Unveiling the Genetic Landscape of Inherited Primary Hemostasis Disorders by Whole-Exome Sequencing: Insights from a Multicenter Study

Abstract

Background

Inherited primary hemostasis disorders (IPHD) comprise a clinically and genetically heterogeneous spectrum, including inherited platelet disorders (IPD), heritable disorders of connective tissue (HDCT) associated with bleeding, and bleeding disorders of unknown cause (BDUC). Their phenotypic overlap and limited access to specific functional testing make genetic analysis essential for accurate diagnosis. This study aimed to investigate the genetic basis of patients with IPHD through whole-exome sequencing (WES), providing genotype–phenotype correlations to guide clinical management.

Material and Methods

A total of 170 probands were included: 114 with IPD (67%), 28 with HDCT (16%), and 28 with BDUC (16%).

Results

Definitive genotype–phenotype correlation was achieved in 83 probands (49%), identifying 98 unique candidate variants across 48 genes. Notably, 19 patients carried variants in different genes that can contribute to the phenotype. A partial genotype–phenotype correlation was achieved in 19 probands (11%), while no variants were identified in the remaining 68 (40%), especially in the BDUC group.

Conclusion

This multicenter study represents the first integrated analysis using a single workflow for patients with IPHD, encompassing not only IPD and BDUC but also HDCT. The high rate of genotype–phenotype correlations achieved, the identification of 68 previously undescribed variants, and the evidence of a shared and overlapped genetic and phenotypic profile among IPHD patients demonstrate the clinical value of WES. The study advocates for a paradigm shift in the clinical diagnosis of IPHD, from traditional phenotype-driven assessment to genotype-informed strategies, positioning WES as a first-line tool together with basic laboratory tests, allowing faster and more accurate diagnosis and personalized patient care.

Keywords

whole-exome sequencing - inherited primary hemostasis disorders - inherited platelet disorders - heritable disorders of connective tissue - bleeding disorders of unknown cause

Introduction

Inherited bleeding disorders can arise from defects in either primary or secondary hemostasis. Unlike congenital coagulopathies (CC), which are well-characterized deficiencies or dysfunctions of clotting factors, inherited primary hemostasis disorders (IPHD) remain comparatively underexplored.[1] [2] Their true prevalence is still uncertain, largely due to the complexity of their clinical presentation and the limited availability of standardized and accessible diagnostic tools.[3]

These IPHD encompass a heterogeneous spectrum of conditions associated with mucocutaneous bleeding tendency[4]: (1) inherited platelet disorders (IPD), which result from abnormalities in platelet count and/or function; (2) heritable disorders of connective tissue (HDCT), typically associated with joint hypermobility (JH) and a broad spectrum of other signs, including bleeding tendency[5] [6]; and (3) bleeding disorder of unknown cause (BDUC), when standard hemostatic tests yield normal results, highlighting the diagnostic challenges these cases represent.[7]

Current diagnostic approaches for IPHD rely on a combination of thorough phenotypic assessment, detailed family history, and basic hemostatic tests. The gold standard tool for evaluating bleeding severity is the Bleeding Assessment Tool of the International Society of Thrombosis and Haemostasis (ISTH-BAT).[8] For IPD, phenotypic assessment also includes specialized laboratory testing for evaluating platelet aggregation, secretion of granules, and surface glycoprotein expression.[5] On the other hand, clinical diagnosis of HDCT relies on the evaluation of JH, skin hyperlaxity, and other specific clinical features, as no laboratory tests are available aside from biochemical analysis of collagen, which requires a tissue biopsy.[9] Specifically, the most widely used approaches to evaluate JH are the Beighton score (BS) and the Brighton criteria (BC).[10] [11] [12] [13] However, when bleeding predominates in HDCT patients, hematology referrals often focus on hemostatic testing, potentially overlooking these specialized assessments and limiting diagnostic accuracy.

In recent years, genetic analysis carried out primarily through gene panel sequencing has emerged as a powerful diagnostic tool in the evaluation of IPHD.[14] [15] As a step toward standardization, the Scientific Standardization Committee (SSC) on Genomics in Thrombosis and Hemostasis of the ISTH established a curated list of 99 TIER 1 genes with diagnostic-grade evidence associated with IPD, CC, and thrombotic disorders (https://www.isth.org/page/ginth_genelists; accessed June 2025).[14] In addition, a set of TIER 2 genes supported by more limited evidence, currently comprising 11 genes, is continuously reviewed. In contrast, no standardized classification exists for genes associated with HDCT and bleeding. Nonetheless, the most common HDCT linked to bleeding, Ehlers–Danlos syndrome (EDS), involves up to 20 genes.[13] Moreover, genes implicated in other IPHD, such as hereditary hemorrhagic telangiectasia (HHT) and endothelial dysfunction, further expand the genetic landscape of these disorders.[16] In this context, the advent of whole-exome sequencing (WES) has been transformative, allowing for the simultaneous analysis of thousands of genes without the need for prior target selection. Compared with targeted gene panels, WES provides broader diagnostic coverage with greater efficiency, while reducing overall cost and turnaround time. This technology has proven especially useful in uncovering the molecular basis of IPD,[17] [18] but its diagnostic yield is markedly reduced in patients with BDUC.[18] Moreover, integration of HDCT into the IPHD diagnostic workflow remains limited, despite a recent WES-based study from our group demonstrating its contribution to bleeding phenotypes.[19] Due to overlapping clinical features, variable expressivity, and often the absence of laboratory findings, IPHD are frequently overlooked or misclassified. Identifying their genetic basis enhances diagnostic accuracy, genetic counseling, and personalized management.[20] Furthermore, molecular diagnosis is crucial for IPD are associated with increased malignancy risk, underscoring the need for integrated phenotypic and genetic evaluation.[21] [22]

Building on this evidence, the aim of this study was to elucidate through WES the genetic landscape of patients with suspected IPHD—including IPD, HDCT, and BDUC—with a focus on uncovering clinically meaningful genotype–phenotype correlations. Importantly, this work also addresses the complexities faced by genetic laboratories receiving samples from diverse centers with limited access to advanced functional assays, reinforcing the central role of genetic testing for timely and accurate diagnosis.

