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Demographics for breast cancers vary widely among nations. The frequency of germline mutations in breast cancers, which reflects the hereditary cases, has not been investigated adequately and accurately in highly-consanguineous Pakistani population. In the present discovery case series, germ-line mutations in twenty-seven breast cancer candidate genes were investigated in eighty-four sporadic breast cancer patients along with the clinical correlations. The germ-line variants were also assessed in two healthy gender-matched controls. The clinico-pathological features were evaluated by descriptive analysis and Pearson χ2 test (with significant p-value 40 years) and ethnicity. The breast cancer gene panel assay was carried out by BROCA, which is a genomic capture, massively parallel next generation sequencing assay on Illumina Hiseq2000 with 100bp read lengths. Copy number variations were determined by partially-mapped read algorithm. Once the mutation was identified, it was validated by Sanger sequencing. The ethnic analysis stratified on the basis of age showed that the frequency of breast cancer at young age (≤40 years) was higher in Sindhis (n = 12/19; 64%) in contrast to patients in other ethnic groups. Majority of the patients had stage III (38.1%), grade III (50%), tumor size 2–5 cm (54.8%), and invasive ductal carcinoma (81%). Overall, the analysis revealed germ-line mutations in 11.9% of the patients, which was not significantly associated with younger age or any particular ethnicity. The mutational spectrum was restricted to three genes: BRCA1, BRCA2, and TP53. The identified mutations consist of seven novel germ-line mutations, while three mutations have been reported previously. All the mutations are predicted to result in protein truncation. No mutations were identified in the remaining twenty-four candidate breast cancer genes. The present study provides the framework for the development of hereditary-based preventive and treatment strategies against breast cancers in Pakistani population.

The relative contributions of both copy number variants (CNVs) and single nucleotide polymorphisms (SNPs) to the additive genetic variance of carcass traits in cattle is not well understood. A detailed understanding of the relative importance of CNVs in cattle may have implications for study design of both genomic predictions and genome-wide association studies. The first objective of the present study was to quantify the relative contributions of CNV data and SNP genotype data to the additive genetic variance of carcass weight, fat, and conformation for 945 Charolais, 923 Holstein-Friesian, and 974 Limousin sires. The second objective was to jointly consider SNP and CNV data in a least absolute selection and shrinkage operator (LASSO) regression model to identify genomic regions associated with carcass weight, fat, and conformation within each of the three breeds separately. A genomic relationship matrix (GRM) based on just CNV data did not capture any variance in the three carcass traits when jointly evaluated with a SNP-derived GRM. In the LASSO regression analysis, a total of 987 SNPs and 18 CNVs were associated with at least one of the three carcass traits in at least one of the three breeds. The quantitative trait loci (QTLs) corresponding to the associated SNPs and CNVs overlapped with several candidate genes including previously reported candidate genes such as MSTN and RSAD2, and several potential novel candidate genes such as ACTN2 and THOC1. The results of the LASSO regression analysis demonstrated that CNVs can be used to detect associations with carcass traits which were not detected using the set of SNPs available in the present study. Therefore, the CNVs and SNPs available in the present study were not redundant forms of genomic data.

Background: Whole-exome sequencing (WES) has been recommended as a first-tier clinical diagnostic test for individuals with neurodevelopmental disorders (NDDs). We aimed to identify the genetic causes of 17 children with developmental delay (DD) and/or intellectual disability (ID).Methods: WES and exome-based copy number variation (CNV) analysis were performed for 17 patients with unexplained DD/ID.Results: Single-nucleotide variant (SNV)/small insertion or deletion (Indel) analysis and exome-based CNV calling yielded an overall diagnostic rate of 58.8% (10/17), of which diagnostic SNVs/Indels accounted for 41.2% (7/17) and diagnostic CNVs accounted for 17.6% (3/17).Conclusion: Our findings expand the known mutation spectrum of genes related to DD/ID and indicate that exome-based CNV analysis could improve the diagnostic yield of patients with DD/ID.

