Controlled Variable Selection from Summary Statistics Only? A Solution via GhostKnockoffs and Penalized Regression
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929248694960128 |
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| author | Chen, Zhaomeng He, Zihuai Chu, Benjamin B. Gu, Jiaqi Morrison, Tim Sabatti, Chiara Candès, Emmanuel |
| author_facet | Chen, Zhaomeng He, Zihuai Chu, Benjamin B. Gu, Jiaqi Morrison, Tim Sabatti, Chiara Candès, Emmanuel |
| contents | Identifying which variables do influence a response while controlling false positives pervades statistics and data science. In this paper, we consider a scenario in which we only have access to summary statistics, such as the values of marginal empirical correlations between each dependent variable of potential interest and the response. This situation may arise due to privacy concerns, e.g., to avoid the release of sensitive genetic information. We extend GhostKnockoffs (He et al. [2022]) and introduce variable selection methods based on penalized regression achieving false discovery rate (FDR) control. We report empirical results in extensive simulation studies, demonstrating enhanced performance over previous work. We also apply our methods to genome-wide association studies of Alzheimer's disease, and evidence a significant improvement in power. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12724 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Controlled Variable Selection from Summary Statistics Only? A Solution via GhostKnockoffs and Penalized Regression Chen, Zhaomeng He, Zihuai Chu, Benjamin B. Gu, Jiaqi Morrison, Tim Sabatti, Chiara Candès, Emmanuel Methodology Genomics Applications Identifying which variables do influence a response while controlling false positives pervades statistics and data science. In this paper, we consider a scenario in which we only have access to summary statistics, such as the values of marginal empirical correlations between each dependent variable of potential interest and the response. This situation may arise due to privacy concerns, e.g., to avoid the release of sensitive genetic information. We extend GhostKnockoffs (He et al. [2022]) and introduce variable selection methods based on penalized regression achieving false discovery rate (FDR) control. We report empirical results in extensive simulation studies, demonstrating enhanced performance over previous work. We also apply our methods to genome-wide association studies of Alzheimer's disease, and evidence a significant improvement in power. |
| title | Controlled Variable Selection from Summary Statistics Only? A Solution via GhostKnockoffs and Penalized Regression |
| topic | Methodology Genomics Applications |
| url | https://arxiv.org/abs/2402.12724 |