Skip to main content
NCBI home page
Frontiers in Genetics logoLink to Frontiers in Genetics
. 2026 Oct 16;17:1947419. doi: 10.3389/fgene.2026.1947419

Clinically relevant chromosomal abnormalities and chromosomal polymorphisms in 7,530 patients undergoing reproductive genetic counseling: a retrospective cytogenetic study in a Chinese Han population

Songhang Wu 1,2, Hongyan Shang 1,2, Wenhua Wang 1,2, Dan Chen 1,2, Wennan Wu 1,2,*
PMCID: PMC13720713  PMID: 42964492

Abstract

Background

Chromosomal aberrations are important causes of reproductive disorders, but their clinical interpretation is often complicated by the presence of benign chromosomal polymorphisms that may obscure the true diagnostic yield of karyotyping. The aim of this study was to evaluate peripheral blood karyotyping in patients undergoing reproductive genetic counseling and to distinguish clinically relevant chromosomal abnormalities from chromosomal polymorphisms.

Methods

A total of 7,530 patients who visited the reproductive center and the obstetrics and gynecology genetic clinic of the First Affiliated Hospital of Fujian Medical University from January 2016 to December 2022 were included. Peripheral blood karyotyping was performed using lymphocyte culture, routine chromosome preparation, and G-banding. Cytogenetic findings were classified into clinically relevant chromosomal abnormalities and chromosomal polymorphisms. Chromosomal polymorphisms were analyzed separately and were not counted as the primary diagnostic outcome. Correspondence analysis was performed using an observed-count matrix linking major clinical manifestations with representative karyotypic findings.

Results

A total of 552 abnormal cytogenetic findings were detected, corresponding to an overall cytogenetic finding rate of 7.33% (552/7,530). Among them, 246 cases were classified as clinically relevant chromosomal abnormalities, giving a primary diagnostic rate of 3.27% (246/7,530), whereas 306 cases were chromosomal polymorphisms, accounting for 4.06% of the total cohort and 55.43% of all abnormal cytogenetic findings. In correspondence analysis, the first two dimensions explained 77.9% and 15.2% of the total inertia, respectively. The global chi-square test indicated a non-random association between clinical manifestations and representative karyotypic findings (chi-square = 69.92, df = 16, P < 0.001).

Conclusion

Peripheral blood karyotyping remains a useful first-line cytogenetic test in reproductive genetic counseling. Clinically relevant chromosomal abnormalities and chromosomal polymorphisms should be reported and interpreted separately. Phenotype-guided interpretation may improve the clinical value of karyotyping and help identify patients who require additional molecular confirmation or multidisciplinary counseling.

Keywords: chromosomal abnormalities, chromosomal polymorphism, correspondence analysis, genetic counseling, karyotype, reproductive disorder

1. Introduction

Reproductive disorders are multifactorial conditions. Uterine structural abnormalities, endocrine imbalances, thrombophilia, environmental factors, and genetic factors may contribute to infertility, recurrent miscarriage, and repeated implantation failure (Crain et al., 2008; Ho et al., 2017). The global prevalence of infertility has been estimated at 8%–12% among couples of reproductive age, and population-based data from China have reported a substantial burden of infertility (Vander Borght and Wyns, 2018; Zhou et al., 2018).

Chromosomal aberrations are important causes of reproductive disorders. Patients with infertility, severe spermatogenic impairment, recurrent miscarriage, or developmental abnormalities of the reproductive system may have a higher likelihood of clinically relevant karyotypic findings than unselected populations (Caseiro et al., 2015; Guerri et al., 2019; Kuroda et al., 2020; Gunes and Esteves, 2021). Peripheral blood karyotyping remains a first-line cytogenetic method for detecting numerical abnormalities, balanced structural rearrangements, sex chromosome abnormalities, and other large-scale chromosomal changes (Gardner et al., 2012; Gonzales et al., 2016).

However, interpretation of karyotype results in reproductive genetic counseling is not always straightforward. Chromosomal polymorphisms, including inv(9), qh ± variants, and D/G group heteromorphisms, are commonly reported in cytogenetic laboratories, but their clinical significance remains controversial. Mixing these variants with clinically relevant abnormalities may overestimate the diagnostic yield of karyotyping. Therefore, this study separately analyzed clinically relevant chromosomal abnormalities and chromosomal polymorphisms in 7,530 patients undergoing reproductive genetic counseling, and explored phenotype-karyotype relationships using correspondence analysis.