Alzheimer’s Disease (AD) is a progressive neurologic disease and the most common form of dementia. While the causes of AD are not completely understood, genetics plays a key role in the etiology of AD, and thus finding genetic factors holds the potential to uncover novel AD mechanisms. For this study, we focus on copy number variation (CNV) detection and burden analysis. Leveraging whole-genome sequence (WGS) data released by Alzheimer’s Disease Sequencing Project (ADSP), we developed a scalable bioinformatics pipeline to identify CNVs. This pipeline was applied to 1,737 AD cases and 2,063 cognitively normal controls. As a result, we observed 237,306 and 42,767 deletions and duplications, respectively, with an average of 2,255 deletions and 1,820 duplications per subject. The burden tests show that Non-Hispanic-White cases on average have 16 more duplications than controls do (p-value 2e-6), and Hispanic cases have larger deletions than controls do (p-value 6.8e-5).

Copy number variations (CNVs) are important structural variations that can cause significant phenotypic diversity. Reliable CNVs mapping can be achieved by identification of CNVs from different genetic backgrounds. Investigations on the characteristics of overlapping between CNV regions (CNVRs) and protein-coding genes (CNV genes) or miRNAs (CNV-miRNAs) can reveal the potential mechanisms of their regulation. In this study, we used 50 K SNP arrays to detect CNVs in Duroc purebred pig. A total number of 211 CNVRs were detected with a total length of 118.48 Mb, accounting for 5.23% of the autosomal genome sequence. Of these CNVRs, 32 were gains, 175 losses, and four contained both types (loss and gain within the same region). The CNVRs we detected were non-randomly distributed in the swine genome and were significantly enriched in the segmental duplication and gene density region. Additionally, these CNVRs were overlapping with 1,096 protein-coding genes (CNV-genes), and 39 miRNAs (CNV-miRNAs), respectively. The CNV-genes were enriched in terms of dosage-sensitive gene list. The expression of the CNV genes was significantly higher than that of the non-CNV genes in the adult Duroc prostate. Of all detected CNV genes, 22.99% genes were tissue-specific (TSI > 0.9). Strong negative selection had been underway in the CNV-genes as the ones that were located entirely within the loss CNVRs appeared to be evolving rapidly as determined by the median dN plus dS values. Non-CNV genes tended to be miRNA target than CNV-genes. Furthermore, CNV-miRNAs tended to target more genes compared to non-CNV-miRNAs, and a combination of two CNV-miRNAs preferentially synergistically regulated the same target genes. We also focused our efforts on examining CNV genes and CNV-miRNAs functions, which were also involved in the lipid metabolism, including DGAT1, DGAT2, MOGAT2, miR143, miR335, and miRLET7. Further molecular experiments and independent large studies are needed to confirm our findings.

Schizophrenia is a chronic, devastating mental disorder with complex genetic components. Given the advancements in the molecular genetic research of schizophrenia in recent years, there is still a lack of genetic tests that can be used in clinical settings. Chromosomal microarray analysis (CMA) has been used as first-tier genetic testing for congenital abnormalities, developmental delay, and autism spectrum disorders. This study attempted to gain some experience in applying chromosomal microarray analysis as a first-tier genetic test for patients with schizophrenia. We consecutively enrolled patients with schizophrenia spectrum disorder from a clinical setting and conducted genome-wide copy number variation (CNV) analysis using a chromosomal microarray platform. We followed the 2020 “Technical Standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen)” to interpret the clinical significance of CNVs detected from patients. We recruited a total of 60 patients (36 females and 24 males) into this study. We detected three pathogenic CNVs and one likely pathogenic CNV in four patients, respectively. The detection rate was 6.7% (4/60, 95% CI: 0.004–0.13), comparable with previous studies in the literature. Also, we detected thirteen CNVs classified as uncertain clinical significance in nine patients. Detecting these CNVs can help establish the molecular genetic diagnosis of schizophrenia patients and provide helpful information for genetic counseling and clinical management. Also, it can increase our understanding of the pathogenesis of schizophrenia. Hence, we suggest CMA is a valuable genetic tool and considered first-tier genetic testing for schizophrenia spectrum disorders in clinical settings.

