Guy Hindley

The paper quantifies how heritability is distributed across genomic regions for different polygenicity levels in complex traits, showing exons contribute less and intergenic regions more in highly polygenic traits.

This paper uses genetic data from psychiatric disorders to prioritize drug targets and compounds, identifying genetic support for existing medications and opportunities for drug repurposing.

This study analyzes GWAS data from nearly 1 million cases to show widespread genetic overlap between neurological and psychiatric disorders, with shared biological processes and divergent effect sizes.

The paper introduces trivariate MiXeR to quantify genetic overlap between three complex phenotypes using GWAS summary statistics, demonstrating its ability to detect complex patterns of genetic overlap.

MiXeR-Pred improves polygenic prediction by leveraging genetic overlap between complex phenotypes, outperforming primary GWAS and MTAG approaches.

This study identifies 114 novel genomic loci for anxiety and finds extensive genetic overlap with schizophrenia, bipolar disorder, major depression, and ADHD.

This study identifies 14 novel opioid use disorder loci and 7 shared loci with schizophrenia, bipolar disorder, and major depression using genome-wide association summary statistics.

The paper identifies abundant genetic pleiotropy across neuroimaging modalities using a multivariate genome-wide association study in two cohorts.

The paper introduces Meta-MOSTest, an extension of MOSTest for meta-analysis of multivariate GWAS, showing it maintains statistical power and type I error control across multiple cohorts.

This study uses a multivariate genetic approach to analyze personality and cognitive traits, identifying pleiotropy and improving polygenic prediction.

This study conducts a GWAS on MRI-derived body composition measures, identifying genetic loci and genes associated with body composition and cardiometabolic diseases, with liver fat showing strong genetic associations.

The paper uses a multivariate GWAS approach to identify 780 genetic loci associated with cortical morphology, showing a 10-fold increase in loci compared to univariate methods.