Congenital Fibrinogen Deficiencies: Not So Rare

Abstract

Congenital fibrinogen deficiencies (CFDs), traditionally considered rare monogenic disorders, are now recognized as more prevalent and genetically complex than previously thought. Indeed, the symptoms manifested in CFD patients, such as bleeding and thrombosis, are likely to result from variation in several genes rather than solely driven by variants in one of the three fibrinogen genes, FGB, FGA, and FGG. This review highlights recent advances in understanding the genetic causes of CFD and their variability, facilitated by the growing use and availability of next-generation sequencing data. Using gnomAD v4.1.0. data, which includes more than 800,000 individuals, we provide updated global prevalence estimates for CFDs based on frequencies of predicted deleterious variants in FGB, FGA, and FGG. Recessively inherited fibrinogen deficiencies (homozygous genotypes) could be present in around 29 individuals per million, while dominantly inherited deficiencies (heterozygous genotypes) may be present in up to 15,000 per million. These increased estimates can be attributed to the inclusion of broader, more diverse genetic datasets in the new version of gnomAD, thus capturing a greater range of rare variants and homozygous cases.

 

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Keywords

fibrinogen - fibrin - bleeding - prevalence

Introduction

Within seconds of vascular injury, hemostasis is initiated. The first step, known as primary hemostasis, involves platelet adhesion to the damaged vessel wall, mediated primarily by von Willebrand factor (vWF), and platelet aggregation facilitated by soluble fibrinogen.[1] [2] The initial platelet plug is inherently unstable without the contribution of secondary hemostasis, also known as the coagulation cascade. This cascade culminates in the activation of thrombin and the conversion of soluble fibrinogen into an insoluble polymer fibrin.[3] Fibrin stabilizes the growing blood clot by forming a robust yet flexible three-dimensional scaffold that effectively stops bleeding and allows wound healing to proceed.[4] Finally, to maintain and restore normal vessel function, fibrinolysis is initiated.[5]

Fibrinogen is a hexamer assembled in hepatocytes from two copies each of three polypeptide chains. The structure of fibrinogen, its conversion to fibrin monomers after thrombin cleavage, and the steps allowing the formation of the three-dimensional fibrin network have been well described elsewhere.[4] Once assembled and after quality control, fibrinogen is secreted into the bloodstream, where it circulates at concentrations of 2 to 4 g/L in healthy individuals with a half-life of 3 to 5 days.[6] [7] In addition to its role in hemostasis, fibrinogen plays a part in a range of processes that, when disrupted, can contribute to cardiovascular disease, obesity, infection, tumor growth, and neurological disorders.[7] [8] [9] In this light, the quantity, localization, and structure of fibrinogen all hold significant clinical relevance and are influenced by both environmental factors and genetics. Indeed studies suggest that 30 to 50% of the variation in circulating fibrinogen levels is heritable.[10] [11] This review will explore our current understanding of congenital fibrinogen deficiencies (CFDs) and reassess the estimated prevalence of these conditions.

 

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Fibrinogen Genes and Transcripts

Fibrinogen production requires the expression of three closely linked genes on the long arm of chromosome 4: FGB, FGA, and FGG, ordered from centromere to telomere. These genes are organized in a 75-kb cluster, including proximal promoters and four enhancer elements—CNC12, PFE2, E3, and E4.[12] [13] [14] The cluster is organized as a small topologically associating domain, a self-interacting genomic region flanked by CTCF sites, which contributes to the coordinated regulation of the fibrinogen-encoding genes in fibrinogen-expressing cells.[15] Fibrinogen production occurs primarily in hepatocytes. Basal mRNA levels are maintained through constitutive expression with production rates increasing significantly during the acute phase response to inflammation.[16] [17]

The major FGA transcript (encoding the Aα chain) consists of five exons, FGB (encoding the Bβ chain) comprises eight exons, and the major FGG transcript (encoding the γ or γA chain) includes ten exons ([Table 1]). Alternative splicing produces minor isoforms for both FGA and FGG. The FGA Aα-E isoform is a longer transcript containing an additional sixth exon and encodes the Aα-E chain. It is found in homodimeric form (Aα-EBβγ)₂ in around 1% of circulating fibrinogen in adults but is more abundant in fetal blood.[18] [19] [20] Although its functional role remains to be fully elucidated, Aα-E has been shown to play a role in hemostasis during development in zebrafish embryos.[21] Interestingly, the relative abundance of the Aα-E isoform compared with Aα has been shown to increase during infection.[22]

