Genome Wide Association Analysis of a Founder Population Identified TAF3 as a Gene for MCHC in Humans

Abstract

The red blood cell related traits are highly heritable but their genetics are poorly defined. Only 5–10% of the total observed variance is explained by the genetic loci found to date, suggesting that additional loci should be searched using approaches alternative to large meta analysis. GWAS (Genome Wide Association Study) for red blood cell traits in a founder population cohort from Northern Italy identified a new locus for mean corpuscular hemoglobin concentration (MCHC) in the TAF3 gene. The association was replicated in two cohorts (rs1887582, P = 4.25E–09). TAF3 encodes a transcription cofactor that participates in core promoter recognition complex, and is involved in zebrafish and mouse erythropoiesis. We show here that TAF3 is required for transcription of the SPTA1 gene, encoding alpha spectrin, one of the proteins that link the plasma membrane to the actin cytoskeleton. Mutations in SPTA1 are responsible for hereditary spherocytosis, a monogenic disorder of MCHC, as well as for the normal MCHC level. Based on our results, we propose that TAF3 is required for normal erythropoiesis in human and that it might have a role in controlling the ratio between hemoglobin (Hb) and cell volume and in the dynamics of RBC maturation in healthy individuals. Finally, TAF3 represents a potential candidate or a modifier gene for disorders of red cell membrane.

About the Authors:

Giorgio Pistis

Affiliation: Division of Genetics and Cell Biology, San Raffaele Research Institute and Vita Salute University, Milano, Italy

Shawntel U. Okonkwo

Affiliation: Cardiovascular Research Institute, University of California San Francisco, San Francisco, California, United States of America

Michela Traglia

Affiliation: Division of Genetics and Cell Biology, San Raffaele Research Institute and Vita Salute University, Milano, Italy

Cinzia Sala

Affiliation: Division of Genetics and Cell Biology, San Raffaele Research Institute and Vita Salute University, Milano, Italy

So-Youn Shin

Affiliation: Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom

Corrado Masciullo

Affiliation: Division of Genetics and Cell Biology, San Raffaele Research Institute and Vita Salute University, Milano, Italy

Iwan Buetti

Affiliation: Division of Genetics and Cell Biology, San Raffaele Research Institute and Vita Salute University, Milano, Italy

Roberto Massacane

Affiliation: Novi Ligure Hospital, Novi Ligure (Al), Italy

Massimo Mangino

Affiliation: Department of Twin Research & Genetic Epidemiology, King's College London, London, United Kingdom

Swee-Lay Thein

Affiliation: Department of Molecular Hematology, King’s College London, London, United Kingdom

Timothy D. Spector

Affiliation: Department of Twin Research & Genetic Epidemiology, King's College London, London, United Kingdom

Santhi Ganesh

¶Membership of the CHARGE Consortium Hematology Working Group is provided in the Acknowledgments.

Affiliation: Division of Cardiovascular Medicine, University of Michigan Health System, Ann Arbor, Michigan, United States of America

Nicola Pirastu

Affiliation: Medical Genetics, Department of Reproductive Sciences and Development, University of Trieste, Trieste, Italy

Paolo Gasparini

Affiliations Medical Genetics, Department of Reproductive Sciences and Development, University of Trieste, Trieste, Italy, Medical Genetics, Department of Laboratory Medicine, Institute for Maternal and Child Health IRCCS-Burlo Garofolo, Trieste, Italy

Nicole Soranzo

Affiliations Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom, Department of Twin Research & Genetic Epidemiology, King's College London, London, United Kingdom

Clara Camaschella

Affiliation: Division of Genetics and Cell Biology, San Raffaele Research Institute and Vita Salute University, Milano, Italy

Daniel Hart

Affiliations Cardiovascular Research Institute, University of California San Francisco, San Francisco, California, United States of America, Howard Hughes Medical Institute, Program in Gene Function and Expression and Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America

Michael R. Green

Affiliation: Howard Hughes Medical Institute, Program in Gene Function and Expression and Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America

Daniela Toniolo

* E-mail: [email protected]

Affiliations Division of Genetics and Cell Biology, San Raffaele Research Institute and Vita Salute University, Milano, Italy, Institute of Molecular Genetics-CNR, Pavia, Italy

