John A. Morris

The paper identifies target genes and regulatory networks for bone mineral density GWAS loci using STING-seq, linking noncoding variants to gene function in osteoblasts.

This study modulates gene dosage using CRISPR and examines nonlinear transcriptional responses to dosage changes in three master transcription factors, revealing non-monotonic effects and links to disease genes.

The paper compares CRISPRi perturbation screens and eQTLs in identifying target genes for GWAS-associated loci, finding they often identify distinct genes and complement each other.

The paper uses interventional data to infer causal networks for blood traits, revealing regulatory relationships among 788 genes.

The paper identifies target genes and pathways of blood trait loci using CRISPR screens and single-cell RNA sequencing, revealing cis and trans regulatory effects of noncoding variants.

The paper introduces CaRPool-seq, a method using Cas13 for combinatorial RNA perturbations in single cells, and applies it to study myeloid differentiation regulators in AML.

The paper introduces the Effector Index, an algorithm to predict causal genes at GWAS loci using genomic features, achieving 85% AUC in type 2 diabetes validation.

The paper introduces SCEPTRE, a method for analyzing single-cell CRISPR screens that improves calibration and sensitivity by addressing technical confounders through conditional resampling.

This study identifies a risk variant for autoimmune disease that acts as a trans-eQTL regulating IRF1 under immune stimulation, demonstrating its causal role and mechanism.

The paper maps the osteocyte transcriptome in mice, identifies 1239 genes, and links them to skeletal disease susceptibility in humans.

The paper develops an automated pipeline to design CRISPR prime editors for human pathogenic variants, finding that only a small fraction can be targeted but this number increases to over 50,000 with alternative Cas9 and templates.

This paper identifies 518 genetic loci associated with bone mineral density and fracture risk, validates causal genes in mice, and provides insights for osteoporosis drug development.

This paper uses machine learning to predict osteoporotic fracture risk from genotypes, finding that genomic predictions outperform clinical risk factors in identifying fracture risk.