Bayesian Variable Selection from Summary Data, with Application to Joint Fine-Mapping of Multiple Traits

Yuxin Zou
Bayesian methods provide attractive approaches to select relevant variables in multiple regression models, particularly in settings with very highly correlated variables. For example, they are popular in genetic fine-mapping problems, aiming to identify the genetic variants that causally affect some phenotypes of interest. However, Bayesian methods are limited by the computational speed and the interpretability of the posterior distribution. Wang et al. (2020) presented a simple and computationally scalable approach to variable selection, the “Sum...
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.