Shubhabrata Mukherjee

This study uses single-nucleus RNA-seq, ATAC-seq, and Multiome to analyze Alzheimer's disease across multiple brain regions, revealing shared and region-specific cellular vulnerabilities.

This paper performs a large-scale GWAS meta-analysis of neuropathologic lesions in Alzheimer's disease and related dementias, identifying both known and novel genetic associations.

scVIP models single-cell transcriptomes to predict phenotypes and infer trajectories in development and disease.

The paper uses machine learning to predict autopsy-confirmed neuropathology from clinical, neuroimaging, and CSF biomarkers, finding that different modalities best predict specific pathologies.

This paper introduces a Bayesian framework to infer continuous disease states from discrete pathological protein measurements in Alzheimer's Disease, using pseudotime to model progression.

This paper develops an APOE-independent polygenic risk score for Alzheimer's disease that shows associations with cognitive decline and neuropathological markers across diverse populations.

This paper identifies genome-wide pleiotropic loci linking blood pressure and cognitive performance measures using longitudinal data from multiple cohorts.

The paper identifies sex-specific genetic loci associated with memory, executive functioning, and language performance in older adults through genome-wide meta-analyses.

This study examines the association between APOE genotype and cognitive resilience in SuperAgers, finding lower APOE-ε4 and higher APOE-ε2 frequencies in non-Hispanic White SuperAgers compared to controls and Alzheimer's cases.

This study creates an integrated multimodal cell atlas of Alzheimer's disease by analyzing single nuclei transcriptomes, epigenomes, and spatial coordinates across AD stages.

The paper uses BADGERS to identify risk factors and endophenotypes for late-onset Alzheimer's disease by analyzing biobank-wide data.

This study identifies biases in proxy GWAS using parental Alzheimer's history, showing misleading genetic correlations and highlighting design issues in biobank cohorts.

The paper identifies cognitive subgroups in Alzheimer's disease based on MRI-derived atrophy patterns and links them to specific biological pathways using transcriptomic data.

This study identifies genetic variants and pathways associated with cognitive resilience to Alzheimer's disease, finding a novel locus linked to bile acid metabolism and methylation changes.

This study identifies novel genetic modifiers of late-onset Alzheimer's disease by stratifying patients into molecular subtypes using transcriptomic data and whole genome sequencing.

The paper fails to replicate previously reported epistatic interactions between TF and HFE variants and AD risk using a larger dataset, suggesting potential false positives or complex interactions.

The paper introduces a statistical framework for cross-tissue transcriptome-wide association analysis, improving gene-trait association detection and identifying novel genes related to late-onset Alzheimer's disease.

The paper identifies genetic loci associated with cognitively-defined subgroups of late-onset Alzheimer’s disease using SNP data from multiple studies.

DECODER integrates large-scale gene expression data to identify mitochondrial Complex I as a therapeutic target for Alzheimer’s disease.

This study performs a large GWAS of Alzheimer's disease, identifies four new risk loci, and highlights pathways involving immunity, lipid metabolism, and APP/Aβ processing.

This paper introduces a method to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics, identifying significant genetic covariances among 50 complex traits.

The paper introduces GenoSkyline-Plus, a framework for tissue-specific functional annotation of the human genome, showing its utility in identifying immune-related DNA elements linked to late-onset Alzheimer’s disease.

The paper evaluates gene-gene interactions between CLU and MS4A4E variants in Alzheimer’s disease using a large dataset, finding a significant interaction and estimating its population attributable fraction.