Introduction

Erythrocytes comprise 40–50% of blood volume and are key components for the transport of oxygen and carbon dioxide for cellular respiration. In healthy individuals the red blood cell-related traits (hemoglobin (Hb), hematocrit (Hct), red cell count (RBC), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC)) are variable and are determined by both inherited and environmental factors. Besides age and sex, acquired factors such as diet, smoking, body weight, hypoxia, blood loss, infections and inflammation are well known to influence erythropoiesis. Heritability of red blood cell traits is high (h2 = 04 to 06) [1] but the genetic determinants remain largely unknown. A better knowledge of the inherited factors determining variation of erythrocyte traits will be relevant to understand the physiology of red blood cell production and the relationship between red cell size and Hb content. This might have implication also for the so-called anemia of the elderly [2], [3], [4] contributing to cognitive impairment and impaired physical capacity in older people.

Recently, 75 independent genetic loci associated with red blood cell phenotypes have been reported from a meta analysis including >135.000 individuals of European or South Asian ancestry. Together, they explain 4–9% of the phenotypic variance per trait [5]. Among the different approaches considered, the use of selected populations, such as genetically isolated populations, was suggested to account for the remaining variability [6] and help in better defining the genetic component of complex traits. Because of their recent origin from a limited number of founders and of subsequent isolation, such populations have been predicted to facilitate the identification of rare variants in the general population that may be enriched in a founder population or could be more easily detected in association with common variants thanks to increased linkage disequilibrium (LD). The relatively homogeneous genetic background and environmental exposure are well-established characteristics of genetic isolates [7], [8], [9], [10]. Moreover, comparison with environmental effects and collection of longitudinal data over several decades may facilitate investigation of relationships between genetic variation and studied traits, early life events and progression of disease risks.

We have collected health and family data of an Italian isolated population living in a valley of North West Italy in the Apennine Mountains, the Val Borbera (INGI-VB) inhabited by about 3000 descendants from the original population [1], [11]. GWAS of the available red blood cell traits identified a novel locus on chromosome 10, associated with MCHC. The locus encompassed the TAF3 (TATA box binding protein (TBP)-associated factor 3) gene, previously shown to be involved in erythropoiesis in zebrafish and mouse [12]. We present here evidence for a role of TAF3 in expression of cytoskeletal proteins relevant for RBC membrane size and for MCHC in humans.

Materials and Methods

Study subjects

The INGI-VB population [1] is described in Materials S1. Only individuals aged 18 years or older were eligible to participate in the study.

The cohorts used in the study (one group of genetically isolated populations from North East Alps in Italy, the Friuli Venezia Giulia (INGI-FVG) region, and outbred population-based cohorts of European ancestry, TwinsUK, AGES, RS, FHS and InChianti) are described in Materials S1.

Blood tests

Fasting blood samples were obtained in the early morning. Complete blood cell (CBC) count was performed the same day by the diagnostic laboratory of the Novi Ligure (AL, Italy) Hospital, by standard methods [1]. MCHC (mean corpuscular hemoglobin concentration) is expressed as grams of hemoglobin per 100 ml of packed cells and is calculated as the ratio between hemoglobin concentration and hematocrit.

Genotyping and association analysis

One thousand six hundred and sixty-four DNAs from the INGI-VB population were genotyped using the Illumina 370 Quad-CNV array, v3. 343,867 SNPs passed quality control checks (SNP call rate >90%, MAF ≥1%, HWE P-value ≥10–4) and were used in subsequent studies. We also imputed genotypes from the 2.5 millions polymorphic HapMap data set, using MACH (http://www.sph.umich.edu/csg/abecasis/mach/). We chose an estimated r2 >0.3 as a threshold to flag and discard low-quality imputed SNPs.

GWAS of the red blood cell traits was done using the GenABEL and ProbABEL packages [13] that take into account the relatedness among the INGI-VB cohort, using genomic kinship. In order to focus on determinants of normal variation of hematological traits in the general populations we restricted the analysis to those individuals within three standard deviations of the population mean. To normalize the distributions, natural log transformation was applied to MCH, MCHC and MCV and square root transformation was applied to RBC, prior to analysis. We used an additive model including sex and age as covariates for Hb, Hct, MCH, MCV, RBC and sex and age2 for MCHC. Quantile-Quantile (Q-Q) plots of all the GWAS are shown in Fig. S1.

For meta-analysis we used a fixed-effects inverse variance method as implemented in METAL (http://www.sph.umich.edu/csg/abecasis/Metal/index.html).

