A Genome-wide Association Study Identifies Three Loci Associated with Mean Platelet Volume
2008; Elsevier BV; Volume: 84; Issue: 1 Linguagem: Inglês
10.1016/j.ajhg.2008.11.015
ISSN1537-6605
AutoresChrista Meisinger, Holger Prokisch, Christian Gieger, Nicole Soranzo, Divya Mehta, Dieter Rosskopf, Peter Lichtner, Norman Klopp, Jonathan Stephens, Nicholas A. Watkins, Panos Deloukas, Andreas Greinacher, Wolfgang Köenig, Matthias Nauck, Christian Rimmbach, Henry Völzke, Annette Peters, Thomas Illig, Willem H. Ouwehand, Thomas Meitinger, H.-Erich Wichmann, Angela Döring,
Tópico(s)Cardiovascular Disease and Adiposity
ResumoMean platelet volume (MPV) is increased in myocardial and cerebral infarction and is an independent and strong predictor for postevent morbidity and mortality. We conducted a genome-wide association study (GWAS), the KORA (Kooperative Gesundheitsforschung in der Region Augsburg) F3 500K study, and found MPV to be strongly associated with three common single-nucleotide polymorphisms (SNPs): rs7961894 located within intron 3 of WDR66 on chromosome 12q24.31, rs12485738 upstream of the ARHGEF3 on chromosome 3p13-p21, and rs2138852 located upstream of TAOK1 on chromosome 17q11.2. We replicated all three SNPs in another GWAS from the UK and in two population-based samples from Germany. In a combined analysis including 10,048 subjects, the SNPs had p values of 7.24 × 10−48 for rs7961894, 3.81 × 10−27 for rs12485738, and 7.19 × 10−28 for rs2138852. These three quantitative trait loci together accounted for 4%–5% of the variance in MPV. In-depth sequence analysis of WDR66 in 382 samples from the extremes revealed 20 new variants and a haplotype with three coding SNPs and one SNP at the transcription start site associated with MPV (p = 6.8 × 10−5). In addition, expression analysis indicated a direct correlation of WDR66 transcripts and MPV. These findings may not only enhance our understanding of platelet activation and function, but may also provide a focus for several novel research avenues. Mean platelet volume (MPV) is increased in myocardial and cerebral infarction and is an independent and strong predictor for postevent morbidity and mortality. We conducted a genome-wide association study (GWAS), the KORA (Kooperative Gesundheitsforschung in der Region Augsburg) F3 500K study, and found MPV to be strongly associated with three common single-nucleotide polymorphisms (SNPs): rs7961894 located within intron 3 of WDR66 on chromosome 12q24.31, rs12485738 upstream of the ARHGEF3 on chromosome 3p13-p21, and rs2138852 located upstream of TAOK1 on chromosome 17q11.2. We replicated all three SNPs in another GWAS from the UK and in two population-based samples from Germany. In a combined analysis including 10,048 subjects, the SNPs had p values of 7.24 × 10−48 for rs7961894, 3.81 × 10−27 for rs12485738, and 7.19 × 10−28 for rs2138852. These three quantitative trait loci together accounted for 4%–5% of the variance in MPV. In-depth sequence analysis of WDR66 in 382 samples from the extremes revealed 20 new variants and a haplotype with three coding SNPs and one SNP at the transcription start site associated with MPV (p = 6.8 × 10−5). In addition, expression analysis indicated a direct correlation of WDR66 transcripts and MPV. These findings may not only enhance our understanding of platelet activation and function, but may also provide a focus for several novel research avenues. Platelets are anucleate blood cells and play an important role in atherogenesis and atherothrombosis, two key processes underlying cardiovascular disease.1Davi G. Patrono C. Platelet activation and atherothrombosis.N. Engl. J. Med. 2007; 357: 2482-2494Crossref PubMed Scopus (1710) Google Scholar, 2Tsiara S. Elisaf M. Jagroop I.A. Mikhailidis D.P. Platelets as predictors of vascular risk: is there a practical index of platelet activity?.Clin. Appl. Thromb. Hemost. 2003; 9: 177-190Crossref PubMed Scopus (286) Google Scholar MPV is increased in myocardial (MIM 608446, MIM 608557) and cerebral (MIM 601367, MIM 606799) infarction and is an independent and strong predictor for postevent morbidity and mortality.3Martin J.F. Bath P.M. Burr M.L. Mean platelet volume and myocardial infarction.Lancet. 