We enrolled 100 women with nsPOI and 200 healthy controls. A targeted NGS panel covering 72 genes potentially involved in POI was developed using Ampliseq technology (ThermoFisher Scientific). Various bioinformatic tools (Polyphen, Sift, CADD, MutationTaster and the Grantham score) were used to identify potentially pathogenic variants according to ACMG guidelines, while tools such as STRVCTVRE, CADD-SV and X-CNV were used to predict pathogenicity of CNV calls.
Results
Using this panel, we identified mutations in 60% (N=60) of the patients, of whom 23% carried likely pathogenic or pathogenic mutations, and 37% had variants of uncertain significance (VUS). Among these 60 patients, 37 had monogenetic variants and 23 had mutations in two or more genes. In total, we identified 42 genes affected in our Italian of nsPOI cohort. The most frequently mutated genes in our cohort included DNAH5, LAMC1, ADAMTS1/19, HSD17B4, HK3 and AR. Additionally, we detected CNVs in the SYCE, DUSP22 and INHBB genes. Most of the altered genes in our cohort are involved in DNA repair, meiosis and signal transduction. Gene Ontology (GO) analysis revealed that the mutated genes play a key role in oocyte differentiation, folliculogenesis and follicular maturation.
Diagram illustrating gene analysis in primary ovarian insufficiency (POI). On the left, NGS analysis identifies 42 altered genes from a 72-gene panel in 100 POI patients. The right side details ovarian follicle development stages, including oogonia, primary oocytes, primordial follicles, primary follicles, and secondary follicles. Genes involved in meiosis, DNA repair, follicle maturation, and metabolic processes are listed, such as FANCC, FOXO3, AMHR2, and BBS9. The flow progresses from oogonia to secondary follicles, highlighting various processes and genes at each stage.
non-syndromic primary ovarian Insufficiency (nsPOI)next generation sequencing (NGS)NGS panelfemale infertilitygenetic screeningMinistry of Health10.13039/100009647The author(s) declared that financial support was received for this work and/or its publication. This study was supported by Italian Ministry of Health (Young researcher grant 2018, GR-2018-12368359). DL and RV are co-owners of the OvAge patent; its use in this study was free of charge and generated no financial gain.section-at-acceptanceReproductionIntroduction
Primary ovarian insufficiency (POI) is a complex disease affecting approximately 1-4% of women worldwide (1). POI is characterized by amenorrhea in women under of the age of 40, hypoestrogenism, and elevated levels of follicle-stimulating hormone (FSH), leading to progressive loss of ovarian function and infertility (2–4) The long-term consequences of POI include an increased risk of osteoporosis, cardiovascular disease, and mental health disorders (5, 6). As such, POI has a substantial impact on women’s reproductive and overall health. The etiology of POI is heterogeneous and may result from various causes, including iatrogenic and environmental exposures (7, 8) autoimmune and metabolic diseases (9, 10), viral infection (11), X-linked abnormalities (12, 13) and autosomal gene mutations (14, 15).
To date, the genetic mechanism underlying POI remain poorly understood, no standardized diagnostic methods currently exist to detect its genetic basis. POI may present as part of a pleiotropic genetic syndrome or as an isolated, non-syndromic form. The advent of the “omics” era has led to the discovery of several genes implicated in both syndromic and non-syndromic POI due to their key roles in reproductive function (16–28).
Given the high genetic heterogeneity and relatively low prevalence of POI, traditional approaches such as candidate gene studies and genome-wide association studies (GWAS) are limited in their ability to elucidate the underlying genetic causes. While some pathogenic variants have been identified in specific families or subsets of patients, their contribution to the POI phenotype often remains uncertain. This knowledge gap delays the development of accurate diagnostic tools and personalized treatment strategies. To address these limitations, next-generation sequencing (NGS) has emerged as a powerful technology for the comprehensive analysis of genes involved in ovarian function (21).
Recent studies (29–32) have demonstrated the utility of targeted NGS panels for identifying potentially pathogenic variants, allowing the simultaneous analysis of multiple genes and improving diagnostic efficiency in terms of cost and turnaround time. Targeted NGS represents a promising strategy to investigate selected genes associated with ovarian biology and enhance clinical management through more precise etiologic diagnoses (33).
A cohort of 100 women with non-syndromic POI was recruited from the OvAge database, maintained at the Department of Experimental and Clinical Medicine of the Magna Graecia University of Catanzaro. This database contains data on more than 1,000 women, including serum levels of anti-Müllerian hormone (AMH), follicle-stimulating hormone (FSH), estradiol (E2), three-dimensional antral follicle count (AFC), vascular index (VI), flow index (FI), and vascular flow index (VFI). For all patients, ovarian reserve had previously been estimated using our proprietary OvAge algorithm (35). This algorithm is a mathematical formula that combines biochemical and ultrasonographic parameters to produce a single interpretable value, referred to as “OvAge”—an estimate of ovarian age—calculated using the following linear equation: OvAge = 48.05 – 3.14 × AMH + 0.07 × FSH – 0.77 × AFC – 0.11 × FI + 0.25 × VI + 0.1 × AMH × AFC + 0.02 × FSH × AFC. The formula was generated through the use of a Generalized Linear Model (GzLM).
For this study, patients were selected from the database based on the following criteria: onset of oligomenorrhea or menopause before the age of 40, elevated FSH level (> 40 mU/L), estradiol (E2) < 20 pg/ml, anti-mullerian hormone (AMH) < 1 ng/ml. Additionaly, patients were included if the difference between their OvAge and chronological age exceeded 10 years. A control group of 200 healthy women with an OvAge–chronological age difference of ±2 years was also included. The following exclusion criteria were applied to both POI patients and controls: use of estrogens or progestins or breastfeeding within two months prior to enrollment, ongoing pregnancy, history of endometriosis, presence of ovarian follicles larger than 10 mm or other ovarian cystic lesions, chromosomal abnormalities, history of ovarian surgery, chemotherapy/radiotherapy, polycystic ovary syndrome, known autoimmune diseases, chronic, systemic, metabolic and endocrine pathologies, history of drug use. The sample size was estimated a priori using a two-sample test for proportions, according G*Power software to obtain >95% CI. All participants provided written informed consent.
DNA extraction and quality assessment
Genomic DNA was extracted from peripheral blood samples using the PureLink® Genomic Kit (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s protocol. DNA quality and quantity were assessed using the Qubit Fluorometer (Invitrogen) and the 4200 Tape Station Instrument (Agilent Technologies, Inc, Santa Clara, CA, USA). Only high-quality DNA samples were used for subsequent library preparation and sequencing.
Next generation sequencing
NGS was performed using the Ion AmpliSeq™ POI Panel on the Ion Torrent platform (Thermo Fisher Scientific, MA, USA). This custom targeted NGS panel provides complete exon coverage of 72 genes known to be associated with POI (34) (see details in Supplementary File S1). Library preparation was performed using10ng of genomic DNA measured with the Qubit 2.0 fluorometer (Thermo Fisher Scientific). Libraries were prepared both manually according to the Ion AmpliSeq Library Kit Plus and automatically on the Ion Chef™ instrument using the Ion AmpliSeq Kit for Chef DL8 (Thermofisher Scientific). Libraries were deemed suitable for sequencing after quality and quantity assessment. The concentration of each cDNA library, which was prepared manually, was determined on the Agilent 4200 system using the Agilent High Sensitivity DNA Assay (Agilent Technologies) according to the manufacturer’s recommendations. The concentration of the automatically prepared cDNA library pool was determined using the Ion Library TaqMan Quantitation Kit (Thermofisher Scientific). Libraries were diluted to 30 pM and then loaded into the Ion Chef™ instrument (Thermofisher Scientific). The Ion Chef™ instrument utilized the Ion 510™ & Ion 520™ & Ion 530™ Kit – Chef (Thermofisher Scientific) to perform emulsion PCR, enrichment and loading of the Ion S5–520 and/or 530 chip.
