Figure - available from: Frontiers in Medicine
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Graphs of AUC-ROC values measured for different machine learning methods. Each value at the X axis corresponds to a phenotype with a certain contribution of epistasis. For each AUC-ROC value, the boundaries of the 95% confidence interval are indicated. Each graph corresponds to a different dataset composition and feature-to-instance ratio. In all cases, 3-loci epistasis model with heritability of 0.25 was used.
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Background
Polygenic risk score (PRS) prediction is widely used to assess the risk of diagnosis and progression of many diseases. Routinely, the weights of individual SNPs are estimated by the linear regression model that assumes independent and linear contribution of each SNP to the phenotype. However, for complex multifactorial diseases such as A...
