Recommended
More Related Content
What's hot
What's hot (20)
Similar to Variation across 141,456 individuals reveals the spectrum of loss-of-function intolerance of the human genome
Similar to Variation across 141,456 individuals reveals the spectrum of loss-of-function intolerance of the human genome (20)
Recently uploaded
Recently uploaded (20)
Variation across 141,456 individuals reveals the spectrum of loss-of-function intolerance of the human genome
- 1. A brief history of gnomAD Daniel MacArthur April 11, 2019
- 2. Given known mutation rates, it is almost certain that every possible single base change compatible with life exists in a living human The power of seven billion people
- 3. Opportunities and challenges of genetic data aggregation over three million exomes and genomes sequenced Challenges: • difficulty of moving data • inadequate consent and data use permissions • objections to data sharing • inconsistent processing and variant calling
- 4. A history of genome data aggregation at Broad Exome Aggregation Consortium (ExAC) Genome Aggregation Database (gnomAD) • 60,076 exomes • began in 2012, first release Oct 2014 • preprint in Oct 2015 • published in May 2016 • 125,748 exomes and 15,708 genomes • began in 2016, first release Oct 2016 • final publication data release Oct 2018 • 7 preprints in Jan-March 2019
- 5. gnomAD 2.1 samples • Data provided by 109 PIs for 141,456 individuals including 125,748 exomes & 15,708 whole genomes • Primarily from case-control studies of complex adult-onset diseases (e.g. type 2 diabetes, heart attack, neuropsychiatric conditions) • Removed low-quality samples, related individuals, known severe pediatric disease cases plus first-degree relatives • Diverse range of ancestries (57% European, over ~10,000 samples apiece from South Asian, Latino, African/African-American, and East Asian populations)
- 6. gnomAD’s impact • 20.8 M pageviews of the ExAC and gnomAD browsers, by 230,000 users from 166 countries • Aided in the diagnosis of over 50,000 rare disease families • 3,962 papers have cited the ExAC paper
- 7. The gnomAD preprints on bioRxiv http://broad.io/gnomad_lof http://broad.io/gnomad_drugs http://broad.io/gnomad_lrrk2 http://broad.io/tx_annotation http://broad.io/gnomad_mnv http://broad.io/gnomad_uorfs http://broad.io/gnomad_sv
- 8. Thank you Production team Eric Banks Charlotte Tolonen Christopher Llanwarne David Roazen Diane Kaplan Gordon Wade Jeff Gentry Jose Soto Kathleen Tibbetts Kristian Cibulskis Laura Gauthier Louis Bergelson Miguel Covarrubias Nikelle Petrillo Ruchi Munshi Sam Novod Thibault Jeandet Valentin Ruano- Rubio Yossi Farjoun Analysis team Konrad Karczewski Laurent Francioli Grace Tiao Kristen Laricchia Anne O'Donnell- Luria Ben Neale Beryl Cummings Eric Minikel Irina Armean James Ware Kaitlin Samocha Mark Daly Nicola Whiffin Qingbo Wang Ryan Collins Cotton Seed Tim Poterba Arcturus Wang Chris Vittal Structural Variation team Ryan Collins Harrison Brand Konrad Karczewski Laurent Francioli Nick Watts Matthew Solomonson Xuefang Zhao Laura Gauthier Harold Wang Chelsea Lowther Mark Walker Christopher Whelan Ted Brookings Ted Sharpe Jack Fu Eric Banks Michael Talkowski Website team Matthew Solomonson Nick Watts Ben Weisburd Konrad Karczewski Ethics team Andrea Saltzman Molly Schleicher Namrata Gupta Stacey Donnelly Broad Genomics Platform Stacey Gabriel Kristen Connolly Steven Ferriera Funding NIGMS R01 GM104371 (PI: MacArthur) NIDDK U54 DK105566 (PIs: MacArthur and Neale) NHGRI U24 HG010262 (PI: Phillipakis) NIMH R56 MH115957 (PI: Talkowski) The vast majority of the data storage, computing resources, and human effort used to generate this call set were donated by the Broad Institute Coordination Jessica Alföldi
