Improving strategy for discovering interacting genetic variants in association studies
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Revealing the underlying complex architecture of human diseases has received considerable attention since the exploration of genotype-phenotype relationships in genetic epidemiology. Identification of these relationships becomes more challenging due to multiple factors acting together or independently. A deep neural network was trained in the previous work to identify two-locus interacting single nucleotide polymorphisms (SNPs) related to a complex disease. The model was assessed for all two-locus combinations under various simulated scenarios. The results showed significant improvements in predicting SNP-SNP interactions over the existing conventional machine learning techniques. Furthermore, the findings are confirmed on a published dataset. However, the performance of the proposed method in the higher-order interactions was unknown. The objective of this study is to validate the model for the higher-order interactions in high-dimensional data. The proposed method is further extended for unsupervised learning. A number of experiments were performed on the simulated datasets under same scenarios as well as a real dataset to show the performance of the extended model. On an average, the results illustrate improved performance over the previous methods. The model is further evaluated on a sporadic breast cancer dataset to identify higher-order interactions between SNPs. The results rank top 20 higherorder SNP interactions responsible for sporadic breast cancer.
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