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Correlations Between Phenotypes and Biological Process Ontologies in Monogenic Human Diseases

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Interdisciplinary Sciences: Computational Life Sciences Aims and scope Submit manuscript

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.

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References

  1. van Tintelen JP, van der Zwaag PA (2018) Inherited cardiovascular conditions: phenotype-genotype data mining and sharing, and databases. In: Kumar D, Elliott P (eds) Cardiovascular genetics and genomics: principles and clinical practice. Springer, Cham, pp 869–877. https://doi.org/10.1007/978-3-319-66114-8_31

    Chapter  Google Scholar 

  2. Gene Ontology C (2015) Gene ontology consortium: going forward. Nucl Acids Res 43(Data issue):D1049–D1056. https://doi.org/10.1093/nar/gku1179

    Article  CAS  Google Scholar 

  3. Gkoutos GV, Schofield PN, Hoehndorf R (2012) Computational tools for comparative phenomics: the role and promise of ontologies. Mamm Genome 23(9–10):669–679. https://doi.org/10.1007/s00335-012-9404-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Robinson PN, Kohler S, Bauer S, Seelow D, Horn D, Mundlos S (2008) The human phenotype ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet 83(5):610–615. https://doi.org/10.1016/j.ajhg.2008.09.017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Khera AV, Kathiresan S (2017) Genetics of coronary artery disease: discovery, biology and clinical translation. Nat Rev Genet 18(6):331–344. https://doi.org/10.1038/nrg.2016.160

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Herrmann SM, Paul M (2002) Studying genotype–phenotype relationships: cardiovascular disease as an example. J Mol Med (Berl) 80(5):282–289. https://doi.org/10.1007/s00109-002-0330-x

    Article  CAS  Google Scholar 

  7. Daher M (2001) Overview of the world health report 2000 health systems: improving performance. J Med Liban 49(1):22–24

    CAS  PubMed  Google Scholar 

  8. Fong C, Ko DC, Wasnick M, Radey M, Miller SI, Brittnacher M (2010) GWAS analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis. Bioinformatics 26(4):560–564. https://doi.org/10.1093/bioinformatics/btp714

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. McPherson R, Tybjaerg-Hansen A (2016) Genetics of coronary artery disease. Circ Res 118(4):564–578. https://doi.org/10.1161/CIRCRESAHA.115.306566

    Article  CAS  PubMed  Google Scholar 

  10. Wu W, Lu CX, Wang YN, Liu F, Chen W, Liu YT et al (2015) Novel phenotype-genotype correlations of restrictive cardiomyopathy with myosin-binding protein C (MYBPC3) gene mutations tested by next-generation sequencing. J Am Heart Assoc. https://doi.org/10.1161/JAHA.115.001879

    Article  PubMed  PubMed Central  Google Scholar 

  11. Oechslin E, Jenni R (2018) Left ventricular noncompaction: from physiologic remodeling to noncompaction cardiomyopathy. J Am Coll Cardiol 71(7):723–726. https://doi.org/10.1016/j.jacc.2017.12.031

    Article  PubMed  Google Scholar 

  12. Teekakirikul P, Kelly MA, Rehm HL, Lakdawala NK, Funke BH (2013) Inherited cardiomyopathies: molecular genetics and clinical genetic testing in the postgenomic era. J Mol Diagn 15(2):158–170. https://doi.org/10.1016/j.jmoldx.2012.09.002

    Article  PubMed  Google Scholar 

  13. De Backer J, Campens L, Muino ML (2018) Looking for the missing links: challenges in the search for genotype-phenotype correlation in marfan syndrome. Circ Genom Precis Med 11(6):e002185. https://doi.org/10.1161/CIRCGEN.118.002185

    Article  PubMed  Google Scholar 

  14. Cirino AL, Harris S, Lakdawala NK, Michels M, Olivotto I, Day SM et al (2017) Role of genetic testing in inherited cardiovascular disease: a review. JAMA Cardiol 2(10):1153–1160. https://doi.org/10.1001/jamacardio.2017.2352

    Article  PubMed  Google Scholar 

  15. Li Y, Patra JC (2010) Genome-wide inferring gene–phenotype relationship by walking on the heterogeneous network. Bioinformatics 26(9):1219–1224. https://doi.org/10.1093/bioinformatics/btq108

