About this Research Topic
A key public health need is to identify high-risk populations for a given disease to enable improved strategies for screening or prevention. Because multiple human complex diseases are heritable, one important strategy is to stratify individuals based on their genetic information. In other words, we may construct polygenic risk scores (PRSs), which sum the cumulative effect of genetic variants. Perhaps due to the increased sample size of available genome-wide association studies and the improved PRS methods, PRS has gained substantial interest in the human genetics community and has been applied to many complex diseases, such as breast cancer and coronary artery disease etc.
While promising, current PRS only explains a relatively small proportion of the heritability for most complex diseases. To further improve the prediction accuracy and explain the missing heritability, novel statistical methods including sophisticated machine learning approaches are urgently needed. These methods may either integrate external functional information or take account of the genetic architecture.
Furthermore, the utility of PRS has not been fully investigated in specific diseases. More work is needed to better assess potential of PRS, including their potential utility in clinical setting.
We welcome the submission of Original Research, Reviews, Mini-Reviews, Perspectives, and Opinions including, but are not limited to, the following topics:
1. Novel statistical and machine learning methods that improve the prediction accuracy of PRS.
2. PRS investigation for human complex diseases, especially for their potential utility in a clinical setting.
3. Review for the current research in PRS of human diseases.
Keywords: PRS, Polygenic risk score, variant, risk prediction, precision medicine, novel methods
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