Sebastian Guelfi

Splicing accuracy varies across human introns, tissues and age

The paper examines mis-splicing across human introns, tissues, and age using RNA-sequencing data, finding correlations with spliceosome components and age-related neurodegenerative disease genes.

Unravelling cell type specific response to Parkinson's Disease at single cell resolution

The paper analyzes single-cell transcriptomics data from Parkinson's Disease patients and controls to identify cell type-specific molecular responses and pathways involved in neurodegeneration.

Systematic visualisation of molecular QTLs reveals variant mechanisms at GWAS loci

The paper visualises molecular QTLs to better understand genetic effects on gene expression and identifies causal genes at GWAS loci.

Single-nucleus co-expression networks of dopaminergic neurons support iron accumulation as a plausible explanation to their vulnerability in Parkinson's disease

The paper introduces scCoExpNets and CovCoExpNets to analyze cell-type specific gene co-expression networks from single-cell RNA-seq data, identifying iron accumulation as a factor in dopaminergic neuron vulnerability in Parkinson's disease.

Regulation of mitophagy by the NSL complex underlies genetic risk for Parkinson's disease at Chr16q11.2 and on the MAPT H1 allele

The paper identifies KAT8 and KANSL1 as regulators of PINK1-mitophagy linked to Parkinson's disease risk, suggesting a novel pathway for therapeutic targeting.

Analysis of nucleus and cytoplasm-specific RNA fractions demonstrates that a significant proportion of the genetic regulation of gene expression across the human brain occurs post-transcriptionally

The paper shows that a significant portion of gene expression regulation in the human brain occurs post-transcriptionally in the cytoplasm, particularly for synaptic genes.

Leveraging omic features with F3UTER enables identification of unannotated 3'UTRs for synaptic genes

The paper identifies 1,513 unannotated 3'UTRs across 39 human tissues using a machine learning framework, highlighting their enrichment in brain and synaptic function.

Human-lineage-specific genomic elements: relevance to neurodegenerative disease and APOE transcript usage

The paper identifies human-lineage-specific genomic elements, called CNCRs, and shows their enrichment in neurological disease genes and APOE intron-3 retention transcripts in Alzheimer's disease.

A Non-Synonymous SHARPIN Variant is Associated with Limbic Degeneration and Family History of Alzheimer’s Disease

The paper identifies a SHARPIN variant associated with limbic system atrophy and family history of Alzheimer’s disease using MRI and genome-wide data.

Transcriptomic analysis of dystonia-associated genes reveals functional convergence within specific cell types and shared neurobiology with psychiatric disorders

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

Incomplete annotation of disease-associated genes is limiting our understanding of Mendelian and complex neurogenetic 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.

Regulatory sites for known and novel splicing in human basal ganglia are enriched for disease-relevant information

The paper identifies disease-relevant regulatory loci in human basal ganglia using genomic and transcriptomic data from 117 brains, highlighting splicing eQTLs and ASE analyses as sources of cell-specific regulatory information.

ERASE: Extended Randomization for assessment of annotation enrichment in ASE datasets

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

Integration of eQTL and Parkinson’s disease GWAS data implicates 11 disease genes

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.

G2P: Using machine learning to understand and predict genes causing rare neurological disorders

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