Table 1 Comparison of major and minor isoforms of fibrinogen genes (FGB, FGA, FGG)

Gene	Transcript	Exons	Length (bp)	Length (amino acids)	RefSeq ID	Relative circulating levels
FGA	Aα	5	2,209	644	NM_021871.4	Major isoform
	Aα-E	6	3,654	866	NM_000508.5	1%
FGB	Bβ	8	3,641	491	NM_005141.5	Major isoform
FGG	γA or γ	10	1,565	437	NM_000509.6	Major isoform
	γ'	9	2,072	453	NM_021870.3	10–15%

The FGG minor isoform, γ', consists of nine exons, with 20 C-terminal residues encoded by exon 9 replacing the last four amino acids of γ encoded by exon 10.[23] [24] Fibrinogen containing γ' is found in ∼10 to 15% of the total circulating fibrinogen, mostly in heterodimeric form (AαBβγ/AαBβγ').[25] [26] The γ′ chain lacks a platelet-binding site normally present in γ and modifies thrombin and factor XIII activity, affecting clot structure and potentially contributing to thrombosis.[27]

 

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Congenital Fibrinogen Deficiencies

CFDs are a heterogeneous group of conditions caused by pathogenic variants in the FGB, FGA, and FGG genes. Such variants can affect the quantity of fibrinogen (i.e., afibrinogenemia or hypofibrinogenemia) or its quality (i.e., dysfibrinogenemia or hypodysfibrinogenemia; [Fig. 1]).[28] [29]

Fig. 1 Classification of quantitative and qualitative congenital fibrinogen disorders and inheritance modes. Quantitative disorders, including afibrinogenemia and hypofibrinogenemia, involve absent or reduced fibrinogen levels in circulation, with autosomal recessive or mostly dominant inheritance. A subset of hypofibrinogenemia variants can result in hepatic fibrinogen storage disease. Qualitative disorders, such as dysfibrinogenemia and hypodysfibrinogenemia, are characterized by dysfunctional fibrinogen present in circulation, with either normal or reduced levels, typically inherited in an autosomal dominant manner.

 

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Quantitative Defects

Afibrinogenemia, characterized by the complete absence of fibrinogen, follows an autosomal recessive inheritance mode and is mostly caused by homozygous variants, with compound heterozygosity being reported less frequently.[30] For example, in a cohort of 74 afibrinogenemic patients, 98.6% were homozygous, mostly for null variants (i.e., nonsense, splice site, frameshift, or large deletions).[31] As previously observed, within this cohort, most variants were identified in FGA, with the two most frequent being the intron 4 donor splice site variant c.510 + 1G > T (23.6%) previously known as IVS4 + 1G > T or the large 11-kb deletion of FGA (12.2%).[32] [33] [34]

Hypofibrinogenemia is characterized by reduced fibrinogen levels in circulation: <0.5 g/L for severe hypofibrinogenemia, between 0.5 and 0.9 g/L for moderate hypofibrinogenemia, and between 1 and 1.5 g/L for mild hypofibrinogenemia.[35] If one considers the fibrinogen level as the phenotype of interest, hypofibrinogenemia is primarily inherited in an autosomal dominant manner, with heterozygous variants in the fibrinogen gene cluster being sufficient to cause reduced fibrinogen levels.[36] These variants impair transcription, splicing, hexamer assembly, or protein secretion all resulting in lower circulating levels of a normal protein.[7] In contrast to dysfibrinogenemia (see below), there is no dysfunctional fibrinogen in circulation; thus, the fibrin clot formed upon thrombin activation does not contain dysfunctional molecules.

