Skip to main content
NCBI home page
Research and Practice in Thrombosis and Haemostasis logoLink to Research and Practice in Thrombosis and Haemostasis
. 2025 Jun 24;9(5):102955. doi: 10.1016/j.rpth.2025.102955

Genome-wide association and linkage analysis of histidine-rich glycoprotein identifies common variants associated with plasma histidine-rich glycoprotein concentrations

Mary I Underwood 1, Ayse Bilge Ozel 2, Tanay Deepak 3, Beth McGee 4, Dave Siemieniak 4, Rida A Malik 5, Cherie Teney 5, Colin A Kretz 5, Jeffery Weitz 5, Karl C Desch 1,∗
PMCID: PMC12314372  PMID: 40756183

Abstract

Background

The plasma protein histidine-rich glycoprotein (HRG) interacts with multiple plasma ligands with various roles in coagulation, immunity, and angiogenesis. Through its inhibition of factor XIIa, HRG regulates the contact pathway of blood coagulation. Plasma HRG concentrations are highly heritable and vary widely, which may impact HRG function.

Objectives

To determine the genetic factors contributing to HRG variability.

Methods

Plasma HRG concentrations were measured in a healthy sibling cohort of 1152 subjects and a second healthy cohort of 2304 individuals of Irish descent. We performed genome-wide association study and meta-analysis on the European subset of these cohorts. Using the sibling subset of the 2 cohorts (n = 934 in 460 sibships), we explored linkage patterns to identify additional signals associated with variation in HRG concentrations that may be driven by rare variants. Two HRG missense variants associated with decreased HRG concentrations were expressed in vitro.

Results

Narrow-sense heritability was estimated at 69%. Meta-analysis identified an association between HRG concentrations and 2 independent signals at the HRG locus. Variants at these chromosome 3 loci collectively explained 45% of the variation in HRG concentrations. In vitro expression of 2 HRG variants associated with decreased HRG concentrations had no impact on HRG secretion. Linkage analysis of HRG concentrations identified 3 further regions contributing to differences in HRG concentrations.

Conclusion

The results of this genome-wide association study, investigating HRG concentration variation in a healthy population, provide new insights into the genetic control of circulating HRG concentrations and generate data for colocalization and Mendelian randomization studies.

Keywords: complex traits, genetic linkage, genome-wide association study, histidine-rich glycoprotein, partial thrompoplastin time, thrombophilia

Essentials

  • •

    HRG regulates the contact pathway of blood coagulation.

  • •

    An HRG variant is associated with altered aPTT.

  • •

    We identified common genetic variants that are associated with altered blood HRG concentrations.

  • •

    The same variant associated with altered aPTT is also the major determinant of blood HRG concentrations.

1. Introduction

The plasma histidine-rich glycoprotein (HRG) interacts with a variety of hemostatic and fibrinolytic proteins, including heparin [1], plasminogen [2], factor (F)XIIa [3], and fibrinogen [4], thereby regulating hemostasis and fibrinolysis. The human HRG gene maps to chromosome 3, at position 3q28-q29, spans approximately 12 kb [[5], [6], [7]], and consists of 7 exons and 6 introns. HRG is produced by hepatocytes [8] and megakaryocytes and is released from platelet α-granules in response to thrombin stimulation [9]. Once secreted, HRG circulates with a half-life of 2.93 ± 0.36 days [10]. The mature protein (∼80 kDa) consists of 507 amino acids, with a high proportion of histidine and proline residues (∼12% each) [1,8]. Approximately 14% of the protein mass is carbohydrates, with 3 N-glycosylation sites and 2 O-glycosylation sites [11].

Hrg-deficient “knockout” (KO) mice are viable and healthy [12,13] with a prothrombotic phenotype likely due to HRG’s ability to reduce thrombin generation via the intrinsic pathway. High-affinity binding of HRG to FXIIa (KD = 7.5 pM in the presence of Zn2+) inhibits FXII autoactivation by kallikrein or FXIIa-mediated activation of FXI [3]. HRG additionally competitively binds to DNA and RNA, preventing their binding to FXI, and binds to procoagulant polyphosphate, reducing polyphosphate-mediated clot formation [14,15]. In Hrg KO and heterozygotes [13], carotid artery occlusion after FeCl3 injury is accelerated, an effect mediated predominantly by the intrinsic pathway. HRG administration abrogates this effect, supporting its role as a physiological regulator of the intrinsic pathway [13].

The presence of HRG prolongs the activated partial thromboplastin time (aPTT), a measure of FXII activation, in a concentration-dependent manner in vitro [3,16] but has no effect on the prothrombin time [3]. This aPTT test is used as an indicator of the integrity of the intrinsic and common pathways [17]. Clinically, it is used to monitor anticoagulation dosing with anticoagulants, such as heparin, or screen for inherited or acquired coagulation factor deficiencies [17,18]. Reduced aPTT has been implicated as a risk marker for incident and recurrent venous thromboembolism [[19], [20], [21], [22]]. Previous genetic studies have identified associations between variants in HRG and aPTT variability [[23], [24], [25], [26]]; the HRG rs9898 variant accounts for 1% to 6% of this variability [23,26]. Given the influence of HRG on the aPTT, we hypothesize that common genetic variants associated with HRG variation will also be associated with aPTT concentrations.

HRG additionally prolongs the rate of fibrinolysis, as fibrin clot lysis times are shorter in Hrg KO mice compared with controls [12]. This is likely due to HRG’s ability to bind plasminogen. In vivo, a high proportion of HRG circulates in complex with plasminogen, reducing the free plasminogen available for fibrin(ogen) binding and slowing fibrinolysis. Furthermore, the incorporation of HRG into fibrin clots [27,28] results in thinner fibers [27], prolonging the clot lysis time. Patients who develop postthrombotic syndrome after deep vein thrombosis have 19% higher HRG baseline concentrations compared with controls. These increased concentrations are associated with reduced patient clot permeability and increased clot lysis time [29]. HRG may, therefore, contribute to the long-term sequelae of deep vein thrombosis by prothrombotic alterations to the fibrin clot.

Previous studies have suggested that approximately 70% of the variance in HRG concentrations could be explained by genetic factors [30,31], with 59% of this explained by HRG gene variants [31]. The HRG concentration in plasma varies 2-fold, ie, 80 to 150 μg/mL [[32], [33], [34]]. Concentrations are reported to be similar in males and females, significantly lower in neonates, increase with age, and decline in pregnant women during the second and third trimesters [33,35,36]. HRG is a negative acute phase protein [37], and its concentrations are reduced during sepsis [32,38], liver disease [34], acquired immune deficiency syndrome [39], and steroid treatment [39], while it is increased during heart disease. Both increased (>150%) [[40], [41], [42], [43], [44]] and decreased concentrations (20%-50%) [[45], [46], [47], [48]] have been described in patients with thrombosis.

Due to its important role in hemostasis and its variable plasma concentration in healthy populations, we performed a genome-wide association study (GWAS) and linkage analysis to identify genetic variants responsible for variance in HRG concentrations. The results explain 48% of the variation in plasma HRG concentrations.

