Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada.
Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON M5T 3M7, Canada.
Hum Mol Genet. 2024 Jul 22;33(15):1315-1327. doi: 10.1093/hmg/ddae067.
Late-Onset Alzheimer's Disease (LOAD) is a heterogeneous neurodegenerative disorder with complex etiology and high heritability. Its multifactorial risk profile and large portions of unexplained heritability suggest the involvement of yet unidentified genetic risk factors. Here we describe the "whole person" genetic risk landscape of polygenic risk scores for 2218 traits in 2044 elderly individuals and test if novel eigen-PRSs derived from clustered subnetworks of single-trait PRSs can improve the prediction of LOAD diagnosis, rates of cognitive decline, and canonical LOAD neuropathology. Network analyses revealed distinct clusters of PRSs with clinical and biological interpretability. Novel eigen-PRSs (ePRS) from these clusters significantly improved LOAD-related phenotypes prediction over current state-of-the-art LOAD PRS models. Notably, an ePRS representing clusters of traits related to cholesterol levels was able to improve variance explained in a model of the brain-wide beta-amyloid burden by 1.7% (likelihood ratio test P = 9.02 × 10-7). All associations of ePRS with LOAD phenotypes were eliminated by the removal of APOE-proximal loci. However, our association analysis identified modules characterized by PRSs of high cholesterol and LOAD. We believe this is due to the influence of the APOE region from both PRSs. We found significantly higher mean SNP effects for LOAD in the intersecting APOE region SNPs. Combining genetic risk factors for vascular traits and dementia could improve current single-trait PRS models of LOAD, enhancing the use of PRS in risk stratification. Our results are catalogued for the scientific community, to aid in generating new hypotheses based on our maps of clustered PRSs and associations with LOAD-related phenotypes.
迟发性阿尔茨海默病（LOAD）是一种具有复杂病因和高遗传性的异质性神经退行性疾病。其多因素风险特征和大部分未被解释的遗传性表明存在尚未确定的遗传风险因素。在这里，我们描述了 2044 名老年人 2218 种特征的多基因风险评分的“整体”遗传风险图谱，并测试了从单特征 PRS 聚类子网络中得出的新特征 PRS 是否可以改善 LOAD 诊断、认知能力下降率和典型 LOAD 神经病理学的预测。网络分析揭示了具有临床和生物学可解释性的 PRS 聚类。这些聚类中的新特征 PRS（ePRS）显著提高了 LOAD 相关表型的预测，超过了当前最先进的 LOAD PRS 模型。值得注意的是，一个代表与胆固醇水平相关特征的 ePRS 能够提高大脑β-淀粉样蛋白负担模型中可解释的方差 1.7%（似然比检验 P=9.02×10-7）。通过去除 APOE 近端基因座，ePRS 与 LOAD 表型的所有关联都被消除。然而，我们的关联分析确定了以 PRS 为特征的模块，其具有高胆固醇和 LOAD。我们认为这是由于 APOE 区域对 PRS 的影响。我们发现，在重叠的 APOE 区域 SNP 中，LOAD 的 SNP 效应均值显著更高。将血管特征和痴呆的遗传风险因素结合起来，可以改善目前 LOAD 的单特征 PRS 模型，增强 PRS 在风险分层中的应用。我们的结果已被科学界收录，以便根据我们的聚类 PRS 图谱和与 LOAD 相关表型的关联生成新的假设。