- 9. Thank youPrincipal Investigators Daniel MacArthur Aarno Palotie Andres Metspalu Anne Remes Adolfo Correa Andre Franke Ann Pulver Ben Glaser Ben Neale Bong-Jo Kim Bruce Cohen Carlos Pato Carlos A Aguilar Salinas Christina Hultman Christine M. Albert Christopher Haiman Clicerio Gonzalez Colin Palmer Craig Hanis Dan Roden Dan Turner Dana Dabelea Daniel Chasman Danish Saleheen David Altshuler David Goldstein Dawood Darbar Dermot McGovern Diego Ardissino Donald Bowden Dost Ongur Emelia J. Benjamin Erkki Vartiainen Erwin Bottinger Gad Getz George Kirov Gil Atzmon Harlan M. Krumholz Harry Sokol Heribert Schunkert Hilkka Soininen Hugh Watkins Jaakko Kaprio Jaana Suvisaari James Meigs James Ware James Wilson Jaspal Kooner Jaume Marrugat Jeanette Erdmann Jeremiah Scharf John Barnard John Chambers John D. Rioux Jose Florez Josée Dupuis Judy Cho Juliana Chan Kari Mattila Kyong Soo Park Laurent Beaugerie Leif Groop Lorena Orozco Lori Bonnycastle Maija Wessman Mark Daly Mark McCarthy Markku Laakso Martti Färkkilä Matthew Bown Matthew Harms Matti Holi Michael Boehnke Michael O'Donovan Michael Owen Mikko Hiltunen Mikko Kallela Mina Chung Ming Tsuang Moore Shoemaker Nazneen Rahman Nilesh Samani Olle Melander Pamela Sklar Patrick T. Ellinor Patrick Sullivan Peter Nilsson Ramnik Xavier Ravindranath Duggirala Rinse Weersma Roberto Elosua Ronald Ma Ruth Loos Ruth McPherson Samuli Ripatti Sekar Kathiresan Seppo Koskinen Soo Heon Kwak Stephen Glatt Steve McCarroll Steven A. Lubitz Subra Kugathasan Tai Shyong Tariq Ahmad Teresa Tusie Luna Terho Lehtimäki Tim Spector Tõnu Esko Tuomi Tiinamaija Veikko Salomaa Yik Ying Teo Young Jin Kim Jerome Rotter Steven Rich
- 10. Variation across 141,456 individuals reveals the spectrum of loss-of-function intolerance of the human genome Konrad Karczewski April 11, 2019 @konradjk broad.io/gnomad_lof
- 11. Range of LoF impact embryonic lethal recessive disease non-essential complex disease beneficial haploinsufficient disease
- 12. Identifying true LoF variants is challenging • LoFs are rare • LoFs are enriched for artifacts
- 13. Identifying true LoF variants is challenging • LoFs are rare • LoFs are enriched for artifacts
- 14. 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 contains: • 230M variants in 15,708 genomes • 15M variants in 125,748 exomes
- 15. Staggering amounts of pLoFs • gnomAD 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
- 16. Identifying true LoF variants is challenging • LoFs are rare • LoFs are enriched for artifacts
- 17. LOFTEE removes benign variation • LoF filtering plugin to VEP, LOFTEE • Variants retained by LOFTEE are: • rarer, 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 Rarer, more deleterious
- 18. 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 Kaitlin Samocha (Samocha et al. 2014; Lek et al. 2016)
- 19. Most genes are depleted of LoF variation MED13L FNDC3B Phenotype Severe Intellectual Disability Unknown 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
- 20. • 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
- 21. • 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
- 22. • 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
- 23. 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
- 24. 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
- 25. 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 broad.io/gnomad_sv
- 26. 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
- 27. ● ● ● ● ● ● ●● ●● ●● ● ● ●● ●● ●● 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
- 28. 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 Andrea Ganna
- 29. 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 pext: broad.io/tx_annotation
- 30. Now featuring: structural variant calls in the browser gnomad.broadinstitute.org Matt Solomonson Nick Watts
