Evidence map›Paper›PMID 34013287›Full record

ArticlemedRxiv : the preprint server for health sciences2021

Insights into genetic factors contributing to variability in SARS-CoV-2 susceptibility and COVID-19 disease severity.

Matteo D'Antonio, COVID-19 Host Genetics Initiative, Timothy D Arthur, Jennifer P Nguyen, Hiroko Matsui, Agnieszka D'Antonio-Chronowska, Kelly A Frazer

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Matteo D'AntonioDepartment of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
COVID-19 Host Genetics Initiative
Timothy D ArthurBiomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA.
Jennifer P NguyenDepartment of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA.
Hiroko MatsuiInstitute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
Agnieszka D'Antonio-ChronowskaDepartment of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
Kelly A FrazerDepartment of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.

Funding

San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2012 to 2026
$9.7M
NLM NIH HHS T15 LM011271
6 · The paper itself

Abstract

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we applied colocalization to compare summary statistics for 16 GWASs from the COVID-19 Host Genetics Initiative to investigate similarities and differences in their genetic signals. We identified 9 loci associated with susceptibility (one with two independent GWAS signals; one with an ethnicity-specific signal), 14 associated with severity (one with two independent GWAS signals; two with ethnicity-specific signals) and one harboring two discrepant GWAS signals (one for susceptibility; one for severity). Utilizing colocalization we also identified 45 GTEx tissues that had eQTL(s) for 18 genes strongly associated with GWAS signals in eleven loci (1-4 genes per locus). Some of these genes showed tissue-specific altered expression and others showed altered expression in up to 41 different tissue types. Our study provides insights into the complex molecular mechanisms underlying inherited predispositions to COVID-19-disease phenotypes.

Identifiers

PMID34013287
PMCPMC8132261

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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 Socratic graph.