Ian Bernard Hickie

This study identifies genetic and environmental factors associated with seasonal affective disorder and seasonality using a large cohort of individuals with depression.

This study identifies genetic variants associated with healthcare costs across multiple systems, finding polygenic influences and large effects from specific loss-of-function variants.

The paper infers circadian phase from blood biomarkers in UK Biobank using machine learning and defines a new construct, circadian acceleration, with genetic and environmental associations.

The paper identifies biologically distinct subtypes of MDD based on energy-related symptoms and finds genetic and metabolic differences between them.

This study examines how individual polygenic risk scores (PRS) rankings change across GWAS releases and their implications for risk stratification.

This paper identifies 58 genome-wide significant loci for major anxiety disorders using a large meta-analysis and highlights GABAergic signaling as a potential mechanism.

This study identifies 52 genetic variants associated with adolescent-onset depression through a large GWAS, revealing age-specific mechanisms and potential drug targets.

The paper identifies polygenic score differences between antidepressant responders and difficult-to-treat depression groups in the AGDS:Cell-o cohort, suggesting biological distinctions and potential for early stratification.

This study conducts a genome-wide association analysis of generalised anxiety symptom severity, identifying 80 significant variants and showing genetic correlations with other mental and physical health traits.

This study identifies genetic risk loci and factors underlying clinical subtypes of bipolar disorder using GWAS and meta-analysis.

This study identifies genetic risk factors for panic attacks and disorder through genome-wide meta-analysis and links them to multiple body systems and neuronal functions.

This study identifies lifestyle, early-life, and genetic factors associated with brain age gaps in individuals with major depressive disorder compared to controls.

This study performs a multi-ancestral GWAS of DSM-defined nicotine dependence, finding genetic correlations with other substance use disorders and health traits, and identifying a strong general addiction liability factor.

The paper evaluates factors affecting brain-age prediction models using structural neuroimaging data from 35,683 individuals, finding optimal model performance with no site harmonization, age-binning, and a sample size of over 1,600.

This study identifies genetic subtypes of major depressive symptoms by analyzing data from clinical and community cohorts, finding distinct factors for appetite/weight symptoms and missing data patterns.

The paper benchmarks and optimizes a normative modeling framework for brain morphometry across the lifespan using MFPR, showing excellent accuracy and stability.

The paper examines factors influencing consent to prescription record linkage in a depression genetics study, finding phenotypic and genetic associations with participation rates.

The paper analyzes cortical thickness trajectories in 17,075 individuals to show peak thickness in childhood, steep decline in early adulthood, and slower decline later, with notable exceptions and significant inter-individual variability.

The paper analyzes sex differences in brain structure variability across the lifespan using a large dataset, finding greater male variability in subcortical volumes, cortical surface area, and thickness in most regions.

This paper examines brain aging in major depressive disorder (MDD) using a large dataset, finding that MDD patients show increased brain age differences compared to controls.

This study identifies traits associated with polygenic risk for depression using UK Biobank data and finds causal links between depression risk and brain structure, as well as environmental interactions.

This study analyzes subcortical volume changes across the lifespan using MRI data from 18,605 individuals, finding age-related declines in some structures and increases in others.

The paper examines how genetic and environmental factors influence DNA methylation at variably methylated regions in neonatal blood, finding that combined effects best explain methylation variability.

This study investigates genetic associations between major depression and cardio-metabolic traits, finding shared genetic aetiology regardless of depression onset age.

The paper describes the recruitment and characteristics of a large cohort of individuals with depression, highlighting the study's effectiveness in gathering data for genetic and environmental risk factor analysis.

The paper improves age prediction from DNA methylation using larger datasets, showing reduced error and limitations in capturing biological ageing variation.

The paper applies polygenic risk scoring to a large family with bipolar and depressive disorders to study genetic risk patterns and anticipation.

Genome-wide association meta-analysis of 130,664 major depression cases and 330,470 controls identifies 44 independent risk loci enriched in cortical neurons, conserved mammalian regions, splicing networks, and antidepressant targets.