Sanger sequencing
PCR products obtained with the BigDye Direct Cycle Sequencing Kit (Applied Biosystems, Thermofisher Scientific) were purified with the BigDye Terminator Purification Kit (Applied Biosystems) and sequenced with the BigDye Terminator v3.1 (Applied Biosystems, Foster City, CA, USA) using the Genetic Analyzer 3500 Dx (Applied Biosystems). The exons of DNAH5 (ex 66), LAMC1 (ex 17), AR (ex 1), GDF9 (ex 3), ADAMTS19 (ex 2) were amplified using the primer list in Supplementary File S1. The electropherograms were analyzed with SeqScape software, v.4 (Applied Biosystems) and compared with the corresponding reference genomes (DNAH5: NM_001369.3; LAMC1: NM_002293.4; AR: NM_000044.6; GDF9: NM_005260.7; ADAMTS19: NM_133638.6).
Bioinformatic analysis
The raw data generated from NGS were processed using Torrent Suite software v.5.14 which performed sequence alignment, adapter trimming, signal filtering, and quality-based read exclusion. Coverage analysis was performed using the Coverage Analysis plug-in. The coverage criteria for removing sequences from further analysis were: Reads < 200,000; mean depth < 200; uniformity < 90% (see Supplementary File S2 for details). Sequence variants detected within the 1549 amplicons of the 72 genes of the custom panel were analyzed with Ion Reporter version 5.18.2 using ‘Torrent Variant Caller v5.18-2 (Thermo Fisher Scientific). The parameters used to filter the variants were coverage, quality and frequency. The following parameters were set for the POI samples: Coverage ≥ 200, Quality score ≥ 30.
To remove common germline variants, variants detected in the POI cohort were filtered against those found in the control group (N = 200). The POI candidate variants were further filtered by the non-Finnish European population from GnomAD (https://gnomad.broadinstitute.org/) and the European population from 1000 Genomes (https://www.internationalgenome.org/). The potential deleterious effects of the identified variants were assessed using the prediction algorithms SIFT and Polyphen2 (36), CADD (37), Grantham (38), MutatioTaster (39). Finally, the semi-automated process of the InterVar web service (40) and a manual review were used to identify the resulting potentially pathogenic variants. Both methods adhered to the American College of Medical Genetics and Genomics (ACMG) guidelines (41). The criteria used based on Richards et al. are listed in Supplementary File S3.
Copy number variation analysis
CNV analysis was performed using Ion Reporter software version 5.18.2. Briefly, a Hidden Markov Model (HMM)-based algorithm used normalized read coverage across amplicons to predict ploidy values (0, 1, 2, 3, etc.). Prior to CN determination, read coverage is corrected for GC bias and compared to a baseline coverage created from a control group of regions with known ploidy status. For each sample, a MAPD (Median Absolute Pairwise Difference) value is calculated, a metric that measures the noise in read coverage across all amplicons. To create a CNV call, MAPD is <0.4 was used as criteria. We considered the confidence score that filters out CNV regions that are likely to be false positives (42). As recommended by Ion Reporter software, we considered significant only CNVs that showed confidence score > 10. According to commonly used parameters (43), focal CNVs were defined as aberrations <3Mb in size.
Three supervised learning-based tools (STRVCTVRE (https://strvctvre.berkeley.edu/), CADD-SV (https://cadd-sv.bihealth.org/) and X-CNV (http://119.3.41.228/XCNV/search.php) were used to predict the impact of CNV on pathogenicity. These predictors for the effect of structural variation (SV), CADD-SV (44) and StrVCTVRE (45), are trained with a random forest classifier and use a set of genomic features for SVs related to conservation, gene importance, coding region, expression and exon structure. X-CNV (46) is a framework based on the probabilistic value of the XGBoost algorithm that generates a meta-voting prediction (MVP) score to quantitatively measure the pathogenic effect of CNVs. As stated by the developers (44), the CADD-SV score on the Phred scale ranges from 0 (potentially benign) to 48 (potentially pathogenic). The StrVCTVRE score ranges from 0-1, with a score of 1 being more harmful (45). The MVP score of the X-CNV tool (46) indicates that CNVs between 0.46 and 0.76, between 0.16 and 0.46 and between 0.14 and 0.16 have potentially likely pathogenic, uncertain and likely benign effects, respectively.
Q-RT-PCR validation
Genomic DNA was prepared using standard methods. Quantitative real-time PCR (Q-PCR) was performed using the Power SYBR Green PCR Master Mix with the QuantStudio 12K Flex Real Time System (ThermoFisher), as previously described (47). Normalization was performed to the GAPDH DNA content. Relative DNA amounts were calculated using the comparative cycle threshold method (48). Primer sequences are listed in Supplementary File S1. Statistics was performed by Student’s t-test.
ResultsSequencing analysis of POI patients
The aim of this study was to characterize the mutational profile of individuals affected by non-syndromic primary ovarian insufficiency (hereafter referred as nsPOI). To this end, we sequenced all coding exons of 72 genes using the Ampliseq technology on the Ion Torrent platform (Supplementary File S1). The experimental workflow is outlined in Figure 1.
Workflow of the NGS analysis. The flowchart shows the workflow to identify the potential causative genetic variants in our nsPOI patients. Legend: AF, allele frequency; P, pathogenic; LP, likely pathogenic; VUS, variant of uncertain significance; LB, likely benign.
Flowchart illustrating the analysis of the nsPOI cohort and controls through NGS. A total of 100 nsPOI samples underwent targeted NGS, identifying 179 variants. After filtering for allele frequency (AF < 1%), 145 variants remained and were associated with nsPOI patients. In parallel, comparison with 200 control samples resulted in 78 variants specifically associated with nsPOI. Following annotation, these consisted of 2 pathogenic (P), 24 likely pathogenic (LP), 50 variants of uncertain significance (VUS), and 2 likely benign (LB) variants.
Genomic DNA from peripheral blood samples of 100 Italian women with nsPOI and 200 healthy controls was sequenced (see Materials & Methods for inclusion and exclusion criteria). Variants were filtered to exclude those with a frequency greater than 1% or those not present in the non-Finnish European population of the gnomAD and 1000 Genomes databases (Figure 1).
By comparing the nsPOI cohort with healthy controls (Supplementary File S4), we identified 78 unique variants (Supplementary File S5) that were exclusively present in 60 of the 100 nsPOI patients. The remaining 40 patients did not carry any mutation in the analyzed genes (Figure 1). These variants included 70 missense (89,7%), 1 nonsense (1,3%), 1 frameshift (1,3%), 5 in-frame deletions (6,4%) and 1 in-frame insertion (1,3%) variants (Figure 2A). These 78 variants were distributed across in 41 genes (Supplementary File S5) encoding 14 transcription factors, 11 proteins involved in signal transduction and the cell cycle, 10 meiotic factors and 6 enzymes (Figure 2B). Genes involved in meiosis showed the highest mutation frequency (31%), followed by those involved in signal transduction and cell cycle (26%), transcriptional regulation (23%) and enzymatic activity (20%). The Gene Ontology analysis of the mutated genes is presented in Figure 2C and Supplementary File S6. The most enriched functional categories (those including >6 genes) were related to the development of the female reproductive system and gamete generation, although many genes were also involved in transcriptional regulation and maintenance of the stem cell niche.