Material and Methods

Patients

Patients with suspected IPHD evaluated at the hematology departments of 17 Spanish hospitals were recruited between 2019 and 2024 and were classified into three groups based on the following criteria:

1. IPD group: thrombocytopenia (platelet count < 150 × 10⁹/L), and/or evidence of impaired platelet function defined as abnormalities in platelet aggregation, glycoprotein expression, or granule content and/or secretion.

2. HDCT with bleeding group: meeting at least one positive score among BS, BC, and ISTH-BAT. BS was considered positive when ≥ 6 in children, ≥5 in adults under 50 years of age, and ≥4 in adults over 50 years.[10] [12] The ISTH-BAT score was considered positive when ≥ 3 for children, ≥4 for adult males, and ≥6 for adult females.[8]

3. BDUC group: positive ISTH-BAT score with normal coagulation factor levels, not meeting criteria for groups A or B.

Patients with suspected acquired platelet or bleeding disorders were excluded. The study was approved by the research ethics committee of Hospital Universitari Vall d'Hebron and was performed according to the guidelines of the Declaration of Helsinki. Informed consent was obtained from all participants. Results were communicated by the referring clinicians, taking into account the clinical implications of each variant and the families' unique needs, albeit with procedural differences between participating institutions. All patients received a formal report, and follow-up consultations were coordinated with specialized units, ensuring personalized clinical management tailored to each specific family.

Phenotypic Study

Demographic, clinical, and laboratory data were recorded for all patients at the hospital of recruitment. The bleeding severity was assessed using the ISTH-BAT score.[8] Laboratory analysis included basic hemostatic tests and platelet count measurements. In case of suspicion of inherited platelet function defect, platelet aggregation studies and/or platelet glycoprotein expression analysis were performed, when available. For patients with suspected HDCT, JH manifestations were evaluated, when possible, by means of the BS and/or the BC.[10] [11]

WES and Data Analysis

WES was performed at the Laboratori de Coagulopaties Congènites of the Banc de Sang i Teixits following the DNA Prep with Enrichment protocol (Illumina, California, United States) and sequenced in a NextSeq 500 platform (Illumina) as previously described.[23] Bioinformatics analysis was carried out using DRAGEN Enrichment (Illumina), and the resulting variant call format (VCF) files were annotated using Nirvana (https://illumina.github.io/NirvanaDocumentation/). Variants were filtered by quality, population frequency, and variant consequence ([Supplementary Fig. S1], available in the online version only). Subsequently, based on each patient's phenotype, a specific virtual gene panel associated with IPD (117 genes), HDCT (96 genes), or bleeding (86 genes) was applied ([Supplementary Table S1], available in the online version only). For patients in whom no candidate variant was identified with the initially applied panel, the analysis was expanded to the other two panels. Variants were prioritized considering the phenotypic overlap with gene-associated disorders described in the literature, OMIM (https://www.omim.org/), and HPO (https://hpo.jax.org/) databases. Candidate variants were classified according to the American College of Medical Genetics and Genomics (ACMG) guidelines[24] using the Franklin software (QIAGEN, Hilden, Germany. https://franklin.genoox.com/clinical-db/home). Pathogenic (P), likely pathogenic (LP), or variant of uncertain significance (VUS) were maintained for further analysis. Benign (B) and likely benign (LB) variants were considered only if they met at least one of the following criteria: (1) previously described in literature associated with the expected phenotype; (2) deleterious prediction based on in silico algorithms: for missense variants, a deleterious effect was considered when predicted by at least half of the consulted algorithms (SIFT, PolyPhen, AlphaMissense, and REVEL)[25] [26] [27] [28]; for splicing variants, the in silico prediction was based on SpliceAI.[29] The candidate variants were discussed and prioritized in regular multidisciplinary team meetings involving both hematologists and molecular biologists. Sanger sequencing was employed for the validation of candidate variants and, when available, for family segregation studies as previously described.[23]

Results

Patients

The study cohort included 170 unrelated probands and 72 family members. The mean cohort age was 41 years (range: 8–83), and the majority of probands were female (71%). The study includes a previously published subset of 18 pediatric patients,[23] alongside 11 newly recruited cases, expanding the pediatric cohort to 29 patients and enabling a more comprehensive analysis. Based on clinical assessment and laboratory results, probands were classified into three categories: 114 (67%) as IPD, 28 (16%) as HDCT, and 28 (16%) as BDUC ([Supplementary Tables S2–S4], available in the online version only). In the IPD group, low platelet counts (<150 × 10⁹/L) were observed in 70 patients (61%), of whom 46 (66%) presented macrothrombocytopenia (MPV ≥ 11.5 fL or macroplatelets) and 25 (36%) had concomitant platelet function defects. In contrast, isolated platelet function impairment was observed in 44 patients (39%). Bleeding episodes were assessed for 108 patients with IPD, of whom 29 (27%) were reported to be clinically asymptomatic. Regarding HDCT patients, the ISTH-BAT scores ranged from 1 to 16 with a mean of 6.3 ± 3.6. Specifically, 10 had both positive ISTH-BAT and BS and/or BC (36%), 8 scored positive only for the ISTH-BAT score (28%), and the remaining 10 scored positive only for BS and/or BC (36%). In the BDUC group, the mean ISTH-BAT was 10.5 (SD = 3.4) among adult patients and 3.25 (SD = 0.5) among children.

Overview of Genetic Results

Candidate genetic variants were identified in a total of 102 probands. Among these, a definitive genotype–phenotype correlation was established in 83 (49%), mostly among IPD and HDCT groups ([Table 1]; [Fig. 1]). In addition, 19 probands (11%) had partial genotype–phenotype correlation due to limited clinical or laboratory data, insufficient evidence supporting the variant's implication or the presence of a variant explaining only part of the clinical manifestations. In the remaining 68 probands (40%), no variants could be prioritized.

