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Genome-wide association analyses based on whole-genome sequencing in Sardinia provide insights into regulation of hemoglobin levels

Abstract

We report genome-wide association study results for the levels of A1, A2 and fetal hemoglobins, analyzed for the first time concurrently. Integrating high-density array genotyping and whole-genome sequencing in a large general population cohort from Sardinia, we detected 23 associations at 10 loci. Five signals are due to variants at previously undetected loci: MPHOSPH9, PLTP-PCIF1, ZFPM1 (FOG1), NFIX and CCND3. Among the signals at known loci, ten are new lead variants and four are new independent signals. Half of all variants also showed pleiotropic associations with different hemoglobins, which further corroborated some of the detected associations and identified features of coordinated hemoglobin species production.

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Figure 1: Association at the globin gene clusters.
Figure 2: Diagrams of genome-wide associated loci.

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  1. Sankaran, V.G., Xu, J. & Orkin, S.H. Advances in the understanding of haemoglobin switching. Br. J. Haematol. 149, 181–194 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Modell, B. & Darlison, M. Global epidemiology of haemoglobin disorders and derived service indicators. Bull. World Health Organ. 86, 480–487 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Malaria Genomic Epidemiology Network. Reappraisal of known malaria resistance loci in a large multicenter study. Nat. Genet. 46, 1197–1204 (2014).

  4. Pilia, G. et al. Heritability of cardiovascular and personality traits in 6,148 Sardinians. PLoS Genet. 2, e132 (2006).

    PubMed  PubMed Central  Google Scholar 

  5. Menzel, S., Garner, C., Rooks, H., Spector, T.D. & Thein, S.L. HbA2 levels in normal adults are influenced by two distinct genetic mechanisms. Br. J. Haematol. 160, 101–105 (2013).

    Article  CAS  PubMed  Google Scholar 

  6. Bae, H.T. et al. Meta-analysis of 2040 sickle cell anemia patients: BCL11A and HBS1L-MYB are the major modifiers of HbF in African Americans. Blood 120, 1961–1962 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Uda, M. et al. Genome-wide association study shows BCL11A associated with persistent fetal hemoglobin and amelioration of the phenotype of β-thalassemia. Proc. Natl. Acad. Sci. USA 105, 1620–1625 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lettre, G. et al. DNA polymorphisms at the BCL11A, HBS1L-MYB, and β-globin loci associate with fetal hemoglobin levels and pain crises in sickle cell disease. Proc. Natl. Acad. Sci. USA 105, 11869–11874 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Danjou, F. et al. Genetic modifiers of β-thalassemia and clinical severity as assessed by age at first transfusion. Haematologica 97, 989–993 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Danjou, F. et al. A genetic score for the prediction of β-thalassemia severity. Haematologica 100, 452–457 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. van der Harst, P. et al. Seventy-five genetic loci influencing the human red blood cell. Nature 492, 369–375 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Trecartin, R.F. et al. Beta zero thalassemia in Sardinia is caused by a nonsense mutation. J. Clin. Invest. 68, 1012–1017 (1981).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Sidore, C. et al. Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers. Nat. Genet. doi: 10.1038/ng.3368 (14 September 2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Freson, K. et al. Molecular cloning and characterization of the GATA1 cofactor human FOG1 and assessment of its binding to GATA1 proteins carrying D218 substitutions. Hum. Genet. 112, 42–49 (2003).

    Article  CAS  PubMed  Google Scholar 

  16. Nichols, K.E. et al. Familial dyserythropoietic anaemia and thrombocytopenia due to an inherited mutation in GATA1. Nat. Genet. 24, 266–270 (2000).

    Article  CAS  PubMed  Google Scholar 

  17. Kozar, K. et al. Mouse development and cell proliferation in the absence of D-cyclins. Cell 118, 477–491 (2004).

    Article  CAS  PubMed  Google Scholar 

  18. Sankaran, V.G. et al. Cyclin D3 coordinates the cell cycle during differentiation to regulate erythrocyte size and number. Genes Dev. 26, 2075–2087 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Soranzo, N. et al. A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nat. Genet. 41, 1182–1190 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kamatani, Y. et al. Genome-wide association study of hematological and biochemical traits in a Japanese population. Nat. Genet. 42, 210–215 (2010).

