MISS: a non-linear methodology based on mutual information for genetic association studies in both population and sib-pairs analysis
MISS: a non-linear methodology based on mutual information for genetic association studies in both population and sib-pairs analysis
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DOI：
10.1093/bioinformatics/btq273
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发表时间：
2010-08-01
期刊：
影响因子：
5.8
通讯作者：
Perera, Alexandre
中科院分区：
文献类型：
--
作者：
Brunel, Helena;Gallardo-Chacon, Joan-Josep;Perera, Alexandre
动机：发现与疾病相关的遗传变异和表型之间的关联已成为研究复杂疾病的重要工具。在这种情况下，与单基因座搜索相比，多基因座遗传关联可能会揭示更多信息。这项工作的主要目标是提出一个非线性的方法，基于信息论的组合之间的关联发现多个SNP和一个给定的phenotype.Results：所提出的方法，称为MISS（互信息统计意义），已被集成联合与特征选择算法，并已被测试的合成数据集与控制的表型，并在特定的情况下的F7基因。MISS方法与基于群体的研究和同胞对分析中用于遗传关联的多元线性回归（MLR）方法以及最大熵条件概率建模（MECPM）方法进行了对比，该方法搜索预测性多位点相互作用。已经发现F7基因区域内的几组SNP显示与血液中的FVII水平显著相关。所提出的多位点方法揭示了SNP的组合，其解释了比其个体多态性更重要的表型信息。MISS比MLR和MECPM能发现更多的SNPs与表型之间的相关性。大多数标记的SNP作为对血液中蛋白FVII水平具有真实的影响的功能变体出现在文献中。
Motivation: Finding association between genetic variants and phenotypes related to disease has become an important vehicle for the study of complex disorders. In this context, multi-loci genetic association might unravel additional information when compared with single loci search. The main goal of this work is to propose a non-linear methodology based on information theory for finding combinatorial association between multi-SNPs and a given phenotype.Results: The proposed methodology, called MISS (mutual information statistical significance), has been integrated jointly with a feature selection algorithm and has been tested on a synthetic dataset with a controlled phenotype and in the particular case of the F7 gene. The MISS methodology has been contrasted with a multiple linear regression (MLR) method used for genetic association in both, a population-based study and a sib-pairs analysis and with the maximum entropy conditional probability modelling (MECPM) method, which searches for predictive multi-locus interactions. Several sets of SNPs within the F7 gene region have been found to show a significant correlation with the FVII levels in blood. The proposed multi-site approach unveils combinations of SNPs that explain more significant information of the phenotype than their individual polymorphisms. MISS is able to find more correlations between SNPs and the phenotype than MLR and MECPM. Most of the marked SNPs appear in the literature as functional variants with real effect on the protein FVII levels in blood.