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

Published on July 27, 2023

Imagine building a puzzle that requires you to combine puzzle pieces from multiple sets. In the quest to better understand Alzheimer’s disease (AD), scientists have developed a joint genetic score called MetaGRS. They integrated genetic information from AD and 24 other traits, analyzing data from two independent cohorts. When compared to the traditional AD genetic risk score (GRS), MetaGRS showed a striking similarity in its ability to predict AD risk. A one standard deviation increase in MetaGRS resulted in a 57% increase in AD risk, just slightly less than the increase observed with the AD GRS alone. Furthermore, when they examined the impact of a specific gene variant called APOE e4, they found that MetaGRS did not provide a substantial improvement over the traditional model. However, the MetaGRS still outperformed a predictive model without any genetic information. This research showcases the potential of multi-trait genetic modeling to enhance our ability to predict Alzheimer’s disease. Want to dig deeper into the study? Check out the full article!

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.

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