Microduplications and reciprocal microdeletions of chromosome 1q21. 1 and/or 1q21.2 have been linked to variable clinical features, but the underlying pathogenic gene(s) remain unclear. Here we report that distinct microduplications were detected on chromosome 1q21.2 (GRCh37/hg19) in a mother (255 kb in size) and her newborn daughter (443 kb in size), while the same paternal locus was wild-type. Although the two microduplications largely overlap in genomic sequence (183 kb overlapping), the mother showed no clinical phenotype while the daughter presented with several features that are commonly observed on 1q21 microduplication or microdeletion patients, including developmental delay, craniofacial dysmorphism, congenital heart disease and sensorineural hearing loss. NBPF15 and NBPF16, two involved genes that are exclusively duplicated in the proband, may be the cause of the clinical manifestations. This study supports an association between NBPF genes and 1q21 copy number variation disorders.

Background: Low-pass genome sequencing (GS) detects clinically significant copy number variants (CNVs) in prenatal diagnosis. However, detection at improved resolutions leads to an increase in the number of CNVs identified, increasing the difficulty of clinical interpretation and management.Methods: Trio-based low-pass GS was performed in 315 pregnancies undergoing invasive testing. Rare CNVs detected in the fetuses were investigated. The characteristics of rare CNVs were described and compared to curated CNVs in other studies.Results: A total of 603 rare CNVs, namely, 597 constitutional and 6 mosaic CNVs, were detected in 272 fetuses (272/315, 86.3%), providing 1.9 rare CNVs per fetus (603/315). Most CNVs were smaller than 1 Mb (562/603, 93.2%), while 1% (6/603) were mosaic. Forty-six de novo (7.6%, 46/603) CNVs were detected in 11.4% (36/315) of the cases. Eighty-four CNVs (74 fetuses, 23.5%) involved disease-causing genes of which the mode of inheritance was crucial for interpretation and assessment of recurrence risk. Overall, 31 pathogenic/likely pathogenic CNVs were detected, among which 25.8% (8/31) were small (n = 3) or mosaic CNVs (n = 5).Conclusion: We examined the landscape of rare CNVs with parental inheritance assignment and demonstrated that they occur frequently in prenatal diagnosis. This information has clinical implications regarding genetic counseling and consideration for trio-based CNV analysis.

Lung squamous cell carcinoma (LUSC) has a poor clinical prognosis and a lack of available targeted therapies. Therefore, there is an urgent need to identify novel prognostic markers and therapeutic targets to assist in the diagnosis and treatment of LUSC. With the development of high-throughput sequencing technology, integrated analysis of multi-omics data will provide annotation of pathogenic non-coding variants and the role of non-coding sequence variants in cancers. Here, we integrated RNA-seq profiles and copy number variation (CNV) data to study the effects of non-coding variations on gene regulatory network. Furthermore, the 372 long non-coding RNAs (lncRNA) regulated by CNV were used as candidate genes, which could be used as biomarkers for clinical application. Nine lncRNAs including LINC00896, MCM8-AS1, LINC01251, LNX1-AS1, GPRC5D-AS1, CTD-2350J17.1, LINC01133, LINC01121, and AC073130.1 were recognized as prognostic markers for LUSC. By exploring the association of the prognosis-related lncRNAs (pr-lncRNAs) with immune cell infiltration, GPRC5D-AS1 and LINC01133 were highlighted as markers of the immunosuppressive microenvironment. Additionally, the cascade response of pr-lncRNA-CNV-mRNA-physiological functions was revealed. Taken together, the identification of prognostic markers and carcinogenic regulatory mechanisms will contribute to the individualized treatment for LUSC and promote the development of precision medicine.

Genomic studies have been a major approach to elucidating disease etiology and to exploring potential targets for treatments of many complex diseases. Statistical analyses in these studies often face the challenges of multiplicity, weak signals, and the nature of dependence among genetic markers. This situation becomes even more complicated when multi-omics data are available. To integrate the data from different platforms, various integrative analyses have been adopted, ranging from the direct union or intersection operation on sets derived from different single-platform analysis to complex hierarchical multi-level models. The former ignores the biological relationship between molecules while the latter can be hard to interpret. We propose in this study an integrative approach that combines both single nucleotide variants (SNVs) and copy number variations (CNVs) in the same genomic unit to co-localize the concurrent effect and to deal with the sparsity due to rare variants. This approach is illustrated with simulation studies to evaluate its performance and is applied to low-density lipoprotein cholesterol and triglyceride measurements from Taiwan Biobank. The results show that the proposed method can more effectively detect the collective effect from both SNVs and CNVs compared to traditional methods. For the biobank analysis, the identified genetic regions including the gene VNN2 could be novel and deserve further investigation.

