Predicting High-Risk Individuals for Common Diseases Using Multi-Omics and Epidemiological Data - Volume II

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Background

A key public health challenge is to identify individuals at high risk for common diseases in order to enable pre-screening or preventive therapies. Unlike Mendelian diseases, the pathogenesis of common diseases, which are caused by the interactions between multiple genetic and environmental factors, has not been elucidated. Therefore, identifying the risk factors that contribute to the substantial burden of common diseases and how to effectively identify high-risk incident cases from the general population are core goals of precision health.

Recently, polygenic risk scores have been proven to be superior in identifying high-risk individuals using genomic variants. Multi-omics data have been adopted to decipher the disease biological risk factors based on human genome sequencing, metagenome sequencing, single-cell sequencing, etc. However, the nonlinear interactions between the variants and the other types of information (such as transcriptomics, proteomics, microbiomics, lifestyles, lab test results, etc.) are underexplored due to the inherent complexity and the lack of robust, accurate and scalable methods. With the advances of high-throughput sequencing technologies, new computational algorithms and tailored analysis techniques promise to identify novel disease risk factors and ultimately lead to the development of clinically relevant biomarkers for disease prediction. Additionally, the epidemiological factors interacting with these genetic factors also influence the disease onset age and severity. Large public health cohorts such as UK Biobank provide an opportunity to build a more comprehensive and multi-view picture of disease risk factors. These unprecedented and comprehensive data pose an enormous practical challenge in developing computational algorithms for identifying individuals with a high risk for disease from the general population.

We welcome investigators to contribute Original Research as well as Review articles of high quality on both data analytics and computational methods to identify novel biological, epidemiological and/or composite risk factors. The novel risk factors must be rigorously experimentally validated using molecular or genetic validation.

Potential topics include but are not limited to the following:
• Identifying multiomics disease risk biomarkers
• Identifying key driver genes and their interaction network for common diseases
• Identifying metagenomic risk factors associated with common human diseases
• Identifying novel genetic and epidemiological disease risk factors
• Integrating medical images, electronic clinical records, and genotype data for disease early diagnosis, prognostication and prediction of treatment response
• Developing novel statistical methods for computing polygenic risk scores
• Developing novel machine learning models to analyse human genome sequencing, metagenomic sequencing and single-cell multiomic sequencing.

Please note that studies proposing novel biomarkers must include molecular or genetic validation.

Topic Editor Bailiang Li is a shareholder at Personalis Inc. The other Topic Editors declare no competing interests with regard to the Research Topic subject.

Keywords: complex diseases, genomic biomarkers, computational models, machine learning, disease prediction, polygenic risk scores, multiomics

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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