Chirag M. Lakhani

The paper introduces parmigiano, an empirical Bayesian framework that improves rare variant association testing in Alzheimer's disease by integrating functional annotations, identifying 23 candidate genes including 19 new ones.

The paper develops scEEMS models to predict cell-type-specific eQTLs, enhancing discovery of AD risk variants by linking them to cell-type-specific genes.

The paper finds that mosaic chromosomal alterations in blood are associated with increased Alzheimer's disease risk, with variations by ancestry, mCA subtype, APOE ε4 status, and chromosomal location.

This paper improves identification of causal Alzheimer's disease variants by integrating deep learning annotations with functional genomics, resulting in better fine-mapping and polygenic risk prediction across diverse populations.

This study introduces gruyere, a Bayesian model that improves rare variant association analysis by integrating functional annotations, identifying 15 new AD-associated genetic associations.

The paper develops a poly-exposure score (PXS) to assess combined genetic and non-genetic risk factors for four diseases, showing PXS outperforms polygenic risk scores in some cases.