Stephen Newhouse

This study identifies TREM2-dependent genes and pathways in Alzheimer's disease by analyzing RNA sequencing data from brain samples, revealing its impact on microglia, oligodendrocytes, and endothelial cells.

The paper develops two gene expression classification models for Alzheimer’s Disease using blood-derived data, finding that including mixed controls improves specificity but reduces sensitivity.

This study compares transcriptomic profiles of asymptomatic and symptomatic Alzheimer's brains to identify early molecular changes associated with disease vulnerability.

DNAscan is a fast and memory-efficient pipeline for analyzing DNA NGS data, capable of detecting SNVs, indels, SVs, and other variants with minimal computational resources.

This paper performs a meta-analysis of Alzheimer's disease brain transcriptomic data to identify differentially expressed genes and pathways specific to AD, finding 323-1023 genes per brain region and enriched pathways in protein metabolism and viral components.

ALSgeneScanner is a tool that provides automated, detailed reports on ALS-related gene variants from whole genome or exome sequencing data, aiding non-specialists in interpretation.

Project MinE analyzes whole-genome sequencing data from ALS patients and controls to study genetic variation and its implications for disease susceptibility.

This paper conducts a meta-analysis showing that CYP2C19 poor metabolizers have better antidepressant efficacy but similar side effects compared to extensive metabolizers.

This study examines the transcriptional effects of Trem2 loss in mouse brain, revealing disruptions in cell co-expression networks and highlighting a potential role for Trem2 in endothelial cell homeostasis.

This study identifies two SNPs associated with antidepressant response in GENDEP and STAR*D studies but finds limited replication in independent cohorts.

This study uses Bayesian logistic regression to assess if genetic risk factors predict amyloid burden in non-demented individuals, finding that known factors like age and APOE are more predictive than polygenic risk scores.

This study investigates whether polygenic risk scores (PRS) can predict antidepressant response in MDD, finding no significant predictive effect.