Søren Brunak

This study identifies 26 risk loci for fibromyalgia through a genome-wide association analysis of 2.5 million individuals, revealing genetic links to neural functions and chronic pain disorders.

This study identifies genetic variants associated with healthcare costs across multiple systems, finding polygenic influences and large effects from specific loss-of-function variants.

This study evaluates polygenic risk scores (PRS) for colorectal cancer (CRC) screening, showing they stratify risk independently of ancestry and sex but offer limited additional predictive value when combined with FIT.

This paper identifies genetic variants associated with amiodarone-induced thyroid dysfunction and shows they improve risk prediction beyond general thyroid disease PRS.

The paper uses genetic data from Denmark and Britain to infer historical North Sea migrations from the Middle Ages to the Industrial Revolution, identifying distinct population patterns and migration trends.

This study identifies 51 genome-wide significant loci associated with dosage changes in 42 drug pair phenotypes, revealing distinct genetic patterns in polypharmacy.

This study identifies 80 genetic loci and 61 effector genes associated with dilated cardiomyopathy through GWAS and integrates transcriptomic data to uncover biological pathways and cellular states involved in the disease.

The paper develops a Lifestyle Factor Ontology and annotated corpus for detecting lifestyle factors in biomedical texts, achieving 76% F-score with a transformer-based NER system.

This study identifies 66 genetic variants linked to neuroendocrine structure volumes and links some to testosterone levels using UK Biobank data.

This study identifies 31 plasma proteins and expands genetic loci associated with PCOS, highlighting metabolic and hormonal influences and polygenic risk effects on cardio-metabolic outcomes.

This study identifies 40 genetic loci associated with placental weight using GWAS in 179,025 children and parents, revealing shared and distinct genetic influences with fetal growth.

The paper identifies shared genetic regulation across mRNA, proteins, and metabolites in blood, revealing networks and mediators of GWAS variants.

The paper identifies five genetic loci associated with postpartum hemorrhage through a meta-analysis and links them to progesterone receptor binding sites and related traits.

The paper introduces a deep learning framework for polygenic risk score prediction, showing improved performance over traditional methods for various diseases.

This study uses Danish registries to estimate heritability and genetic correlations of disease phenotypes and trajectories, identifying novel genetic associations and sex-specific contributions.

This study uses deep learning to predict blood and urine biomarkers from genetic data, outperforming linear methods and capturing non-linear and epistatic effects.

The paper analyzes whole genome sequences of 150,119 UK Biobank individuals, identifying variants and associations with traits, and provides a haplotype reference panel for imputation.

This study identifies 144 genetic loci associated with abdominal aortic aneurysm and highlights PCSK9 as a therapeutic target through Mendelian Randomization and pre-clinical validation.

SignalP 6.0 predicts all five types of signal peptides with improved accuracy and applies to metagenomic data.

This GWAS identifies common variants near BDKRB2 associated with angioedema from ACEi treatment, showing genetic risk and blood pressure effects.

This paper uses deep learning to predict pancreatic cancer risk from clinical disease trajectories in two large datasets, achieving higher accuracy than models without temporal information.

This study performs a genome-wide association analysis of hemorrhoidal disease in 944,133 individuals, identifying 102 risk loci and linking them to vascular and tissue development pathways.

This study develops machine learning models to predict liver fat content using multi-omic and clinical data from NAFLD patients, achieving higher accuracy than existing tools.

The paper introduces the Cancer LncRNA Census (CLC), a curated resource of lncRNAs with causative roles in cancer, and shows their genomic and evolutionary features.

ActivePathways integrates multivariate omics data to identify enriched pathways, revealing cancer driver genes and prognostic pathways in breast cancer.

The paper identifies calcium signalling defects as a novel cause of congenital heart disease through whole exome sequencing and functional validation in zebrafish.

This study investigates the association between genetic variants in PMAT and OCT1 transporters and metformin gastrointestinal intolerance in type 2 diabetes patients.

The paper identifies bow-tie motifs in the cDC1-specific interactome, showing they connect cellular processes and enable protein multifunctionality.

The paper analyzes the facial and gut microbiomes of vultures to identify microbial elements that contribute to their adaptation to scavenging diets.