2. Methods

2.1. Subjects

This was a retrospective observational study. A total of 7,530 reproductive genetic counseling patients who visited the reproductive center and the obstetrics and gynecology genetic clinic of the First Affiliated Hospital of Fujian Medical University from January 2016 to December 2022 were included in the final analysis. Patients were included if they were referred for infertility, severe spermatogenic impairment, menstrual or endocrine abnormalities, reproductive organ developmental abnormalities, adverse pregnancy outcomes, or reproductive genetic counseling. Patients with incomplete essential clinical or karyotype information, or a recorded history of radiation exposure, were excluded.

Clinical indications were assigned according to the referral diagnosis documented in the electronic medical record. Infertility was defined by the clinical referral diagnosis recorded by the reproductive or obstetrics and gynecology clinic. Oligoasthenospermia/azoospermia was based on the referring diagnosis supported by semen analysis. Hormonal abnormalities included recorded abnormalities in reproductive endocrine evaluation, including abnormalities involving follicle-stimulating hormone, luteinizing hormone, estradiol, progesterone, testosterone, prolactin, or other sex hormone results. Reproductive organ developmental abnormalities included congenital or clinically suspected developmental abnormalities of reproductive organs. Adverse pregnancy outcomes included recurrent miscarriage, embryo arrest, stillbirth, fetal abnormalities, or prior offspring with birth defects as recorded in the referral diagnosis. Genetic counseling indications referred to patients or couples seeking reproductive genetic risk assessment without a more specific categorized indication. The study was approved by the Ethics Committee of the First Affiliated Hospital of Fujian Medical University (Approval No. MTCA, ECFAH of FMU [2015]084-3). The study was conducted in accordance with the Declaration of Helsinki and applicable institutional and national ethical requirements. The participants provided written informed consent to participate in the study.

2.2. G-banding

Peripheral blood (3 mL) was collected into a sodium heparin anticoagulant tube under sterile conditions. Within 1 h, 0.5 mL of blood was inoculated into lymphocyte culture medium and incubated at 37 °C in a humidified atmosphere containing 5% CO2. After 72 h, colchicine was added at a final concentration of 20 μg/mL, and cells were harvested after an additional 2 h. Standard hypotonic treatment, fixation, slide preparation, and G-banding were performed. At least 20 metaphase cells were counted, and at least five metaphases were analyzed for each patient. For suspected mosaicism or confirmed abnormal karyotypes, the analysis was extended to a total of at least 30 metaphases when required, rather than the routine 20 metaphase count for normal samples. The analyzed chromosome resolution was greater than 320 bands. Karyotypes were described according to the International System for Human Cytogenomic Nomenclature (ISCN 2016) (McGowan-Jordan et al., 2016). Karyotype images were captured and analyzed using the Leica GSL120 automated chromosome scanning and image analysis system (Leica Microsystems, Germany).

2.3. Classification of cytogenetic findings

To avoid overestimation of diagnostic yield, cytogenetic findings were classified into two categories before analysis. Clinically relevant chromosomal abnormalities included autosomal numerical abnormalities, sex chromosome numerical abnormalities, autosomal structural abnormalities, sex chromosome structural abnormalities, sex reversal or disorders of sex development-related karyotypes, marker chromosomes, and other abnormalities with potential reproductive implications. Chromosomal polymorphisms included inv(9), 1/9/16qh ± variants, D/G group variants, inv(Y), Yqh±, and other heterochromatic variants. Chromosomal polymorphisms were analyzed separately because their clinical significance remains controversial and they were not counted as the primary clinically relevant diagnostic outcome.

2.4. Statistical analysis

Descriptive statistics were used to summarize the distribution of cytogenetic findings. The overall cytogenetic finding rate was calculated as total abnormal cytogenetic findings divided by all tested patients. The primary diagnostic rate was calculated as clinically relevant chromosomal abnormalities divided by all tested patients. Chromosomal polymorphisms were reported separately.

Correspondence analysis was performed in Python (version 3.11) using a singular value decomposition of the standardized residual matrix. The input matrix was constructed from observed counts linking major clinical manifestations to representative karyotypic categories. Each cell represented the number of patients with a specified clinical manifestation and karyotypic finding. Row and column principal coordinates were calculated and visualized in a two-dimensional biplot using matplotlib. Dimension-level inertia was calculated to quantify the proportion of the total association represented by each dimension. A chi-square test of independence was used to assess the global association between row and column categories. A P value less than 0.05 was considered statistically significant.

3. Results

3.1. Demographic characteristics and overall cytogenetic findings

The study included 7,530 patients aged 18–55 years, including 3,903 males and 3,627 females. Male patients were older than female patients [male vs. female: 31.32 ± 5.09 years vs. 29.65 ± 4.68 years, P < 0.001].

A total of 552 abnormal cytogenetic findings were detected, corresponding to an overall cytogenetic finding rate of 7.33% (552/7,530). After classification, 246 cases were clinically relevant chromosomal abnormalities, corresponding to a primary diagnostic rate of 3.27% (246/7,530). Chromosomal polymorphisms were identified in 306 cases, accounting for 4.06% of the total cohort and 55.43% of all abnormal cytogenetic findings. The overall classification is shown in Table 1.

