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Abstract

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes critical for an organisms 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.

02 /Specification & Classification

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Claude Opus 5.5 · max effort · 8 Nov 2026 · from the full text of version 4 (8 Apr 2020) · CC BY 4.0

From 125,748 exomes and 15,708 genomes, 443,769 high-confidence predicted loss-of-function variants yield LOEUF, a continuous gene intolerance score tracking mouse lethality, cell essentiality, and disease associations.

Natural loss-of-function variants act as human gene knockouts but are rare and artifact-prone, and the earlier pLI (probability of loss-of-function intolerance) from the Exome Aggregation Consortium (ExAC) was dichotomous and underpowered for small genes. Using over twice ExAC's exome sample size, a methylation- and coverage-aware mutation model, and stringent annotation filtering, this work places protein-coding genes on a continuous, confidence-bounded intolerance scale.

Key results

  1. ShownIn a high-precision callset, LOFTEE preferentially removes common, artifact-enriched predicted loss-of-function (pLoF) calls yet spares rare and ClinVar-pathogenic ones, leaving 443,769 high-confidence pLoFs; selection holds methylated CpG stop-gained sites well below synonymous saturation.

    In two gold-standard samples, precision >99% (SNVs) and >98.5% (indels), recall >90% and >82%; de novo recall 97.3% (SNVs), 98% (indels) on 375 validated variants; 413,097 pLoFs on canonical transcripts of 16,694 genes; 1,815 genes (2,636 variants) curated as likely tolerant of biallelic loss; observed 52% of possible methylated CpG stop-gained vs 84.9% of synonymous variants. · A high-quality catalogue of variation; Identifying loss-of-function variants; Figure legends

  2. ShownA refined mutation model enables a continuous, confidence-bounded constraint score (LOEUF, 90% upper confidence bound of the observed/expected pLoF ratio) that supersedes dichotomous pLI and reveals constraint as a continuum, with most genes under at least moderate selection against pLoF variants.

    Synonymous counts fit by the model with r = 0.979; median expected pLoFs per gene 17.9 (ExAC 13.2); 72.1% of genes have >10 expected pLoFs (ExAC 62.8%); median o/e 48%; 19,197 genes binned in deciles of ~1,920; first-decile aggregate o/e ~6%; zero observed pLoFs gives LOEUF 1.34 at 2 expected vs 0.03 at 100 expected. · The LoF intolerance of human genes; Figure legends

  3. ShownLOEUF matches orthogonal data: haploinsufficient genes cluster at the constrained extreme, recessive genes sit mid-range and olfactory receptors are unconstrained, while constrained genes carry fewer rare exonic deletions and are more often essential in heterozygous mouse knockouts and human cells.

    6,735 rare exon-overlapping deletion structural variants from 6,749 genomes correlate with LOEUF (r = 0.13; p = 9.8 × 10^−68); 389 mouse heterozygous-lethal orthologs mean LOEUF 0.488 vs 0.962 for 18,808 others (p = 10^−78); 678 CRISPR-essential genes 0.63 vs 0.964 (p = 9 × 10^−71); 777 non-essential genes 1.34 vs 0.936 (p = 3 × 10^−92). · Validation of LoF intolerance score; Figure legends

  4. ShownDe novo pLoF excess in neurodevelopmental cases concentrates in the most constrained LOEUF decile, schizophrenia rare-variant burden is higher in constrained genes, and common-variant heritability enrichment increases with constraint, most strongly for schizophrenia and educational attainment.

    Intellectual disability/developmental disorder (ID/DD; 5,305 cases vs 2,179 controls): de novo pLoF rate 15-fold higher in the most constrained decile (Fig. 5a legend: ~11-fold), 2.9-fold in the second; autism (6,430 patients): 4.4-fold; LD Score regression on 658 traits: enrichment scales with LOEUF decile, robust to gene size, brain expression and baseline annotations. · Constraint informs disease etiologies; Figure legends

  5. ShownConstrained genes have more protein-interaction partners and broader, higher expression; within genes, constrained transcripts are more highly expressed; at matched sample size, African/African-American samples show lower LOEUF, in line with a larger effective population size.

    Protein-interaction degree vs LOEUF r = −0.14 (p = 1.7 × 10^−51, length-adjusted); breadth across 38 GTEx tissues r = −0.31 and expression level ρ = −0.28 (both p < 1 × 10^−100); 1,740 genes: constrained transcripts 6.34 TPM higher (p = 2.2 × 10^−14); 927 genes at n = 8,128: LOEUF 0.49 (African/African-American) vs 0.62 (European), p = 4.1 × 10^−14. · Biological properties of constraint; Figure legends

Methods, data and reproducibility

Genome Aggregation Database (gnomAD) exomes and genomes were joint-called (BWA-Picard-GATK) and random-forest filtered in Hail; LOFTEE filtered Variant Effect Predictor pLoFs; per-gene observed pLoF counts versus a methylation- and coverage-corrected mutation model gave LOEUF (90% upper bound of observed/expected), tested on orthogonal and disease data.