1992; 339: 1000-1001Abstract PubMed Scopus (12) Google Scholar, 4Bath P. Algert C. Chapman N. Neal B. PROGRESS Collaborative GroupAssociation of mean platelet volume with risk of stroke among 3134 individuals with history of cerebrovascular disease.Stroke. 2004; 35: 622-626Crossref PubMed Scopus (297) Google Scholar Platelets are formed from polyploid bone marrow precursor cells, the megakaryocytes, through a process of proplatelet formation. The volume of platelets is tightly regulated but the precise molecular machinery that controls it is only partially understood and involves outside-in signals emanating from extracellular matrix proteins and growth factors.5Kaushansky K. Historical review: megakaryopoiesis and thrombopoiesis.Blood. 2008; 111: 981-986Crossref PubMed Scopus (238) Google Scholar There is ample evidence that the blood cell indices under which is also MPV have a high level of heritability. In twin studies, heritability estimates for hemoglobin levels and the counts of white blood cells and platelets ranged from 0.37 to 0.89.6Garner C. Tatu T. Reittie J.E. Littlewood T. Darley J. Cervino S. Farrall M. Kelly P. Spector T.D. Thein S.L. Genetic influences on F cells and other hematologic variables: a twin heritability study.Blood. 2000; 95: 342-346PubMed Google Scholar Studies in baboons and rodents confirmed these findings and found (not surprisingly) that also the volumes of red cells and platelets are under genetic control.7Mahaney M.C. Brugnara C. Lease L.R. Platt O.S. Genetic influences on peripheral blood cell counts: a study in baboons.Blood. 2005; 106: 1210-1214Crossref PubMed Scopus (25) Google Scholar We conducted a genome-wide association study (GWAS) in individuals sampled from the KORA (Kooperative Gesundheitsforschung in der Region Augsburg) F3 500K study population. The study population for the GWAS was recruited from the MONICA S3 survey, a population-based sample from the general population living in the region of Augsburg, Southern Germany, which was carried out in 1994/95. The standardized examinations applied in this survey including 4856 participants aged 25 to 74 years (response 75%) have been described in detail elsewhere.8Löwel H. Döring A. Schneider A. Heier M. Thorand B. Meisinger C. MONICA/KORA Study GroupThe MONICA Augsburg surveys—basis for prospective cohort studies.Gesundheitswesen. 2005; 67: S13-S18Crossref PubMed Scopus (150) Google Scholar, 9Wichmann H.E. Gieger C. Illig T. MONICA/KORA Study GroupKORA-gen–resource for population genetics, controls and a broad spectrum of disease phenotypes.Gesundheitswesen. 2005; 67: S26-S30Crossref PubMed Scopus (359) Google Scholar In a follow-up examination of S3 in 2004/05 (KORA F3), 3006 subjects participated. For KORA F3 500K we selected 1644 subjects of these participants then aged 35 to 79 years, including 1606 individuals with MPV values available. Genotyping was performed with the Affymetrix Gene Chip Human Mapping 500K Array Set as described in Döring et al.10Döring A. Gieger C. Mehta D. Gohlke H. Prokisch H. Coassin S. Fischer G. Henke K. Klopp N. Kronenberg F. et al.SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.Nat. Genet. 2008; 40: 430-436Crossref PubMed Scopus (339) Google Scholar In brief, on SNP level from a total of 500,568 SNPs, we excluded for the purpose of this analysis all SNPs on chromosome X, leaving 490,032 autosomal SNPs for the GWA screening step. The X chromosome SNPs were excluded from the analysis because the X chromosome has to be treated differently from the autosomes (note that the Affymetrix Chip used does not assay the Y chromosome). Because most loci on the X chromosome are subject to X chromosome inactivation, it can not be predicted which allele is active. Furthermore, because there is only one copy of X in males, sample sizes and accordingly power are different from the autosomes. From the 490,032 autosomal SNPs, 335,152 (68.39%) SNPs passed all quality control criteria and were selected for the subsequent association analyses. Criteria leading to exclusion were genotyping efficiency <95% (N = 49,325) and minor allele frequency (MAF) 0.05).9,9640.015 (0.0014)3.81 × 10−27rs796189412120849966KORA 500K F3AG99.70.0131,602 (11.92)0.040 (0.0059)2.09 × 10−112.77%UKBS-CC1100.00.6851,220 (11.32)0.033 (0.0063)3.04 × 10−71.90%KORA S497.90.9374,070 (11.18)0.034 (0.0037)7.26 × 10−202.04%SHIP98.20.36283,142 (11.14)0.028 (0.0036)2.61 × 10−141.84%CombinedbNo study heterogeneity (I2 range 0–43, p values > 0.05).10,0340.032 (0.0022)7.24 × 10−48cThe p value excluding the KORA S4 sample (n = 5964) is 1.087 × 10−29.rs21388521724727475KORA 500K F3CT99.91.0001,605 (49.33)−0.017 (0.0037)3.31 × 10−61.34%UKBS-CC199.50.3071,220 (47.80)−0.018 (0.0041)1.62 × 10−51.38%KORA S499.80.53294,139 (47.17)−0.018 (0.0023)1.57 × 10−141.42%SHIP96.20.11233,084 (48.21)−0.011 (0.0024)1.70 × 10−60.74%CombinedbNo study heterogeneity (I2 range 0–43, p values > 0.05).10,048−0.015 (0.0014)7.19 × 10−28Effect sizes (estimates and SE) are given for each copy of the minor allele and are expressed as natural logarithm of MPV.a Violation of HWE equilibrium, also after regenotyping.b No study heterogeneity (I2 range 0–43, p values > 0.05).c The p value excluding the KORA S4 sample (n = 5964) is 1.087 × 10−29. Open table in a new tab Effect sizes (estimates and SE) are given for each copy of the minor allele and are expressed as natural logarithm of MPV. In further analysis in the GWA population, it was examined whether the three lead SNPs are associated with other traits, such as white blood cell count, red blood cell count, mean corpuscular volume, hematocrit, and hemoglobin. None of the lead SNPs showed a significant association (p < 0.05) with any of these traits (data not shown). In the combined sample of 10,048 individuals, the SNP rs7961894 reached a p value of 7.24 × 10−48 (effect per minor allele copy = 0.032 per log fl, CI 0.028–0.037), the SNP rs12485738 a p value of 3.81 × 10−27 (effect per minor allele copy = 0.015 per log fl, CI 0.012–0.017), and the third SNP (rs2138852) a combined p value of 7.19 × 10−28 (effect per minor allele copy = −0.015 per log fl, CI −0.018–−0.013). The reference values were about 15% higher in SHIP than in the other studies, which is best explained by the different analysis platforms with the Coulter-method (KORA, UKBS-CC1) or light scatter analysis (Sysmex SE-9000, SHIP). However, this fact may be negligible for the analysis, provided that the values are not differentially variable over the range. An internal comparison of the methods carried out in the SHIP project resulted in the regression equation Y (fl Sysmex SE-9000) = 1.000∗X (fl Coulter-method) + 1.850, indicating that all values are shifted by the constant value of 1.850 upwards. We carried out an analysis corrected with MPV values for SHIP and found rather higher effect estimates for all three SNPs. We decided to use the conservative uncorrected values resulting in a slight underestimation of the effects. Because the lead SNP in WDR66 reached the best p value and accounted for about 2.0% of the MPV variance, we decided to analyze the coding sequence of WDR66 in more detail (Tables S4 and S5). High-resolution melting analysis was used as mutation scanning technology to analyze the coding region of WDR66. WDR66 exons were PCR amplified with intronic primers with ∼5 ng genomic DNA with a final denaturation step at 94°C for 1 min (0.25 units Thermo-Start Taq DNA polymerase [Abgene], 1× LCGreen Plus [BIOKE], 0.25 μM of each primer; Table S5). High-resolution melting analysis was performed on a LightScanner instrument (Idaho Technology). In the presence of the saturating double-stranded DNA-binding dye, amplicons were slowly heated from 77°C until fully denatured (96°C) while the fluorescence was monitored. Melting curves were analyzed by LightScanner software (Idaho Technology), with normalized, temperature-shifted curves displayed as difference plots (−dF/dT). Detected samples with altered melting curves compared with the average of multiple wild-types were directly sequenced with a BigDye Cycle sequencing kit (Applied Biosystems). We analyzed the sequence of all 21 coding exons and the 5′ UTR in 382 samples selected from the high and low extremes of the MPV distribution in 4000 individuals (KORA S4). We found variants or variation in 4 of the 9 coding SNPs, which were already annotated in dbSNP. None of these showed an association with MPV, but the A allele of the lead SNP rs7961894 was overrepresented in the high-MPV group (p = 1.3 × 10−6, Fisher's exact test for allele distribution, Figure 2; more detailed information in Table S4). In addition, we detected 10 nonsynonymous SNPs, one nonsense and five synonymous variants, a 15 bp and an 18 bp insertion, one 3′ UTR SNP and one SNP (C → T) a single bp upstream of the UCSC annotated 5′ end of the WDR66 transcript (see Table S4). The latter variant (ss107795092) with a minor allele frequency (MAF) of 3.6% falls within a conserved region (LOD = 24, phastCons program) and is significantly overrepresented in the low-MPV group (p = 6.8 × 10−5). This variant is linked (r2 > 0.9, see Table S6) with three other newly discovered coding SNPs (ss107795081-3, p.C304C, p.V307I, and p.R417Q) and they define—in the background of the G allele of the lead SNP rs7961894—a rare haplotype (MAF 2.5%). This haplotype may contribute to the significant association of rs7961894 with MPV, but the strongest association was found for the lead SNP followed by ss107795092 alone. The strong correlation of the SNP prompted us to investigate the transcript levels of WDR66 in a randomly selected subgroup of 323 KORA F3 samples with whole-genome expression profiles available. Gene-expression analysis was performed with the Illumina Human-6 v2 Expression BeadChip as described in Döring et al.10Döring A. Gieger C. Mehta D. Gohlke H. Prokisch H. Coassin S. Fischer G. Henke K. Klopp N. Kronenberg F. et al.SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.Nat. Genet. 2008; 40: 430-436Crossref PubMed Scopus (339) Google Scholar In brief, blood samples were collected under fasting conditions in PAXgene (TM) Blood RNA tubes (PreAnalytiX) and RNA extraction was performed with the PAXgene Blood RNA Kit (QIAGEN). RNA was reverse transcribed and biotin-UTP labeled with the Illumina TotalPrep RNA Amplification Kit (Ambion). The raw data were exported from the Illumina "Beadstudio" Software to R, converted into logarithmic scores, and normalized.10Döring A. Gieger C. Mehta D. Gohlke H. Prokisch H. Coassin S. Fischer G. Henke K. Klopp N. Kronenberg F. et al.SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.Nat. Genet. 2008; 40: 430-436Crossref PubMed Scopus (339) Google Scholar We observed no association between intronic lead SNP rs7961894 and WDR66 transcript level, but a significant association of the levels of the WDR66 transcript with MPV (p = 0.01, Figure 3) via the linear regression model. In addition, we looked at correlation between gene expression and genotypes for the other two lead SNPs and found no significant association. Based on the small samples size for the expression studies, the analysis has a limited power. However, the lacking association between the intronic SNP and WDR expression argues against a direct effect on WDR66 expression. On the other side, the correlation of WDR66 expression with MPV supports the hypothesis that WDR66 is involved in the determination of MPV. In summary, we identified three loci associated with MPV, a quantitative trait that is increasingly recognized as being associated with the post-MI event risk of major complications. These three loci accounted for about 5% of the variance in MPV values in the normal population. All three genes are plausible biological candidates that could modify the process of platelet formation. The process of proplatelet formation is critically dependent on reorganization of cytoskeletal components and localized apoptosis seems to play an important role.5Kaushansky K. Historical review: megakaryopoiesis and thrombopoiesis.Blood. 2008; 111: 981-986Crossref PubMed Scopus (238) Google Scholar, 14Chang Y. Bluteau D. Debili N. Vainchenker W. From hematopoietic stem cells to platelets.J. Thromb. Haemost. 2007; : 318-327Crossref PubMed Scopus (96) Google Scholar WD-repeat proteins are present in all eukaryotes but not in prokaryotes. It is hypothesized that they are involved in the regulation of cellular functions ranging from signal transduction and transcription regulation to cell-cycle control and apoptosis.15Neer E.J. Schmidt C.J. Nambudripad R. Smith T.F. The ancient regulatory-protein family of WD-repeat proteins.Nature. 