Cell culture and transfections

Mouse erythroleukemia (MEL) cells [14] were a kind gift from Merav Socolovsky {University of Massachusetts Medical School}. K562 cells were obtained from the American Type Culture Collection [ATCC® CCL-243]. MEL cells were maintained in Dulbecco’s Modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS). Erythroid differentiation was induced by the addition of dimethyl sulfoxide (DMSO) to 2% in culture medium. Human K562 cells were maintained in RPMI supplemented with 10% FBS.

ShRNA- induced knockdowns

Two anti human TAF3 shRNA constructs, TRCN0000016610, and TRCN0000016612, were obtained from The RNAi Consortium (Broad Institute). Two anti-mouse TAF3 shRNA constructs, SM2441e7, and SM2496h12 and the control scrambled shRNA were obtained from Open Biosystems (Huntsville, AL, USA). These constructs were electroporated into cells using the Amaxa Nucleofector AAD-1001 electroporator (Lonza, Walkersville, MD, USA). Cell line Nucleofector Kit V (Cat. No. VCA-1003) was used according to manufacturer’s instructions (Lonza, Walkersville, MD, USA).

Chromatin immunoprecipitation (ChIP)

ChIP was performed using standard protocols, with the following modifications: sequential cross-linking was performed using ethylene glycol bis[succinimidyl succinate] (EGS) and formaldehyde as follows. Cells were crosslinked in 1.5 mM EGS in PBS for 20 minutes at room temperature, followed by the addition of formaldehyde to 1% final concentration for 10 minutes. Affinity purified rabbit anti-TAF3 was obtained from Bethyl Laboratories (Montgomery, TX, USA), catalog No. A302–360A. Isolated chromatin was sheared using a Diagenode Bioruptor (Liege, Belgium). Fold enrichment was calculated as normalized fold differences of cycle thresholds [2–(−ΔΔCt)] from specific IP over non specific IgG after quantitative PCR.

Quantitative RT-PCR

Total RNA was isolated and reverse transcription performed, followed by quantitative real-time PCR using SYBR Green on an 7500 Fast Real Time PCR System (Applied Biosystems-Life Technologies Corporation, Carlsbad, CA, USA). For all reactions, the Ct values of samples were analyzed after subtracting the signal obtained with the non-silencing shRNA (for RNAi) controls. Fold knockdown or gene expression level was calculated by normalizing the expression of the target gene following knockdown to that obtained using a non-silencing shRNA. TAF3 recruitment levels were determined by normalizing levels to those obtained with no antibody controls.

The primers used are listed in Tables S1 and S2.

Ethics Statements

INGI-VB. The study was approved by San Raffaele Hospital and Regione Piemonte ethical committees. Written informed consent was obtained from every participant to the study as indicated by the San Raffaele Hospital and Regione Piemonte ethical committees.

INGI-FVG. Ethics approval was obtained from the Ethics Committee of the Burlo Garofolo Children Hospital in Trieste. Written informed consent was obtained from every participant to the study as indicated by the Burlo Garofolo Children Hospital Ethics Committee.

TWINSUK. Twins largely volunteered unaware of any their phenotypic status in relationship to SAA or any other phenotypic trait of interest to the authors and they gave written fully informed consent under a protocol reviewed and approved by the St Thomas’ Hospital Local Research Ethics Committee.

AGES. The AGES-Reykjavik Study GWAS was approved by the National Bioethics Committee (VSN: 00–063) and the Data Protection Authority. Written informed consent was obtained from every participant to the study as indicated by the National Bioethics Committee

FHS. The research protocols of the Framingham Heart Study are reviewed and approved annually by the Institutional Review Board of the Boston University Medical Center and by the Observational Studies Monitoring Board of the National Heart, Lung and Blood Institute. Since 1971, written consent has been obtained from participants before each examination.

InCHIANTI. The ethical committee of the INRCA “I Fraticini,” Florence, Italy, approved the InCHIANTI study protocol. All participants signed an informed participation consent.

