Anna K. Greenwood

The paper identifies cross-domain drivers of Alzheimer's disease risk by integrating network analysis across biological domains.

The paper compares the transcriptome of Kat5 knockout mice to AD datasets, finding similar gene expression patterns and showing KAT5 localization in AD neurons.

The paper performs large-scale proteomic analysis of Alzheimer's disease brain regions across different racial and ethnic groups, identifying AD-related protein changes and APOE4 concordance with genotype.

This paper presents a multi-omics dataset from multi-ethnic Alzheimer's disease brain donors, including transcriptome, genome, and proteome data, to improve precision medicine for underrepresented populations.

This paper identifies 8 genes across 6 genomic loci associated with Alzheimer's disease risk using transcriptome-wide association study and validates them with summary Mendelian randomization.

The paper estimates molecular pseudotime for Alzheimer's disease progression using RNA-Seq data from human brain samples, showing strong concordance with pathological markers and cognitive diagnosis.

This paper performs a large-scale eQTL meta-analysis across cerebral cortical and cerebellar brain regions, identifying distinct eQTL patterns and demonstrating improved power over existing studies.