Genomic characteristics of the nsPOI cohort. (A) Pie chart showing the frequency of each mutation type identified in nsPOI patients. (B) Histogram displaying the prevalence of variants per gene. The 78 filtered variants were found in 41 genes, encoding (from left to right): 10 meiotic factors, 11 proteins related to signal transduction/cell cycle, 14 transcription factors, and 6 enzymes. The y-axis indicates the frequency of variants in each gene (blue) and the percentage of patients carrying variants in that gene (pink). Percentages of each gene class correspond to the sum of blue bars. (C) Tree plot showing hierarchical clustering of positively enriched GO terms among the 41 mutated genes. Circle size reflects the number of genes per GO term; circle color indicates adjusted p-value significance. Clusters are annotated with representative keywords. (D) Pie chart presenting the proportion of patients with LP/P or VUS variants versus those with no detectable mutations (negative).
Panel A: A donut chart shows mutation types with missense mutations at 89.7%, frameshift at 6.4%, others at 1.3% each. Panel B: A bar graph depicts gene mutations, divided into categories like meiosis/DNA repair and signaling transduction. The percentage of mutations and patients is indicated. Panel C: A dendrogram presents gene ontology terms related to reproductive system development and regulation processes. Panel D: A pie chart displays mutation impacts: 40% negative, 37% VUS, and 23% LP/P.
To classify the identified variants, we followed the ACMG guidelines (41), using five predictive algorithms (SIFT, PolyPhen-2, MutationTaster, CADD, and Grantham) for optimal interpretive accuracy (see Materials and Methods and Supplementary File S3 for details). Variants were classified as likely benign/benign (LB/B), likely pathogenic/pathogenic (LP/P) and of uncertain significance (VUS). In total, we identified 2 LB variants, 24 LP variants, 2 P variants, 50 VUS variants. Overall, 40% of patients were mutation-negative, while 60% carried at least one variant. Among these, 23% had LP/P variants and 37% had only VUS (Figure 2D). Representative Sanger sequencing validations of selected mutations are shown in Supplementary Figure S1.
Genes mutated in a cohort from southern ItalyThe most frequently mutated genes in our cohort
The protein Dynein axonemal heavy chain 5 encoded by the DNAH5 gene was mutated in twelve patients (12%). Three patients carried the S3774P variant, two had I3568T, two patients carried the variant T806I. Seven additional patients each presented a distinct mutation: A769V, S2605L, N1420D, L1339R, N934S, Q2949E, and Y4308C. Notably, S3774P and I3568T co-occurred in two patients. Except for T806I, N934S, L1339R, and I3568T, which were located in linker domains, the remaining mutations resided in conserved functional domains (Figure 3A). Mutations A769V and N1420D were located in the N-terminal region 1 (DHC_N1) and region 2 (DHC_N1) of the dynein heavy chain, respectively; mutations S2605L and Q2949E were located in the P-loop containing the dynein motor region D3 (AAA_7) and D4 (AAA_7), respectively; mutation S3774P was located in the ATP-binding dynein motor region D5 (AAA_9); and Y4308C was located in the dynein heavy chain region D6 of the dynein motor (Figure 3A). Mutations S2605L and N934S were classified as likely pathogenic, while the remaining mutations were categorized as variants of uncertain significance (VUS) (Table 1).
Mutation mapper plots of the most frequently mutated genes in nsPOI patients. The plots show the distribution of variants in DNAH5(A), LAMC1(B), HK3(C), HSD17B4(D), and PCDH11X(E). Amino acid changes are indicated above each lollipop, and line length represents the number of patients carrying the corresponding variant. Plots were generated using the Mutation Mapper tool on the cBioPortal platform (https://www.cbioportal.org/mutation_mapper).
Five mutation-mapping diagrams (A–E) show protein domains with nsPOI-associated variants. Panel A (DNAH5) displays S3774P, I3568T, T806I, A769V, S2605L, N1420D, L1339R, N934S, Q2949E, and Y4308C across dynein domains. Panel B (LAMC1) shows R1011H, Y1035S, A1239V, and A1335S, including variants in the laminin EGF domain. Panel C (HK3) presents C237R, I347T, Q600H, and P676S within hexokinase domains. Panel D (HSD17B4) includes I53M, C214S, R658H, and A741S, two in the SCP2 domain. Panel E (PCDH11X) shows T790S in the protocadherin domain and R1010I in the C-terminal region. Axes indicate protein length and mutation frequency.
Variants identified by targeted NGS in our POI cohort.
N. of patients
Gene
Type of mutation
AA change
gnomAD_NEF
EUR_AF
SIFT
PolyPhen
CADD
Mutation taster
Grantham
ACMG criteria
Classification
3
DNAH5
missense
S3774P
0.00013
NA
0
0.529
27.8
B
74
PP2, PP3, PM2, PM1
LP
2
DNAH5
missense
I3568T
0.00881
NA
0.23
0.001
21.9
B
89
PP2, PM2, PM1
VUS
1
DNAH5
missense
Q2949E
0.00315
0.002
0.08
0.002
21.4
D
29
PP2, PM2, PM1
VUS
1
DNAH5
missense
S2605L
NA
NA
0.08
0.028
23.7
D
145
PP2, PP3, PM6, PM1, PM2
LP
1
DNAH5
missense
N1420D
0.00041
0.0061
0.28
0.005
22.8
B
23
PP2, PM2, PM1, PM2
VUS
1
DNAH5
missense
L1339R
0.00019
0.002
0.6
0
18.98
B
102
PP2, PM2
VUS
1
DNAH5
missense
N934S
NA
NA
0.89
0
0.11
B
46
PP2, PM2
VUS
2
DNAH5
missense
T806I
0.01763
NA
0.28
0
15.24
B
58
PP2, PM2
VUS
1
DNAH5
missense
A769V
0.07048
NA
1
0
0.42
B
64
PP2, PM2
VUS
1
DNAH5
missense
Y4308C
0.09694
NA
0.05
0.03
18.84
B
194
PP2, BS1, BP4, BP6
LB
3
LAMC1
missense
R1011H
0.00078
0.001
0.07
1
27.1
D
29
PP2, PM5, PM2, PM1
LP
1
LAMC1
missense
Y1035S
NA
NA
0
1
28.4
D
144
PM2, PM5, PP2, PP3
LP
1
LAMC1
missense
A1239V
NA
NA
0.37
0.858
17.33
B
64
PM2, PP2
VUS
1
LAMC1
missense
A1335S
NA
NA
0.01
0.999
23.3
B
99
PP2, PP3, PM2, PM6
LP
1
HSD17B4
missense
I53M
NA
NA
NA
NA
5.95
B
