Lin Wan-Yu, Tiwari Hemant K, Gao Guimin, Zhang Kui, Arcaroli John J, Abraham Edward, Liu Nianjun
Department of Biostatistics, University of Alabama at Birmingham, USA.
Ann Hum Genet. 2012 May;76(3):246-60. doi: 10.1111/j.1469-1809.2012.00706.x.
Testing multiple markers simultaneously not only can capture the linkage disequilibrium patterns but also can decrease the number of tests and thus alleviate the multiple-testing penalty. If a gene is associated with a phenotype, subjects with similar genotypes in this gene should also have similar phenotypes. Based on this concept, we have developed a general framework that is applicable to continuous traits. Two similarity-based tests (namely, SIMc and SIMp tests) were derived as special cases of the general framework. In our simulation study, we compared the power of the two tests with that of the single-marker analysis, a standard haplotype regression, and a popular and powerful kernel machine regression. Our SIMc test outperforms other tests when the average R(2) (a measure of linkage disequilibrium) between the causal variant and the surrounding markers is larger than 0.3 or when the causal allele is common (say, frequency = 0.3). Our SIMp test outperforms other tests when the causal variant was introduced at common haplotypes (the maximum frequency of risk haplotypes >0.4). We also applied our two tests to an adiposity data set to show their utility.
同时检测多个标记不仅可以捕捉连锁不平衡模式，还可以减少检测次数，从而减轻多重检验的惩罚。如果一个基因与一种表型相关，那么该基因中具有相似基因型的受试者也应该具有相似的表型。基于这一概念，我们开发了一个适用于连续性状的通用框架。作为该通用框架的特殊情况，我们推导了两种基于相似性的检验（即SIMc检验和SIMp检验）。在我们的模拟研究中，我们将这两种检验的功效与单标记分析、标准单倍型回归以及一种流行且强大的核机器回归的功效进行了比较。当因果变异与周围标记之间的平均R²（连锁不平衡的一种度量）大于0.3或者当因果等位基因常见时（例如，频率=0.3），我们的SIMc检验优于其他检验。当因果变异出现在常见单倍型中时（风险单倍型的最大频率>0.4），我们 的SIMp检验优于其他检验。我们还将我们的两种检验应用于一个肥胖数据集以展示它们的实用性。