2. Material and Methods

2.1. GABC cohort

A total of 1189 healthy individuals (ages 14-35) representing 507 sibships (with 2-6 siblings per family) from the University of Michigan, Ann Arbor, were recruited between June 26, 2006, and January 30, 2009. Study participants signed an online consent form [49]. Subjects with unexpected sibling relationships and discrepancies, as well as those with sex mismatches, were removed, resulting in a final dataset of 1152 subjects with 502 sibships. Please see previous publications for further details about the Genes and Blood Clotting (GABC) cohort and subsequent quality control (QC) steps [50,51].

2.2. TSS cohort

A total of 2438 healthy individuals of Irish ethnicity (ages 18-28) were recruited from the University of Dublin, Trinity College, in the 2003-2004 academic year. Participants signed a written consent upon enrollment, in accordance with the Declaration of Helsinki. The University of Dublin, Trinity College, affiliated with the Dublin Federated Hospitals Research Ethics Committee, gave ethical approval. After sample quality filtering steps, 2304 samples remained for analysis. Further information on the Trinity Student Study (TSS) cohort and subsequent QC steps can be found in previous publications [[50], [51], [52], [53]].

2.3. HRG antigen concentrations

HRG antigen concentrations were measured in the GABC and TSS cohorts using a custom AlphaLISA (Perkin Elmer, Revvity) with an affinity-purified sheep anti-HRG polyclonal antibody. This antibody was amine-coupled to acceptor beads (Perkin Elmer, Revvity) according to the manufacturer’s recommendations. Acceptor beads, biotinylated affinity-purified sheep anti-HRG polyclonal antibody (Affinity Biologicals), and streptavidin donor beads (Perkin-Elmer, Revvity) were used to detect HRG concentrations.

2.4. Genotyping, phenotyping, and data processing

Details of the genotyping and data QC have been previously published [50]. The Illumina HumanOmni1-Quad_v1-0_B array was used for genotyping the DNA samples in the GABC and TSS cohorts. Genotypes of both cohorts were imputed using the Michigan Imputation Server with TopMED Freeze 5b (Mixed Ancestry, build b38) as the reference panel. There were 39,980,749 autosomal markers in the final imputed dataset for the GABC cohort, with 34,112,094 of these having an imputation quality (R2 ≥ .3) that passed the final QC filters (genotype missing call rate ≥ 0.1 and minor allele frequency [MAF] ≥ 0.2). Similarly, there were 34,849,703 autosomal markers in the final imputed dataset for the TSS cohort, with 28,227,517 having an imputation quality of ≥ 0.3, of which 7,317,746 were kept after applying the same final QC filters as above.

Raw HRG concentrations were log10-transformed to normalize the data and adjusted for age, gender, and 2 to 3 strongly associated genotype principal component scores. This was done for the GABC cohort (N = 1152) and TSS cohort (N = 2304).

2.5. Heritability and variance explained estimations

Heritability and variance explained estimations were calculated using genotypes and the GCTA software package [54]. Heritability was also estimated using intraclass correlation (ICC) of HRG concentrations in sibships and the Merlin-Regress package [55] in the GABC cohort.

2.6. Association, meta-analysis, and linkage

GWAS and linkage analysis were done as previously described [[50], [51], [52]]. In brief, we used Plink (v1.9) [56], assuming unrelatedness for the TSS cohort, and EMMAX [57], accounting for sibling relationships and population structure in the GABC cohort. For both cohorts, we calculated the genomic control factor [58] to assess the degree of residual population stratification. Haplotypes were defined, and haplotype association analysis was run in the combined GABC and TSS cohorts using Plink (v1.9) [56]. Meta-analysis was conducted with the data from TSS cohort in the second study using METAL [59] with inverse sample size as the variance.

Linkage analysis was run using Merlin-Regress [55]. We evaluated the genome-wide significance of the linkage results with a locus-counting approach [60], as previously described [50]. We compared the observed logarithm of the odds (LOD) scores of the top independent regions of interest with the null distribution of LOD scores of their corresponding equal-ranked independent regions of interest >500 simulated datasets with randomized phenotypes.

2.7. Cloning of HRG missense variants

HRG complementary DNA was expressed in the mammalian expression plasmid pDEST (Invitrogen), and HRG cDNA was polymerase chain reaction (PCR) amplified using pCMV6-AC-GFP as template (Origene). pDEST was PCR amplified using pDEST-pcDNA5 (Invitrogen) as a template. Primers were designed by Gibson Assembly, and PCR fragments were assembled using NeBuilder HiFi DNA Assembly Mix (New England Biologicals). Primers used to produce variants p.R448C (c.C1342T) and p.P204S (c.C610T) are shown in Supplementary Table S1. Plasmids were sequenced to ensure no mutations were introduced during PCR.

2.8. Creation of HRG stable cell lines

Purified plasmids were transfected into Flp-In T-REx 293 cells (Thermo Fisher Scientific) using Lipofectamine-3000 and Flp recombinase (Life Technologies). Stable lines were selected using 200 μg/mL hygromycin, and cells were cultured in Dulbecco's Modified Eagle's Medium (Invitrogen) containing 10% fetal bovine serum in a humidified 37 °C chamber with 5% CO2. The vector contains a tetracycline operon for inducible complementary DNA expression and ampicillin and hygromycin resistance genes.

2.9. Analysis of HRG and GAPDH concentrations in cell supernatant and lysates

To measure HRG secretion, stably expressing reference or variant HRG were plated into T75 flasks. The next day, Opti-MEM Reduced Serum Medium (Thermo Fisher Scientific) was added to flasks, in addition to 1 μg/mL tetracycline to induce HRG expression. After 72 hours, conditioned media were collected. Cells were pelleted and lysed as previously described [61].

HRG concentrations in unconcentrated cell supernatants were measured using the same in-house AlphaLISA used to measure plasma HRG concentrations (described above). Values are expressed as a percentage of recombinant reference HRG. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) concentrations in cell lysates were measured using AlphaLISA SureFire Ultra Total GAPDH assay (Revvity) according to the manufacturer’s recommendations. HRG concentrations in media were normalized based on the corresponding GAPDH concentrations in cell lysates.

3. Results

3.1. HRG concentrations and HRG heritability

Demographic information of the 2 cohorts has been previously described [50]. The HRG concentrations measured in plasma are shown in Supplementary Figure S1. The median HRG concentrations in the GABC and TSS cohorts were 87.4 IU/dL and 95.7 IU/dL, respectively. The first and third quantiles of the distributions spanned a 2-fold difference in the GABC cohort (63.1 IU/dL-123.3 IU/dL) and a 2-fold difference in the TSS cohort (71.3 IU/dL-124.4 IU/dL). Estimates of HRG heritability were consistent across the 3 different methods for the GABC European-only cohort: 69% using Merlin-Regress, 66% using ICC, and 60% using GCTA. GABC and TSS combined cohort heritability using the known sibship relationships yielded an ICC of 60%.

