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Keywords:

  • bleeding;
  • gene;
  • ITGA2;
  • risk;
  • von Willebrand disease

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

Summary.  We analyzed the association of bleeding severity with candidate gene haplotypes within pedigrees of 11 index cases of von Willebrand disease (VWD) type 2 (two type 2A, three type 2B and six type 2M), using the QTL Association model (MENDEL 5.5). In addition to the 11 index cases, these pedigrees included 47 affected and 49 unaffected relatives, as defined by VWF mutations and/or phenotype. A bleeding severity score was derived from a detailed history and adjusted for age. Donors were genotyped using a primer extension method, and eight candidate genes were selected for analysis. VWF antigen (or ristocetin cofactor activity) levels had the strongest influence on bleeding severity score. After Bonferroni correction for multiple testing, only ITGA2 promoter haplotype -52T was associated with an increased bleeding severity score (P < 0.01). This association remained statistically significant when the three type 2B pedigrees were excluded (P = 0.012) or when gender-specific bleeding categories were excluded (P < 0.01). The major haplotypes of seven other candidate genes, GP1BA, ITGA2B, ITGB3, GP6, VWF, FGB, and IL6, were not associated with bleeding severity. These results establish that genetic differences in the expression of the integrin subunit α2 can influence the bleeding phenotype of VWD type 2 and complement our previous findings in VWD type 1. Genetically controlled attenuation of platelet collagen receptor expression can influence risk for morbidity in clinical settings where hemostasis is compromised.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

von Willebrand disease (VWD) is subdivided into two major groups, where there is either a quantitative deficiency of von Willebrand factor (VWF; types 1 and 3) or a qualitative abnormality (type 2). Type 1 VWD is defined as a partial deficiency of VWF in plasma and/or platelets, reflected by a concomitant reduction of VWF activity and antigen with a normal distribution of high molecular weight forms. Type 2 VWD is subdivided into four distinct subtypes, each with a different pathophysiologic mechanism. In type 2A VWD, there is an absence of high molecular weight VWF multimers in plasma and an impairment of those platelet functions, which are dependent on VWF binding to the platelet GPIb complex. In type 2B VWD, there is also a loss of the largest plasma VWF multimers resulting from an increased affinity for the platelet GPIb complex. Type 2M VWD is characterized by decreased VWF-dependent platelet functions but apparently normal VWF multimers. Finally, type 2N (Normandy) VWD is associated with a functional defect in the N-terminal region of the molecule where the binding site for coagulation factor VIII resides, but a normal distribution of plasma VWF multimers.

The ristocetin cofactor activity (VWF:RCo) is used in the diagnosis of VWD because it correlates with the ability of VWF to interact with the platelet GPIb complex. The VWF:RCo assay, together with VWF antigen (VWF:Ag), is employed as the first step in the diagnosis of VWD types. The VWF:RCo/Ag ratio discriminates type 1 from type 2A, 2B and 2M VWD, as reported in the guidelines for the diagnosis and treatment of VWD in Italy. In type 1 VWD patients and normal subjects, VWF:RCo and VWF:Ag values are equivalent, resulting in a VWF:RCo/Ag ratio that is above the normal lower limit. On the contrary, VWF:RCo levels lower than VWF:Ag, with a VWF:RCo/Ag ratio below the normal lower limit, are indicative of type 2A, 2B and 2M VWD.

A positive bleeding history since childhood with symptoms observed in at least two different sites is considered the most important criteria in the clinical diagnosis of VWD. Genetic follow-up will often identify mutations that inhibit the synthesis/secretion or enhance the clearance of VWF [1]. Quite often, in the clinical and laboratory diagnosis of VWD, individuals with very similar or identical VWF levels can exhibit a quite different bleeding tendency. These exceptions to the paradigm have confounded both the diagnosis and prognostic predictability of VWD and can influence the choice of treatment for the patient.