- 31. Acknowledgments • Laurent Francioli • Grace Tiao • Beryl Cummings • Jack Kosmicki • Andrea Ganna • Qingbo Wang • Kaitlin Samocha Ben Neale • Daniel Birnbaum • Jessica Alföldi Kristen Laricchia • Matt Solomonson Nick Watts • Ryan Collins Harrison Brand • Raymond K. Walters Kate Tashman • Daniel Rhodes Moriel Singer-Berk Eleina England Eleanor G. Seaby • Hail team Tim Poterba Cotton Seed Arcturus Wang • Laura Gauthier Yossi Farjoun Eric Banks • Analytic and Translational Genetics Unit • Mark Daly • Daniel MacArthur broad.io/gnomad_lof
- 32. Evaluating potential drug targets through human loss-of- function genetic variation Eric Vallabh Minikel April 11, 2019 @cureffi broad.io/gnomad_drugs
- 33. Why study LoF variants in drug discovery? • LoF variants can be an in vivo, whole human, lifelong model of inhibition of a target. from Plenge 2013, PMID: 23868113
- 34. Why study LoF variants in drug discovery? • LoF variants can be an in vivo, whole human, lifelong model of inhibition of a target. • With caveats: • drug effect may not exactly mimic LoF • developmental effects • tissue-specific effects • dosage • difference in our ancestors' environment vs. our environment
- 35. How do drug targets compare to all genes & specific gene lists in constraint?
- 36. How do drug targets compare to all genes & specific gene lists in constraint?
- 37. How constrained are some well-known drug targets?
- 38. How constrained are some well-known drug targets? • 19% of all drug targets (N=73, including 53 targets of inhibitors, antagonists, etc.) have obs/exp < 13%, the average for haploinsufficient genes
- 39. How constrained are some well-known drug targets? • 19% of all drug targets (N=73, including 53 targets of inhibitors, antagonists, etc.) have obs/exp < 13%, the average for haploinsufficient genes • These include some chemotherapy targets but also aspirin, statins, and antimuscarinics!
- 40. How constrained are some well-known drug targets? • Not all chemotherapy targets are so constrained
- 41. How constrained are some well-known drug targets? • Drug targets span the full spectrum – constraint alone should not rule a potential target in or out
- 42. Can we find and phenotype LoF individuals for a gene of interest? • If you can find them, phenotyping of LoF individuals (het or hom) can be deeply informative for safety and/or efficacy • Examples: PCSK9, APOC3, CETP, LPA, HAO1... • Questions for today: • Is it always realistic to expect to find enough LoF heterozygotes or homozygotes to permit your analysis of interest? • What is the best strategy to go about finding them? • How should you curate pLoF variants before starting to recontact?
- 43. Cumulative allele frequency of LoF variants • Cumulative allele frequency (CAF) = Σ(AF) for all LoF variants • Define p = proprtion of the haplotypes in population that are LoF • In an outbred population: • LoF het frequency = 2p(1-p) • LoF hom / compound het frequency = p2 • This analysis: • Use gnomAD data to compute p for each gene • Predict the hom/compound het frequency for each gene in the population — assuming this genotype is not lethal (!)
- 46. What about bottlenecked populations? https://ecologyblog0112.weebly.com/population-genetics-in-the-conservation-of-biodiversity.html
- 50. Which populations to sequence? • For the near future, analysis for most genes will need to focus on heterozygotes, regardless of population • For finding homozygotes, best strategy is to sample diverse bottlenecked populations and consanguineous individuals
- 51. How to curate? • "the more interesting something looks, the less likely it is to be real" • Solutions: • LOFTEE (Karczewski 2019, broad.io/gnomad_lof) • Expression-aware annotation (Cummings 2019, broad.io/tx_annotation) • Deep manual curation is still important Non-random distribution of pLoFs across the coding sequence is suspicious • Next up: examples of curation of 3 genes with different error modes
- 52. HTT • Cumulative pLoF allele frequency: 6.2% • Mostly driven by several common variants in exon 1 • Highly suspicious given the lethal mouse knockout phenotype!