3.2. HRG variants associate with HRG concentrations in the GABC and TSS cohorts

Although the GABC study represents several ethnicities, we restricted our analysis to those of European descent to control for population substructure. Our association analysis of the 934 subjects of European ancestry identified 147 single-nucleotide polymorphisms (SNPs) with P < 6.8 × 10-9 (= .05/7,361,402) at or close to the HRG gene on chromosome 3 (Figure 1A). The top SNP was rs9898(T) (MAF = 0.33; effect size [β] = −0.19 ± SD 0.066; P = 9.4 × 10-132).

Figure 1.

Figure 1

Open in a new tab

Association of histidine-rich glycoprotein (HRG) concentrations in the Genes And Blood Clotting and Trinity Student Study cohorts. Manhattan plot of −log10-fold(P) values for each single-nucleotide polymorphism tested. The dotted line marks the significance threshold (P value = .05/number of single-nucleotide polymorphisms tested) for genome-wide significance. Quantile-quantile plots showing the presence of any inflation are included with the corresponding association result. Genome-wide association study results for the (A) GABC cohort (934 patients of European ancestry) and (B) TSS replication cohort (2304 patients of European ancestry) are shown.

In the replication TSS cohort, there were 241 significant SNPs identified at or close to the HRG locus on chromosome 3 with P < 6.8 × 10-9 (=.05/7,317,746). In agreement with GABC, the top SNP identified was rs9898(T) (MAF = 0.36; β = −0.16 ± 0.0043; P = 1.2 × 10-240; Figure 1B). The quantile-quantile plots of the observed vs expected −log10-fold(P) values of both cohorts showed no inflation (lambda = 1.02) and demonstrated a strong deviation from expected values (Figure 1), driven by the significant signals on chromosome 3.

There was 147 markers significant in both the GABC and TSS cohorts. The 147 significant SNPs identified in the GABC cohort were also present in the TSS cohort. There was strong agreement in directionality of β for these SNPs in the 2 cohorts (R2 = .95; Figure 2A). Similarly, 237 of the 241 significant SNPs in the TSS cohort were also present in the GABC dataset (of which 90 were insignificant in GABC). There was also strong agreement in directionality of β for these shared SNPs in the 2 cohorts (R2 = .95; Figure 2B).

Figure 2.

Figure 2

Open in a new tab

Comparison of the effect sizes of top single-nucleotide polymorphism (SNP) signals in Genes And Blood Clotting (GABC) cohort with the top significant SNP signals in Trinity Student Study (TSS) cohort. (A) Top significant SNPs (P < 6.8 × 10-9) in GABC cohort (147) were plotted against their values in the TSS cohort. (B) Similarly, the top significant SNPs in TSS cohort (241, of which 237 are shown here) were plotted against their values in GABC cohort.

3.3. Meta-analysis of GABC and TSS cohorts

The meta-analysis of the GABC and TSS associations identified 246 significant SNPs (P < 6.6 × 10-9 [=.05/7,582,323]) on chromosome 3 at or close to the HRG locus (Supplementary Table S2). The top SNP identified again was rs9898(T) (MAF = 0.35; β = −0.17 ± 0.0036; P = 1.1 × 10-338; Figure 3A, Supplementary Figure S3). Of these SNPs, 169 were significant in the GABC cohort (matching all of the significant SNPs in GABC), and 237 were significant in the TSS cohort. After conditioning on the top SNP rs9898, the remaining 136 significant SNPs mapped to chromosome 3 (more specifically, the following gene loci: CRYGS, TBCCD1, FETUB, HRG, ASHG, and KNG1; Figure 3B, Supplementary Figure S4). After conditioning on rs9898, the top SNP was rs7625980, an HRG intronic variant (position = 186,672,838; MAF = 0.22; β = −0.13 ± 0.0049; P = 4.5 × 10-120). After conditioning on rs9898 and rs7625980, no significant SNPs remained (Figure 3C), suggesting 2 independent linkage disequilibrium (LD) blocks were associated with HRG concentrations at the HRG locus. This also indicates that signals in nearby loci, such as KNG1, are due to LD with variants in HRG and not due to variation at these nearby loci. The KNG1 SNP rs710446 is associated with multiple traits [62]. In the meta-analysis, this SNP was significant, P = 1.62 × 10-11, but not when conditioned on rs9898. Overall, the meta-analysis explained 44.6% of the variance in HRG concentrations, and 40.7% was explained by rs9898 alone.

Figure 3.

Figure 3

Open in a new tab

Meta-analysis of single-nucleotide polymorphisms (SNPs) identified in both cohorts before and after conditioning on top SNPs. Manhattan plot of −log10-fold(P) values for each SNP tested. The dotted line marks the significance threshold of 8.18 × 10-9 for genome-wide significance (−log10-fold; P = .6.6 × 10-9). (A) Top SNPs identified in meta-analysis of GABC and TSS cohorts (Plink) + GABC934 (EMMAX). (B) Top SNPS identified in both cohorts after conditioning on rs9898. (C) Top SNPS identified in both cohorts after conditioning on both rs9898 and rs7625980.

3.4. Linkage analysis

Using the sibling structure in the GABC cohort and siblings in the TSS cohort, we performed linkage analysis. Our initial study identified 4546 SNPs with LOD score ≥ 3 (Figure 4A). As LOD scores may be inflated due to unmodelled LDs between SNPs, a clustering algorithm was used in MERLIN. All genotyped and imputed SNPs were divided into LD clusters, yielding ∼37,000 clusters across the genome, and a linkage LOD score was calculated for each cluster as previously described [50]. Cluster analysis identified 288 clusters with a LOD score ≥ 3 (Figure 4A) and revealed a strong signal on chromosome 3 (peak LOD = 10.1) and chromosome 2 (peak LOD = 3.2).

Figure 4.

Figure 4

Open in a new tab

Linkage analysis using Multipoint Engine for Rapid Likelihood Inference (MERLIN)-regress of GABC and TSS cohorts. (A) Logarithm of the odds (LOD) scores for GABC and TSS cohorts using approximately 37,000 clusters. There were 288 clusters with a LOD score ≥ 3 (dotted line shows LOD = 3). Clusters were defined in MERLIN to model independent regions of linkage. (B) Comparison of the observed maximal LOD scores in the top 10 independent linkage regions (shown as red dots) with their corresponding equal-ranked LOD score distributions in 500 null simulations (shown as boxplots). P values for the original cluster LOD scores at each rank (red) in comparison with the corresponding null LOD distribution are displayed at the bottom.

Similar to our previous locus-counting approach [50], we evaluated the significance of the genome-wide linkage results by comparing the original peak cluster LOD scores (within the independent regions of linkage [IRL]) with the null distributions of their equally ranked counterparts, created by randomizing the phenotypes across individuals while keeping the LDs modeled by the clusters the same. These simulations were done 500 times, and the IRLs were defined as 40 cM around the peak LOD score. We observed 3 IRLs that had higher peak LOD scores than their equally ranked null distributions (Figure 4B). A peak LOD score of 10.1 on chromosome 3 approximately 189.0 Mb (P value = 4.07 × 10-13 and simulation-based empirical P value = 0). This IRL contains the HRG gene. The second-highest region was on chromosome 2 (∼234.7 Mb), with a peak LOD score of 3.2 (P value = 5.30 × 10-6 and simulation-based empirical P value = ∼0). The third was on chromosome 18 (∼47.4 Mb), with a peak LOD score of 1.6 (P value = 2.55 × 10-4 and simulation-based empirical P value = .01). The 3 linkage intervals explained 2.5% of the variation in the HRG concentrations using GCTA.