We selected haplotypes of five platelet glycoprotein genes that have already been implicated in risk for thrombosis and/or bleeding. These are: GPIbα (GP1BA) [2,3], GPVI (GP6) [4], integrin α2 (ITGA2) [5], integrin αIIb (ITGA2B) [6], and integrin β3 (ITGB3) [7] (see Table 1 in reference 12). We also selected haplotypes of two glycoprotein ligands and one cytokine gene that can influence the expression levels of these glycoproteins and thus have an impact on platelet function or activity. These are the VWF gene VWF [8], the fibrinogen Bβ chain gene FGB [9] and the interleukin-6 gene IL6 [10]. Haplotype differences in each of these eight genes can potentially influence the efficiency of hemostasis, and our objective here was to analyze the association of these haplotypes with risk for bleeding in VWD type 2 through an analysis of index cases and their family members.

Table 1.  von Willebrand disease (VWD) type 2 families
Family Subtype*von Willebrand factor mutation
  1. *Phenotypic classification of VWD subtypes according to Sadler (11).

F12MR1374H
F22MY1321C
F32MNot identified
F42BR1306W
F52AV1665E
F62M VicenzaR1205H
F72M VicenzaR1205H
F82AV1607D
F92M VicenzaNot identified
F102BR1308L
F11Mixed (2B and 1)P1337L, C275R

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

Criteria for enrollment of VWD patients and healthy individuals

This study was organized in 11 different families from the Milan area. VWD type 2 patients were classified according to the previous definitions of the Scientific Standardization Committee (SSC) on VWF of the International Society on Thrombosis and Haemostasis (ISTH) [11]. This project was approved by the local IRB of the University Hospital of Milan. After signing an informed consent, 11 index cases, 47 affected and 49 unaffected relatives were enrolled on the basis of a diagnosis of VWD type 2 according to the records of the database available at the Angelo Bianchi Bonomi Hemophilia Thrombosis Center of Milan. The 11 families with associated VWF mutations and subtype classification are listed in Table 1.

In this blinded study, the subjects were recruited, interviewed, assigned a bleeding severity score and tested for all laboratory and clinical parameters in Milan. DNA samples were then coded without knowledge of these data or the identification of the family members and their relationships, and the genotyping and statistical analysis were performed at The Scripps Research Institute (TSRI).

Standardized criteria to evaluate bleeding history

A bleeding history was derived from detailed questionnaires, and a score was compiled, as described [1]. This questionnaire was administered to all the affected and non-affected members (including index cases) of the different families from the same well trained hematologist who was attending the out-patient care unit at the Angelo Bianchi Bonomi Hemophilia Thrombosis Center. In order to avoid bias during the interview, the hematologist was not aware at that moment, which patient was considered the index case. The severity of bleeding episodes was ranked from 0 to 5, as shown in Table 2, in each of 11 bleeding manifestation categories: epistaxis; cutaneous symptomatic bleeding; bleeding from minor wounds; oral cavity bleeding; gastrointestinal bleeding; bleeding associated with tooth extraction; surgery; muscle hematoma; hemarthrosis; post-partum hematoma; and menorrhagia. The bleeding score used in this study, in which a ranking from 0 to 5 is employed, is a modification of the previous scoring method used in our study of VWD type 1 patients [12] in order to more fully describe the severity of bleeding in all 11 categories. The numerical sum of the scores for each category was then divided by the age of the individual and multiplied by 100 to arrive at the bleeding severity score adjusted for age. Adjustment of the bleeding score for age of the individual accomplishes two objectives: it normalizes the cumulative bleeding history with respect to age, and it generates a variable that is normally distributed, a requisite for the QTL association analysis.

Table 2.  Criteria for the calculation of the Bleeding Severity Score in patients with Von Willebrand disease Thumbnail image of

In certain cases that are indicated in the text, the Bleeding Score was computed based on nine categories, excluding the gender-specific categories Post-Partum Bleeding and Menorrhagia.

