Leonardo Bottolo

SPARK reconstructs enhancer-based gene regulatory networks using single-cell multiomics data and prior knowledge, revealing regulatory links associated with immune disease GWAS variants.

This study shows that single-cell experiments often lack power to detect small-effect genes, even with 600 donors, and highlights the impact of sequencing depth and cell counts on results.

The paper identifies trimodal QTLs that jointly affect enhancer accessibility, promoter contacts, and gene expression in monocytes, revealing genetic links between enhancer activity and chromatin boundaries.

The paper uses single-cell Mendelian randomisation to identify cell-type specific genetic effects on brain disease and behaviour, revealing 118 genes with causal associations to 16 brain phenotypes.

This paper introduces MrDAG, a Bayesian Mendelian randomization method that models complex dependencies among multiple exposures and outcomes to improve causal effect estimation, highlighting education and smoking as key interventions for mental health.

This paper estimates the penetrance of rare variants in cardiomyopathy genes using cross-sectional data from genetic testing, finding substantial penetrance for dominant cardiomyopathies and variant-specific differences.

This paper introduces Multi-response Mendelian randomization (MR2) to identify shared and distinct exposures for multiple disease outcomes, showing improved power and accuracy by accounting for residual correlations between outcomes.

The paper identifies a trans locus in rats that causes a protein length-dependent shift in translational efficiency, affecting cardiac hypertrophy and sarcomere protein stoichiometry.

The paper introduces a Bayesian SUR model for high-dimensional QTL analysis, efficiently handling correlated metabolite data and genetic variants.

EPISPOT improves molecular QTL mapping by integrating epigenetic annotations to detect and interpret genetic hotspots with greater statistical power and biological insight.

This paper introduces CardioBoost, a disease-specific variant classifier for inherited cardiac conditions, showing improved accuracy in predicting pathogenicity compared to genome-wide tools.

MethylCal is a Bayesian tool that calibrates methylation levels by jointly analyzing CpGs in DMRs or CpG islands, improving accuracy in methylation quantification and clinical applications.

ABBA detects differentially methylated regions in WGBS data, identifies disease-associated methylation changes, and proposes a mechanism for glomerulonephritis via Ifitm3 promoter hypermethylation.