Juan Pablo Casas

This study examines the association between maternal and fetal HLA heterozygosity and preeclampsia risk in a large multi-ancestry cohort, finding protective and risk associations for specific HLA loci.

The paper uses Mendelian Randomization to predict adverse drug reactions by linking genetic variants to gene expression and protein abundance across large datasets.

This study uses large biobanks to identify gene-trait pairs with causal evidence, rediscover approved drug targets, and develop a predictive model for drug development.

This study uses Mendelian randomization to identify two proteins, CCL27 and TNFRSF14, that influence subsequent cardiovascular events after a stroke, suggesting inflammation as a contributing factor.

Population-stratified GWAS of 2,068 traits in 635,969 Million Veteran Program participants, 29% genetically non-European, yields 98,485 associations and 57,601 fine-mapped signals with largely shared cross-population architecture.

This study identifies novel genetic risk variants and potential drug targets for heart failure using GWAS and Mendelian randomization proteomics.

This study compares the genetic architecture of heart failure with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF), finding distinct genetic associations and highlighting HFpEF's heterogeneity.

This study identifies genetic variants associated with severe COVID-19 and finds shared genetic architecture with complex diseases using EHR data from the Million Veteran Program.

The paper uses whole genome sequencing to identify genetic loci associated with platelet aggregation, including novel variants in RGS18, SVEP1, and PEAR1.

The paper integrates genomic and proteomic data to identify genetic variants influencing host proteins interacting with SARS-CoV-2, revealing potential drug targets and disease mechanisms.

This study identifies 114 genome-wide significant loci for schizophrenia and bipolar disorder, with specific subphenotype associations and four genes distinguishing the disorders.

The paper discusses how integrating druggable target genes with disease susceptibility genes can improve drug development success rates by reducing target specification errors.

The paper connects GWAS loci to druggable genes, compounds, and clinical indications to identify drug development opportunities and design a genotyping array for druggable genome-wide association studies.