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PubMed2026

Computational strategies for copy number variation detection, disease association, and beyond.

Copy number variations (CNVs) are key structural variations that contribute to human genetic diversity, evolution, and disease susceptibility. Advances in sequencing technologies and computational methods have improved CNV detection, yet association studies remain challenged by methodological limitations and a lack of standardisation. This review provides an overview of computational strategies for germline CNV detection and disease association. We highlight the value of CNV analysis for uncovering genetic contributions to complex traits and disease risk and outline an analysis workflow including key benchmarking methods. We also discuss current challenges and future directions for advancing CNV detection and association analysis.

Humans↗

PubMed2024

Diagnostic yield of exome sequencing-based copy number variation analysis in Mendelian disorders: a clinical application.

Next-generation sequencing (NGS) coupled with bioinformatic tools has revolutionized the detection of copy number variations (CNVs), which are implicated in the emergence of Mendelian disorders. In this study, we evaluated the diagnostic yield of exome sequencing-based CNV analysis in 449 patients with suspected Mendelian disorders. We aimed to assess the diagnostic yield of this recently utilized method and expand the clinical spectrum of intragenic CNVs. The cohort underwent whole exome sequencing (WES) and clinical exome sequencing (CES). Using GATK-gCNV, we identified 12 pathogenic CNVs that correlated with their clinical findings and resulting in a diagnostic yield of 2.67%. Importantly, the study emphasizes the role of CNVs in the etiology of Mendelian disorders and highlights the value of exome sequencing-based CNV analysis in routine diagnostic processes.

Humans↗

PubMed2026

Genomic and evolutionary analysis reveals dynamic variations of MKK3 gene, a key regulator for seed dormancy in barley.

Barley (Hordeum vulgare L.) is an important crop in the world, and its seed dormancy is primarily controlled by a mitogen-activated protein kinase kinase 3 (MKK3) gene. Although kinase activity of MKK3 and its roles in barley post-domestication have been widely studied, the pre-domestication evolution of MKK3 and the spread of nondormant alleles among global barley varieties remain largely unexplored. In this study, we analyzed MKK3 sequences in barley and its wild progenitor (Hordeum spontaneum K. Koch) and identified two polymorphic miniature inverted-repeat transposable elements (MITEs). Comparative analyses indicated that the insertions/excision of the MITEs predated the current estimates of barley domestication. Examination of the barley pangenomes coupled with droplet digital polymerase chain reaction revealed extensive copy number variation of MKK3 and suggested that transposons likely contributed to tandem amplification of the MKK3 gene on chromosome 5H. Additionally, approximately 1-Kb MKK3 sequences were found on chromosomes 1H and 6H. Further analysis indicated that these short MKK3 sequences were captured by a CACTA transposon that also contained fragments from four other expressed genes. The acquisition of MKK3 was estimated to be between 1.9 and 2.5 million years ago. Together, these findings illuminate the dynamic pre-domestication evolution of the MKK3 gene and identify three divergent MKK3 haplotype groups including a unique lineage predominant in Ethiopian germplasm. This study highlights the contribution of transposons to structural diversification and evolutionary differentiation of the MKK3 locus and provides helpful information for understanding the complex history of MKK3 gene in barley and also for improving preharvest sprouting tolerant varieties under distinct natural conditions.

Hordeum↗

PubMed2003

Large-scale variation among human and great ape genomes determined by array comparative genomic hybridization.