TABLE 1.

Overall classification of cytogenetic findings in the cohort.

Category Included findings Cases Detection rate in total cohort (%) Proportion among abnormal cytogenetic findings (%) Interpretation
Clinically relevant chromosomal abnormalities Numerical and structural abnormalities of autosomes and sex chromosomes, sex reversal/DSD-related karyotypes, marker chromosomes, and others a 246 3.27 44.57 Primary diagnostic outcome; interpreted according to phenotype and reproductive history
Chromosomal polymorphisms inv(9), 1/9/16qh±, D/G group variants, inv(Y), Yqh±, and other heterochromatic variants 306 4.06 55.43 Analyzed separately because clinical significance remains controversial; not counted as the primary diagnostic outcome
Total abnormal cytogenetic findings All findings above 552 7.33 100.00 Overall cytogenetic finding rate
Open in a new tab

Detection rate was calculated using the total cohort of 7,530 patients as the denominator. Chromosomal polymorphisms were analyzed separately and were not included in the primary clinically relevant diagnostic rate.

a

Includes autosomal numerical abnormalities, sex chromosome numerical abnormalities, autosomal structural abnormalities, sex chromosome structural abnormalities, sex reversal/disorders of sex development (DSD)-related karyotypes, marker chromosomes, and other findings with potential reproductive implications.

3.2. Descriptive distribution of clinically relevant chromosomal abnormalities

Among the 552 abnormal cytogenetic findings, sex chromosome numerical abnormalities accounted for 146 cases (1.94% of the total cohort; 26.45% of all abnormal cytogenetic findings), autosomal structural abnormalities for 83 cases (1.10%; 15.04%), autosomal numerical abnormalities for 7 cases (0.09%; 1.27%), sex chromosome structural abnormalities for 4 cases (0.05%; 0.72%), and sex reversal or disorders of sex development-related karyotypes for 6 cases (0.08%; 1.09%). Together, these categories constituted 246 clinically relevant chromosomal abnormalities (3.27% of the total cohort). Detailed distribution is shown in Table 2.

TABLE 2.

Distribution of clinically relevant chromosomal abnormalities.

Category Cases Detection rate in total cohort (%) Proportion among abnormal cytogenetic findings (%) Representative clinical context
Autosomal numerical abnormalities 7 0.09 1.27 Miscarriage, birth defects, infertility-related indications
Sex chromosome numerical abnormalities 146 1.94 26.45 Azoospermia, oligozoospermia, amenorrhea, Turner syndrome-related features
Autosomal structural abnormalities 83 1.10 15.04 Balanced translocation, Robertsonian translocation, inversion
Sex chromosome structural abnormalities 4 0.05 0.72 Y chromosome structural abnormalities and X chromosome deletion
Sex reversal/DSD-related karyotypes 6 0.08 1.09 46,XX male; 46,XY female or related DSD findings
Total clinically relevant abnormalities 246 3.27 44.57 Primary diagnostic outcome
Open in a new tab

Proportion was calculated using all 552 abnormal cytogenetic findings as the denominator. Chromosomal polymorphisms are not included in this table.

3.3. Chromosomal polymorphisms

Chromosomal polymorphisms were identified in 306 cases. The most frequent variant was inv(9) (114 cases), accounting for 37.25% of chromosomal polymorphisms and 20.65% of all abnormal cytogenetic findings. This was followed by 1/9/16qh ± variants (87 cases; 28.43% of polymorphisms), D/G group variants (51 cases; 16.67%), Yqh± (37 cases; 12.09%), inv(Y) (14 cases; 4.58%), and rare variants including 19qh+, 10qh+, and inv(1). Detailed distribution of chromosomal polymorphisms is provided in Supplementary Table S1.

3.4. Age distribution

The highest overall cytogenetic finding rate was observed in the 26–30-year age group (8.09%), followed by the 18–25-year group (7.84%), 46–55-year group (7.58%), 41–45-year group (7.51%), 31–35-year group (6.53%), and 36–40-year group (5.91%). Because some age subgroups had small sample sizes, age-specific patterns should be interpreted descriptively.

3.5. Correspondence analysis

Correspondence analysis was performed using an observed-count matrix of major clinical manifestations and representative karyotypic findings. The matrix included 513 representative findings from major categories selected for phenotype-karyotype visualization (Table 3). The global chi-square test indicated a non-random association between clinical manifestations and representative karyotypic findings (chi-square = 69.92, df = 16, P < 0.001). The first two dimensions explained 77.9% and 15.2% of the total inertia, respectively, with a cumulative inertia of 93.1%.