As an alternative to a fixed interval of ± 20 cM around the peak, we can also look at LOD score support intervals around the maximum LOD score. While the 1 LOD score cutoff around our top signal on chromosome 3 spanned 186.8 to 189.2 Mb (and did not include HRG), the 2 LOD score cutoff yielded an interval of 186.2 to 189.7 Mb, so we looked at genes within a 2 LOD core interval. Our second most significant signal was on chromosome 2, with a 2 LOD score interval of 231.0 to 235.5 Mb. Our third significant signal was on chromosome 18, with a 2 LOD support interval spanning 23.3 to 77.4 Mb.

HRG is located within the 2 LOD score of chromosome 3, and this gene alone could have driven the phenotype. No hemostasis-related genes were found within the 2 LOD interval of chromosomes 2, 3, and 18. Glycosyltransferases, such as B4GALT6 (chromosome 18) and ST6GAL1 (chromosome 3), were included in these regions (HRG is both glycosylated and sialylated [11]), as well as LMAN1 (chromosome 18), which is important in the trafficking of secreted proteins. The causative genes responsible for these linkage signals remain to be determined.

3.5. In vitro expression of HRG variants

Two missense variants present in each HRG haplotype block (rs9898 and rs1042445), associated with reduced HRG concentrations (Supplementary Figure S2), were expressed to analyze their effects on secretion in vitro. The first variant, rs9898 (p.P204S, c.C610T), the top SNP identified in both cohorts, led to the substitution of a proline residue with a serine. The top SNP identified in the second haplotype block was an intronic variant rs7625980 (c.741+568T>C; P = 4.5 × 10-120). Three additional SNPs with the same P value and in LD with rs7625980 were also identified: 1 missense rs1042445 (p.R448C, c.C1342T) and 2 intronic rs7614709 and rs1863622 (P = 4.5 × 10-120).

Three forms of HRG, reference, rs9898(T), and rs1042445(T), were expressed in stably transfected cell lines. HRG concentrations measured in supernatant samples were decreased compared with the reference (Figure 5A). This difference was significant for p.P204S (P = .0217). However, when normalized to the number of cells (estimated with GAPDH concentrations; Figure 5B, C), there was no significant difference in HRG concentrations between the conditioned media of reference and variant-expressing cells. This suggests that the differences observed in GWAS were not related to decreased secretion rates. Our in vitro model only tested coding sequence variation effects on HRG secretion. Alternatively, these variants could drive changes in HRG plasma clearance rates or change RNA expression patterns due to noncoding variants in LD with the missense variants.

Figure 5.

Figure 5

Open in a new tab

Histidine-rich glycoprotein (HRG) concentrations in reference (Ref) and variant recombinant HRG expressed in vitro. (A) HRG concentrations were measured in the supernatant of cells expressing reference (Ref) or alternate variant HRG before glyceraldehyde 3 phosphate dehydrogenase (GAPDH) normalization. (B) GAPDH concentrations were measured in cells expressing Ref or alternate variant HRG. (C) HRG concentrations in the supernatant of Ref or alternate variant cells expressing HRG were normalized based on GAPDH concentrations. Data shown are from 2 biological replicates, with 3 replicates per experiment. An unpaired t-test was used to compare variants with Ref HRG. ns, not significant, ∗P < .05.

4. Discussion

In this GWAS, we identified genetic factors that contribute to HRG concentrations. The young age and healthy status of our subjects limited the impact of nongenetic environmental factors on HRG concentrations. Our heritability estimates (60%-69%) are in line with previous studies, which estimated heritability of 69% [30] and 70% [31] based on family studies.

HRG inhibits activation of the intrinsic pathway of coagulation and, consequently, the aPTT, a widely used clinical test of the coagulation system. HRG concentrations vary 2-fold in plasma [[32], [33], [34]], so understanding factors contributing to this variation and, subsequently, aPTT may be clinically useful.

The top SNP identified was rs9898 (c.C610T, p.Pro204Ser), located within the HRG gene. The affected residue is located between 2 β strands (ß3 and ß4), and in rabbits, this amino acid exists as a serine rather than a proline, suggesting a subtle effect of substitution on protein structure and function. It lies within the N2 domain, which binds to polyphosphate but does not inhibit polyphosphate-induced FXII autoactivation [63]. The HRG N-terminal region has been implicated in binding to plasminogen [64], immunoglobulin G, C1q, heparin, and heparan sulfate [65]. The rs1042445 variant, one of the top SNPs identified in the second haplotype block after conditioning, lies within the C-terminal region of HRG, which has also been implicated in binding to plasminogen and thrombospondin.

rs9898 was the top SNP in HRG associated with variance in aPTT [[23], [24], [25], [26]], explaining 1% to 6% of this variation [23,26]; the rs9898 minor T allele is associated with a decrease in the aPTT [23,25,26]. As the rs9898 SNP contributes to decreased aPTT, which is associated with venous thrombosis, the association between this SNP and venous thrombosis has been investigated [66]. No association was found, suggesting that this variant does not directly affect venous thromboembolism risk. Given that the presence of HRG in plasma prolongs the aPTT, this suggests that the rs9898 minor T allele may be associated with a decrease in HRG concentrations or function. In agreement with this, in our study, we found that this variant was associated with decreased HRG plasma concentrations. Similarly, the second independent variant, rs1042445, was associated with decreased HRG concentrations in plasma.

However, when these variants were expressed in vitro, normalized HRG concentrations were unchanged, suggesting these variants have no effect on HRG secretion. In line with this observation, we observed no impact of these variants on HRG mRNA concentrations using the genotype-tissue expression portal, which is in agreement with another study [67]. One possible explanation for the discrepancy between plasma and in vitro secretion may be that these variants affect HRG clearance. Interestingly, the presence of a serine residue at position 204 introduces an N-glycosylation site at Arg 202, resulting in a 2 kDa increase in molecular weight [68]. Given the increased glycosylation, this variant may affect HRG concentrations through differences in the rate of HRG clearance, the main site of which is the liver and spleen [69]. For other plasma proteins, such as von Willebrand factor, mutations or changes in glycosylation have been shown to affect the rate of clearance [70]. Alternatively, other variants in LD with rs9898 or rs1042445 could be driving the phenotype through RNA expression mechanisms.