Laboratory assays

Blood samples were drawn and processed for VWF multimeric analysis and hemostasis tests as previously described [13]. The bleeding time (BT) was measured by the Simplate II device (General Diagnostics, Morris Plane, NJ, USA). VWF:Ag was measured by enzyme-linked immunosorbent assay (ELISA), and VWF:RCo was measured by aggregometry of formalin-fixed platelets, as described [13] or by an ELISA, using recombinant GPIb, as recently reported in detail[14]. All FVIII/VWF measurements were expressed in international units (IU), with reference to a plasma pool standardized against the International Reference Preparation for FVIII/VWF-related activities. The phenotypic distinction between VWD type 2A, 2B and 2M was also confirmed by DNA analysis of affected patients, as previously reported [15].

Pedigrees of the families

The 11 pedigrees are shown in Fig. 1. For each pedigree, the VWD subtype and the causative VWF mutation are indicated. In two pedigrees in which a mutation has not yet been defined (families 3 and 9), the subtype diagnosis is based on phenotype and VWF multimer analysis. Within each pedigree, individuals are identified by a coded number (roman numeral = generation; arabic numeral = individual). Index cases are noted by a bold horizontal arrow, and below each symbol, the values for (top to bottom) bleeding severity score, VWF:Ag and VWF:RCo are indicated. The bleeding scores indicated in Fig. 1 are those computed on the basis of all 11 categories. Affected individuals are designated by black (or grey) symbols and are defined as those members who express at least one mutant VWF haplotype (see Table 2). Family 11 represents a unique case in which two mutant VWF haplotypes have been identified, one inherited from the paternal side (P1337L); the other inherited from the maternal side (C275R). In this case, those family members expressing the former are indicated in black; those expressing the latter, grey.

imageimageimageimage

Figure 1. Pedigree charts. In each panel, the von Willebrand disease (VWD) subtype and the involved von Willebrand factor (VWF) mutation(s) are indicated beneath the family designations. Black symbols indicate affected individuals (as well as grey symbols in family 11), based on phenotype and DNA mutation analysis (where available), and white symbols represent otherwise healthy individuals. Below each symbol are indicated (top to bottom): Family code (generation: individual); bleeding severity score/age; VWF antigen (VWF:Ag) level; and ristocetin cofactor activity (VWF:RCo) level. Bold, diagonal arrows indicate index cases within each pedigree. Family symbols represented by dashed lines are deceased or otherwise unavailable for study and are only represented here to indicate the familial relationship between the remaining family members. n = not tested.

PCR amplification of the target polymorphic DNA fragment

DNA was isolated from citrate-anticoagulated whole blood using the DNeasy Tissue kit (Qiagen, Inc., Valencia, CA, USA) according to the manufacturer's instructions. PCR primer pairs were designed to amplify specific fragments of DNA encompassing each of SNP, and genotypes were called based on clustering of data from primer extension assays, as previously described [16].

Statistical analysis: QTL association

In the QTL Association model, implemented using the software package MENDEL 5.5 [17], genotypes at the candidate gene locus are treated as predictors modifying the quantitative trait [18]. For each candidate gene, MENDEL assesses association by conducting a likelihood ratio test to determine whether the haplotype regression coefficients are significantly different from zero. Continuous variables, such as plasma VWF:RCo levels or plasma VWF:Ag levels are handled as covariates. The haplotypes associated with minimum or maximum values for each of the quantitative traits, BT, plasma VWF:Ag levels, plasma VWF:RCo levels, and bleeding severity score were computed, and the significance of the association indicated by a P-value. When necessary to achieve a normal distribution, quantitative traits were subjected to appropriate transformations and checked for normality by the Kolmogorov–Smirnov test (SigmaStat 3.0; Systat Software, Inc., Point Richmond, CA, USA). P-values were adjusted 16-fold for multiple testing, using the Bonferroni method [19].

