An interpretable Alzheimer’s disease oligogenic risk score informed by neuroimaging biomarkers improves risk prediction and stratification

Frontiers in Aging Neuroscience

Aging Neuroscience

Christos Davatzikos¹  Heng Huang²  Jingxuan Bao³  Erica H. Suh³  Sang-Hyuk Jung³  Zixuan Wen³  Garam Lee⁴  Dokyoon Kim⁵  Li Shen⁵  Kwangsik Nho⁶  Andrew J. Saykin⁶  Paul M. Thompson⁷ 

[1] Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States;Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States;Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States;Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States;Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Republic of Korea;Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States;Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States;Department of Radiology and Imaging Sciences, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States;Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States;

关键词: polygenic risk score;    Alzheimer’s disease;    mild cognitive impairment;    genetics;    predictive markers;   

DOI  :  10.3389/fnagi.2023.1281748

 received in 2023-08-23, accepted in 2023-10-06,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Suh, Lee, Jung, Wen, Bao, Nho, Huang, Davatzikos, Saykin, Thompson, Shen and Kim.