Anders Martin Dale

NBDC Data Tools automate dataset preparation from ABCD and HBCD releases, enabling reproducible and reusable analysis workflows in R and Python.

The paper quantifies the contribution of structural variants to complex trait heritability using MiXeRSV, finding SVs explain up to 32% of heritability in some traits.

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.

The paper identifies shared genetic variants between major psychiatric disorders and retinal traits, revealing molecular mechanisms linking retinal morphology to neurodevelopmental and neurotransmission processes.

This study examines how BMI relates to brain microstructure across adolescence, finding nonlinear associations that intensify at higher BMI percentiles.

ABCDscores is an R package that computes summary scores for the ABCD Study dataset, offering a flexible, transparent, and reproducible solution for data analysis.

This study investigates genetic overlaps between psychiatric disorders, cardiometabolic diseases, and brain structure to understand shared and distinct genetic pathways contributing to comorbidity.

The paper introduces a 3D brain geometry toolkit for multisite neuroimaging analysis, showing subcortical shape measures improve diagnostic and treatment prediction compared to traditional volume features.

FEMA-Long enables efficient modeling of time-dependent effects in longitudinal data, discovering genetic variants with time-dependent effects on infant growth 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 paper develops a germline polygenic score (PRSagg) to predict prostate cancer aggressiveness and validates it across multiple datasets.

This paper improves polygenic prediction of height in an admixed Latino cohort by incorporating genetic ancestry, achieving a 0.1 increase in R² compared to using polygenic scores alone.

Bivariate GSA-MiXeR is a tool that models genetic correlations between two traits to identify genomic regions associated with psychiatric and neurodegenerative disorders using single-cell RNA sequencing data.

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 conducts a GWAS of placental efficiency in term infants, identifies genetic loci, and links PLE to monoaminergic pathways and placental tissue expression.

The paper introduces a scalable toolkit for 3D surface-based brain analysis, demonstrating its utility in mapping subcortical shape variations across 21 international samples.

The paper uses an extended mixed-effects model to analyze age-related changes in brain structure across a large adolescent cohort, finding distinct developmental patterns in cortical and subcortical regions.

This study identifies distinct genetic overlaps between Alzheimer's and Parkinson's disease and metabolic markers, revealing disease-specific pathways involving lipid metabolism and synaptic processes.

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

This study compares old and new local ancestry inference methods in Hispanic/Latino populations using HCHS/SOL data, finding high similarity but some regional discrepancies.

This study shows that a single ECT session causes transient, reversible brain microstructural changes resembling vasogenic edema, which may precede neuroplasticity and therapeutic effects.

The paper identifies extensive phenotypic and genetic links between systemic metabolism and brain morphology using UK Biobank data.

This paper introduces and validates Laminar Population Analysis (LPA), a method to decompose extracellular potentials in the mouse visual system by identifying contributions from different neural populations and pathways.

The paper develops a polygenic risk model for prostate cancer using VA data and validates it in external cohorts, showing its clinical utility in risk stratification.

The paper conducts GWAS on brain volumes, identifies genetic loci, and develops polygenic scores with cross-ancestry predictive ability and causal links to neuropsychiatric disorders.

This study identifies shared genetic loci between common epilepsies and general cognitive ability using LDSC, MiXeR, and conjFDR analyses.

Finemap-MiXeR improves genetic finemapping accuracy by using a variational Bayesian approach, outperforming existing methods in synthetic and real data.

The paper estimates genetic, environmental, and subject-specific variance components in cortical morphometry using longitudinal data from the ABCD Study.

The paper uses the ABCD Study to determine optimal replication sample sizes for reproducible brain-behavior associations using neuroimaging data.

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.