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Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes

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., A large empirical assessment of sequence-resolved structural variants from 14,891 genomes across diverse global populations in the Genome Aggregation Database (gnomAD) provides a reference map for disease-association studies, population genetics, and diagnostic screening.

A Correction to this paper has been published: https://doi.org/10.1038/s41586-020-03176-6.

SummaryGenetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes critical for an organism’s function will be depleted for such variants in natural populations, while non-essential genes will tolerate their accumulation. However, predicted loss-of-function (pLoF) variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here, we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence pLoF variants in this cohort after filtering for sequencing and annotation artifacts. Using an improved human mutation rate model, we classify human protein-coding genes along a spectrum representing tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve gene discovery power for both common and rare diseases.

Aberrant HGF-MET (hepatocyte growth factor-met proto-oncogene) signaling activation via interactions with surrounding stromal cells in tumor microenvironment has significant roles in malignant tumor progression. However, extracellular proteolytic regulation of HGF activation, which is influenced by the tumor microenvironment, and its consequential effects on melanoma malignancy remain uncharacterized. In this study, we identified SPINT2 (serine peptidase inhibitor Kunitz type 2), a proteolytic inhibitor of hepatocyte growth factor activator (HGFA), which has a significant role in the suppression of the HGF-MET pathway and malignant melanoma progression. SPINT2 expression is significantly lower in metastatic melanoma tissues compared with those in early-stage primary melanomas, which also corresponded with DNA methylation levels isolated from tissue samples. Treatment with the DNA-hypomethylating agent decitabine in cultured melanoma cells induced transcriptional reactivation of SPINT2, suggesting that this gene is epigenetically silenced in malignant melanomas. Furthermore, we show that ectopically expressed SPINT2 in melanoma cells inhibits the HGF–induced MET-AKT (v-Akt murine thymoma viral oncogene) signaling pathway and decreases malignant phenotype potential such as cell motility and invasive growth of melanoma cells. These results suggest that SPINT2 is associated with tumor-suppressive functions in melanoma by inhibiting an extracellular signal regulator of HGF, which is typically activated by tumor–stromal interactions. These findings indicate that epigenetic impairment of the tightly regulated cytokine-receptor communications in tumor microenvironment may contribute to malignant tumor progression.