Results

Identification of a new locus for MCHC in the INGI-VB population

GWAS of haemoglobin concentration and related parameters was performed in 1664 genotyped individual from INGI-VB and many of the SNPs previously identified [15], [16], [17] as associated to RBC related traits were replicated. Some of the SNPs represented nominally significant replicas, others had a highly significant p-value (Table S3). In this analysis we also identified two novel GW significant loci, both associated with MCHC (Table 1). One, rs1155865 on chromosome 4 (p-value  = 3.05E–08), was in a region devoid of any known gene A group of genes could be found only more than 1 Mb down stream. This association could not be replicated. The second SNP, rs1887582, on chromosome 10 was borderline GW significant (p-value  = 3.75E–07). The presence of the variant significantly decreased MCHC from an average value of 33.13 g/dl in AA homozygotes to 32.65 g/dl in GG homozygotes. The association of the SNP rs1887582 could be replicated in two additional cohorts (INGI-FVG and Twins UK) (Table 2). The combined p-value was highly significant (beta  = −0.0059, SE  = 0.0010, p-value  = 4.25E–09, HET p-value  = 0.591) (Fig. S2). No other red blood cell trait was significantly associated with rs1887582.

[Figure omitted. See PDF.]

Table 1. SNPs associated with MCHC in the INGI-VB cohort.

https://doi.org/10.1371/journal.pone.0069206.t001

[Figure omitted. See PDF.]

Table 2. rs1887582 replica.

https://doi.org/10.1371/journal.pone.0069206.t002

We could study four additional cohorts (RS, FHS, AGES and InChianti) : rs1887582 variant was significantly associated in AGES (p-value  = 8.70E–04). The other populations did not show association: in all three, the variant had an opposite sign effect (Table 3).

[Figure omitted. See PDF.]

Table 3. rs1887582 association in general population cohorts.

https://doi.org/10.1371/journal.pone.0069206.t003

The new MCHC locus encompasses the TAF3 gene

The SNP rs1887582 mapped to the TAF3 gene (Fig 1). A number of additional SNPs, in LD with rs1887582 appeared associated with MCHC when the imputed SNP dataset was analyzed. The imputed SNP rs11255458 presented the lowest p-value (beta  = −0.0073, SE  = 0.0014, p-value  = 2.28E–07) and had an r2  = 0.88 with rs1887582. The peak of association mapped within the TAF3 gene. The recombination and LD map of the region are shown in Fig. 1A (CEU cohort from HapMap) and 1B (INGI-VB cohort). One recombination hot spot, separating two LD blocks and the variant from the promoter of the gene, was localized in the middle of the gene, proximal to association peak. Association analysis of MCHC conditioned on rs11255458 did not identify independent signals and confirmed that the association is due to LD to the SNP rs11255458.

[Figure omitted. See PDF.]

Figure 1. Regional association plot and linkage disequilibrium pattern at the TAF3 locus.

(A) Association of genotyped and imputed SNPs. The top imputed SNP (rs11255458) is highlighted in violet, the other SNPs are colored according to their degree of linkage disequilibrium (r2) with rs11255458. The chromosomal positions (NCBI human genome Build 36) of the SNPs are plotted against genomic control-adjusted -log10 p-value. The estimated recombination rates (cM/Mb) from HapMap CEU release 22 are shown as gray lines. (B) The D'-based LD map was build using genotyped and imputed data of the INGI-VB population.

https://doi.org/10.1371/journal.pone.0069206.g001

Interestingly, in K562 erythroid cells the locus is enriched for the histone modification H3K4me1, a histone mark associated with enhancer regions (Fig. S3). The rs11255458 SNP introduces a C/T change in the binding site for a repressor transcription factor, YY1, as determined by TESS analysis of the sequence [CACACAC/TTT) in both C and T forms ( http://www.cbil.upenn.edu/cgi-bin/tess/tess ). YY1 may direct histone deacetylases and histone acetyltransferases to promoter /enhancer regions. A C/T mutation in YY1 binding site may thus disrupt or enhance the erythrocyte-specific transcription of TAF3.

