About this Research Topic
With the advances of sequencing and experimental techniques, the molecular mechanisms of human Mendelian diseases have been more or less elucidated. However, there are also several complex diseases, such as Alzheimer's disease, heart disease, and immune diseases, where disease/pathology development involves the interaction of large numbers of biomolecules across multi-molecular levels including DNA, RNA, proteins, and methylation, as well as the impact of environmental and human lifestyle factors. The understanding of such diseases is one of the biggest challenges in modern biology and medical sciences. The progress in this field will shed light on complex disease pathology, prevention, prognosis, diagnosis, and treatment in a personalized manner.
In recent years, large amounts of data from human genome sequencing, metagenome sequencing, and information about the impact of environmental and lifestyle factors on complex diseases have been produced, collected, and stored in large scale databases such as the National Alzheimer's Coordinating Center (NACC) database and the database of Genotypes and Phenotypes (dbGaP). For example, the UK Biobank with 500,000 participants has genotyping data, electronic health records, lifestyle surveys, and a wide range of biomarkers measured from the blood and urine samples. The large amount of unstudied data poses a big challenge, as well as a great opportunity, to reveal the secrets behind complex diseases using machine learning, statistics, and bioinformatics tools along with validation through experimental work. In fact, many computational studies have already been performed within this research area; however, most are focused on disease-associated factors at a single-molecular level, such as genetic factors, epigenetic factors, environmental factors, and so on. A more systematic study on the interactions among these factors, alongside experimental validation, might present a comprehensive view on the disease pathogenicity and thus may hold the key to truly understanding complex diseases.
We welcome investigators to contribute Original Research as well as Review articles on computational methods, experimental validations, and clinical applications in studying associations between complex diseases and various biomarkers and environmental factors. Potential topics include but are not limited to the following:
• Computational methods in inferring complex disease-associated biomolecules including DNA, RNA, protein, methylation, and so on, and experimental validation of key biomolecules. The areas include but are not limited to genome-wide association studies (GWAS), disease gene identification, disease associated network analysis and expression quantitative trait loci analysis, genetic and/or epigenetic fine-mapping for complex diseases, and new methods for rare variant burden test
• Computational methods in studying associations and interactions between complex diseases and metagenomics, and experimental validation of key disease-associated microbes
• Computational methods in Mendelian randomization for inferring the causal relationship between complex diseases and environmental or lifestyle factors, and experimental validation of the key identified parameters
• Identifying the regulation or interactions among complex disease-associated factors at different levels including biomolecules, metagenomics, environmental and lifestyle factors, etc.
• Computational methods in detangling the associations among multiple correlated complex diseases, and their experimental validation
• Computational methods in re-purposing drugs for complex diseases and experimental validation in cell lines, animal models, and clinical trials
• Integrating medical images and sequencing data for diagnosing and analyzing complex diseases
• Clinical applications of biomarkers, novel computational tools, and new drugs for complex diseases
Dr. Jialiang Yang is the Vice President of Genesis (Beijing) Co. Ltd. All other Topic Editors have no competing interests to declare in relation to this Research Topic.
Keywords: Complex Diseases, Genetic Factors, Environmental Factors, Association Study, Computational Models, Big-Data Mining