As this was an opportunistic secondary use study, we did not recruit any participants.

Supplemental Figure 1 Method: All MS runs were compared and clustered using standard artMS ( https://github.com/biodavidjm/artMS ) procedures on observed feature intensities computed by MaxQuant. Supplemental Figure 1 shows all Pearson's pairwise correlations between MS runs, and are clustered according to similar correlation patterns. Supplemental Figure 2 Method: See main text. Supplemental Figure 3 Method: PFAM domain enrichment analysis. The enrichment of individual PFAM domains (or PFAM clans) 1 was calculated with a hypergeometric test where success is defined as number of domains, and the number of trials is the number of individual preys pulled-down with each viral bait. The population values were the numbers of individual PFAM domains and clans in the human proteome.To make sure that the p-values that signify enrichment were meaningful, we only considered PFAM domains that have been pulled-down at least three times with any SARS-CoV-2 protein, and which occur in the human proteome at least five times. In SI Figure 3 we show PFAM domains/clans with the lowest p-value for a given viral bait protein.

A Correction to this paper has been published: https://doi.org/10.1038/s41586-020-03174-8.

Structural variants (SVs) rearrange large segments of DNA1 and can have profound consequences in evolution and human disease2,3. As national biobanks, disease-association studies, and clinical genetic testing have grown increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD)4 have become integral in the interpretation of single-nucleotide variants (SNVs)5. However, there are no reference maps of SVs from high-coverage genome sequencing comparable to those for SNVs. Here we present a reference of sequence-resolved SVs constructed from 14,891 genomes across diverse global populations (54% non-European) in gnomAD. We discovered a rich and complex landscape of 433,371 SVs, from which we estimate that SVs are responsible for 25–29% of all rare protein-truncating events per genome. We found strong correlations between natural selection against damaging SNVs and rare SVs that disrupt or duplicate protein-coding sequence, which suggests that genes that are highly intolerant to loss-of-function are also sensitive to increased dosage6. We also uncovered modest selection against noncoding SVs in cis-regulatory elements, although selection against protein-truncating SVs was stronger than all noncoding effects. Finally, we identified very large (over one megabase), rare SVs in 3.9% of samples, and estimate that 0.13% of individuals may carry an SV that meets the existing criteria for clinically important incidental findings7. This SV resource is freely distributed via the gnomAD browser8 and will have broad utility in population genetics, disease-association studies, and diagnostic screening.