10
PM6, PM1, PM2
LP
1
HSD17B4
missense
C214S
0.00004
NA
0.04
0.217
28.4
NA
112
PP3, PM2
VUS
1
HSD17B4
missense
R658H
NA
NA
0.03
0.984
23.6
B
103
PP3, PM1, PM2
VUS
1
HSD17B4
missense
A741S
NA
NA
0
0.752
26.5
B
99
PP3, PM6, PM1, PM2
LP
1
HK3
missense
P676S
0.00021
0.001
0.02
0.588
18.49
B
74
PP2, PP3, PM2
VUS
2
HK3
missense
Q600H
0.01082
0.008
0.15
0
19.02
B
24
PP2, PM2, PM1
VUS
1
HK3
missense
I347T
0.00011
NA
0
0.904
23.8
B
89
PP2, PP3, PM2, PM1
LP
1
HK3
missense
C237R
NA
NA
0
1
24.1
D
180
PM2, PM5, PP2, PP3
LP
3
PCDH11X
missense
T790S
0.03674
NA
0.08
0.035
5.332
B
32
PM2, PM1, PP2
VUS
1
PCDH11X
missense
R1010I
0.04897
NA
0
0.999
23.2
B
64
PM1, PM2, PP3, PP2
LP
2
AR
deletion
Q74_Q80del
NA
NA
NA
NA
13.48
D
NA
PM4, PM1, PM2
LP
1
AR
deletion
Q66_Q80del
NA
NA
NA
NA
13.48
D
NA
PM4, PM1, PM2
LP
1
NOTCH2
missense
I1693T
NA
NA
0.01
0.039
22.8
B
32
PM2, PP2
VUS
1
NOTCH2
missense
R318L
0.01764
NA
0.12
0.001
22.4
B
102
PM2, PM1, PP2
VUS
1
NOTCH2
missense
A147S
0.00885
NA
0.34
0.932
22.7
B
99
PM1, PM2, PP2, PP3
LP
1
ADAMTS19
missense
R64C
NA
NA
0
0.959
18.07
B
180
PM1, PM2, PP2, PP3
LP
1
ADAMTS19
missense
L117V
NA
NA
0.02
0
12.17
B
32
PM1, PM2
VUS
1
ADAMTS19
missense
G202S
0.00018
NA
0.35
0.005
13.87
B
56
PM1, PM2, BS1, BP4, BP6
LB
1
FANCM
missense
A48D
NA
NA
0.01
0.068
14.8
B
126
PM6, PM1, PM2
LP
1
FANCM
missense
L57F
0.00282
0.002
0.01
0.068
14.75
B
126
PM2, PM1
VUS
1
FANCM
missense
P1255L
NA
NA
0
0.078
na
B
22
PM6, PM1, PM2
LP
1
FANCC
missense
E273Q
0.01759
NA
0.01
0.993
25.3
B
29
PM2, PM1, PP3
VUS
1
FANCC
frameshift
I121Tfs*7
NA
NA
NA
NA
32
NA
NA
PVS1, PM2, PM1
P
2
GDF9
missense
R454C
0.0041
0.004
0
1
28.8
B
180
PS1, PM2, PM1, PP3
LP
1
SPIDR
nonsense
S64*
NA
NA
NA
NA
26.9
NA
NA
PVS1, PM2, PM1
P
1
SPIDR
missense
R294K
0.00051
0.0031
1
0.047
17.5
B
26
PM2, PM1
VUS
1
POU5F1
missense
T116S
NA
NA
0.11
0.062
6.86
B
58.0
PM2, PP2
VUS
1
POU5F1
missense
P65R
NA
NA
0.0
0.495
11.43
B
103.0
PM6, PM2, PP2
VUS
1
SF1
missense
G517A
0.01772
NA
0.02
0.998
24.8
B
60
PM2, PP2, PP3
VUS
1
SF1
deletion
G39_P47del
NA
NA
NA
NA
19.52
NA
NA
PM2, PM4, PM6, PP2
LP
1
SOHLH1
deletion
G323del
0.01191
NA
NA
NA
11.44
NA
NA
PM6, PM2, PM4
LP
1
AMHR2
missense
G153R
NA
NA
0
1
25.7
B
125
PM6, PM1, PM2, PP3
LP
1
TP63
missense
R487C
0.00019
NA
0
0.997
31
D
180
PM2, PM1, PM5, PP2
LP
1
NANOS3
duplication
G171dup
NA
NA
NA
NA
14.8
NA
NA
PM1, PM2, PM4
LP
1
MRPS22
missense
V23L
NA
NA
0.02
0.043
9.9
B
32
PM1, PM2, PM6
LP
1
FOXL2
deletion
A234del
0.00107
NA
NA
NA
16.23
NA
NA
PM1, PM2, PM4
LP
The table shows the most frequent variants identified in 60/100 patients in our cohort. For each variant the allele frequency in population databases (GnomAD, 1000 genomes_EUR), the pathogenicity prediction scores (Sift, Polyphen, Mutation Taster, CADD, Grantham) and the final classification according to ACMG criteria are reported. Abbreviations: GnomA NEF, Genome Aggregation Database Non-Finnish European; Eur AF, European population Allele Frequency; B, benign; D, deleterious; NA, not available; P, pathogenic; LP, likely pathogenic; VUS, variant of uncertain significance; LB, likely benign.
Six patients had 4 missense mutations (R1011H, Y1035S, A1239V, A1335S) in the protein encoded by the laminin subunit gamma 1 (LAMC1) gene. The R1011H mutation, which is localized in the laminin EGF domain (Figure 3B), was present in 3 patients. All mutations had a very low frequency or were not present in the public control databases, and their prediction scores supported potential pathogenicity at different levels (Table 1).
The gene encoding hexokinase 3 (HK3) was mutated in five patients carrying 4 missense mutations of the gene (C237R, I347T, Q600H, P676S). The mutations C237R and I347T are located in the hexokinase 2 domain, while the mutation Q600H is located in the hexokinase 1 domain and the mutation P676S is located in the junction domain between the hexokinase 1 and hexokinase 2 domains at the C-terminus (Figure 3C). In particular, C237R and I347T are likely pathogenic variants (Table 1).
Four patients had four different missense mutations of the HSD17B4 gene encoding 17-beta-hydroxysteroid dehydrogenase 4, which is involved in the peroxisomal beta-oxidation pathway for fatty acids. The I53M and C214S mutations were located in non-functional domains, while the R658H and A741S missense mutations were located in the sterol carrier protein 2 (SCP2) domain, which is responsible for the binding and transfer of sterols and phospholipids (Figure 3D). The mutations I53M and A741S were classified as likely pathogenic variants (Table 1). In our cohort, four patients had missense mutations in PCDH11X. The mutation T790S, located in the protocadherin domain, was found in three patients and R1010I, at the C-terminus of the protein, was present in one patient and was predicted likely pathogenic (Figure 3E, Table 1).
Variants in known POI-associated genes
We also identified mutations in known POI-related genes involved in folliculogenesis and ovarian function, many of which have been validated in mouse models (49).
Several X-linked genes are known contributors to POI pathogenesis (50, 51). In addition to PCDH11X, we identified Androgen receptor (AR) mutations in three patients. Two distinct deletions (Q66–80del and Q74–80del) within the poly-Q repeat at the N-terminus were classified as pathogenic (Figure 4A, Supplementary Figure S1).
Mutation mapper plots of genes with LP/P variants in nsPOI patients. The plots show the distribution of mutations in AR(A), ADAMTS19(B), FANCM(C), FANCC(D), GDF9(E), SPIDR(F), and SF1(G). Amino acid changes are indicated above each lollipop, and line length reflects the number of patients carrying the corresponding variant. Plots were generated using the Mutation Mapper tool on the cBioPortal platform (https://www.cbioportal.org/mutation_mapper).
