Aron S. Buchman

This study uses multimodal transcriptomic data to enhance Alzheimer's disease risk gene discovery, identifying 161 risk genes with proteomic support and an interconnected network of hub genes.

This study integrates multi-omics data from DLPFC with GWAS summary data to identify PD risk genes, finding significant overlaps between bulk and cell-type-aware TWAS and PWAS results.

This study integrates multi-omics data from aged human brains to identify cross-system biological factors and molecular subtypes associated with Alzheimer's disease heterogeneity.

The paper defines cognitive resilience as differential cognitive decline rates after adjusting for neuropathologies, develops a framework to quantify it using molecular signatures from post-mortem brain data, and translates these findings to living individuals.

The paper uses an omnibus xWAS pipeline to integrate snRNA-seq and GWAS data, identifying 91 independent cell-type-specific risk genes for Alzheimer's disease dementia.

This study integrates spatial transcriptomics and snRNA-seq data to improve differential gene expression analysis of AD-related phenotypes, identifying novel genes and pathways not detectable with traditional methods.

This study identifies TDP-43-driven splicing dysregulation as a common mechanism in ALS, FTD, and Alzheimer's disease, linking them through mis-spliced mRNAs like APP.

This paper standardizes postmortem human brainstem sampling along the rostrocaudal axis to reduce heterogeneity and improve reproducibility across studies.

This study translates post-mortem brain multi-omics AD dementia subtypes into blood, MRI, and psychological markers in living individuals.

The paper investigates structural variants (SVs) in AD/ADRD traits using whole genome sequencing data from two cohort studies, identifying SVs linked to disease phenotypes and suggesting new risk loci.

This paper introduces PentaMind, a deep learning model that extracts cognitive features from hand-drawn intersecting pentagons, improving detection of cognitive decline in older adults.

SR-TWAS improves TWAS power by combining multiple reference panels and regression methods through ensemble learning, identifying risk genes for Alzheimer's and Parkinson's diseases.

The paper introduces PWAS-O, an omnibus proteome-wide association study method that identifies 43 risk genes for Alzheimer's disease dementia, including 5 novel ones.

This paper introduces BFGWAS_QUANT, a scalable Bayesian functional GWAS method that improves variant prioritization in Alzheimer's disease by integrating multivariate quantitative annotations.

This paper introduces VC-TWAS, a novel variance-component TWAS method that improves power for studying complex diseases like Alzheimer's dementia by relaxing assumptions about genetic effects.

This study identifies 15 genetic loci associated with muscle weakness in older adults through a large meta-analysis, revealing new pathways linked to aging and health outcomes.

This paper introduces BGW-TWAS, a Bayesian method for transcriptome-wide association studies that incorporates both cis- and trans-eQTL information, improving power and identifying novel associations with Alzheimer's disease.