Ferkingstad et al. [71] used proteomics and aptamer-based SomaScan to look for associations between plasma protein concentrations and genetic variants in an Icelandic population. The authors also identified associations between the rs9898 and rs1042445 variants and HRG concentrations. The β statistic values for rs9898 were positive, but negative for rs1042445. We found that both variants were associated with decreased HRG concentrations. Sun et al. [72] evaluated associations between common genetic variants in participants of the UK Biobank and plasma protein concentrations measured using O-link technology. We queried this database to check for an association between the rs9898 and rs1042445 gene variants and HRG protein concentrations. No association between the rs1042445 allele and HRG concentrations was found. However, in the Sun study, rs9898 was associated with decreased HRG and lower plasminogen.

Protein quantitative trait loci (pQTLs) detected using SomaScan [71] were compared with pQTLs using O-link as part of the Systematic and Combined AnaLysis of Olink Proteins consortium [73]. The authors replicated 64% of 355 pQTL associations [71]. Correlations between proteins measured using O-link and SomaScan varied from 0.01 to 0.95, with a median correlation of 0.76; HRG was not measured in this comparison. These discrepancies are likely due to the limitations of both of these assays. Katz et al. [74] compared both platforms and concluded that the O-link platform was more reliable at specifically identifying a protein target with a higher number of phenotypic associations based on previous genetic studies. On the other hand, SomaScan platforms had greater measurement precision and identified a larger number of proteins across the proteome [74]. O-link antibody-based measurements are semiquantitative and can only be used for relative comparisons of protein concentrationconcentrations [75]. SomaScan is more likely to be susceptible to missense variants due to modifications in electric charge (due to amino acid differences), which can affect the binding of negatively charged aptamers [75]. Similarly, missense variants may affect antibody binding using SomaScan or antibody-based enzyme-linked immunosorbent assay [52]. The advantage of our study is that we specifically measured HRG concentrations using a well-established polyclonal anti-HRG antibody.

In our previous study of plasma plasminogen concentrations, we did not find an association between HRG gene variants and plasminogen [51]. This could be due to lower statistical power in our study. Surface- and glycosaminoglycan-bound HRG regulates plasminogen activation by tissue plasminogen activator [64,76,77]. The ability of HRG to tether plasminogen to glycosaminoglycans has been proposed to play an important role in facilitating plasminogen activation [76,77]. Plasmin cleavage of HRG enhances HRG binding to plasminogen, although the physiological relevance of this cleavage is unknown. The rs9898 polymorphism influences plasmin cleavage of HRG, reducing it slightly [11], suggesting that this variant may affect its interaction with plasminogen.

The sibling structure of the GABC and TSS cohorts enabled linkage analysis, which identified regions on 3 different chromosomes, together accounting for 2.5% of the HRG variability. The identity of the genes impacting HRG concentrations in these linkage intervals is unclear, although the signal at chromosome 3 includes HRG that could be driven by a combination of known common variants and genotyped rare variants. These areas need to be fine-mapped to understand the causative genes responsible.

In conclusion, we identified genetic factors that contribute to HRG concentrations. Common HRG variants on chromosome 3 showed expected significant signals in both our test and replication cohorts, collectively explaining 47.0% of the variation in HRG concentrations. The direction of the most significantly associated SNPs was in strong agreement between both cohorts. Linkage analysis of the HRG concentrations using the siblings in both cohorts identified 3 regions, which explained 2.5% of the HRG concentration variation. These results provide new insight into the genetic control of circulating HRG concentrations. Our in vitro results demonstrate that the missense p.P204S and p.R448C variants do not affect HRG secretion in cell culture, suggesting that increased clearance may drive the differences in these missense variant HRG concentrations.

Acknowledgments

We thank Professor David Ginsburg, University of Michigan, for his advice on experiments and participants of the Genes And Blood Clotting and Trinity Student Study cohorts.

Funding

This work was funded by National Institutes of Health/National Heart Lung Blood Institute, United States, R01 HL172780.

Author contributions

M.I.U. cloned and expressed histidine-rich glycoprotein (HRG) variants in vitro and wrote the manuscript. A.B.O. analyzed genome-wide association and linkage data and wrote the manuscript. T.D. expressed HRG variants in vitro. B.M. carried out AlphaLISA experiments. D.S. analyzed genome-wide association and linkage data. R.A.M. characterized HRG expression in vitro. C.T. did the HRG cloning and expression. C.A.K. and J.W. cloned the HRG antibody, analyzed data, and edited the manuscript. K.C.D. designed the experiments, carried out AlphaLISA experiments, and wrote the manuscript.

Relationship disclosure

There are no competing interests to disclose.

Footnotes

Handling Editor: Dr Henri Spronk

The online version contains supplementary material available at https://doi.org/10.1016/j.rpth.2025.102955

Supplementary material

Supplementary Table S1
mmc1.docx (18.7KB, docx)
Supplementary Table S2
mmc2.xlsx (65.7KB, xlsx)

Supplementary Figure S1.

Supplementary Figure S1

Open in a new tab

Supplementary Figure S2.

Supplementary Figure S2

Open in a new tab

Supplementary Figure S3.

Supplementary Figure S3

Open in a new tab

Supplementary Figure S4.

