Imagine navigating through a maze where every turn represents a risk factor for Alzheimer’s disease (AD). Traditional Polygenic Risk Scores (PRS) have been struggling to accurately predict an individual’s likelihood of developing AD, especially for those with mild cognitive impairment (MCI). But fear not! A brilliant team of scientists has now developed adORS, an interpretable Alzheimer’s disease oligogenic risk score. By analyzing the genes that show strong associations with neuroimaging biomarkers, this new score offers improved accuracy in predicting AD risk and stratifying patients. Think of adORS as a treasure map, highlighting specific genetic clues like ATF6, EFCAB11, ING5, SIK3, and CD46. These clues have been linked to similar neurodegenerative diseases and supported by AD-related literature. With adORS, researchers can now unlock the individual genetic secrets behind AD and pave the way for personalized treatments. This breakthrough not only enhances the design of clinical trials and disease-modifying therapies but also provides hope for tailored interventions targeting high-risk individuals. So grab your microscope and delve into the fascinating realm of genetics to help crack the code of Alzheimer’s disease!

IntroductionStratification of Alzheimer’s disease (AD) patients into risk subgroups using Polygenic Risk Scores (PRS) presents novel opportunities for the development of clinical trials and disease-modifying therapies. However, the heterogeneous nature of AD continues to pose significant challenges for the clinical broadscale use of PRS. PRS remains unfit in demonstrating sufficient accuracy in risk prediction, particularly for individuals with mild cognitive impairment (MCI), and in allowing feasible interpretation of specific genes or SNPs contributing to disease risk. We propose adORS, a novel oligogenic risk score for AD, to better predict risk of disease by using an optimized list of relevant genetic risk factors.MethodsUsing whole genome sequencing data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort (n  =  1,545), we selected 20 genes that exhibited the strongest correlations with FDG-PET and AV45-PET, recognized neuroimaging biomarkers that detect functional brain changes in AD. This subset of genes was incorporated into adORS to assess, in comparison to PRS, the prediction accuracy of CN vs. AD classification and MCI conversion prediction, risk stratification of the ADNI cohort, and interpretability of the genetic information included in the scores.ResultsadORS improved AUC scores over PRS in both CN vs. AD classification and MCI conversion prediction. The oligogenic model also refined risk-based stratification, even without the assistance of APOE, thus reflecting the true prevalence rate of the ADNI cohort compared to PRS. Interpretation analysis shows that genes included in adORS, such as ATF6, EFCAB11, ING5, SIK3, and CD46, have been observed in similar neurodegenerative disorders and/or are supported by AD-related literature.DiscussionCompared to conventional PRS, adORS may prove to be a more appropriate choice of differentiating patients into high or low genetic risk of AD in clinical studies or settings. Additionally, the ability to interpret specific genetic information allows the focus to be shifted from general relative risk based on a given population to the information that adORS can provide for a single individual, thus permitting the possibility of personalized treatments for AD.

Read Full Article (External Site)