TABLE 3.

Observed-count matrix used for correspondence analysis.

Clinical manifestation XXY-related karyotypes 45,X/mosaic Turner Balanced translocation Robertsonian translocation Chromosomal polymorphisms
Oligoasthenospermia/azoospermia 49 4 10 3 40
Infertility 67 5 20 8 176
Adverse pregnancy outcomes 0 0 3 2 8
Irregular menstruation/amenorrhea 0 1 9 2 40
Genetic counseling 11 0 7 6 42
Open in a new tab

Cells are observed counts linking major clinical manifestations with representative karyotypic categories. The matrix included 513 representative findings and was used for correspondence analysis.

The two-dimensional biplot showed phenotype-karyotype correspondence patterns. Oligoasthenospermia/azoospermia was positioned close to XXY-related karyotypes, adverse pregnancy outcomes were positioned closer to balanced and Robertsonian translocations, and infertility was positioned near chromosomal polymorphisms. Categories located near the origin contributed less to discrimination along the first two dimensions than categories positioned farther from the origin. Because no bootstrap or permutation-based stability assessment was performed, the biplot should be interpreted as an exploratory visualization rather than a confirmatory clustering analysis (Figure 1).

FIGURE 1.

Scatter plot visualizing clinical manifestations and karyotypic findings using blue circles and orange triangles, respectively, mapped along Dimension 1 (seventy-seven point nine percent inertia) and Dimension 2 (fifteen point two percent inertia); labels identify specific findings, such as adverse pregnancy outcomes, genetic counseling, infertility, balanced translocation, and other conditions.

Open in a new tab

Correspondence analysis biplot of clinical manifestations and representative karyotypic findings. The first two dimensions explained 77.9% and 15.2% of the total inertia, respectively. Points located closer to each other indicate stronger correspondence in the analyzed matrix. The biplot was based on observed-count data and should be interpreted as an exploratory visualization of phenotype-karyotype patterns.

4. Discussion

In this retrospective observational study of 7,530 patients undergoing reproductive genetic counseling, we separated clinically relevant chromosomal abnormalities from chromosomal polymorphisms to avoid overestimating diagnostic yield. The overall cytogenetic finding rate was 7.33%, while the primary diagnostic rate for clinically relevant chromosomal abnormalities was 3.27%. This distinction is important because chromosomal polymorphisms accounted for more than half of all reported cytogenetic findings but should not be interpreted in the same manner as numerical abnormalities, structural rearrangements, sex reversal, or sex chromosome abnormalities.

The descriptive distribution of clinically relevant abnormalities suggests that sex chromosome numerical abnormalities and autosomal structural abnormalities are important cytogenetic categories in reproductive genetic counseling. XXY-related karyotypes, Turner syndrome-related karyotypes, X/Y structural abnormalities, marker chromosomes, and sex reversal or disorders of sex development-related karyotypes require phenotype-based interpretation and, when appropriate, additional molecular confirmation such as FISH, CMA, CNV-seq, NGS, SRY testing, or AZF microdeletion testing. These techniques serve complementary roles in the cytogenetic workup: FISH (fluorescence in situ hybridization) is used for rapid detection of specific chromosomal sequences and mosaicism; CMA (chromosomal microarray analysis) and CNV-seq (copy number variation sequencing) detect submicroscopic copy number variations; NGS (next-generation sequencing) enables comprehensive genomic analysis; SRY (sex-determining region Y) testing detects the Y-chromosomal gene critical for testicular development; and AZF (azoospermia factor) microdeletion testing identifies deletions in the AZFa, AZFb, and AZFc regions on the Y chromosome, which are important causes of male infertility (Wapner et al., 2012; Salehi et al., 2006; Sukumaran et al., 2013; Cioppi et al., 2021).

Correspondence analysis provided an exploratory visualization of the relationship between clinical manifestations and representative karyotypic findings. The close positioning of oligoasthenospermia/azoospermia and XXY-related karyotypes is biologically and clinically plausible, as Klinefelter syndrome is a well-known chromosomal cause of severe male infertility. Likewise, the correspondence of adverse pregnancy outcomes with balanced and Robertsonian translocations is consistent with the role of structural rearrangements in unbalanced gamete formation, miscarriage, embryo arrest, and offspring with chromosomal imbalance (Dutta et al., 2011; Zhang et al., 2018). However, because correspondence analysis was exploratory and no bootstrap stability analysis was performed, these patterns should be interpreted as phenotype-guided visualization rather than definitive causal evidence.

