Michael E. Weale

The paper introduces MystraColoc, a Bayesian algorithm for multi-trait colocalisation, showing improved performance in clustering genetic association signals across GWAS datasets.

This study evaluates how polygenic risk scores can optimize screening programs to reduce premature deaths by tailoring screening ages based on genetic risk.

The paper releases and evaluates polygenic risk scores for 53 diseases and traits in the UK Biobank and shows they outperform existing scores.

The paper shows that a significant portion of gene expression regulation in the human brain occurs post-transcriptionally in the cytoplasm, particularly for synaptic genes.

This paper develops an integrated risk tool combining polygenic risk scores with clinical risk factors to improve coronary artery disease prediction, showing better performance than existing tools.

This study identifies cell-type-specific expression of dystonia-associated genes and links them to shared genetic and biological mechanisms with psychiatric disorders.

This study shows that differences in GWAS signals across populations can be explained by factors like MAF and LD without needing true causal effect size differences.

The paper uses PheWAS across large cohorts to identify genetic associations with drug targets and predict drug safety and efficacy.

This study analyzes genetic data from Zoroastrians in Iran and India to explore population structure, gene flow, and selection, revealing patterns of endogamy, admixture timing, and disease-related selection.

The paper uses GWAS summary statistics to identify enrichment of heritability in immune system annotations for Alzheimer’s and Parkinson’s diseases.