Table 1 Genotype of patients with definitive genotype–phenotype correlation classified in the three recruitment groups: Inherited platelet disorder (IPD), heritable disorders of connective tissue (HDCT), and bleeding disorder of unknown cause (BDUC)

IPD group
Patient	Gene	Transcript	Exon	Intron	HGVSc	HGVSp	Genotype	ACMG	Described
RBDS0001	TUBB1	ENST00000217133	4/4	–	c.629T > C	p.(Ile210Thr)	Het	LP	No
	ACTN1	ENST00000394419	2/22	–	c.137G > A	p.(Arg46Gln)	Het	P	Yes
RBDS0002	MYH9	ENST00000216181	41/41	–	c.5797C > T	p.(Arg1933Ter)	Het	P	Yes
RBDS0003	ITGA2B	ENST00000262407	–	11/29	c.998 + 1G > A	–	Het	LP	No
	TUBB1	ENST00000217133	1/4	–	c.35del	p.(Cys12LeufsTer12)	Het	P	Yes
RBDS0010	ACTN1	ENST00000394419	7/22	–	c.673G > A	p.(Glu225Lys)	Het	LP	Yes[a]
RBDS0011	TUBB1	ENST00000217133	4/4	–	c.752G > A	p.(Arg251His)	Het	VUS	Yes[a]
	SRC	ENST00000373558	8/12	–	c.893C > A	p.(Thr298Asn)	Het	VUS	No[a]
RBDS0012	CYCS	ENST00000305786	3/3	–	c.193C > G	p.(Leu65Val)	Het	VUS	No[a]
RBDS0014	ACTN1	ENST00000394419	10/22	–	c.952_960del	p.(Asp318_Arg320del)	Het	VUS	No[a]
RBDS0016	GP1BB	ENST00000366425	2/2	–	c.119G > A	p.(Gly40Glu)	Het	B	Yes
RBDS0021	RUNX1	ENST00000437180	–	8/8	c.967 + 2_967 + 5del	–	Het	P	Yes[a]
RBDS0023	ITGA2B	ENST00000262407	18/30	–	c.1821G > A	p.(Thr607 = )	Het	VUS	No
	ACTN1	ENST00000394419	18/22	–	c.2255G > A	p.(Arg752Gln)	Het	LP	Yes[a]
RBDS0026	F7	ENST00000375581	9/9	–	c.808G > A	p.(Glu270Lys)	Het	LP	No[a]
	GFI1B	ENST00000339463	–	10/10	c.814 + 1G > A	–	Het	P	Yes[a]
RBDS0030	GP1BA	ENST00000329125	2/2	–	c.595T > C	p.(Ser199Pro)	Het	VUS	No
RBDS0039	RUNX1 haploinsufficiency due to heterozygous deletion of the distal arm of chromosome 21
RBDS0045	F11	ENST00000403665	15/15	–	c.1796G > A	p.(Cys599Tyr)	Het	LP	No[a]
	UNC13D	ENST00000207549	6/32	–	c.467G > A	p.(Arg156Gln)	Het	VUS	No
RBDS0047	NBEAL2	ENST00000450053	37/54	–	c.5936G > A	p.(Arg1979Gln)	Hom	VUS	No[a]
RBDS0048	GFI1B	ENST00000339463	–	9/10	c.648 + 1G > A	–	Het	LP	No[a]
RBDS0051	GATA1	ENST00000376670	6/6	–	c.1042G > A	p.(Glu348Lys)	Hem	VUS	No
RBDS0054	UNC13D	ENST00000207549	11/32	–	c.887C > T	p.(Pro296Leu)	Het	VUS	No
	VWF	ENST00000261405	14/52	–	c.1534_1536del	p.(Leu512del)	Het	VUS	No
	HPS1	ENST00000325103	11/20	–	c.972del	p.(Met325TrpfsTer6)	Het	P	Yes
RBDS0055	CDC42	ENST00000315554	2/6	–	c.101C > A	p.(Pro34Gln)	Het	LP	Yes
RBDS0056	ITGB3	ENST00000559488	7/15	–	c.1025A > G	p.(Asn342Ser)	Het	VUS	No
RBDS0057	ANKRD26	ENST00000376087	4/34	–	c.542C > T	p.(Thr181Ile)	Het	B	No
RBDS0059	NBEAL2	ENST00000450053	41/54	–	c.6657C > A	p.(Phe2219Leu)	Hom	LP	Yes
RBDS0061	GFI1B	ENST00000339463	10/11	–	c.737G > A	p.(Arg246Gln)	Het	VUS	No
RBDS0062	IKZF5	ENST00000368886	5/5	–	c.1160G > A	p.(Gly387Glu)	Het	VUS	No
	PLA2G4A	ENST00000368886	18/18	–	c.2128G > A	p.(Glu710Lys)	Het	VUS	No
	ACTN1	ENST00000394419	11/22	–	c.1091T > C	p.(Ile364Thr)	Het	VUS	No
RBDS0068	ETV6	ENST00000396373	6/8	–	c.1138T > C	p.(Trp380Arg)	Het	VUS	Yes
RBDS0069	THPO	ENST00000204615	3/6	–	c.62C > A	p.(Ser21Tyr)	Het	VUS	No
RBDS0081	P2RY12	ENST00000302632	3/3	–	c.197_198del	p.(Thr66SerfsTer4)	Het	VUS	No
RBDS0082	PTPN11	ENST00000351677	12/16	–	c.1403C > T	p.(Thr468Met)	Het	P	Yes
	RUNX1	ENST00000437180	7/9	–	c.623A > T	p.(Gln208Leu)	Het	VUS	No
RBDS0091	ITGA2B	ENST00000262407	18/30	–	c.1821G > A	p.(Thr607 = )	Het	VUS	No
	COL5A1	ENST00000371817	4/66	–	c.597C > G	p.(Ile199Met)	Het	B	No
RBDS0106	CD36	ENST00000435819	13/17	–	c.1004_1006del	p.(Glu335del)	Het	VUS	No
			–	8/16	c.429 + 2T > C	–	Het	P	Yes
RBDS0109	ETV6	ENST00000396373	6/8	–	c.1106G > A	p.(Arg369Gln)	Het	P	Yes
RBDS0121	RUNX1	ENST00000437180	9/9	–	c.1262_1263insGGGG	p.(Glu422GlyfsTer179)	Het	LP	No
	TBXA2R	ENST00000375190	2/3	–	c.100T > C	p.(Phe34Leu)	Het	VUS	No
RBDS0122	NF1	ENST00000358273	30/58	–	c.3989A > C	p.(Glu1330Ala)	Het	VUS	No
	GP1BB	ENST00000366425	2/2	–	c.119G > A	p.(Gly40Glu)	Het	B	Yes
RBDS0123	MASTL	ENST00000375940	6/12	–	c.707C > A	p.(Ser236Ter)	Het	VUS	No
RBDS0127	CDC42	ENST00000315554	2/6	–	c.101C > A	p.(Pro34Gln)	Het	LP	Yes
	TRPM7	ENST00000313478	2/39	–	c.38A > C	p.(Lys13Thr)	Het	VUS	No
RBDS0128	GFI1B	ENST00000339463	9/11	–	c.521C > A	p.(Thr174Asn)	Het	VUS	Yes
RBDS0135	ACTN1	ENST00000394419	22/22	–	c.2623C > T	p.(Pro875Ser)	Het	VUS	No