Six mutation-mapping diagrams (A–G) display protein domains and nsPOI-associated variants. Panel A (AR) shows N-terminal poly-Q deletions Q74–80del and Q66–80del. Panel B (ADAMTS19) includes missense variants R64C, L117V, and G202S. Panel C (FANCM) shows A48D, L57F, and P1255L. Panel D (FANCC) presents the missense E273Q and the frameshift I121Tfs7. Panel E (GDF9) shows R454C. Panel F (SPIDR) contains the nonsense S64 and missense R294K within the DUF4502 domain. Panel G (SF1) displays G39_P47del in the helix-hairpin domain and G517A in a low-complexity region. Axes indicate protein length and mutation frequency.
Mutations were also found in ADAMTS1 and ADAMTS19, which encode metalloproteinases involved in ovarian extracellular matrix remodeling (52).
Three patients carried R64C, L117V, and G202S missense variants in ADAMTS19 (Figure 4B). The R64C mutation, located in the pro-domain crucial for enzyme folding and activity, was deemed likely pathogenic (Table 1). Additionally, six patients carried VUS in ADAMTS1, further discussed below.
In recent years, the Fanconi anemia complementation group (FANC) genes have also been implicated in POI (22, 23). Mutations were identified in FANCM (3%), FANCC (2%), and FANCG (1%) in our cohort. In FANCM, three missense mutations (A48D, L57F, P1255L) were found (Figure 4C, Table 1). Although not located in known domains, A48D and L57F were predicted likely pathogenic. FANCC mutations included a potentially pathogenic E273Q substitution and a frameshift mutation (I121Tfs*7) causing a premature stop codon (Figure 4D, Table 1). One patient carried the W122C mutation in the FANCG protein (Table 2). This substitution occurred at an interspecies conserved residue, which given the physicochemical distance between the W and C residues (Grantham score=215) should result in a severe disruption of the protein structure, although it was classified as VUS.
Mutations in genes carrying only VUS.
N. of patients
Gene
Type of mutation
AA change
gnomAD_NEF
EUR_AF
SIFT
PolyPhen
CADD
Mutation taster
Grantham
ACMG criteria
Classification
1
ADAMTS1
missense
A806V
0.01759
NA
0.03
0.581
21.8
B
64
PP3, PM2, PM1
VUS
3
ADAMTS1
missense
T732I
0.00012
NA
0.05
0.18
20.7
B
89
PM2, PM1
VUS
2
ADAMTS1
missense
T514A
0.00012
0.001
0.01
0
18.49
B
58
PM2
VUS
3
POF1B
missense
R315C
0.00074
0.0013
0
1
31
B
180
PM2, PM1
VUS
1
BNC1
missense
G661R
NA
NA
0.31
0.007
14.9
B
125
PM2, PP2
VUS
1
BNC1
missense
E209K
0.02638
NA
0
1
29.3
D
56
PM2, PM5, PP2
VUS
1
CDKN1B
missense
S7C
NA
NA
0.0
1.0
28.1
B
112
PM2, PP3
VUS
1
CDKN1B
missense
P117S
0.06262
NA
0.02
0.167
18.56
D
74
PM2, PS3
VUS
1
WT1
missense
S325L
0.008814
NA
0
0.981
32
NA
145
PM2, PM1, PP3
VUS
1
WT1
missense
G37S
0.0000398
NA
0.01
0.008
15.6
B
56
PM2, PM1
VUS
1
MSH4
missense
S10L
NA
NA
0.31
0
11.6
B
145
PM2
VUS
1
MSH4
missense
L35I
0.0003181
0.002
0.03
0.041
8.8
B
5
PM2
VUS
1
WDR62
missense
S275L
0.07042
NA
0
1
29.5
B
145
PM2, PP3
VUS
1
WDR62
missense
Y336C
0.03519
NA
0.29
1
23.6
B
194
PM2, PP3
VUS
2
BRSK1
missense
P764A
0.005974
0.005
0.22
0
15.03
B
27
PM2, PP2
VUS
1
FANCG
missense
W122C
0.0001589
NA
0
1
26.9
B
215
PM2, PM1, PP3
VUS
1
SOHLH2
missense
S417L
0.000203
0.001
0
0.947
23
B
145
PM2, PM1, PP3
VUS
1
SYCE1
missense
R59Q
0.000229
NA
0.01
1
26.6
B
43
PM2, PM1
VUS
1
AGTR2
missense
G21V
0.004099
0.0026
na
na
8.7
B
109
PM2
VUS
1
FOXO1
missense
V564M
0.008816
NA
0.46
0.296
22.6
B
21
PM2
VUS
1
FOXO3
missense
A140S
0.0001551
NA
0.7
0.052
15.1
B
99
PM2, PM1
VUS
1
BBS9
missense
T865A
0.0005225
0.002
0.06
0.0
15.34
B
58
PM2, BP4
VUS
1
DUSP22
missense
A171T
0.02637
NA
0.85
0.0
11.68
B
58.0
BP4
VUS
1
LARS2
missense
S315L
0.01762
NA
0.64
0.002
20.5
B
145.0
PM2, PM1
VUS
1
NUPR1
missense
R74H
0.0000176
NA
0.18
0.76
23.4
B
29.0
PM2, PM, PP3
VUS
1
POLR3H
missense
V99I
0.02638
NA
0.02
0.544
32
D
29.0
PM2, PP3, PM1
VUS
1
SALL4
missense
G251R
0.008865
NA
0.2
0.661
8.9
B
125.0
PM2, PP2
VUS
1
DMC1
missense
A76T
NA
NA
0.04
0.104
24.6
D
58
PP3, PM2, PM1
VUS
FOR each variant the allele frequency in population databases (GnomAD, 1000 genomes_EUR), the pathogenicity prediction scores (Sift, Polyphen, Mutation Taster, CADD, Grantham) and the final classification according to ACMG criteria are reported. Abbreviations: GnomA NEF, Genome Aggregation Database non-Finnish European; Eur AF, European population Allele Frequency; B, benign; D, deleterious; NA, not available; VUS, variant of uncertain significance.
The gene encoding growth differentiation factor 9 (GDF9), crucial for folliculogenesis and oocyte development, was mutated (R454C) in two patients. The R454C mutation occurs at a conserved amino acid position and is likely to have functionally detrimental effects (Figure 4E, Table 1).
Two patients carried mutations in the Scaffolding Protein Involved in DNA Repair (SPIDR) gene, which encodes a scaffold protein involved in HR repair. Both a nonsense mutation (S64*) and a missense mutation (R294K) occurred in the DUF4502 domain, the role of which is not yet fully understood (Figure 4F, Table 1). The mutation S64* introduces a premature stop codon near the N-terminus and was classified as highly pathogenic.
Two mutations were found in the splicing factor 1 (SF1) gene (Figure 4G, Table 1). A deletion G39_P47del, in the helix-hairpin domain at the N-terminus of SF1 (53) was classified as LP. This helix-hairpin domain is required for cooperative recognition of 3’ splice sites by stabilizing a unique quaternary arrangement of the SF1-U2AF65 RNA complex during assembly of the spliceosome (54). In contrast, G517A is categorized as VUS in the low complexity region.