- 53. HTT • common LoFs are all alignment artifacts at polyQ and polyP repeat regions • after filtering & curation, cumulative pLoF allele frequency: 0.013%
- 54. MAPT • Cumulative pLoF allele frequency: 14%
- 55. MAPT • almost all pLoFs are in exons not expressed in the brain! • the remainder are various artifacts • after filtering & curation, cumulative pLoF allele frequency: 0% • Transcript-aware expression – see Cummings et al, broad.io/tx_annotation
- 56. PRNP • Slightly constrained – 6 observed, 10 expected • All in N terminus or very near C terminus
- 57. PRNP • N-terminal variants are true LoF. In N terminus, not constrained at all (obs/exp = 6/6.05 = 99%) • C-terminal truncating variants cause disease through gain-of-function (literature variants added). The gnomAD C-terminal frameshift turns out to be a dementia case!
- 58. Very different answers before/after curation CAF gene before after HTT 6.2% 0.013% LRRK2 0.23% 0.09% MAPT 14% 0% PRNP 0.0035% 0.0021% SNCA 0.0012% 0% SOD1 0.0060% 0.0038%
- 59. Very different answers before/after curation CAF prevalence gene before after LoF hets GoF disease HTT 6.2% 0.013% 1 in 3,800 1 in 2,400-4,400 LRRK2 0.23% 0.09% 1 in 500 1 in 3,300 MAPT 14% 0% not observed 1 in 5,000 – 31,000 PRNP 0.0035% 0.0021% 1 in 18,000 1 in 50,000 SNCA 0.0012% 0% not observed 1 in 360,000 SOD1 0.0060% 0.0038% 1 in 26,000 1 in 27,000-83,000
- 60. Very different answers before/after curation CAF prevalence gene before after LoF hets GoF disease HTT 6.2% 0.013% 1 in 3,800 1 in 2,400-4,400 LRRK2 0.23% 0.09% 1 in 500 1 in 3,300 MAPT 14% 0% not observed 1 in 5,000 – 31,000 PRNP 0.0035% 0.0021% 1 in 18,000 1 in 50,000 SNCA 0.0012% 0% not observed 1 in 360,000 SOD1 0.0060% 0.0038% 1 in 26,000 1 in 27,000-83,000
- 61. Very different answers before/after curation • Even without recontact & phenotyping, curation can be highly informative CAF prevalence gene before after LoF hets GoF disease HTT 6.2% 0.013% 1 in 3,800 1 in 2,400-4,400 LRRK2 0.23% 0.09% 1 in 500 1 in 3,300 MAPT 14% 0% not observed 1 in 5,000 – 31,000 PRNP 0.0035% 0.0021% 1 in 18,000 1 in 50,000 SNCA 0.0012% 0% not observed 1 in 360,000 SOD1 0.0060% 0.0038% 1 in 26,000 1 in 27,000-83,000
- 62. Very different answers before/after curation • Even without recontact & phenotyping, curation can be highly informative • But remember, even MAPT and SNCA might be great drug targets! CAF prevalence gene before after LoF hets GoF disease HTT 6.2% 0.013% 1 in 3,800 1 in 2,400-4,400 LRRK2 0.23% 0.09% 1 in 500 1 in 3,300 MAPT 14% 0% not observed 1 in 5,000 – 31,000 PRNP 0.0035% 0.0021% 1 in 18,000 1 in 50,000 SNCA 0.0012% 0% not observed 1 in 360,000 SOD1 0.0060% 0.0038% 1 in 26,000 1 in 27,000-83,000
