Genome-Wide Association Studies-Based Machine Learning for Prediction of Age-Related Macular Degeneration Risk

Because age-related macular degeneration (AMD) is a progressive disorder and advanced AMD is currently hard to cure, an accurate and informative prediction of a person's AMD risk using genetic information is desirable for early diagnosis and potential individualized clinical management. The objective of this study was to develop and validate novel prediction models for AMD risk using large genome-wide association studies datasets with different machine learning approaches.

Genotype data from 32,215 Caucasian individuals with age of ≥50 years from the International AMD Genomics Consortium in dbGaP were used to establish and test prediction models for AMD risk. Four different machine learning approaches-neural network, lasso regression, support vector machine, and random forest-were implemented. A standard logistic regression model using a genetic risk score was also considered.

All machine learning-based methods achieved satisfactory performance for predicting advanced AMD cases (vs. normal controls) (area under the curve = 0.81-0.82, Brier score = 0.17-0.18 in a separate test dataset) and any stage AMD (vs. normal controls) (area under the curve = 0.78-0.79, Brier score = 0.18-0.20 in a separate test dataset). The prediction performance was further validated in an independent dataset of 783 subjects from UK Biobank (area under the curve = 0.67).

By applying multiple state-of-art machine learning approaches on large AMD genome-wide association studies datasets, the predictive models we established can provide an accurate estimation of an individual's AMD risk profile based on genetic information along with age. The online prediction interface is available at: https://yanq.shinyapps.io/no_vs_amd_NN/.

The accurate and individualized risk prediction model interface will greatly improve early diagnosis and enhance tailored clinical management of AMD.