Neurodevelopmental disorders (NDDs) are a heterogeneous class of brain diseases, with a complex genetic basis estimated to account for up to 50% of cases. Nevertheless, genetic diagnostic yield is about 20%. Array-comparative genomic hybridization (array-CGH) is an established first-level diagnostic test able to detect pathogenic copy number variants (CNVs), however, most identified variants remain of uncertain significance (VUS). Failure of interpretation of VUSs may depend on various factors, including complexity of clinical phenotypes and inconsistency of genotype-phenotype correlations. Indeed, although most NDD-associated CNVs are de novo, transmission from unaffected parents to affected children of CNVs with high risk for NDDs has been observed. Moreover, variability of genetic components overlapped by CNVs, such as long non-coding genes, genomic regions with long-range effects, and additive effects of multiple CNVs can make CNV interpretation challenging. We report on 12 patients with complex phenotypes possibly explained by complex genetic mechanisms, including involvement of antisense genes and boundaries of topologically associating domains. Eight among the 12 patients carried two CNVs, either de novo or inherited, respectively, by each of their healthy parents, that could additively contribute to the patients’ phenotype. CNVs overlapped either known NDD-associated or novel candidate genes (PTPRD, BUD13, GLRA3, MIR4465, ABHD4, and WSCD2). Bioinformatic enrichment analyses showed that genes overlapped by the co-occurring CNVs have synergistic roles in biological processes fundamental in neurodevelopment. Double CNVs could concur in producing deleterious effects, according to a two-hit model, thus explaining the patients’ phenotypes and the incomplete penetrance, and variable expressivity, associated with the single variants. Overall, our findings could contribute to the knowledge on clinical and genetic diagnosis of complex forms of NDD.

Nagashima-type palmoplantar keratoderma (NPPK) is characterized by non-progressive, diffuse, and cross-gradient hyperkeratosis caused by mutations in the SERPINB7 gene on chromosome 18q21.33. Chromosome 18q deletion syndrome (18q- syndrome) is a terminal deletion or microdeletion syndrome characterized by intellectual disability and congenital malformations. This paper describes an 18-year-old man with palmoplantar keratoderma and diffuse white matter abnormalities in the brain. Trio-based exome sequencing (ES) revealed a suspected mosaic compound heterozygous mutation for c.796C>T (p.Arg266∗) in exon 8 inherited from the mother and a de novo exons 4–6 deletion of SERPINB7. Additional copy number variant (CNV) analysis of the ES data indicated a heterozygous gross deletion of 18q22.3-q23. The two SERPINB7 gene variants were verified by Sanger sequencing and quantitative real-time polymerase chain reaction (qRT-PCR). Finally, low-coverage whole-genome sequencing (WGS) confirmed the 18q22.3-q23 deletion and additionally detected a mosaic 18q21.33-q22.3 deletion, together explaining NPPK and the neurological phenotypes of the proband. The gross deletion of all exons of SERPINB7 was revealed for the first time. More rarely, c.796C>T (p.Arg266∗) was likely to be mosaic, while the exon deletion was mosaic. In conclusion, the combination of multiple molecular genetic testing methods provides comprehensive informative molecular findings and promotes the diagnosis of complex diseases, as in this case.