- 63. Suggested guidelines for evaluating drug targets based on LoF • It's complicated - no simple formula, evaluate each target on a case-by-case basis • Filter and curate • Consider positional distribution • Calculate cumulative allele frequency • Experimentally validate loss-of-function • Don't eliminate a gene from consideration just because you can't find LoF individuals • Read the pre-print: broad.io/gnomad_drugs
- 64. Acknowledgments • Contact: eminikel@broadinstitute.org / danmac@broadinstitute.org • Funding: NIH F31 AI22592 • Many thanks to East London Genes & Health • Thanks to co-authors: Konrad, Beryl, Nicky, Jessica; Stuart Schreiber; Hilary Martin, Richard Trembath, & David van Heel (ELGH); gnomAD consortium & production team • FYI: Sonia & Eric's thesis defenses (primary prevention targeting PRNP) – April 16, 9:00a – 11:00a, Broad Auditorium broad.io/gnomad_drugs
- 65. From LoF to phenotype: a pilot study using LRRK2 Nicky Whiffin @nickywhiffin Research fellow, Imperial College London Irina ArmeanAaron Kleinman broad.io/gnomad_lrrk2
- 66. • Gain of function missense variants in LRRK2 cause early- onset Parkinson’s • LRRK2 is over-activated in general Parkinson’s GoF LRRK2 variants cause Parkinson’s
- 67. • Gain of function missense variants in LRRK2 cause early- onset Parkinson’s • LRRK2 is over-activated in general Parkinson’s • Multiple pharma companies now pursuing LRRK2 inhibitors as generalised Parkinson’s therapy GoF LRRK2 variants cause Parkinson’s
- 68. • Early pre-clinical model organism studies – KO animals have lung, liver and renal phenotypes Early concerns for toxicity
- 69. • Early pre-clinical model organism studies – KO animals have lung, liver and renal phenotypes Is partial reduction of LRRK2 protein levels safe in humans? Early concerns for toxicity
- 70. Cohorts included gnomAD v2.1 141,456 sequenced individuals Case-control and cohort studies 23andMe >4 million research- consented individuals Genotyped and imputed
- 71. Cohorts included 633 LRRK2 LoF carriers (123 unique variants) 348 LRRK2 LoF carriers (117 unique variants) - LOFTEE low confidence 255 LRRK2 LoF carriers (111 unique variants) - Manual curation
- 73. Cohorts included Genotyped or imputed LRRK2 LoF variants 8 variants Subset of carriers for each variant sent for Sanger validation 3 variants in 1103 carriers (749 Sanger confirmed) - < 5 validated carriers - Failed validation - Manual curation 633 LRRK2 LoF carriers (123 unique variants) 348 LRRK2 LoF carriers (117 unique variants) - LOFTEE low confidence 255 LRRK2 LoF carriers (111 unique variants) - Manual curation
- 74. Cohorts included <5 homozygotes Genotyped or imputed LRRK2 LoF variants 8 variants Subset of carriers for each variant sent for Sanger validation 3 variants in 1103 carriers (749 Sanger confirmed) - < 5 validated carriers - Failed validation - Manual curation 633 LRRK2 LoF carriers (123 unique variants) 348 LRRK2 LoF carriers (117 unique variants) - LOFTEE low confidence 255 LRRK2 LoF carriers (111 unique variants) - Manual curation
- 75. pLoF variants are evenly spread across the protein...
