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. 2012 Dec 20;492(7429):369-75.
doi: 10.1038/nature11677. Epub 2012 Dec 5.

Seventy-five genetic loci influencing the human red blood cell

Pim van der Harst  1 , Weihua Zhang, Irene Mateo Leach, Augusto Rendon, Niek Verweij, Joban Sehmi, Dirk S Paul, Ulrich Elling, Hooman Allayee, Xinzhong Li, Aparna Radhakrishnan, Sian-Tsung Tan, Katrin Voss, Christian X Weichenberger, Cornelis A Albers, Abtehale Al-Hussani, Folkert W Asselbergs, Marina Ciullo, Fabrice Danjou, Christian Dina, Tõnu Esko, David M Evans, Lude Franke, Martin Gögele, Jaana Hartiala, Micha Hersch, Hilma Holm, Jouke-Jan Hottenga, Stavroula Kanoni, Marcus E Kleber, Vasiliki Lagou, Claudia Langenberg, Lorna M Lopez, Leo-Pekka Lyytikäinen, Olle Melander, Federico Murgia, Ilja M Nolte, Paul F O'Reilly, Sandosh Padmanabhan, Afshin Parsa, Nicola Pirastu, Eleonora Porcu, Laura Portas, Inga Prokopenko, Janina S Ried, So-Youn Shin, Clara S Tang, Alexander Teumer, Michela Traglia, Sheila Ulivi, Harm-Jan Westra, Jian Yang, Jing Hua Zhao, Franco Anni, Abdel Abdellaoui, Antony Attwood, Beverley Balkau, Stefania Bandinelli, François Bastardot, Beben Benyamin, Bernhard O Boehm, William O Cookson, Debashish Das, Paul I W de Bakker, Rudolf A de Boer, Eco J C de Geus, Marleen H de Moor, Maria Dimitriou, Francisco S Domingues, Angela Döring, Gunnar Engström, Gudmundur Ingi Eyjolfsson, Luigi Ferrucci, Krista Fischer, Renzo Galanello, Stephen F Garner, Bernd Genser, Quince D Gibson, Giorgia Girotto, Daniel Fannar Gudbjartsson, Sarah E Harris, Anna-Liisa Hartikainen, Claire E Hastie, Bo Hedblad, Thomas Illig, Jennifer Jolley, Mika Kähönen, Ido P Kema, John P Kemp, Liming Liang, Heather Lloyd-Jones, Ruth J F Loos, Stuart Meacham, Sarah E Medland, Christa Meisinger, Yasin Memari, Evelin Mihailov, Kathy Miller, Miriam F Moffatt, Matthias Nauck, Maria Novatchkova, Teresa Nutile, Isleifur Olafsson, Pall T Onundarson, Debora Parracciani, Brenda W Penninx, Lucia Perseu, Antonio Piga, Giorgio Pistis, Anneli Pouta, Ursula Puc, Olli Raitakari, Susan M Ring, Antonietta Robino, Daniela Ruggiero, Aimo Ruokonen, Aude Saint-Pierre, Cinzia Sala, Andres Salumets, Jennifer Sambrook, Hein Schepers, Carsten Oliver Schmidt, Herman H W Silljé, Rob Sladek, Johannes H Smit, John M Starr, Jonathan Stephens, Patrick Sulem, Toshiko Tanaka, Unnur Thorsteinsdottir, Vinicius Tragante, Wiek H van Gilst, L Joost van Pelt, Dirk J van Veldhuisen, Uwe Völker, John B Whitfield, Gonneke Willemsen, Bernhard R Winkelmann, Gerald Wirnsberger, Ale Algra, Francesco Cucca, Adamo Pio d'Adamo, John Danesh, Ian J Deary, Anna F Dominiczak, Paul Elliott, Paolo Fortina, Philippe Froguel, Paolo Gasparini, Andreas Greinacher, Stanley L Hazen, Marjo-Riitta Jarvelin, Kay Tee Khaw, Terho Lehtimäki, Winfried Maerz, Nicholas G Martin, Andres Metspalu, Braxton D Mitchell, Grant W Montgomery, Carmel Moore, Gerjan Navis, Mario Pirastu, Peter P Pramstaller, Ramiro Ramirez-Solis, Eric Schadt, James Scott, Alan R Shuldiner, George Davey Smith, J Gustav Smith, Harold Snieder, Rossella Sorice, Tim D Spector, Kari Stefansson, Michael Stumvoll, W H Wilson Tang, Daniela Toniolo, Anke Tönjes, Peter M Visscher, Peter Vollenweider, Nicholas J Wareham, Bruce H R Wolffenbuttel, Dorret I Boomsma, Jacques S Beckmann, George V Dedoussis, Panos Deloukas, Manuel A Ferreira, Serena Sanna, Manuela Uda, Andrew A Hicks, Josef Martin Penninger, Christian Gieger, Jaspal S Kooner, Willem H Ouwehand, Nicole Soranzo, John C Chambers
Affiliations

Seventy-five genetic loci influencing the human red blood cell

Pim van der Harst et al. Nature. .