Genome editing in pigs has been made efficient, practical, and economically viable by the CRISPR/Cas9 platform, representing a promising new era in translational modeling of human disease for research and preclinical development of therapies and devices. Porcine embryo microinjection provides a universally available, efficient option over somatic-cell nuclear transfer, but requires that critical considerations be made in genotypic validation of the models that routinely go unaddressed. Accurate validation of genotypes is especially important when modeling genetic disorders, such as neurofibromatosis type 1 (NF1) that exhibits complex genotype–phenotypic relationships. NF1, an autosomal dominant disorder, is particularly hard to model as it manifests very differently across patients, and even within families, with over 3,000 disease-associated mutations of the neurofibromin 1 (NF1) gene identified. The precise nature of the mutations plays a role in the complex phenotypic presentation of the disorder that includes benign and malignant peripheral and central nervous system tumors, a variety of motor deficits and debilitating cognitive impairments and musculoskeletal, cardiovascular, and gastrointestinal disorders. NF1 can also often involve mutations in passenger genes such as TP53. In this manuscript, we describe the creation of three novel porcine models of NF1 and a model additionally harboring a mutation in TP53 by embryo microinjection of CRISPR/Cas9. We present the challenges encountered in validation of genotypes and the methodological strategies developed to counter the hurdles. We present simple options for quantifying level of mosaicism: a quantitative method (targeted amplicon sequencing) for small edits such as SNPs and indels and a semiquantitative method (competitive PCR) for large edits. Characterization of mosaicism allowed for strategic selection of founder pigs for rapid, economical expansion of genetically defined lines. We also present commonly observed unexpected DNA repair products (i.e., structural variants or cryptic alleles) that are refractory to PCR amplification and thus evade detection. We present the use of copy number variance assays to overcome hurdles in detecting cryptic alleles. The report provides a framework for genotypic validation of porcine models created by embryo microinjection and the expansion of lines in an efficient manner.

Background: Mutations in the STRC (MIM 606440) gene, inducing DFNB16, are considered a major cause of mild–moderate autosomal recessive non-syndromic hearing loss (ARNSHL). We conducted a systematic review and meta-analysis to determine the global prevalence and characteristics of STRC variations, important information required for genetic counseling.Methods: PubMed, Google Scholar, Medline, Embase, and Web of Science were searched for relevant articles published before January 2021.Results: The pooled prevalence of DFNB16 in GJB2-negative patients with hearing loss was 4.08% (95% CI: 0.0289–0.0573), and the proportion of STRC variants in the mild–moderate hearing loss group was 14.36%. Monoallelic mutations of STRC were 4.84% (95% CI: 0.0343–0.0680) in patients with deafness (non-GJB2) and 1.36% (95% CI: 0.0025–0.0696) in people with normal hearing. The DFNB16 prevalence in genetically confirmed patients (non-GJB2) was 11.10% (95% CI: 0.0716–0.1682). Overall pooled prevalence of deafness–infertility syndrome (DIS) was 36.75% (95% CI: 0.2122–0.5563) in DFNB16. The prevalence of biallelic deletions in STRC gene mutations was 70.85% (95% CI: 0.5824–0.8213).Conclusion: Variants in the STRC gene significantly contribute to mild–moderate hearing impairment. Moreover, biallelic deletions are a main feature of STRC mutations. Copy number variations associated with infertility should be seriously considered when investigating DFNB16.

The purpose of this study was to explore the copy number variations (CNVs) associated with miscarriage during early and middle pregnancy and provide useful genetic guidance for pregnancy and prenatal diagnosis. A total of 505 fetal specimens were collected and CNV sequencing (CNV-seq) analysis was performed to determine the types and clinical significance of CNVs, and relevant medical records were collected. The chromosomal abnormality rate was 54.3% (274/505), among which the numerical chromosomal abnormality rate was 40.0% (202/505) and structural chromosomal abnormality rate was 14.3% (72/505). Chromosomal monosomy mainly occurred on sex chromosomes, and chromosomal trisomy mainly occurred on chromosomes 16, 22, 21, 15, 13, and 9. The incidence of numerical chromosomal abnormalities in ≥35 year-old age pregnant women was significantly higher than p < 0.001). There were 168 genes in VOUS + pCNV regions. 41 functions and 12 pathways (p < 0.05) were enriched of these genes by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Some meaningful genetic etiology information such as genes and pathways has been obtained, it may provide useful genetic guidance for pregnancy and prenatal diagnosis.

