Abstract
Myeloproliferative neoplasms (MPNs) are blood cancers that are characterized by the excessive production of mature myeloid cells and arise from the acquisition of somatic driver mutations in haematopoietic stem cells (HSCs). Epidemiological studies indicate a substantial heritable component of MPNs that is among the highest known for cancers1. However, only a limited number of genetic risk loci have been identified, and the underlying biological mechanisms that lead to the acquisition of MPNs remain unclear. Here, by conducting a large-scale genome-wide association study (3,797 cases and 1,152,977 controls), we identify 17 MPN risk loci (P < 5.0 × 10−8), 7 of which have not been previously reported. We find that there is a shared genetic architecture between MPN risk and several haematopoietic traits from distinct lineages; that there is an enrichment for MPN risk variants within accessible chromatin of HSCs; and that increased MPN risk is associated with longer telomere length in leukocytes and other clonal haematopoietic states—collectively suggesting that MPN risk is associated with the function and self-renewal of HSCs. We use gene mapping to identify modulators of HSC biology linked to MPN risk, and show through targeted variant-to-function assays that CHEK2 and GFI1B have roles in altering the function of HSCs to confer disease risk. Overall, our results reveal a previously unappreciated mechanism for inherited MPN risk through the modulation of HSC function.
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Data availability
Summary statistics for variants with fine-mapped PP > 0.1% from the full GWAS meta-analysis (UKBB, Finngen and 23andMe) are available in Supplementary Table 5. Full summary statistics from 23andMe data cannot be reported owing to a clause in the 23andMe data transfer agreement, intended to protect the privacy of the 23andMe research participants. Thus, we provide full summary statistics for the MPN meta-analysis comprising UK Biobank and Finngen cohorts on GWAS Catalog under the accession code GCST90000032 (https://www.ebi.ac.uk/gwas/downloads/summary-statistics). To fully recreate our meta-analysis results for MPN: researchers can (1) obtain MPN summary statistics from 23andMe (https://research.23andme.com/dataset-access/); and (2) conduct a meta-analysis of our summary statistics with the 23andMe summary statistics. For downloads of FinnGen summary statistics, information on how to access individual level FinnGen data by application to responsible agencies (FinBB and THL), and other collaborative access inquiries, please see https://www.finngen.fi/en/for_researchers. Individual genetic and phenotypic data for the following cohorts are available by application: UKBB (https://www.ukbiobank.ac.uk) and Million Veteran Program (https://www.research.va.gov/MVP/research.cfm).
Code availability
Code and source data required for reproducing results and figures discussed herein are available on GitHub (https://github.com/sankaranlab/mpn-gwas).
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Acknowledgements
We thank members of the Sankaran laboratory for comments; W. Zhou for technical guidance on the implementation of SAIGE; and the research participants and employees of 23andMe, UKBB, FinnGen and the Million Veteran Program. This research has been conducted using the UKBB Resource under application 31063. E.L.B. received support from the Howard Hughes Medical Institute Medical Research Fellowship. S.K.N. received support through a Scholar Award from the American Society of Hematology. This work was supported by the Claudia Adams Barr Program for Innovative Cancer Research, the New York Stem Cell Foundation, the MPN Research Foundation, the Leukemia & Lymphoma Society, and National Institutes of Health grants (R01 DK103794 and R01 HL146500 to V.G.S.). V.G.S. is a New York Stem Cell Foundation-Robertson Investigator.
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E.L.B. and V.G.S. conceived the study. E.L.B., S.K.N. and V.G.S. designed the study. S.K.N., X.L., O.I.G., D.E.K. and M.M. performed experiments. E.L.B., X.L., A.G.B., J.K., M.T., A.H., T.K., C.A.L., A.L.d.L.P., D.E.K., B.L. and C.E. performed computational and statistical analyses. C.C. and B.M.N. contributed to genetic analysis of UKBB. A.G.B., C.E., P.N., P.W.F.W., K.C., S.P., J.M.G., C.J.O. and S.K. contributed to genetic analysis of the Million Veteran Program. J.K., A.H., T.K., A.P. and M.J.D. contributed to genetic analysis of FinnGen. M.T., B.L. and A.R. contributed to analysis of the Human Cell Atlas. V.C., C.P.N. and N.J.S. contributed to genetic analysis of leukocyte telomere length. C.J.W. and A.d.l.C. contributed to genetic analysis of AML. A.L.d.L.P., B.N., J.E.D. and M.M. contributed ideas and insights. V.G.S. supervised all experimental and analytic aspects of this work. E.L.B., S.K.N., X.L. and V.G.S. wrote the manuscript with input from all authors. All authors read and approved the final version of the manuscript.
