Konstantin Strauch

This study identifies 14 susceptibility loci for Ewing sarcoma through a genome-wide association meta-analysis, revealing genetic interactions with somatic EWSR1::FLI1 fusions.

GeDi streamlines functional enrichment results by clustering gene sets and integrating PPI data, improving interpretation and hypothesis generation.

This study identifies PTPRK as a gene modulated by TMPRSS2::ERG fusion and a regulatory SNP, showing its role in prostate cancer progression and potential as a biomarker.

The paper presents a multi-omics cell line atlas for Ewing sarcoma, identifying new target genes and regulatory mechanisms of EWSR1-ETS fusion oncogenes.

The paper reconstructs regulatory networks underlying trans-QTL hotspots using human multi-omics data and biological priors, identifying novel networks related to schizophrenia and lean body mass.

GeneTonic provides an R/Bioconductor package to streamline RNA-seq data interpretation by integrating differential expression and functional enrichment analyses into interactive visualizations and reports.

This study identifies two genome-wide significant susceptibility loci for acute myeloid leukemia through a meta-analysis of GWAS data.

The paper identifies novel genetic loci associated with blood pressure that interact with sleep duration across multiple ancestries.

This study performs a large GWAS on dilated cardiomyopathy, identifying two new loci and confirming two existing ones, with potential therapeutic implications.

annoFuse filters and prioritizes oncogenic gene fusions from RNA-Seq data, improving identification of clinically relevant fusions in cancer datasets.

This study identifies 137 genome-wide significant loci associated with DNA methylation biomarkers of ageing using DNAm and SNP data from over 41,000 individuals.

The paper identifies SNPs with sex-specific effects on lung function traits using UK Biobank data, finding a significant interaction at rs7697189 near HHIP.

The paper identifies a DNA methylation site in the LPA promoter associated with Lp(a) levels and shows that a SNP at this site influences Lp(a) through direct genetic effect, not methylation.

DeepWAS integrates regulatory effect predictions of variants into GWAS to identify regulatory SNPs associated with diseases and traits.

This study identifies 210 genetic loci associated with the PR interval through multi-ancestry GWAS, highlighting their role in cardiac conduction and cardiovascular disease.

This paper evaluates the causal effect of fibrinogen on coronary heart disease using Mendelian randomization, finding a small effect with multi-variant methods but not with single variant approaches.

The paper identifies 49 novel blood lipid loci associated with gene-sleep interactions in 126,926 individuals across 5 ancestry groups, with gene-short-sleep interactions explaining 4.25% of triglyceride variance.

The paper identifies cis-epistasis at the LPA locus involving two SNPs that interact to modulate coronary artery disease risk and related traits.

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.

The paper evaluates missing data handling strategies in untargeted metabolomics data, finding that KNN imputation with variable pre-selection is robust across multiple evaluation schemes.

This paper introduces SAPPHO methods to detect pleiotropy in GWAS by modeling shared patterns of genetic associations, finding 16 additional and 124 novel loci in meta-analysis.

This study identifies 47 novel genetic loci associated with PR interval through genome-wide association meta-analysis across multiple populations.

This study fine-maps type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps, identifying 73 variants with >80% posterior probability of association.

The paper performs a GWAS on blood plasma proteome data from two cohorts to identify SNP-protein associations linked to various diseases and creates a genome-proteome network and web tool for analysis.

This paper shows that DNA methylation data can capture ancestry information and introduces a method to infer ancestry from methylation data without genetic data.