Yakov A. Tsepilov

PEGASUS defines a community reporting standard for GWAS predicted effector genes: a metadata record, a full locus-by-gene evidence matrix, and a derived author-prioritised gene list.

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

VIDRA models genetic dose-response relationships to predict drug mechanisms, therapeutic potential, and adverse events using phenotypically associated variants.

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 performs a federated cis-eQTL meta-analysis across single-cell datasets to identify cell-type-specific genetic effects and link them to disease mechanisms.

This study shows that plasma protein quantitative trait loci (pQTL) support therapeutic hypotheses more effectively than other genetic evidence, increasing the likelihood of drug development success.

FastHer provides a fast and accurate method for estimating local heritability from GWAS summary statistics, achieving GREML-level accuracy with significantly reduced computational time.

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.

The paper identifies co-eQTLs that alter gene co-expression relationships in specific cell types, linking these variants to immune disease associations.

This study identifies 16 new genetic loci associated with plasma N-glycosylation in 10,000 individuals and links them to liver disease and inflammatory proteins.

This paper decomposes the genetic background of chronic back pain into shared and unshared components, identifying genes associated with different subphenotypes and biological pathways.

SHAHER is a framework that identifies shared genetic factors across correlated traits using GWAS summary statistics, showing improved performance in simulations and real datasets.

sumSTAAR is a framework for gene-based association analysis using GWAS summary statistics, offering flexible method combinations and improved computational efficiency.

This paper describes a biobank protocol for studying lumbar disc degeneration using clinical and omics data from 1100 participants.

The paper identifies genetic loci regulating glycosylation of transferrin and IgG, finding both shared and protein-specific associations and proposing tissue-specific transcription factor regulation.

This study identifies three genetic loci associated with chronic widespread musculoskeletal pain, with the RNF123 locus replicated in independent cohorts.

The paper identifies 190 genes associated with neuroticism through gene-based GWAS analysis, with 38 novel genes and evidence of pleiotropy and pathogenic variants.

This study combines multiple GWAS of ageing traits to identify genetic components associated with human ageing and finds LPA and VCAM1 as potential drug targets.

The paper identifies shared genetic factors for chronic musculoskeletal pain across different anatomic sites using genetically independent phenotypes derived from UK Biobank data.

This study identifies 7 genetic loci associated with varicose veins and finds genetic overlaps with traits like intelligence and smoking.

This paper develops a multi-trait framework for discovering, replicating, and interpreting pleiotropic loci using GWAS summary statistics, identifying novel genetic associations without additional genotyping.

This study identifies twelve genetic loci associated with human blood plasma N-glycome composition through genome-wide association analysis, revealing both direct and indirect genetic influences on glycosylation.

This study conducts a large GWAS on back pain, identifies genetic loci, and explores genetic correlations with risk factors, revealing complex genetic architecture and molecular pathways.

The paper introduces a network-based conditional GWAS method to analyze metabolomics data, identifying additional loci by improving genetic effect precision and handling pleiotropic scenarios.

This paper conducts a genome-wide meta-analysis of chronic back pain in 158,025 individuals, identifying three genetic loci associated with the condition.

The paper analyzes coat color alleles and mtDNA haplotypes in Russian cats to study population genetics and phylogeography, finding genetic homogeneity and unique mitotypes.