Comprehensive maps of genetic interactions in mammalian cells are daunting to construct because of the large number of potential interactions, ~ 2 × 108 for protein coding genes. We previously used co-inheritance of distant genes from published radiation hybrid (RH) datasets to identify genetic interactions. However, it was necessary to combine six legacy datasets from four species to obtain adequate statistical power. Mapping resolution was also limited by the low density PCR genotyping. Here, we employ shallow sequencing of nascent human RH clones as an economical approach to constructing interaction maps. In this initial study, 15 clones were analyzed, enabling construction of a network with 225 genes and 2359 interactions (FDR < 0.05). Despite its small size, the network showed significant overlap with the previous RH network and with a protein-protein interaction network. Consumables were ≲ $50 per clone, showing that affordable, high quality genetic interaction maps are feasible in mammalian cells.

BackgroundAs immunotherapy has received attention as new treatments for brain cancer, the role of inflammation in the process of glioma is of particular importance. Increasing studies have further shown that long non-coding RNAs (lncRNAs) are important factors that promote the development of glioma. However, the relationship between inflammation-related lncRNAs and the prognosis of glioma patients remains unclear. The purpose of this study is to construct and validate an inflammation-related lncRNA prognostic signature to predict the prognosis of low-grade glioma patients.MethodsBy downloading and analyzing the gene expression data and clinical information of the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) patients with low-grade gliomas, we could screen for inflammatory gene-related lncRNAs. Furthermore, through Cox and the Least Absolute Shrinkage and Selection Operator regression analyses, we established a risk model and divided patients into high- and low-risk groups based on the median value of the risk score to analyze the prognosis. In addition, we analyzed the tumor mutation burden (TMB) between the two groups based on somatic mutation data, and explored the difference in copy number variations (CNVs) based on the GISTIC algorithm. Finally, we used the MCPCounter algorithm to study the relationship between the risk model and immune cell infiltration, and used gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to explore the enrichment pathways and biological processes of differentially expressed genes between the high- and low-risk groups.ResultsA novel prognostic signature was constructed including 11 inflammatory lncRNAs. This risk model could be an independent prognostic predictor. The patients in the high-risk group had a poor prognosis. There were significant differences in TMB and CNVs for patients in the high- and low-risk groups. In the high-risk group, the immune system was activated more significantly, and the expression of immune checkpoint-related genes was also higher. The GSEA, GO, and KEGG analyses showed that highly expressed genes in the high-risk group were enriched in immune-related processes, while lowly expressed genes were enriched in neuromodulation processes.ConclusionThe risk model of 11 inflammation-related lncRNAs can serve as a promising prognostic biomarker for low-grade gliomas patients.

BackgroundProstate cancer (PCa) is among the leading causes of cancer mortality. Dicycloplatin is a newer generation platinum-based drug that has less side effects than cisplatin and carboplatin. However, its effects in PCa is mixed due to lack of appropriate stratifying biomarkers. Aiming to search for such biomarkers, here, we analyze a group of PCa patients with different responses to dicycloplatin.MethodsWe carried out whole-exome sequencing on cell-free DNA (cfDNA) and matched leukocyte DNA from 16 PCa patients before treatment with dicycloplatin. We then compared the clinical characteristics, somatic mutations, copy number variants (CNVs), and mutational signatures between the dicycloplatin-sensitive (nine patients) and dicycloplatin-resistant (seven patients) groups and tested the identified mutations, CNV, and their combinations as marker of dicycloplatin response.ResultsThe mutation frequency of seven genes (SP8, HNRNPCL1, FRG1, RBM25, MUC16, ASTE1, and TMBIM4) and CNV rate of four genes (CTAGE4, GAGE2E, GAGE2C, and HORMAD1) were higher in the resistant group than in the sensitive group, while the CNV rate in six genes (CDSN, DPCR1, MUC22, TMSB4Y, VARS, and HISTCH2AC) were lower in the resistant group than in the sensitive group. A combination of simultaneous mutation in two genes (SP8/HNRNPCL1 or SP8/FRG1) and deletion of GAGE2C together were found capable to predict dicycloplatin resistance with 100% sensitivity and 100% specificity.ConclusionWe successfully used cfDNA to monitor mutational profiles of PCa and designed an effective composite marker to select patients for dicycloplatin treatment based on their mutational profile.

