Identifying genetic interactions associated with late-onset Alzheimer's disease.
Identifying genetic interactions associated with late-onset Alzheimer's disease.
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DOI：
10.1186/s13040-014-0035-z
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发表时间：
2014
期刊：
影响因子：
4.5
通讯作者：
Visweswaran S
中科院分区：
文献类型：
--
作者：
Floudas CS;Um N;Kamboh MI;Barmada MM;Visweswaran S
从全基因组关联研究（GWAS）获得的数据中识别遗传相互作用可以帮助理解复杂疾病的遗传基础。然而，GWAS中大量的单核苷酸多态性（SNP）使得遗传相互作用的识别在计算上具有挑战性。我们开发了贝叶斯组合方法（Bayesian Combinatorial Method，简称BMM），可以识别出与疾病具有高度统计相关性的SNP对。我们将SNP应用于两个晚发性阿尔茨海默病（LOAD）GWAS数据集，以识别与已知阿尔茨海默病相关SNP相互作用的SNP。我们还比较了PLINK中实现的logistic回归。对来自两个GWAS数据集的前200个数据集SNP的基因的基因本体论分析显示出LOAD相关术语的过度表达。两个数据集共有四个基因：APOE和APOC 1，它们与LOAD有很好的关联，CAMK 1D和FBXL 13，以前与LOAD无关，但有证据表明参与LOAD。还发现了来自前30个数据集SNP的其他基因的支持证据。在鉴定几种参与LOAD发病机制的SNPs方面，由于主效应小，单变量分析无法鉴定出这些SNPs。这些结果提供了支持，应用SNP从高维GWAS数据集识别潜在的遗传变异，如SNP。本文的在线版本（doi：10.1186/s13040-014-0035-z）包含补充材料，可供授权用户使用。
Identifying genetic interactions in data obtained from genome-wide association studies (GWASs) can help in understanding the genetic basis of complex diseases. The large number of single nucleotide polymorphisms (SNPs) in GWASs however makes the identification of genetic interactions computationally challenging. We developed the Bayesian Combinatorial Method (BCM) that can identify pairs of SNPs that in combination have high statistical association with disease. We applied BCM to two late-onset Alzheimer’s disease (LOAD) GWAS datasets to identify SNPs that interact with known Alzheimer associated SNPs. We also compared BCM with logistic regression that is implemented in PLINK. Gene Ontology analysis of genes from the top 200 dataset SNPs for both GWAS datasets showed overrepresentation of LOAD-related terms. Four genes were common to both datasets: APOE and APOC1, which have well established associations with LOAD, and CAMK1D and FBXL13, not previously linked to LOAD but having evidence of involvement in LOAD. Supporting evidence was also found for additional genes from the top 30 dataset SNPs. BCM performed well in identifying several SNPs having evidence of involvement in the pathogenesis of LOAD that would not have been identified by univariate analysis due to small main effect. These results provide support for applying BCM to identify potential genetic variants such as SNPs from high dimensional GWAS datasets. The online version of this article (doi:10.1186/s13040-014-0035-z) contains supplementary material, which is available to authorized users.
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DOI：
10.1038/nrg2579
发表时间：
2009-06
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
Cordell HJ
通讯作者：
Cordell HJ
影响因子：
12.3
作者：
Avramopoulos D
通讯作者：
Avramopoulos D
DOI：
10.1056/nejmra0808700
发表时间：
2009-04-23
期刊：
The New England journal of medicine
影响因子：
--
作者：
Hardy J;Singleton A
通讯作者：
Singleton A
影响因子：
5.8
作者：
Johnson, Andrew D.;Handsaker, Robert E.;de Bakker, Paul I. W.
通讯作者：
de Bakker, Paul I. W.
影响因子：
3.5
作者：
Blacker, D;Bertram, L;Tanzi, RE
通讯作者：
Tanzi, RE