Evidence map›Paper›PMID 34001247›Full record

ArticleGenome medicine2021

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

Liuyang Wang, Thomas J Balmat, Alejandro L Antonia, Florica J Constantine, Ricardo Henao, Thomas W Burke, Andy Ingham, Micah T McClain, Ephraim L Tsalik, Emily R Ko and 6 more

Open access · goldAbstract read
In one paragraph

Article in Genome medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 5 pooled it
10.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

53 citing papers in PubMed, 5 syntheses or guidelines pooled it, 71 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors at 1 institution in 1 country.

Liuyang WangDepartment of Molecular Genetics and Microbiology, School of Medicine, Duke University, 0049 CARL Building Box 3053, 213 Research Drive, Durham, NC, 27710, USA.
Thomas J BalmatDuke Research Computing, Duke University, Durham, NC, 27710, USA.
Alejandro L AntoniaDepartment of Molecular Genetics and Microbiology, School of Medicine, Duke University, 0049 CARL Building Box 3053, 213 Research Drive, Durham, NC, 27710, USA.
Florica J ConstantineCenter for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27710, USA.
Ricardo HenaoCenter for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27710, USA.
Thomas W BurkeCenter for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27710, USA.
Andy InghamDuke Research Computing, Duke University, Durham, NC, 27710, USA.
Micah T McClainCenter for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27710, USA.
Ephraim L TsalikDepartment of Molecular Genetics and Microbiology, School of Medicine, Duke University, 0049 CARL Building Box 3053, 213 Research Drive, Durham, NC, 27710, USA.
Emily R KoCenter for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27710, USA.
Geoffrey S GinsburgCenter for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27710, USA.
Mark R DeLongDuke Research Computing, Duke University, Durham, NC, 27710, USA.
Xiling ShenDepartment of Biomedical Engineering, Woo Center for Big Data and Precision Health, Duke University, Durham, NC, 27710, USA.
Christopher W WoodsCenter for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27710, USA.
Elizabeth R HauserDuke Molecular Physiology Institute and Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, 27710, USA.
Dennis C KoDepartment of Molecular Genetics and Microbiology, School of Medicine, Duke University, 0049 CARL Building Box 3053, 213 Research Drive, Durham, NC, 27710, USA. dennis.ko@duke.edu.ORCID 0000-0002-0113-5981
Duke University · US

Funding

Antibacterial Resistance Leadership Group (ARLG)UM1AI104681 · NIAID · DUKE UNIVERSITY · PI Vance G. Fowler, Henry F HENRY CHAMBERS · 2013 to 2026
$180.8M
HUMAN GENETIC VARIATION REGULATING SALMONELLA HOST-PATHOGEN INTERACTIONS AND DISEASE SUSCEPTIBILITYR01AI118903 · NIAID · DUKE UNIVERSITY · PI KO, DENNIS CHUN-YONE · 2015 to 2024
$4.2M
AN ATLAS OF SHARED GENETIC ARCHITECTURE CONNECTING CELL BIOLOGY AND HUMAN DISEASER21AI133305 · NIAID · DUKE UNIVERSITY · PI KO, DENNIS CHUN-YONE, WANG, LIUYANG · 2018 to 2019
$440k
NIAID NIH HHS R01 AI118903NIAID NIH HHS UM1 AI104681
6 · The paper itself

Abstract

backgroundWhile 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.

resultsHere, 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)) 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 associated with both severe COVID-19 and other human traits demonstrated colocalization of the GWAS signal at the ABO locus with plasma protein levels of a reported receptor of SARS-CoV-2, CD209 (DC-SIGN). This finding points to a possible mechanism whereby glycosylation of CD209 by ABO may regulate COVID-19 disease severity.

conclusionsThus, 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. The iCPAGdb web portal is accessible at http://cpag.oit.duke.edu and the software code at https://github.com/tbalmat/iCPAGdb .

Indexed as

Databases, Nucleic AcidGenetic Predisposition to DiseaseLinkage DisequilibriumMultifactorial InheritancePolymorphism, Single NucleotideCOVID-19Genome-Wide Association StudyHumansSARS-CoV-2ColocalizationCross-phenotype associationGoutHi-HOSTIdiopathic pulmonary fibrosisLD-scoreMacular telangiectasiaPheWASPleiotropyrs12610495rs2869462rs505922

Identifiers

PMID34001247
PMCPMC8127495
OpenAlexW3162254480

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.