Comparisons of multi-marker association methods to detect association between a candidate region and disease.
Comparisons of multi-marker association methods to detect association between a candidate region and disease.
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DOI：
10.1002/gepi.20448
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发表时间：
2010-04
影响因子：
2.1
通讯作者：
Zhao, Hongyu
中科院分区：
文献类型：
--
作者：
Ballard, David H.;Cho, Judy;Zhao, Hongyu
与单个标记分析相比，联合使用多个标记的信息可能更有效地揭示基因组区域与性状之间的关联。在这篇文章中，我们比较了七种多标记方法的性能。这些方法包括（1）单标记分析（2）固定效应回归模型，其中预测因子是该区域中观察到的基因型、解释遗传变异比例的主成分或基于基因型的傅立叶变换的预测因子;（3）方差成分分析。在我们的模拟研究中，我们考虑遗传模型，其中关联是由于一个，两个或三个标记，并且致病标记具有不同的等位基因频率。我们使用来自区域中所有标记的信息或仅来自标记标记的信息。我们的模拟结果表明，当有一个致病变异，最好的评分标记方法是首选，而方差分量法和主成分法的工作以及更常见的致病变异。当有一个以上的致病变异，主成分方法似乎表现良好，在所有的情况下研究。当这些方法应用于分析炎症性肠道疾病数据集的基因中或附近的所有标记与疾病状态之间的关联时，基于主成分方法的分析比其他方法在生物学上得到更一致的发现。
The joint use of information from multiple markers may be more effective to reveal association between a genomic region and a trait than single marker analysis. In this article, we compare the performance of seven multi-marker methods. These methods include (1) single marker analysis (either the best-scoring single nucleotide polymorphism in a candidate region or a combined test based on Fisher’s method); (2) fixed effects regression models where the predictors are either the observed genotypes in the region, principal components that explain a proportion of the genetic variation, or predictors based on Fourier transformation for the genotypes; and (3) variance components analysis. In our simulation studies, we consider genetic models where the association is due to one, two, or three markers, and the disease-causing markers have varying allele frequencies. We use information from either all the markers in a region or information only from tagging markers. Our simulation results suggest that when there is one disease-causing variant, the best-scoring marker method is preferred whereas the variance components method and the principal components method work well for more common disease-causing variants. When there is more than one disease-causing variant, the principal components method seems to perform well over all the scenarios studied. When these methods are applied to analyze associations between all the markers in or near a gene and disease status for an inflammatory bowel disease data set, the analysis based on the principal components method leads to biologically more consistent discoveries than other methods.
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