Thomas Rolland

The paper introduces a multiscale resource integrating genetics, brain imaging, and clinical data from autistic individuals and their relatives to explore genetic and neurobiological patterns in autism.

This study uses genome-wide meta-regression to analyze social behavior across different contexts, ages, and reporters, identifying six genetic loci and showing modest heritability.

The paper develops predictive models for intellectual disability in autistic children by integrating genetic variants and developmental milestones, achieving an AUC ROC of 0.65 and identifying 10% of ID cases.

The paper constructs a PPI network of 100 high-confidence ASD genes, identifies novel interactions, and uses AlphaFold-Multimer to prioritize variants for functional validation in organoids.

The paper maps the odds ratio for autism associated with rare LoF variants in autism-associated genes and shows sub-diagnostic effects in undiagnosed individuals.

This study identifies genetic correlates of phenotypic heterogeneity in autism by analyzing common and de novo variants across core features and co-occurring disabilities.

The paper examines how rare and common genomic variants affect brain functional connectivity, revealing how polygenicity and pleiotropy influence psychiatric conditions and brain networks.

The paper constructs a high-quality yeast binary interactome map and compares inner- and outer-complexome organization, finding the outer-complexome contains more interactions and exhibits higher functional heterogeneity.

The paper presents HuRI, a comprehensive human protein-protein interaction map, and demonstrates its utility in understanding cellular function and disease mechanisms.

This study identifies modifier genes in Phelan-McDermid syndrome patients by analyzing 22q13 rearrangements and their association with clinical features.