An interpretable Alzheimer's disease oligogenic risk score informed by neuroimaging biomarkers improves risk prediction and stratification.
An interpretable Alzheimer's disease oligogenic risk score informed by neuroimaging biomarkers improves risk prediction and stratification.
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DOI：
10.3389/fnagi.2023.1281748
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发表时间：
2023
影响因子：
4.8
通讯作者：
中科院分区：
文献类型：
--
作者：
使用多基因风险评分（PRS）将阿尔茨海默病（AD）患者分层为风险亚组，为临床试验和疾病修饰疗法的开发提供了新的机会。然而，AD的异质性继续对PRS的临床广泛使用构成重大挑战。PRS仍然不适合证明风险预测的足够准确性，特别是对于轻度认知障碍（MCI）的个体，以及允许对导致疾病风险的特定基因或SNP进行可行的解释。我们提出了adORS，一种新的寡基因风险评分AD，以更好地预测疾病的风险，通过使用相关的遗传风险因素的优化列表。使用来自阿尔茨海默病神经影像学倡议（ADNI）队列（n = 1，545）的全基因组测序数据，我们选择了20个与FDG-PET和AV 45-PET表现出最强相关性的基因，这些基因被认为是检测AD功能性脑变化的神经影像学生物标志物。将该基因子集纳入adORS中，与PRS相比，评估CN与AD分类和MCI转换预测的预测准确性、ADNI队列的风险分层以及评分中包含的遗传信息的可解释性。在CN与AD分类和MCI转换预测中，adORS改善了AUC评分超过PRS。即使没有APOE的帮助，寡基因模型也改进了基于风险的分层，从而反映了ADNI队列与PRS相比的真实患病率。解释分析显示，adORS中包含的基因，如ATF 6、EFCAB 11、ING 5、SIK 3和CD 46，已在类似的神经退行性疾病中观察到和/或得到AD相关文献的支持。与传统的PRS相比，adORS可能在临床研究或环境中被证明是区分AD高或低遗传风险患者的更合适的选择。此外，解释特定遗传信息的能力允许焦点从基于给定人群的一般相对风险转移到adORS可以为单个个体提供的信息，从而允许AD的个性化治疗的可能性。
Stratification 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. Using 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. adORS 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. Compared 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.
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影响因子：
16.6
作者：
Arbore G;West EE;Rahman J;Le Friec G;Niyonzima N;Pirooznia M;Tunc I;Pavlidis P;Powell N;Li Y;Liu P;Servais A;Couzi L;Fremeaux-Bacchi V;Placais L;Ferraro A;Walsh PR;Kavanagh D;Afzali B;Lavender P;Lachmann HJ;Kemper C
通讯作者：
Kemper C
影响因子：
4.2
作者：
Guerreiro RJ;Gustafson DR;Hardy J
通讯作者：
Hardy J
预测阿尔茨海默病的纵向测量和层次分类框架
影响因子：
4.6
作者：
Huang M;Yang W;Feng Q;Chen W;Alzheimer’s Disease Neuroimaging Initiative
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative
影响因子：
4.7
作者：
Chen, Bei Jun;Mills, James D.;Janitz, Michael
通讯作者：
Janitz, Michael
影响因子：
2.2
作者：
Du, Yayun;Liu, Xiaoli;Wu, Xiaopan
通讯作者：
Wu, Xiaopan