Nelson Bautista Salazar

The paper identifies ancestry-specific genetic loci associated with autism spectrum disorder using admixture mapping across diverse populations.

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

The paper curates 222 genes associated with autism spectrum disorder (ASD) using the EAGLE framework, identifying 78 with definitive evidence and highlighting differential expression patterns between ASD and broader neurodevelopmental disorders.

The paper re-evaluates genome-wide significance thresholds for low-frequency variants in autism, identifying novel variants and their functional roles.

The paper evaluates the role of compound heterozygous deletion events in autism by analyzing 9,766 families and finds modest enrichment of rare DelCH events in autistic individuals.

EAGLE-AI automates gene-autism linkage evidence extraction and scoring with high accuracy, though challenges remain in handling unscreened papers and supplementary materials.

The paper identifies DDX53 genetic variants in individuals with ASD, showing a link between DDX53 and ASD, and highlights implications for genetic testing and mouse modeling.

This study performs an X chromosome-wide association study (XWAS) in ASD, identifying 59 variants associated with ASD, including novel candidate genes like FGF13.