Large-scale genomic rearrangements are a major force of evolutionary change and the ascertainment of such events between the human and great ape genomes is fundamental to a complete understanding of the genetic history and evolution of our species. Here, we present the results of an evolutionary analysis utilizing array comparative genomic hybridization (array CGH), measuring copy-number gains and losses among these species. Using an array of 2460 human bacterial artificial chromosomes (BACs) (12% of the genome), we identified a total of 63 sites of putative DNA copy-number variation between humans and the great apes (chimpanzee, bonobo, gorilla, and orangutan). Detailed molecular characterization of a subset of these sites confirmed rearrangements ranging from 40 to at least 175 kb in size. Surprisingly, the majority of variant sites differentiating great ape and human genomes were found within interstitial euchromatin. These data suggest that such large-scale events are not restricted solely to subtelomeric or pericentromeric regions, but also occur within genic regions. In addition, 5/9 of the verified variant sites localized to areas of intrachromosomal segmental duplication within the human genome. On the basis of the frequency of duplication in humans, this represents a 14-fold positional bias. In contrast to previous cytogenetic and comparative mapping studies, these results indicate extensive local repatterning of hominoid chromosomes in euchromatic regions through a duplication-driven mechanism of genome evolution.

Animals↗

PubMed2026

Genomic Profiling of Anophthalmia/Microphthalmia-Associated CNVs Reveals Complex Genotype-Phenotype Correlations and Incomplete Penetrance.

BACKGROUND: Anophthalmia/microphthalmia (A/M) is a severe congenital ocular malformation characterized by the complete absence or small size of the eye bulb. Interpreting copy number variations (CNVs) in A/M is challenged by variable genotype-phenotype correlations and reduced penetrance. This study investigated the genetic etiology of A/M-associated CNVs. METHODS: Genomic profiling was performed on four unrelated families presenting with ocular anomalies or harboring A/M-susceptible CNVs. Variants were evaluated by integrating American College of Medical Genetics and Genomics (ACMG) guidelines with clinical phenotypes and familial segregation. RESULTS: An inherited 8.13 Mb deletion (8p23.3p23.1) in Patient 1 was excluded due to genotype-phenotype mismatch. Patients 2 and 3 harbored de novo pathogenic deletions involving OTX2 (14q22.3) and SOX2 (3q26.33), causing typical A/M. Case 4 revealed a 14q22.2q23.1 deletion encompassing OTX2 in a fetus and mother without ocular anomalies, consistent with the incomplete penetrance of OTX2-related microphthalmia. Thus, CNV-induced haploinsufficiency causes A/M with high phenotypic variability. CONCLUSION: Accurate CNV interpretation requires robust genotype-phenotype correlation and careful assessment of incomplete penetrance to prevent diagnostic pitfalls and improve genetic counseling.

Female↗

PubMed2025

The CAP/ACMG CYCGH proficiency testing program: 10 years in review.

PURPOSE: The College of American Pathologists has offered proficiency testing (PT) for the detection of copy-number variations (CNV) in the constitutional setting (CYCGH) since 2008. We review and summarize data from the CYCGH PT program, including participant performance over time, changes made to the program, and ungraded challenges. METHODS: The PT challenges from 2011 through 2021 (22 total mailings) and changes to the program over time were reviewed. Laboratory enrollment and performance were assessed. RESULTS: Overall participation has increased over time, and laboratories have maintained a high level of proficiency. The major changes to the program have occurred twice during the time span examined. Reasons for challenges not meeting consensus were varied. The use of ungraded challenges was also discussed. CONCLUSION: The CYCGH PT program is challenging because it assesses both analytical performance and interpretation as a single analyte. The program has evolved over time to address the changes in the field of CNV detection. During this time, additional technologies with the ability to detect CNVs have emerged, and the possibility of developing a platform-agnostic CNV PT program is being explored.

Humans↗

PubMed2024

Prenatal diagnosis and molecular cytogenetic analysis of pure chromosome 10p15.3 microdeletion using chromosomal microarray analysis.

