Improving strategy for discovering interacting genetic variants in association studies
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 independent...
| Main Authors: | , |
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| Format: | Conference Paper |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/48977 |
| _version_ | 1848758137174097920 |
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| author | Uppu, S. Krishna, Aneesh |
| author_facet | Uppu, S. Krishna, Aneesh |
| author_sort | Uppu, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:39:12Z |
| format | Conference Paper |
| id | curtin-20.500.11937-48977 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:39:12Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-489772017-09-13T15:47:18Z Improving strategy for discovering interacting genetic variants in association studies Uppu, S. Krishna, Aneesh 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. 2016 Conference Paper http://hdl.handle.net/20.500.11937/48977 10.1007/978-3-319-46687-3_51 restricted |
| spellingShingle | Uppu, S. Krishna, Aneesh Improving strategy for discovering interacting genetic variants in association studies |
| title | Improving strategy for discovering interacting genetic variants in association studies |
| title_full | Improving strategy for discovering interacting genetic variants in association studies |
| title_fullStr | Improving strategy for discovering interacting genetic variants in association studies |
| title_full_unstemmed | Improving strategy for discovering interacting genetic variants in association studies |
| title_short | Improving strategy for discovering interacting genetic variants in association studies |
| title_sort | improving strategy for discovering interacting genetic variants in association studies |
| url | http://hdl.handle.net/20.500.11937/48977 |