Two-stage two-locus models in genome-wide association.
Two-stage two-locus models in genome-wide association.
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DOI：
10.1371/journal.pgen.0020157
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发表时间：
2006-09-22
期刊：
影响因子：
4.5
通讯作者：
Cardon, Lon R.
中科院分区：
文献类型：
--
作者：
Evans, David M.;Marchini, Jonathan;Morris, Andrew P.;Cardon, Lon R.
对模式生物的研究表明，上位性可能在人类复杂疾病和性状的病因学中起重要作用。随着大规模全基因组关联研究时代的快速临近，重要的是要量化在人类中使用实际样本量是否有可能检测到相互作用的基因座，以及当采用单基因座方法时，未检测到的上位性在多大程度上会对检测关联的能力产生不利影响。因此，我们研究了在广泛的包含不同程度上位性的双基因座数量性状模型中检测关联的能力。我们比较了使用忽略相互作用效应的单基因座模型、允许相互作用的完整双基因座模型，以及最重要的两种两阶段策略检测关联的能力，在两阶段策略中，最初使用单基因座检验确定的一部分基因座使用完整双基因座模型进行分析。尽管多重检验带来了惩罚，但在考虑的许多情况下，拟合完整双基因座模型比单基因座检验表现更好，特别是与检测单个基因座的尝试相比。使用两阶段策略减少了在基因组中进行详尽的双基因座搜索相关的计算负担，但当基因座相互作用时，其效力不如详尽搜索。两阶段方法也增加了遗漏在边缘贡献较小效应的相互作用基因座的风险。基于我们广泛的模拟，我们的结果表明，涉及基因组中所有标记成对组合的详尽搜索可能为单基因座扫描在识别对表型方差有中等比例贡献的相互作用基因座方面提供有用的补充。
尽管人们越来越认识到尝试绘制人类中的基因相互作用可能是一项有成效的努力，但对于检测它们的最佳策略，特别是在全基因组关联的情况下（其中潜在比较的数量巨大），尚未达成共识。在本文中，作者比较了四种不同搜索策略在全基因组关联中检测相互作用基因座的性能——单基因座搜索、详尽的双基因座搜索，以及两种两阶段程序，其中最初使用单基因座检验确定的一部分基因座使用完整双基因座模型进行分析。他们的结果表明，当基因座相互作用时，在基因组中进行详尽的双基因座搜索优于两阶段策略，并且在许多情况下能够识别出仅使用单基因座搜索无法识别的基因座。他们的发现表明，涉及基因组中所有标记成对组合的详尽搜索可能为单基因座扫描在识别对表型方差有中等比例贡献的相互作用基因座方面提供有用的补充。
Studies in model organisms suggest that epistasis may play an important role in the etiology of complex diseases and traits in humans. With the era of large-scale genome-wide association studies fast approaching, it is important to quantify whether it will be possible to detect interacting loci using realistic sample sizes in humans and to what extent undetected epistasis will adversely affect power to detect association when single-locus approaches are employed. We therefore investigated the power to detect association for an extensive range of two-locus quantitative trait models that incorporated varying degrees of epistasis. We compared the power to detect association using a single-locus model that ignored interaction effects, a full two-locus model that allowed for interactions, and, most important, two two-stage strategies whereby a subset of loci initially identified using single-locus tests were analyzed using the full two-locus model. Despite the penalty introduced by multiple testing, fitting the full two-locus model performed better than single-locus tests for many of the situations considered, particularly when compared with attempts to detect both individual loci. Using a two-stage strategy reduced the computational burden associated with performing an exhaustive two-locus search across the genome but was not as powerful as the exhaustive search when loci interacted. Two-stage approaches also increased the risk of missing interacting loci that contributed little effect at the margins. Based on our extensive simulations, our results suggest that an exhaustive search involving all pairwise combinations of markers across the genome might provide a useful complement to single-locus scans in identifying interacting loci that contribute to moderate proportions of the phenotypic variance. Although there is growing appreciation that attempting to map genetic interactions in humans may be a fruitful endeavor, there is no consensus as to the best strategy for their detection, particularly in the case of genome-wide association where the number of potential comparisons is enormous. In this article, the authors compare the performance of four different search strategies to detect loci which interact in genome-wide association—a single-locus search, an exhaustive two-locus search, and two, two-stage procedures in which a subset of loci initially identified with single-locus tests are analyzed using a full two-locus model. Their results show that when loci interact, an exhaustive two-locus search across the genome is superior to a two-stage strategy, and in many situations can identify loci which would not have been identified solely using a single-locus search. Their findings suggest that an exhaustive search involving all pairwise combinations of markers across the genome may provide a useful complement to single-locus scans in identifying interacting loci that contribute to moderate proportions of the phenotypic variance.
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影响因子：
9.8
作者：
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通讯作者：
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DOI：
10.1073/pnas.45.7.984
发表时间：
1959-01-01
影响因子：
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DOI：
10.1017/s0016672304006779
发表时间：
2004-06-01
期刊：
GENETICAL RESEARCH
影响因子：
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作者：
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通讯作者：
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影响因子：
64.8
作者：
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通讯作者：
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