BACKGROUND: The literature contains exceedingly limited reports on chromosome 10p15.3 microdeletions. In the present study, two cases of fetuses with pure terminal 10p15.3 microdeletion syndrome in a Chinese population were examined, with the objective of enhancing understanding of the genotype-phenotype correlation associated with 10p15.3 microdeletions. METHODS: Two fetuses with chromosome 10p15.3 microdeletion were identified from a cohort of 5,258 cases undergoing amniocentesis. Karyotyping and chromosomal microarray analysis (CMA) was conducted to assess chromosomal abnormalities and detect copy number variations (CNVs) within the families, respectively. RESULTS: In Family 1, the fetus exhibited a 556.2-Kb deletion in the 10p15.3 region, encompassing OMIM genes such as DIP2C and ZMYND11, and presented with increased nuchal translucency on prenatal ultrasound examination. Parental CMA analysis revealed that the 10p15.3 microdeletion was inherited from the father, who displayed mild language impairment. In Family 2, a comparable 10p15.3 microdeletion was identified in a fetus presenting with asymmetric butterfly vertebrae at T10 and T12, along with mild scoliosis of the spine. Family 1 elected to terminate the pregnancy, while Family 2 chose to continue. At a follow-up conducted at one year and eight months, the child demonstrated delays in both speech and motor development. CONCLUSION: The present study is the first to report two cases of pure terminal chromosome 10p15.3 microdeletion syndrome in fetuses, offering valuable insights for the prenatal diagnosis of 10p15.3 microdeletion syndrome. Further, it is the first to describe mild clinical features, specifically limited to language impairment, in a patient with 10p15.3 microdeletion syndrome.

Female↗

PubMed2022

Phenotypic Impact of Rare Potentially Damaging Copy Number Variation in Obsessive-Compulsive Disorder and Chronic Tic Disorders.

BACKGROUND: Recent studies report an important-and previously underestimated-role of rare variation in risk of obsessive-compulsive disorder (OCD) and chronic tic disorders (CTD). Using data from a large epidemiological study, we evaluate the distribution of potentially damaging copy number variation (pdCNV) in OCD and CTD, examining associations between pdCNV and the phenotypes of probands, including a consideration of early- vs. late-diagnoses. METHOD: The Obsessive-Compulsive Inventory-Revised (OCI-R) questionnaire was used to ascertain psychometric profiles of OCD probands. CNV were identified genome-wide using chromosomal microarray data. RESULTS: For 993 OCD cases, 86 (9%) were identified as pdCNV carriers. The most frequent pdCNV found was at the 16p13.11 region. There was no significant association between pdCNV and the OCI-R total score. However, pdCNV was associated with Obsessing and Checking subscores. There was no significant difference in pdCNV frequency between early- vs. late-diagnosed OCD probands. Of the 217 CTD cases, 18 (8%) were identified as pdCNV carriers. CTD probands with pdCNV were significantly more likely to have co-occurring autism spectrum disorder (ASD). CONCLUSIONS: pdCNV represents part of the risk architecture for OCD and CTD. If replicated, our findings suggest pdCNV impact some OCD symptoms. Genes within the 16p13.11 region are potential OCD risk genes.

Humans↗

PubMed2024

Contribution of copy number variations to education, socioeconomic status and cognition from a genome-wide study of 305,401 subjects.

Educational attainment (EA), socioeconomic status (SES) and cognition are phenotypically and genetically linked to health outcomes. However, the role of copy number variations (CNVs) in influencing EA/SES/cognition remains unclear. Using a large-scale (n = 305,401) genome-wide CNV-level association analysis, we discovered 33 CNV loci significantly associated with EA/SES/cognition, 20 of which were novel (deletions at 2p22.2, 2p16.2, 2p12, 3p25.3, 4p15.2, 5p15.33, 5q21.1, 8p21.3, 9p21.1, 11p14.3, 13q12.13, 17q21.31, and 20q13.33, as well as duplications at 3q12.2, 3q23, 7p22.3, 8p23.1, 8p23.2, 17q12 (105 kb), and 19q13.32). The genes identified in gene-level tests were enriched in biological pathways such as neurodegeneration, telomere maintenance and axon guidance. Phenome-wide association studies further identified novel associations of EA/SES/cognition-associated CNVs with mental and physical diseases, such as 6q27 duplication with upper respiratory disease and 17q12 (105 kb) duplication with mood disorders. Our findings provide a genome-wide CNV profile for EA/SES/cognition and bridge their connections to health. The expanded candidate CNVs database and the residing genes would be a valuable resource for future studies aimed at uncovering the biological mechanisms underlying cognitive function and related clinical phenotypes.

