Epistatic Features and Machine Learning Improve Alzheimer's Disease Risk Prediction Over Polygenic Risk Scores.

Autor:	Hermes S; Parabon NanoLabs, Inc., Reston, VA, USA., Cady J; Parabon NanoLabs, Inc., Reston, VA, USA., Armentrout S; Parabon NanoLabs, Inc., Reston, VA, USA., O'Connor J; Parabon NanoLabs, Inc., Reston, VA, USA., Holdaway SC; Parabon NanoLabs, Inc., Reston, VA, USA., Cruchaga C; Department of Psychiatry, Washington University, St. Louis, MO, USA.; Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University, St. Louis, MO, USA., Wingo T; Goizueta Alzheimer's Disease Center, Emory University School of Medicine, Atlanta, GA, USA.; Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA.; Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA., Greytak EM; Parabon NanoLabs, Inc., Reston, VA, USA.
Jazyk:	angličtina
Zdroj:	Journal of Alzheimer's disease : JAD [J Alzheimers Dis] 2024; Vol. 99 (4), pp. 1425-1440.
DOI:	10.3233/JAD-230236
Abstrakt:	Background: Polygenic risk scores (PRS) are linear combinations of genetic markers weighted by effect size that are commonly used to predict disease risk. For complex heritable diseases such as late-onset Alzheimer's disease (LOAD), PRS models fail to capture much of the heritability. Additionally, PRS models are highly dependent on the population structure of the data on which effect sizes are assessed and have poor generalizability to new data. Objective: The goal of this study is to construct a paragenic risk score that, in addition to single genetic marker data used in PRS, incorporates epistatic interaction features and machine learning methods to predict risk for LOAD. Methods: We construct a new state-of-the-art genetic model for risk of Alzheimer's disease. Our approach innovates over PRS models in two ways: First, by directly incorporating epistatic interactions between SNP loci using an evolutionary algorithm guided by shared pathway information; and second, by estimating risk via an ensemble of non-linear machine learning models rather than a single linear model. We compare the paragenic model to several PRS models from the literature trained on the same dataset. Results: The paragenic model is significantly more accurate than the PRS models under 10-fold cross-validation, obtaining an AUC of 83% and near-clinically significant matched sensitivity/specificity of 75%. It remains significantly more accurate when evaluated on an independent holdout dataset and maintains accuracy within APOE genotype strata. Conclusions: Paragenic models show potential for improving disease risk prediction for complex heritable diseases such as LOAD over PRS models.
Databáze:	MEDLINE
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