Andrew Singleton

This study assesses genetic variation in Alzheimer's and Parkinson's diseases across ancestries, finding distinct population attributable risks for different loci.

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 paper combines two global monogenic Parkinson's disease initiatives to create a comprehensive cohort and infrastructure for studying multi-ancestry genetic factors and disease expression.

This paper reviews 123 neurodegenerative disease GWAS and finds 82% focus on European ancestry, with non-European studies identifying fewer risk loci.

This study identifies six novel genetic loci associated with Parkinson's disease mortality and motor progression using genome-wide survival analysis.

This study evaluates federated learning models for multi-omics Parkinson's disease prediction, finding they perform comparably to centrally trained models with minor performance differences.

The paper investigates the association between the non-coding GBA1 rs3115534 variant and REM sleep behavior disorder in Nigerians with Parkinson's disease.

The paper identifies druggable target genes for neurodegenerative diseases using Mendelian Randomization and provides a web platform for exploring therapeutic targets.

This paper conducts a multi-ancestry genome-wide meta-analysis of Parkinson's disease, identifying 78 independent loci and 23 genes associated with PD risk.

The paper outlines the protocol for integrating genetic and clinical data from a global cohort of over 150,000 participants to study the genetic causes of sporadic Parkinson's disease.

The paper examines MAPT haplotype frequencies in Nigerian Africans and finds no association between the H1 haplotype and Parkinson's disease risk or age at onset.

The paper identifies immune-related cell types and genetic loci associated with three neurodegenerative diseases using single cell RNA sequencing and genome-wide association studies.

The paper performs a genome-wide meta-analysis of CSF biomarkers in Alzheimer's and Parkinson's disease cohorts to identify genetic associations and disease-specific patterns.

This study conducts a multi-ancestry GWAS meta-analysis of Alzheimer's disease, identifies two novel loci, and assesses polygenic risk score generalizability across populations.

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.

This study investigates the genetic and genomic links between NBIA and PD, finding no strong association and suggesting they may be distinct conditions.

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 presents a multi-layered molecular dataset from iPSC-derived dopaminergic neurons to model Parkinson's disease genetic risk and provides a data browser for accessing these resources.

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.

The paper establishes a protocol for whole-genome sequencing to identify genetic causes of Parkinson's disease, focusing on monogenic variants and their clinical implications.

This study analyzes Y chromosome haplogroups and gene expression in Parkinson's disease but finds no strong associations, suggesting male prevalence may have other causes.

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

This study identifies 34 genome-wide significant short-tandem repeat (STR) loci associated with Parkinson's disease risk and shows their functional impact on gene expression and disease pathways.

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 identifies and replicates epistatic variant-variant interactions in Parkinson's disease across genetically diverse populations using a new pipeline called VARI3.

The paper shows that a non-coding genetic variant affects LRRK2 expression specifically through microglia, highlighting the importance of cell type in understanding non-coding variation's role in Parkinson's disease.

This paper develops an automated ML model to predict Parkinson's Disease using multi-modal data, achieving high AUC scores and constructing gene networks relevant to PD.

This study identifies pathogenic variants in dystonia-parkinsonism genes in 12.5% of SWEDD cases in the PPMI cohort, highlighting the importance of genetic testing in this patient group.