Pekka Jousilahti

The paper identifies pulmonary primary oxysterol and bile acid synthesis as a prognostic biomarker for pulmonary arterial hypertension, outperforming current clinical risk scores.

This study integrates polygenic risk scores and gut metagenomic data to improve prediction of common diseases, showing enhanced predictive performance over conventional risk factors.

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.

Greengenes2 aligns 16S rRNA and shotgun metagenomic data using a whole-genome phylogeny, showing agreement in principal coordinates, taxonomy, and phenotype effect size.

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

This study uses gut microbiome data and machine learning to improve liver disease risk prediction, showing that combining microbiome features with conventional risk factors enhances model performance.

In 5,959 Finns with genotypes and stool metagenomes, GWAS found 583 independent SNP-taxon associations; dairy intake modified LCT effects on Bifidobacterium, and ABO-linked Faecalicatena lactaris tracked secretor status.

This study identifies 118 genetic loci associated with 2,319 metabolite features and finds 46 causal effects of metabolites on common diseases using genome-wide association and Mendelian randomization.

This study shows common genetic variants significantly contribute to migraine family clustering, with higher burden in familial cases compared to population-based cases.

This study investigates the association between CpG methylation and educational attainment, finding limited significant associations that may be influenced by confounding factors.