Kajsa Atterling Brolin

The paper identifies novel cross-trait associations using polytranscriptomic scores in Parkinson's disease and improves prediction models with multi-PTS integration.

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

This study identifies genetic variants associated with REM sleep behavior disorder in Parkinson's disease and explores genetic correlations with neuropsychiatric traits.

The paper analyzes the association between PGLYRP2 variants and Parkinson's disease across diverse ancestries and sexes, finding no significant sex-dependent effect for rs892145-T but a potential protective effect for rs959117-T in African American and Caribbean females.

This paper analyzes genetic variation in Parkinson's disease across multiple ancestries, revealing ancestry-specific patterns and highlighting the importance of diverse populations in genetic research.

The paper validates a mitochondrial polygenic score for Parkinson's disease across diverse populations, finding strong associations in Europeans and Ashkenazi Jews but not in other ancestries.

This paper compares transcriptomic profiles in En1-hemizygous mice to identify gene expression differences linked to susceptibility to Parkinson's-like neurodegeneration.

The paper describes a virtual hackathon event that brought together early career scientists to develop tools for Parkinson's disease research using open science principles.

The paper investigates RIC3 variants' association with Parkinson's Disease in multiple cohorts and finds no significant association.

This study identifies five RBD risk loci through genome-wide association and finds differential expression of SNCA-AS1 and SCARB2 in brain regions.

The paper presents a GWAS locus browser for Parkinson's disease that helps prioritize genes associated with risk variants from multiple GWAS studies.