Lloyd T. Elliott

The paper evaluates the impact of sample overlap in GWAS meta-analysis across different significance thresholds and shows minimal impact when overlap is small.

The paper identifies DNA methylation variability and differential methylation regions associated with aging-related health decline and constructs an epigenetic biomarker that outperforms methylation-only biomarkers in predicting mortality.

This paper identifies latent subgroups in functional brain organisation using large-scale resting-state fMRI data from UK Biobank participants, revealing associations with cognitive, lifestyle, and health phenotypes.

The paper introduces Q-FSNet and Q-DirichNet, sparse Bayesian neural networks for identifying physiological sweet spots in metabolomic data from the Canadian Longitudinal Study on Aging.

The paper develops a QSM pipeline to analyze brain magnetic susceptibility in UK Biobank data, finding associations with iron, disease, and genetic variants.

This paper improves population genetic data compression and GWAS computation using finite-state entropy algorithms, achieving 10-40% speed and size improvements over dictionary compression methods.

This paper introduces a method to estimate genetic similarity matrices using phylogenetic trees, showing consistency with genotype-based matrices and enabling analysis without genotype data.

The paper expands genetic associations with brain imaging phenotypes using updated UK Biobank data, finding more genetic variants and associations, including on the X chromosome.

The paper identifies 62 distinct modes of brain aging from multimodal imaging data, showing varied genetic and lifestyle associations.

This study identifies genetic loci specific to periventricular and deep white matter hyperintensities, revealing distinct genetic architectures and potential biological mechanisms.

The paper uses MRI data to show that white matter microstructure predicts functional connectivity between brain regions, with genetic variants linked to this relationship.

This study performs genome-wide association analyses on brain imaging phenotypes from UK Biobank, identifying 148 significant SNP-imaging associations with potential relevance to neurological and psychiatric disorders.

This paper introduces dynamic graphical models for directed functional connectivity and evaluates their performance using simulated and human fMRI data.

Quality-controlled genotypes for 488,377 UK Biobank participants at 805,426 markers, imputed to ~96 million variants, plus ancestry PCs, kinship (30.3% have close relatives enrolled) and HLA calls.