Dániel Süveges

This paper analyzes 100,526 GWAS to identify pleiotropic genes and their therapeutic implications, finding that highly pleiotropic genes are associated with safety liabilities and that combining PAV support with intermediate pleiotropy improves therapeutic success.

The paper introduces a framework for extracting biological evidence from literature to aid drug discovery, identifying millions of associations between targets, diseases, and drugs.

The paper uses network-based expansion of GWAS results to identify pleiotropic gene modules linked to multiple traits and demonstrates their application in studying inflammatory bowel disease.

The paper uses whole-genome sequencing to analyze the genetic architecture of 257 cardiometabolic protein biomarkers, identifying 131 sequence variant associations and constructing polygenic scores that explain up to 45% of protein variation.

This paper calls copy number variants from whole-genome sequencing data of 6,898 individuals using a regression tree-based method, identifying 1,320 CNVs and their potential clinical impact.

This study identifies 64 new genetic signals for osteoarthritis using UK Biobank data, including 52 novel loci, and highlights potential therapeutic targets with existing drugs.

This study evaluates very low depth whole genome sequencing for complex trait association studies, showing it can efficiently discover association signals comparable to imputed GWAS chip designs.

The paper performs deep whole genome sequencing on 1,457 individuals to investigate rare variant contributions to cardiometabolic traits, identifying new regulatory variants and gene-trait associations.

This study identifies 9 new osteoarthritis loci using UK Biobank data and finds causal effects of higher body mass index on osteoarthritis.