Rudolph E. Tanzi

Genedex integrates gene signatures from Alzheimer's disease studies to contextualize gene expression across pathology, cell types, and brain regions, revealing new insights into disease mechanisms.

This study conducts GWAS on short tandem repeats (STRs) to identify novel genetic mechanisms in Alzheimer's disease using data from ~330,000 individuals.

The paper shows statins and mevalonate pathway inhibition increase STMN2 and ATF3, promoting neuronal regeneration in neurodegenerative disease.

The paper investigates cell-to-cell communication in Alzheimer's disease to characterize disease biology and suggest new avenues for therapeutic intervention.

The paper identifies context-dependent regulatory variants in Alzheimer's disease by analyzing their effects across different cellular contexts using MPRAs and machine learning.

This study shows ACAT1 inhibition increases microglial uptake of amyloid β via sTREM2 and LRP1, offering new therapeutic targets for Alzheimer's disease.

This paper introduces a chemiluminescence probe, ADLumin-1, that enables both generic and precise detection of misfolded proteins in neurodegenerative disease models.

This study identifies molecular and cellular signatures of cognitive resilience in Alzheimer's disease by integrating multi-omics data from brain tissues.

This study identifies 16 new Alzheimer's disease susceptibility loci through multi-ancestry GWAS using whole genome sequencing data from diverse cohorts.

This study uses 3D direct neuronal reprogramming of AD patient fibroblasts to recapitulate AD neuropathology, showing A{beta} deposition, tauopathy, and neuronal death, and identifies potential therapeutic targets.

This paper develops polygenic hazard score models using lasso for Cox's proportional hazards to predict Alzheimer's free survival, showing improved accuracy and personalized survival curves.

This paper develops a platform combining graphene-assisted Raman spectroscopy and machine learning to rapidly screen Alzheimer's disease biomarkers in mouse brains, achieving 98% classification accuracy.

This study identifies two novel Alzheimer's disease-associated genes, DTNB and DLG2, by analyzing rare variants in whole-genome sequencing data from AD family datasets.

This paper demonstrates the use of bioluminescence imaging with AkaLumine to report amyloid beta levels in Alzheimer's disease models and shows its potential for monitoring disease progression and therapy.

The paper conducts GWAS on CSF Alzheimer's disease biomarkers, identifying genetic associations with NF-L, YKL-40, and Ng levels.

This paper introduces a smoothed version of the Lassosum penalty to improve integrated risk models, showing increased accuracy in predicting Alzheimer's and COPD risks compared to the original Lassosum and LDpred2.

The paper conducts a genome-wide association study (GWAS) on Alzheimer’s disease CSF biomarkers in the EMIF-AD dataset, identifying novel genetic signals and confirming existing associations.

The paper identifies two SARS-CoV-2 genome loci associated with increased COVID-19 mortality risk using logistic regression on whole-genome sequencing data.

This study identifies dedifferentiation and neuronal repression as key mechanisms in Familial Alzheimer's Disease using patient-derived neurons and multi-omics analysis.

This study shows decreased VE-cadherin levels in AD patients and mouse models, linked to amyloid pathology and plasma sVEC as a potential biomarker for AD risk.