Personalized Risk Assessment for Alzheimer's Disease Using Genetic Scores
Author Information
Author(s): Eftychia Bellou, Woori Kim, Ganna Leonenko, Feifei Tao, Emily Simmonds, Ying Wu, Niklas Mattsson-Carlgren, Oskar Hansson, Michael W. Nagle, Valentina Escott-Price
Primary Institution: Cardiff University
Hypothesis
Can polygenic risk scores improve the prediction of Alzheimer's disease risk across different methodologies?
Conclusion
The study benchmarks the best strategies for deriving and modeling polygenic risk scores for predicting Alzheimer's disease.
Supporting Evidence
- The best prediction accuracy was achieved using two predictors: APOE and remaining PRS.
- Microglial PRS showed comparable accuracy to the whole genome.
- Discrepancies in risk scores were largely due to the GWAS statistics used.
Takeaway
Researchers are trying to figure out how to better predict who might get Alzheimer's disease by looking at their genes.
Methodology
The study compared prediction accuracy of polygenic risk scores in two cohorts using various modeling approaches and genetic data.
Potential Biases
Potential biases due to sample overlap and differences in GWAS summary statistics.
Limitations
The sample sizes are relatively small and may differ in case and control ascertainment methods.
Participant Demographics
Participants included 223 Alzheimer's disease cases and 345 healthy controls from ADNI123, and 170 Alzheimer's disease cases and 596 healthy controls from BioFINDER.
Statistical Information
P-Value
p<0.001
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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