    Article  CAS  PubMed  Google Scholar 

  21. Kathiresan, S. et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet. 41, 56–65 (2009).

    Article  CAS  PubMed  Google Scholar 

  22. Jarvik, G.P. et al. Genetic and nongenetic sources of variation in phospholipid transfer protein activity. J. Lipid Res. 51, 983–990 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lettre, G. et al. Genome-wide association study of coronary heart disease and its risk factors in 8,090 African Americans: the NHLBI CARe Project. PLoS Genet. 7, e1001300 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kettunen, J. et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat. Genet. 44, 269–276 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hirose, Y. et al. Human phosphorylated CTD–interacting protein, PCIF1, negatively modulates gene expression by RNA polymerase II. Biochem. Biophys. Res. Commun. 369, 449–455 (2008).

    Article  CAS  PubMed  Google Scholar 

  26. Lessard, S., Beaudoin, M., Benkirane, K. & Lettre, G. Comparison of DNA methylation profiles in human fetal and adult red blood cell progenitors. Genome Med. 7, 1 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Riddell, J. et al. Reprogramming committed murine blood cells to induced hematopoietic stem cells with defined factors. Cell 157, 549–564 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Holmfeldt, P. et al. Nfix is a novel regulator of murine hematopoietic stem and progenitor cell survival. Blood 122, 2987–2996 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kawane, K. et al. Requirement of DNase II for definitive erythropoiesis in the mouse fetal liver. Science 292, 1546–1549 (2001).

    Article  CAS  PubMed  Google Scholar 

  30. Porcu, S. et al. Klf1 affects DNase IIα expression in the central macrophage of a fetal liver erythroblastic island: a non-cell-autonomous role in definitive erythropoiesis. Mol. Cell. Biol. 31, 4144–4154 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhou, D., Liu, K., Sun, C.-W., Pawlik, K.M. & Townes, T.M. KLF1 regulates BCL11A expression and γ- to β-globin gene switching. Nat. Genet. 42, 742–744 (2010).

    Article  CAS  PubMed  Google Scholar 

  32. Siatecka, M. & Bieker, J.J. The multifunctional role of EKLF/KLF1 during erythropoiesis. Blood 118, 2044–2054 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Satta, S. et al. Compound heterozygosity for KLF1 mutations associated with remarkable increase of fetal hemoglobin and red cell protoporphyrin. Haematologica 96, 767–770 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Borg, J. et al. Haploinsufficiency for the erythroid transcription factor KLF1 causes hereditary persistence of fetal hemoglobin. Nat. Genet. 42, 801–805 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Perseu, L. et al. KLF1 gene mutations cause borderline HbA2. Blood 118, 4454–4458 (2011).

    Article  CAS  PubMed  Google Scholar 

  36. 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

  37. Su, A.I. et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl. Acad. Sci. USA 101, 6062–6067 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Raychaudhuri, S. et al. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 5, e1000534 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Andrews, N.C. The NF-E2 transcription factor. Int. J. Biochem. Cell Biol. 30, 429–432 (1998).

    Article  CAS  PubMed  Google Scholar 

  41. Hoogewijs, D. et al. Androglobin: a chimeric globin in metazoans that is preferentially expressed in Mammalian testes. Mol. Biol. Evol. 29, 1105–1114 (2012).

    Article  CAS  PubMed  Google Scholar 

  42. Iolascon, A., Perrotta, S. & Stewart, G.W. Red blood cell membrane defects. Rev. Clin. Exp. Hematol. 7, 22–56 (2003).

    CAS  PubMed  Google Scholar 

  43. Moayyeri, A., Hammond, C.J., Valdes, A.M. & Spector, T.D. Cohort profile: TwinsUK and Healthy Ageing Twin Study. Int. J. Epidemiol. 42, 76–85 (2013).

