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- 1. Variation across 141,456 individuals reveals the spectrum of loss-of-function intolerance of the human genome Konrad Karczewski March 2, 2019 @konradjk broad.io/gnomad_lof
- 2. Range of LoF impact embryonic lethal recessive disease non-essential complex disease beneficial haploinsufficient disease
- 3. Identifying true LoF variants is challenging • LoFs are rare • LoFs are enriched for artifacts
- 4. Identifying true LoF variants is challenging • LoFs are rare • LoFs are enriched for artifacts
- 5. • gnomAD: 125,748 exomes and 15,708 whole genomes Increasing the scale of reference databases
- 6. gnomAD 2.1.1 • Data provided by 109 PIs • 1.3 and 1.6 petabytes of BAM files • Uniformly processed and joint called • 12 and 24 terabyte VCFs • Developed a novel QC pipeline • Complete pipeline publicly available: broad.io/gnomad_qc • All QC and analysis performed using Hail: hail.is • Scalable to thousands of CPUs • Enabled rapid iteration (few hours for each component, few days for entire process) Grace Tiao Laurent Francioli Broad Genomics Platform Broad Data Sciences Platform Tim PoterbaCotton Seed
- 7. gnomAD 2.1.1 • Sub-continental ancestry • Subsets: • controls-only • non-neuro/non-psychiatric • non-cancer • non-TOPMed Bravo Grace Tiao Laurent Francioli http://gnomad.broadinstitute.org
- 8. Staggering amounts of variation synonymous missense pLoF 0 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 0 40,000 80,000 125,748 Sample size Numberobserved • gnomAD 2.1 contains: • 230M variants in 15,708 genomes • 15M variants in 125,748 exomes
- 9. Staggering amounts of pLoFs • gnomAD 2.1 contains: • 230M variants in 15,708 genomes • 15M variants in 125,748 exomes • Of these, we observe 515,326 predicted loss-of- function (pLoF) variants • Stop-gained • Essential splice • Frameshift indel pLoF 0 100,000 200,000 300,000 400,000 0 40,000 80,000 125,748 Sample size Numberobserved
- 10. Identifying true LoF variants is challenging • LoFs are rare • LoFs are enriched for artifacts
- 11. LOFTEE removes benign variation • LoF filtering plugin to VEP, LOFTEE • Variants retained by LOFTEE are: • more rare, and thus • more deleterious • After filtering, we discover 443,769 high-confidence pLoFs in gnomAD https://github.com/konradjk/loftee ● ● ● ● 0.00 0.05 0.10 0.15 synonymous missense low confidence pLoF high confidence pLoF MAPS
- 12. Detecting genes depleted for pLoFs • Mutational model that predicts the number of SNVs in a given functional class we would expect to see in each gene in a cohort • Now incorporating methylation, improved coverage correction, LOFTEE • Previously transformed into the probability of LoF intolerance (pLI) • Applying to 125,748 gnomAD exomes • Median of 17.3 pLoFs expected per gene • Direct estimate of observed/expected ratio • New scores: LOEUF • Upper bound of 90% confidence interval Kaitlin Samocha (Samocha et al. 2014; Lek et al. 2016)
- 13. Most genes are depleted of LoF variation MED13L FNDC3B Phenotype Severe Intellectual Disability None Observed Expected Obs/Exp (CI) Observed Expected Obs/Exp (CI) Synonymous 462 465 0.993 (0.92-1.07) 271 266 1.02 (0.92-1.13) pLoF 0 102 0 (0-0.029) 0 68 0 (0-0.043) • Many are extremely depleted (<20% observed compared to expected) • Including most known (curated) haploinsufficient genes • Using upper bound of confidence interval corrects for small genes 0 500 1000 1500 0.0 0.5 1.0 1.5 Observed/Expected Numberofgenes 0 200 400 600 800 0.0 0.5 1.0 1.5 2.0 LOEUF Numberofgenes
- 14. • Binning this spectrum into deciles Resolving the spectrum of LoF intolerance Haploinsufficient Autosomal Recessive Olfactory Genes 0% 20% 40% 0% 20% 40% 60% 80% 100% LOEUF decile Percentofgenelist More depleted More constrained More tolerant Less constrained
