A unified quantile framework for nonlinear heterogeneous transcriptome-wide associations
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866915051637571584 |
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| author | Wang, Tianying Ionita-Laza, Iuliana Wei, Ying |
| author_facet | Wang, Tianying Ionita-Laza, Iuliana Wei, Ying |
| contents | Transcriptome-wide association studies (TWAS) are powerful tools for identifying gene-level associations by integrating genome-wide association studies and gene expression data. However, most TWAS methods focus on linear associations between genes and traits, ignoring the complex nonlinear relationships that may be present in biological systems. To address this limitation, we propose a novel framework, QTWAS, which integrates a quantile-based gene expression model into the TWAS model, allowing for the discovery of nonlinear and heterogeneous gene-trait associations. Via comprehensive simulations and applications to both continuous and binary traits, we demonstrate that the proposed model is more powerful than conventional TWAS in identifying gene-trait associations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_12081 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | A unified quantile framework for nonlinear heterogeneous transcriptome-wide associations Wang, Tianying Ionita-Laza, Iuliana Wei, Ying Methodology Applications Transcriptome-wide association studies (TWAS) are powerful tools for identifying gene-level associations by integrating genome-wide association studies and gene expression data. However, most TWAS methods focus on linear associations between genes and traits, ignoring the complex nonlinear relationships that may be present in biological systems. To address this limitation, we propose a novel framework, QTWAS, which integrates a quantile-based gene expression model into the TWAS model, allowing for the discovery of nonlinear and heterogeneous gene-trait associations. Via comprehensive simulations and applications to both continuous and binary traits, we demonstrate that the proposed model is more powerful than conventional TWAS in identifying gene-trait associations. |
| title | A unified quantile framework for nonlinear heterogeneous transcriptome-wide associations |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2207.12081 |