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P.N. reports research grants from Amgen, Apple and Boston Scientific, and is a scientific advisor to Apple and Blackstone Life Sciences, all unrelated to the present work. A.R. is a cofounder of and equity holder in Celsius Therapeutics, and a member of the scientific advisory boards for Thermo Fisher Scientific, Neogene Therapeutics and Syros Pharmaceuticals. S.K. is an employee of Verve Therapeutics, and holds equity in Verve Therapeutics, Maze Therapeutics, Catabasis and San Therapeutics. He is a member of the scientific advisory boards for Regeneron Genetics Center and Corvidia Therapeutics, and he has served as a consultant for Acceleron, Eli Lilly, Novartis, Merck, Novo Nordisk, Novo Ventures, Ionis, Alnylam, Aegerion, Haug Partners, Noble Insights, Leerink Partners, Bayer Healthcare, Illumina, Color Genomics, MedGenome, Quest and Medscape. The remaining authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 Flowchart of genetic association analyses.
Flowchart of the quality control steps and analysis methods for the three discovery-phase genome-wide association studies in the UK Biobank, 23andMe and FinnGen, followed by replication in the Million Veteran Program.
Extended Data Fig. 2 MPN GWAS cohort-specific effect sizes.
a, Forest plot displaying cohort-specific odds ratios for lead variants of the 17 loci reaching genome-wide significance after replication. Sample sizes are: UKBB, n = 1,086 cases and 407,155 controls; 23andMe, n = 1,223 cases and 252,140 controls; FinnGen, n = 640 cases and 176,259 controls; MVP, n = 848 cases and 317,423 controls. Data represent odds ratios and 95% CI. b, Overall correlation of effect sizes between MVP cohort and combined discovery cohort (UKBB + 23andMe + FinnGen) for all 24 variants reaching suggestive significance (P < 1 × 10−6) which underwent replication (P = 3.76 × 10−5, two-tailed Pearson correlation). c, Forest plot displaying cohort-specific odds ratios for lead variants of the three most-significant loci in the meta-analysis: the JAK2 46/1 haplotype and two independent signals at the TERT locus. MVP_jak2 = JAK2V617F phenotype in MVP, MVP_jak2_or_mpn = JAK2V617F or ICD-based MPN definition in MVP. Data are odds ratios and 95% CI. Sample sizes are: UKBB, n = 1,086 cases and 407,155 controls; 23andMe, n = 1,223 cases and 252,140 controls; FinnGen, n = 640 cases and 176,259 controls; MVP_jak2, n = 848 cases and 317,423 controls; MVP_jak2_or_mpn, n = 2,203 cases and 218,607 controls.
Extended Data Fig. 3 Assessing the distribution and prevalence of the MPN polygenic risk score in UK Biobank.
a, Density distribution of the MPN PRS within the UK Biobank. b, Receiver operating characteristic curves for MPN predictions (n = 1,086 cases and 407,155 controls), using information from age, sex, genotyping array and ancestry-informed principal components (AUC1, blue) alone, or with the addition of PRS (AUC2, orange). c, Odds ratio (mean and 95% CI) for MPN acquisition according to deciles of the PRS (n = 1,086 cases and 407,155 controls), with decile 1 (10% of individuals with lowest PRS) as the reference group. d, Prevalence of MPN within each decile of the PRS in the UK Biobank population (n = 1,086 MPN cases, 407,155 controls). e, MPN cases and controls in the UK Biobank were stratified into three groups according to their PRS – low, intermediate, and high defined as the lowest quintile, the middle three quintiles, and the highest quintile of the PRS distribution respectively. For carriers and non-carriers of the JAK2 46/1 haplotype, the odds ratio for MPN was calculated in a logistic regression model with PRS group, age, sex and the top ten principal components of ancestry as covariates. Non-carriers with intermediate PRS served as the reference group. Data are odds ratios and 95% CI. f, Fine-mapped 95% credible sets for all 25 MPN risk loci reaching suggestive significance, stratified by the number of variants comprising each credible set. g, The fine-mapped posterior probability of causality for the highest fine-mapped variant in each locus credible set. h, Variants within the 95% credible sets and PP > 0.001 across all regions, grouped by genomic annotation.
Extended Data Fig. 4 Shared genetic associations between MPN risk and other phenotypes.
a, Schematic depicting the trajectory of undifferentiated haematopoietic stem and progenitor cells (HSPCs) into various committed cell types: lymphocytes (LYMPH), monocytes (MONO), neutrophils (NEUT), basophils (BASO), eosinophils (EO), red blood cells (RBC) and platelets (PLT). b, Regional association plots at the TERT locus (±50 kb from lead variant), showing the associations of variants with leukocyte telomere length and MPN. The colours of the points depict pairwise LD (r2) to sentinel variant rs7705526. The two conditionally independent lead variants for both traits, rs7705526 and rs2853677, are labelled. c, Individual single-nucleotide polymorphisms (SNPs) associated with telomere length and their effect sizes on MPN risk (n = 2,949 cases and 835,554 controls), calculated using the fixed effects meta-analysis method. Aggregate Mendelian randomization (MR) effects, calculated from three different methods (weighted median, inverse-variance weighted and Egger regression), are shown at the bottom. Data are MR effect sizes and standard errors. Red colour indicates significance. d, MR leave-one-out sensitivity analysis, showing MR effect estimates using the inverse variance weighted approach after excluding each individual SNP from the analysis (n = 2,949 cases and 835,554 controls). Data are MR effect sizes and standard errors. e, PheWAS of MPN risk variants. We tested fine-mapped MPN risk variants (PP > 0.10 or lead variant) for associations with 1,130 well-represented case–control phenotypes from the UKBB, calculated by two-tailed logistic mixed model association test. Shown in this heat map are the top MPN-associated variants at each locus with one or more associations reaching Bonferroni-corrected significance (P = 0.05/1,130 phenotypes = 4.4 × 10−5, or abs(z-score) = 4.08). Heat map colour indicates association z-score. All variant effects are oriented with respect to the risk-increasing MPN allele. Phenotypes are divided into major clinical categories, as listed in the annotations above the heat map.