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

Phenotypic and molecular diagnosis of VWD type 2 patients

The affected and non affected members of the 11 families (F1 through F11) enrolled in this study are described in the pedigrees in Fig. 1. The phenotypic data are depicted within the pedigree charts, and the VWD type 2 diagnoses were confirmed by the finding of characteristic mutations within the VWF gene (Table 1). Six families (F1, F2, F3, F6, F7, and F9) were classified VWD type 2M according to phenotypic and molecular data. Affected members of F1 and F2 carry mutations within the A1 domain of VWF, while affected members of F6 and F7 exhibit the mutation typically observed in VWD type 2M ‘Vicenza’ [20].

Three families (F5, F8, and F11) were initially enrolled with a diagnosis of VWD type 2A. Molecular analysis confirmed the diagnosis of VWD in two of these families, and F5 and F8 each carry one of the previously defined mutations within the A2 domain of VWF. In the case of the third family, F11, the two severely affected members originally diagnosed as VWD 2A were found to be double heterozygotes for two different mutations [21]. Since one of these mutations, P1337L, is typically associated with the phenotypic pattern of VWF type 2B, conferring an enhanced interaction of VWF with platelet GPIb, we have included F11 in the group of VWD type 2B. Two additional VWD type 2B families (F4 and F10) were originally enrolled in the study. The affected members of F4 carry a mutation R1306W within the A1 domain of VWF already reported in VWD type 2B, while the affected members of F10 are characterized by a novel VWD type 2B mutation R1308L, recently described [22].

Data processing

The variables, bleeding severity score/age, BT and plasma VWF:Ag level, were each adjusted to generate a normal distribution, which is a requirement for quantitative traits analysis by QTL association. Normal distributions, as defined by the Kolmogorov–Smirnov test (SigmaStat 3.0), were obtained with the square root of the bleeding severity score/age, the reciprocal of the square root of the BT, and the logarithm (base 10) of the VWF:Ag level. In each case, the data do not differ significantly from the pattern expected if the data were drawn from a population with a normal distribution.

Descriptive statistics

Within nine families (excluding F3 and F9), affected individuals were defined as those with one or more of the mutant VWF haplotypes. In F3 and F9, affected members are those who express the VWD type 2M phenotype, based on laboratory findings and VWF multimer analyses. Comparisons of relevant variables between unaffected and affected members of the 11 pedigrees are summarized in Table 3. Among the affected individuals, bleeding severity scores (11 categories) ranged from 3 to 69; among unaffected kindred, 0 to 15. The gender-adjusted bleeding severity scores (nine categories) ranged from 0 to 69 among affected individuals and 0 to 15 among unaffected kindred. BTs ranged from 3 to 35 min among affected individuals and 3 to 7 min among unaffected kindred. A statistically significant difference (P < 0.01) was observed between unaffected and affected pedigree members with respect to each of the relevant variables, VWF:Ag, VWF:VIII, bleeding severity score/age (whether unadjusted or gender-adjusted), and BT. With respect to age, the difference was modest and barely statistically significant (P = 0.05). The ratio of males to females in each group were not statistically different (P = 0.92)

Table 3.  Comparison of relevant variables among affected and unaffected members of the 12 pedigrees
VariableUnaffectedAffectedP*
MeanSDRangeMeanSDRange
  1. NA, not applicable; BT, bleeding time; VWF:Ag, von Willebrand factor antigen; VWF:RCo, ristocetin cofactor activity.

  2. *Mann–Whitney rank sum test (SigmaStat 3.01). Chi-square test (SigmaStat 3.01). Bleeding Score based on 11 categories. §Bleeding score based on nine categories, excluding the gender-specific categories post-partum hemorrhage and menorrhagia.

VWF:RCo92.428.846–14219.625.82–110NA
VWF:Ag100.026.968–13537.837.74–214<0.01
Factor VIII131.446.693–24958.641.812–238<0.01
Score/age5.24.50–1526.416.63–69<0.01
Score/age§4.33.80–1523.415.80–69<0.01
 BT4.81.13–711.67.23–35<0.01
 Age52.621.810–9343.821.113–910.05
  n  n P
Female 29  30 0.92
Male 25  25  

The gene haplotype frequencies among the members of the 11 pedigrees, when considered as a single population, are not significantly different from those of a normal control group from Milan (data not shown).

