Howard J. Jacob

This study identifies 1,242 novel genetic variants and 31 novel genes associated with frailty through genome-wide association and proteomics integration.

This study conducts a genome-wide meta-analysis across three biobanks to identify new genetic loci associated with lumbar disc herniation, expanding the known genetic landscape.

The paper demonstrates the feasibility of conducting clinical recall studies from the FinnGen biobank to collect customized clinical data from participants with Alzheimer's disease or mild cognitive disorder.

This study conducts a GWAS of preeclampsia and hypertensive disorders of pregnancy, identifying 18 genome-wide significant loci, including novel associations with blood pressure and placental function.

The paper uses a large knowledge graph to identify potential drug repurposing candidates by linking drugs to diseases through genetic evidence.

The paper performs single-variant and gene-based association analyses of 4,529 phenotypes using exome data from 426,370 UK Biobank individuals, finding associations linked to variant frequency and deleteriousness.

Finnish-enriched low-frequency variants, often coding, underlie many GWAS signals across 1,932 register endpoints in 218,792 Finns; a 15-disease meta-analysis with Estonian and UK biobanks adds 30 unreported associations.

The paper develops a deep learning classifier to distinguish somatic and germline mutations in tumor-only samples, achieving high accuracy across tissue types and reducing racial bias in TMB estimates.

The paper establishes reference samples with high-coverage whole-genome sequencing to create truth sets for somatic mutation and germline variant detection using NGS technologies.

This study evaluates reproducibility and accuracy of cancer mutation detection using whole-genome and whole-exome sequencing across multiple sequencing centers and bioinformatics pipelines.

The paper describes exome sequencing of 200,643 UK Biobank participants, providing a rich resource of coding variants for genetic and drug discovery research.

The paper introduces a multivariate GWAS framework that identifies functional variants and disease associations by transforming multivariate results into univariate phenotypes for follow-up analysis.