TAF3 interacts with the SPTA1 promoter and is required for its regulated expression

MCHC, the ratio between Hb and Hct, may be altered by changes in membrane structure that modify the surface/volume ratio of the erythrocyte, as observed in hereditary spherocytosis. TAF3 encodes a member of the large family of cofactors that specifically interact with TRF3 (TBP-related factor 3), one of the TATA binding proteins (TBP). We tested whether TAF3 could influence MCHC through transcriptional regulation of SPTA1, a gene encoding alpha spectrin, a red cell protein that links the plasma membrane to the actin cytoskeleton and is one of the genetic determinant of MCHC in disease [18] and in the normal range[15] (see also Table S3). We analyzed the expression of the gene in human K562 cells expressing shRNAs designed to target the TAF3 transcript. We observed a 50% reduction in the level of SPTA1 expression, by qRT-PCR analysis, in cell treated with both shRNAs compared with control scrambled shRNA(NS-shRNA) (Fig. 2A). We also transiently introduced constructs bearing control or Taf3 shRNAs into mouse erythroleukemia MEL cells. Transfected MEL cells were induced to erythroid differentiation for 4 days, and then monitored for the expression of SPTA1 by qRT-PCR analysis. As shown in Fig. 2 A, knockdown of TAF3 strongly reduced the expression of the mouse gene as well.

[Figure omitted. See PDF.]

Figure 2. The SPTA1 gene is transcriptionally regulated by TAF3.

(A) Knockdown of TAF3 by shRNA and its effect on SPTA1 expression in human K562 cells, and in differentiating mouse MEL cells. 4 days after induction. Expression was monitored by qRT-PCR. NS: control scrambled shRNA; TAF3kd1 and TAF3 kd2 and anti Taf3 shRNAs described in Methods. (B) Chromatin immunoprecipitation analysis of the recruitment of TAF3 and TBP to the indicated promoters in K562 cells shows that TAF3 is associated with SPTA1. TAF3: ChIP with anti Taf3 Ab; IgG: control chromatin immunoprecipitation with IgG addition. Asterisks indicate a statistical significant difference (*  = P<0.05; **  = P<0.01) between control and TAF3 knockdown data for MEL and K562 cells, and between specific immunoprecipitations and IgG control immunoprecipitations.

https://doi.org/10.1371/journal.pone.0069206.g002

To determine whether TAF3 directly regulates transcription of SPTA1, we performed chromatin immunoprecipitation assays in K562 cells and examined TAF3 and TBP recruitment to the SPTA1 promoter. We observed significant recruitment of TAF3 and TBP in NS shRNA K562 cells. The observed reduction in TBP recruitment to the SPTA1 promoter following TAF3 knockdown [Fig 2b] is consistent with the known stable association of TAF3 and TBP as components of the TFIID complex [19], [20]. These data confirm a role for TAF3 in regulating the transcription of SPTA1.

Discussion

We report here a new locus, associated with MCHC levels identified by GWAS of red blood cell traits in the INGI-VB founder population. The association to decreased MCHC level of the minor allele could be replicated in other populations, where it did not reach GW significance, confirming that isolated populations may be relevant tools for the identification of loci that escape large meta-analysis. Variants, rare in the general population, may have drifted to higher frequency in the genetic isolate, while the recent origin from a limited number of founders could have produced increased LD and homogenous LD maps. The SNP rs1887582 was indeed found at a 30% higher frequency of 0.22 in INGI-VB compared to 0.17 of most other cohorts. The presence of a recombination hot spot in the middle of the genetic association peak could have produced several different haplotypes in outbred populations, and increased the genetic heterogeneity. A unique haplotype in the isolated INGI-VB population, resulting from the less heterogeneous genetic background, could account for the differences in the p-values of the association in the different cohorts. Finally, the observation that the rs11255458 SNP introduces a C/T change in the binding site for a repressor transcription factor, YY1 in a putative enhancer region suggests that it could be the genetic background of the isolate that allows the identification of the variant as causative of an increase in MCHC.

The novel locus highlighted a transcription factor, TAF3, essential in hematopoiesis in zebrafish and in the mouse [12]. TAF3 encodes a member of the large family of cofactors that specifically interact with TRF3, one of the TATA binding proteins (TBP) and participates in recruitment of the multi-subunit core promoter recognition complex [12], [21]. We present here evidence for a role of this gene in human, in expression of proteins relevant for RBC membrane organization. The red cell has to maintain its structural integrity, membrane stability and deformability that allow erythrocyte survival in the circulation, especially in the spleen, in order to assure oxygen delivery to the tissues. Membrane deformability is mainly achieved by maintenance of vertical links between cytoskeleton components and the red cell membrane lipid bilayer. Weakening these links leads to decreased membrane surface, increased cell volume and increased MCHC. The role of cytoskeletal proteins in determining normal MCHC variation was confirmed by the finding of DNA variation(s) in SPTA1 associated with MCHC in the normal population [15] and in our sample (table S3). Mutations in SPTA1 were found in the monogenic disorder of MCHC, hereditary spherocytosis (HS, MIM ID #182900). Our results in K562 and MEL cells demonstrate a direct role for TAF3 in the transcriptional control of SPTA1, and bring further insights into the molecular pathways controlling red blood cell cytoskeleton.

