Emily Greenfest‐Allen

A TAD-informed aging-brain xQTL atlas of multi-modal and cell-type-resolved regulatory variation

The paper presents an xQTL atlas of regulatory variation in the aging human brain, integrating multiple modalities and cell types across four studies, identifying more variant-molecular-trait associations by including TAD boundaries.

Integrated genomic analysis and CRISPRi implicates EGFR in Alzheimer's disease risk

The paper identifies EGFR as a potential therapeutic target in Alzheimer's disease by linking a risk variant to increased EGFR expression in brain cell types through genomic analysis and CRISPRi validation.

Alzheimer's Disease Sequencing Project Release 4 Whole Genome Sequencing Dataset

The paper provides a whole-genome sequencing dataset of 36,361 ADSP participants, including variant annotations, phenotypes, and a linkage disequilibrium panel for Alzheimer's Disease research.

NIAGADS: A Comprehensive National Data Repository for Alzheimer's Disease and Related Dementia Genetics and Genomics Research

NIAGADS provides a national data repository for Alzheimer's Disease and related dementia genetics and genomics research, hosting genome and exome sequence data from over 209,000 samples.

NIAGADS Alzheimer's GenomicsDB: A resource for exploring Alzheimer's Disease genetic and genomic knowledge

The paper introduces GenomicsDB, a public knowledgebase for Alzheimer's disease genetic datasets and annotations, enabling data sharing and discovery through harmonized GWAS summary statistics and functional annotations.

FILER: large-scale, harmonized FunctIonaL gEnomics Repository

FILER provides a large-scale, harmonized functional genomics data catalog with streamlined access to over 50,000 datasets and efficient querying capabilities.

SparkINFERNO: A scalable high-throughput pipeline for inferring molecular mechanisms of non-coding genetic variants

SparkINFERNO efficiently identifies causal non-coding genetic variants and their molecular mechanisms using GWAS data and functional genomics datasets.

iterativeWGCNA: iterative refinement to improve module detection from WGCNA co-expression networks

iterativeWGCNA improves module detection in WGCNA by iteratively refining gene clusters and minimizing information loss.