1994; 371: 297-300Crossref PubMed Scopus (1291) Google Scholar Our expression experiment indicates a direct correlation of WDR66 transcript level and MPV. Previous studies have shown that ARHGEF3 (XPLN), which encodes the rho guanine-nucleotide exchange factor 3 (RhoGEF3), is expressed in the brain, skeletal muscle, heart, kidney, and platelets as well as macrophage and neuronal cell tissues.16Arthur W.T. Ellerbroek S.M. Der C.J. Burridge K. Wennerberg K. XPLN, a guanine nucleotide exchange factor for RhoA and RhoB, but not RhoC.J. Biol. Chem. 2002; 277: 42964-42972Crossref PubMed Scopus (107) Google Scholar RhoGEFs activate RhoGTPases, which play an important role in many cellular processes such as regulation of cell morphology, cell aggregation, cytoskeletal rearrangements, and transcriptional activation.17Thiesen S. Kübart S. Ropers H.H. Nothwan H.G. Isolation of two novel human RhoGEFs, ARHGEF3 and ARHGEF4, in 3p13–21 and 2q22.Biochem. Biophys. Res. Commun. 2000; 273: 364-369Crossref PubMed Scopus (25) Google Scholar TAOK1, which is expressed in a wide variety of different tissues that include brain, heart, lung, testis, skeletal muscle, placenta, thymus, prostate, and spleen, encodes the TAO kinase 1 peptide (hTAOK1 also known as MARKK or PSK2) a microtubule affinity-regulating kinase that has been identified recently as an important regulator of mitotic progression, required for both chromosome congression and checkpoint-induced anaphase delay.18Draviam V.M. Stegmeier F. Nalepa G. Sowa M.E. Chen J. Liang A. Hannon G.J. Sorger P.K. Harper J.W. Elledge S.J. A functional genomic screen identifies a role for TAO1 kinase in spindle-checkpoint signalling.Nat. Cell Biol. 2007; 9: 556-564Crossref PubMed Scopus (86) Google Scholar TAOK1 activates c-Jun N-terminal kinase (JNK) and induces apoptotic morphological changes that include cell contraction, membrane blebbing, and apoptotic body formation.19Zihni C. Mitsopoulos C. Tavares I.A. Baum B. Ridley A.J. Morris J.D. Prostate-derived sterile 20-like kinase 1-alpha induces apoptosis. JNK- and caspase-dependent nuclear localization is a requirement for membrane blebbing.J. Biol. Chem. 2007; 282: 6484-6493Crossref PubMed Scopus (27) Google Scholar In conclusion, to our knowledge we identified the first three quantitative trait loci associated with MPV in the general population. Identification of primary genetic determinants of MPV may not only enhance our understanding of platelet activation and function, but may also provide a focus for several novel research avenues. The MONICA/KORA Augsburg studies were financed by the Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany, and supported by grants from the German Federal Ministry of Education and Research (BMBF). Part of this work was funded by the German National Genome Research Network (NGFN) and the European Union-sponsored project Cardiogenetics (LSH-2005-037593). Our research was supported within the Munich Center of Health Sciences (MC Health) as part of LMUinnovativ. SHIP is part of the Community Medicine Research net (CMR) of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research, the Ministry of Cultural Affairs, as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania. The SHIP genotyping was supported by the future fund of the state government of Mecklenburg-Vorpommern (UG 07 034). The establishment and genotyping of the UKBS-CC1 collection was funded by the Wellcome Trust and by a National Institutes of Health Research Grant to NHSBT. We thank the staff of the DNA Collections and Genotyping Facilities at the Wellcome Trust Sanger Institute for sample preparation. We gratefully acknowledge the contribution of G. Eckstein, T. Strom and K. Heim, A. Löschner, R. Hellinger, and all other members of the Helmholtz Zentrum München genotyping staff in generating and analyzing the SNP and RNA data set and G. Fischer and B. Kühnel for data management and statistical analyses. We thank all members of field staffs who were involved in the planning and conduct of the MONICA/KORA Augsburg, UKBS-CC1, and SHIP studies. Finally, we express our appreciation to all study participants. No conflict of interest relevant to this article was reported. Download .pdf (.25 MB) Help with pdf files Document S1. One Figure and Six Tables The URLs for data presented herein are as follows:Genome browser, http://genome.ucsc.edu/Markov Chain Haplotyping Package, http://www.sph.umich.edu/csg/abecasis/mach/METAL Package, http://www.sph.umich.edu/csg/abecasis/Metal.index.htmlOnline Mendelian Inheritance in Man (OMIM), http://www.ncbi.nlm.nih.gov/Omim/The R project for Statistical Computing, http://www.r-project.org/Sequenom, http://www.sequenom.comSNP database, http://www.ncbi.nlm.nih.gov/SNP/
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