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The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders 1-4 , but attempts to assess constraint for non-protein-coding regions have proved more difficult. Here we aggregate, process and release a dataset of 76,156 human genomes from the Genome Aggregation Database (gnomAD)-the largest public open-access human genome allele frequency reference dataset-and use it to build a genomic constraint map for the whole genome (genomic non-coding constraint of haploinsufficient variation (Gnocchi)). We present a refined mutational model that incorporates local sequence context and regional genomic features to detect depletions of variation. As expected, the average constraint for protein-coding sequences is stronger than that for non-coding regions. Within the non-coding genome, constrained regions are enriched for known regulatory elements and variants that are implicated in complex human diseases and traits, facilitating the triangulation of biological annotation, disease association and natural selection to non-coding DNA analysis. More constrained regulatory elements tend to regulate more constrained protein-coding genes, which in turn suggests that non-coding constraint can aid the identification of constrained genes that are as yet unrecognized by current gene constraint metrics. We demonstrate that this genome-wide constraint map improves the identification and interpretation of functional human genetic variation., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variations in human disease has not been explored at scale. Exome-sequencing studies of population biobanks provide an opportunity to systematically evaluate the impact of rare coding variations across a wide range of phenotypes to discover genes and allelic series relevant to human health and disease. Here, we present results from systematic association analyses of 4,529 phenotypes using single-variant and gene tests of 394,841 individuals in the UK Biobank with exome-sequence data. We find that the discovery of genetic associations is tightly linked to frequency and is correlated with metrics of deleteriousness and natural selection. We highlight biological findings elucidated by these data and release the dataset as a public resource alongside the Genebass browser for rapidly exploring rare-variant association results., Competing Interests: K.J.K. is a consultant for Vor Biopharma. B.M.R.-G., X.Z., F.R., S.E., A.J.G., M.R., J.W., H.J., and J.W.D. are employees of AbbVie, Inc. M.R. is an employee of and owns stock in AbbVie, Inc. E.A.T., D.S., P.G.B., and H.R. are employees of Biogen and hold stocks/stock options in Biogen. H.I.K., X.C., X.H., and M.R.M. are employees of Pfizer. D.K. holds stock in the private company TriNetX, LLC. D.S.P. was an employee of Genomics plc. All the analyses reported in this paper were performed as part of D.S.P.’s previous employment at the Massachusetts General Hospital and Broad Institute. N.A.W. owns stock in Pfizer. L.D.G. receives funding from Intel and Illumina. D.G.M. is a founder with equity of Goldfinch Bio and serves as a paid advisor to GSK, Variant Bio, Insitro, and Foresite Labs. H.L.R. is a member of the scientific advisory board at Genome Medical. A.A.P. is a Venture Partner at GV. He has received consulting fees from Novartis and receives funding from Bayer, IBM, Microsoft, Alphabet, Intel, GSK, Pfizer, and Illumina. M.J.D. is a founder of Maze Therapeutics. B.M.N. is a member of the scientific advisory board at Deep Genomics and RBNC Therapeutics, a member of the scientific advisory committee at Milken, and a consultant for Camp4 Therapeutics, Merck, and Biogen., (© 2022 The Author(s).)

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 1 . 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 predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.