Rare cases of homozygosity have also been reported in hypofibrinogenemia, often resulting in the severe form of the disease.[36] [37] When considering clinical phenotypes (e.g., bleeding), penetrance is not complete since individuals with heterozygous variants can remain asymptomatic.[38] Although many hypofibrinogenemic patients are heterozygous for variants that cause afibrinogenemia in homozygosity, fewer null variants are identified in hypofibrinogenemia compared with afibrinogenemia.[31] [39] Instead, missense variants, mostly located in the C-terminal domains of the β and γ chains, are the most common variant type, identified in 60.4% of a cohort of 44 patients.[31] In a subset of hypofibrinogenemia, specific variants in the C-terminus of the γ chain escape the normal degradation pathway used to eliminate mutant or misassembled fibrinogen, causing the accumulation of fibrinogen aggregates within the endoplasmic reticulum of hepatocytes and a liver disease known as hepatic fibrinogen storage disease.[35]

 

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Qualitative Defects

Dysfibrinogenemia, where fibrinogen is produced at normal levels but is functionally defective, typically follows an autosomal dominant inheritance mode. In contrast to hypofibrinogenemia, heterozygous variants result in the production, assembly, and secretion of dysfunctional fibrinogen molecules into circulation. Even in the presence of normal fibrinogen hexamers, upon thrombin cleavage the incorporation of mutant fibrin molecules into the clot results in an impaired fibrin network.[40] Rare cases of homozygosity have been reported in dysfibrinogenemia.[41] The vast majority of genotyped cases (>70%) are heterozygous for one of the “hotspot” missense variants affecting either residue FGA p.Arg35 at the Aα thrombin cleavage site crucial for the first steps of fibrin polymerization or residue FGG p.Arg301 crucial for the D:D interaction, also essential for fibrin network formation.[31] [42] [43] While most fibrinogen variants do not demonstrate a clear correlation between genotype and clinical phenotype, a few dysfibrinogenemia-causing variants are strongly associated with an increased risk of thrombotic disease.[44] The underlying mechanisms for these particular cases are likely variant-specific and may involve impaired interactions with thrombin, plasminogen, or tissue plasminogen activator, resulting in abnormal fibrin clot formation and altered clot architecture, or impaired clot lysis.[35] [45] [46] [47] Hypodysfibrinogenemia, a disorder that combines both quantitative and qualitative defects, can be inherited either as autosomal dominant or recessive, depending on whether a single variant is sufficient to cause reduced levels of defective fibrinogen in circulation or not. A patient's genotype may be heterozygous, compound heterozygous, or homozygous. Variants in two different fibrinogen genes may be present, with one variant leading to a “hypo” phenotype and the other to a “dys” phenotype. This explains why many variants observed in hypodysfibrinogenemia patients are also present in those with other fibrinogen disorders.[48]

 

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Clinical Heterogeneity

Congenital fibrinogen disorders present a spectrum of clinical phenotypes, ranging from asymptomatic individuals to those experiencing mild to severe bleeding and/or thrombotic events.[49] Clinical heterogeneity is particularly evident within hypofibrinogenemia and dysfibrinogenemia, while afibrinogenemia consistently leads to a more severe bleeding phenotype due to the complete absence of fibrinogen in circulation.[39] [42] [50] [51] Afibrinogenemia cases are therefore typically diagnosed following bleeding episodes, as is the case for most symptomatic hypofibrinogenemia patients while dysfibrinogenemia is more often detected through preoperative screening or family diagnosis.[39] Both quantitative and qualitative fibrinogen disorders can present with thrombosis, which can seem paradoxical, particularly in cases of absent fibrinogen.[35] This can be explained by clots forming through vWF-mediated platelet aggregation, but without fibrin resulting in loosely packed, unstable thrombi prone to embolization.[38] [52] Additionally, without fibrinogen in circulation, its antithrombin function is lost, leaving thrombin free to promote further platelet activation and thrombus formation.[53]

Diagnosis is based on the assessment of both functional and antigenic levels of fibrinogen, alongside clinical presentation, as recommended by established guidelines.[35] Capturing detailed information with clinical questionnaires and the use of standardized tools, such as the ISTH bleeding assessment tool, is important for evaluating clinical variability and to test correlations between genetic information and the observed phenotype.[54] For instance, it was shown in a multivariant analysis of dysfibrinogenemia cases that sex, fibrinogen level, activity/antigen ratios, and the common “hotspot” missense variants were not associated with bleeding outcomes or the risk of thrombosis, suggesting additional reasons exist for the clinical heterogeneity observed.[42]

