Donna M. Werling

The paper uses an AI-driven framework to model perturbations in brain organoids and identifies candidate genes for autism using genomic and clinical data.

This study shows that using a T2T-CHM13 recombination map and haplotype panel improves phasing and imputation accuracy compared to GRCh38.

CWAS-Plus identifies noncoding variant associations in whole-genome sequencing data using cell-type-specific functional data, finding links to autism and Alzheimer's disease.

This study introduces SUPERGNOVA, a framework for estimating local genetic correlations, and identifies 150 trait pairs with significant local genetic correlations, including autism spectrum disorder and cognitive performance.

The paper quantifies developmental changes in voltage-gated sodium channel exon expression in human and mouse neocortex, identifying key splicing transitions associated with neurological disorders.

The paper uses whole-genome and RNA sequencing from the human prefrontal cortex to identify genetic variants and transcriptomic changes associated with neurodevelopment and neuropsychiatric disorders.

This study examines the role of noncoding variation in autism spectrum disorder using whole genome sequencing of 2,076 genomes, finding no significant association after correction for multiple testing.

The paper discusses the role of whole genome sequencing in psychiatric disorders through the WGSPD consortium, aiming to assess its utility compared to other genotyping methods.