Hirotaka Iwaki

Repeat expansions in Parkinson's disease and parkinsonism across ancestries: insights from a global genetic cohort

This study identifies pathogenic and intermediate repeat expansions in Parkinson's disease and parkinsonism across 11 ancestries, highlighting ATXN2's role in PD risk and ancestry-specific patterns.

CARDBiomedBench: A Benchmark for Evaluating Large Language Model Performance in Biomedical Research

CARDBiomedBench evaluates LLM performance in biomedical research using a benchmark focused on neurodegenerative diseases, revealing significant limitations in response quality and safety.

TMEM175, SCARB2 and CTSB associations with Parkinson's disease risk across populations.

This study identifies genetic associations between lysosomal genes and Parkinson's disease risk across diverse populations using GWAS and whole genome sequencing data.

Long-read sequencing identifies FGF14 repeat expansions in Parkinson's disease

The paper identifies FGF14 GAA repeat expansions in Parkinson's disease using long-read sequencing and finds they occur in both patients and controls.

Large-scale genetic characterization of Parkinson's disease in the African and African admixed populations

This study identifies genetic variants associated with Parkinson's disease in African and African admixed populations, highlighting differences from European and Asian populations.

Does COMT Play a Role in Parkinson's Disease Susceptibility Across Diverse Ancestral Populations?

This study investigates the association between COMT genetic variants and Parkinson's disease risk across diverse ancestries but finds no significant enrichment of COMT risk alleles in PD patients after multiple testing correction.

The Global Landscape of Genetic Variation in Parkinson's disease: Multi-Ancestry Insights into Established Disease Genes and their Translational Relevance

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.

Plasma glucosylceramide levels are regulated by ATP10D and are not involved in Parkinson's disease pathogenesis.

The paper investigates the role of ATP10D and GlcCer in Parkinson's disease, finding ATP10D regulates plasma GlcCer levels but is not involved in PD pathogenesis.

The Neurodegenerative Disease Knowledge Portal: Propelling Discovery Through the Sharing of Neurodegenerative Disease Genomic Resources

The paper introduces the Neurodegenerative Disease Knowledge Portal (NDKP), an open-science platform aggregating genomic datasets for neurodegenerative diseases to enable collaborative analysis and discovery.

GenoTools: An Open-Source Python Package for Efficient Genotype Data Quality Control and Analysis

GenoTools provides a Python package for genotype data QC, ancestry estimation, and GWAS, enabling scalable and reproducible analysis of large genetic datasets.

Assessment of common genetic variation in Alzheimer's and Parkinson's diseases reveals global distinction in population attributable risk

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

NeuroBooster Array: A Genome-Wide Genotyping Platform to Study Neurological Disorders Across Diverse Populations

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.

Genome-wide determinants of mortality and motor progression in Parkinson's disease

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

omicSynth: an Open Multi-omic Community Resource for Identifying Druggable Targets across Neurodegenerative Diseases

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

Multi-ancestry genome-wide meta-analysis in Parkinson's disease

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.

Genome-wide Association Identifies Novel Etiological Insights Associated with Parkinsons Disease in African and African Admixed Populations

Intronic GBA1 variant rs3115534-G, common in African and African admixed ancestry but rare elsewhere, raises Parkinson's disease risk (OR 1.58) and lowers onset age.

Global Parkinson's Genetics Program (GP2) Complex Network Protocol: Defining the causes of sporadic Parkinson's disease

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.

Genetic meta-analysis of levodopa induced dyskinesia in Parkinson's disease

This paper identifies three novel genetic variants associated with levodopa-induced dyskinesia in Parkinson's disease and confirms associations with ANKK1 and BDNF, while also finding gender, age at onset, and anxiety as risk factors.

Genome-wide meta-analysis of CSF biomarkers in Alzheimer's disease and Parkinson's disease cohorts

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.

Genetic risk factor clustering within and across neurodegenerative diseases

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

The IPDGC/GP2 Hackathon - an open science event for training in data science, genomics, and collaboration using Parkinson's disease data

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.

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

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.

Large-scale Rare Variant Burden Testing in Parkinson's Disease Identifies Novel Associations with Genes Involved in Neuro-inflammation

Analysis of Y Chromosome Haplogroups in Parkinson's Disease

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.

Multi-Modality Machine Learning Predicting Parkinsons 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.

Application of Aligned-UMAP to longitudinal biomedical studies

This paper applies Aligned-UMAP to longitudinal biomedical datasets to reveal time-dependent patterns and provides best practices for its use.

Investigation of Autosomal Genetic Sex Differences in Parkinsons disease

This study investigates sex-specific genetic factors in Parkinson's disease using GWAS but finds no significant genetic differences between male and female cases.

The Parkinson's Disease DNA Variant Browser

The paper introduces a web browser for Parkinson's disease genomic data, aggregating results from 102,127 participants to aid in identifying risk genes and variants.

A population scale analysis of rare SNCA variation in the UK Biobank

The paper analyzes rare SNCA variation in the UK Biobank cohort, identifying copy number variants but no pathogenic missense variants or PD cases among carriers.

Genome-wide association studies of cognitive and motor progression in Parkinson's disease

This paper identifies genetic variants associated with cognitive and motor progression in Parkinson's disease using GWAS and longitudinal cohort data.