复杂疾病模型快速参数求解算法

Fast algorithm to calculate parameters of complex disease model

全基因组关联研究(GWAS)是在探究人类复杂疾病相关基因的重要方法,实用有效的算法是GWAS成功的关键,因此根据疾病模型生成模拟数据对GWAS算法进行比较测试具有重要的意义。模拟测试要求根据各种输入的控制量计算出疾病模型的相关参数,但是目前缺乏相关公开的算法。提出了一个求解这些参数的分支限界算法。大量实验测试表明该算法能快速精确地计算出疾病模型的相关参数,可用于搭建GWAS算法测试平台。

Genome-Wide Association Study（GWAS）is an important method to search for susceptible genes of complex diseases, and practical and effective algorithms are in need in GWAS. Testing GWAS algorithm on simulation data based on different disease model is important in comparing their performances. Generating the simulation data requires calculating the parameters of diseases models according to the control parameters, but there are no corresponding public algorithms. This paper proposes a branch and bound algorithm to compute the parameters of diseases models. Extensive experimental results show that the algorithm works out quickly with accurate parameters of diseases models. The algorithm can be used in constructing test platforms for GWAS algorithm.