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Floating Feature Selection for multiloci association of quantitative traits in sib-pairs analysis

机译:SIB对分析中多层定量性状关联的浮动特征选择

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Finding association between genotypic differences and disease traits has become one of the main objectives in current genetic research. It has been published that some of the underlying factors in the dynamics of the coagulation process have a genetic compound, showing significant hereditability. This is the case of the Factor VII. In this work, we propose a method for selecting sets of Single Nucleotide Polymorphisms (SNPs) of the F7 gene that are significantly related with the phenotype (Factor VII levels). The methodology is applied to the sib pairs from the GAIT project sample. The method consists of an adapted Sequential Floating Feature Selection (SFFS) algorithm. This algorithm is applied with two relevance criteria, one linear and one non linear. The SNPs sets found with linear models are included in the sets found with non linear techniques. The results fit in with previous results in clinical area.
机译:发现基因型差异和疾病性状之间的关联已成为当前遗传研究的主要目标之一。已经公布,凝血过程的动态中的一些潜在因素具有遗传化合物,显示出显着的近贫困性。这是因子VII的情况。在这项工作中,我们提出了一种选择与表型(因子VII水平)显着相关的F7基因的单核苷酸多态性(SNP)的三种核苷酸多态性(SNP)的方法。该方法应用于来自步态项目样本的SIB对。该方法包括适应的顺序浮动特征选择(SFF)算法。该算法应用于两个相关性标准,一个线性和一个非线性。使用线性模型找到的SNP组包含在具有非线性技术的集合中。结果适用于临床区域的先前结果。

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