- 76. ...and are genuinely LoF lymphoblastoid cells from individuals with heterozygous LRRK2 LoF CRISPR-edited embryonic stem cells differentiated into cardiomyocytes Jamie Marshall Homozygous reference Homozygous reference p.Cys1313Ter p.Arg1483Ter p.Arg1693Ter
- 77. • 1,358 carriers of 111 pLoF variants • Appear to be true LoF But what effect do these have on human health? A curated dataset of LRRK2 pLoF individuals
- 78. No effect on overall mortality
- 79. • 60 LRRK2 LoF carriers in gnomAD had available phenotype data • Genomic Psychiatric Cohort, Pakistan Risk of Myocardial Infarction Study, Swedish Schizophrenia and Bipolar Studies, the FINRISK study, the BioMe Biobank, the Estonian Biobank • Very diverse sources including EHRs and questionnaires Manual curation of gnomAD phenotype data Jessica Alföldi
- 80. • 60 LRRK2 LoF carriers in gnomAD had available phenotype data • Genomic Psychiatric Cohort, Pakistan Risk of Myocardial Infarction Study, Swedish Schizophrenia and Bipolar Studies, the FINRISK study, the BioMe Biobank, the Estonian Biobank • Very diverse sources including EHRs and questionnaires • Manually assessed for lung, liver, kidney, CV, nervous system, immune system phenotypes and cancer • No enrichment for any adverse phenotypes • No sign of syndromic phenotypes Manual curation of gnomAD phenotype data Jessica Alföldi
- 81. No differences across 77 serum biomarkers Entire cohort LRRK2 LoF carriers
- 82. No association with any phenotypes in 23andMe
- 83. • ~1 in 550 humans has a heterozygous pLoF variant in LRRK2 • ~50% reduction in LRRK2 protein • likely across all tissues throughout life • No discernable negative impact across >1100 carriers • No effect on overall mortality • No enrichment for any assessed phenotypes • Suggests that partial LRRK2 inhibitors should be well-tolerated, even with chronic administration • Demonstrates the power of large-scale genetics to assess tolerability for drug discovery Key message for LRRK2 drug development
- 84. Acknowledgements Irina Armean Jamie Marshall Eric Minikel Konrad Karczewski Beryl Cummings Laurent Francioli Kristen Laricchia Qingbo Wang James Ware Jessica Alföldi Daniel MacArthur Aaron Kleinman Anna Guan Babak Alipanahi Peter Morrison the 23andMe Research Team Paul Cannon Genome Aggregation Database Production Group Genome Aggregation Database Consortium Marco Baptista Kalpana Merchant Aki Havulinna Bozenna Iliadou Jung-Jin Lee Grish Nadkarni Cole Whiteman Mark Daly Tõnu Esko Christina Hultman Ruth Loos Lili Milani Aarno Palotie Carlos Pato Michele Pato Danish Saleheen Patrick Sullivan
Editor's Notes
- Citations, diagnoses, impact
- I'm going to talk some cool things you can do with this dataset to understand the impact of loss-of-function variation on the human genome.
- 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…
- Because true LoFs are deleterious, a number of factors conspire to make them difficult to characterize. In particular...
- 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, following the same pattern of discovery. 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... 1649 confident homozygous. With a high-quality catalog of predicted loss-of-function variants, we can not only look at genes which have LoF variants in the general population, but also genes where we don’t see any LoFs.
- 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 this metric is a conservative estimate that takes into account the gene size. As our sample sizes grow, LOEUF will converge to the o/e ratio, but for now this is a useful metric.
- So we can bin this metric into deciles, which I'll show on every slide from here on out with the left ...
- 116 - manually curated haploinsufficient developmental delay genes
- 1164
- 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 early lethal in mouse are more likely to have an ortholog 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, 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. For more information on this dataset, see the recently posted preprint from Ryan and crew
- If we look at the burden of de novo LoFs in patients with developmental delay or intellectual disability, we observe a 15-fold increased rate in the top 10% most constrained genes in the genome, in cases compared to controls. 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 of 600 traits from UKBiobank, 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
- Thanks to the efforts of Matt and Nick
- Not depleted LOEUF = 0.64
- Not depleted LOEUF = 0.64
- Not depleted LOEUF = 0.64
- Not depleted LOEUF = 0.64
- By LoF I mean nonsense, frameshift or essential splice site variants Manual curation Variant quality metrics Reads on IGV LoF rescue either by co-localised variants or cryptic/alternative splice sites
- ‘GC’ still works as a strong splice donor site
- Protein domains - Chi-square P=0.23
- Thank Jamie by name “Western blot” of protein levels
- Kolmogorov-Smirnov P=0.085 and 0.46 respectively Last known age not survival
- ~4 million individuals, over 1000 of which are known carriers giving reasonable power to detect an association