RBDS0137	TUBB1	ENST00000217133	4/4	–	c.326G > A	p.(Gly109Glu)	Het	VUS	Yes
RBDS0145	FLNA	ENST00000369850	35/48	–	c.5579_5580del	p.(Val1860GlyfsTer43)	Het	LP	No
RBDS0149	ITGB3	ENST00000559488	11/15	–	c.1889G > T	p.(Cys630Phe)	Het	VUS	No
RBDS0150	GP1BB	ENST00000366425	2/2	–	c.22dup	p.(Ala8GlyfsTer24)	Het	LP	No
RBDS0153	TUBB1	ENST00000217133	1/4	–	c.35del	p.(Cys12LeufsTer12)	Het	P	Yes
RBDS0160	ANKRD26	ENST00000376087	23/34	–	c.2599C > T	p.(Arg867Ter)	Het	LP	No
RBDS0162	MYH9	ENST00000216181	41/41	–	c.5797C > T	p.(Arg1933Ter)	Het	P	Yes
RBDS0163	COL1A1	ENST00000225964	36/51	–	c.2558T > C	p.(Ile853Thr)	Het	VUS	No
	RUNX1	ENST00000437180	–	7/8	c.614-2_614-1insTATT	–	Het	LP	No
RBDS0169	RUNX1	ENST00000437180	8/9	–	c.939_950del	p.(Ala315_Ser318del)	Het	VUS	No
RBDS0170	HPS1	ENST00000325103	10/20	–	c.875_878del	p.(Asp292AlafsTer38)	Het	P	Yes
RBDS0178	GATA1	ENST00000376670	6/6	–	c.950G > A	p.(Arg317Gln)	Het	VUS	No
RBDS0190	ACTN1	ENST00000394419	2/22	–	c.137G > A	p.(Arg46Gln)	Het	P	Yes
RBDS0191	GATA1	ENST00000376670	6/6	–	c.920G > A	p.(Arg307His)	Het	LP	Yes
RBDS0192	RUNX1	ENST00000437180	9/9	–	c.1100del	p.(Gly367AlafsTer227)	Het	LP	No
RBDS0194	GFI1B	ENST00000339463	9/11	–	c.648G > C	p.(Gln216His)	Het	VUS	No
RBDS0196	GP1BB	ENST00000366425	2/2	–	c.119G > A	p.(Gly40Glu)	Het	B	Yes
RBDS0201	GFI1B	ENST00000339463	–	10/10	c.815-1G > C	–	Het	LP	No
RBDS0205	UNC13D	ENST00000207549	31/32	–	c.3048C > T	p.(Gly1016 = )	Het	VUS	No
	IKZF5	ENST00000368886	5/5	–	c.1160G > A	p.(Gly387Glu)	Het	VUS	No
	RUNX1	ENST00000437180	5/9	–	c.434del	p.(Arg145LysfsTer7)	Het	LP	No
RBDS0209	GP1BA	ENST00000329125	2/2	–	c.677del	p.(Asn226ThrfsTer30)	Het	LP	No
	TPM4	ENST00000344824	2/9	–	c.141G > T	p.(Gln47His)	Het	VUS	No
RBDS0218	TBXA2R	ENST00000375190	2/3	–	c.346G > C	p.(Gly116Arg)	Het	VUS	No
RBDS0223	RUNX1	ENST00000437180	8/9	–	c.939_950del	p.(Ala315_Ser318del)	Het	VUS	No
RBDS0236	ITGA2B	ENST00000262407	26/30	–	c.2665C > G	p.(Arg889Gly)	Het	VUS	No
RBDS0245	ACTN1	ENST00000394419	22/22	–	c.2717C > T	p.(Thr906Met)	Het	VUS	No
RBDS0250	TBXA2R	ENST00000375190	2/3	–	c.100T > C	p.(Phe34Leu)	Het	VUS	No
HDCT group
Patients	Gene	Transcript	Exon	Intron	HGVSc	HGVSp	Genotype	ACMG	Described
RBDS0032	COL1A2	ENST00000297268	17/52	–	c.850G > A	p.(Glu284Lys)	Het	LP	No
RBDS0065	COL3A1	ENST00000304636	44/51	–	c.3230G > A	p.(Gly1077Asp)	Het	LP	No
RBDS0092	COL1A1	ENST00000225964	5/51	–	c.436C > A	p.(Pro146Thr)	Het	VUS	Yes
RBDS0095	TNXB	ENST00000375244	3/44	–	c.1066_1078del	p.(Arg356GlyfsTer211)	Het	LP	No
	FBN2	ENST00000508053	9/71	–	c.377G > A	p.(Arg126His)	Het	VUS	No
RBDS0100	COL3A1	ENST00000304636	27/51	–	c.1889G > A	p.(Gly630Glu)	Het	VUS	No
RBDS0114	TGFBR2	ENST00000359013	7/8	–	c.1484A > G	p.(Tyr495Cys)	Het	P	Yes
RBDS0115	COL1A2	ENST00000297268	50/52	–	c.3691A > G	p.(Thr1231Ala)	Het	VUS	No
RBDS0130	COL5A2	ENST00000374866	52/54	–	c.4067A > G	p.(Asp1356Gly)	Het	VUS	No
RBDS0133	COL5A1	ENST00000371817	13/66	–	c.1637C > T	p.(Ala546Val)	Het	VUS	No
RBDS0200	COL2A1	ENST00000380518	46/54	–	c.3215C > A	p.(Pro1072His)	Het	VUS	No
RBDS0202	COL1A2	ENST00000297268	36/52	–	c.2168A > G	p.(Asn723Ser)	Het	VUS	No
RBDS0203	COL11A1	ENST00000358392	3/67	–	c.328G > C	p.(Gly110Arg)	Het	B	No
			23/67	–	c.2101C > T	p.(Pro701Ser)	Het	VUS	No
	ABCC6	ENST00000205557	27/31	–	c.3770C > A	p.(Pro1257His)	Het	VUS	Yes
RBDS0211	COL1A2	ENST00000297268	50/52	–	c.3574A > G	p.(Lys1192Glu)	Het	VUS	No
RBDS0225	BMPR2	ENST00000374580	6/13	–	c.710G > A	p.(Arg237His)	Het	VUS	No
RBDS0226	COL1A2	ENST00000297268	42/52	–	c.2717G > A	p.(Arg906His)	Het	LP	Yes
BDUC group
Patients	Gene	Transcript	Exon	Intron	HGVSc	HGVSp	Genotype	ACMG	Described
RBDS0075	COL1A2	ENST00000297268	49/52	–	c.3313G > A	p.(Gly1105Ser)	Het	VUS	No
	TGFBR1	ENST00000552516	9/9	–	c.1511G > C	p.(Gly504Ala)	Het	VUS	Yes
RBDS0077	PTPN11	ENST00000351677	8/16	–	c.923A > G	p.(Asn308Ser)	Het	P	Yes
RBDS0094	ACVRL1	ENST00000388922	4/10	–	c.353_360dup	p.(Leu121SerfsTer4)	Het	P	Yes
RBDS0098	ABCC6	ENST00000205557	–	24/30	c.3506 + 2_3506 + 5del	–	Het	P	Yes
RBDS0142	SOS1	ENST00000402219	10/23	–	c.1297G > A	p.(Glu433Lys)	Het	LP	Yes
RBDS0210	GP1BA	ENST00000329125	2/2	–	c.137C > T	p.(Pro46Leu)	Het	VUS	No
				–	c.1697G > A	p.(Gly566Asp)	Het	VUS	No
RBDS0234	GATA1	ENST00000376670	6/6	–	c.1019del	p.(Gly340AlafsTer14)	Het	LP	No

