Publications by authors named "Anastasia Illarionova"

Background: Until recently, about three-quarters of all monogenic Parkinson's disease (PD) studies were performed in European/White ancestry, thereby severely limiting our insights into genotype-phenotype relationships at a global scale.

Objective: To identify the multi-ancestry spectrum of monogenic PD.

Methods: The first systematic approach to embrace monogenic PD worldwide, The Michael J.

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Until recently, about three-quarters of all monogenic Parkinson's disease (PD) studies were performed in European/White ancestry, thereby severely limiting our insights into genotype-phenotype relationships at global scale. The first systematic approach to embrace monogenic PD worldwide, The Michael J. Fox Foundation Global Monogenic PD (MJFF GMPD) Project, contacted authors of publications reporting individuals carrying pathogenic variants in known PD-causing genes.

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The Monogenic Network of the Global Parkinson's Genetics Program (GP2) aims to create an efficient infrastructure to accelerate the identification of novel genetic causes of Parkinson's disease (PD) and to improve our understanding of already identified genetic causes, such as reduced penetrance and variable clinical expressivity of known disease-causing variants. We aim to perform short- and long-read whole-genome sequencing for up to 10,000 patients with parkinsonism. Important features of this project are global involvement and focusing on historically underrepresented populations.

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The Foundational Data Initiative for Parkinson Disease (FOUNDIN-PD) is an international collaboration producing fundamental resources for Parkinson disease (PD). FOUNDIN-PD generated a multi-layered molecular dataset in a cohort of induced pluripotent stem cell (iPSC) lines differentiated to dopaminergic (DA) neurons, a major affected cell type in PD. The lines were derived from the Parkinson's Progression Markers Initiative study, which included participants with PD carrying monogenic PD variants, variants with intermediate effects, and variants identified by genome-wide association studies and unaffected individuals.

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Article Synopsis
  • * A virtual 3-day hackathon brought together 49 early-career scientists from 12 countries, focusing on building tools and pipelines specifically for PD research, with each team working on one of nine distinct projects.
  • * The hackathon not only generated resources to enhance research but also provided training and networking opportunities, ultimately fostering creative problem-solving and collaboration essential for emerging researchers in data science.
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Article Synopsis
  • The study aimed to identify genetic risk factors for Parkinson disease (PD) by focusing on structural variants (SVs) rather than just single nucleotide variants, which only cover a small part of genetic variation.
  • Researchers conducted a large-scale genome-wide association study using data from 7,772 individuals and discovered SVs linked to PD risk in a sample of 2,585 cases and 2,779 controls.
  • They validated three novel SVs associated with PD, including a significant deletion within the gene LRRN4, marking this research as the most thorough analysis of SVs' role in PD risk to date.
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In October 2021, 59 scientists from 14 countries and 13 U.S. states collaborated virtually in the Third Annual Baylor College of Medicine & DNANexus Structural Variation hackathon.

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Background: Parkinson's disease (PD) is a neurodegenerative disease with an often complex component identifiable by genome-wide association studies. The most recent large-scale PD genome-wide association studies have identified more than 90 independent risk variants for PD risk and progression across more than 80 genomic regions. One major challenge in current genomics is the identification of the causal gene(s) and variant(s) at each genome-wide association study locus.

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