Comparison of strategies to detect epistasis from eQTL data.

Comparison of strategies to detect epistasis from eQTL data.
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DOI:
10.1371/journal.pone.0028415
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发表时间:
2011
期刊:
影响因子:
3.7
通讯作者:
Bergmann S
Bergmann S
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Kapur K;Schüpbach T;Xenarios I;Kutalik Z;Bergmann S

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全基因组关联研究有助于识别与复杂性状(如人类疾病或基因表达表型)相关的遗传变异。有人提出,通过考虑基因座对之间的相互作用来扩展现有的分析方法可能会发现额外的遗传效应。然而,大量可能的双标记测试提出了重大的计算和统计挑战。虽然已经提出了几种检测上位效应的策略,并对特定的表型进行了测试,但到目前为止,还没有系统的尝试使用真实的数据来比较它们的性能。我们利用连锁和eQTL研究的数千个基因表达性状,比较不同策略的表现。我们发现,使用标记和表型之间的边缘关联的信息来检测上位效应产生了较低的错误发现率(FDR)比一个策略,只使用生物注释在酵母,而从人类数据的结果是不确定的。对于未来的研究,其目的是发现上位效应,我们建议纳入有关SNP和表型之间的边缘关联的信息,而不是仅仅依赖于生物学注释。发现上位性效应的改进方法将导致对复杂遗传效应的更全面的理解。
Genome-wide association studies have been instrumental in identifying genetic variants associated with complex traits such as human disease or gene expression phenotypes. It has been proposed that extending existing analysis methods by considering interactions between pairs of loci may uncover additional genetic effects. However, the large number of possible two-marker tests presents significant computational and statistical challenges. Although several strategies to detect epistasis effects have been proposed and tested for specific phenotypes, so far there has been no systematic attempt to compare their performance using real data. We made use of thousands of gene expression traits from linkage and eQTL studies, to compare the performance of different strategies. We found that using information from marginal associations between markers and phenotypes to detect epistatic effects yielded a lower false discovery rate (FDR) than a strategy solely using biological annotation in yeast, whereas results from human data were inconclusive. For future studies whose aim is to discover epistatic effects, we recommend incorporating information about marginal associations between SNPs and phenotypes instead of relying solely on biological annotation. Improved methods to discover epistatic effects will result in a more complete understanding of complex genetic effects.
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