Finally, we identified likely pathogenic variants of SOHLH1, AMHR2, TP63, NANOS3, MRPS22, FOXL2 and DMC1 genes in one patient each. These mutations were absent or novel in public databases and had consistently high pathogenicity scores across prediction tools.
Of particular note, we identified a novel Gly323 deletion in SOHLH1, within a low-complexity region involved in folliculogenesis, where other deleterious missense variants have been described (55).
In addition, one patient carries a mutation of the TP63 gene, which codes for a transcription factor of the p53 family. TP63 is expressed in primordial and primary follicles and preserve the germ line integrity (56). The R487C mutation is located in the linker domain but predicted as potentially deleterious (57, 58).
Variants of uncertain significance in functionally relevant genes
Our analysis also identified additional 20 genes that carried only VUS. As previously described, six patients harbored three missense mutations (T514A, T732I, A806V) in another member of the ADAMTS family, ADAMTS1 (Table 2). Two patients carried the T514A mutation, located in a linker domain (Figure 5A). The T732I variants, present in 3 patients, and the A806V were both located within the ADAM spacer domain (Figure 5A). Notably, T732I had the highest Grantham score among the three, indicating that the substitution of threonine—a small, polar, hydrophilic amino acid—with isoleucine—a large, non-polar, hydrophobic residue—could significantly alter protein structure.
Mutation mapper plots of genes with VUS variants in nsPOI patients. The plots show the distribution of mutations in ADAMTS1 (A), BNC1 (B), CDKN1B (C), WT1 (D), and WDR62 (E). Amino acid changes are indicated above each lollipop, and line length corresponds to the number of patients carrying the respective variant. Plots were generated using the Mutation Mapper tool on the cBioPortal platform (https://www.cbioportal.org/mutation_mapper). CDKN1B and WDR62 plots were modified with Biorender.
Five mutation-mapping diagrams (A–E) present protein domains and nsPOI-associated VUS. Panel A (ADAMTS1) shows missense variants T514A, T732I, and A806V, located in linker or spacer domains. Panel B (BNC1) displays E209K and G661R within linker regions of the zinc-finger protein. Panel C (CDKN1B) highlights S7C in the Neurogenin-2 binding domain and P117S in the Jab1/CSN5 interaction domain. Panel D (WT1) includes G37S in the N-terminal DNA-binding region and S325L in a zinc-finger motif. Panel E (WDR62) shows S275L and Y336C in the first WD40 domain. Axes indicate protein length (x-axis) and mutation frequency (y-axis).
Three patients carried the same variant (R315C) in the POF1B gene, which has been previously associated with POI (59–61). Although classified as VUS, this variant was predicted to be deleterious by four out of five in silico algorithms (Table 2).
The BNC1 gene, encoding the zinc finger protein Basonuclin 1, is highly expressed in ovarian germ cells and plays a role in transcriptional regulation (62, 63). We identified two missense mutations (E209K and G661R) in the linker domain of the BNC1 protein (Figure 5B, Table 2). The E209K mutation was predicted to be deleterious by the SIFT, PolyPhen, CADD and MutationTaster algorithms, likely due to the change in charge affecting structural stability and DNA binding capacity.
In the CDKN1B gene, which encodes the cell cycle inhibitor p27^Kip1, two missense variants were identified: S7C, located in the Neurogenin-2 binding domain at the N-terminus (64), and P117S which replaces a highly conserved proline within the Jab1/CSN5 binding domain (Figure 5C).
In the WT1 gene, which encodes the WT1 transcription factor, we found two missense mutations (Figure 5D). The G37S mutation was located at the N-terminus, which is characterized by a proline- and glutamine-rich DNA-binding domain, while the S325L mutation was located in one of the four zinc finger motifs at the C-terminus (ZnF_CH2H2 domain). Several variants have been described for this gene, including G37S and S325L, both of which are listed with very low frequency in the gnomAD database.
Two patients carried two different mutations, S10L and L35I, in the N-terminus of the protein encoded by the MutS homolog 4 (MSH4) gene, which is involved in the mismatch repair process.
We found two mutations in the gene encoding the WD repeat-containing protein 62 (Figure 5E). The mutations S275L and Y336C were located in the first WD40 domain of MABP1/WDR62 (65) of the protein, which is involved in the interaction with Aurora A and binding to microtubules at the spindle pole (66).
In addition, we found VUS mutations in the genes SYCE1, AGTR2, FOXO1, FOXO3, BBS9, DUSP22, LARS2, NUPR1, POLR3H, SALL4 in one patient each. See Table 2 and Supplementary File S5.
Patients with monogenic or polygenic variants
Among the 60 patients with genetic variants, we found that 37 had alterations in a single gene (monogenic patients), while 23 carried variants in two or more genes (polygenic patients) (Figure 6A, Supplementary File S7). Of the 37 monogenic patients, 27 (73%) carried VUS, while 10 (27%) had likely pathogenic mutations (Table 3). One patient carried the truncating pathogenic variant, which occurs in the SPIDR gene.
Co-occurrence of mutations in the nsPOI cohort. (A) The graph shows the percentage of POI patients in whom one or more genes are simultaneously mutated. (B) Stacked bar plot reports the number of mutated genes (y-axis) per patients (x-axis). Gene names are displayed in the bars.
Diagram A shows a pie chart divided into monogenic (37%) and polygenic (23%) components. The polygenic section is further divided into digenic (15%), trigenic (6%), and quadrigenic (2%) parts. Diagram B presents a bar chart illustrating the number of mutated genes across different patients, each labeled with specific gene mutations.
The table lists the 37 patients that could be explained by monogenic variants.
Patient_ID
Gene
Chr
Type of mutation
AA change
Classification
N52
DNAH5
5
Missense
S3774P
LP
DNAH5
5
Missense
I3568T
VUS
N57
DNAH5
5
Missense
Q2949E
VUS
NPMA_4
DNAH5
5
Missense
N1420D
VUS
N63
DNAH5
5
Missense
L1339R
VUS
N13
HK3
5
Missense
P676S
VUS
N120
HK3
5
Missense
Q600H
VUS
NPMA_11
HK3
5
Missense
Q600H
VUS
N103
HK3
5
Missense
I347T
LP
NPMA_7
HK3
5
Missense
C237R
LP
N73
NOTCH2
1
Missense
I1693T
VUS
N82_v1
NOTCH2
1
Missense
R318L
VUS
NPMA_8
NOTCH2
1
Missense
A147S
LP
N54
LAMC1
1
Missense
R1011H
LP
NPMA_3
LAMC1
1
Missense
R1011H
LP
N117
MSH4
1
Missense
S10L
VUS
N102
MSH4
1
Missense
L35I
VUS
NPMA_12
PCDH11X
X
Missense
T790S
VUS
NPMA29
PCDH11X
X
Missense
T790S
VUS
N58
POU5F1
6
Missense
T116S
VUS
N2
POU5F1
6
Missense
P65R
VUS
N118
BNC1
15
Missense
G661R
VUS
N62
BNC1
15
Missense
E209K
VUS
NPMA14
HSD17B4
5
Missense
R658H
VUS
N105
ADAMTS1
21
Missense
T732I
VUS
N104
AMHR2
12
Missense
G153R
LP
NPMA17
AR
X
In_Frame_Del
Q74_Q80del
LP
N113
FANCG
9
Missense
W122C
VUS
NPMA_6
FANCM
14
Missense
L57F
VUS
NPMA18
BRSK1
19
Missense
P764A
VUS
N91
CDKN1B
12
Missense
P117S
VUS
NPMA25
DMC1
22
Missense
A76T
VUS
N15
DUSP22
6
Missense
A171T
VUS
N70
MRPS22
3
Missense
V23L
LP
N78
SPIDR
8
Nonsense
S64*
P
N92
SYCE1
10
Missense
R59Q
VUS
NPMA_49
WDR62
19
Missense
Y336C
VUS
N109
WT1
11
Missense
G37S
VUS
P, pathogenic; LP, likely pathogenic; VUS, variant of uncertain significance.
