Markku Laakso

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 analyzes rare coding variants in smoking and drinking behavior using exome chip data, finding that they contribute to phenotypic variation but require larger samples for individual variant associations.

This study identifies 46 novel genetic loci associated with alcohol consumption and suggests shared genetic mechanisms with neuropsychiatric disorders using GWAS and functional analyses.

The paper quantifies the overlap between GWAS genes for 62 complex traits and Mendelian disorder genes, finding significant enrichment and elevated effect sizes for phenotypically-matched genes.

This study identifies rare genetic variants associated with type 2 diabetes using exome sequencing of 20,791 cases and 24,440 controls, and provides a Bayesian framework to improve variant prioritization.

The paper investigates the impact of rare protein truncating variants on 13 quantitative traits and 10 diseases using whole-exome sequencing data from 100,296 individuals, finding increased risk for several disorders and associations with other phenotypes.

This paper clusters T2D genetic loci using multi-trait associations to identify disease mechanisms and subtypes, finding distinct clinical outcomes linked to each cluster.

The paper identifies genetic-environment interactions in skeletal muscle gene expression using allele-specific expression and clinical traits as environmental proxies.

The paper identifies DNA methylation associations with metabolic syndrome traits in adipose tissue and develops a biomarker for type II diabetes risk.

This study fine-maps type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps, identifying 73 variants with >80% posterior probability of association.

The paper develops a method to handle missing data in genetic meta-analysis, identifying novel low-frequency variants in nicotine dependence genes with improved accuracy.

This study identifies heterozygous RFX6 protein truncating variants as a cause of MODY with reduced penetrance, using genetic analysis and cohort comparisons.

The paper develops methods to analyze multi-allelic variants in large sequence datasets, showing improved power and unbiased estimates in association tests.

The paper integrates gene expression and GWAS data to identify novel genes associated with obesity-related traits.