峻的挑战。我们开发了一种基于贝叶斯网络和最小描述长度原理的方法来检测上位相互作用。我们使用从 70 个不同遗传模型生成的 28000 个模拟数据集，将其检测基因-基因相互作用的能力及其效率与组合方法多因素降维 (MDR) 进行比较。我们进一步将该方法应用于从涉及晚发阿尔茨海默病 (LOAD) 的 GWAS 中获得的超过 300,000 个 SNP。我们的方法优于 MDR，并且我们证实了之前的结果，表明 GAB2 基因与 LOAD 相关。据我们所知，这是第一次使用高维全基因组数据集成功进行基于模型的上位分析。
It is believed that interactions among genes (epistasis) may play an important role in susceptibility to common diseases. To study the underlying genetic variants of diseases, genome-wide association studies (GWAS) that simultaneously assay several hundreds of thousands of SNPs are being increasingly used. Often, the data from these studies are analyzed with single-locus methods. However, epistatic interactions may not be easily detected with single-locus methods. As a result, both parametric and nonparametric multi-locus methods have been developed to detect such interactions. However, efficiently analyzing epistasis using high-dimensional genome-wide data remains a crucial challenge. We develop a method based on Bayesian networks and the minimum description length principle for detecting epistatic interactions. We compare its ability to detect gene-gene interactions and its efficiency to that of the combinatorial method multifactor dimensionality reduction (MDR) using 28000 simulated data sets generated from 70 different genetic models We further apply the method to over 300,000 SNPs obtained from a GWAS involving late onset Alzheimer’s disease (LOAD). Our method outperforms MDR and we substantiate previous results indicating that the GAB2 gene is associated with LOAD. To our knowledge, this is the first successful model-based epistatic analysis using a high-dimensional genome-wide data set.
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DOI：
10.1056/nejmoa072366
发表时间：
2007-08-02
期刊：
The New England journal of medicine
影响因子：
--
作者：
Samani NJ;Erdmann J;Hall AS;Hengstenberg C;Mangino M;Mayer B;Dixon RJ;Meitinger T;Braund P;Wichmann HE;Barrett JH;König IR;Stevens SE;Szymczak S;Tregouet DA;Iles MM;Pahlke F;Pollard H;Lieb W;Cambien F;Fischer M;Ouwehand W;Blankenberg S;Balmforth AJ;Baessler A;Ball SG;Strom TM;Braenne I;Gieger C;Deloukas P;Tobin MD;Ziegler A;Thompson JR;Schunkert H;WTCCC and the Cardiogenics Consortium
通讯作者：
WTCCC and the Cardiogenics Consortium
影响因子：
9.8
作者：
Ritchie, MD;Hahn, LW;Moore, JH
通讯作者：
Moore, JH
影响因子：
5.8
作者：
Hahn, LW;Ritchie, MD;Moore, JH
通讯作者：
Moore, JH
影响因子：
2.9
作者：
Millstein, J;Siegmund, KD;Gauderman, J
通讯作者：
Gauderman, J
影响因子：
3
作者：
Coffey CS;Hebert PR;Ritchie MD;Krumholz HM;Gaziano JM;Ridker PM;Brown NJ;Vaughan DE;Moore JH
通讯作者：
Moore JH