Machine Learning SNP Based Prediction for Precision Medicine - Ho, Daniel Sik Wai; Schierding, William; Wake, Melissa; Saffery, Richard; O’Sullivan, Justin (Frontiers in Genetics, 2019)
In the past decade, precision genomics based medicine has emerged to provide tailored and effective healthcare for patients depending upon their genetic features. Genome Wide Association Studies have also identified population based risk genetic variants for common and complex diseases. In order to meet the full promise of precision medicine, research is attempting to leverage our increasing genomic understanding and further develop personalised medical healthcare through ever more accurate disease risk prediction models. Polygenic risk scoring and machine learning are two primary approaches for disease risk prediction. However, the results of polygenetic risk scoring remain limited due to the limitations of the approaches. By contrast, significant results have been obtained using machine learning algorithms to model complex disease risk. This increase in predictive abilities results from the ability of machine learning algorithms to handle multi-dimensional data. Here, we provide an overview of polygenetic risk scoring and machine learning in complex disease risk prediction. We highlight essential issues of recent machine learning application developments and describes how machine-learning approaches can lead to improved complex disease prediction, which helps to deliver future personalized healthcare according to the genetic features. Finally, we discuss how potential future research applications of machine learning prediction models might provide tissue-specific targets for customised and preventive early interventions in managing complex diseases.