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FDHE-IW: A Fast Approach for Detecting High-Order Epistasis in Genome-Wide Case-Control Studies

机译:FDHE-IW:一种快速探测基因组案例控制研究中的高阶简历

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Detecting high-order epistasis in genome-wide association studies (GWASs) is of importance when characterizing complex human diseases. However, the enormous numbers of possible single-nucleotide polymorphism (SNP) combinations and the diversity among diseases presents a significant computational challenge. Herein, a fast method for detecting high-order epistasis based on an interaction weight (FDHE-IW) method is evaluated in the detection of SNP combinations associated with disease. First, the symmetrical uncertainty ( SU ) value for each SNP is calculated. Then, the top-k SNPs are isolated as guiders to identify 2-way SNP combinations with significant interaction weight values. Next, a forward search is employed to detect high-order SNP combinations with significant interaction weight values as candidates. Finally, the findings were statistically evaluated using a G -test to isolate true positives. The developed algorithm was used to evaluate 12 simulated datasets and an age-related macular degeneration (AMD) dataset and was shown to perform robustly in the detection of some high-order disease-causing models.
机译:检测在基因组关联研究中的高阶简历(GWASS)在表征复杂的人类疾病时具有重要性。然而,巨大数量的单核苷酸多态性(SNP)组合和疾病之间的多样性具有重要的计算挑战。在此,在检测与疾病相关的SNP组合的检测中评估了基于相互作用重量(FDHE-IW)方法来检测高阶简历的快速方法。首先,计算每个SNP的对称不确定性(SU)值。然后,将顶-K SNP作为指导者隔离,以识别具有显着的相互作用重量值的双向SNP组合。接下来,采用前向搜索来检测具有显着的交互权重值作为候选的高阶SNP组合。最后,使用G -TEST进行统计评估结果以隔离真实的阳性。发达的算法用于评估12个模拟数据集和年龄相关的黄斑变性(AMD)数据集,并显示在检测到一些高阶疾病导致模型中的鲁棒性。

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