 

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Identification of Pathogenic Variants and Modifier Alleles

Genotyping has traditionally relied on Sanger sequencing to identify pathogenic variants, many of which are single nucleotide variants (SNVs). The visualization of more than 330 causative SNVs placed on the coding sequences of FGB, FGA, and FGG reveals important structure-function insight for several domains of the fibrinogen molecule.[29] [53] With such allelic heterogeneity, the number of newly identified causative variants continues to grow.

Next-generation sequencing (NGS) approaches, such as whole-exome sequencing (WES) and whole-genome sequencing (WGS), now enable more comprehensive detection of variants, including those beyond the primary pathogenic variant. This is especially important in cases where a single variant may not fully explain the phenotype, emphasizing the need to explore other alleles.[55] In the general population, the fact that variants such as factor V Leiden and prothrombin G20210A can tip an individual's hemostatic balance toward thrombosis is well established.[56] [57] In the presence of a bleeding disorder, such as hemophilia, the presence of factor V Leiden or prothrombin G20210A results in a milder bleeding phenotype, a discovery that has inspired novel therapeutic approaches for treating the disease.[58] [59] Similarly, the genetically determined ABO blood group of an individual is known to influence vWF and consequently factor VIII plasma levels, thereby modifying the clinical severity of von Willebrand disease.[60] [61] While knowledge of the impact of these variants preceded NGS, this analysis now allows the simultaneous identification of well-known genetic modifiers, alongside the primary pathogenic variant, as well as additional variants that may further influence the phenotype. However, the increasing detection of variants of uncertain significance presents a significant challenge since proving the functional impact of such variants remains difficult, especially if they are rare.

 

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Prevalence of CFDs

The increased accessibility and use of NGS has not only made it easier to identify additional variants beyond the primary pathogenic variant but has also led to an explosion in the availability of genetic data.[62] This has been driven by large, publicly accessible (de-identified) human sequencing datasets, pioneered by initiatives such as the 1000 Genomes Project in 2010.[63] Previously, the frequency of pathogenic variants was assessed through candidate gene sequencing within small case–control cohorts.[64] Current access to NGS data from hundreds of thousands of individuals from diverse populations greatly improves the accuracy of variant frequency estimates and helps identify new disease-causing variants.[64] [65] [66] [67] [68] Given the rates of natural variation, it is highly probable that all genetic variants compatible with life exist in the human population, making population-scale genetics incredibly valuable for uncovering the impact of variants in the genome.[64]

Public datasets such as the UK Biobank, which includes genetic and extensive phenotypic information from over half a million individuals, have been pivotal in identifying genetic risk factors for conditions like obesity.[69] [70] These datasets enable the linkage of specific genotypes to clinical traits, as demonstrated in a recent population-scale study leveraging data from over 937,000 individuals. The study assembled the largest cohort to date of double heterozygotes for factor V Leiden and prothrombin G20210A variants, providing precise effect size estimates for the risk of venous thromboembolism (VTE). It was shown that double heterozygous genotypes may be as frequent as factor V Leiden homozygotes, with a similar increased risk of VTE. This example underscores the power of population-scale genomics to revisit established genetic risk factors, while delivering far more accurate risk assessments than were previously achievable.[71]

While the Genome Aggregation Database (gnomAD) does not provide individual genotype or phenotype data, its aggregated summary data provide variant allele frequencies which have become important for understanding genetic variation.[72] The database is an essential resource for variant interpretation, greatly improving the ability to distinguish between common variants and those that are rare and potentially pathogenic.[64] gnomAD data also provide insight into gene constraint (intolerance to variation) and allows a better estimate of prevalence (i.e., the percentage of a population with a causal genotype for a genetic disorder). Regarding CFDs, the global incidence of the recessively inherited deficiency, afibrinogenemia, was historically estimated at 1 in a million individuals.[73] The prevalences of hypofibrinogenemia and dysfibrinogenemia were difficult to estimate, largely since many individuals are asymptomatic and only a few specialized clinical centers were reporting the data. However, as these disorders typically follow an autosomal dominant inheritance pattern (a pathogenic variant on one of the two fibrinogen alleles is sufficient to cause the deficiency), they are obviously more common.

