Integrative Genomics Analysis Reveals a Novel 21q22.11 Locus Contributing to Susceptibility of COVID-19

Abstract

The systematic identification of host genetic risk factors is essential for the understanding and treatment of COVID-19. By performing a meta-analysis of two independent genome-wide association (GWAS) summary datasets (N = 680,128), a novel locus at 21q22.11 was identified to be associated with COVID-19 infection (rs9976829 in IFNAR2 and upstream of IL10RB, OR = 1.16, 95% CI = 1.09 - 1.23, P = 2.57*10-6). The rs9976829 represents a strong splicing quantitative trait locus (sQTL) for both IFNAR2 and IL10RB genes, especially in lung tissue (P = 1.8*10-24). Gene-based association analysis also found IFNAR2 was significantly associated with COVID-19 infection (P = 2.58*10-7). Integrative genomics analysis of combining GWAS with eQTL data showed the expression variations of IFNAR2 and IL10RB have prominent effects on COVID-19 in various types of tissues, especially in lung tissue. The majority of IFNAR2-expressing cells were dendritic cells (40%) and plasmacytoid dendritic cells (38.5%), and IL10RB-expressing cells were mainly nonclassical monocytes (29.6%). IFNAR2 and IL10RB are targeted by several interferons-related drugs. Together, our results uncover 21q22.11 as a novel susceptibility locus for COVID-19, in which individuals with G alleles of rs9976829 have a higher probability of COVID-19 susceptibility than those with non-G alleles.

Single Cell Sequencing Analysis Identifies Genetics-Modulated ORMDL3+ Cholangiocytes Having Higher Metabolic Effects On Primary Biliary Cholangitis

“…IFI16 is reported to be an innate immune sensor for intracellular DNA [37]. Pathway enrichment analysis revealed that there were 55 significant KEGG pathways enriched by these 71 down-regulated DEGs (FDR < 0.05, Figure 6e and Supplemental Table S15), of which several such as inflammatory bowel disease, rheumatoid arthritis, Th17 cell differentiation, cytokine-cytokine receptor interaction, and chemokine signaling pathway have been demonstrated to implicate in inflammatory-related diseases [11,38,39].…”

“…We referenced the method used in previous studies [11,67,68,72] to perform an in silico permutation analysis of 100,000 times of random selections (N Total) for validating the consistency of results from S-MultiXcan analysis (Geneset #1, N = 308, FDR ≤ 0.05) with other results from three distinct analyses: MAGMA analysis (Geneset #2: N = 563, FDR ≤ 0.05), S-PrediXcan on liver (Geneset #3: N = 76, FDR ≤ 0.05), and S-PrediXcan on blood (Geneset #4: N = 115, FDR ≤ 0.05).…”

“…Nevertheless, the GWAS approach has generally focused on examining the genetic associations of millions of single nucleotide polymorphisms (SNPs) and only a handful of SNPs with a genome-wide significance (P ≤ 5×10 -8 ) are reported. There exist many common SNPs with small marginal effects were neglected [11,12]. Moreover, the vast majority of reported SNPs were mapped within non-coding genomic regions [12].…”

“…It is plausible to infer that these noncoding SNPs may modulate the expression levels of corresponding risk genes rather than change the functions of their proteins. Thus, combination of GWAS summary statistics and other different types of data that characterize tissue-and cell-type-specific activity, including expression quantitative trait loci (eQTL) [11], DNase I-hypersensitive sites (DHS) [13], and histone marks [14], contributes to highlight disease-related risk genes and cell types.…”

Integrated multi-omics data analysis identifies a novel genetics-risk gene ofIRF4associated with prognosis of oral cavity cancer

“…To determine whether there exist prominently consistent results between genes from MAGMA analysis (N = 1,324, P < 0.05) and genes from S-MultiXcan analysis (N = 1,366, P < 0.05), we used a permutation method [11] to carry out a in silico permutation analysis with 100,000 times of random selections. In the first step, we calculated the overlapped genes between MAGMA and S-MultiXcan (N observation = 472 genes).…”