A method for detecting epistasis in genome-wide studies using case-control multi-locus association analysis.
A method for detecting epistasis in genome-wide studies using case-control multi-locus association analysis.
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DOI：
10.1186/1471-2164-9-360
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发表时间：
2008-07-31
期刊：
影响因子：
4.4
通讯作者：
Ruiz A
中科院分区：
文献类型：
--
作者：
Gayán J;González-Pérez A;Bermudo F;Sáez ME;Royo JL;Quintas A;Galan JJ;Morón FJ;Ramirez-Lorca R;Real LM;Ruiz A
阐明复杂疾病的遗传基础的困难在于许多因素可以影响疾病的发展。其中一些遗传效应可能以复杂的方式相互作用，证明目前的单位点方法无法检测到。我们开发了一种称为无假设临床克隆（HFCC）的分析工具，用于在病例对照设计中搜索全基因组上位性。HFCC结合了用于全基因组上位性检测的相对快速的计算算法，以及在多位点组合中测试各种不同上位性模型的灵活性。HFCC具有很好的能力来检测在各种遗传模型和噪声条件下模拟的多位点相互作用。最重要的是，HFCC可以用大数据集完成详尽的全基因组上位性搜索，如用帕金森病患者和对照组的400，000个SNP集所证明的。随着当前大量个体和遗传标记的遗传研究的可用性，HFCC可以在识别逃脱标准单位点关联分析的上位性效应方面产生很大影响。
The difficulty in elucidating the genetic basis of complex diseases roots in the many factors that can affect the development of a disease. Some of these genetic effects may interact in complex ways, proving undetectable by current single-locus methodology. We have developed an analysis tool called Hypothesis Free Clinical Cloning (HFCC) to search for genome-wide epistasis in a case-control design. HFCC combines a relatively fast computing algorithm for genome-wide epistasis detection, with the flexibility to test a variety of different epistatic models in multi-locus combinations. HFCC has good power to detect multi-locus interactions simulated under a variety of genetic models and noise conditions. Most importantly, HFCC can accomplish exhaustive genome-wide epistasis search with large datasets as demonstrated with a 400,000 SNP set typed on a cohort of Parkinson's disease patients and controls. With the current availability of genetic studies with large numbers of individuals and genetic markers, HFCC can have a great impact in the identification of epistatic effects that escape the standard single-locus association analyses.
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