Humans↗

PubMed2026

Overexpression of a subset of long intergenic noncoding RNAs in uterine serous carcinoma predicts poor prognosis.

The evaluation and prediction of uterine serous carcinoma (USC), a type of endometrial cancer that is more severe than endometrioid adenocarcinoma, remain challenging. Long noncoding RNAs (lncRNAs) are frequently dysregulated in human cancers. This study assessed the expression patterns and prognostic values of long intergenic noncoding RNAs (lincRNAs) in USC. RNA sequencing, copy number variation (CNV), and clinical data from The Cancer Genome Atlas were used to investigate various lncRNAs in endometrial cancer. LincRNAs, a major subclass of lncRNAs, exhibit specific expression patterns modulated by CNVs and act as predictors of poor prognosis, survival, and recurrence in USC. Functional analyses were conducted to investigate the roles of lncRNAs in USC. Finally, the expression of these lincRNAs was verified in 32 pairs of USCs collected from the hospital over 3 years. A series of lincRNAs were found to be specifically expressed in USC compared with other lncRNAs and regulated by CNV. Moreover, these specific upregulated lincRNAs, particularly ENSG00000281406, ENSG00000226791, ENSG00000269903, and ENSG00000204277, demonstrated poor prognoses for survival and recurrence in USC. Functionally, our analysis showed that ENSG00000281406 positively correlated with the Wnt signaling pathway, whereas ENSG00000226791, ENSG00000269903, and ENSG00000204277 negatively correlated with the T-cell receptor signaling pathway. Importantly, we confirmed that ENSG00000204277 negatively correlated with CD8+ T-cell immune infiltration in USC. Our results highlight that these lincRNAs can serve as new biomarkers for the prognostic prediction of USC. In particular, ENSG00000204277 may be used as a therapeutic target for USC.

Humans↗

PubMed2024

Whole exome sequencing analysis of 167 men with primary infertility.

BACKGROUND: Spermatogenic failure is one of the leading causes of male infertility and its genetic etiology has not yet been fully understood. METHODS: The study screened a cohort of patients (n = 167) with primary male infertility in contrast to 210 normally fertile men using whole exome sequencing (WES). The expression analysis of the candidate genes based on public single cell sequencing data was performed using the R language Seurat package. RESULTS: No pathogenic copy number variations (CNVs) related to male infertility were identified using the the GATK-gCNV tool. Accordingly, variants of 17 known causative (five X-linked and twelve autosomal) genes, including ACTRT1, ADAD2, AR, BCORL1, CFAP47, CFAP54, DNAH17, DNAH6, DNAH7, DNAH8, DNAH9, FSIP2, MSH4, SLC9C1, TDRD9, TTC21A, and WNK3, were identified in 23 patients. Variants of 12 candidate (seven X-linked and five autosomal) genes were identified, among which CHTF18, DDB1, DNAH12, FANCB, GALNT3, OPHN1, SCML2, UPF3A, and ZMYM3 had altered fertility and semen characteristics in previously described knockout mouse models, whereas MAGEC1,RBMXL3, and ZNF185 were recurrently detected in patients with male factor infertility. The human testis single cell-sequencing database reveals that CHTF18, DDB1 and MAGEC1 are preferentially expressed in spermatogonial stem cells. DNAH12 and GALNT3 are found primarily in spermatocytes and early spermatids. UPF3A is present at a high level throughout spermatogenesis except in elongating spermatids. The testicular expression profiles of these candidate genes underlie their potential roles in spermatogenesis and the pathogenesis of male infertility. CONCLUSION: WES is an effective tool in the genetic diagnosis of primary male infertility. Our findings provide useful information on precise treatment, genetic counseling, and birth defect prevention for male factor infertility.

Humans↗

PubMed2025

Deep clinical and genetic analysis of 17p13.3 region: 38 pediatric patients diagnosed using next-generation sequencing and literature review.

