The prediction of Alzheimer’s disease through multi-trait genetic modeling

Kaylyn Clark, Wei Fu, Chia Lun Liu, Pei Chuan Ho, Hui Wang, Wan Ping Lee, Shin Yi Chou, Li San Wang, Jung Ying Tzeng

Research output: Contribution to journalArticlepeer-review


To better capture the polygenic architecture of Alzheimer’s disease (AD), we developed a joint genetic score, MetaGRS. We incorporated genetic variants for AD and 24 other traits from two independent cohorts, NACC (n = 3,174, training set) and UPitt (n = 2,053, validation set). One standard deviation increase in the MetaGRS is associated with about 57% increase in the AD risk [hazard ratio (HR) = 1.577, p = 7.17 E-56], showing little difference from the HR for AD GRS alone (HR = 1.579, p = 1.20E-56), suggesting similar utility of both models. We also conducted APOE-stratified analyses to assess the role of the e4 allele on risk prediction. Similar to that of the combined model, our stratified results did not show a considerable improvement of the MetaGRS. Our study showed that the prediction power of the MetaGRS significantly outperformed that of the reference model without any genetic information, but was effectively equivalent to the prediction power of the AD GRS.

Original languageEnglish
Article number1168638
JournalFrontiers in Aging Neuroscience
Publication statusPublished - 2023

All Science Journal Classification (ASJC) codes

  • Ageing
  • Cognitive Neuroscience


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