TAF3 variation does not affect other RBC traits. We propose that it might be specifically involved in control of RBC volume and ultimately in the regulation of the ratio between Hb and cell volume and in the dynamics of RBC maturation in normal individuals. The mechanisms that regulate number, size and Hb concentration in normal erythropoiesis are poorly understood. Our results shed some light in a process continuously taking place in normal individuals, as ∼2.5E–11 new RBC are released every day from the bone marrow in the peripheral circulation to replace those that are continuously cleared and this number can increase up to 20 fold in cases of severe anemia [22], [23].

Finally, as mutations in various membrane and cytoskeleton components have been described in disorder affecting RBC geometry and particularly cell surface to volume ratio, TAF3 may represent a candidate gene for rare cases of HS or other inherited red cell membrane defects, non linked to known genes or a modifier gene for rare cytoskeleton disorders affecting the severity of disease phenotype.

Supporting Information

[Figure omitted. See PDF.]

Figure S1.

Quantile-quantile plots for the traits indicated. The x axis shows -log10 transformed expected P values, while the y axis indicates -log10 transformed observed P values. The corresponding genomic inflation factor (λ) is also shown for each trait.

https://doi.org/10.1371/journal.pone.0069206.s001

(DOC)

Figure S2.

Forest plots of effect size and direction for rs1887582. The contributing effect from each study is shown by a black square, with confidence intervals indicated by horizontal lines. The contributing weight of each study to the meta-analysis is indicated by the size of the square. The combined meta-analysis estimate is shown at the bottom of each graph.

https://doi.org/10.1371/journal.pone.0069206.s002

(DOC)

Figure S3.

Graphical representation of the TAF3 gene intron where the rs112355458 SNP maps. The rs112355458 SNP is indicted by the red arrow. The locus is enriched for the histone modification H3K4me1, a histone mark associated with enhancer regions. The rs11255458 SNP may disrupt the erythrocyte-specific transcription of TAF3 by generating a binding site for a repressor complex.

https://doi.org/10.1371/journal.pone.0069206.s003

(DOC)

Materials S1.

https://doi.org/10.1371/journal.pone.0069206.s004

(DOC)

Table S1.

Primers for gene expression analysis in MEL cells.

https://doi.org/10.1371/journal.pone.0069206.s005

(DOC)

Table S2.

Primers for ChIP analysis in K562 cells.

https://doi.org/10.1371/journal.pone.0069206.s006

(DOC)

Table S3.

Comparison of the GWAS results of the present study with previous studies.

https://doi.org/10.1371/journal.pone.0069206.s007

(DOC)

Acknowledgments

The CHARGE Consortium Hematology Working Group list of authors and affiliations is as follows

Santhi K. Ganesh1,2,3, Neil A. Zakai 4, Frank J. A. van Rooij5, Albert V. Smith6, Ming-Huei Chen7,8, Michael Nalls 9,10, Anna Kottgen 11, Nicole L. Glazer 12,13, Abbas Dehghan5, Thor Aspelund 6, Qiong Yang7,14, Toshiko Tanaka10,15,16, Andrew Jaffe 11, Joshua C. M. Bis 13, Germaine Verwoert 5, Caroline S. Fox 1, Jack M. Guralnik9, Georg Ehret3, Kenneth Rice 17, Ben A. Oostra5, Gudny Eiriksdottir6, Daniel Levy1, Kushang V. Patel9, Eric Boerwinkle18, Jerome I. Rotter19, Albert Hofman 5, Dena Hernandez10, Gang Zheng20, Andrew B. Singleton10, Josef Coresh 11, Thomas Lumley 17, André G. Uitterlinden5, Lenore J. Launer9, L. Adrienne Cupples7,14, Cornelia M. van Duijn5, Vilmundur Gudnason6, Bruce M. Psaty12,13,21,22,23, Luigi Ferrucci10,15, Aravinda Chakravarti3, Christopher J. O'Donnell1, Jacqueline C. M Witteman5, Susan Furth24, Mary Cushman12, Tamara B. Harris9, JingPing Lin20.

