Computational resources associating diseases with genotypes, phenotypes and exposures

Abstract

The causes of a disease and its therapies are not only related to genotypes, but also associated with other factors, including phenotypes, environmental exposures, drugs and chemical molecules. Distinguishing disease-related factors from many neutral factors is critical as well as difficult. Over the past two decades, bioinformaticians have developed many computational resources to integrate the omics data and discover associations among these factors. However, researchers and clinicians are experiencing difficulties in choosing appropriate resources from hundreds of relevant databases and software tools. Here, in order to assist the researchers and clinicians, we systematically review the public computational resources of human diseases related to genotypes, phenotypes, environment factors, drugs and chemical exposures. We briefly describe the development history of these computational resources, followed by the details of the relevant databases and software tools. We finally conclude with a discussion of current challenges and future opportunities as well as prospects on this topic.

Keywords: database; disease phenotype; environmental exposure; genotype; software tool; web platform.

Figure 1

Development history of disease-related computational resources. The development of disease-related databases, software tools and web platforms, is depicted over the timeline. According to scopes and applications, the computational resources are classified into different groups.

Figure 2

Framework of a comprehensive web platform. A comprehensive web platform should integrate various disease-related information including genotypes, phenotypes, environmental factors, life styles and so on. The available information in the platform should be homogenously annotated by controlled vocabularies and community-driven ontologies, such as GenBank, dbSNP and miRbase for genotypes, HPO and DO for phenotypes, EFO and ChEBI for environmental factors and life styles, DrugBank and PubChem for drugs. Moreover, the platform should have solid scoring models to prioritize associations between different factors, such as genotype-phenotype associations (GPAs), environmental factor-phenotype associations (EFPAs), genotype-environmental factor-phenotype associations (GEFPAs), phenotype-treatment associations (PTAs), genotype-treatment associations (GTAs) and genotype-phenotype-treatment associations (GPTAs).