Abbreviations: ACMG, American College of Medical Genetics; B, benign; Hem, hemizygous; Het, heterozygous; HGVSc, Human Genome Variation Society coding DNA sequence; HGVSp, Human Genome Variation Society protein sequence; Hom, homozygous; LP, likely pathogenic; P, pathogenic; VUS, variant of uncertain significance.

^a Variants reported in a previous publication of our group.[23]

Fig. 1 Diagnostic yield of patients classified in the three different recruitment groups: inherited platelet disorders (IPD), heritable disorders of connective tissue (HDCT), and bleeding disorders of unknown cause (BDUC).

Patients with Definitive Genotype–Phenotype Correlation

Of the 83 probands with a definitive genotype–phenotype correlation, 62 had a single candidate variant, mostly in heterozygosis state, except for two patients (RBDS0047 and RBDS0059) who were homozygous for a variant in NBEAL2, responsible for Gray Platelet syndrome with an autosomal recessive (AR) inheritance. The remaining 21 probands presented at least two variants: two (RBDS0106 and RBDS0210) reported variants in the same gene, whereas the other 19 had candidate variants in two or more genes.

A total of 108 candidate variants (98 unique) were identified across 48 genes ([Fig. 2A]). Most candidate variants (69%) were novel. According to ACMG criteria, 56% were classified as VUS, 40% were classified as P or LP, and 4% were classified as B but fulfilled the predefined selection criteria ([Fig. 2B]; [Supplementary Table S5], available in the online version only). Family segregation studies were performed for 24 probands.

Fig. 2 Summary and classification of candidate variants identified in patients with definitive genotype–phenotype correlation. (A) Graphical representation of the affected genes considering the number of unique variants identified in each and presented in descending order. (B) Classification of the candidate variants according to the American College of Medical Genetics (ACMG) guidelines. (C) Proportion and number of candidate variants identified in the three recruitment groups (IPD, HDCT, and BDUC) stratified by gene panel category (IPD, HDCT, and bleeding).

IPD Group

Sixty-one out of 114 probands (54%) reached a definitive diagnosis ([Fig. 1]). A total of 81 candidate variants (71 unique) were identified in 35 genes ([Table 1]). The most frequently affected gene was RUNX1, followed by ACTN1, GFI1B, GATA1, GP1BA and TUBB1. Sixteen patients (14%) carried candidate variants in genes linked to increased risk of hematological malignancies (RUNX1, ANKRD26, and ETV6). Notably, the majority of candidate variants in RUNX1 (7/8) were loss-of-function, including four frameshift variants, two splice sites variant, and one complete gene deletion, a well-documented mechanism affecting RUNX1.[30] The deletion was identified as a mosaic haploinsufficiency in patient RBDS0039, confirming the referring hospital's report of mosaic chromosome 21 monosomy ([Supplementary Fig. S2], available in the online version only).

