Matti Pirinen

This paper introduces a Bayesian framework for meta-analyzing GWAS across diverse phenotypes, showing increased power compared to frequentist methods.

The paper conducts GWAS and PheWAS on plasma lipid species to identify genetic associations and their links to diseases beyond traditional lipid measures.

The paper investigates the genetic contributions to intellectual disability, finding rare variants are more common in severe cases and common variants contribute to all severity levels.

This study uses haplotype sharing among Finns to reconstruct recent population history and identify regions with increased rare disease variant sharing.

biMM efficiently estimates genetic variances and covariances for cohorts with high-dimensional phenotype measurements using a bivariate linear mixed model.

The paper compares genome sequences from Finnish and British populations to identify genetic variants enriched in a population bottleneck, finding significant enrichment of rare variants in Finns.

metaCCA performs multivariate meta-analysis of genome-wide association studies using canonical correlation analysis, showing agreement with pooled individual-level data.

FINEMAP efficiently identifies causal variants in genome-wide association studies using a shotgun stochastic search algorithm, achieving accurate results faster than existing methods.