- 15. • Known haploinsufficient genes have ~10% of the expected pLoFs Resolving the spectrum of LoF intolerance Haploinsufficient Autosomal Recessive Olfactory Genes 0% 20% 40% 0% 20% 40% 60% 80% 100% LOEUF decile Percentofgenelist
- 16. • Autosomal recessive genes are centered around 60% of expected Resolving the spectrum of LoF intolerance Haploinsufficient Autosomal Recessive Olfactory Genes 0% 20% 40% 0% 20% 40% 60% 80% 100% LOEUF decile Percentofgenelist Gene list from: Blekhman et al., 2008 Berg et al., 2013
- 17. Haploinsufficient Autosomal Recessive Olfactory Genes 0% 20% 40% 0% 20% 40% 60% 80% 100% LOEUF decile Percentofgenelist • Some genes, e.g. olfactory receptors, are unconstrained Resolving the spectrum of LoF intolerance
- 18. Constraint metrics reflect mouse and cellular knockout phenotypes Qingbo Wang 0% 10% 20% 30% 0% 20% 40% 60% 80% 100% LOEUF decile Percentofmouse hetlethalknockoutgenes Cell essential Cell non−essential 0% 5% 10% 15% 20% 25% 0% 20% 40% 60% 80% 100% LOEUF decile Percentofessential/ non−essentialgenes Hart et al., 2017 Eppig et al., 2015 Motenko et al., 2015
- 19. Constraint spectrum reflects patterns of structural variation • Called SVs in 14,245 individuals with 30X WGS • 9,860 rare (<1%), biallelic, autosomal LoF deletions • Occurrence correlates with SNV constraint metric Ryan Collins Harrison Brand ● ● ● ● ● ● ● ● ● ● 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 0% 20% 40% 60% 80% 100% LOEUF decile Aggregatedeletion SVobserved/expected Preprint coming soon!
- 20. Constraint metrics are correlated with biological relevance Protein-protein interactions ● ● ● ● ● ● ● ● ● ● 5 10 15 20 25 0% 20% 40% 60% 80% 100% LOEUF decile Meannumberof protein−proteininteractions Gene expression ● ● ● ● ● ● ● ● ● ● 0 10 20 30 0% 20% 40% 60% 80% 100% LOEUF decile Numberoftissueswhere canonicaltranscriptisexpressed Disease-associated ● ● ● ● ● ● ● ● ● ● 0% 10% 20% 0% 20% 40% 60% 80% 100% LOEUF decile ProportioninOMIM
- 21. Population-based constraint • Repeating this constraint calculation for each population (8,128 individuals per population) • Lower mean o/e and LOEUF in African/African-Americans • Consistent with increased efficiency of selection in populations with larger effective population sizes ● ● ● ● ● 0.45 0.50 0.55 0.60 African/ African−American Latino East Asian European South Asian LOEUF
- 22. ● ● ● ● ● ● ●● ●● ●● ● ● ●● ●● ●● synonymoussynonymoussynonymoussynonymoussynonymoussynonymoussynonymoussynonymoussynonymoussynonymoussynonymoussynonymoussynonymoussynonymoussynonymoussynonymoussynonymoussynonymoussynonymoussynonymous pLoFpLoFpLoFpLoFpLoFpLoFpLoFpLoFpLoFpLoFpLoFpLoFpLoFpLoFpLoFpLoFpLoFpLoFpLoFpLoF 0 5 10 15 0% 20% 40% 60% 80% 100% LOEUF decile Rateratiofordenovo variantsinID/DDcases comparedtocontrols Constraint improves rare disease diagnosis • Patients with developmental delay/intellectual disability are 15X more likely to have an de novo LoF in a constrained gene • 8,095 de novos in 5,305 cases • 2,623 de novos in 2,179 controls • Integrating expression data improves this further Jack Kosmicki Beryl Cummings broad.io/tx_annotation
- 23. Constraint informs common disease etiologies • Compared to genome-wide background, SNPs near constrained genes are enriched in their contribution to heritability of common traits • In particular, traits that previously1 showed an enrichment of ultra-rare variants are also enriched among constrained genes ● ● ● ● ● ● ● ● ● ● 1.0 1.2 1.4 0% 20% 40% 60% 80% 100% LOEUF decile Partitioningheritability enrichment Schizoprenia Qualifications: College or University degree Duration to first press of snap−button in each round Educational attainment Bipolar 10-2 10 -4 10 -6 10-8 10-10 10 -12 10 -14 Activities Cardiovascular Cognitive Environment Hematological Metabolic Nutritional Ophthalmological Psychiatric Reproduction Respiratory Skeletal Social Interactions Other Traitenrichment p−value 1Ganna et al. 2018 AJHG
