Developing an oligogenic risk score for Alzheimer's disease (adORS). (20th December 2022)

• Record Type: Journal Article
• Authors: Lee, Garam; Suh, Erica H; Jung, Sang‐Hyuk; Wen, Zixuan; Shen, Li; Kim, Dokyoon; Initiative, Disease Neuroimaging
• Is Part Of: Alzheimer's & dementia. Volume 18(2022)Supplement 6
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.066011
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Polygenic risk score (PRS) has become a popular approach for estimating an individual's genetic risk for disease, recently including Alzheimer's disease. However, its limitations reveal the need to improve genetic modeling of the disease, i.e. pinpointing relevant genetic drivers for score calculation and improving interpretability and clinical utility. We propose a novel genetic risk score for Alzheimer's disease (adORS), which—compared to PRS—combines a smaller set of more informative risk variants using known biomarkers to better predict those with high or low risk of disease, as well as mild cognitive impairment (MCI) conversion to AD. Method: Genetic variants of 1, 704 patients (364 controls, 550 AD cases, 273 MCI converters [MCIc], and 517 MCI non‐converters [MCInc]) were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Participants were classified based on diagnosis at their last visit. Variants were transformed into a gene‐based burden to amplify the variance held by each variable, enabling smooth model estimation and gene‐level interpretation. The risk score was estimated based on genes exhibiting high correlations with well‐known biomarkers, FDG and AV45. PRS were generated with clumping and thresholding using the PLINK data analysis toolset. We compared the performances between adORS and PRS on two tasks: controls vs AD classification and MCI conversion prediction. Result: Top 20 highly correlated genes including known AD‐relevant genes such as TOMM40 and APOC1 were used to calculate the AD risk score. Overall, adORS demonstrated better prediction performance than PRS (Table 1). adORS also showed better distinguishability between cases and controls and predicted lower risk scores in controls and MCInc patients versus higher risk scores in AD cases and MCIc. Additionally, adORS is well‐calibrated compared to PRS (Figure 1). The number of AD and MICc patients increases smoothly as the risk score increases. Conclusion: We proposed a new method to calculate an AD‐specific genetic risk score. The main advantage of adORS is to use only significant AD‐relevant genes for calculation, yielding a score that is a better predictor of AD status and progression. Experiments demonstrated that adORS has enhanced performance of prediction accuracy, calibration, and interpretability over PRS.