Extended Data Fig. 5 Characterizing MPN target genes.
a, Target genes prioritized on the basis of noncoding criteria (red boxes) and coding consequences (blue boxes) and scored based on the number of criteria met. Only the highest-scoring gene per locus is reported, and for noncoding loci, only genes with a score of 2 or more are reported. b, Average expression (log2-transformed counts per million (CPM)) of MPN target genes (n = 15) across 16 primary haematopoietic cell types. Black diamonds indicate the mean expression of all non-zero expressed protein-coding genes in each cell type. Box plots show the median at the centre, with the top and bottom of the box indicating the interquartile range. Whiskers extend either to the maximum and minimum value or to 1.5 × the interquartile range. c, Protein–protein interaction network showing known and predicted associations between the protein products of MPN target genes, generated with the STRING database. d, Top-enriched biological annotations for MPN target genes identify key pathways associated with haematopoiesis and oncogenesis.
Extended Data Fig. 6 Structural basis for CHEK2 homodimer disruption by mutation of Ile157.
a, The crystal structure of the CHEK2 (forkhead-associated (FHA) kinase domain) homodimer (PDB: 3I6U). The FHA domain of molecule A (mol A) is shown in cyan and the kinase domain is coloured green. A second CHEK2 (mol B) has both domains coloured white. The two CHEK2 molecules are nearly symmetric—coiling around the central axis (black rod). The location of each Ile157 residue is marked with an asterisk. b, A magnified window showing details of the interactions. Ile157 links the FHA of one CHEK2 molecule (white) to the kinase domain of a second (green). The side chain of I157 mediates an FHA–kinase hydrophobic interface, interacting with Phe238 and Leu236 on the kinase domain. c, The second interface of the CHEK2 dimer (180° rotation from b) is nearly identical. A threonine residue at position 157 would diminish these hydrophobic interfaces and destabilize the CHEK2 dimer, as has been previously reported34.
Extended Data Fig. 7 CHEK2 is required for apoptosis of cycling HSPCs, but not for lineage commitment.
a, Assessment of IR-induced cell death of cycling HSPCs and myeloid progenitors following sublethal irradiation, after treatment with CHEK2 inhibitor (n = 3) or DMSO control (n = 3) (two-sided paired t-test). n is the number of biologically independent experiments. Data are mean ± s.e.m. b, Numbers (left) and percent (right) of HSPC colonies formed after CHEK2 inhibition (CHEK2 inhibitor II, Sigma 220486) (n = 4) versus DMSO control (n = 4). n is the number of biologically independent experiments. Data are mean ± s.e.m.
Extended Data Fig. 8 Supplementary data for variant-to-function studies at the GFI1B locus.
a, Map of the lentiviral constructs designed to assess enhancer activity at rs524137. b, Histogram displays GFP mean fluorescence intensity (MFI) of haematopoietic K562 cells infected with promoter-only versus promoter-and-enhancer lentiviral constructs. Compared to mock uninfected control cells, cells infected with the construct carrying both GFI1B promoter and enhancer show greater GFP intensity. c, FACS gating for sorting and identifying the primitive CD34+CD45RA−CD90+CD133+EPCR+ITGA3+ LT-HSC population in the day-7 CD34+ HSPCs presented in Fig. 4g–i. d, Schematic of colony-replating assays using human HSPCs edited with GFI1B coding (CDS) or enhancer guides (ENH). e, Representative western blot measuring GFI1B protein expression 5 days after CRISPR–Cas9 targeting with non-targeting control (NT), or coding regions of GFI1B (g1, g2). Lamin B was expression used as a loading control. Lamin B controls were probed on the same blot as the GFI1B. Similar results were obtained in three independent experiments. For gel source data, see Supplementary Fig. 3.
Extended Data Fig. 9 Schematics of the variant-to-function arcs for MPN risk loci.
a, CHEK2; b, GFI1B.
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This file contains Supplementary Figs 1-3, a Supplementary Note, a list of contributors of the Million Veteran Program, and Supplementary References.
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Bao, E.L., Nandakumar, S.K., Liao, X. et al. Inherited myeloproliferative neoplasm risk affects haematopoietic stem cells. Nature 586, 769–775 (2020). https://doi.org/10.1038/s41586-020-2786-7
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DOI: https://doi.org/10.1038/s41586-020-2786-7
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