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Keywords:

  • genetics;
  • myocardial infarction;
  • platelets;
  • quantitative traits

Abstract

  1. Top of page
  2. Abstract
  3. MI/CAD risk loci
  4. QTLs for platelet phenotypes
  5. The Cambridge HaemAtlas
  6. Zebrafish as model organism for fast screening of novel candidate genes
  7. Future developments
  8. Disclosure of Conflict of Interests
  9. References

Summary.  The era of Genome-Wide Association Studies (GWAS) commenced in 2007 with the study of the Wellcome Trust Case Control Consortium (WTCCC) which for the first time ever showed that risk loci can be identified by scanning the complete genome for sequence variation in large numbers of cases of disease and healthy controls. We and others have expanded on this effort and successfully identified the first 11 risk loci for myocardial infarction (MI) and coronary artery disease (CAD). Studies on quantitative traits provide an alternative approach to identify MI/CAD risk loci. This review captures the early successes in the emerging field of disease genomics.


MI/CAD risk loci

  1. Top of page
  2. Abstract
  3. MI/CAD risk loci
  4. QTLs for platelet phenotypes
  5. The Cambridge HaemAtlas
  6. Zebrafish as model organism for fast screening of novel candidate genes
  7. Future developments
  8. Disclosure of Conflict of Interests
  9. References

MI and CAD have a substantial genetic determination. From 2007 onwards, we and others have used GWAS to uncover the genetics roots of both conditions [1,2]. A combined analysis of the first 2 GWAS confirmed the initial association at chromosome 9p21.3 identified by the WTCCC, replicated two more loci on 2q36.3 and 6q25.1 and revealed four more putative loci [3]. A replication study in 11 550 further cases and 11 205 controls from 9 European studies again not only confirmed the association at the 9p21.3 locus, but also confirmed the associations at 1p13.3, 1q41, and 10q11.21 [4]. The associations with 2q36.3 and 6q25.1 were borderline. A similar study from the MIGen consortium replicated 6 loci discovered in our earlier studies and identified three additional ones (2q33, 6p24, 21q22) [5]. A novel approach for the analysis of GWAS is to take the haplotype structure into account and this identified the SLC22A3-LPAL2-LPA gene cluster as a strong susceptibility locus for CAD [6]. A three-stage analysis of GWAS data with a final wet-lab replication in 25 000 subjects identified a risk locus on 3q22.3 [7]. Results from fine-mapping studies are now awaited to identify more precisely the causal sequence variants. Cumulatively, the 11 risk alleles at best explain only 20% of the overall heritability of CAD/MI. A large number of additional risk loci therefore remain to be identified. It is expected that studies on quantitative traits with relevance to MI/CAD are an alternative route for their discovery. Therefore, we initiated research to identify quantitative trait loci (QTLs) for the function, number (PLT) and volume (MPV) of platelets.

QTLs for platelet phenotypes

  1. Top of page
  2. Abstract
  3. MI/CAD risk loci
  4. QTLs for platelet phenotypes
  5. The Cambridge HaemAtlas
  6. Zebrafish as model organism for fast screening of novel candidate genes
  7. Future developments
  8. Disclosure of Conflict of Interests
  9. References

Platelets play a pivotal role in atherosclerosis, thrombus formation and wound healing, all three important for the pathophysiology of CAD/MI. Platelets are rapidly activated at sites of plaque rupture by a range of physiological agonists, including collagen and ADP. There is some evidence that platelet hyper-responsiveness confers risk for MI/CAD [8] and MPV represents a strong, independent predictor of post-event outcome in MI [9]. Platelets are formed in the bone marrow from megakaryocytes (MKs) through the process of proplatelet formation (PPF). MKs are derived from the haematopoietic stem cell (HSC) by sequential steps of fate determination and proliferation that is regulated by complex combinatorial networks of transcription factors (TFs) [10]. The output of haematopoiesis, being the number of mature blood cells and their volume are tightly controlled and in the healthy population, MPV is inversely correlated with PLT. Studies in rodents, primates and twins have confirmed that blood cell quantitative traits are highly heritable. The mechanism by which these traits are regulated is only in part understood and most insights have been obtained from studies of inherited forms of aberrant regulation in man and from knock-out studies in mice. To identify further key regulators and to test the hypothesis that some of the QTLs for number, volume and function of platelets are also risk loci for CAD/MI we set out to discover such QTLs. Genotyping results from three control collections of GWAS were analyzed and this analysis identified the first four QTLs for MPV on chromosome 3p13-p21, 7q22.3, 12q24.3 and 17q11.2 [11,12]. For all but one of the observed associations, putative candidate genes could be inferred, but this was not possible for the association signal on chromosome 7q22.3, where the association signal was located in a 65 Kb haplotype block, which does not harbour any genes [11]. Computational analysis of this association region suggested the presence of binding sites for GATA1/2 and EVI1 and changes in the binding kinetics of either TF due to the sequence variation may explain the association with platelet volume, count and function.

