Correlations Between Phenotypes and Biological Process Ontologies in Monogenic Human Diseases
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ORIGINAL RESEARCH ARTICLE
Correlations Between Phenotypes and Biological Process Ontologies in Monogenic Human Diseases Chenfang Zhang1 · Georgi Z. Genchev1,2,3 · Dennis Bergau4 · Hui Lu1,2 Received: 8 October 2019 / Revised: 17 November 2019 / Accepted: 6 December 2019 © International Association of Scientists in the Interdisciplinary Areas 2020
Abstract A substantial body of research is focused to improve the understanding of the relationship between genotypes and phenotypes. Genotype–phenotype studies have shown promise in improving disease diagnosis in humans and identification of specific clinical phenotypes may be helpful in developing more effective therapeutic and diagnostic strategies. To expand on the existing paradigm of evaluating genotypes and phenotypes, we present an investigation of the correlation between biological processes as represented by genomic information and phenotypes in human disease. We focus on monogenic diseases and link biological process and phenotype utilizing information from the Online Mendelian Inheritance in Man, the Gene Ontology, and the Human Phenotype Ontology comprehensive genomic, phenotypic, and disease information resources. Our study uncovers 4661 statistically significant associations and identifies novel correlations between biological processes and phenotypes. We find new relationships between unique phenotype–genotype pairs related to cardiovascular diseases and hypertelorism, which suggests that differences between certain phenotype–genotype association may be the key to the divergence of corresponding phenotypes. Although the application of correlating genotype, phenotype, and biological processes may help to guide diagnosis and treatment of diseases, further investigation and more specific gene ontology descriptions are still required to elucidate mechanisms of action. Keywords Biological process · Gene ontology · Human phenotype ontology · Genotype–phenotype correlation
1 Introduction
* Hui Lu [email protected] Chenfang Zhang [email protected] Georgi Z. Genchev [email protected] Dennis Bergau [email protected] 1
Department of Bioinformatics and Biostatistics, SJTU‑Yale Joint Center for Biostatistics and Data Science, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
2
Center for Biomedical Informatics, Shanghai Children’s Hospital, Shanghai, China
3
Bulgarian Institute for Genomics and Precision Medicine, Sofia, Bulgaria
4
Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
Next-generation sequencing (NGS) technologies enable clinicians to access and evaluate genotypic information of monogenic disease patients with relative ease and in an expedient and cost-effective manner. In clinical centers, panel sequencing of genes known to be associated with certain disease are routinely performed and results are processed using bioinformatics pipelines [1]. At the same time, a wide array of research efforts is focused to better understand disease genotypes, as we
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