  • gnomAD 2.1.1 exomes (unrelated, QC-passing) from 199,558 aggregated, largely from adult-onset common-disease case-control studies; over 1.3 petabytes raw · human · n = 125,748 individuals
  • gnomAD 2.1.1 genomes (unrelated, QC-passing) from 20,314 aggregated; over 1.6 petabytes raw · human · n = 15,708 individuals
  • Callset truth sets: parent-offspring trios within the callsets, independently validated de novo variants, and two gold-standard samples (NA12878, synthetic diploid mixture) · human · n = 4,568 exome and 212 genome trios; 375 validated de novos
  • Rare autosomal exon-overlapping deletions (structural variants) called in a gnomAD whole-genome subset · human · n = 6,735 deletions in 6,749 individuals
  • Validation gene sets: curated haploinsufficient, recessive and olfactory receptor lists; mouse heterozygous-lethal orthologs; CRISPR-screen essential/non-essential human genes · human; mouse (knockout orthologs) · n = 389 mouse-lethal; 678 essential; 777 non-essential genes
  • GTEx gene- and transcript-level tissue expression, plus protein-protein interaction network degree · human · n = 38 tissues
  • Independent rare-variant cohorts: de novo variants in intellectual disability/developmental disorder (ID/DD) and autism, plus schizophrenia case-control burden data · human · n = 5,305 ID/DD cases, 2,179 controls; 6,430 autism patients
  • Common-variant GWAS results from UK Biobank and large consortia, used for heritability partitioning · human · n = 658 traits (276 independent)
Methods & tools
BWA-Picard-GATK joint variant-calling pipeline, Hail, Random forest variant filter, compared with GATK Variant Quality Score Recalibration (VQSR), Variant Effect Predictor (VEP), LOFTEE v1.0 (Loss-Of-Function Transcript Effect Estimator), Mutation rate model with methylation and coverage correction, yielding LOEUF, pLI (ExAC constraint metric, used as comparator), MAPS (mutability-adjusted proportion of singletons), PCA-HDBSCAN platform inference; UMAP ancestry projection, LD Score regression, ClinVar, OMIM and Pharos annotations, GERP scores (used to tune the END_TRUNC filter)
Software & environment
Docker image konradjk/gnomad_lof_paper:0.2 packages code and software to regenerate figures; processing and analysis in Hail; pLoF filtering with LOFTEE v1.0 on Variant Effect Predictor annotations; data release gnomAD 2.1.1.
Reproducibility gaps
Only aggregate variant frequencies and annotations are released (no restrictions); individual-level genotypes, phenotypes, and the external disease cohorts are not described as available, and clinical data exist for a small subset only.
Limitations (6)
  • LOFTEE's stringency drops some genuine pLoFs (e.g., terminal truncations that escape nonsense-mediated decay but delete C-terminal domains), annotation misses inactivating missense and regulatory variants, and uncharacterized decay-escape mechanisms may still yield false calls. · stated by the authors
  • Sampling remains far from saturation: only about half of possible methylated CpG stop-gained variants are observed, and resolving selection on each loss-of-function variant would require millions to hundreds of millions of people. · stated by the authors
  • Ancestral diversity is limited: Middle Eastern, central and southeast Asian, Oceanian and most African populations are nearly absent, and the smallest exome population (8,128 individuals) leaves per-gene population comparisons weakly powered. · stated by the authors
  • LOEUF mainly reflects selection against heterozygous loss, not homozygous intolerance; about 30% of coding genes stay underpowered owing to length, high scores mix tolerant and small genes, and genes with post-reproductive phenotypes can appear tolerant. · stated by the authors
  • The key ID/DD de novo enrichment is reported inconsistently (15-fold for the most constrained decile in the main text, about 11-fold in the Fig. 5a legend), so its exact magnitude should be verified before citation. · noted in this summary
  • Several validation correlations are statistically strong but small in magnitude (r = 0.13 for exonic deletions, r = −0.14 for interaction degree), so LOEUF explains little per-gene variance in these orthogonal measures. · noted in this summary

Who should read:Read this if you interpret coding variants or prioritize candidate disease genes and need to know how gnomAD's LOEUF constraint score is built, validated, and differs from pLI.

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