Kuk Anthony Y C, Nott David J, Yang Yaning
Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore.
Department of Statistics and Finance, University of Science and Technology, Hefei, China.
J Hum Genet. 2014 Apr;59(4):198-205. doi: 10.1038/jhg.2014.1. Epub 2014 Jan 23.
There is much recent interest in finding rare genetic variants associated with various diseases. Owing to the scarcity of rare mutations, single-variant analyses often lack power. To enable pooling of information across variants, we use a random effect formulation within a retrospective modeling framework that respects the retrospective data collecting mechanism of case-control studies. More concretely, we model the control allele frequencies of the variants as random effects, and the systematic differences between the case and control frequencies as fixed effects, resulting in a mixed model. The use of Poisson approximation and gamma-distributed random effects results in a generalized negative binomial distribution for the joint distribution of the control and case frequencies. Variants are selected by conducting stepwise likelihood ratio tests. The superiority of the proposed method over two existing variant selection methods is demonstrated in a simulation study. The effects of non-gamma random effects and correlated variants are also found to be not too detrimental in the simulation study. When the proposed procedure is applied to identify rare variants associated with obesity, it identifies one additional variant not picked up by existing methods.
近期，人们对寻找与各种疾病相关的罕见基因变异有着浓厚兴趣。由于罕见突变的稀缺性，单变异分析往往缺乏效力。为了能够汇总各变异的信息，我们在一个回顾性建模框架内采用随机效应公式，该框架尊重病例对照研究的回顾性数据收集机制。更具体地说，我们将变异的对照等位基因频率建模为随机效应，病例和对照频率之间的系统差异建模为固定效应，从而得到一个混合模型。泊松近似和伽马分布随机效应的使用导致对照和病例频率的联合分布呈现广义负二项分布。通过进行逐步似然比检验来选择变异。在一项模拟研究中证明了所提出方法相对于两种现有变异选择方法的优越性。在模拟研究中还发现，非伽马随机效应和相关变异的影响并非过于有害。当将所提出的程序应用于识别与肥胖相关的罕见变异时，它识别出了现有方法未发现的一个额外变异。