    Article  PubMed  Google Scholar 

  44. Sangerman, J. et al. Mechanism for fetal hemoglobin induction by histone deacetylase inhibitors involves γ-globin activation by CREB1 and ATF-2. Blood 108, 3590–3599 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Goh, S.-H. et al. A newly discovered human α-globin gene. Blood 106, 1466–1472 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Farrell, J.J. et al. A 3-bp deletion in the HBS1L-MYB intergenic region on chromosome 6q23 is associated with HbF expression. Blood 117, 4935–4945 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Stadhouders, R. et al. HBS1L-MYB intergenic variants modulate fetal hemoglobin via long-range MYB enhancers. J. Clin. Invest. 124, 1699–1710 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Zeller, T. et al. Genetics and beyond—the transcriptome of human monocytes and disease susceptibility. PLoS ONE 5, e10693 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Bhatnagar, P. et al. Genome-wide association study identifies genetic variants influencing F-cell levels in sickle-cell patients. J. Hum. Genet. 56, 316–323 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Bauer, D.E. & Orkin, S.H. Update on fetal hemoglobin gene regulation in hemoglobinopathies. Curr. Opin. Pediatr. 23, 1–8 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Bauer, D.E. et al. An erythroid enhancer of BCL11A subject to genetic variation determines fetal hemoglobin level. Science 342, 253–257 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Grundberg, E. et al. Global analysis of DNA methylation variation in adipose tissue from twins reveals links to disease-associated variants in distal regulatory elements. Am. J. Hum. Genet. 93, 876–890 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Karolchik, D. et al. The UCSC Genome Browser database: 2014 update. Nucleic Acids Res. 42, D764–D770 (2014).

    Article  CAS  PubMed  Google Scholar 

  54. Rosenbloom, K.R. et al. ENCODE data in the UCSC Genome Browser: year 5 update. Nucleic Acids Res. 41, D56–D63 (2013).

    Article  CAS  PubMed  Google Scholar 

  55. Steinberg, M.H. & Adams, J.G. Hemoglobin A2: origin, evolution, and aftermath. Blood 78, 2165–2177 (1991).

    CAS  PubMed  Google Scholar 

  56. Pistis, G. et al. Rare variant genotype imputation with thousands of study-specific whole-genome sequences: implications for cost-effective study designs. Eur. J. Hum. Genet. 23, 975–983 (2015).

    Article  PubMed  Google Scholar 

  57. Goldstein, J.I. et al. zCall: a rare variant caller for array-based genotyping: genetics and population analysis. Bioinformatics 28, 2543–2545 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Li, Y., Willer, C.J., Ding, J., Scheet, P. & Abecasis, G.R. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816–834 (2010).

    PubMed  PubMed Central  Google Scholar 

  59. Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G.R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kang, H.M. et al. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Abecasis, G.R., Cherny, S.S., Cookson, W.O. & Cardon, L.R. Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nat. Genet. 30, 97–101 (2002).

    Article  CAS  PubMed  Google Scholar 

  62. R Core Development Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).

  63. Origa, R. et al. Complexity of the α-globin genotypes identified with thalassemia screening in Sardinia. Blood Cells Mol. Dis. 52, 46–49 (2014).

    Article  CAS  PubMed  Google Scholar 

  64. Naitza, S. et al. A genome-wide association scan on the levels of markers of inflammation in Sardinians reveals associations that underpin its complex regulation. PLoS Genet. 8, e1002480 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Howie, B.N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    PubMed  PubMed Central  Google Scholar 

  66. Menzel, S. et al. A QTL influencing F cell production maps to a gene encoding a zinc-finger protein on chromosome 2p15. Nat. Genet. 39, 1197–1199 (2007).