Interestingly, eight candidate variants were identified in at least two probands ([Supplementary Table S6], available in the online version only). Of these, four variants had been previously described in association with IPD, whereas four were novel VUS with very low allele frequency.

Moreover, in addition to the candidate variant identified in IPD-associated genes, seven patients were also carriers of candidate variants in other genes contributing to their clinical phenotype. Specifically, patients RBDS0026, RBDS0045, and RBDS0054 had heterozygous variants in coagulation genes (F7, F11, and VWF, respectively) that correlated with slightly reduced factor levels. Patients RBDS0082 and RBDS0122, referred for suspected platelet dysfunction and Noonan syndrome (NS), were found to carry additional candidate variants in PTPN11 and NF1, respectively, confirming the clinical suspicion. Finally, patients RBDS0091 and RBDS0163 carried candidate variants in IPD genes (ITGA2B and RUNX1, respectively) as well as in EDS-related genes (COL1A1 and COL5A1, respectively). The combination of both variants correlates with their clinical presentation of thrombocytopenia combined with JH ([Fig. 2C]).

HDCT Group

Fifteen out of 28 patients (54%) harbored 18 candidate variants across 13 different genes ([Table 1]; [Fig. 1]). The most frequently affected gene was COL1A2, which is associated with osteogenesis imperfecta and EDS arthrochalasia type. Specifically, nine patients carried variants in EDS genes (COL1A1, COL1A2, COL3A1, COL5A1, COL5A2, and TNXB). Four patients carried variants in genes associated with Marfanoid phenotype (COL11A1, COL2A1, FBN2, TGFBR2). Of these, two carried additional variants in TNXB and ABCC6, respectively. Notably, the ABCC6 variant, associated with pseudoxanthoma elasticum, correlated with the patient's bleeding manifestations and family history of sudden death.[31] In the last patient (RBDS0225), who presented an intracranial aneurysm with hemorrhage, no HDCT-related variants were identified, but harbored a candidate variant in BMPR2, a gene associated with HHT, consistent with the observed phenotype ([Fig. 2C]).

BDUC Group

Seven out of 28 patients (25%) achieved a definitive genotype–phenotype correlation ([Table 1]; [Fig. 1]). Three patients harbored variants in genes of the bleeding panel, responsible for HHT (ACVRL1) or NS (PTPN11 and SOS1). Two patients carried candidate variants in HDCT-related genes: patient RBDS0098 with an ABCC6 variant potentially explaining her bleeding and tissue fragility, and patient RBDS0075 with COL1A2 and TGFBR1 variants that likely explain the JH phenotype observed in his family. The remaining two patients carried candidate variants in IPD-associated genes. Patient RBDS0210 carried two novel candidate variants in GP1BA, consistent with AD type A2 Bernard Soulier syndrome (BSS; OMIM # 153670), correlating with the bleeding phenotype and normal platelet count but high MPV. However, the allelic phase of the variants was not determined due to the unavailability of family members for testing. Finally, the female patient RBDS0234, with severe bleeding (ISTH-BAT = 12) and normal platelet count and aggregation, harbored a candidate variant in GATA1 that was inherited by her son, who had impaired platelet aggregation ([Fig. 2C]).

Patients with Partial Genotype–Phenotype Correlation

A total of 19 patients presented a partial genotype–phenotype correlation attributable to several reasons. Of them, 14 belonged to the IPD group, 3 to the HDCT group, and 2 to the BDUC group ([Fig. 1]; [Supplementary Table S7], available in the online version only). Four patients (21%) had insufficient clinical or laboratory data to establish a definitive genotype–phenotype correlation. Ten patients (53%) carried heterozygous variants in genes with limited evidence or associated with recessive IPHD. This is the case of patient RBDS0097, who carried a heterozygous variant in HPS6, associated with AR Hermansky–Pudlak syndrome (HPS) that causes oculocutaneous albinism and bleeding. Although heterozygous HPS6 variants have not been clearly linked to IPD, patients with heterozygous HPS1 variants have been reported exhibiting mild clinical features, including abnormal platelet aggregation and bleeding tendencies.[32] This raises the possibility that heterozygous variants in HPS-related genes may contribute to a mild bleeding phenotype, as proposed in this patient. However, further evidence is required to establish the causal relationship. Finally, five patients (26%) harbored variants explaining only part of the phenotype. For instance, patient RBDS0085, who experienced bleeding and JH, carried a variant in GFI1B, correlating with her bleeding and borderline platelet count (160 × 10⁹/L), but no variant was identified to explain her JH.

Discussion

Here, we report the genetic insights and genotype–phenotype correlations identified in a cohort of 170 Spanish families with IPHD. Recruited patients presented signs of IPD, HDCT associated with bleeding, or BDUC with a positive ISTH-BAT score. To our knowledge, this is the first cohort specifically designed to integrate these three categories and address the overlap of bleeding phenotypes encountered in routine clinical practice within a single WES-based framework. Consistent with previous studies,[15] [17] [19] [33] most patients included in our cohort were females, with a higher prevalence (86%) observed in the HDCT and BDUC groups.

A complete genotype–phenotype correlation was achieved in 83 probands, accounting for 49% of total cases. Among them, we identified 108 candidate variants, of which 98 were unique. The majority of candidate variants were novel (69%), which contributes to expanding our understanding of the molecular basis of these pathologies, providing valuable insights into their underlying genetic mechanisms. In addition, 55 candidate variants were classified as VUS (56%), reflecting the challenge of assigning a definitive pathogenicity due to limited functional or segregation data. This underscores the importance of developing larger population-based studies and promoting data sharing to facilitate variant reclassification as additional cases, segregation, and detailed phenotypic information become available.