Discovery of platelet function QTLs

As is the case for MPV and PLT, the platelet response to agonist activation also varies between individuals, but shows consistency within an individual. Several studies suggest strong heritability, but the genetic basis of this variation has not been properly explored. We selected collagen-related peptide (CRP-XL) and ADP as agonists because they perform a major role in platelet activation after plaque rupture [13, 14]. In a first step, we ascertained the response of platelets to both agonists in the platelet function cohort of 500 healthy individuals (PFC). The second step was the selection of 97 candidate genes encoding proteins with known roles in either or both signalling cascades. The third step was to enhance information about sequence variation by sequencing the candidate genes in 48 reference DNA samples. From the enriched dataset, 1327 SNPs were selected for typing in the 500 DNA samples of the PFC. The statistical analysis identified 17 independent SNPs which were associated with platelet function (P < 0.005) [15]. Further investigations with platelets of known genotype explored the mechanisms behind the associations for ITPR1, PEAR1 and VAV3. These data provide novel insights into key hubs in the signaling networks downstream of GPVI and the G-protein-coupled receptors P2Y1 and P2Y12. Association studies of the QTL-SNPs for platelet function and volume and the risk of CAD/MI are currently underway.

The Cambridge HaemAtlas

  1. Top of page
  2. Abstract
  3. MI/CAD risk loci
  4. QTLs for platelet phenotypes
  5. The Cambridge HaemAtlas
  6. Zebrafish as model organism for fast screening of novel candidate genes
  7. Future developments
  8. Disclosure of Conflict of Interests
  9. References

To aid the interpretation of QTL studies and to direct functional genomics studies on MI/CAD risk genes, we have generated a gene expression atlas for the main cellular elements in the blood from healthy individuals. Using Illumina arrays, we have compared the gene expression profiles of the precursors of erythrocytes and platelets (erythroblasts and MKs), the four main lymphocyte populations (B-cells, cytotoxic and helper T-cells, natural killer cells), granulocytes and monocytes. A bioinformatics analysis was performed focusing on TFs, immunoglobulin superfamily members and lineage-specific transcripts [16]. We observed that the numbers of lineage-specific transcripts varies by two orders of magnitude, ranging from five for cytotoxic T lymphocytes to 878 for granulocytes. In addition, we have identified novel co-expression patterns for key TFs. The question can now be posed whether one of the mechanisms by which QTL SNPs modify platelet phenotypes is by changing TF binding. The HaemAtlas dataset has already been used in conjunction with studies of MPV QTLs and in the characterization of tetraspanins [11,17]. In another example, we performed a functional genomics study on four of the 75 transcripts encoding transmembrane membrane proteins that are uniquely expressed in MKs and platelets, but not in the remaining seven cell types [18].

Zebrafish as model organism for fast screening of novel candidate genes

  1. Top of page
  2. Abstract
  3. MI/CAD risk loci
  4. QTLs for platelet phenotypes
  5. The Cambridge HaemAtlas
  6. Zebrafish as model organism for fast screening of novel candidate genes
  7. Future developments
  8. Disclosure of Conflict of Interests
  9. References

In a first step, we confirmed by immuno-blot and fluorescence with polyclonal antibodies that BAMBI, DCBLD2, ESAM and LRRC32 were present on the membrane of human platelets. In a second step, we determined the suitability of the vertebrate Danio rerio (zebrafish) for functional screening of novel platelet genes by reverse genetics [18]. We investigated the phenotype induced by antisense morpholino oligonucleotide (MO)-based knockdown in a laser-induced arterial thrombosis model. To validate the model, the genes for GPIIb (αIIb) and Factor VIII were targeted. MO-injected fish showed normal thrombus initiation, but severely impaired thrombus growth, consistent with the mouse knockout phenotypes and concomitant knockdown of both resulted in spontaneous bleeding. Knockdown of the four novel platelet proteins altered arterial thrombosis, as demonstrated by modified kinetics of thrombus initiation and/or development. By mining the HaemAtlas, four novel regulators of platelet function were identified, with BAMBI and LRRC32 promoting and DCBLD2 and ESAM inhibiting thrombus formation [18].

Future developments

  1. Top of page
  2. Abstract
  3. MI/CAD risk loci
  4. QTLs for platelet phenotypes
  5. The Cambridge HaemAtlas
  6. Zebrafish as model organism for fast screening of novel candidate genes
  7. Future developments
  8. Disclosure of Conflict of Interests
  9. References

Meta-analysis of results from GWAS will provide additional power to identify further sequence variants, which confer risk of MI/CAD. It is also expected that some of the common and rare copy-number variants (CNV) will contribute to disease risk [19,20]. The 1000-Genome project will provide an enormous enhancement of information about sequence variation and this will undoubtedly lead to the production of 4th generation genotyping arrays for further GWAS [21,22]. Ultra-high throughput sequencing will eventually facilitate studies in which the sequence of the complete genome of thousands of MI/CAD cases and controls will be determined. Meanwhile studies on QTLs for height have shown that the overall heritability is controlled by hundreds of loci with each allele exerting a very modest effect on phenotype [23]. It is reasonable to assume that the same will be the case for loci controlling platelet phenotypes, but it is to be expected that rare loss-of and gain-of function variants of QTL will exert a much stronger effect on phenotype than common sequence variation. We are testing this assumption by using blood cell quantitative traits as a model. One other innovative approach to study the mechanism by which sequence variation modifies platelet phenotypes is to determine the binding sites of TFs at a genome-wide level. Chromatin Immuno-Precipitation combined with ultra-high throughput sequencing (ChIP-Seq) of TF-bound DNA will inform the development of Bayesian networks of gene regulation during megakaryopoiesis [24].

References

  1. Top of page
  2. Abstract
  3. MI/CAD risk loci
  4. QTLs for platelet phenotypes
  5. The Cambridge HaemAtlas
  6. Zebrafish as model organism for fast screening of novel candidate genes
  7. Future developments
  8. Disclosure of Conflict of Interests
  9. References
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