Genetic correlations

The results are depicted in Table 4. For each gene, the haplotype that is associated with the minimum values of the quantitative trait in question is designated the minimum haplotype, while the haplotype that is associated with maximal values is designated the maximum haplotype.

Table 4.  QTL Association with ITGA2-52 haplotypes
 MinimumMaxiumumcorrected P-value
HaplotypeEstimateHaplotypeEstimate
  1. VWD, von Willebrand disease.

  2. *Excludes the gender-specific categories: post-partum hemorrhage and menorrhagia

ALL VWD type 2 families
Bleeding Score (11 categories)C−0.2269T0.2269<0.01
*Bleeding Score (nine categories)C−0.2228T0.22280.012
VWD Type 2A + 2M families only
Bleeding Score (11 categories)C−0.3213T0.3213<0.01
*Bleeding Score (nine categories)C−0.2834T0.2834<0.01

When data from all 11 of the VWD type 2 families is analyzed (Table 4), ITGA2 haplotype -52T is most often associated with higher bleeding severity scores. Conversely, ITGA2 haplotype -52C is most often associated with lower bleeding scores. This association is statistically significant when either the unadjusted score or the gender-adjusted score are evaluated (P < 0.01 and P = 0.012, respectively). When data from only the eight type 2A and type 2M families were considered, the same statistically significant associations were found (P < 0.01). These results can be interpreted to mean that haplotype -52T is associated with bleeding risk, while haplotype -52C is protective. Prior to correction for multiple testing, a weak relationship was seen with ITGA2B haplotypes, where haplotype 1 (Ile843; Baka) was associated with increased history of bleeding, while haplotype 2 (Ser843; Bakb) was protective (uncorrected P = 0.013). However, this association did not remain statistically significant following correction for multiple testing (P = 0.22). None of the other candidate gene haplotypes exhibited a statistically significant association with bleeding severity score (data not shown).

With respect to BT, there were no statistically significant associations with any of the haplotypes of the nine candidate genes (data not shown).

The QTL Association model employed in this analysis also permits a simultaneous assessment of the impact of other parameters (covariates). In this manner, the influence of each parameter (VWF:RCo level, VWF:Ag level, gender, and age) on bleeding severity score and BT, relative to candidate gene haplotypes, can be determined. A parameter estimate and standard error of the estimate are computed. Under the null hypothesis, the ratio of the absolute value of the estimate to the corresponding standard error is approximately standard normal. A ratio of 2.0 or greater is arbitrarily accepted to indicate a significant effect, and a comparison of the ratios provide an indication of the relative impact of each parameter.

With regard to Bleeding Severity Score (Table 5), parameter estimates clearly show that VWF level (whether VWF:Ag or VWF:RCo) has the strongest impact on the score (ratio = 6.00 and 5.96, respectively). This was an inverse effect, as reflected in the negative estimate, such that an increase in VWF level was associated with a decrease in the score. ITGA2 Haplotype -52T also exerted an inverse effect on the score, generating a ratio of 3.79. It is noteworthy that neither gender nor age had a significant impact on the score. These results depicted in Table 5 are generated from data obtained with all 11 VWD type 2 families using the unadjusted (11 category) Bleeding Severity Score. Comparable findings were made when only type 2A and 2M families were analyzed and the gender-adjusted score was utilized (data not shown).

Table 5.  Parameter estimates for the Bleeding Severity Score*
ParameterEstimateSERatio
  1. VWF:Ag, von Willebrand factor antigen; VWF:RCo, ristocetin cofactor activity.

  2. *Data obtained for all 11 VWD type 2 families using the 11 Category Bleeding Score

  3. (Absolute value of the estimate)/SE.