BACKGROUND: Chromosome 17p13.3 is a region of genomic instability associated with different neurodevelopmental diseases. The malformation spectrum of 17p13.3 microdeletions ranges from an isolated lissencephaly sequence to Miller-Dieker syndrome, while 17p13.3 microduplications result in autism, learning disabilities, microcephaly and other brain malformations. This study aims to provide a more comprehensive delineation of the clinical and genetic characteristics associated with 17p13.3 alterations. METHODS: We retrospectively analyzed the next-generation sequencing (NGS) data of more than 40 thousand patients from January 2016 to December 2021 and identified 38 pediatric patients with copy-number variations (CNVs) or single-nucleotide variations (SNVs) in 17p13.3 region. Published patients with CNVs in the 17p13.3 region were also collected and we performed a Chi-square test to compare the phenotype spectrum of microdeletions and microduplications. RESULTS: Among the 27 CNV patients, 20 patients with microdeletions and 7 patients with microduplications were found. PAFAH1B1 was the most frequently deleted gene and CRK was the most frequently duplicated gene. Affected genes in 11 SNV patients included PAFAH1B1 and PRPF8. Developmental delay was the most common abnormality detected in the 38 patients (29/38, 76.3%). Of note, Case 10 presented omphalocele and Case 23 presented scoliosis, webbed neck and bone cyst, all of which were unusual variant phenotypes in this region. The Chi-square test revealed that epilepsy, lissencephaly and short stature were statistically significant with microdeletions, while behavioral abnormalities and hand and foot abnormalities were significant with microduplications (p < 0.01). CONCLUSIONS: While PAFAH1B1, YWHAE and CRK are associated with major phenotypes of 17p13.3, RTN4RL1 may be involved in white matter changes and HIC1 might contribute to the occurrence of omphalocele. This study provided a comprehensive understanding of genetic information and phenotype spectrum of the 17p13.3 region.

Humans↗

PubMed2025

Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.

Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug and a thorough understanding of differences in drug responses among individuals continue to pose significant challenges. This study introduced a deep learning model PASO, which integrated transformer encoder, multi-scale convolutional networks and attention mechanisms to predict the sensitivity of cell lines to anticancer drugs, based on the omics data of cell lines and the SMILES representations of drug molecules. First, we use statistical methods to compute the differences in gene expression, gene mutation, and gene copy number variations between within and outside biological pathways, and utilized these pathway difference values as cell line features, combined with the drugs' SMILES chemical structure information as inputs to the model. Then the model integrates various deep learning technologies multi-scale convolutional networks and transformer encoder to extract the properties of drug molecules from different perspectives, while an attention network is devoted to learning complex interactions between the omics features of cell lines and the aforementioned properties of drug molecules. Finally, a multilayer perceptron (MLP) outputs the final predictions of drug response. Our model exhibits higher accuracy in predicting the sensitivity to anticancer drugs comparing with other methods proposed recently. It is found that PARP inhibitors, and Topoisomerase I inhibitors were particularly sensitive to SCLC when analyzing the drug response predictions for lung cancer cell lines. Additionally, the model is capable of highlighting biological pathways related to cancer and accurately capturing critical parts of the drug's chemical structure. We also validated the model's clinical utility using clinical data from The Cancer Genome Atlas. In summary, the PASO model suggests potential as a robust support in individualized cancer treatment. Our methods are implemented in Python and are freely available from GitHub (https://github.com/queryang/PASO).

Deep Learning↗

PubMed2025

HER2 alterations across solid tumors: implications for comprehensive testing.