Genes exclusively mutated in monogenic patients included HK3, NOTCH2, MSH4, BNC1, POU5F1, DUSP22, FANCG, MRPS22, and SYCE1 (Table 3). Four of the 12 patients with DNAH5 mutations had only a VUS mutation of the gene.
Among the polygenic cases, 15 patients had mutations in two genes (15%), 6 patients had mutations in three genes (6%) and only 2 patients had mutations in four genes (2%) (Figure 6B, Supplementary File S7). Of particular interest, two patients shared the same combination of mutations in the ADAMTS1 (T732I) and GDF9 (R454C) proteins, while two other patients had concurrent but different mutations in the ADAMTS1 and DNAH5 proteins: ADAMTS1-A806V and DNAH5-S2605L; ADAMTS1-T514A and DNAH5-T806I.
POI genes subjected to CNVs
All POI patients were also screened for germline CNVs using amplicon-based NGS data generated with Ion Reporter software. A pooled DNA sample from patients in the control group served as reference baseline. Based on the MAPD values, 99 out of 100 patients were eligible for CNV analysis (see Materials and Methods). CNVs were detected in 11% of patients, involving SYCE1, DUSP22, and INHBB genes (Table 4).
Chromosome regions with CNVs identified in POI cohort.
Patient
Region
Gene
Type of CNV
CNV length (bp)
Co-occurring mutated genes
Amino acid change
CADD_SV Phred score
StrVCTVRE
X-CNV(MVP)
N38
10q26.3
SYCE1
gain
11349
3.558
0.351
0.187
N58
10q26.3
SYCE1
gain
11349
POU5F1
T116S
N75
10q26.3
SYCE1
gain
11349
PCDH11X
T790S
SALL4
G251R
N85
10q26.3
SYCE1
gain
11349
AR
Q66_Q80del
FOXL2
A234del
NPMA40
10q26.3
SYCE1
gain
11349
LAMC1
A1335S
AR
Q74_Q80del
FOXO3
A140S
N47
6p25.3
DUSP22
loss
58438
11.06
0.354
0.227
N57
6p25.3
DUSP22
gain
58438
DNAH5
Q2949E
13.79
0.264
0.154
N90
6p25.3
DUSP22
gain
58438
NPMA_3
6p25.3
DUSP22
gain
58438
LAMC1
R1011H
NPMA_7
6p25.3
DUSP22
gain
58438
HK3
C237R
N61
2q14.2
INHBB
loss
3890
13.86
0.671
0.700
The table shows CNVs and co-occurred mutations in genes previously associated to POI. For each CNV chromosome region, type of CNV, genomic coordinates CNV length, gene, CADD SV Phred, StrVCTVRE, X-CNV(MVP), co-occurred mutated genes and their amino acid changes scores are indicated.
Five patients had CN amplifications of the SYCE1 gene on chromosome 10q26.3 and all were identified as focal (<3Mb). Four of the five patients with copy number gains also had missense mutations and deletions in other genes (Table 4). Notably, one patient with SYCE1 CN gains also had LP variants in AR (Q66_Q80del) and FOXL2 (A234del) (Table 4), while the other had LP mutations of LAMC1 (A1335S) and AR (Q74_Q80del) and a VUS (A140S) in the FOXO3 gene.
Five patients had focal CNVs of the DUSP22 gene on chromosome 6p25. Four patients had CN gains of the gene, while one patient had a copy number loss of the gene. Three of the four patients with copy number gain also had a pathogenic mutation each in DNAH5 (Q2949E), LAMC1 (R1011H) and HK3 (C237R) (Table 3).
We also found a copy number loss of the INHBB gene on chromosome 2q14.2 in a patient who had no mutations in other genes (Table 3).
For pathogenetic classification of the identified CNV, we used the StrVCTVRE, MVP and CADD_SV_Phred scores (see Material and Methods for more details). According to the scores obtained, the microdeletion found in the INHBB gene was classified as likely pathogenic. Supplementary Figure S2 shows representative Q-PCR analysis of CNVs in SYCE1, DUSP22 and INHBB genes.
In the present study, by integrating sequencing data with a panel of 72 POI-related genes, we identified 78 rare variants distributed across 41 genes, which were absent in 200 matched controls. Variants were classified following ACMG guidelines, resulting in a diagnostic yield of 60%, with 23% of patients carrying pathogenic or likely pathogenic (LP/P) variants and 37% harboring only variants of uncertain significance (VUS).
Our findings further support the notion that nsPOI is genetically heterogeneous disorder, with both monogenic and polygenic contributions. Notably, DNAH5 emerged as the most frequently mutated gene (12%), with several variants located in conserved motor domains.
DNAH5 encodes a microtubule-associated motor protein involved in ciliary motility. Mutations in DNAH5 have previously been linked to primary ciliary dyskinesia (PCD) (67), as well as non-syndromic asthenozoospermia and hypospadias (68, 69). Aboura and colleagues identified chromosomal amplifications at 5p14.3, the locus of DNAH5, in a POI cohort. To our knowledge, this is the first report describing DNAH5 mutations in nsPOI patients.
We also identified mutations in the gene encoding the laminin subunit gamma-1 (LAMC1). LAMC1 is known to interact with other laminin family proteins in the extracellular matrix to promote ovarian follicle development (70, 71). Overexpression of LAMC1 is involved in the progression of gynecologic cancers (72, 73) and predicts poor prognosis in gastric and esophageal cancers (74–77). In contrast, only one missense mutation (78) and one SNP haplotype (79) have been associated with increased POI risk without functional characterization. In this work, we identified four novel missense mutations (R1011H, Y1035S, A1239V, A1335S) in LAMC1 that are very rare or absent in the general population. In particular, the mutation R1011H, which is localized in the EGF_like domain and occurred in three different patients, was classified as likely pathogenic.
Our data also highlight the relevance of the ADAMTS family in ovarian function (80, 81). We detected pathogenic or VUS variants in both ADAMTS1 and ADAMTS19, proteases known to regulate folliculogenesis and ovulation via extracellular matrix remodeling (82–84).
The role of ADAMTS1 and ADAMTS19 in reproductive function has been demonstrated using mouse models: ADAMTS1 homozygous knock-out mice had fewer ovarian follicles (85), while the protease ADAMTS19 is overexpressed in the gonads of female mice (86). Regarding the role of ADAMTS19 in nsPOI, so far only SNPs in intronic regions of ADAMTS19 have been potentially associated with POI in both Caucasian (87) and Asian populations (79). In our study, we described three novel mutations in ADAMTS1 (A806V, T732I and T514A) found in six patients. In particular, the mutation T732I, which occurred in three patients, is located in the spacer domain, which is necessary for association with ECM components and regulation of enzyme activity (88). In addition, we identified three missense mutations (R64C, L117V, G202S) of ADAMTS19, of which the R64C mutation was classified as likely pathogenic according to the ACMG criteria.
