Eric E. Schadt

The paper examines motivations and outcomes of elective genomic sequencing across research, clinical, and commercial contexts, finding most participants found results medically important but few shared them with healthcare providers.

The paper generates intestinal organoids from pediatric IBD patients and controls to study epithelial phenotypes linked to genetic drivers of the disease, identifying gene expression patterns and pathways associated with inflammation.

This study compares gene expression in living and postmortem human brain tissue, finding significant differences that challenge the assumption that postmortem data accurately represents living brain gene expression.

The paper constructs context-specific brain gene regulatory networks from bulk and single-cell RNA-seq data using Bayesian and deep learning methods to study Autism Spectrum Disorder mechanisms.

This study shows that DUSP6 overexpression in the dorsal hippocampus of male 5xFAD mice reduces amyloid load and improves memory, while also reducing neuroinflammation in both sexes.

This study compares A-to-I RNA editing in postmortem and living human brain tissues, revealing significant differences in editing levels and their association with cellular processes and neurological conditions.

The paper compares gene expression differences between living and postmortem human brain samples using snRNA-seq and develops a model to account for these differences in analysis.

The paper identifies KLF16 as an inhibitory regulator of osteogenic differentiation through transcriptomic analysis of human iPSC-derived cells and in vivo validation.

The paper identifies anti-correlated gene selection as a method to prevent false discovery of subpopulations in scRNA-seq by reducing erroneous subdivisions in clustering.

This paper benchmarks scRNAseq clustering pipelines using a self-supervised approach, identifying a novel metabolic gene signature linked to hepatocellular carcinoma survival.

The paper identifies IL10RB as a key regulator of COVID-19 host susceptibility and severity through integrative analysis of genetic and transcriptomic data, and validates potential drug repositioning candidates.

The paper evaluates a comprehensive pan-ethnic carrier screening panel across 282 monogenic diseases in 397,540 patients, showing it identifies 10-times more at-risk carrier couples than a two-gene panel and provides cost savings.

This paper shows that common scRNA-seq batch correction methods erase biological variation, and proposes new measures to assess batch effects and their impact on cluster composition and topologies.

This study evaluates the prognostic value of polygenic risk scores for schizophrenia and finds they do not improve outcome prediction beyond clinical features.

This study evaluates a polygenic risk score for breast cancer in women aged 70 and older, finding it improves risk prediction beyond conventional factors.

The paper identifies lipid-lowering genetic variants in older adults without CHD, showing their association with lower LDL-C and reduced statin use.

This study examines the effect of APOE genotypes and polygenic risk scores on dementia and cognitive decline in a longitudinal cohort of healthy older individuals, finding increased risks for APOE ε4 carriers and high PRS tertiles.

The paper introduces iGDA, a tool for accurately detecting and phasing minor SNVs from long-read sequencing data, even at low frequencies.

The paper identifies proinflammatory monocytes/macrophages associated with resistance to PD-1/PD-L1 blockade in urothelial cancer using bulk and single-cell RNA sequencing.

This study identifies shared molecular signatures between chronic fatigue syndrome and other fatiguing illnesses using RNA-sequencing data from blood samples.

This study ranks prostate cancer tumors along the IFN-γ/PD-L1 axis to prioritize patients for immune checkpoint therapy and identifies immune resistance mechanisms.

The paper develops a computational systems biology approach to identify novel microglial-specific targets for the phagosome in Alzheimer's disease using integrated transcriptome and genotype data.

The paper identifies protein network modules in Alzheimer's disease brains and cerebrospinal fluid that are associated with energy metabolism and neuroinflammation, suggesting potential biomarkers and therapeutic targets.

This paper introduces ANDRU, a human data-driven approach to discover geroprotectors by targeting aging and age-related disease subnetworks, identifying potential targets and compounds in adipose and artery tissues.

This paper introduces BridGE, a method to detect pathway-level genetic interactions from GWAS data, revealing significant interactions in multiple diseases.

This study integrates transcriptome data with genetic variation to identify gene-trait associations and uncover biological mechanisms underlying complex traits in humans and mice.

This study characterizes the epigenome of C. difficile and identifies a conserved DNA methyltransferase that impacts sporulation and other biological processes.

The paper compares RNAseq methods for analyzing medium spiny neurons in the nucleus accumbens, finding that nuclear-FACS-seq identifies more differentially expressed genes and GWAS hits than other methods.

This study performs a multi-omic analysis of acne patients and non-acne individuals to identify molecular subnetworks associated with disease status.

Coexpression meta-analysis of 2114 human brain RNA-seq samples across the AMP-AD consortium identifies five consensus Alzheimer's disease transcriptional clusters, including two sex-heterogeneous clusters and a previously unreported proteostasis cluster.