    Article  CAS  PubMed  Google Scholar 

  67. Myers, A.J. et al. A survey of genetic human cortical gene expression. Nat. Genet. 39, 1494–1499 (2007).

    Article  CAS  PubMed  Google Scholar 

  68. Stranger, B.E. et al. Population genomics of human gene expression. Nat. Genet. 39, 1217–1224 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Veyrieras, J.-B. et al. High-resolution mapping of expression-QTLs yields insight into human gene regulation. PLoS Genet. 4, e1000214 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Dimas, A.S. et al. Common regulatory variation impacts gene expression in a cell type–dependent manner. Science 325, 1246–1250 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Pickrell, J.K. et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768–772 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Fehrmann, R.S.N. Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. PLoS Genet. 7, e1002197 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Innocenti, F. et al. Identification, replication, and functional fine-mapping of expression quantitative trait loci in primary human liver tissue. PLoS Genet. 7, e1002078 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Montgomery, S.B., Lappalainen, T., Gutierrez-Arcelus, M. & Dermitzakis, E.T. Rare and common regulatory variation in population-scale sequenced human genomes. PLoS Genet. 7, e1002144 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Degner, J.F. et al. DNaseI sensitivity QTLs are a major determinant of human expression variation. Nature 482, 390–394 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Gaffney, D.J. et al. Dissecting the regulatory architecture of gene expression QTLs. Genome Biol. 13, R7 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Wright, F.A., Shabalin, A.A. & Rusyn, I. Computational tools for discovery and interpretation of expression quantitative trait loci. Pharmacogenomics 13, 343–352 (2012).

    Article  CAS  PubMed  Google Scholar 

  78. Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Westra, H.-J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Battle, A. et al. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res. 24, 14–24 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Fairfax, B.P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Willer, C.J., Li, Y. & Abecasis, G.R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work is dedicated to Antonio Cao, Renzo Galanello and Maurizio Longinotti, who devoted their scientific lives to understanding, preventing and treating hematological diseases in Sardinia. We are also grateful to M.S. Ristaldi and M.G. Marini for knowledge and insight that they freely shared with us. Finally, we thank all the volunteers who generously participated in this study and made this research possible. The SardiNIA study was funded in part by the US National Institutes of Health (National Institute on Aging, National Heart, Lung, and Blood Institute, and National Human Genome Research Institute). This research was supported by National Human Genome Research Institute grants HG005581, HG005552, HG006513 and HG007022; by National Heart, Lung, and Blood Institute grant HL117626; by the Intramural Research Program of the US National Institutes of Health, National Institute on Aging, contracts N01-AG-1-2109 and HHSN271201100005C; by Sardinian Autonomous Region (L.R. number 7/2009) grant cRP3-154; by grant FaReBio2011 “Farmaci e Reti Biotecnologiche di Qualità”; and by the PB05 InterOmics MIUR Flagship Project. The TwinsUK study was funded by the Wellcome Trust; the European Community's Seventh Framework Programme (FP7/2007-2013); and the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. Genotyping in the replication cohorts was performed by the Wellcome Trust Sanger Institute and National Eye Institute via the US National Institutes of Health/Center for Inherited Disease Research (CIDR). S.L.T. was supported by the Medical Research Council, UK (grant G0000111, ID51640), and S. Menzel received funding from the British Society for Haematology (start-up grant).

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G.R.A., D.S. and F.C. conceived the study. F.D., D.S., S.S. and F.C. drafted the manuscript. F.D., M.Z., M.U., P.M., S.L.T., G.R.A., D.S., S.S. and F.C. revised the manuscript. F.B., A. Maschio and A.A. performed sequencing experiments. M. Pitzalis, G.R.A. and S.S. selected samples for sequencing. F.D., C.S., M.S., E.P., G.P. and S.S. carried out genetic association analyses in the SardiNIA cohort. C.S. analyzed DNA sequence data. M.Z., F.B. and A. Mulas carried out SNP array genotyping. M.Z. designed the validation strategy, and M.Z., F.B. and A. Mulas verified genotypes by Sanger sequencing and TaqMan genotyping. L.P. performed genotyping of –α 3.7 deletion type I. M. Pala created an automatized pipeline to query the public eQTL repositories. P.M. and R.G. provided genotypes and phenotypic data for patients with β-thalassemia. S.B. and R.G. supervised the characterization of the hemoglobins in the SardiNIA cohort. F.D. analyzed the cohort of patients with β-thalassemia. S. Menzel, T.D.S. and S.L.T. provided replication samples. S. Metrustry analyzed replication samples. L.L. provided IT support for sequencing and genotype data processing and analyses. D.S. and F.C. supervised the study. All authors reviewed and approved the final manuscript.

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Correspondence to Fabrice Danjou or Francesco Cucca.

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Danjou, F., Zoledziewska, M., Sidore, C. et al. Genome-wide association analyses based on whole-genome sequencing in Sardinia provide insights into regulation of hemoglobin levels. Nat Genet 47, 1264–1271 (2015). https://doi.org/10.1038/ng.3307

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