Jin Lina, Zhu Wensheng, Yu Yaqin, Kou Changgui, Meng Xiangfei, Tao Yuchun, Guo Jianhua
Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, 130024, China; School of Public Health, Jilin University, Changchun, Jilin, 130021, China.
Ann Hum Genet. 2014 Mar;78(2):141-53. doi: 10.1111/ahg.12049. Epub 2013 Dec 16.
In case-control studies, association analysis was designed to test whether genetic variants were associated with human diseases. To evaluate the association, analysing one genetic marker at a time suffered from weak power, because of the correction for multiple testing and possibly small genetic effects. An alternative strategy was to test simultaneous effects of multiple markers, which was believed to be more powerful. However, when the number of markers under investigation was large, they would be subjected to weak power as well, because of the greater degrees of freedom. To conquer these limitations in case-control studies, we proposed a novel method that could test joint association of several loci (i.e. haplotype), with only a single degree of freedom. In this research, we developed a nonparametric approach, which was based on U-statistics. We also introduced a new kernel for U-statistic, which could combine the haplotype structure information, and was expected to enhance the power. Simulations indicated that our proposed approach offered merits in identifying the associations between diseases and haplotypes. Application of our method to a study of candidate genes for internalising disorder illustrated its virtue in utility and interpretation, and provided an excellent result in detecting the associations.
在病例对照研究中，关联分析旨在检验基因变异是否与人类疾病相关。为评估这种关联性，一次分析一个遗传标记的方法由于多重检验校正以及可能存在的微小基因效应而效能较低。另一种策略是检验多个标记的同时效应，这种方法被认为更具效能。然而，当所研究的标记数量很大时，由于自由度更高，它们也会面临效能较低的问题。为克服病例对照研究中的这些局限性，我们提出了一种新方法，该方法可以检验几个位点（即单倍型）的联合关联性，且仅有一个自由度。在本研究中，我们开发了一种基于U统计量的非参数方法。我们还为U统计量引入了一种新的核函数，它可以结合单倍型结构信息，并有望提高效能。模拟结果表明，我们提出的方法在识别疾病与单倍型之间的关联方面具有优势。将我们的方法应用于内化障碍候选基因的研究中，展示了其在实用性和解释性方面的优点，并在检测关联方面取得了优异的结果。