Chromosomal polymorphisms were common in this cohort. Similar observations have been reported in previous cytogenetic studies of infertile populations and reproductive medicine cohorts (Madon et al., 2005; Minocherhomji et al., 2009; Sahin et al., 2008; Li et al., 2020). Nevertheless, the clinical significance of inv(9), qh ± variants, D/G group variants, and other heterochromatic variants remains controversial. Many of these variants are also observed in phenotypically normal individuals. Therefore, they should be reported descriptively and interpreted together with clinical phenotype, reproductive history, partner karyotype, and embryo or products-of-conception genetic results when available. Assigning direct pathogenic causality to chromosomal polymorphisms without supporting clinical or molecular evidence should be avoided.

Based on our cohort findings, routine laboratory practice, and published reproductive genetics literature, we proposed a clinical interpretation and genetic counseling framework for representative karyotypic findings (Table 4). This framework is not intended to replace formal professional guidelines or multidisciplinary clinical decision-making. Rather, it provides a practical structure for laboratory geneticists, reproductive medicine specialists, obstetricians, and genetic counselors to communicate cytogenetic results, determine whether additional testing is needed, and identify findings that require individualized reproductive counseling. In multidisciplinary team practice, such a framework may help separate clinically actionable findings from variants of uncertain or controversial significance.

TABLE 4.

Proposed clinical interpretation and genetic counseling framework for representative karyotypic findings.

Karyotypic finding Main associated clinical features Additional evaluation/testing to consider Clinical/genetic counseling considerations
47,XXY/mosaic XXY Azoospermia, severe oligoasthenospermia, male infertility Andrological and endocrine evaluation; semen and fertility assessment; additional Y-chromosome testing when clinically indicated Fertility counseling and individualized discussion of reproductive options, including assisted reproductive strategies when appropriate (Zitzmann et al., 2021)
Turner syndrome-related karyotypes Amenorrhea, ovarian insufficiency, infertility, reproductive organ developmental abnormalities Endocrine, cardiovascular, and reproductive assessment; additional genetic characterization when indicated by the karyotype and phenotype Counseling regarding fertility potential, pregnancy-related risks, and individualized reproductive planning; multidisciplinary follow-up when appropriate (Gravholt et al., 2024)
Balanced or Robertsonian translocation Recurrent miscarriage, adverse pregnancy outcomes, infertility Partner karyotyping and individualized reproductive genetic assessment Counseling regarding reproductive and recurrence risks; PGT-SR and prenatal diagnostic options may be discussed according to individual reproductive plans (ESHRE Guideline Group on RPL, 2023)
Marker chromosome Azoospermia, infertility, developmental abnormalities, or uncertain phenotype Further characterization by FISH and/or genomic copy-number analysis when the origin or genomic content cannot be adequately defined by conventional karyotyping Interpretation should consider marker origin, genomic content, mosaicism, phenotype, and inheritance; multidisciplinary evaluation may be appropriate in complex cases (Yu et al., 2012; Joksic et al., 2024)
Sex reversal/DSD-related karyotypes Infertility, azoospermia, amenorrhea, gonadal dysgenesis Endocrine evaluation, imaging, biochemical assessment, and phenotype-directed genetic testing, including SRY or other targeted molecular analyses when appropriate Multidisciplinary evaluation involving clinical genetics, endocrinology, urology/andrology, and/or gynecology as appropriate; individualized genetic and psychosocial counseling (Lee et al., 2016; Cools et al., 2018)
X/Y chromosome structural abnormalities Amenorrhea, azoospermia, oligozoospermia, infertility Phenotype-directed endocrine and molecular evaluation; Y-chromosome/AZF testing in males with azoospermia or severe oligozoospermia when appropriate Reproductive counseling should be individualized according to the specific structural abnormality, phenotype, reproductive implications, and potential transmission risk (Krausz et al., 2024; Gravholt et al., 2024)
Chromosomal polymorphisms Infertility or adverse reproductive history, but also observed in phenotypically normal individuals No reflex molecular testing solely on the basis of a chromosomal polymorphism; further evaluation should be guided by phenotype, reproductive history, and other clinical findings Interpret cautiously and avoid assigning direct pathogenic causality without supportive phenotypic, familial, reproductive, or molecular evidence (Ralapanawe et al., 2023; Li et al., 2020)
Open in a new tab

This proposed framework was developed based on the findings of the present cohort and published clinical guidelines, consensus statements, and reproductive genetics literature. Additional evaluation or testing should be considered according to the specific karyotypic finding, clinical phenotype, and reproductive history. The framework is intended to support laboratory interpretation, multidisciplinary referral, and genetic counseling and should not replace formal professional guidelines or individualized clinical decision-making. AZF, azoospermia factor; DSD, differences/disorders of sex development; FISH, fluorescence in situ hybridization; PGT-SR, preimplantation genetic testing for structural rearrangements; SRY, sex-determining region Y.

