Mads E. Hauberg

The paper recalculates CNV penetrance for intellectual disability, showing lower estimates due to methodological differences, and recommends phenotype-specific risk reporting.

The paper identifies ZBTB7A as a key regulator of MDD-specific chromatin signatures and astrocyte-mediated stress vulnerability in the orbitofrontal cortex.

This study maps chromatin accessibility in Alzheimer's disease using ATAC-seq, identifying regulatory changes and 3D genomic alterations linked to disease.

This study identifies common genetic variants influencing intrinsic brain networks using rsfMRI data from 44,190 individuals, finding associations with cognitive and clinical traits.

The paper identifies acetylated chromatin domains associated with dysfunction in schizophrenia and bipolar disorder by analyzing histone modifications in prefrontal cortex samples.

This study identifies common genetic variants influencing white matter microstructure using dMRI data from 42,919 individuals, revealing genetic links to neuropsychiatric disorders and other traits.

This paper performs a large-scale eQTL meta-analysis across cerebral cortical and cerebellar brain regions, identifying distinct eQTL patterns and demonstrating improved power over existing studies.

The paper functionally annotates rare structural variants in the human brain by integrating SV calling with RNA-seq data to assess their regulatory effects and prioritize pathogenic deletions.

This study identifies five genome-wide significant loci and seven shared with other traits in a large ASD GWAS, highlighting polygenic heterogeneity and the value of large-scale GWAS in ASD.

The paper investigates polygenic associations between ADHD and literacy/language abilities, finding that genetic confounding with educational attainment masks ADHD-specific reading impairments.

Genome-wide association meta-analysis of 20,183 ADHD cases and 35,191 controls finds 12 significant loci; signal concentrates in conserved regions, constrained genes, and CNS regulatory elements.

This paper introduces a Bayesian framework to identify regulatory effects at GWAS risk loci by integrating GWAS summary data with molecular QTL data.