PURPOSE: ERBB2 (HER2) alterations (eg, overexpression, amplification, and mutations) are known to drive tumor progression. These changes, particularly in non-breast and gastric/gastroesophageal cancers, remain poorly characterized. With pan-tumor approval of HER2-targeted therapies like Trastuzumab deruxetecan (T-DXd), understanding ERBB2 alterations across diverse cancers is crucial. METHODS: HER2 analysis was conducted on 653 solid tumor specimens at the University of Alabama, using immunohistochemistry (IHC), copy number (CN) variation (CNV) assessment, and mutational profiling. The correlation between CN amplification and IHC expression was evaluated using Somers' D ordinal association. RESULTS: Of the 653 cases, HER2 IHC scores were distributed as 3+ (3.1%), 2+ (13.2%), and 1+ (19.8%), with 63.9% being IHC-negative. ERBB2 CN amplification was observed in 3.1%, with 75% exhibiting IHC3+. Pathogenic mutations were found in 3.1%, with low IHC3+ rates (5%). Among samples with ERBB2 mutations, only 3 had CN amplifications (1-positive, 2-intermediate). Somers'-D analysis revealed a strong association between CNV and IHC expression (D = 0.73, P < .001). CONCLUSION: This study highlights ERBB2 alterations across diverse cancers, demonstrating their heterogeneity and clinical significance. ERBB2 mutation-carrying tumors are less likely to have HER2 protein 3+ expression or CN amplification, indicating the need for comprehensive genomic analysis to identify those patients. In the context of pan-tumor approval of T-DXd for HER2, findings support integrating genomic and phenotypic data to enhance diagnostic precision and inform therapeutic decision-making. Comprehensive ERBB2 (HER2) testing across tumor types is essential to expand access to HER2-targeted therapies.

Humans↗

PubMed2025

Detection of copy number variations by chromosomal microarray analysis in disorders of sex development of unexplained molecular etiology and association with clinical findings.

PURPOSE: Despite advances in genetic diagnostics, the molecular cause of a significant proportion of DSDs remains unknown. The aim of this study was to identify copy number variations (CNVs) using chromosomal microarray analysis (CMA) technology in DSD patients with previously undetected molecular genetic etiology and to investigate their phenotypic associations with these variations. METHODS: This study included DSD cases without chromosomal abnormalities and without any variants detected by sequence analysis methods, including whole-exome sequencing analysis. We evaluated variant pathogenicity according to the American College of Medical Genetics and Genomics guidelines and recorded the phenotypic findings of the cases. All pathogenic variants were subjected to segregation analysis. RESULTS: Of the 20 patients included in the study, 16 (80%) were classified as 46,XY DSD and 4 (20%) as 46,XX DSD. Initial clinical diagnoses in this 46,XX DSD group included gonadal dysgenesis in two patients (50%) and androgen excess in the remaining two (50%). Among the 46,XY DSD patients, five patients (31.25%) were presumed to be androgen insensitive, nine (56.25%) were diagnosed with defects in androgen biosynthesis, and two (12.5%) had gonadal dysgenesis. CMA detected 38 CNVs in 16 patients (80%), comprising 12 deletions (31.6%) and 26 duplications (68.4%). Three pathogenic CNVs were detected in 3 patients (15%), whereas 27 variants of uncertain significance were identified in 13 patients (65%). CONCLUSION: In selected cases, the diagnostic approach should incorporate CMA to elucidate the molecular etiology of DSD. Furthermore, CMA may prove to be an invaluable tool in the search for new genes responsible for DSD.

Humans↗

PubMed2025

The MTORC1 signaling pathway related gene POLR3G serves as a potential prognostic biomarker in Hepatocellular Carcinoma.