This study has several limitations. First, it was a single-center retrospective study, and selection bias could not be avoided. Second, clinical indication definitions were based on referral diagnoses recorded in the medical record; detailed standardized definitions, disease duration, and complete hormone profiles were not available for every patient. Third, complete partner karyotypes, longitudinal reproductive outcomes, and embryo or products-of-conception genetic results were not available for all patients, limiting our ability to directly evaluate the reproductive impact of specific findings, especially chromosomal polymorphisms. Fourth, correspondence analysis was based on representative karyotype categories and should be regarded as exploratory. Finally, G-banding has inherent resolution limitations and cannot reliably detect submicroscopic copy-number variants or single-gene defects. Therefore, conventional karyotyping should be regarded as complementary to, rather than competitive with, molecular genomic testing. When clinically indicated, additional testing such as CMA/CNV-seq or NGS should be considered to identify abnormalities beyond the resolution of conventional cytogenetics.

Conventional karyotyping and contemporary genomic technologies should be regarded as complementary rather than competing diagnostic approaches in reproductive genetics. G-banded karyotyping provides a genome-wide overview of chromosome number and large-scale structure and remains particularly useful for detecting aneuploidies, balanced translocations, Robertsonian translocations, large inversions, sex chromosome abnormalities, and chromosomal mosaicism. Its major limitation is relatively low resolution, which prevents reliable detection of submicroscopic copy-number variants and single-gene defects. In contrast, CMA and CNV-seq provide higher resolution for detecting submicroscopic copy-number alterations but generally cannot reliably identify balanced chromosomal rearrangements. NGS is particularly valuable for identifying pathogenic variants underlying single-gene disorders but does not replace chromosome-level evaluation when numerical or structural abnormalities are suspected. OGM is an emerging high-resolution technology capable of detecting a broad spectrum of balanced and unbalanced structural variants, although its availability, standardization, and clinical validation in reproductive medicine remain evolving (Chebly, 2026). Therefore, conventional karyotyping remains particularly important when aneuploidy, balanced rearrangements, chromosomal mosaicism, or large structural abnormalities are suspected.

Looking ahead, the integration of conventional karyotyping with emerging genomic technologies will likely shape the future of reproductive genetic diagnosis. A phenotype-guided, tiered approach—using karyotyping when chromosomal numerical or structural abnormalities are suspected, followed by CMA/CNV-seq for submicroscopic CNVs and NGS for suspected single-gene disorders—may improve diagnostic comprehensiveness while maintaining clinical practicality. OGM may further expand the detection of structural variants as its clinical standardization and validation progress. Artificial intelligence-assisted karyotype analysis may also improve analytical efficiency in high-volume laboratories. Ultimately, future reproductive genetic diagnosis is likely to integrate conventional cytogenetics and molecular genomic technologies according to the clinical phenotype and suspected genetic mechanism.

In conclusion, peripheral blood karyotyping remains a useful first-line cytogenetic test in reproductive genetic counseling. Clinically relevant chromosomal abnormalities and chromosomal polymorphisms should be reported and interpreted separately. The primary diagnostic rate for clinically relevant abnormalities was 3.27%, whereas chromosomal polymorphisms accounted for 4.06% of the cohort. Phenotype-guided interpretation, supported by correspondence analysis and an explicitly defined counseling framework, may improve communication between cytogenetic laboratories and reproductive medicine teams. Additional molecular testing should be considered when conventional karyotyping is insufficient for clinical interpretation.

Acknowledgments

We thank the staff and patients at our hospital for providing the samples used in this study.

Funding Statement

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Natural Science Foundation of Fujian Province (grant number 2023J01607), and the National Natural Science Foundation of China (grant numbers 81902130).

Footnotes

Edited by: Jared C. Roach, Institute for Systems Biology (ISB), United States

Reviewed by: Alain Chebly, Saint Joseph University, Lebanon

Elisavet Kouvidi, Institute of Child Health, Greece

Data availability statement

The datasets analyzed in this study are not publicly available due to patient privacy and ethical restrictions. De-identified data may be made available by the corresponding author upon reasonable request, subject to institutional approval and applicable ethical requirements.

Ethics statement

The study was approved by the Ethics Committee of the First Affiliated Hospital of Fujian Medical University (Approval No. MTCA, ECFAH of FMU [2015]084-3). The study was conducted in accordance with the Declaration of Helsinki and applicable institutional and national ethical requirements. The participants provided their written informed consent to participate in this study.