N6-methyladenosine (m6A) RNA modification can alter gene expression and function by regulating RNA splicing, stability, translocation, and translation. It is involved in various types of cancer. However, its role in gliomas is not well known. This study aimed to determine the prognostic value of the m6A RNA methylation regulator in gliomas and investigate the underlying mechanisms of the aberrant expression of m6A-related genes.mRNA expression profiles and clinical information of 448 glioma samples were obtained from The Cancer Genome Atlas and cBioportal. The expression of m6A-related genes in normal controls and low-grade glioma and glioblastoma was obtained from Gene Expression Profiling Interactive Analysis. Further, m6A-related gene expression and its relationship with prognosis were obtained through The Chinese Glioma Genome Atlas (CGGA). Multivariate Cox regression analyses were performed, and a nomogram was built with potential risk factors based on a multivariate Cox analysis to predict survival probability. Online tools such as Gene Set Enrichment Analysis, STRING, Cytoscape, and Molecular Complex Detection were applied for bioinformatics analysis and to investigate the underlying mechanisms of the aberrant expression of m6A-related genes. The multivariate Cox regression analysis found that higher expression levels of YTHDC2 and insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3, also called IMP3) were independent negative and positive prognostic factors for overall survival (OS), respectively. Data from the CGGA database showed that IGF2BP3 expression increased when the tumor grade increased. Receiver operating characteristic (ROC) curve was used to evaluate the predictive specificity and sensitivity. The area under the ROC curve indicated that the OS prediction was 0.92 (1-year) and 0.917 (3-years), indicating that m6A-related genes could predict patient survival. In addition, IGF2BP3 was closely related to the shorter survival period of patients. Copy number variation and DNA methylation, but not somatic mutations, might contribute to the abnormal upregulation of IGF2BP3 in gliomas. Significantly altered genes were identified, and the protein–protein interaction network was constructed. Based on the data presented, our study identified several m6A-related genes, especially IGF2BP3, that could be potential prognostic biomarkers of gliomas. The study unveiled the potential regulatory mechanism of IGF2BP3 in gliomas.

ObjectiveTo evaluate the effectiveness of non-invasive prenatal screening (NIPS) in prenatal screening of fetal pathogenic copy number variants (CNVs).Materials and MethodsWe evaluated the prenatal screening capacity using traditional and retrospective approaches. For the traditional method, we evaluated 24,613 pregnant women who underwent NIPS; cases which fetal CNVs were suggested underwent prenatal diagnosis with chromosomal microarray analysis (CMA). For the retrospective method, we retrospectively evaluated 47 cases with fetal pathogenic CNVs by NIPS. A systematic literature search was performed to compare the evaluation efficiency.ResultsAmong the 24,613 pregnant women who received NIPS, 124 (0.50%) were suspected to have fetal CNVs. Of these, 66 women underwent prenatal diagnosis with CMA and 13 had true-positive results. The positive predictive value (PPV) of NIPS for fetal CNVs was 19.7%. Among 1,161 women who did not receive NIPS and underwent prenatal diagnosis by CMA, 47 were confirmed to have fetal pathogenic CNVs. Retesting with NIPS indicated that 24 of these 47 cases could also be detected by NIPS, representing a detection rate (DR) of 51.1%. In total, 10 publications, namely, six retrospective studies and four prospective studies, met our criteria and were selected for a detailed full-text review. The reported DRs were 61.10–97.70% and the PPVs were 36.11–80.56%. The sizes of CNVs were closely related to the accuracy of NIPS detection. The DR was 41.9% (13/31) in fetuses with CNVs ≤ 3 Mb, but was 55.0% (11/20) in fetuses with CNVs > 3 Mb. Finally, to intuitively show the CNVs accurately detected by NIPS, we mapped all CNVs to chromosomes according to their location, size, and characteristics. NIPS detected fetal CNVs in 2q13 and 4q35.ConclusionThe DR and PPV of NIPS for fetal CNVs were approximately 51.1% and 19.7%, respectively. Follow-up molecular prenatal diagnosis is recommended in cases where NIPS suggests fetal CNVs.

