Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455–0392, USA.
Genet Epidemiol. 2010 Nov;34(7):680-8. doi: 10.1002/gepi.20529.
To detect genetic association with common and complex diseases, many statistical tests have been proposed for candidate gene or genome-wide association studies with the case-control design. Due to linkage disequilibrium (LD), multi-marker association tests can gain power over single-marker tests with a Bonferroni multiple testing adjustment. Among many existing multi-marker association tests, most target to detect only one of many possible aspects in distributional differences between the genotypes of cases and controls, such as allele frequency differences, while a few new ones aim to target two or three aspects, all of which can be implemented in logistic regression. In contrast to logistic regression, a genomic distance-based regression (GDBR) approach aims to detect some high-order genotypic differences between cases and controls. A recent study has confirmed the high power of GDBR tests. At this moment, the popular logistic regression and the emerging GDBR approaches are completely unrelated; for example, one has to choose between the two. In this article, we reformulate GDBR as logistic regression, opening a venue to constructing other powerful tests while overcoming some limitations of GDBR. For example, asymptotic distributions can replace time-consuming permutations for deriving P-values and covariates, including gene-gene interactions, can be easily incorporated. Importantly, this reformulation facilitates combining GDBR with other existing methods in a unified framework of logistic regression. In particular, we show that Fisher's P-value combining method can boost statistical power by incorporating information from allele frequencies, Hardy-Weinberg disequilibrium, LD patterns, and other higher-order interactions among multi-markers as captured by GDBR.
为了检测常见和复杂疾病的遗传关联，已经提出了许多统计测试方法，用于基于病例对照设计的候选基因或全基因组关联研究。由于连锁不平衡（LD），多标记关联测试可以通过 Bonferroni 多重测试调整获得比单标记测试更强的功效。在许多现有的多标记关联测试中，大多数旨在检测病例和对照组基因型分布差异的许多可能方面之一，例如等位基因频率差异，而少数新方法旨在针对两个或三个方面，所有这些都可以在逻辑回归中实现。与逻辑回归相反，基于基因组距离的回归（GDBR）方法旨在检测病例和对照组之间某些高阶基因型差异。最近的一项研究证实了 GDBR 测试的高功效。目前，流行的逻辑回归和新兴的 GDBR 方法完全没有关系；例如，人们必须在两者之间做出选择。在本文中，我们将 GDBR 重新表述为逻辑回归，为构建其他强大的测试开辟了途径，同时克服了 GDBR 的一些限制。例如，可以用耗时的置换来代替渐近分布来推导出 P 值，并且可以轻松地包含协变量，包括基因-基因相互作用。重要的是，这种重新表述便于将 GDBR 与逻辑回归的统一框架中的其他现有方法相结合。特别是，我们表明，Fisher 的 P 值组合方法可以通过整合 GDBR 捕获的等位基因频率、Hardy-Weinberg 不平衡、LD 模式和其他多标记之间的高阶相互作用等信息来提高统计功效。
