Abstract
The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders1,2,3,4, 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.
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Data availability
The aggregated allele frequency dataset is available in a browser at https://gnomad.broadinstitute.org, with bulk downloads for VCF files and Hail tables, as well as all constraint statistics described in this manuscript. Additionally, we provide a subset of the dataset that includes individual-level data for the HGDP85 and 1000 Genomes projects86—the generation and use of this dataset is described in a companion manuscript75. There are no restrictions on the aggregate data released. External datasets used in this study are available in the following public resources: ENCODE cCREs, https://screen-v2.wenglab.org/; super enhancers, http://www.licpathway.net/sedb/download.php; FANTOM5 enhancers, https://fantom.gsc.riken.jp/5/datafiles/reprocessed/hg38_latest/extra/enhancer/; miRNA, https://genome.ucsc.edu/cgi-bin/hgTables (All GENCODE V32 track); FANTOM5 lncRNA, https://fantom.gsc.riken.jp/cat/v1/#/genes; GWAS Catalog, https://genome.ucsc.edu/cgi-bin/hgTables (GWAS Catalog track); GWAS fine-mapping, https://www.finucanelab.org/data; CNV morbidity map of DD, https://genome.ucsc.edu/cgi-bin/hgTables (Development Delay track); ClinVar, https://genome.ucsc.edu/cgi-bin/hgTables (ClinVar Variants track); TOPMed, https://bravo.sph.umich.edu/freeze8/hg38/downloads; ClinGen, https://genome.ucsc.edu/cgi-bin/hgTables (ClinGen track); MGI, https://www.informatics.jax.org/; OMIM, https://www.omim.org/; Roadmap Epigenomics Enhancer-Gene Linking, https://ernstlab.biolchem.ucla.edu/roadmaplinking/; GTEx https://gtexportal.org/home/datasets.
Code availability
All code to perform quality control of the resource is publicly available at https://github.com/broadinstitute/gnomad_qc, and many of the functions are documented in a Python package (gnomad) at https://broadinstitute.github.io/gnomad_methods/index.html. The code to compute the constraint statistics is available at https://github.com/atgu/gnomad_nc_constraint.
Change history
15 January 2024
A Correction to this paper has been published: https://doi.org/10.1038/s41586-024-07050-7
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Acknowledgements
The authors thank the individuals whose data is in gnomAD for their contributions to research. Development of the Genome Aggregation Database was supported by NIDDK U54DK105566 and the NHGRI of the National Institutes of Health under award number U24HG011450. Additional funding for Genome Aggregation Database Consortium members is listed in the Supplementary Information. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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S.C., L.C.F., J.K.G., Q.W., A.O.-L., H.L.R., M.J.D., B.M.N., D.G.M. and K.J.K. contributed to the writing of the manuscript and generation of figures. S.C., R.L.C., M.K. and K.J.K. contributed to the analysis of data. L.C.F., Q.W., C.V., L.D.G., T.P., C.S., M.E.T., B.M.N. and K.J.K. developed tools and methods. L.C.F., J.K.G., J.A., M.W.W., Y.T., W.P., M.T.Y., Z.K., Y.F., E.B., S.D., S.G., N.G., S.F., C.T., S.N., L.B., D.R., V.R.-R., M.C., C.L., N.P., G.W., T.J., R.M., K.T., A.R.M., G.T. and K.J.K. contributed to the production and quality control of the gnomAD dataset. N.A.W., R.G., M.S. and K.J.K. contributed to the gnomAD browser. All authors listed under The Genome Aggregation Database Consortium contributed to the generation of the primary data incorporated into the gnomAD resource. All authors reviewed the manuscript.
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K.J.K. is a consultant for Vor Biopharma, Tome Biosciences, and is on the Scientific Advisory Board of Nurture Genomics. D.G.M. is a paid advisor to GSK, Insitro, Variant Bio and Overtone Therapeutics, and has previously received research support from AbbVie, Astellas, Biogen, BioMarin, Eisai, Merck, Pfizer and Sanofi-Genzyme.
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Extended data figures and tables
Extended Data Fig. 1 Construction of mutational model and Gnocchi score.
a,b, Estimation of trinucleotide context-specific mutation rates. The proportion of possible variants observed for each substitution and context in 76,156 gnomAD genomes (y-axis) is exponentially correlated with the absolute mutation rate estimated from 1,000 downsampled genomes (x-axis). Fit lines were modeled separately for human autosomes (a) and chromosome X (b). c, Estimation of the effects of regional genomic features on mutation rates. The effects of 13 genomic features at four scales (window sizes 1kb-1Mb; x-axis) on the mutation rate of 32 trinucleotide contexts (y-axis) are shown, colored by the coefficient from regressing de novo mutations (DNMs) on each specific feature and window size. Red/Blue color indicates a positive/negative effect of increasing the feature value on mutation rates; grey crosses indicate significant features at the smallest possible window size after Bonferroni correction for 13×4 = 52 tests. Abbreviations: LCR=low-complexity region, SINE/LINE=short/long interspersed nuclear element, Dist=Distance, Recomb=Recombination, Methyl=Methylation. d,e, The distribution of Gnocchi score as a function of expected and observed variation. Each point represents the Gnocchi score of a 1kb window on the genome (N = 1,984,900 on autosomes (d) and N = 57,729 on chromosome X (e)), which quantifies the deviation of observed variation from expectation. A positive Gnocchi score (red) indicates depletion of variation (observed<expected) and the higher the score the stronger the depletion; the red dashed line indicates the 99th percentile of Gnocchi scores across the autosomes (d) or chromosome X (e).
