David A. Knowles

The paper introduces Leaflet, a binomial mixture model for analyzing alternative splicing in single-cell data, and applies it to the Tabula Muris brain dataset to identify cell-state specific splicing patterns.

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

This paper introduces MCFA, an unsupervised integration method for multi-omic data, and applies it to five omics types across 614 samples to infer shared and private factors.

The paper presents three computational models to analyze fast-scan cyclic voltammetry data from mice, quantifying presynaptic dopamine kinetics and the role of synucleins in neurotransmission.

This paper introduces DeepMR, a method to estimate causal relationships between genomic marks using deep learning models, and applies it to validate and discover new TF relationships in reprogramming.

The paper introduces Welch-weighted Egger regression to reduce false positives in Mendelian randomization due to correlated pleiotropy, showing improved performance on real and simulated data.

The paper identifies environmental modulators of genetic risk factors for complex traits by integrating perturbation data with GWAS.

This paper introduces bimmer, a method to infer causal networks from observational genomics data by estimating direct and mediated effects using bidirectional mediated Mendelian randomization.

The paper characterizes the dynamic multi-omic molecular response to acute exercise and identifies biomarkers of fitness.

The paper identifies three novel complex structural variation patterns in cancer genomes using genome graph analysis across 2,833 tumor WGS.

The paper maps stimulation-responsive chromatin changes in immune cells and identifies variants linked to autoimmunity.

The paper identifies clinically relevant cell type interactions in the lung tumor microenvironment using RNA-seq data from sorted cell populations.

This paper evaluates transcriptome-wide association studies (TWAS) for causal gene prioritization, showing its strengths and limitations in identifying causal genes at GWAS loci.

This study identifies genetic variants influencing the transcriptomic response to doxorubicin in cardiomyocytes and links them to anthracycline-induced cardiotoxicity risk.

The paper identifies genetic-environment interactions in skeletal muscle gene expression using allele-specific expression and clinical traits as environmental proxies.

LeafCutter quantifies RNA splicing variation from short-read RNA-seq data without transcript annotations, identifying more sQTLs and improving disease gene associations.

RolyPoly links GWAS summary statistics with single-cell RNA-seq to identify trait-relevant cell types and genes, finding associations between microglia and Alzheimer's disease, and oligodendrocytes and schizophrenia.