通过聚类、单变量关联、深度和机器学习组学进行的特征集优化用于生物标志物发现的广泛关联研究（DMWAS），如在GTEx试验数据集上测试的心脏病发作导致的死亡
Feature set optimization by clustering, univariate association, Deep & Machine learning omics Wide Association Study (DMWAS) for Biomarkers discovery as tested on GTEx pilot dataset for death due to heart attack
摘要：
单变量和多变量方法已被广泛用于全基因组关联研究中。除了编码SNPs，我们提倡使用聚类作为一种新的方法来编码基因组中的结构变异，SV，如缺失和插入多态性（DIP），拷贝数变异（CNVs），易位，倒位等，其可用作独立的特征变量值，用于通过人工智能方法进行下游计算以预测内端表型。我们介绍了一个基于聚类的编码方案的结构变化和omics为基础的分析。我们使用深度学习和其他机器学习技术进行了完整的所有基因组变异与表型的关联，尽管也可以应用其他方法，如遗传算法。在GTEx V7试点DNA变异数据集上应用SV的这种编码和SNP的独热编码，我们能够使用DMWAS的各种方法获得高准确性，特别是发现逻辑回归对于心脏病发作死亡（MHHRTATT）表型的效果最好。然后将作为特征集的基因组变体按照对疾病或性状表型的影响力的降序排列，我们称之为优化，并且还考虑了顶部单变量关联。发现在染色体3和位置192063195处的变体Id P1_M_061510_3_402_P与MHHRTATT最高度相关。我们在此列出了MHHRTATT表型死因的十大优化基因组变异特征集。
英文摘要：
Univariate and multivariate methods for association of the genom-ic variations with the end-or-endo phenotype have been widely used for genome wide association studies. In addition to encoding the SNPs, we advocate usage of clustering as a novel method to encode the structural variations, SVs, in genomes, such as the deletions and insertions polymorphism (DIPs), Copy Number Variations (CNVs), translocation, inversion, etc., that can be used as an independent fea-ture variable value for downstream computation by artificial intelli-gence methods to predict the endo-or-end phenotype. We introduce a clustering based encoding scheme for structural variations and om-ics based analysis. We conducted a complete all genomic variants association with the phenotype using deep learning and other ma-chine learning techniques, though other methods such as genetic al-gorithm can also be applied. Applying this encoding of SVs and one-hot encoding of SNPs on GTEx V7 pilot DNA variation dataset, we were able to get high accuracy using various methods of DMWAS, and particularly found logistic regression to work the best for death due to heart-attack (MHHRTATT) phenotype. The genom-ic variants acting as feature sets were then arranged in descending order of power of impact on the disease or trait phenotype, which we call optimization and that also uses top univariate association into account. Variant Id P1_M_061510_3_402_P at chromosome 3 & position 192063195 was found to be most highly associated to MHHRTATT. We present here the top ten optimized genomic va-riant feature set for the MHHRTATT phenotypic cause of death.
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