Peter Sørensen

This study uses genetic correlations to prioritize drug groups for repurposing in type 2 diabetes, identifying several drug classes with significant genetic relevance.

The paper introduces gact, an R package that integrates GWAS summary statistics with genomic resources to identify causal genes and pathways, demonstrating its application in coronary artery disease and type 2 diabetes.

This paper introduces mr.mash-rss, a method for polygenic prediction using GWAS summary statistics, showing improved accuracy over existing methods in various scenarios.

This paper evaluates Bayesian Linear Regression derived gene set tests, showing they outperform MAGMA in detecting genetic associations with complex diseases.

This study evaluates Bayesian Linear Regression models for gene set prioritization in complex diseases, showing improved performance in identifying T2D-related pathways using multi-trait analysis.

The paper expands the R package qgg with Bayesian Linear Regression models for genomic analysis of complex traits using individual data or GWAS summary statistics.

This paper evaluates Bayesian Linear Regression models with BayesC and BayesR priors as fine mapping tools, comparing them to existing methods and showing improved F1 scores and credible set properties.

This study performs a GWAS on medication-use data from 335,744 UK Biobank individuals, identifying 59 genetic loci and showing that medication-use is a highly polygenic trait with 18% heritability from common variants.

The paper introduces the qgg R package for large-scale quantitative genetic analyses, including genetic parameter estimation and predictive modeling of complex traits using human height data from the UK Biobank.