Jaanika Kronberg

This study performs a large-scale GWAS on 249 metabolic traits in 619,372 individuals, identifying 88,604 associations and using MR to explore causal links between metabolites and diseases.

The paper infers parent-of-origin effects in ~265,000 individuals using a novel method, identifying over 30 novel POEs and confirming known associations, supporting the parental conflict hypothesis.

The paper uses Estonian Biobank data to identify genetic loci associated with 5,491 disease phenotypes, finding 3,222 genome-wide significant loci and 754 protein-altering variant-trait associations.

The paper reviews the Estonian Biobank's role in advancing genomics research and personalized medicine through its comprehensive data and translational focus.

The paper develops metabolomic risk scores from half a million blood samples to predict 12 common diseases, showing they outperform polygenic scores and benefit from repeat measurements.

OTTERS is a TWAS framework that uses summary-level eQTL reference data to estimate eQTL weights and perform omnibus TWAS, showing improved power and applicability.

The paper uses blood eQTL meta-analysis to explore the polygenic architecture of complex traits, finding that trans-eQTLs are more informative for identifying causal genes than cis-eQTLs.

This paper introduces TWMR, a Mendelian Randomization method that identifies gene-trait associations by integrating GWAS and eQTL data, revealing 3,913 novel associations across 43 human phenotypes.

This study identifies 139 common and 4 rare genetic variants associated with type 2 diabetes through GWAS and integrates gene expression and epigenomic data to highlight potential functional genes and regulatory mechanisms.

The paper identifies gene targets for brain-related traits using transcriptomic and methylomic data from blood, finding strong correlations between genetic effects in brain and blood tissues.