A first update on mapping the human genetic architecture of COVID-19

Abstract

Data availabilitySummary statistics generated by COVID-19 Host Genetics Initiative are available online (https://www.covid19hg.org/results/r6/). The analyses described here use the freeze 6 data. The COVID-19 Host Genetics Initiative continues to regularly release new data freezes. Summary statistics for samples from individuals of non-European ancestry are not currently available owing to the small individual sample sizes of these groups, but the results for 23 loci lead variants are reported in Supplementary Table 3. Individual-level data can be requested directly from the authors of the contributing studies, listed in Supplementary Table 1.

Exome-wide association study to identify rare variants influencing COVID-19 outcomes: Results from the Host Genetics Initiative

“…The resulting score (either 0, 1, or 2) for each mask was then regressed on each of our three phenotypes using logistic regression, controlling for age, age^2, sex, sex*age, sex*age 2 , and 10 common variant (MAF > 1%) genetic principal components (the same covariates as for COVID-19 HGI GWASs [ 7 , 8 ]). Additionally, given that population genetic structure and its confounding effect on phenotypes is different at the rare variant level [ 26 ], we also used the first 20 genetic principal components from rare variants (MAF<1%) as covariates in all our analyses.…”

“…However, given that COVID-19 critical illness remains a rare phenomenon [ 51 ], our severe disease phenotype results are unlikely to be strongly affected by this. Finally, the use of population control is a long-established strategy in GWAS burden tests [ 7 , 8 , 11 , 22 , 52 ], and the statistical power gain from increasing our sample size is likely to have counter-balanced the misclassification bias.…”

“…Previous work on COVID-19 host genetics using genome-wide association studies (GWASs) revealed 23 statistically robust genetic loci associated with either COVID-19 severity or susceptibility [7][8][9][10][11]. Given that most GWASs use genetic data obtained from genome-wide genotyping followed by imputation to measure the association between a phenotype and genetic variation, their reliability and statistical power declines as a variant's frequency decreases, especially at allele frequencies of less than 1% [12].…”

MUC22, HLA-A, and HLA-DOB variants and COVID-19 in resilient super-agers from Brazil

“…Also, MUC22 is approximately 100 kb from CCHCR1 gene, the most important signal in the MHC region for Covid-19 susceptibility, according to the Covid-19 Host Genetics Consortium (Covid19hg - ). All CCHCR1 variants associated with Covid-19 ( 29 , 61 ) are intronic and were not captured by our genotyping method (whole-exome sequencing). The exomic CCHCR1 variants included in our survey do not correlate with Covid-19 severity.…”

Host Genetic Risk Factors Associated with COVID-19 Susceptibility and Severity in Vietnamese

“…Additionally, the most recent GWAS study on European, Latino, and African American patients revealed novel risk variants associated with COVID-19 severity, which were located in chromosomes 19p13.3, 12q24.13, and 21q22.1 [ 6 ]. Most recently, an updated GWAS study by COVID-19 Host Genetics Initiative additionally revealed 11 significant loci, which consisted of three genes in new loci ( SFTPD , MUC5B, and ACE2 ), compared with 13 loci previously described [ 8 ]. Scientific reports in Asian populations also discovered host genetic factors related to COVID-19 infection and progression such as ACE2 in Chinese patients [ 9 ], ACE2 and TMPRSS2 in Japanese patients [ 10 ], 5q32 and 12q22 in Thai patients [ 11 ], and HLA system in South Asian patients [ 12 ].…”