Kim Sung, Zhang Kui, Sun Fengzhu
Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, California, USA.
BMC Genet. 2003 Dec 31;4 Suppl 1(Suppl 1):S9. doi: 10.1186/1471-2156-4-S1-S9.
Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environmental interactions. We used a set association method that uses single-nucleotide polymorphism markers to locate genetic variation responsible for complex diseases in which multiple genes are involved. Here we extended the set association method from bi-allelic to multiallelic markers. In addition, we studied the type I error rates and power for both approaches using simulations based on the coalescent process. Both bi-allelic set association (BSA) and multiallelic set association (MSA) tests have the correct type I error rates. In addition, BSA and MSA can have more power than individual marker analysis when multiple genes are involved in a complex disease. We applied the MSA approach to the simulated data sets from Genetic Analysis Workshop 13. High cholesterol level was used as the definitive phenotype for a disease. MSA failed to detect markers with significant linkage disequilibrium with genes responsible for cholesterol level. This is due to the wide spacing between the markers and the lack of association between the marker loci and the simulated phenotype.
复杂疾病通常由多个基因与环境因素之间复杂的相互作用引起。大多数现有的连锁和关联方法是在假定简单疾病模型的情况下开发的，用于识别单个易感基因，而忽略了任何可能的基因-基因和基因-环境相互作用。我们使用了一种集合关联方法，该方法利用单核苷酸多态性标记来定位涉及多个基因的复杂疾病的遗传变异。在此，我们将集合关联方法从双等位基因标记扩展到多等位基因标记。此外，我们使用基于合并过程的模拟研究了两种方法的I型错误率和检验效能。双等位基因集合关联（BSA）和多等位基因集合关联（MSA）检验都具有正确的I型错误率。此外，当复杂疾病涉及多个基因时，BSA和MSA比单个标记分析具有更高的检验效能。我们将MSA方法应用于遗传分析研讨会13的模拟数据集。高胆固醇水平被用作一种疾病的决定性表型。MSA未能检测到与负责胆固醇水平的基因具有显著连锁不平衡的标记。这是由于标记之间的间距较大以及标记位点与模拟表型之间缺乏关联。
