AI Article Synopsis
- The project is a partnership between the IHCC and DAC aimed at creating a genomic risk assessment tool for Alzheimer's disease that incorporates data from diverse ethnic backgrounds.
- The GIRA model combines genetic factors, like polygenic risk scores and specific gene haplotypes, with non-genetic data such as age and sex.
- Early findings show the GIRA model can effectively identify individuals at higher risk for Alzheimer's, with implications for future clinical use and research.
Article Abstract
Background: As a collaboration model between the International HundredK+ Cohorts Consortium (IHCC) and the Davos Alzheimer's Collaborative (DAC), our aim was to develop a trans-ethnic genomic informed risk assessment (GIRA) algorithm for Alzheimer's disease (AD).
Methods: The GIRA model was created to include polygenic risk score calculated from the AD genome-wide association study loci, the apolipoprotein E haplotypes, and non-genetic covariates including age, sex, and the first three principal components of population substructure.
Results: We validated the performance of the GIRA model in different populations. The proteomic study in the participant sites identified proteins related to female infertility and autoimmune thyroiditis and associated with the risk scores of AD.
Conclusions: As the initial effort by the IHCC to leverage existing large-scale datasets in a collaborative setting with DAC, we developed a trans-ethnic GIRA for AD with the potential of identifying individuals at high risk of developing AD for future clinical applications.
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Source |
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10854406 | PMC |
| http://dx.doi.org/10.1002/alz.13378 | DOI Listing |
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