Department of Statistics, School of Mathematical Sciences, Heilongjiang University, Harbin, 150080, China.
J Hum Genet. 2018 Jan;63(1):37-45. doi: 10.1038/s10038-017-0354-2. Epub 2017 Nov 7.
Next-generation sequencing technology not only presents a new method for the detection of human genomic structural variation, but also provides a large number of genetic data of rare variants for us. Currently, how to detect association between human complex diseases and rare variants using genetical data has attracted extensive attention. In the field of medicine, many people's health and disease conditions are measured by ordinal response variables, namely, the trait value reflects the development stage or severity of a certain condition. However, most existing methods to test for association between rare variants and complex diseases are designed to deal with dichotomous or quantitative traits. Association analysis methods of ordinal traits are relatively fewer, and considering ordinal traits as dichotomous and quantitative traits will inevitably lose some valuable information in the original data. Therefore, in this paper, we extend an existing method of adaptive combination of P values (ADA) and propose a new method of association analysis for ordinal trait based on it (called OR-ADA) to test for possible association between ordinal trait and rare variants. In our method, we establish a cumulative logistic regression model, in which the regression coefficients are estimated by the Newton-Raphson algorithm and the likelihood ratio test is used to test the association. Through a large number of simulation studies and an example, we demonstrate the performance of the new method and compare it with several methods. The analysis results show that the OR-ADA strategy is robust to the signs of effects of causal variants and more powerful under many scenarios.
下一代测序技术不仅为人类基因组结构变异的检测提供了新方法，还为我们提供了大量罕见变异的遗传数据。目前，如何利用遗传数据检测人类复杂疾病与罕见变异之间的关联已引起广泛关注。在医学领域，许多人的健康和疾病状况是通过有序反应变量来衡量的，即特征值反映了某种状况的发展阶段或严重程度。然而，目前大多数用于检测罕见变异与复杂疾病之间关联的方法都是针对二分类或定量性状设计的。用于分析有序性状关联的方法相对较少，并且将有序性状视为二分类和定量性状不可避免地会丢失原始数据中的一些有价值的信息。因此，本文扩展了一种现有的基于 P 值自适应组合的方法（ADA），并在此基础上提出了一种新的基于有序性状的关联分析方法（称为 OR-ADA），用于检验有序性状与罕见变异之间的可能关联。在我们的方法中，我们建立了一个累积逻辑回归模型，其中使用牛顿-拉斐逊算法估计回归系数，并使用似然比检验来检验关联。通过大量的模拟研究和一个实例，我们验证了新方法的性能，并将其与几种方法进行了比较。分析结果表明，OR-ADA 策略对因果变异效应的符号具有稳健性，并且在许多情况下更具威力。