Previously gnomAD data have been used in the field of thrombosis and hemostasis to estimate disease prevalences including for CFDs.[74] [75] [76] [77] Using data from gnomAD v.2.0 which included exome/genome data from ∼140,000 individuals, Paraboschi et al suggested that genotypes that could lead to a recessively inherited fibrinogen deficiency were ten times higher than previously reported, with significant variation across the eight genetic ancestry groups listed.[75] Prevalences ranged from 1 per million in East Asians to 24.5 per million in non-Finnish Europeans (which excludes Finns due to the distinct genetic characteristics of this population, shaped by historical bottlenecks and isolation). The global prevalence of genotypes that could lead to a dominantly inherited fibrinogen deficiency, such as hypofibrinogenemia or dysfibrinogenemia, was estimated to be around 11,000 per million individuals.

We aimed to reevaluate these estimates using gnomAD v4.1.0, released in 2024, which includes WES and WGS data for 807,162 individuals (730,947 exomes and 76,215 genomes). A significant increase in sample size comes from the inclusion of 416,555 exomes from the UK Biobank, alongside a threefold increase in non-European individuals. This dataset reveals an average of two SNVs every three bases, offering comprehensive coverage of human variation. Using this extensive resource, we generated new global prevalence estimates for homozygous and heterozygous genotypes that could result in a fibrinogen disorder ([Fig. 2]).

Fig. 2 Filtering process for the identification of high-confidence variants in FGB, FGA, and FGG focusing on those likely to cause a fibrinogen deficiency. First, coding variants were selected from RefSeq transcripts (+15 bp either side), including major and minor transcripts. Variants were then filtered based on their predicted consequences, ClinVar classifications, and in silico predictions. The cumulative allele frequency of potentially deleterious variants was calculated for major transcripts (Aα, Bβ, γ') only. Frequencies of individuals heterozygous or homozygous for these variants were obtained using the Hardy-Weinberg principle.

To this end, gnomAD v4.1.0 data were downloaded, variants predicted to be likely pathogenic were selected, and the cumulative allele frequency (cAF) for each gene was calculated by summing the allele frequencies of all filtered variants. To ensure high-confidence data and minimize false positives, only short variants (SNVs and indels—small insertions and deletions) that passed all filters in both exomes and genomes or passed in one dataset and were absent from the other were included. Coding regions (+15 bp either side, so splice sites are included) for all major and minor isoforms ([Table 1]) were initially analyzed for completeness to capture variants in all expressed transcripts, while also observing those specific to each isoform. Additional variant annotation was performed using the Ensembl Variant Effect Predictor (VEP), incorporating supplementary data and in silico predictions.[78] Several strict filtering criteria were applied. First, null variants were selected based on predicted consequences (e.g., frameshift, stop gained, start lost). Splice variants not already classified as null but predicted to be high impact or likely pathogenic were then selected, defined by a SpliceAI score > 0.8 or a Combined Annotation Dependent Depletion (CADD) score > 30.[79] [80] For the inclusion of missense variants and other remaining variant types, additional filters were applied by selecting those labeled as pathogenic in ClinVar, excluding those classified as benign, and including variants meeting specific in silico criteria: classified as “likely pathogenic” by AlphaMissense, CADD score >30, or predicted as “deleterious” by SIFT and at the same time “probably damaging” by PolyPhen.[81] [82] [83] [84]