The HSD17B4 gene, also known as D-bifunctional protein (DBP), is a bifunctional enzyme involved in the conversion of androstenedione to testosterone and estrone to estradiol. In this study, we found four different missense mutations (I53M, C214S, R658H and A741S) in HSD17B4 gene in 4 different patients. Studies on Perrault syndrome (89) provided evidence of the importance of HSD174B for healthy ovarian function. In addition, Puyan et al. found a haplotype and two missense SNPs in the HSD17B4 gene associated with susceptibility to POF in a genetic case-control association study (79).
Remarkably, 31% of mutated genes in our study were involved in DNA repair and meiosis, pathways essential for oocyte integrity. We found mutations in three different genes of the FANC family, FANCM, FANCC and FANCG, whose role in repair during HR in meiosis has been recently investigated (90). Our results support the observations that defects in the FANC genes can impair normal oogenesis. In our cohort, 3 of the five mutations identified in the FANCM and FANCC genes were classified as LP or P (Table 2). In addition, we found mutations in the gene MSH4, which is required for optimal reciprocal recombination and appropriate segregation of homologous chromosomes during meiosis I (91). Mutations of this gene have recently been associated with failure of gametogenesis in both sexes (92). A causal role may also be attributed to mutations in the homologous recombination repair gene SPIDR gene, whose alteration has been associated with gonadal disgenesia (93) and ovarian failure (23).
We have also identified novel variants in genes previously associated with POI such as PCDH11X, AR, TP63, BNC1, WT1, CDKN1B, SYCE1 as well as previously described mutations such as R454C in GDF9, which is considered one of the causative alterations in POI (94). It is noteworthy that, to our knowledge, no POI-associated missense mutations for PCDH11X have been reported to date, only CNV (52). In addition, three patients had two different deletions in the poly-Q region of the AR gene. Previous reports have described (95–97) only point mutations of the gene encoding the AR in POI patients, while short poly-Q polymorphisms have been associated with poorer prognosis only in endometrial cancer (98).
Recent data have identified TP63 mutations in syndromic and non-syndromic POI, associated with oocyte apoptosis and early ovarian reserve depletion (17, 61, 99). The R487C mutation identified in our study has previously been linked to colorectal cancer (57), low-grade gliomas (58), and more recently, to pyroptosis-related gene networks (58, 100, 101). This supports a possible role for p63 as a high-risk biomarker in cancer and reproductive disorders.
Interestingly, the P117S mutation in the cell cycle inhibitor CDKN1B has been associated with multiple endocrine neoplasia type IV (MEN4) (102, 103). This mutation is located in the domain of binding to Jab1/CSN5, which promotes the translocation of p27 from the nucleus to the cytoplasm, thereby favoring cell proliferation (104).
POI-associated genes include the Synaptonemal Complex Central Element 1 (SYCE1) gene which encodes a member of the synaptonemal complex that links homologous chromosomes during prophase I of meiosis. Allelic variants of this gene have been associated with premature ovarian failure (105) and spermatogenic failure. We found a VUS in this gene (R59K) that was reported as a somatic mutation in a patient with malignant skin cancer (106).
Of particular interest, several variants were shared among multiple patients, and distinct mutations in the same gene were observed across the cohort. In 37 patients, POI could be attributed to monogenic variants, while in 23, mutations in two or more genes suggested a polygenic etiology. We found that two patients had a specific combination of mutations in the ADAMTS1 (T732I) and GDF9 (R454C) genes. Two patients carried both DNAH5 variants, S3774P and I3568T. As parental DNA was unavailable, phasing could not be established, and compound heterozygosity for an autosomal recessive mechanism remains unconfirmed. Segregation studies in relatives and analysis of a larger cohort are planned to assess the contribution of this co-mutation to nsPOI.
CNV analysis identified alterations in SYCE1, DUSP22, and INHBB. Of these, only the INHBB CNV was classified as likely pathogenic by prediction tools. Although SNV mutations in the INHBB gene were reported in a previous study (107), no evidence of POI-causing CNV in this gene has been described to date. INHBB encodes the βB subunit of inhibin/activin dimers which regulate granulosa cell proliferation and folliculogenesis (108, 109). Loss of INHBB function may impair activin-mediated signaling, leading to defective follicular maturation and contributing to premature ovarian failure. We speculate that CNVs involving DUSP22 may dysregulate JNK signaling (110), thereby promoting apoptosis during follicle maturation. Collectively, these findings suggest that CNVs may play a significant role in the pathogenesis of nsPOI. From a clinical perspective, identification of pathogenic variants—including both SNVs and CNVs—offers opportunities for translation into patient care. Preimplantation genetic testing (PGT) could help prevent transmission of deleterious variants in families with a history of POI. Moreover, women at increased risk could be counseled regarding early fertility preservation strategies, such as oocyte cryopreservation. As the disrupted signaling pathways become more clearly defined, these insights may also pave the way for targeted therapeutic approaches, including pharmacological modulation and, in the future, gene-based interventions.
In summary, we employed a customized targeted NGS panel to analyze DNA from 100 POI patients and 200 controls. The approach yielded a diagnostic rate of 60%, implicating 42 genes. These findings provide new insights into the complex genetic architecture of POI.
We acknowledge that the pathogenic potential of many variants identified requires confirmation through functional validation in cell and animal models. Several VUS in our cohort appear to be strong candidates for reclassification as “likely pathogenic.” Consistent with recent reports, a substantial proportion of VUS are ultimately reclassified, although the average time to reclassification is approximately 2–3 years, underscoring the need for periodic reanalysis (111–113).
Notably, variants such as BNC1 E209K, CDKN1B P117S, WT1 S325L, WDR62 S275L, FANCG W122C, SOHLH2 S147L, and SYCE R59K showed high deleteriousness scores, were located in conserved domains, and were absent or extremely rare in population databases. However, additional functional or segregation evidence will be required to support their reinterpretation.
Expanding the cohort size may further improve sensitivity and strengthen genetic associations. We are acknowledge the absence of segregation testing in family members, which could have clarified the significance of several VUS. This was primarily due to budgetary constraints, as the project was supported by a ministerial grant that covered only affected patients and did not extend to relatives. Despite these limitations, the integration of clinical, hormonal, and ultrasonographic data (via the OvAge algorithm) with targeted NGS represents a promising strategy for early POI diagnosis and personalized management. While available treatments can assist with fertility, the irreversible depletion of ovarian reserve in POI remains incurable. As affected women are also at increased risk for comorbidities that reduce life expectancy, early identification of genetic risk could improve long-term outcomes and quality of life.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.
Ethics statement
The studies involving humans were approved by Comitato Etico Centrale Calabria. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2025.1659701/full#supplementary-material
Sanger sequencing analysis. Sanger sequencing was used to confirm the indicated mutations of DNAH5(A), GDF9(B), LAMC1(C, D), and AR(E) genes.
Q-RT-PCR analysis. The graphs show the CN gains of SYCE1(A) and DUSP22(B) and the CN losses of INHBB(C) in each of the indicated patients. Values are expressed as CN ratios compared to a pool of control samples arbitrarily set as a reference for the diploid state (CN = 2). Statistical significance was calculated in relation to the pool of control samples: *p<0.05, **p<0.01.
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Edited by: Richard Ivell, University of Nottingham, United Kingdom
Reviewed by: Vidhu Dhawan, ESIC Medical College (Faridabad), India