Author contributions

SW: Data curation, Methodology, Writing – original draft. HS: Writing – review and editing, Formal Analysis. WWa: Writing – review and editing, Data curation, Formal Analysis. DC: Formal Analysis, Writing – review and editing. WWu: Supervision, Writing – original draft, Funding acquisition.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2026.1947419/full#supplementary-material

Table1.docx (12.9KB, docx)

References

  1. Caseiro A. L., Regalo A., Pereira E., Esteves T., Fernandes F., Carvalho J. (2015). Implication of sperm chromosomal abnormalities in recurrent abortion and multiple implantation failure. Reprod. Biomed. Online 31, 481–485. 10.1016/j.rbmo.2015.07.001 [DOI] [PubMed] [Google Scholar]
  2. Chebly A. (2026). Cytogenetics in the genomics era: why karyotyping still matters. BMC Med. Genomics 19, 10. 10.1186/s12920-025-02296-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Cioppi F., Rosta V., Krausz C. (2021). Genetics of azoospermia. Int. J. Mol. Sci. 22, 3264. 10.3390/ijms22063264 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Cools M., Nordenström A., Robeva R., Hall J., Westerveld P., Flück C., et al. (2018). Caring for individuals with a difference of sex development (DSD): a consensus statement. Nat. Rev. Endocrinol. 14, 415–429. 10.1038/s41574-018-0010-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Crain D. A., Janssen S. J., Edwards T. M., Heindel J., Ho S. M., Hunt P., et al. (2008). Female reproductive disorders: the roles of endocrine-disrupting compounds and developmental timing. Fertil. Steril. 90, 911–940. 10.1016/j.fertnstert.2008.08.067 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Dutta U. R., Rajitha P., Pidugu V. K., Dalal A. B. (2011). Cytogenetic abnormalities in 1162 couples with recurrent miscarriages in southern region of India: report and review. J. Assist. Reprod. Genet. 28, 145–149. 10.1007/s10815-010-9492-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. ESHRE Guideline Group on RPL, Bender Atik R., Christiansen O. B., Elson J., Kolte A. M., Lewis S., et al. (2023). ESHRE guideline: recurrent pregnancy loss: an update in 2022. Hum. Reprod. Open 2023 (1), hoad002. 10.1093/hropen/hoad002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Gardner R. J. M., Sutherland G. R., Shaffer L. G. (2012). Chromosome Abnormalities and Genetic Counseling. 4th Edn. New York, NY: Oxford University Press. [Google Scholar]
  9. Gonzales P. R., Carroll A. J., Korf B. R. (2016). Overview of clinical cytogenetics. Curr. Protoc. Hum. Genet. 89 (8.1.1–8.1.13), 1–8. 10.1002/0471142905.hg0801s89 [DOI] [PubMed] [Google Scholar]
  10. Gravholt C. H., Andersen N. H., Christin-Maitre S., Davis S. M., Duijnhouwer A., Gawlik A., et al. (2024). Clinical practice guidelines for the care of girls and women with Turner syndrome. Eur. J. Endocrinol. 190, G53–G151. 10.1093/ejendo/lvae050 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Guerri G., Maniscalchi T., Barati S., Gerli S., Di Renzo G. C., Della Morte C., et al. (2019). Non-syndromic monogenic female infertility. Acta Biomed. 90, 68–74. 10.23750/abm.v90i10-S.8763 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Gunes S., Esteves S. C. (2021). Role of genetics and epigenetics in male infertility. Andrologia 53, e13586. 10.1111/and.13586 [DOI] [PubMed] [Google Scholar]
  13. Ho S. M., Cheong A., Adgent M. A., Veevers J., Suen A. A., Tam N. N. C., et al. (2017). Environmental factors, epigenetics, and developmental origin of reproductive disorders. Reprod. Toxicol. 68, 85–104. 10.1016/j.reprotox.2016.07.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Joksic I., Toljic M., Milacic I., Stankovic A., Karadzov Orlic N., Mikovic Z. (2024). Small supernumerary marker chromosomes in prenatal diagnosis—molecular characterization and clinical outcomes. Front. Genet. 14, 1326985. 10.3389/fgene.2023.1326985 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Krausz C., Navarro-Costa P., Wilke M., Tüttelmann F. (2024). EAA/EMQN best practice guidelines for molecular diagnosis of Y-chromosomal microdeletions: state of the art 2023. Andrology 12, 487–504. 10.1111/andr.13514 [DOI] [PubMed] [Google Scholar]
