Hirotaka Iwaki

The paper presents a GWAS locus browser for Parkinson's disease that helps prioritize genes associated with risk variants from multiple GWAS studies.

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

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

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.

This study simulates clinical trials to show genetic differences between trial arms can lead to false negatives and positives, especially in small samples, and suggests genetic balancing improves outcomes.

Across 37,688 Parkinson's cases, 18,618 proxy-cases and 1.4M controls, 90 risk signals map to 78 loci (37 new); an 1805-variant polygenic score explains 26-36% of heritability.

The paper identifies genetic modifiers of risk and age at onset in GBA-associated Parkinson disease and Lewy body dementia using large case-control datasets and genome-wide association studies.

This study conducts a GWAS to identify genetic determinants of Parkinson's disease age of onset, finding associations with SNCA, TMEM175, and GBA, and highlighting alpha-synuclein's role in disease onset.

This study identifies two genome-wide significant variants associated with Parkinson's disease progression and replicates known risk variants' effects on motor and cognitive decline.