Mina Ryten

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 identifies microglial activation as a factor contributing to cortical thinning in epilepsy by integrating neuroimaging and gene expression data.

This study uses transcriptomic profiling to identify common and distinct molecular features across Lewy Body diseases, highlighting RNA splicing dysregulation and potential therapeutic targets.

PhenoExam is an R package and web tool that performs phenotype and disease enrichment analysis on gene sets, detecting significant phenotypic similarities and differences across databases.

The paper identifies 38 genes regulating a-synuclein cell-to-cell transfer using an siRNA screen and shows genetic overlap with Parkinson's disease.

The paper identifies gene-specific mutational signatures caused by replication errors and endogenous DNA damage using CRISPR-Cas9 knockouts and whole-genome sequencing.

This paper introduces dasper, an R/Bioconductor package that detects pathogenic splicing events from RNA-sequencing data with higher accuracy than existing methods.

The paper introduces GMSCA, a tool to enhance gene multifunctionality predictions in bulk-tissue co-expression networks, revealing new roles for genes like SNCA in oligodendrocytes.

The paper identifies a genetic variant in OAS1 linked to increased Alzheimer's disease risk and severe COVID-19 outcomes, showing its role in microglial inflammation.

The paper examines mitochondrial-nuclear gene relationships in human brain tissue, finding cell-type specific variations and disruptions in Alzheimer’s disease.

This paper identifies genetic pathways and cell types involved in ALS using polygenic risk scores and Mendelian randomization, finding convergence on specific biological processes and cell types.

CoExp Web provides a web tool and software for analyzing and sharing co-expression network models.

The paper identifies pathway-specific polygenic risk factors and transcriptomic networks associated with Parkinson disease using large-scale genetic and expression data.

This study identifies cell-type-specific expression of dystonia-associated genes and links them to shared genetic and biological mechanisms with psychiatric disorders.

The paper identifies and validates novel transcription regions across tissues, improving gene annotation for 63% of OMIM-morbid genes and highlights tissue-specific and functionally significant variants.

The paper identifies common LRRK2 locus variation associated with survival in progressive supranuclear palsy, linking it to gene expression regulation and suggesting therapeutic implications for tauopathies.

The paper integrates gene essentiality data from human and mouse screens to classify genes into FUSIL categories, identifying developmental lethal genes associated with rare diseases.

The paper investigates the relationship between gene expression and brain atrophy in genetic frontotemporal dementia, finding correlations between atrophy patterns and specific gene sets related to vascular, glial, and neuronal functions.

ERASE assesses enrichment of GWAS risk loci in ASE data and identifies splicing-related ASEs associated with Parkinson's disease.

This paper integrates eQTL and Parkinson’s disease GWAS data to identify 11 disease genes and pathways involved in PD risk through colocalisation, TWAS, and network analysis.

The paper identifies 5 new Alzheimer's risk loci by comparing mouse amyloid microglial response with human GWAS gene-based SNPs.

This study identifies a genetic variant at the TRIM11 locus associated with Progressive Supranuclear Palsy phenotype through GWAS and validates it in independent cohorts.

The paper develops a machine learning model to predict genes causing rare neurological disorders and shows it accurately identifies disease-associated genes.

The paper assesses benign genetic variability in elderly individuals without neurodegenerative disease to aid in distinguishing pathogenic mutations.

The paper uses GWAS summary statistics to identify enrichment of heritability in immune system annotations for Alzheimer’s and Parkinson’s diseases.