Mischa Lundberg

The paper identifies key cell types and genes involved in skeletal disease using single-cell analysis and validates their roles in mouse models.

This study shows that as ASD and ADHD diagnoses have increased, the genetic risk associated with these conditions has decreased over time.

This study identifies novel Alzheimer's disease genes and epistasis using machine learning GWAS tools, capturing more phenotypic variance and discovering new loci with epistatic interactions.

The paper investigates how L1 retrotransposition potential declines with age through methylation changes and promoter shortening in mice.

The paper introduces PA-FGRS, a method to estimate genetic liability from family records, and shows it improves MDD classification and GWAS power when combined with genotypes.

This study identifies genetic determinants of hip joint space width and finds they have opposing causal effects on hip osteoarthritis risk through distinct mechanisms.

RFlocalfdr thresholds variable importance from Random Forests using an empirical Bayes approach, achieving high accuracy and speed for high-dimensional datasets.

The paper identifies genes involved in human bone disease by analyzing transcriptomic changes during zebrafish scale regeneration.

This paper estimates indirect parental genetic effects on offspring phenotypes using virtual parental genotypes derived from sibling and half-sibling pairs, demonstrating the feasibility of this approach with GWAS data.

This study identifies genetic and environmental risk factors for chronic pain using GWAS and Mendelian randomization, finding associations with depression, anxiety, smoking, and BMI.

A web platform bundling LD-score regression, DEPICT, MetaXcan, SMR, GSMR and fastBAT over uploaded or 1,500-plus public GWAS summary statistics, with precomputed gene, pathway and tissue results.