Makaela Mews

This study evaluates two sequence-to-function models for predicting regulatory variant effects across ancestries using blood eQTL data, finding better performance for high-confidence variants in African American populations.

This study performs a multi-ancestry transcriptome-wide association study (TWAS) to identify shared and population-specific genetic effects in Alzheimer's disease using RNA-seq and genotype data from multiple cohorts.

HORNET identifies causal genes and their regulatory networks using eQTLs and GWAS data, showing differential gene expression causality in schizophrenia across brain tissues.

The paper evaluates methylation clocks in genetically diverse populations and finds they are less accurate in admixed cohorts and fail to consistently predict age acceleration in Alzheimer's disease cases.