We’re excited to announce the awardees for the Michael J. Fox Foundation Training (MJFF) and Integrated Learning Support Grants! Thank you to everyone who submitted an application – we received many thoughtful proposals on several important Parkinson’s disease-related research questions. If you weren’t selected this time, stay tuned for future funding opportunities supported by the Data Community of Practice.
Please read on to learn more about the researchers and projects selected!
2026 MJFF Training and Integrated Learning Support Grantees
Translating the Endolysosomal System for Therapeutics in Parkinson’s Disease (TEST-PD)
Investigators:
Dr. Prashanth Kukkle (Neurologist & Movement Disorders Specialist - Parkinson’s Disease & Movement Disorders Clinic)
Dr. Hari Prasad (Senior Research Scientist – India Institute of Science; @Charaiveti)
Investigating the Genetic Basis of Parkinson’s Disease in the South African Population Through Whole Genome and Mitochondrial DNA Analysis
Investigator: Kathryn Step (PhD Candidate in Human Molecular Genetics – Stellenbosch University; @kathrynstep)
Systems-level Transcriptomic Rewiring Across the Prodromal-to-Clinical Spectrum of Parkinson’s Disease
Investigator: Johan Largo González (PhD candidate in Biological Sciences – Pontificia Universidad Javeriana; @Johan)
Identifying BSN as a Novel Genetic Risk Factor for Parkinson’s Disease
Investigator: Ahamed P. Kaladiyil (PhD Candidate – School of Medicine, Western Sydney University; @AhamedPK)
A Multi-Modal Cross-Attention Transformer Architecture for Fusing Imaging, Genomic, and Multi-Omic Data to Stratify Parkinson’s Disease Patients
Investigator: Buddhiprabha Erabadda (Postdoctoral Researcher – Translational Neuroscience and Dementia Research Group, University of Oxford; @buddhi)
Please join us in congratulating the awardees of these five funded projects! Do you have ideas for a data reuse or analysis project in need of supplemental funding? Please feel free to reach out or share your ideas for future funding opportunities below.
First, I would like to sincerely thank the Michael J. Fox Foundation for this opportunity and for supporting early-career researchers. I am truly grateful to be part of this community.
My project focuses on understanding how the transcriptome is reorganized across the progression of Parkinson’s disease (PD), from prodromal stages to clinically established disease. Using RNA sequencing data from the Parkinson’s Progression Markers Initiative, I will combine differential expression analysis, co-expression network modeling, and interpretable machine learning to identify stage-dependent changes in gene regulation. The goal is to move beyond single-gene analyses and instead capture how gene networks are rewired as the disease progresses.
I expect to identify transcriptomic modules and key network features that change across disease stages, including potential regulatory hubs associated with early disease transitions. Additionally, interpretable machine learning approaches will help prioritize the most biologically relevant features distinguishing prodromal and clinical PD. The results will be prepared for publication and shared through open and reproducible workflows, and I look forward to sharing them with this community in the near future.
This project aims to provide a systems-level understanding of Parkinson’s disease progression, which may contribute to identifying early biomarkers. By focusing on transcriptomic rewiring, the study seeks to better capture the dynamic nature of the disease. I also hope this work contributes to strengthening the presence of Latin American researchers in global neurodegeneration research and promotes open science practices.
Finally, I am a PhD student in Biological Sciences at the Pontificia Universidad Javeriana (Bogotá, Colombia), with a background in bioinformatics. My research has primarily focused on mild cognitive impairment in the Colombian population, integrating multi-omics data to better understand neurodegenerative processes. This project represents my first step into Parkinson’s disease research, and I am excited to contribute to this field while engaging with the broader research community.
Thank you again for this opportunity. I look forward to connecting with and learning from this community.
We are truly grateful to be part of this vibrant community and want to express our sincere thanks for this wonderful opportunity. Below is a brief overview of our project, and we are excited to network and learn from all of you.
Title: Translating the Endolysosomal System for Therapeutics in Parkinson’s Disease (TEST-PD)
Brief Description of the Project:This project aims to address pathway burdens in Parkinson’s disease (PD) by focusing specifically on the underexplored role of the endolysosomal system. We will analyse genetic data from the Global Parkinson’s Genetics Program (GP2) for endolysosomal pathway genes—including known contributors such as GBA1, LRRK2, VPS35, and ATP13A2, as well as novel candidate genes—and compare these with data from under-represented South Asian/Indian PD patients. The goal is to identify how genetic variations contribute to PD pathogenesis and to explore their potential as therapeutic targets. Future studies will involve developing cellular models to study the functional consequences of these genetic alterations and to test potential interventions.
Anticipated Results/Outcomes:This project will generate a comprehensive variant profile comparing GP2 cohorts with South Asian/Indian PD patients, assessing whether endolysosomal genes such as GBA1 and LRRK2 exhibit similar or divergent effects across populations. It will also identify novel endolysosomal genes linked to PD and explore population-specific genetic contributions to disease risk. Successful completion will enable assessment of pathway burdens and pinpoint key mutations, offering insights into disease mechanisms and potential therapeutic targets within the endolysosomal pathway. These outcomes will inform future studies involving the development of models to study how identified genetic alterations impact cellular function, thereby guiding strategies to restore endolysosomal function and slow or halt PD progression .