- 24. Data publicly released with no publication restrictions gnomad.broadinstitute.org Matt Solomonson Nick Watts Gene model with transcripts Pathogenic Clinvar Variants Dataset selection box Tissue isoform expression Constraint metrics
- 25. Coming soon! Structural variant calls in the browser gnomad.broadinstitute.org Matt Solomonson Nick Watts
- 26. • broad.io/gnomad_lof – Flagship LoF manuscript (these slides) • broad.io/gnomad_drugs – Applications to drug targets • broad.io/gnomad_lrrk2 – LRRK2 LoF carriers do not exhibit phenotypes • broad.io/gnomad_uorfs – Deleterious 5’ UTR variants • broad.io/tx_annotation – Transcript-based annotation • Coming soon: • broad.io/gnomad_mnv – Mechanisms of multi-nucleotide variants • broad.io/gnomad_sv – Structural variant map of 15K genomes
- 27. Acknowledgments • Laurent Francioli • Grace Tiao • Kaitlin Samocha • Ben Neale • Jessica Alföldi • Kristen Laricchia • Genomics Platform • Data Science Platform • Daniel MacArthur • Mark Daly • Mike Talkowski • Beryl Cummings • Matt Solomonson • Ben Weisburd • Hail team • Tim Poterba • Cotton Seed • Analytic and Translational Genetics Unit • Genome Aggregation Database (gnomAD) • Full list of consortia and contributors at: gnomad.broadinstitute.org/about broad.io/gnomad_lof
Editor's Notes
- I'm going to talk about work I've done with the gnomAD consortium on loss-of-function variation.
- Imagine if we could put each of the 20K genes in the genome along a spectrum of sensitivity to functional disruption, that is, the clinical or phenotypic impact that a loss-of-function variant might have in that gene. For instance, here over on the left are genes where we’ll never see LoF variants in living humans as these would be incompatible with human life. In the middle are variants and genes that we typically study in the clinical genetics space, from causal variants for dominant and recessive diseases to risk factors for complex disease. On the right, we have genes that are relatively tolerant of LoF variation, potentially even homozygous inactivation. And unlike in model organisms, where we can effectively engineer such mutations, there are obvious technical and ethical barriers to doing so in humans. But when we sequence healthy individuals or individuals with common diseases, we find plenty of genes inactivated, in the form of naturally-occurring predicted loss-of-function variants (or pLoFs). However…
- In order to discover the high-impact rare LoF variants, our group and others have undertaken large sequencing projects to catalog natural genetic variation. Today, I'm going to describe recent efforts using the larger Genome Aggregation Database (gnomAD) which comprises 125K exomes and 15K genomes, which we’ve now released to the public. We’ve released version 2.1.1 which…
- We’ve released version 2.1.1 which comprises about 3 PB of BAM files, generously provided by over 100 PIs. Thanks to the tireless efforts of the Broad Genomics and Data Sciences Platforms, these have been run through a uniform pipeline and joint called into 36 terabytes of VCFs. If you've been using the ExAC or gnomAD datasets, we recommend switching to this new release now, as we’ve developed a novel QC pipeline that we believe increases the quality of the dataset substantially.
- This dataset contains a substantial amount of variation, including... Here you can see the number of variants discovered in the exomes, broken down by functional class, as a function of sample size, which follow approximately a square root law. If we zoom into the predicted LoFs...