1 National Heart, Lung and Blood Institute, Division of Intramural Research, Bethesda, Maryland, United States of America; 2 National Human Genome Research Institute, Division of Intramural Research, Bethesda, Maryland, United States of America; 3 McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America; 4 Departments of Medicine and Pathology, University of Vermont, Burlington, Vermont, United States of America; 5 Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands; 6 Icelandic Heart Association, Kopavogur, Iceland; 7 Framingham Heart Study, Framingham,Massachusetts, United States of America; 8 Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, United States of America; 9 Laboratory for Epidemiology, Demography, and Biometry, National Institute of Aging, NIH, Bethesda, Maryland, United States of America; 10 Intramural Research Program, Laboratory of Neurogenetics National Institute on Aging, NIH, Bethesda, Maryland, United States of America; 11 Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States of America; 12 Department of Medicine, University of Washington, Seattle, Washington, United States of America; 13 Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America; 14 Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America; 15 Longitudinal Studies Section – Clinical Research Branch, National Institute of Aging, NIH, Baltimore, Maryland, United States of America; 16 Medstar Research Institute, Baltimore, Maryland, United States of America; 17 Department of Biostatistics, University of Washington, Seattle, Washington, United States of America; 18 Human Genetics Center, University of Texas Health Science Center, Houston, Texas, United States of America; 19 Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America; 20 Division of Cardiovascular Sciences of Extramural Research, National Heart, Lung and Blood Institute, NIH, Bethesda, Maryland, United States of America; 21 Department of Epidemiology, University of Washington, Seattle, Washington, United States of America; 22 Department of Health Services, University of Washington, Seattle, Washington, United States of America; 23 Center for Health Studies Group Health, Seattle, Washington, United States of America; 24 Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America.

Val Borbera (INGI-VB)

We thank the inhabitants of the Val Borbera that made this study possible, the local administrations, the Tortona and Genova archdiocese and the ASL-22, Novi Ligure (Al) for support. We particularly thank Federico Caligaris-Cappio (Oncology-Hsr) and the internal medicine MDs of the San Raffaele Hospital, Maria Rosa Biglieri and Diego Sabbi for their contribution to clinical data collection. We thank Massimo Ginestri for help with laboratory data analysis, and Fiammetta Viganò and Mai Baalbaki for technical support.

Friuli Venezia Giulia study (INGI-FVG)

We thank the inhabitants of the FVG villages that made this study possible and the local administrations.

Twins UK (TUK)

We thank the staff from the Genotyping Facilities at the Wellcome Trust Sanger Institute for sample preparation, Quality Control and Genotyping; Le Centre National de Génotypage, France, led by Mark Lathrop, for genotyping; Duke University, North Carolina, USA, led by David Goldstein, for genotyping; and the Finnish Institute of Molecular Medicine, Finnish Genome Center, University of Helsinki, led by Aarno Palotie. Genotyping was also performed by CIDR as part of an NEI/NIH project grant.

CHARGE Consortium

The authors acknowledge the essential role of the Cohorts for Heart and Aging Research in Genome Epidemiology (CHARGE) Consortium in development and support of this manuscript. CHARGE members participating in the replicas included The Netherland’s Rotterdam Study (RS), Framingham Heart Study (FHS) and the Age, Gene/Environment Susceptibility- Reykjavik Study (AGES) Study and InCHIANTI.

Author Contributions

Conceived and designed the experiments: DT DH MG CC. Performed the experiments: GP DH SUO. Analyzed the data: GP MT. Wrote the paper: DT CC. Provided the informatics tools for data collection and analysis: CS CM IB. Organized the INGI-VB data collection: CS RM CC DT. Provided the data on their replica populations: S-YS MM S-LT TDS SG NP PG. Provided the replica populations: NS, CHARGE Consortium.

Citation: Pistis G, Okonkwo SU, Traglia M, Sala C, Shin S-Y, Masciullo C, et al. (2013) Genome Wide Association Analysis of a Founder Population Identified TAF3 as a Gene for MCHC in Humans. PLoS ONE 8(7): e69206. https://doi.org/10.1371/journal.pone.0069206

References

1. Traglia M, Sala C, Masciullo C, Cverhova V, Lori F, et al. (2009) Heritability and demographic analyses in the large isolated population of Val Borbera suggest advantages in mapping complex traits genes. PLoS One 4: e7554.

