Nurlan Kerimov

The paper visualises molecular QTLs to better understand genetic effects on gene expression and identifies causal genes at GWAS loci.

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.

Finnish-enriched low-frequency variants, often coding, underlie many GWAS signals across 1,932 register endpoints in 218,792 Finns; a 15-disease meta-analysis with Estonian and UK biobanks adds 30 unreported associations.

Fine-mapping 148 complex traits in three biobanks with combined n = 811,261 yields 4,518 causal variant-trait pairs, 285 of them repeating in more than one population.

The paper presents the eQTL Catalogue, a resource containing uniformly processed eQTLs from 21 studies, enabling integrative analysis and identification of cell-type-specific eQTLs and disease colocalisations.

The paper identifies trans-acting genetic variants that control gene modules through co-expression analysis in blood cell types.