Supplementary Figure S4

Open in a new tab

References

  • 1.Lijnen H.R., Hoylaerts M., Collen D. Neutralization of heparin activity by binding to human histidine-rich glycoprotein. Thromb Res. 1983;29:443–446. doi: 10.1016/0049-3848(83)90248-7. [DOI] [PubMed] [Google Scholar]
  • 2.Lijnen H.R., Hoylaerts M., Collen D. Isolation and characterization of a human plasma protein with affinity for the lysine binding sites in plasminogen. Role in the regulation of fibrinolysis and identification as histidine-rich glycoprotein. J Biol Chem. 1980;255:10214–10222. [PubMed] [Google Scholar]
  • 3.MacQuarrie J.L., Stafford A.R., Yau J.W., Leslie B.A., Vu T.T., Fredenburgh J.C., et al. Histidine-rich glycoprotein binds factor XIIa with high affinity and inhibits contact-initiated coagulation. Blood. 2011;117:4134–4141. doi: 10.1182/blood-2010-07-290551. [DOI] [PubMed] [Google Scholar]
  • 4.Vu T.T., Stafford A.R., Leslie B.A., Kim P.Y., Fredenburgh J.C., Weitz J.I. Histidine-rich glycoprotein binds fibrin(ogen) with high affinity and competes with thrombin for binding to the γ-chain. J Biol Chem. 2011;286:30314–30323. doi: 10.1074/jbc.M111.253831. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Koide T. Human histidine-rich glycoprotein gene: evidence for evolutionary relatedness to cystatin supergene family. Thromb Res. Suppl 1988;8:91–97. doi: 10.1016/0049-3848(88)90158-2. [DOI] [PubMed] [Google Scholar]
  • 6.Hennis B.C., Frants R.R., Bakker E., Vossen R.H., van der Poort E.W., Blonden L.A., et al. Evidence for the absence of intron H of the histidine-rich glycoprotein (HRG) gene: genetic mapping and in situ localization of HRG to chromosome 3q28-q29. Genomics. 1994;19:195–197. doi: 10.1006/geno.1994.1046. [DOI] [PubMed] [Google Scholar]
  • 7.van den Berg E.A., le Clercq E., Kluft C., Koide T., van der Zee A., Oldenburg M., et al. Assignment of the human gene for histidine-rich glycoprotein to chromosome 3. Genomics. 1990;7:276–279. doi: 10.1016/0888-7543(90)90551-5. [DOI] [PubMed] [Google Scholar]
  • 8.Koide T., Foster D., Yoshitake S., Davie E.W. Amino acid sequence of human histidine-rich glycoprotein derived from the nucleotide sequence of its cDNA. Biochemistry. 1986;25:2220–2225. doi: 10.1021/bi00356a055. [DOI] [PubMed] [Google Scholar]
  • 9.Leung L.L., Harpel P.C., Nachman R.L., Rabellino E.M. Histidine-rich glycoprotein is present in human platelets and is released following thrombin stimulation. Blood. 1983;62:1016–1021. [PubMed] [Google Scholar]
  • 10.Lijnen H.R., DeCock F., Collen D. Turnover of human histidine-rich glycoprotein in healthy subjects and during thrombolytic therapy. Thromb Res. 1981;23:121–131. doi: 10.1016/0049-3848(81)90245-0. [DOI] [PubMed] [Google Scholar]
  • 11.Zou Y., Pronker M.F., Damen J.M.A., Heck A.J.R., Reiding K.R. Genotype-dependent N-glycosylation and newly exposed O-glycosylation affect plasmin-induced cleavage of histidine-rich glycoprotein (HRG) J Biol Chem. 2024;300 doi: 10.1016/j.jbc.2024.105683. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Tsuchida-Straeten N., Ensslen S., Schäfer C., Wöltje M., Denecke B., Moser M., et al. Enhanced blood coagulation and fibrinolysis in mice lacking histidine-rich glycoprotein (HRG) J Thromb Haemost. 2005;3:865–872. doi: 10.1111/j.1538-7836.2005.01238.x. [DOI] [PubMed] [Google Scholar]
  • 13.Vu T.T., Zhou J., Leslie B.A., Stafford A.R., Fredenburgh J.C., Ni R., et al. Arterial thrombosis is accelerated in mice deficient in histidine-rich glycoprotein. Blood. 2015;125:2712–2719. doi: 10.1182/blood-2014-11-611319. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Vu T.T., Leslie B.A., Stafford A.R., Zhou J., Fredenburgh J.C., Weitz J.I. Histidine-rich glycoprotein binds DNA and RNA and attenuates their capacity to activate the intrinsic coagulation pathway. Thromb Haemost. 2016;115:89–98. doi: 10.1160/TH15-04-0336. [DOI] [PubMed] [Google Scholar]
  • 15.Malik R.A., Zhou J., Fredenburgh J., Truong T.K., Crosby J.R., Revenko A.S., et al. Polyphosphate-induced thrombosis in mice is factor XII-dependent and is attenuated by histidine-rich glycoprotein. Blood Adv. 2021;5:3540–3551. doi: 10.1182/bloodadvances.2021004567. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Vestergaard A.B., Andersen H.F., Magnusson S., Halkier T. Histidine-rich glycoprotein inhibits contact activation of blood coagulation. Thromb Res. 1990;60:385–396. doi: 10.1016/0049-3848(90)90221-w. [DOI] [PubMed] [Google Scholar]
  • 17.Kamal A.H., Tefferi A., Pruthi R.K. How to interpret and pursue an abnormal prothrombin time, activated partial thromboplastin time, and bleeding time in adults. Mayo Clin Proc. 2007;82:864–873. doi: 10.4065/82.7.864. [DOI] [PubMed] [Google Scholar]
  • 18.Boender J., Kruip M.J., Leebeek F.W. A diagnostic approach to mild bleeding disorders. J Thromb Haemost. 2016;14:1507–1516. doi: 10.1111/jth.13368. [DOI] [PubMed] [Google Scholar]
  • 19.Hron G., Eichinger S., Weltermann A., Quehenberger P., Halbmayer W.M., Kyrle P.A. Prediction of recurrent venous thromboembolism by the activated partial thromboplastin time. J Thromb Haemost. 2006;4:752–756. doi: 10.1111/j.1538-7836.2006.01868.x. [DOI] [PubMed] [Google Scholar]
  • 20.Zakai N.A., Ohira T., White R., Folsom A.R., Cushman M. Activated partial thromboplastin time and risk of future venous thromboembolism. Am J Med. 2008;121:231–238. doi: 10.1016/j.amjmed.2007.10.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Legnani C., Mattarozzi S., Cini M., Cosmi B., Favaretto E., Palareti G. Abnormally short activated partial thromboplastin time values are associated with increased risk of recurrence of venous thromboembolism after oral anticoagulation withdrawal. Br J Haematol. 2006;134:227–232. doi: 10.1111/j.1365-2141.2006.06130.x. [DOI] [PubMed] [Google Scholar]
  • 22.Tripodi A., Chantarangkul V., Martinelli I., Bucciarelli P., Mannucci P.M. A shortened activated partial thromboplastin time is associated with the risk of venous thromboembolism. Blood. 2004;104:3631–3634. doi: 10.1182/blood-2004-03-1042. [DOI] [PubMed] [Google Scholar]
