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Univariate and multivariate methods for association of the genom-icvariations with the end-or-endo phenotype have been widely used for genome wideassociation studies. In addition to encoding the SNPs, we advocate usage ofclustering as a novel method to encode the structural variations, SVs, ingenomes, such as the deletions and insertions polymorphism (DIPs), Copy NumberVariations (CNVs), translocation, inversion, etc., that can be used as anindependent fea-ture variable value for downstream computation by artificialintelli-gence methods to predict the endo-or-end phenotype. We introduce aclustering based encoding scheme for structural variations and om-ics basedanalysis. We conducted a complete all genomic variants association with thephenotype using deep learning and other ma-chine learning techniques, thoughother methods such as genetic al-gorithm can also be applied. Applying thisencoding of SVs and one-hot encoding of SNPs on GTEx V7 pilot DNA variationdataset, we were able to get high accuracy using various methods of DMWAS, andparticularly found logistic regression to work the best for death due toheart-attack (MHHRTATT) phenotype. The genom-ic variants acting as feature setswere then arranged in descending order of power of impact on the disease ortrait phenotype, which we call optimization and that also uses top univariateassociation into account. Variant Id P1_M_061510_3_402_P at chromosome 3 &position 192063195 was found to be most highly associated to MHHRTATT. Wepresent here the top ten optimized genomic va-riant feature set for theMHHRTATT phenotypic cause of death.

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