Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio 44106-7281, USA.
Genet Epidemiol. 2012 Jul;36(5):480-7. doi: 10.1002/gepi.21642. Epub 2012 May 30.
Current genome-wide association studies still heavily rely on a single-marker strategy, in which each single nucleotide polymorphism (SNP) is tested individually for association with a phenotype. Although methods and software packages that consider multimarker models have become available, they have been slow to become widely adopted and their efficacy in real data analysis is often questioned. Based on conducting extensive simulations, here we endeavor to provide more insights into the performance of simple multimarker association tests as compared to single-marker tests. The results reveal the power advantage as well as disadvantage of the two- vs. the single-marker test. Power differentials depend on the correlation structure among tag SNPs, as well as that between tag SNPs and causal variants. A two-marker test has relatively better performance than single-marker tests when the correlation of the two adjacent markers is high. However, using HapMap data, two-marker tests tended to have a greater chance of being less powerful than single-marker tests, due to constraints on the number of actual possible haplotypes in the HapMap data. Yet, the average power difference was small whenever the one-marker test is more powerful, while there were many situations where the two-marker test can be much more powerful. These findings can be useful to guide analyses of future studies.
目前的全基因组关联研究仍然严重依赖于单标记策略，其中每个单核苷酸多态性（SNP）都单独测试与表型的关联。尽管考虑多标记模型的方法和软件包已经可用，但它们的广泛采用速度较慢，其在实际数据分析中的功效经常受到质疑。基于广泛的模拟，我们在这里努力提供更多的见解，以了解与单标记测试相比，简单的多标记关联测试的性能。结果揭示了两种标记测试相对于单标记测试的优势和劣势。功率差异取决于标记 SNP 之间以及标记 SNP 与因果变异体之间的相关结构。当两个相邻标记之间的相关性较高时，双标记测试比单标记测试具有相对更好的性能。然而，由于 HapMap 数据中实际可能的单倍型数量的限制，使用 HapMap 数据时，双标记测试往往比单标记测试的效能更小。然而，每当单标记测试更强大时，平均功率差异很小，而在许多情况下，双标记测试可以更强大。这些发现可以帮助指导未来研究的分析。