  • 23.Houlihan L.M., Davies G., Tenesa A., Harris S.E., Luciano M., Gow A.J., et al. Common variants of large effect in F12, KNG1, and HRG are associated with activated partial thromboplastin time. Am J Hum Genet. 2010;86:626–631. doi: 10.1016/j.ajhg.2010.02.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Gaunt T.R., Lowe G.D., Lawlor D.A., Casas J.P., Day I.N. A gene-centric analysis of activated partial thromboplastin time and activated protein C resistance using the HumanCVD focused genotyping array. Eur J Hum Genet. 2013;21:779–783. doi: 10.1038/ejhg.2012.242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Tang W., Schwienbacher C., Lopez L.M., Ben-Shlomo Y., Oudot-Mellakh T., Johnson A.D., et al. Genetic associations for activated partial thromboplastin time and prothrombin time, their gene expression profiles, and risk of coronary artery disease. Am J Hum Genet. 2012;91:152–162. doi: 10.1016/j.ajhg.2012.05.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Weng L.C., Cushman M., Pankow J.S., Basu S., Boerwinkle E., Folsom A.R., et al. A genetic association study of activated partial thromboplastin time in European Americans and African Americans: the ARIC Study. Hum Mol Genet. 2015;24:2401–2408. doi: 10.1093/hmg/ddu732. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Leung L.L. Interaction of histidine-rich glycoprotein with fibrinogen and fibrin. J Clin Invest. 1986;77:1305–1311. doi: 10.1172/JCI112435. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Ząbczyk M., Stachowicz A., Natorska J., Olszanecki R., Wiśniewski J.R., Undas A. Plasma fibrin clot proteomics in healthy subjects: relation to clot permeability and lysis time. J Proteomics. 2019;208 doi: 10.1016/j.jprot.2019.103487. [DOI] [PubMed] [Google Scholar]
  • 29.Siudut J., Natorska J., Son M., Plens K., Undas A. Increased levels of histidine-rich glycoprotein are associated with the development of post-thrombotic syndrome. Sci Rep. 2020;10 doi: 10.1038/s41598-020-71437-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Boomsma D.I., Hennis B.C., van Wees A.G., Frants R.R., Kluft C. A parent-twin study of plasma levels of histidine-rich glycoprotein (HRG) Thromb Haemost. 1993;70:848–851. [PubMed] [Google Scholar]
  • 31.Hennis B.C., Boomsma D.I., van Boheemen P.A., Engesser L., Kievit P., Dooijewaard G., et al. An amino acid polymorphism in histidine-rich glycoprotein (HRG) explains 59% of the variance in plasma HRG levels. Thromb Haemost. 1995;74:1497. 50. [PubMed] [Google Scholar]
  • 32.Lijnen H.R., Jacobs G., Collen D. Histidine-rich glycoprotein in a normal and a clinical population. Thromb Res. 1981;22:519–523. doi: 10.1016/0049-3848(81)90115-8. [DOI] [PubMed] [Google Scholar]
  • 33.Morgan W.T., Koskelo P., Koenig H., Conway T.P. Human histidine-rich glycoprotein. II. Serum levels in adults, pregnant women and neonates. Proc Soc Exp Biol Med. 1978;158:647–651. doi: 10.3181/00379727-158-40265. [DOI] [PubMed] [Google Scholar]
  • 34.Saito H., Goodnough L.T., Boyle J.M., Heimburger N. Reduced histidine-rich glycoprotein levels in plasma of patients with advanced liver cirrhosis. Possible implications for enhanced fibrinolysis. Am J Med. 1982;73:179–182. doi: 10.1016/0002-9343(82)90175-9. [DOI] [PubMed] [Google Scholar]
  • 35.Corrigan J.J., Jr., Jeter M.A., Bruck D., Feinberg W.M. Histidine-rich glycoprotein levels in children: the effect of age. Thromb Res. 1990;59:681–686. doi: 10.1016/0049-3848(90)90428-f. [DOI] [PubMed] [Google Scholar]
  • 36.Drasin T., Sahud M. Blood-type and age affect human plasma levels of histidine-rich glycoprotein in a large population. Thromb Res. 1996;84:179–188. doi: 10.1016/0049-3848(96)00174-0. [DOI] [PubMed] [Google Scholar]
  • 37.Saigo K., Yoshida A., Ryo R., Yamaguchi N., Leung L.L. Histidine-rich glycoprotein as a negative acute phase reactant. Am J Hematol. 1990;34:149–150. doi: 10.1002/ajh.2830340212. [DOI] [PubMed] [Google Scholar]
  • 38.Kuroda K., Wake H., Mori S., Hinotsu S., Nishibori M., Morimatsu H. Decrease in histidine-rich glycoprotein as a novel biomarker to predict sepsis among systemic inflammatory response syndrome. Crit Care Med. 2018;46:570–576. doi: 10.1097/CCM.0000000000002947. [DOI] [PubMed] [Google Scholar]
  • 39.Morgan W.T. Serum histidine-rich glycoprotein levels are decreased in acquired immune deficiency syndrome and by steroid therapy. Biochem Med Metab Biol. 1986;36:210–213. doi: 10.1016/0885-4505(86)90127-1. [DOI] [PubMed] [Google Scholar]
  • 40.Engesser L., Kluft C., Briet E., Brommer E.J. Familial elevation of plasma histidine-rich glycoprotein in a family with thrombophilia. Br J Haematol. 1987;67:355–358. doi: 10.1111/j.1365-2141.1987.tb02357.x. [DOI] [PubMed] [Google Scholar]
  • 41.Castaman G., Ruggeri M., Burei F., Rodeghiero F. High levels of histidine-rich glycoprotein and thrombotic diathesis. Report of two unrelated families. Thromb Res. 1993;69:297–305. doi: 10.1016/0049-3848(93)90027-l. [DOI] [PubMed] [Google Scholar]
  • 42.Falkon L., Gari M., Montserrat I., Borrell M., Fontcuberta J. Familial elevation of plasma histidine-rich glycoprotein. A case associated with recurrent venous thrombosis and high PAI-1 levels. Thromb Res. 1992;66:265–270. doi: 10.1016/0049-3848(92)90197-i. [DOI] [PubMed] [Google Scholar]
  • 43.Hoffmann J.J., Hennis B.C., Kluft C., Vijgen M. Hereditary increase of plasma histidine-rich glycoprotein associated with abnormal heparin binding (HRG Eindhoven) Thromb Haemost. 1993;70:894–899. [PubMed] [Google Scholar]
  • 44.Ehrenforth S., Aygoren-Pursun E., Hach-Wunderle V., Scharrer I. Prevalence of elevated histidine-rich glycoprotein in patients with thrombophilia—a study of 695 patients. Thromb Haemost. 1994;71:160–161. [PubMed] [Google Scholar]
  • 45.Shigekiyo T., Ohshima T., Oka H., Tomonari A., Azuma H., Saito S. Congenital histidine-rich glycoprotein deficiency. Thromb Haemost. 1993;70:263–265. [PubMed] [Google Scholar]
  • 46.Souto J.C., Garí M., Falkon L., Fontcuberta J. A new case of hereditary histidine-rich glycoprotein deficiency with familial thrombophilia. Thromb Haemost. 1996;75:374–375. [PubMed] [Google Scholar]
  • 47.Shigekiyo T., Yoshida H., Kanagawa Y., Satoh K., Wakabayashi S., Matsumoto T., et al. Histidine-rich glycoprotein (HRG) Tokushima 2: novel HRG deficiency, molecular and cellular characterization. Thromb Haemost. 2000;84:675–679. [PubMed] [Google Scholar]
  • 48.Luo J., Zhang W., Zeng Q., Zhou W., Cao Q., Zhou W. Familial early-onset deep venous thrombosis associated with a novel HRG mutation. Eur J Med Genet. 2018;61:68–71. doi: 10.1016/j.ejmg.2017.10.019. [DOI] [PubMed] [Google Scholar]
