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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2410.23425 |
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| _version_ | 1866912096926564352 |
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| author | Subirana-Granés, Marc Hoffman, Jill Zhang, Haoyu Akirtava, Christina Nandi, Sutanu Fotso, Kevin Pividori, Milton |
| author_facet | Subirana-Granés, Marc Hoffman, Jill Zhang, Haoyu Akirtava, Christina Nandi, Sutanu Fotso, Kevin Pividori, Milton |
| contents | Understanding the genetic basis of complex traits is a longstanding challenge in the field of genomics. Genome-wide association studies (GWAS) have identified thousands of variant-trait associations, but most of these variants are located in non-coding regions, making the link to biological function elusive. While traditional approaches, such as transcriptome-wide association studies (TWAS), have advanced our understanding by linking genetic variants to gene expression, they often overlook gene-gene interactions. Here, we review current approaches to integrate different molecular data, leveraging machine learning methods to identify gene modules based on co-expression and functional relationships. These integrative approaches, like PhenoPLIER, combine TWAS and drug-induced transcriptional profiles to effectively capture biologically meaningful gene networks. This integration provides a context-specific understanding of disease processes while highlighting both core and peripheral genes. These insights pave the way for novel therapeutic targets and enhance the interpretability of genetic studies in personalized medicine. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23425 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Genetic studies through the lens of gene networks Subirana-Granés, Marc Hoffman, Jill Zhang, Haoyu Akirtava, Christina Nandi, Sutanu Fotso, Kevin Pividori, Milton Molecular Networks Understanding the genetic basis of complex traits is a longstanding challenge in the field of genomics. Genome-wide association studies (GWAS) have identified thousands of variant-trait associations, but most of these variants are located in non-coding regions, making the link to biological function elusive. While traditional approaches, such as transcriptome-wide association studies (TWAS), have advanced our understanding by linking genetic variants to gene expression, they often overlook gene-gene interactions. Here, we review current approaches to integrate different molecular data, leveraging machine learning methods to identify gene modules based on co-expression and functional relationships. These integrative approaches, like PhenoPLIER, combine TWAS and drug-induced transcriptional profiles to effectively capture biologically meaningful gene networks. This integration provides a context-specific understanding of disease processes while highlighting both core and peripheral genes. These insights pave the way for novel therapeutic targets and enhance the interpretability of genetic studies in personalized medicine. |
| title | Genetic studies through the lens of gene networks |
| topic | Molecular Networks |
| url | https://arxiv.org/abs/2410.23425 |