Among the 807,162 individuals, 5,383 different variants were identified across all fibrinogen transcripts ([Table 2]). Of these, the three major isoforms accounted for 4,211 variants: 1,557 in Aα, 1,641 in Bβ, and 1,013 in γA. The two minor isoforms, AαE and γ', contributed 868 and 304 unique variants, respectively. Focusing on the three major isoforms, 798 variants were classified as potentially pathogenic after applying the described filtering criteria. The cAF of the selected variants in the cohort is 0.007515, generated by summing the cAF for each gene. Using the cAF and the Hardy-Weinberg equilibrium equation (p2 + 2pq + q² = 1), the global prevalence of fibrinogen disorder genotypes was estimated. For dominantly inherited disorders such as dysfibrinogenemia, heterozygotes (2pq) are of primary concern, as the presence of a single variant allele in the fibrinogen genes is likely sufficient to cause the disorder.[36] Accordingly 15,000 individuals per million are expected to be heterozygous for a potentially pathogenic variant and could theoretically exhibit a disease phenotype of, dysfibrinogenemia or moderate hypofibrinogenemia. This estimate is higher than previously calculated.[75] Likewise for recessively inherited forms, such as afibrinogenemia or severe hypofibrinogenemia, the global frequency of homozygotes (q²) for the filtered variants is estimated at ∼29.4 individuals per million, higher than the figure of 24.5 per million in non-Finnish Europeans previously reported.[75]

Table 2 Global estimated prevalence of inherited fibrinogen disorders

Transcript	Total variants	Total likely pathogenic variants	Cumulative allele frequency[a]	Heterozygotes[b] (per million)	Homozygotes[b] (per million)
FGA	1,557	303	0.001158	2,300	1.3
FGB	1,641	272	0.001188	2,400	1.4
FGG (without Ala108Gly)	1,013 (1,012)	223 (222)	0.005169 (0.002138)	10,300 (4,270)	26.7 (4.57)
Total (without Ala108Gly)	4,211 (4,210)	798 (797)	0.007515 (0.004484)	15,000 (8,970)	29.4 (7.27)

^a The cumulative allele frequency was calculated by summing the allele frequencies of all variants predicted to be likely pathogenic for each gene in the gnomAD v4.1.0 dataset.

^b Calculated based on Hardy-Weinberg equilibrium.

This increase in the estimated disease prevalences can be attributed to several factors. First, the number of identified genetic variants associated with disease continues to grow. Indeed, Baxter et al showed that for 13 nominated genes, the average number of predicted loss-of-function variants per gene increased approximately fourfold between gnomAD v2 and v4.[68] Similarly, there was a twofold increase in the number of variants per gene listed as pathogenic or likely pathogenic in ClinVar.[68] Many of these newly identified variants are rare, with the average allele frequencies of v4 variants being markedly lower than those already present in v2. These variants have the potential to significantly impact a phenotype, and although rare, the cumulative sum contributes to an increased incidence of potentially pathogenic alleles.

Regarding the estimated frequency of homozygotes, not all homozygous individuals will necessarily present with afibrinogenemia, the most severe form of CFD. In some cases, individuals may carry two pathogenic alleles resulting in severe hypofibrinogenemia or dysfibrinogenemia.[37] [41] This is particularly relevant for the FGG p.Ala108Gly variant, which has been linked to hypofibrinogenemia in case reports, as well as lower fibrinogen levels in several GWAS studies, with the variant predicted to cause a 0.2- to 0.7-g/L reduction in fibrinogen levels per Gly allele.[85] [86] [87] [88] [89] [90] [91] In gnomAD v2, no homozygous individuals were identified for FGG p.Ala108Gly. In contrast, gnomAD v4 identified 12 homozygous individuals for this variant. These homozygous cases are more likely associated with severe hypofibrinogenemia than afibrinogenemia. This prediction is based on our study of a patient with a large 14.8 Mb deletion including the entire fibrinogen gene cluster and FGG p.Ala108Gly on the non-deleted allele, who had fibrinogen levels of 0.7 g/L, consistent with hypofibrinogenemia despite a homozygous-like genotype for .Ala108Gly.[55]

Further examination of the FGG p.Ala108Gly variant underscores the role of genetic ancestry in disease prevalence, with a frequency of 0.38% in non-Finnish Europeans compared with 0.06% in South Asians, a more than sixfold difference. This disparity is likely the result of a founder effect, which enriches certain variants within specific populations, as this variant is inherited as part of a distinct haplotype.[92] The inclusion of 416,555 individuals from the UK Biobank, where this variant is relatively frequent (0.39%), increases the overall estimated prevalence. For example, when this variant is excluded from the analysis, the cAF decreases to 0.0045 ([Table 2]). Consequently, the frequency of heterozygotes (2pq) drops to ∼8,970 individuals per million, and the frequency of homozygotes (q²) falls to 7.3 individuals per million. These findings clearly demonstrate how population-specific variants significantly influence global prevalence estimates.