VWF:Ag−0.01140.00196.00
VWF:RCo−0.01370.00235.96
ITGA2 -52C−0.22690.05983.79
Age−0.00650.00401.63
Female−0.11370.07691.48
ITGA2 -52T0.22690.05983.79
Male0.11370.07691.48

As noted above, none of the candidate gene haplotypes exerted a significant influence on BT. On the contrary, VWF level was again the single parameter most responsible for variation in BT, while age and gender did not have a significant influence (Table 6).

Table 6.  Parameter estimates for the bleeding time*
ParameterEstimateSERatio*
  1. VWF:Ag, von Willebrand factor antigen; VWF:RCo, ristocetin cofactor activity.

  2. *Data obtained for all 11 von Willebrand disease type 2 families.

VWF:Ag−0.01200.00235.22
VWF:RCo−0.01440.00285.14
Age−0.00880.00521.69
Female−0.02870.09760.29

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

Existing ITGA2 haplotype frequency data and more recent information from the International HapMap project can be combined to define linkage disequilibrium (LD) between previously characterized single nucleotide polymorphisms (SNPs), such as -52 T/C and 807 T/C [23]. Thus, the -52 T/C polymorphism is apparently one of the oldest associated with ITGA2 and distinguishes two ancient ITGA2 haplotypes. Additional polymorphisms arose within a haplotype block downstream from -52 T/C, including 807 T/C and 1648 A/G, generating several more haplotypes.

In a previous study of VWD type 1 pedigrees [12], we found a significant association between haplotype 2 (807C) and severity of bleeding (high score), while a modest and not statistically insignificant association was seen with -52T. In the case of the type 2 pedigrees in this study, the influence of -52T and its association with increased bleeding score was greater than had been found in the previous type 1 study, while statistical significance was not observed with haplotype 2. These seemingly disparate findings in two separate studies are actually complementary manifestations of a similar genetic influence. Based on our own genotype data and the report of DiPaola et al. [23], there is strong LD between -52T and 807C, defined by two haplotypes with a combined frequency in the Italian (Milan) population of 0.35. Both of these SNPs have been associated with attenuated expression of platelet integrin α2β1 and thereby could increase the risk for bleeding. On the contrary, additional haplotypes are characterized by -52C in association with either 807C or 807T, and these have equivalent frequencies in the same population (0.37 and 0.27, respectively). Consequently, in a given pedigree, the polymorphism that is most strongly associated with the bleeding phenotype will be influenced not only by that SNP itself, but the genetic background (haplotype distribution) of each trio within the pedigree. In the end, the outcome is similar, and the conclusion is that ITGA2 haplotypes associated with decreased expression can increase the risk for bleeding.

The involvement of ITGA2 haplotypes in risk for bleeding in VWD is certainly not a coincidence. In mice that are deficient in α2β1 [24], platelet adhesion and thrombus formation are moderately impaired but not completely eliminated. Also, mouse strain differences in platelet aggregability by collagen have been recently correlated with a difference in expression of integrin α2β1, but not other prominent receptors, such as GPVI, GPIbα or integrin αIIbβ3 [25]. Quantitative differences in human platelet α2β1 have also been correlated with inheritance of ITGA2 haplotypes [5,26]. While we have addressed the association of ITGA2 haplotypes that confer low receptor density with risk for bleeding in VWD, others have reported a correlation between those haplotypes that confer high receptor density and risk for arterial thrombosis in younger men with a history of myocardial infarction [27,28], women who are heavy smokers [29], patients with diabetic retinopathy [30] and younger patients with stroke [31].

Despite its variability and poor heritability, the level of VWF, measured as antigen or ristocetin cofactor, remains the single most important parameter associated with bleeding severity and BTs in families of VWD type 1 or 2. In addition, ITGA2 haplotypes are important modifiers of disease outcome, demonstrating that genetically controlled attenuation of platelet collagen receptor integrin α2β1 expression can influence risk for morbidity in clinical settings, such as VWD, where hemostasis is already compromised.

Acknowledgement

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

This study was supported by NHLBI grants HLO75821 and HL46979 awarded to T.J. Kunicki.

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  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References
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