Samantha Hong

The paper generates a collection of 65 isogenic human stem cell lines with PD-associated mutations to study Parkinson's disease, revealing that most genetic variation comes from cell culture rather than genome editing.

The paper introduces a genome browser for Parkinson's disease genomic data, integrating WGS and CES samples with variant annotations and ancestry stratification.

CNV-Finder uses deep learning to detect copy number variations from array data, improving accuracy and reducing manual workload for downstream analyses.

The paper evaluates the NeuroBooster array's performance in genotyping pathogenic variants in Parkinson's disease across ancestries and validates their frequencies in diverse populations.

The paper uses long-read sequencing to identify structural variants and their impacts on gene expression and methylation in diverse human brain samples.

GenoTools provides a Python package for genotype data QC, ancestry estimation, and GWAS, enabling scalable and reproducible analysis of large genetic datasets.

Cluster Buster improves SNP genotyping quality by reducing no-calls and enhancing genotype accuracy for neurodegenerative disease research.