An additional factor contributing to the increased prevalence estimates is the more recent inclusion of databases like the UK Biobank which do not exclude individuals based on phenotype. This broader inclusion may lead to greater representation of individuals with rare diseases, contrasting with the Exome Aggregation Consortium (ExAC), which primarily focused on exome sequencing data from “ostensibly healthy” individuals.[93] In the latest gnomAD dataset, numerous homozygous cases for disease-causing variants have been identified that were previously only observed in heterozygosity in gnomAD v2, as exemplified by FGG p.Ala108Gly.

A more precise estimate of disease prevalence for specific congenital fibrinogen disorders can be obtained by analyzing well-characterized causative variants. As already discussed, more than 70% of diagnosed dysfibrinogenemia cases result from variants affecting the FGA p.Arg35 or FGG p.Arg301 amino acids, the so-called hot-spots.[31] In gnomAD, variants affecting these positions include p.Arg35His, p.Arg35Cys, and p.Arg35Ser in FGA, and p.Arg301His and p.Arg301Cys in FGG. The cAF for these five variants in gnomAD is 3.47 × 10⁻⁵, indicating that ∼69 per million individuals are expected to have dysfibrinogenemia due to these five variants alone. Notably, no individuals in gnomAD were homozygous for these variants.

There are several limitations to our global prevalence estimates based on gnomAD v4 data. First, our analysis considers the presence of the genotype, regardless of whether individuals will develop the disease. If the genotype is not fully penetrant, genetic estimates may be higher than actual disease prevalence, as shown in studies on penetrance and variable expressivity in monogenic diseases.[94] Second, our analysis includes only the coding regions and splice sites of the three major isoforms, as these transcripts are the most abundant in circulation and, therefore, the most clinically relevant. Variants outside the coding regions, affecting for instance regulatory elements (e.g., the enhancers CNC12, PFE2, E3, and E4 or the fibrinogen gene promoters) which were not analyzed here, could also contribute to fibrinogen deficiencies, although no cases with variants in these elements have been reported to date. Our analysis also excludes structural variants (SVs) which include the FGA 11 kb deletion and copy number variants (CNVs). While gnomAD has released SV and CNV data for some samples, these are not yet available for the entire cohort. Indeed, identifying SVs and CNVs through short-read sequencing, such as exome sequencing, remains imperfect, complicating their inclusion in such analyses. It is also important to consider that the variants included in this analysis are not entirely specific to congenital fibrinogen disorders; the filtering process also captures potentially pathogenic variants associated with other fibrinogen-related conditions, such as renal amyloidosis. This disorder, characterized by amyloid deposits formed from abnormal fibrinogen, predominantly affects the kidneys.[95] Consequently, the reported cAF reflects the prevalence of fibrinogen-related diseases more broadly. Finally, as previously mentioned, since a substantial portion of the gnomAD v4.1.0 data is contributed by the UK Biobank, our estimates may not be accurate for the underrepresented populations in these genomic databases.

 

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Perspectives

Future research will continue to study large cohort databases to improve understanding of genetic variation in fibrinogen disorders. Pairing global genotype data with more detailed health records will allow to better understand the penetrance of suspected disease alleles and shed further light on the likely oligogenic nature of symptoms in patients with a CFD. By assessing the combined effects of multiple genetic variants, it will become possible to measure how additional modifiers impact disease severity. Ideally, this could lead to predictive models that anticipate a patient's symptoms before they become symptomatic, enabling preventive intervention and improving patient care.

 

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Conflict of Interest

The authors declare that they have no conflict of interest.

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Publication History

Received: 17 October 2024

Accepted: 03 January 2025

Article published online:
12 March 2025