Mika Ala‐Korpela

This study identifies 400+ genetic loci associated with circulating metabolic traits and highlights pleiotropy and novel disease pathways.

The paper introduces a Bayesian SUR model for high-dimensional QTL analysis, efficiently handling correlated metabolite data and genetic variants.

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

The paper examines the effects of CETP and HMGCR genetic inhibition on lipoprotein profiles and CHD risk, finding distinct impacts on cholesterol and triglyceride compositions.

The paper identifies alpha-1 antitrypsin as a key contributor to GlycA's predictive power for disease risk and mortality across multiple conditions.

This study identifies 588 SNP-metabolite associations using the METSIM cohort, revealing pleiotropy, temporal changes, and age-related genetic control shifts.

Exome sequencing in Finnish populations identifies rare trait-associated alleles with higher frequencies and geographical clustering, revealing 30 novel associations with quantitative traits.

This study identifies genetic and environmental factors that disrupt trait correlations, showing environmental perturbations reduce correlations and genetic factors influence correlation patterns.

This paper introduces a reference-free method for generating polygenic risk scores (PRS) that avoids over-fitting and computational inefficiencies, and demonstrates applications in pleiotropy detection and mixed-model association testing.

The paper integrates transcriptomic, metabolomic, and genomic data to identify gene networks and their genetic regulation linked to immune function and circulating metabolites.