Human infection with SARS-CoV-2, the causative agent of COVID-19, leads to a remarkably diverse spectrum of outcomes, ranging from asymptomatic to fatal. Recent reports suggest that both clinical and genetic risk factors may contribute to COVID-19 susceptibility and severity. To investigate genetic risk factors, we collected over 500,000 COVID-19 survey responses between April and May 2020 with accompanying genetic data from the AncestryDNA database. We conducted sex-stratified and meta-analyzed genome-wide association studies (GWAS) for COVID-19 susceptibility (positive nasopharyngeal swab test, ncases=2,407) and severity (hospitalization, ncases=250). The severity GWAS replicated associations with severe COVID-19 near ABO and SLC6A20 (P<0.05). Furthermore, we identified three novel loci with P<5x10-8. The strongest association was near IVNS1ABP, a gene involved in influenza virus replication, and was associated only in males. The other two novel loci harbor genes with established roles in viral replication or immunity: SRRM1 and the immunoglobulin lambda locus. We thus present new evidence that host genetic variation likely contributes to COVID-19 outcomes and demonstrate the value of large-scale, self-reported data as a mechanism to rapidly address a health crisis.
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.
RNA molecules of all types fold into complex secondary and tertiary structures that are important for their function and regulation. Structural and catalytic RNAs such as ribosomal RNA (rRNA) and transfer RNA (tRNA) are central players in protein synthesis, and only function through their proper folding into intricate three-dimensional structures. Studies of messenger RNA (mRNA) regulation have also revealed that structural elements embedded within these RNA species are important for the proper regulation of their total level in the transcriptome. More recently, the discovery of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) has shed light on the importance of RNA structure to genome, transcriptome, and proteome regulation. Due to the relatively small number, high conservation, and importance of structural and catalytic RNAs to all life, much early work in RNA structure analysis mapped out a detailed view of these molecules. Computational and physical methods were used in concert with enzymatic and chemical structure probing to create high-resolution models of these fundamental biological molecules. However, the recent expansion in our knowledge of the importance of RNA structure to coding and regulatory RNAs has left the field in need of faster and scalable methods for high-throughput structural analysis. To address this, nuclease and chemical RNA structure probing methodologies have been adapted for genome-wide analysis. These methods have been deployed to globally characterize thousands of RNA structures in a single experiment. Here, we review these experimental methodologies for high-throughput RNA structure determination and discuss the insights gained from each approach.
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