BEATRICE: Bayesian Fine-mapping from Summary Data using Deep Variational Inference

We introduce a novel framework BEATRICE to identify putative causal variants from GWAS summary statistics (https://github.com/sayangsep/Beatrice-Finemapping). Identifying causal variants is challenging due to their sparsity and to highly correlated variants in the nearby regions. To account for thes...

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Detalles Bibliográficos
Autores principales: Ghosal, Sayan, Schatz, Michael C., Venkataraman, Archana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055416/
https://www.ncbi.nlm.nih.gov/pubmed/36993396
http://dx.doi.org/10.1101/2023.03.24.534116

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055416/
https://www.ncbi.nlm.nih.gov/pubmed/36993396
http://dx.doi.org/10.1101/2023.03.24.534116

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