In recent work, Wang et al introduced the “Sum of Single Effects”(SuSiE) model, and showed
that it provides a simple and efficient approach to fine-map** genetic variants from …

In genome-wide association studies (GWAS) it is now common to search for, and find,
multiple causal variants located in close proximity. It has also become standard to ask …

We introduce a simple new approach to variable selection in linear regression, with a
particular focus on quantifying uncertainty in which variables should be selected. The …

The advancement of high‐throughput genomic assays has led to enormous growth in the
availability of large‐scale biological datasets. Over the last two decades, these increasingly …

Fine-map** aims to identify causal variants impacting complex traits. We propose
PolyFun, a computationally scalable framework to improve fine-map** accuracy by …

We present a comprehensive statistical framework to analyze data from genome-wide
association studies of polygenic traits, producing interpretable findings while controlling the …

Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data
suffers notoriously from false positives. Approaches to control the false discovery rate (FDR) …

Fine-map** aims to identify causal genetic variants for phenotypes. Bayesian fine-
map** algorithms (for example, SuSiE, FINEMAP, ABF and COJO-ABF) are widely used …

Selecting relevant features associated with a given response variable is an important
problem in many scientific fields. Quantifying quality and uncertainty of a selection result via …

We consider the problem of conditional independence testing: given a response and
covariates, we test the null hypothesis that. The conditional randomization test was recently …