Extended Data Fig. 2 Comparison of Gnocchi score between coding and non-coding regions.
a, The proportion of highly constrained windows (Gnocchi ≥ 4) as a function of the percentage of coding sequences in a window (left to right: N = 1,906/49,525, 3,244/55,676, 2,240/18,461, 1,506/7,094, 969/3,519, 569/1,946, 364/1,223, 283/910, 243/724, 10,392/30,138). The intervals (x-axis) are left exclusive and right inclusive. “Exonic only” refers to the 1kb windows created from directly concatenating coding exons into 1kb sequences. Error bars indicate standard errors of the proportions. b, The exonic-only regions (N = 27,875; purple) present a significantly higher Gnocchi score than regions that are exclusively non-coding (N = 1,843,559; blue). Dashed lines indicate the medians. c, The proportion of highly constrained windows (Gnocchi≥4) as a function of the proportion of exonic windows being added to the dataset of non-coding windows. d, Gnocchi score percentiles of non-coding versus exonic windows. About 0.05% (100-99.95%) and 3.12% (100-96.88%) of the non-coding windows exhibit similar constraint to the 90th and 50th of exonic regions, respectively.
Extended Data Fig. 3 Estimation of constraint for aggregated regulatory annotations.
a,b, Gnocchi scores of aggregated promoter (dark purple), enhancer (light purple), microRNA (miRNA; dark blue), and long non-coding RNA (lncRNA; light blue) annotations are compared against those of exonic (a) and non-coding (b) regions at a 1kb scale. The Gnocchi score percentiles of each annotation (y-axis) are benchmarked by the score deciles of exonic or non-coding regions (10–100 percentiles; x-axis); the grey dashed vertical line indicates the median (50th percentile).
Extended Data Fig. 4 Applications of Gnocchi for characterizing non-coding regions in addition to existing functional annotations.
a, Use of Gnocchi for prioritizing non-coding regions with or without a regulatory annotation (N = 464,504 and 1,379,055, respectively). Constrained non-coding regions are enriched for GWAS variants, independent of the candidate cis-regulatory element (cCRE) annotation from ENCODE. Error bars indicate 95% confidence intervals of the odds ratios. b, Use of Gnocchi in statistical fine-mapping. The increase in posterior inclusion probability (PIP) when incorporating Gnocchi score as a functional prior into previous fine-mapping results (that used a uniform prior; denoted as PIPGnocchi and PIPunif, respectively) is shown for 164 new likely causal associations with a PIPGnocchi ≥0.8 as a function of PIPGnocchi.
Extended Data Fig. 5 Comparison of Gnocchi and other predictive metrics in prioritizing non-coding variants.
a, Receiver operating characteristic (ROC) curves of Gnocchi and other seven metrics in classifying putative functional non-coding variants (“positive” variant set) – left to right: 9,229 GWAS Catalog variants, 2,191 GWAS fine-mapping variants, a subset of 140 high-confidence fine-mapped variants, and 1,026 likely pathogenic variants – against “negative” variant set randomly drew from the population with a similar allele frequency (AF). AF>5% and allele count (AC) = 1 were applied respectively for matching the three GWAS variant sets and the likely pathogenic variant set, based on their AF distributions in TOPMed (shown in b). b, AUCs of the classification with a varying AF threshold for the negative variant set. As most GWAS variants are common and most likely pathogenic variants are very rare (not seen in the population), AF>5% and AC = 1 were applied respectively in the primary analyses shown in a.
Extended Data Fig. 6 Comparison of constraint scores built from different mutational models and genomic windows.
Gnocchi (presented in this study) outperforms the scores rebuilt from mutational models that only consider local sequence context – trinucleotide (trimer-only) or heptanucleotide (heptamer-only) – without adjustment on mutation rate by regional genomic features, and the performance is robust to the artificial break of genomic windows when computed at a 1kb sliding by 100bp scale.
Extended Data Fig. 7 Pairwise correlations between different constraint/conservation metrics.
The Spearman’s rank correlation between each pair of the eight metrics was computed based on the mean value of each score on 1kb windows across the genome.
Extended Data Fig. 8 Power of constraint detection.
a,b, The sample size required for well-powered non-coding constraint detection. The percentage of non-coding regions powered to detect constraint (Gnocchi ≥ 4) at a 1kb (a) and 100bp (b) scale under varying levels of selection (depletion of variation) is shown as a function of log-scaled sample size. Lighter color indicates milder deletion of variation (weaker selection), which requires a larger sample size to detect constraint; the grey dashed vertical line indicates the current sample size of 76,156 genomes. Dotted curves (left to right) benchmark the 95th, 90th, and 50th percentile of depletion of variation observed in coding exons of similar size. The number of samples required to obtain an 80% detection power is labeled at corresponding benchmarks. c, AUCs of Gnocchi scores computed on different window sizes in identifying putative functional non-coding variants. 1kb (used in this study) presents the optimal window size with high performance while maintaining reasonable resolution. d, AUCs of Gnocchi scores computed from different subsets of gnomAD in identifying putative functional non-coding variants. While with an equal sample size, the downsampled dataset with diverse ancestries presents higher performance than the Non-Finnish European (NFE)-only dataset.
Supplementary information
Supplementary Information
This file provides detailed information about the aggregation, processing, and release of 76,156 human genomes from the Genome Aggregation Database (gnomAD), including Supplementary Figs. 1–8, Supplementary Tables 1–3, and descriptions of supplementary datasets.
Supplementary Datasets
This zipped file contains supplementary dataset items 1–6: see Supplementary Information for supplementary dataset guide.
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Chen, S., Francioli, L.C., Goodrich, J.K. et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625, 92–100 (2024). https://doi.org/10.1038/s41586-023-06045-0
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DOI: https://doi.org/10.1038/s41586-023-06045-0
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