2. Beutler E, Waalen J (2006) The definition of anemia: what is the lower limit of normal of the blood hemoglobin concentration? Blood 107: 1747–1750.

3. Eisenstaedt R, Penninx BW, Woodman RC (2006) Anemia in the elderly: current understanding and emerging concepts. Blood reviews 20: 213–226.

4. Sala C, Ciullo M, Lanzara C, Nutile T, Bione S, et al. (2008) Variation of hemoglobin levels in normal Italian populations from genetic isolates. Haematologica 93: 1372–1375.

5. van der Harst P, Zhang W, Mateo Leach I, Rendon A, Verweij N, et al.. (2012) Seventy-five genetic loci influencing the human red blood cell. Nature.

6. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, et al. (2009) Finding the missing heritability of complex diseases. Nature 461: 747–753.

7. Sabatti C (2009) Service SK, Hartikainen AL, Pouta A, Ripatti S, et al (2009) Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat Genet 41: 35–46.

8. Service S, DeYoung J, Karayiorgou M, Roos JL, Pretorious H, et al (2006) Magnitude and distribution of linkage disequilibrium in population isolates and implications for genome-wide association studies. Nat Genet 38: 556–560.

9. Varilo T, Laan M, Hovatta I, Wiebe V, Terwilliger JD, et al. (2000) Linkage disequilibrium in isolated populations: Finland and a young sub-population of Kuusamo. Eur J Hum Genet 8: 604–612.

10. Varilo T, Peltonen L (2004) Isolates and their potential use in complex gene mapping efforts. Curr Opin Genet Dev 14: 316–323.

11. Colonna V, Pistis G, Bomba L, Mona S, Matullo G, et al. (2013) Small effective population size and genetic homogeneity in the Val Borbera isolate. European journal of human genetics : EJHG 21: 89–94.

12. Hart DO, Santra MK, Raha T, Green MR (2009) Selective interaction between Trf3 and Taf3 required for early development and hematopoiesis. Dev Dyn 238: 2540–2549.

13. Aulchenko YS, Ripke S, Isaacs A, van Duijn CM (2007) GenABEL: an R library for genome-wide association analysis. Bioinformatics 23: 1294–1296.

14. Nudel U, Salmon JE, Terada M, Bank A, Rifkind RA, et al. (1977) Differential effects of chemical inducers on expression of beta globin genes in murine erythroleukemia cells. Proceedings of the National Academy of Sciences of the United States of America 74: 1100–1104.

15. Ganesh SK, Zakai NA, van Rooij FJ, Soranzo N, Smith AV, et al. (2009) Multiple loci influence erythrocyte phenotypes in the CHARGE Consortium. Nature genetics 41: 1191–1198.

16. Kamatani Y, Matsuda K, Okada Y, Kubo M, Hosono N, et al. Genome-wide association study of hematological and biochemical traits in a Japanese population. Nat Genet 42: 210–215.

17. Soranzo N, Spector TD, Mangino M, Kuhnel B, Rendon A, et al. (2009) A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nat Genet 41: 1182–1190.

18. An X, Mohandas N (2008) Disorders of red cell membrane. Br J Haematol 141: 367–375.

19. Gangloff YG, Pointud JC, Thuault S, Carre L, Romier C, et al. (2001) The TFIID components human TAF(II)140 and Drosophila BIP2 (TAF(II)155) are novel metazoan homologues of yeast TAF(II)47 containing a histone fold and a PHD finger. Molecular and cellular biology 21: 5109–5121.

20. Vermeulen M, Mulder KW, Denissov S, Pijnappel WW, van Schaik FM, et al. (2007) Selective anchoring of TFIID to nucleosomes by trimethylation of histone H3 lysine 4. Cell 131: 58–69.

21. Juven-Gershon T, Kadonaga JT (2010) Regulation of gene expression via the core promoter and the basal transcriptional machinery. Developmental biology 339: 225–229.

22. Higgins JM, Mahadevan L (2010) Physiological and pathological population dynamics of circulating human red blood cells. Proceedings of the National Academy of Sciences of the United States of America 107: 20587–20592.

23. Lew VL, Raftos JE, Sorette M, Bookchin RM, Mohandas N (1995) Generation of normal human red cell volume, hemoglobin content, and membrane area distributions by “birth” or regulation? Blood 86: 334–341.