  16. Kuroda S., Usui K., Sanjo H., Takeshima T., Kawahara T., Uemura H., et al. (2020). Genetic disorders and male infertility. Reprod. Med. Biol. 19, 314–322. 10.1002/rmb2.12336 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Lee P. A., Nordenström A., Houk C. P., Ahmed S. F., Auchus R., Baratz A., et al. (2016). Global disorders of sex development update since 2006: perceptions, approach and care. Horm. Res. Paediatr. 85, 158–180. 10.1159/000442975 [DOI] [PubMed] [Google Scholar]
  18. Li S. J., Cheng Y. X., Ye S., Zhou D. N., Zhang Y., Yin T. L., et al. (2020). Chromosomal polymorphisms associated with reproductive outcomes after IVF-ET. J. Assist. Reprod. Genet. 37, 1703–1710. 10.1007/s10815-020-01793-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Madon P. F., Athalye A. S., Parikh F. R. (2005). Polymorphic variants on chromosomes probably play a significant role in infertility. Reprod. Biomed. Online 11, 726–732. 10.1016/s1472-6483(10)61691-4 [DOI] [PubMed] [Google Scholar]
  20. McGowan-Jordan J., Simons A., Schmid M. (2016). ISCN 2016: An International System for Human Cytogenomic Nomenclature (Basel: Karger; ). [Google Scholar]
  21. Minocherhomji S., Athalye A. S., Madon P. F., Kulkarni D., Uttamchandani S. A., Parikh F. R. (2009). A case-control study identifying chromosomal polymorphic variations as forms of epigenetic alterations associated with the infertility phenotype. Fertil. Steril. 92, 88–95. 10.1016/j.fertnstert.2008.05.071 [DOI] [PubMed] [Google Scholar]
  22. Ralapanawe M. S. B., Khattak H., Hapangama H. R., Weerakkody G. R., Papadopoulou A., Gallos I. (2023). Chromosomal polymorphisms in assisted reproduction: a systematic review and meta-analysis. Hum. Fertil. (Camb.) 26, 687–698. 10.1080/14647273.2022.2051614 [DOI] [PubMed] [Google Scholar]
  23. Sahin F. I., Yilmaz Z., Yuregir O. O., Bulakbasi T., Ozer O., Zeyneloglu H. B. (2008). Chromosome heteromorphisms: an impact on infertility. J. Assist. Reprod. Genet. 25, 191–195. 10.1007/s10815-008-9216-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Salehi L. B., Scarciolla O., Vanni G. F., Nardone A. M., Frajese G., Novelli G., et al. (2006). Identification of a novel mutation in the SRY gene in a 46,XY female patient. Eur. J. Med. Genet. 49, 494–498. 10.1016/j.ejmg.2006.03.003 [DOI] [PubMed] [Google Scholar]
  25. Sukumaran A., Desmangles J. C., Gartner L. A., Buchlis J. (2013). Duplication of dosage sensitive sex reversal area in a 46,XY patient with normal sex determining region of Y causing complete sex reversal. J. Pediatr. Endocrinol. Metab. 26, 775–779. 10.1515/jpem-2012-0354 [DOI] [PubMed] [Google Scholar]
  26. Vander Borght M., Wyns C. (2018). Fertility and infertility: definition and epidemiology. Clin. Biochem. 62, 2–10. 10.1016/j.clinbiochem.2018.03.012 [DOI] [PubMed] [Google Scholar]
  27. Wapner R. J., Martin C. L., Levy B., Ballif B. C., Eng C. M., Zachary J. M., et al. (2012). Chromosomal microarray versus karyotyping for prenatal diagnosis. N. Engl. J. Med. 367, 2175–2184. 10.1056/NEJMoa1203382 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Yu S., Fiedler S. D., Brawner S. J., Joyce J. M., Zhou X. G., Liu H. Y. (2012). Characterizing small supernumerary marker chromosomes with combination of multiple techniques. Cytogenet. Genome Res. 136, 6–14. 10.1159/000334271 [DOI] [PubMed] [Google Scholar]
  29. Zhang T., Sun Y., Chen Z., Li T. (2018). Traditional and molecular chromosomal abnormality analysis of products of conception in spontaneous and recurrent miscarriage. BJOG 125, 414–420. 10.1111/1471-0528.15052 [DOI] [PubMed] [Google Scholar]
  30. Zhou Z., Zheng D., Wu H., Li R., Xu S., Kang Y., et al. (2018). Epidemiology of infertility in China: a population-based study. BJOG 125, 432–441. 10.1111/1471-0528.14966 [DOI] [PubMed] [Google Scholar]
  31. Zitzmann M., Aksglaede L., Corona G., Isidori A. M., Juul A., T'Sjoen G., et al. (2021). European Academy of Andrology guidelines on Klinefelter syndrome endorsing organization: European society of endocrinology. Andrology 9, 145–167. 10.1111/andr.12909 [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Table1.docx (12.9KB, docx)

Data Availability Statement

The datasets analyzed in this study are not publicly available due to patient privacy and ethical restrictions. De-identified data may be made available by the corresponding author upon reasonable request, subject to institutional approval and applicable ethical requirements.

Articles from Frontiers in Genetics are provided here courtesy of Frontiers Media SA

RESOURCES