Potential Impact of Research: This project has the potential to significantly advance our understanding of PD by identifying key genetic variants that impact the endolysosomal system. By comparing GP2 cohorts with South Asian/Indian patients, it will address critical gaps in PD genetics and genetic diversity. The identification of population-specific genetic risk factors will guide future targeted therapies. The research could also have broader implications for other neurodegenerative diseases, such as Alzheimer’s disease, in which endolysosomal dysfunction is a central feature. Ultimately, findings could lead to more precise and effective PD therapies, improving patient outcomes and quality of life.
I would like to express my sincere gratitude for this funding opportunity, which will support my transition from a PhD candidate to a postdoctoral researcher. This support is instrumental in enabling continuity in my research and the development of independent projects within the field of Parkinson’s disease genomics.
Brief description of project
This project investigates whether mitochondrial DNA (mtDNA) haplogroups are associated with Parkinson’s disease (PD) risk in ancestrally diverse South African populations. Using both NeuroBooster Array (NBA) genotyping data and whole genome sequencing (WGS) data, we will perform rigorous quality control, assess population structure, and extract mitochondrial variants for haplogroup assignment using HaploGrep. Haplogroup distributions will be compared between PD cases and controls using regression-based association analyses adjusted for demographic and ancestry-related covariates. Integrating NBA and WGS data enables improved variant resolution and more accurate haplogroup classification across diverse populations.
Anticipated results / outcomes
We expect to generate high-confidence mtDNA haplogroup assignments across all participants, along with comprehensive summaries of haplogroup frequencies stratified by ancestry and case-control status. The study will identify mtDNA haplogroups associated with PD risk, supported by effect estimates, confidence intervals, and multiple-testing-corrected significance values. Additional outcomes include ancestry-stratified and sensitivity analyses to assess robustness, as well as reproducible computational workflows for mtDNA analysis using both array and sequencing data.
Potential impact of research
This work will provide novel insights into the role of mitochondrial genetic variation in PD within underrepresented African populations. By incorporating both NBA and WGS data, the study enhances resolution and inclusivity in mitochondrial analyses, contributing to a more complete understanding of PD genetic architecture. The findings will support GP2’s goal of equitable global representation in PD genomics and may help inform future risk stratification, biological understanding, and targeted research in diverse populations.
I am currently in the final stages of my PhD thesis submission, and this project will serve as my first study as I transition into a postdoctoral position at Stellenbosch University, South Africa. While my PhD research has focused on association analyses and pathogenic variant screening, mitochondrial DNA has not yet been explored in this cohort. This presents a valuable opportunity to generate novel insights and expand the scope of our understanding of Parkinson’s disease genetics in our population. I look forward to seeing what results we obtain and how we can further refine and tailor our analytical pipelines for broader use within the research community.
Many thanks to Greg and everyone else behind the MJFF Training and Integrated Learning Support Grants for this great opportunity.
As it stands, patient stratification is a critical bottleneck in Parkinson’s disease (PD) clinical trials. While single-modality biomarkers exist, PD heterogeneity necessitates a multi-modal approach.
To address this, over the next year, I am planning to develop a deep learning framework to cluster PD patients using multiple modalities, initially focusing on four simultaneous modalities from the MJFF Parkinson’s Progression Markers Initiative (PPMI) dataset: Whole Genome Sequencing (WGS), proteomics, metabolomics, and structural imaging. The results will be validated in an independent MJFF dataset for robustness. Once the project is complete, all model weights, code, and documentation will be made available via GitHub.
By defining PD subtypes based on underlying molecular biology rather than clinical symptoms, this model will directly facilitate targeted patient stratification for future clinical trials, accelerating the shift towards personalised medicine in PD.
This is my first time working with the PPMI dataset, so I am very excited to learn from and share the findings with the MJFF community.
I am excited to announce and describe our project “Identifying BSN as a Novel Genetic Risk Factor for Parkinson’s Disease” that has been awarded the MJFF Training and Integrated Learning Support Grant. I am extremely grateful to the Micheal J Fox Foundation for their support. Also excited to be a part of this community of researchers. Do get in touch if you’re interested and would like to know more or collaborate.
Brief description of the project
Parkinson’s disease (PD) patients are found to be highly heterogenous in their clinical presentations. Our preliminary analysis found a link between genetic polymorphisms in the BSN gene with the occurrence of Freezing of gait and Shuffling gait features in PD subjects. We, therefore, hypothesize a strong link between the genetic composition of a subject and their clinical features.
We aim to validate our findings of BSN gene’s involvement in gait defects with bigger global datasets and potentially establish a stronger link between genetics and clinical features. Using statistical approaches, we will identify correlations and validate effects by accounting for confounding factors. We also aim to characterize rare and commonly found mutations in BSN as benign or pathogenic using in silico tools.
Anticipated results / outcomes
Overall, this project will link the data from whole genome sequencing with the presentation of different clinical features to explain the clinical heterogeneity found in PD subjects. This information would also help to predict the occurrence and potentially the severity of each of these clinical features. This would enable the identification of multiple other correlations between genetic composition and clinical features.
The mutations identified and characterised, primarily in the BSN gene, will pave the way for further molecular biology experiments that can help explain the pathological mechanisms involved in disease.
Potential impact of research
The information generated from this project will help predict the disease progression of patients at an early stage, ideally at diagnosis, including the disease course of either tumour dominant (TD) or Postural instability and gait dominant (PIGD). This will help take measures such as preventative treatments and personalised medicine for different clinical features like freezing of gait, postural instability, etc.
