Objective: We’re developing a scalable pipeline to infer local ancestry (LA) for individuals in the Global Parkinson’s Genetics Program (GP2). This approach aims to enhance genetic analysis across both admixed and non-admixed populations, improving the understanding of Parkinson’s disease (PD) risk by incorporating ancestry information at the chromosomal level. By leveraging local ancestry, we seek to uncover population-specific risk factors.

Background: PD is a complex, multifaceted neurodegenerative disorder with a genetic basis that is not yet fully understood. While both common and rare genetic variants contribute to PD, understanding the role of local ancestry, especially in admixed populations, offers a promising approach to uncovering genetic risk factors.

Method: Our methodological approach consists of several key phases: (1) Quality Control (QC): We collect genetic data including covariates such as sex, age, and status, followed by genotyping QC to ensure data integrity from GP2 release 9 (DOI: 10.5281/zenodo.14510099), (2) Phasing and Imputation: After QC, we phase and impute the data using the TOPMed imputation server, focusing on high-quality variants. (3) LA Panel Building: We conduct a supervised ADMIXTURE1 analysis to identify non-admixed samples to use as references for enhancing our local ancestry inference using Gnomix2. (4) Association Studies: We will perform three approaches to analyze genetic associations: Tractor3, Ancestry Treatment as Covariates (ATT), and admixture mapping (both case-only and case-control) using admix-kit4 and GENESIS5.

Results: Testing showed that expanding from a 3-way to a 5-way reference panel improved ancestry predictions, particularly in chromosomes 8, 19, and 22. Gnomix generated accurate ancestry predictions without rephasing. The most significant changes were in the ancestral classification of GBA1 gene variants, with population-attributable risks varying by local ancestry.

Conclusion:

Our pipeline provides a scalable method for accurately determining local ancestry, crucial for identifying population-specific genetic risk factors in PD. By improving ancestry predictions with tools like Gnomix and leveraging diverse reference panels, we can uncover how genetic risk varies across ancestral backgrounds.

References: 1. Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19, 1655–1664 (2009).
2. Website. doi: https://doi.org/10.1101/2021.09.19.460980.
3. Atkinson, E. G. et al. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat Genet 53, 195–204 (2021).
4. Hou, K. et al. Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations. bioRxiv (2023) doi:10.1101/2023.09.30.560263.
5. Gogarten, S. M. et al. Genetic association testing using the GENESIS R/Bioconductor package. Bioinformatics 35, 5346–5348 (2019).

« Back to 2025 International Congress

MDS Abstracts - https://www.mdsabstracts.org/abstract/assessing-individual-risk-through-determination-of-local-ancestry-insights-from-the-global-parkinsons-genetics-program/