This study aims to investigate the prognostic significance and potential biological functions of the MTORC1 signaling pathway-associated gene POLR3G in Hepatocellular carcinoma (HCC). A prognostic risk model for HCC was developed by integrating HCC-related datasets and associated clinical data obtained from The Cancer Genome Atlas (TCGA) database. The GSVA website was employed to analyze the model genes across pan-cancer datasets, focusing on copy number variations (CNV), single nucleotide variations (SNV), methylation differences, drug sensitivity and immune cell infiltration profiles. Subsequently, we examined the expression levels and prognostic significance of POLR3G in HCC. Utilizing Spearman correlation analysis, we identified genes associated with POLR3G. Furthermore, Gene Set Enrichment Analysis (GSEA) was employed to elucidate the potential signaling pathways in which POLR3G may be involved. The relationship between POLR3G expression and immune cell abundance in HCC samples was assessed using the ssGSEA algorithm. Finally, the impact of POLR3G on HCC cell proliferation was validated through CCK-8 and EDU cell proliferation assays. Through univariate Cox regression analysis and LASSO regression analysis, we established a prognostic risk model for HCC comprising 13 genes. The analysis revealed that individuals categorized in the low-risk group had a markedly improved overall survival probability relative to those in the high-risk group. POLR3G exhibited a markedly elevated expression in HCC tissues when compared to adjacent normal tissues. The expression of POLR3G was correlated with tumor grade, and elevated POLR3G expression was associated with poor prognosis in HCC patients. Furthermore, the expression level of POLR3G was found to be correlated with the level of immune cell infiltration. Knockdown of POLR3G significantly inhibited the proliferative capacity of hepatocellular carcinoma cells. The findings suggest that POLR3G may serve as a potential biomarker influencing the prognosis of hepatocellular carcinoma patients by modulating the tumor immune microenvironment.

Humans↗

PubMed2025

Comprehensive chromosomal abnormality detection: integrating CNV-Seq with traditional karyotyping in prenatal diagnostics.

BACKGROUND: This study aimed to evaluate the efficacy of copy number variation sequencing (CNV-Seq) in detecting chromosomal abnormalities in prenatal diagnosis, comparing its performance with traditional karyotype analysis. METHODS: A retrospective analysis was conducted on 1001 prenatal samples collected between April 2021 and December 2023. Samples were analyzed using both CNV-Seq and karyotype analysis. The detection rates of chromosomal abnormalities were compared between the two methods across various prenatal diagnostic indications. Clinical follow-up was performed to assess pregnancy outcomes. RESULTS: CNV-Seq detected chromosomal abnormalities in 89 of 1,001 cases (8.9%), compared to 50 cases (5.0%) identified by traditional karyotyping. CNV-Seq not only detected all abnormalities identified by karyotyping, including common aneuploidies such as trisomy 21 and sex chromosome abnormalities, but also uncovered 53 additional pathogenic submicroscopic CNVs associated with 33 known syndromes. The detection rates of CNV-Seq were significantly higher in high-risk groups, such as those identified by non-invasive prenatal testing (HR-NIPT) and maternal serum screening (HR-MSS), demonstrating superior sensitivity and accuracy in prenatal diagnostics. CONCLUSION: CNV-Seq demonstrated superior sensitivity in detecting chromosomal abnormalities, particularly submicroscopic alterations, compared to traditional karyotyping. The study highlights the potential of CNV-Seq as a valuable tool in prenatal diagnostics, offering improved detection of genetic abnormalities and guiding clinical decision-making. However, a combined approach using both CNV-Seq and karyotype analysis is recommended for comprehensive prenatal genetic screening.

Humans↗

PubMed2025

In silico generation of synthetic cancer genomes using generative AI.

Understanding how genomic alterations drive cancer is key to advancing precision oncology. To detect these alterations, accurate algorithms are used; however, due to privacy concerns, few deeply sequenced cancer genomes can be shared, limiting benchmarking and representing a major obstacle to the improvement of analytic tools. To address this, we developed OncoGAN, a generative AI model combining adversarial networks and variational autoencoders to create realistic synthetic cancer genomes. Trained on large-scale genomic datasets, OncoGAN accurately reproduces somatic mutations, copy number alterations, and structural variants across cancer types while preserving donors' privacy. The synthetic genomes reflect tumor-specific mutational signatures and positional mutation patterns. Using DeepTumour, we validated the synthetic data's fidelity, showing high concordance between generated and predicted tumors. Moreover, augmenting the training data with synthetic genomes improved DeepTumour's accuracy, underscoring OncoGAN's potential to generate shareable datasets with known ground truths for benchmarking and enhancement of cancer genome analysis tools.

Humans↗