Prediction of Polygenic Risk Score by Machine Learning and Deep Learning Methods in Genome-wide Association Studies

Abstract

Polygenic risk score (PRS) is a method that using multiple SNPs simultaneously and can be calculated as a typical disease risk score. It is useful method for precision and personalised medicine. Calculating PRS with the classical method, it is frequently used to use two different data sets which are training and testing sets. It is a disadvantage for the classical method. By using a single data set, machine learning (ML) and deep learning (DL) methods both avoid the problem of overfitting and can be used as a good alternative method. Genome-wide Association Studies (GWAS) data were generated with the PLINK Program by replicating a hundred times at different allele frequencies and different sample size. We applied two different ML algorithms which are Support Vector Machine (SVM) and Random Forest (RF) as well as DL approach. ML methods can obtain more consistent results in terms of case-control separation compared to PRS calculated with the classical method (PRS). The use of ML and DL methods as an alternative to classical methods to calculate PRS has been suggested.