Across disease groups, 62 probands classified in the IPD group reached definitive genotype–phenotype correlation (54%), which is in line with previous studies.[15] [17] [18] Notably, patients with thrombocytopenia exhibited a higher rate of definitive correlations (70%), with RUNX1 and ACTN1 being the most prevalent genes, accounting for 17% of candidate variants. In contrast, definite correlations were achieved in only 27% of patients presenting solely with thrombocytopathy. This disparity likely reflects both biological and technical factors: platelet count reduction is easily measurable and associated with several genes involved in megakaryocyte differentiation, whereas qualitative platelet function defects are more complex to assess and often yield nonspecific results, thus hampering precise phenotypic characterization and subsequent genetic interpretation. These findings emphasize the need for continued efforts to refine phenotypic and functional characterization, as establishing the precise molecular cause of patients with thrombocytopathy can substantially improve the diagnostic yield of patients with IPD.[30] Although most candidate variants identified were located in the TIER1 or TIER2 genes of the ISTH-SSC on Genomics in Thrombosis and Hemostasis, literature review supported the inclusion of candidate variants in CD36, FLNA, and UNC13D in five patients. Two CD36 heterozygous variants were identified in patient RBDS0106, with ISTH-BAT = 9, 70% reduction of GPIV, recurrent miscarriages, and placental hematoma that are likely attributable to an immune reaction against fetuses expressing GPIV.[34] Regarding FLNA, despite its being implicated in a wide spectrum of rare diseases, specific variants have been reported in patients with isolated macrothrombocytopenia,[35] as is the case with patient RBDS0145. Finally, although UNC13D is classically responsible for AR Hemophagocytic lymphohistiocytosis, it has been recently linked to impaired platelet granule content and/or secretion with AD inheritance, a phenotype also observed in patients RBDS0045, RBDS0054, and RBDS0205. In this sense, the assumption of AR inheritance for some IPD is being challenged by recent findings suggesting potential dominant effects of certain variants. BSS was among the first IPD described to exhibit both AR and AD inheritance,[36] a pattern later extended to platelet dysfunction associated with heterozygous variants in HPS1 and P2RY12.[37] [38] [39] [40] [41] [42] [43] [44] The identification of three probands within our cohort (RBDS0054, RBDS0081, RBDS0170) with candidate variants in these genes provides further evidence supporting their role in the pathogenesis of platelet dysfunction.

Notably, the HDCT group demonstrated a definitive genotype–phenotype correlation in 15 patients (54%), predominantly driven by variants in EDS-related genes. To our knowledge, this is the first genetic study specifically including HDCT patients in the context of a cohort of individuals with suspected IPHD. Nevertheless, the role of collagen in platelet adhesion, activation, and hemostasis maintenance is increasingly being recognized, and some studies have already included a few EDS-associated genes when analyzing patients with bleeding disorders, successfully identifying candidate variants.[18] [19] [32] [45] However, the role of HDCT as a contributor to bleeding tendencies has remained largely uncharacterized. Bleeding severity within our HDCT group showed marked variability, with ISTH-BAT score ranging from 1 to 16. Notably, the most severe bleeding phenotype was observed in patient RBDS0065, who carried the novel COL3A1:c.3230G > A variant accounting for his severe bleeding manifestations, including spontaneous rectus muscle hematoma, a fatal intestinal perforation, and additional congenital anomalies. Although in this case an earlier diagnosis would not have prevented these life-threatening events, establishing a molecular diagnosis remains essential to guide clinical management, avoid harmful interventions, and provide appropriate genetic counseling. The remaining probands with a suspicion of HDCT exhibited milder phenotypes, with bleeding manifestations resembling those observed in other IPHD, hindering the distinction of their underlying origin unless JH is considered. This study highlights the importance of including HDCT assessment in the differential diagnosis of patients presenting with mucocutaneous bleeding, as connective tissue integrity is essential for primary hemostasis, and overlooking these disorders may lead to missed or delayed diagnoses. In this sense, we anticipate that analyzing HDCT-associated genes and including patients with bleeding and signs of HDCT may set a precedent for future genetic studies addressing IPHD, potentially providing answers for many patients who currently remain undiagnosed.[19]

The BDUC group had the lower rate of cases with definitive genotype–phenotype correlation (25%), which was achieved in 7 patients. This outcome is explained by the fact that these patients present positive ISTH-BAT scores in the absence of abnormal laboratory findings to guide the investigation. Nevertheless, our study achieved a higher diagnostic rate than a previous reported BDUC cohort (18%), where only 11 genes were analyzed, highlighting the added value of broader gene panel analysis.[18] Notably, the identification of candidate variants in genes included in the IPD or HDTC panels in four of the seven patients with definitive genotype–phenotype correlation reflects the molecular complexity and heterogeneity among BDUC patients and emphasizes the need for comprehensive genetic testing. An illustrative example is the prioritization of the novel GATA1:c.1019del variant in patient RBDS0234, providing further evidence supporting the association between female carriers and a bleeding tendency despite normal platelet counts.[32]

Interestingly, 19 patients of the entire cohort carried candidate variants in different genes. In some cases, these variants converged in similar phenotypes, suggesting a potential cumulative effect on the clinical presentation, while in others accounted for distinct syndromic manifestations, reflecting the complexity of genotype–phenotype relationships in this group. Among them, 12 patients (RBDS0001, RBDS0003, RBDS0011, RBDS0023, RBDS0062, RBDS0075, RBDS0095, RBDS0121, RBDS0127, RBDS0203, RBDS0205, RBDS0209) carried variants affecting genes within the same pathway or panel, while the other 7 (RBDS0026, RBDS0045, RBDS0054, RBDS0082, RBDS0091, RBDS0122, RBDS0163) had variants across genes located in different panels. This IPHD genetic complexity advocate for an integrative approach that recognizes both the potential for synergistic genetic effects and the value of WES as a versatile and integrative tool for disentangling these complex disorders.

