Bae Harold, Perls Thomas, Steinberg Martin, Sebastiani Paola
Department of Biostatistics, Boston University School of Public Health.
New England Centenarian Study, Section of Geriatrics, Department of Medicine, Boston University School of Medicine.
Bayesian Anal. 2015 Mar;10(1):53-74. doi: 10.1214/14-BA880.
We present a coherent Bayesian framework for selection of the most likely model from the five genetic models (genotypic, additive, dominant, co-dominant, and recessive) commonly used in genetic association studies. The approach uses a polynomial parameterization of genetic data to simultaneously fit the five models and save computations. We provide a closed-form expression of the marginal likelihood for normally distributed data, and evaluate the performance of the proposed method and existing method through simulated and real genome-wide data sets.
我们提出了一个连贯的贝叶斯框架，用于从遗传关联研究中常用的五种遗传模型（基因型、加性、显性、共显性和隐性）中选择最可能的模型。该方法使用遗传数据的多项式参数化来同时拟合这五种模型并节省计算量。我们给出了正态分布数据边际似然的闭式表达式，并通过模拟和真实的全基因组数据集评估了所提方法和现有方法的性能。
