Erik Ingelsson

The paper integrates genetic colocalizations with physiological and pharmacological perturbations to identify causal genes for cardiometabolic diseases.

This study uses Mendelian randomization to show that increased alcohol consumption is associated with higher cardiometabolic risks, including elevated blood pressure and mortality, but lower triglycerides and HbA1C.

The paper uses a CRISPR/Cas9-based approach in zebrafish to identify genes affecting heart rate variability and confirms the role of known genes while highlighting a novel gene.

The paper identifies pQTLs for cardiovascular proteins and uses them to evaluate drug targets and suggest new candidates for precision medicine.

The paper analyzes genetic variants at the PCSK9 locus to assess their phenotypic effects on lipid levels and disease outcomes, finding concordance with PCSK9 inhibitor trials for myocardial infarction but discordance for type 2 diabetes.

The paper uses DeGAs to identify genetic components across 2,138 phenotypes in the UK Biobank, revealing novel adipocyte biology through variant and gene contributions to obesity-related 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 study conducts a large GWAS on heart failure, identifying 12 genetic loci with associations to CAD, atrial fibrillation, and reduced left ventricular function, and provides causal evidence for several risk factors.

The paper develops a zebrafish pipeline to study dyslipidemia and atherosclerosis, identifying potential causal genes and showing drug effects.

The paper develops and evaluates prediction models for cardiovascular diseases and type 2 diabetes using EHR data, showing high discrimination performance for most conditions.

The paper conducts genome-wide association studies on urinary biomarkers in the UK Biobank, identifying 78 novel loci and confirming existing associations, linking these biomarkers to chronic diseases.

The paper develops and validates a next-gen risk engine for predicting multiple cardiovascular diseases using UK Biobank data, achieving moderate-to-high discrimination and good calibration.

The paper analyzes gene expression and splicing changes in human aging using longitudinal RNA sequencing data from the PIVUS study, finding stability in most genetic regulation but dynamic changes in a subset of genes.

This study identifies 93 genetic loci associated with kidney function across four ancestry groups and highlights causal effects on kidney-related diseases.

The paper quantifies the risk of left ventricular outflow tract obstructive heart defects (LVOTO) from different NOTCH1 variants in two European cohorts, identifying significant intronic variants and loss-of-function variants as risk factors.

This study evaluates the use of whole blood transcriptome sequencing for diagnosing rare diseases, finding an 8.5% diagnostic rate across 56 undiagnosed cases.

This study identifies age-related DNA methylation changes in old Swedish twins over 20 years, showing stability across cohorts and genetic influences independent of age.

The paper identifies five genes modulating coronary artery disease risk through smooth muscle cells by integrating eQTL and GWAS data with transcriptomic and epigenomic analyses.

This paper identifies a protective effect of PCSK9 loss-of-function variant rs11591147 against ischemic stroke using UK Biobank data.

This study uses Mendelian randomization to show lower birthweight causally increases risk of CAD and T2D in adulthood, independent of adult BMI and hypertension.

The paper assesses the medical relevance of protein-truncating variants across 337,208 UK Biobank individuals, identifying 27 associations with 135 phenotypes and highlighting pleiotropic effects.

This study identifies 101 genetic loci associated with hand grip strength in 334,925 UK Biobank participants and links grip strength to brain-related gene expression and health outcomes.

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 develops a workflow for large-scale non-targeted metabolomic profiling in human studies, detecting over 8,000 metabolic features and identifying 90 metabolites associated with hsCRP.