  • 49.Desch K., Li J., Kim S., Laventhal N., Metzger K., Siemieniak D., Ginsburg D. Analysis of informed consent document utilization in a minimal-risk genetic study. Ann Intern Med. 2011;155:316–322. doi: 10.1059/0003-4819-155-5-201109060-00009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Desch K.C., Ozel A.B., Siemieniak D., Kalish Y., Shavit J.A., Thornburg C.D., et al. Linkage analysis identifies a locus for plasma von Willebrand factor undetected by genome-wide association. Proc Natl Acad Sci U S A. 2013;110:588–593. doi: 10.1073/pnas.1219885110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Ma Q., Ozel A.B., Ramdas S., McGee B., Khoriaty R., Siemieniak D., et al. Genetic variants in PLG, LPA, and SIGLEC 14 as well as smoking contribute to plasma plasminogen levels. Blood. 2014;124:3155–3164. doi: 10.1182/blood-2014-03-560086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Ozel A.B., McGee B., Siemieniak D., Jacobi P.M., Haberichter S.L., Brody L.C., et al. Genome-wide studies of von Willebrand factor propeptide identify loci contributing to variation in propeptide levels and von Willebrand factor clearance. J Thromb Haemost. 2016;14:1888–1898. doi: 10.1111/jth.13401. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Mills J.L., Carter T.C., Scott J.M., Troendle J.F., Gibney E.R., Shane B., et al. Do high blood folate concentrations exacerbate metabolic abnormalities in people with low vitamin B-12 status? Am J Clin Nutr. 2011;94:495–500. doi: 10.3945/ajcn.111.014621. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Yang J., Lee S.H., Goddard M.E., Visscher P.M. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82. doi: 10.1016/j.ajhg.2010.11.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Abecasis G.R., Cherny S.S., Cookson W.O., Cardon L.R. Merlin--rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet. 2002;30:97–101. doi: 10.1038/ng786. [DOI] [PubMed] [Google Scholar]
  • 56.Chang C.C., Chow C.C., Tellier L.C., Vattikuti S., Purcell S.M., Lee J.J. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7. doi: 10.1186/s13742-015-0047-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Kang H.M., Sul J.H., Service S.K., Zaitlen N.A., Kong S.Y., Freimer N.B., et al. Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 2010;42:348–354. doi: 10.1038/ng.548. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Devlin B., Roeder K. Genomic control for association studies. Biometrics. 1999;55:997–1004. doi: 10.1111/j.0006-341x.1999.00997.x. [DOI] [PubMed] [Google Scholar]
  • 59.Willer C.J., Li Y., Abecasis G.R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–2191. doi: 10.1093/bioinformatics/btq340. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Wiltshire S., Cardon L.R., McCarthy M.I. Evaluating the results of genomewide linkage scans of complex traits by locus counting. Am J Hum Genet. 2002;71:1175–1182. doi: 10.1086/342976. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Hesketh G.G., Youn J.Y., Samavarchi-Tehrani P., Raught B., Gingras A.C. Parallel exploration of interaction space by BioID and affinity purification coupled to mass spectrometry. Methods Mol Biol. 2017;1550:115–136. doi: 10.1007/978-1-4939-6747-6_10. [DOI] [PubMed] [Google Scholar]
  • 62.Temprano-Sagrera G., Sitlani C.M., Bone W.P., Martin-Bornez M., Voight B.F., Morrison A.C., et al. Multi-phenotype analyses of hemostatic traits with cardiovascular events reveal novel genetic associations. J Thromb Haemost. 2022;20:1331–1349. doi: 10.1111/jth.15698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Truong T.K., Malik R.A., Yao X., Fredenburgh J.C., Stafford A.R., Madarati H.M., et al. Identification of the histidine-rich glycoprotein domains responsible for contact pathway inhibition. J Thromb Haemost. 2022;20:821–832. doi: 10.1111/jth.15631. [DOI] [PubMed] [Google Scholar]
  • 64.Borza D.B., Morgan W.T. Acceleration of plasminogen activation by tissue plasminogen activator on surface-bound histidine-proline-rich glycoprotein. J Biol Chem. 1997;272:5718–5726. doi: 10.1074/jbc.272.9.5718. [DOI] [PubMed] [Google Scholar]
  • 65.Jones A.L., Hulett M.D., Parish C.R. Histidine-rich glycoprotein binds to cell-surface heparan sulfate via its N-terminal domain following Zn2+ chelation. J Biol Chem. 2004;279:30114–30122. doi: 10.1074/jbc.M401996200. [DOI] [PubMed] [Google Scholar]
  • 66.Morange P.E., Oudot-Mellakh T., Cohen W., Germain M., Saut N., Antoni G., et al. KNG1 Ile581Thr and susceptibility to venous thrombosis. Blood. 2011;117:3692–3694. doi: 10.1182/blood-2010-11-319053. [DOI] [PubMed] [Google Scholar]
  • 67.Hong M.G., Dodig-Crnković T., Chen X., Drobin K., Lee W., Wang Y., et al. Profiles of histidine-rich glycoprotein associate with age and risk of all-cause mortality. Life Sci Alliance. 2020;3 doi: 10.26508/lsa.202000817. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Hennis B.C., van Boheemen P.A., Wakabayashi S., Koide T., Hoffmann J.J., Kievit P., et al. Identification and genetic analysis of a common molecular variant of histidine-rich glycoprotein with a difference of 2kD in apparent molecular weight. Thromb Haemost. 1995;74:1491–1496. [PubMed] [Google Scholar]
  • 69.Tugues S., Roche F., Noguer O., Orlova A., Bhoi S., Padhan N., et al. Histidine-rich glycoprotein uptake and turnover is mediated by mononuclear phagocytes. PLOS One. 2014;9 doi: 10.1371/journal.pone.0107483. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.O'Sullivan J.M., Ward S., Lavin M., O'Donnell J.S. von Willebrand factor clearance - biological mechanisms and clinical significance. Br J Haematol. 2018;183:185–195. doi: 10.1111/bjh.15565. [DOI] [PubMed] [Google Scholar]
  • 71.Ferkingstad E., Sulem P., Atlason B.A., Sveinbjornsson G., Magnusson M.I., Styrmisdottir E.L., et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53:1712–1721. doi: 10.1038/s41588-021-00978-w. [DOI] [PubMed] [Google Scholar]
  • 72.Sun B.B., Chiou J., Traylor M., Benner C., Hsu Y.H., Richardson T.G., et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature. 2023;622:329–338. doi: 10.1038/s41586-023-06592-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Folkersen L., Gustafsson S., Wang Q., Hansen D.H., Hedman Å.K., Schork A., et al. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals. Nat Metab. 2020;2:1135–1148. doi: 10.1038/s42255-020-00287-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Katz D.H., Robbins J.M., Deng S., Tahir U.A., Bick A.G., Pampana A., et al. Proteomic profiling platforms head to head: leveraging genetics and clinical traits to compare aptamer- and antibody-based methods. Sci Adv. 2022;8 doi: 10.1126/sciadv.abm5164. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Edfors F., Iglesias M.J., Butler L.M., Odeberg J. Proteomics in thrombosis research. Res Pract Thromb Haemost. 2022;6 doi: 10.1002/rth2.12706. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Borza D.B., Shipulina N.V., Morgan W.T. Effects of histidine-proline-rich glycoprotein on plasminogen activation in solution and on surfaces. Blood Coagul Fibrinolysis. 2004;15:663–672. doi: 10.1097/00001721-200412000-00006. [DOI] [PubMed] [Google Scholar]
  • 77.Jones A.L., Hulett M.D., Altin J.G., Hogg P., Parish C.R. Plasminogen is tethered with high affinity to the cell surface by the plasma protein, histidine-rich glycoprotein. J Biol Chem. 2004;279:38267–38276. doi: 10.1074/jbc.M406027200. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table S1
mmc1.docx (18.7KB, docx)
Supplementary Table S2
mmc2.xlsx (65.7KB, xlsx)

Articles from Research and Practice in Thrombosis and Haemostasis are provided here courtesy of Elsevier

RESOURCES