    Article  CAS  PubMed  Google Scholar 

  16. Kohler S, Doelken SC, Mungall CJ, Bauer S, Firth HV, Bailleul-Forestier I et al (2014) The human phenotype ontology project: linking molecular biology and disease through phenotype data. Nucl Acids Res 42(Database issue):D966–D974. https://doi.org/10.1093/nar/gkt1026

    Article  CAS  PubMed  Google Scholar 

  17. Yu G, Wang LG, Han Y, He QY (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16(5):284–287. https://doi.org/10.1089/omi.2011.0118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504. https://doi.org/10.1101/gr.1239303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Schwartz PJ, Priori SG, Spazzolini C, Moss AJ, Vincent GM, Napolitano C et al (2001) Genotype–phenotype correlation in the long-QT syndrome: gene-specific triggers for life-threatening arrhythmias. Circulation 103(1):89–95. https://doi.org/10.1161/01.cir.103.1.89

    Article  CAS  PubMed  Google Scholar 

  20. Kayvanpour E, Sedaghat-Hamedani F, Amr A, Lai A, Haas J, Holzer DB et al (2017) Genotype–phenotype associations in dilated cardiomyopathy: meta-analysis on more than 8000 individuals. Clin Res Cardiol 106(2):127–139. https://doi.org/10.1007/s00392-016-1033-6

    Article  CAS  PubMed  Google Scholar 

  21. Evans DG (1991) Dominantly inherited microcephaly, hypotelorism and normal intelligence. Clin Genet 39(3):178–180. https://doi.org/10.1111/j.1399-0004.1991.tb03008.x

    Article  CAS  PubMed  Google Scholar 

  22. Tonni G, Ventura A, Centini G, De Felice C (2008) First trimester three-dimensional transvaginal imaging of alobar holoprosencephaly associated with proboscis and hypotelorism (ethmocephaly) in a 46, XX fetus. Congenit Anom (Kyoto) 48(1):51–55. https://doi.org/10.1111/j.1741-4520.2007.00171.x

    Article  Google Scholar 

  23. Davis C, Lauritzen CG (2009) Frontobasal suture distraction corrects hypotelorism in metopic synostosis. J Craniofac Surg 20(1):121–124. https://doi.org/10.1097/SCS.0b013e318190e25d

    Article  PubMed  Google Scholar 

  24. Kovats T, Tomcsanyi J (2009) Bradycardia and B-type natriuretic peptide. Int J Cardiol 135(2):238–239. https://doi.org/10.1016/j.ijcard.2008.03.086

    Article  PubMed  Google Scholar 

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Acknowledgements

This work is supported by National Key R&D Program of China 2018YFC0910500, Science and Technology Commission of Shanghai Municipality (STCSM) Grant No. 17DZ22512000, Foundation of Shanghai Municipal Commission of Health and Family Planning Grant No. 2018ZHYL0223, and Shanghai Jiaotong University School of Medicine No. TM201501, No.TM201623.

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Authors and Affiliations

  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

    Chenfang Zhang, Georgi Z. Genchev & Hui Lu

  2. Center for Biomedical Informatics, Shanghai Children’s Hospital, Shanghai, China

    Georgi Z. Genchev & Hui Lu

  3. Bulgarian Institute for Genomics and Precision Medicine, Sofia, Bulgaria

    Georgi Z. Genchev

  4. Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA

    Dennis Bergau

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  1. Chenfang Zhang
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  2. Georgi Z. Genchev
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  3. Dennis Bergau
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  4. Hui Lu
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Contributions

CZ, GG, DB, and HL designed the study. CZ collected and processed the data. CZ, GG, DB analyzed the results. CZ, GG, DB, and HL wrote and revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hui Lu.

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On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Zhang, C., Genchev, G.Z., Bergau, D. et al. Correlations Between Phenotypes and Biological Process Ontologies in Monogenic Human Diseases. Interdiscip Sci Comput Life Sci 12, 547–554 (2020). https://doi.org/10.1007/s12539-020-00400-9

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  • DOI: https://doi.org/10.1007/s12539-020-00400-9

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