Danny Antaki

This study identifies somatic copy number variants (sCNVs) in schizophrenia cases, revealing their contribution to risk and specific NRXN1 disruptions.

DeepMosaic detects mosaic single nucleotide variants without control samples using deep convolutional neural networks, achieving high accuracy and uncovering many variants missed by existing methods.

This study shows that autism spectrum disorder risk is influenced by rare variants, polygenic risk, and sex, with sex-specific effects on genetic liability and behavioral traits.

The paper evaluates brain somatic mosaicism in the adult human to identify rules governing cellular distribution during embryogenesis.

The paper identifies genetic variants responsible for gene expression differences between C57BL/6 and C57BL/10 mouse substrains.

This study uses cortical organoids derived from patients with 16p11.2 CNV to show altered neurodevelopmental processes, including changes in organoid size, neuronal maturation, and migration.

The paper analyzes sperm mosaic mutations in healthy males, finding that many are stable over time and pose a lifelong transmission risk to offspring.

The paper quantifies autism recurrence risk by detecting paternal sperm mosaicism through single molecule genotyping and whole genome sequencing.

This study identifies a large number of structural variants in human genomes using multiple sequencing technologies and presents them as a gold standard for future studies.

This study identifies rare paternally-inherited structural variants contributing to autism by disrupting noncoding regions and coding genes, with differential parental contributions.

SV2 is a machine-learning algorithm that improves structural variant genotyping and detects de novo mutations from paired-end sequencing data.

The paper identifies 1.2 million structural variants in 235 individuals, showing high de novo mutation diversity and complex mutation clusters, but finds no increased genome rearrangement rate for ASD.