Sonja W. Scholz

The paper integrates cell-specific gene expression and chromatin accessibility to identify neurodegenerative disease risk loci specific to microglia.

This study uses snRNA-seq to analyze age-related transcriptional changes in four brain regions across young and aged individuals, identifying region-specific age-associated genes and biological processes.

The paper introduces DRAGEN, a scalable mosaic variant caller that detects low VAF variants with high accuracy and efficiency, and presents a benchmark for low-VAF variants.

The paper investigates genetic interactions between SNCA and APOE in synucleinopathies using GWAS data and finds no significant epistatic interactions after multiple testing correction.

The paper shows that DNA methylation is reset in iPSCs compared to donor PBMCs, with unique methylation sites influenced by genetics in each cell type.

The paper identifies a protective variant CREB3R119G that reduces ALS risk and progression by modulating CREB3 activity in vulnerable neurons.

The paper compares single-cell whole genome amplification methods and reference genomes for detecting somatic copy number variants in human brain tissue.

The paper introduces the NeuroBooster Array, a genome-wide genotyping platform designed to study neurological disorders across diverse populations by capturing genetic variation and enabling imputation of disease-related variants.

This study identifies nine risk loci and two additional risk genes for restless legs syndrome through genome-wide association and transcriptome-wide analysis.

Sniffles2 improves SV detection accuracy and speed, enabling mosaic and population-level SV calling with applications in neurological disorders.

This study clusters patients with neurodegenerative diseases based on genetic risk variants, revealing overlapping genetic etiology and subsets with no common risk factors.

This study identifies a new susceptibility locus, PLA2G4C, for Multiple System Atrophy through a genome-wide association study and functional analysis of a risk variant.

The paper identifies local genetic correlations between neurodegenerative and neuropsychiatric diseases using LAVA, revealing shared genomic regions and potential therapeutic targets.

This paper develops a scalable protocol for Oxford Nanopore long-read sequencing, achieving high SNP and structural variant detection with haplotype-resolved methylation calls, suitable for large-scale genomics projects.

This study uses machine learning to identify Parkinson's disease subtypes and predict progression in two cohorts, finding three subtypes with distinct progression rates and high prediction accuracy.

This study identifies regional genetic correlations between neurodegenerative diseases and immune-related traits using sc-eQTLs and GWAS data, revealing immune-mediated risk loci.

The paper identifies and characterizes a reference iPSC line, KOLF2.1J, for standardized use in collaborative studies across multiple differentiation protocols and assays.

This paper performs a large meta-analysis of GWAS in dementia with Lewy bodies, identifying sex-specific genetic risk signals and highlighting the need for larger cohorts.

This study finds that blood mitochondrial DNA copy number is consistently associated with personality traits and mediates the link between personality and mortality in two genetic cohorts.

This study identifies 3,154 high-confidence structural variants in Parkinson's disease using short-read sequencing and validates some with long-read data, highlighting limitations of short-read methods.

The paper presents two methods to accurately detect GBA variants and reciprocal recombinants, showing higher GBA variant frequency in Lewy body dementia compared to Parkinson's disease.

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 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.

This paper uses machine learning to predict Alzheimer's disease progression rates and classify clinical subtypes based on early clinical data.

The paper identifies de-novo SPTLC1 mutations in juvenile ALS patients and suggests serine supplementation may alleviate symptoms.

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

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.

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.

This study shows that a polygenic risk score increases the likelihood of Parkinson's disease in LRRK2 p.G2019S carriers, with stronger effects in younger individuals.