A partial genotype–phenotype correlation was achieved for 19 patients (11%), mostly due to insufficient information regarding the variant or the affected gene. In some cases, specific tests would be required to determine the contribution of the identified variants to platelet dysfunction and/or bleeding tendency. However, the availability of these tests is often limited and restricted to specialized reference centers, creating a significant obstacle for comprehensive functional and molecular characterization. For example, the heterozygous missense variant GP6:c.242G > A identified in patient RBDS0058 is located in a gene with an AR inheritance pattern and could only be confirmed through flow cytometry, which was not available.[46] [47] Consequently, many potentially relevant variants remain without functional validation, leading to diagnostic uncertainty and a possible underestimation of their clinical contribution, highlighting the need for broader access to advanced platelet functional assays and collaborative networks to improve diagnostic accuracy.

Unfortunately, no candidate variant could be identified in 68 patients (40%), with a higher prevalence in the BDUC group (68%), as previously discussed. In this regard, the lack of identified candidate variants may reflect insufficient phenotypic characterization, as discussed above, technical limitations inherent to the analysis, discrepancies with ACMG guideline interpretations, and the currently unknown genetic basis of certain IPHD. Technically, the failure to identify candidate variants could be attributed to the inherent constraints of WES in detecting deep intronic and large structural variants. Furthermore, discrepancies in the application of ACMG guidelines hamper variant interpretation and prioritization.[48] An illustrative example is the GATA1:c.1208C > T variant, identified in two unrelated hemizygous patients (RBDS0007 and RBDS0239). This variant could not be prioritized because, although it has not been found in a homozygous state (it has been reported in only 7 hemizygous males out of 380,757 individuals in gnomAD), it is currently classified as benign under ACMG guidelines based on the BS2 criterion (presence of > 2 homozygous individuals). This suggests that the BS2 threshold may be inappropriate for genes with X-linked inheritance, such as GATA1, and could warrant reconsideration. In this context, initiatives aimed at standardizing variant interpretation are of great value, such as the ClinGen Platelet Disorders Variant Curation Expert Panel, recently established to curate clinically relevant variants in platelet genes, starting with ITGA2B and ITGB3.[49] Finally, it cannot be ruled out that some undiagnosed patients carry variants in genes not yet associated with these IPHD, such as hypermobile EDS.[50] To address this diagnostic gap, when new disease-associated genes are identified, virtual gene panels are updated, and unsolved cases are reanalyzed. In addition, multidisciplinary meetings allow continuous phenotypic refinement, which can guide variant reinterpretation. Furthermore, particularly for variants identified in low-evidence genes, segregation studies represent a powerful tool to provide additional evidence for variant pathogenicity. Moreover, in unsolved patients with phenotypes strongly suggestive of IPHD, such as congenital (macro)thrombocytopenia, complementary approaches, including RNA-sequencing, are being pursued to identify splicing defects or dysregulated pathways not detectable by WES. We therefore anticipate that the combination of reanalysis, improved phenotypic characterization, and discovery of novel disease-associated genes will progressively increase diagnostic yield over time.

In conclusion, this extensive and multicenter study provides a comprehensive framework to address the genetic and clinical heterogeneity of IPHD. The simultaneous and standardized inclusion and WES of patients with IPD, HDCT, and BDUC enabled both cross-category comparison and the uncovering of genetic and phenotypic overlap. The identification of the molecular cause in 49% of patients and the detection of 68 novel variants reinforce the clinical relevance of WES as an early step, alongside basic phenotypic testing, for the evaluation of patients with suspected IPHD. Moreover, the identification of 19 patients carrying multiple candidate variants in different genes supports the idea of a shared clinical presentation across IPHD. The significant diagnostic yield obtained in patients with HDCT highlights the importance of considering these disorders as contributors to the bleeding phenotype. These results also contribute to redefining heterogeneous entities such as BDUC under a more precise genetic framework. However, the limited access to specialized functional assays highlights the need to strengthen collaborative networks and promote large-scale population studies to support variant validation and reclassification. Overall, this work paves the way toward a genotype-driven and individualized diagnostic approach, fostering the implementation of precision medicine in patients with IPHD.

Contributors' Statement

F.V. and I.C. developed the hypothesis and designed the research. N.V., M.C., J.M., O.B., R.B., S.G., J.M.B., N.S., R.S., R.M.A., E.F.-M., R.A., L.L.-A., O.R., F.J.L., L.Q.P., C.V.E., A.T., S.M., E.A., and R.P. collected IPHD patients and reported the clinical data presented in this study. P.B., N.C., L.R., C.L., and N.G. performed the molecular analyses. P.B., N.B., F.V., and I.C. identified and validated the candidate variants. L.M.-F., I.G.-M., and C.H. provided support in the analysis and organization of the results. P.B. wrote the manuscript. All authors revised the final version of the manuscript.

Conflict of Interest

The authors declare that they have no conflict of interest.

Acknowledgment

The authors are very grateful to all the clinicians who have participated by contributing samples to this project: Anna Bustins (Institut Català d'Oncologia (ICO), Hospital Dr. Josep Trueta), Gabriela Ghita (Institut Català d'Oncologia (ICO), Hospital Dr. Josep Trueta), Elena Pina (Hospital Universitari de Bellvitge), Elena Palmer (Hospital Universitari de Bellvitge), Cristina Marzo (Hospital Universitari Arnau de Vilanova).

Data Availability Statement

Data available in the article [Supplementary Material] (available in the online version only). Additional data can be available on request from the authors.

Ethical Approval

This study was approved by the research ethics committee of Hospital Universitari Vall d'Hebron and was conducted according to the Declaration of Helsinki.

Informed Consent

All patients provided written informed consent for the study.

^‡ These authors contributed equally to this article.

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Publication History

Received: 24 October 2025

Accepted after revision: 04 February 2026

Accepted Manuscript online:
11 February 2026

Article published online:
25 February 2026