Abstract

Anaemia is a chief determinant of global ill health, contributing to cognitive impairment, growth retardation and impaired physical capacity. To understand further the genetic factors influencing red blood cells, we carried out a genome-wide association study of haemoglobin concentration and related parameters in up to 135,367 individuals. Here we identify 75 independent genetic loci associated with one or more red blood cell phenotypes at P < 10(-8), which together explain 4-9% of the phenotypic variance per trait. Using expression quantitative trait loci and bioinformatic strategies, we identify 121 candidate genes enriched in functions relevant to red blood cell biology. The candidate genes are expressed preferentially in red blood cell precursors, and 43 have haematopoietic phenotypes in Mus musculus or Drosophila melanogaster. Through open-chromatin and coding-variant analyses we identify potential causal genetic variants at 41 loci. Our findings provide extensive new insights into genetic mechanisms and biological pathways controlling red blood cell formation and function.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. Gene-expression patterns for 121 putative candidate genes, and tissue distribution of NDRs
a, Heat-map of candidate genes in the Differentiation Map of Hematology. Cell acronyms refer to original source (summarized in Supplementary Table 15). Expression above a log2 signal intensity (SI) of 6 is consistently above background. b, −log10 P of the signed-rank test for candidate genes being more highly expressed in each cell type than non-candidate genes. c, Time-course of differentiation of cord-blood haematopoietic stem cells cultured along the erythroid lineage. Putative candidate genes are shown as upregulated (red), downregulated (blue) or with the slope not being significantly different from zero (grey). d, Tissue distribution of NDRs containing a potential causal variant. NDRs were ranked by peak score (proportional to their peak height in FAIRE-seq). The rankings were then used to divide the NDRs into cumulative tranches to explore the effect of calling-thresholds on results (left bar, tranche containing the 5,000 top-ranked NDRs of each cell type; penultimate bar, tranche containing the 50,000 top-ranked NDRs of each cell type). The solid line indicates the number of SNPs overlapping the tranche-specific NDRs that are potential causal variants (defined as a sentinel SNP from the red blood cell GWAS, or a SNP in high linkage disequilibrium (r2 >0.8) and located within 1 Mb of a sentinel SNP; right-hand y axis); the bar summarizes the tissue distribution of these SNPs (as a percentage of tranche-specific total). The right-hand bar represents the expected tissue distribution for the SNPs under the null hypothesis. Results show that the potential causal variants are most commonly found in erythroblast-specific NDRs, and that this is true across the spectrum of peak-calling thresholds.
Figure 2
Figure 2. RNAi silencing in D. melanogaster
a, Plasmatocytes imaged by green fluorescent protein expression (light green spots on posterior dorsal end of L3 larvae) from wild-type (WT) cells and cells with RNAi silencing of orthologues of the following human genes: CRHR1 (106381, increased cell counts (CC)), KIT (13502, decreased CC) and CCNA2 (32421, increased CC). Numbers represent the unique Flybase IDs corresponding to the D. melanogaster orthologues. Scale bar, 0.5 mm. Bottom right, plasmatocyte size is also increased in CCNA2 compared to wild type. Scale bars, 0.1 mm. b, Crystal cells (black spots visualized by heating larvae to 60 °C) in wild-type larvae, and in RNAi silencing of ATP5O (12794, increased CC), UBE2L3 (110767, decreased CC) or ATP2B4 (101743, aggregated). Scale bars, 0.5 mm.
Figure 3
Figure 3. Association of SNP score with red blood cell phenotypes
Results presented as odds ratio (95% confidence interval) for participants in each SNP score quartile (Q) having phenotype level in the top quartile versus the lowest quartile of the respective population distribution, compared to people in the lowest quartile of SNP score (Q1, reference group). HB, haemoglobin; n, number of participants in the respective comparison of SNP score quartiles.
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