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基于泛函线性模型的基因水平关联性分析

郭浩1,刘湘涛2,龚浩博1,黄健飞1   

  1. 1. 扬州大学数学科学学院,  扬州 225002; 2. 天佳吉 瑞基因科技有限公司, 合肥 238000
  • 出版日期:2019-11-25 发布日期:2020-03-20

郭浩,刘湘涛,龚浩博,黄健飞. 基于泛函线性模型的基因水平关联性分析[J]. 系统科学与数学, 2019, 39(11): 1885-1894.

GUO Hao,LIU Xiangtao, GONG Haobo, HUANG Jianfei. Gene Level Association Analysis Based on Functional Linear Model[J]. Journal of Systems Science and Mathematical Sciences, 2019, 39(11): 1885-1894.

Gene Level Association Analysis Based on Functional Linear Model

GUO Hao1 ,LIU Xiangtao2 ,GONG Haobo1 ,HUANG Jianfei1   

  1. 1. College of Mathematical Sciences, Yangzhou University, Yangzhou 225002; 2. Tianjia Genomes Tech Co. Ltd., Hefei 238000
  • Online:2019-11-25 Published:2020-03-20

采用泛函线性模型进行基因水平关联性分析时, 需要对基因片段上离散位点的遗传变异值进行数值逼近. 为了改善传统样条函数在逼近时精度不高, 且在推导时比较耗时的问题, 文章提出了采用勒让德多项式来进行数值逼近, 并利用该类多项式的正交性来提高获得泛函线性模型的效率. 通过分析模拟的基因数据, 文章提出的 方法可以在控制好第一类统计错误的前提下, 提高统计检验能力, 并减少计算时间. 因此, 在采用泛函线性模型进行基因水平关联分析时, 使用勒让德多项式估计的模型比传统的样条函数模型更有实际应用价值.

When carrying out the gene level association analysis based on functional linear model, it's required to numerically approximate the values of genetic variants in each genes. In order to improve the accuracy of approximations and reduce the time-consuming problem by using the traditional spline function in deducing functional linear model, this paper proposes a functional linear model based on Legendre polynomials, which can enhance the accuracy and efficiency of approximations in getting functional linear model due to the orthogonality. By analyzing the simulated genetic data, it knows that the proposed method can keep the reasonable type 1 error, enhance the statistical power, and reduce the computational time. Therefore, the functional linear model with Legendre polynomials has more practical application values than the traditional functional linear model in gene level association analysis.

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