Affiliations: [a] Parabon NanoLabs, Inc., Reston, VA, USA
| [b] Department of Psychiatry, Washington University, St. Louis, MO, USA
| [c] Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University, St. Louis, MO, USA
| [d] Goizueta Alzheimer’s Disease Center, Emory University School of Medicine, Atlanta, GA, USA
| [e] Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| [f] Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
Correspondence:
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Correspondence to: Ellen McRae Greytak, Parabon NanoLabs, Inc., Reston, VA, USA. E-mail: ellen@parabon.com.
Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
Abstract: 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.
Keywords: Alzheimer’s disease, data mining, deep learning, epistasis, machine learning, predictive genetic testing
