0c85ef2a7ae60b49c2ce4f3175a9f3a54cda412f kate Mon Nov 9 13:56:11 2020 -0800 Restore lost Description section, and remove section about multi-region. refs #26351 diff --git src/hg/makeDb/trackDb/human/covidMuts.html src/hg/makeDb/trackDb/human/covidMuts.html index 22cb9a3..fbf2275 100644 --- src/hg/makeDb/trackDb/human/covidMuts.html +++ src/hg/makeDb/trackDb/human/covidMuts.html @@ -1,119 +1,132 @@ -
+This track shows variants associated with monogenic congenital defects of immunity to the SARS-CoV-2 +virus identified by the COVID Human Genetic Effort. This international consortium aims +to detect rare or common monogenic inborn immunity errors (IEI) underlying severe forms of COVID-19 +in previously healthy individuals, as well as rare or common monogenic variations that make certain +individuals resistant to infection by SARS-CoV2 virus despite repeated exposure. +
-Unlike a regular genome browser track, the COVID-19 Immunity Variants track displays all -affected genes in multi-region view, -showing the variants within the coordinates of the gene, but hiding all the intergenic bases. To -return to the default Genome Browser view, click on the "-" button next to the "region" box. +The major feature of the small set of variants in this track is that they are functionally tested +to be deleterious and genetically tested to be disease-causing. Specifically, +rare variants were predicted to be loss-of-function (LOF) at 13 human loci known to govern +TLR3- and IRF7-dependent type I interferon (IFN) immunity to influenza virus in patients with +life-threatening COVID-19 pneumonia, relative to subjects with asymptomatic or benign infection. +These genetic defects display incomplete penetrance for influenza respiratory distress and only +manifested clinically upon infection with the more virulent SARS-CoV-2.
+ +Only eight genes and few variants are contained in this track. Use the links in the table to browse the gene of interest:
Gene Name | Human GRCh37/hg19 Assembly | Human GRCh38/hg38 Assembly |
---|---|---|
TLR3 | chr4:186990309-187006252 | chr4:186069152-186088069 |
IRF7 | chr11:612555-615999 | chr11:612591-615970 |
UNC93B1 | chr11:67758575-67771593 | chr11:67991100-68004097 |
TBK1 | chr12:64845840-64895899 | chr12:64452120-64502114 |
TICAM1 | chr19:4815936-4831754 | chr19:4815932-4831704 |
IRF3 | chr19:50162826-50169132 | chr19:49659570-49665875 |
IFNAR1 | chr21:34697214-34732128 | chr21:33324970-33359864 |
IFNAR2 | chr21:34602231-34636820 | chr21:33229974-33264525 |
Variant calls in 12 autosomal IFN-related genes from whole exome or genome data with a MAF lower than 0.001 (gnomAD v2.1.1) and experimental demonstration of loss-of-function (LOF) were considered for statistical analysis. The proportion of individuals carrying at least one pLOF variant was compared between cases and controls by means of logistic regression with the likelihood ratio test. The first three principal components of the PCA were included in the logistic regression model to account for ethnic heterogeneity of the cohorts. Analysis of enrichment in rare (MAF < 0.001) synonymous variants of the 12 genes was performed to check the calibration of the burden test. PCA was conducted with Plink v1.9 software on whole exome and genome sequencing data and 1000 Genomes (1kG) Project phase 3 public database as reference, using 27,480 exonic variants with a minor allele frequency >0.01 and a call rate >0.99. The odds ratio was also estimated by logistic regression and adjusted for ethnic heterogeneity.
The raw data can be explored interactively with the Table Browser, or the Data Integrator. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information.
Thanks to the COVID Human Genetic Effort contributors for making these data available, and in particular to Qian Zhang at the Rockefeller University for review and input during browser track development.
Zhang Q, Bastard P, Liu Z, Le Pen J, Moncada-Velez M, Chen J, Ogishi M, Sabli IKD, Hodeib S, Korol C et al. Inborn errors of type I IFN immunity in patients with life-threatening COVID-19. Science. 2020 Sep 24;. PMID: 32972995