- ...we observe over half a million LoFs, and I should clarify that when I'm talking about pLoFs today, I'm referring to stop-gained, essential splice, and frameshift variants.
- So now that we've increased our sensitivity and discovered a bunch of rare putative LoFs, now we'd like to increase our specificity...
- To this end, we've created a tool called LOFTEE, a plugin to VEP that filters out common error modes based on first principles, and importantly, does not use frequency. In spite of that, when we look at the mutability adjusted proportion of singletons, or MAPS, a metric of deleteriousness based on frequency, LOFTEE filters out variants that have a frequency spectrum consistent with missense variants, while variants that are retained are much more rare on average and thus more deleterious. After filtering...
- A few years ago, Kaitlin Samocha built a mutational model to predict... And we've now built on this model with a number of improvements to refine the model and increase specificity. Previously, this constraint metric was defined in a metric called pLI. However, now that we're applying to a larger dataset, with our greater resolution, we can use the more interpretable observed/expected ratio, and build a confidence interval around this value, which can give us a conservative estimate of the observed to expected ratio.
- Using this method, we can return to the question of where genes fall on this spectrum of LoF tolerance. Most genes have a depletion of pLoFs (that is, observed/expected less than 1), and many are extremely depleted, including most known HI genes. Just a note for anyone who has used the pLI scores previously, we've now flipped the scale, so the genes over on the left side are high pLI genes. So these improvements solidify our ability to detect constraint, here are two very clear examples. LoFs in MED13L previously demonstrated to cause severe ID, facial features, and cardiac phenotypes. FNDC3B has no known human phenotype, but results in death at birth when knocked out in mice. But if you find a rare disease patient with an LoF in this gene, you might be concerned. Some of you may notice this tick on the left side, where observed/expected is zero. This can happen due to extreme constraint, or small genes (say, observed = 0, expected = 2). At larger sample sizes, this will even itself out and we could use the observed/expected ratio, but for now, we can use the upper bound of the confidence interval, which we term LOEUF, resulting in a much smoother distribution, which I’ll use from here on out.
- So we can bin this metric into deciles, which I'll show on every slide from here on out with the left ...
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- 350 so the metric is well-calibrated, and importantly this means we now have improved LoF tolerance scores for all protein-coding genes in the genome
- This fits with what we see in model systems, where genes that are embryonically lethal in mouse are more likely to be in the human constrained genes. Similarly, in CRISPR screens, genes that are essential for cell viability are also more likely to be constrained and the opposite for the confidently non-essential genes.
- We next explored the correlation between constraint against SNVs with patterns of structural variation. Ryan and Harrison called SVs in 14K individuals, identifying about 10K rare biallelic autosomal LoFs that disrupt gene function. When they looked at the occurrence of SVs in each of the constraint deciles, they found that on average, the constrained genes had a strong depletion of structural variation, If here with more than 4 mins to go: Important to note that this is not a per-gene SV metric, as even this dataset of 15K has less than one rare LoF SV per gene.
- One interesting aspect we can explore with the new constraint framework is population-specific constraint, and ask whether genes are constrained in one population or another. While our sample size for each population is too small to detect constraint against individual elements, we can see that on average, constraint is most effective in the African/African-American population, consistent with…
- If we look at the burden of de novo LoFs in patients with developmental delay or intellectual disability, we observe a 15-fold increased burden in the top 10% most constrained genes in the genome. So in other words, this lowest decile contains genes where a single LoF mutation will prevent you, by an estimable amount, from progressing through a healthy development during childhood.
- Finally, we can investigate how these constraint metrics relate to common disease biology. In a partitioning heritability analysis, we find that SNPs near constrained genes are enriched for heritability of common traits. If we zoom in on which traits have the strongest enrichment for heritability among constrained genes, we find schizophrenia, bipolar disorder, and educational attainment, which is consistent with previous work that marked these traits as enriched for ultra-rare coding variants
- And you can load TTN!
- And you can load TTN!
- If anyone is interested in learning more about the dataset and what you can do with it, we’ve put 5 papers on biorxiv with 2 to come in the next few weeks on different aspects.