An atlas connecting shared genetic architecture of human diseases and molecular phenotypes provides insight into COVID-19 susceptibility.

Autor:	Wang L; Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA., Balmat TJ; Duke Research Computing, Duke University, Durham, NC 27710, USA., Antonia AL; Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA., Constantine FJ; Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC 27710, USA., Henao R; Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC 27710, USA., Burke TW; Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC 27710, USA., Ingham A; Duke Research Computing, Duke University, Durham, NC 27710, USA., McClain MT; Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC 27710, USA.; Durham Veterans Affairs Health Care System, Durham, NC 27705, USA.; Division of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA., Tsalik EL; Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA.; Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC 27710, USA.; Durham Veterans Affairs Health Care System, Durham, NC 27705, USA.; Division of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA., Ko ER; Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC 27710, USA.; Department of Hospital Medicine, Duke Regional Hospital, Durham, NC, 27705, USA., Ginsburg GS; Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC 27710, USA., DeLong MR; Duke Research Computing, Duke University, Durham, NC 27710, USA., Shen X; Department of Biomedical Engineering, Woo Center for Big Data and Precision Health, Duke University, Durham, NC 27710, USA., Woods CW; Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC 27710, USA.; Durham Veterans Affairs Health Care System, Durham, NC 27705, USA.; Division of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA., Hauser ER; Duke Molecular Physiology Institute and Department of Biostatistics and Bioinformatics, Duke University Medical Center Durham, NC 27710, USA.; Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC 27705, USA., Ko DC; Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27710, USA.; Division of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA.; Lead contact.
Jazyk:	angličtina
Zdroj:	MedRxiv : the preprint server for health sciences [medRxiv] 2020 Dec 22. Date of Electronic Publication: 2020 Dec 22.
DOI:	10.1101/2020.12.20.20248572
Abstrakt:	While genome-wide associations studies (GWAS) have successfully elucidated the genetic architecture of complex human traits and diseases, understanding mechanisms that lead from genetic variation to pathophysiology remains an important challenge. Methods are needed to systematically bridge this crucial gap to facilitate experimental testing of hypotheses and translation to clinical utility. Here, we leveraged cross-phenotype associations to identify traits with shared genetic architecture, using linkage disequilibrium (LD) information to accurately capture shared SNPs by proxy, and calculate significance of enrichment. This shared genetic architecture was examined across differing biological scales through incorporating data from catalogs of clinical, cellular, and molecular GWAS. We have created an interactive web database (interactive Cross-Phenotype Analysis of GWAS database (iCPAGdb); http://cpag.oit.duke.edu) to facilitate exploration and allow rapid analysis of user-uploaded GWAS summary statistics. This database revealed well-known relationships among phenotypes, as well as the generation of novel hypotheses to explain the pathophysiology of common diseases. Application of iCPAGdb to a recent GWAS of severe COVID-19 demonstrated unexpected overlap of GWAS signals between COVID-19 and human diseases, including with idiopathic pulmonary fibrosis driven by the DPP9 locus. Transcriptomics from peripheral blood of COVID-19 patients demonstrated that DPP9 was induced in SARS-CoV-2 compared to healthy controls or those with bacterial infection. Further investigation of cross-phenotype SNPs with severe COVID-19 demonstrated colocalization of the GWAS signal of the ABO locus with plasma protein levels of a reported receptor of SARS-CoV-2, CD209 (DC-SIGN), pointing to a possible mechanism whereby glycosylation of CD209 by ABO may regulate COVID-19 disease severity. Thus, connecting genetically related traits across phenotypic scales links human diseases to molecular and cellular measurements that can reveal mechanisms and lead to novel biomarkers and therapeutic approaches. Competing Interests: Competing interests The author(s) declare no competing interests.
Databáze:	MEDLINE
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