BackgroundHereditary cancer predisposition syndromes account for approximately 10% of cancer cases. Next generation sequencing (NGS) based multi-gene targeted panels is now a frontline approach to identify pathogenic mutations in cancer predisposition genes in high-risk families. Recent evolvement of NGS technologies have allowed simultaneous detection of sequence and copy number variants (CNVs) using a single platform. In this study, we have analyzed frequency and nature of sequence variants and CNVs, in a Canadian cohort of patients, suspected with hereditary cancer syndrome, referred for genetic testing following specific genetic testing guidelines based on patient’s personal and/or family history of cancer.MethodsA 2870 patients were subjected to a single NGS based multi-gene targeted hereditary cancer panel testing algorithm to identify sequence variants and CNVs in cancer predisposition genes at our reference laboratory in Southwestern Ontario. CNVs identified by NGS were confirmed by alternative techniques like Multiplex ligation-dependent probe amplification (MLPA).ResultsA 15% (431/2870) patients had a pathogenic variant and 36% (1032/2870) had a variant of unknown significance (VUS), in a cancer susceptibility gene. A total of 287 unique pathogenic variant were identified, out of which 23 (8%) were novel. CNVs identified by NGS based approach accounted for 9.5% (27/287) of pathogenic variants, confirmed by alternate techniques with high accuracy.ConclusionThis study emphasizes the utility of NGS based targeted testing approach to identify both sequence and CNVs in patients suspected with hereditary cancer syndromes in clinical setting and expands the mutational spectrum of high and moderate penetrance cancer predisposition genes.

ObjectivesThe objective of the study was to explore the added value of whole-exome sequencing (WES) in abnormal fetuses with detailed prenatal ultrasound and postnatal phenotype with normal karyotype and chromosomal microarray analysis (CMA).MethodsParents of fetuses with structural abnormalities by prenatal ultrasound who consented to provide fetal samples were prospectively recruited from January 2017 to December 2019. With aneuploidies or cases with copy number variations (CNVs) excluded, WES was performed for cases with normal karyotype and CMA results. Detailed prenatal ultrasound and postnatal imaging or pathology features were recommended for further interpretation of genetic variants.ResultsWES was performed for 94 eligible fetuses, DNA samples of which were extracted from 53 parent–fetus trios and 41 proband-only fetal tissues. A diagnostic genetic variant was identified in 37 (39.4%) of 94 fetuses, and 34 (64.2%) were detected in 53 trios, which was significantly greater than 3 (7.3%) in 41 proband-only cases (p < 0.001). In 34 trios with diagnostic genetic variants, 23 (67.6%) were de novo and 11 (32.4%) were inherited with two homozygous and nine heterozygous variants. Fourteen (14.9%) of 94 fetuses had a variant of uncertain significance (VUS). Among 94 cases, six affected pregnancies continued and 88 terminated, and 57 of 88 terminated cases underwent postmortem examinations. With accurate phenotypes demonstrated by prenatal ultrasound and postnatal autopsies, the clinical phenotypes were correlated in 33 (89.2%) of 37 cases with specific genotypes, with the highest matching ratio in skeletal diseases (20/33, 60.6%).ConclusionWES has added value in the genetic diagnosis of abnormal fetuses with normal karyotypes and CMA, particularly in skeletal diseases. Using WES in various anomalous fetuses can broaden the understanding of prenatal phenotypes and genetic variants.

Copy number variation (CNV) may contribute to the development of complex diseases. However, due to the complex mechanism of path association and the lack of sufficient samples, understanding the relationship between CNV and cancer remains a major challenge. The unprecedented abundance of CNV, gene, and disease label data provides us with an opportunity to design a new machine learning framework to predict potential disease-related CNVs. In this paper, we developed a novel machine learning approach, namely, IHI-BMLLR (Integrating Heterogeneous Information sources with Biweight Mid-correlation and L1-regularized Logistic Regression under stability selection), to predict the CNV-disease path associations by using a data set containing CNV, disease state labels, and gene data. CNVs, genes, and diseases are connected through edges and then constitute a biological association network. To construct a biological network, we first used a self-adaptive biweight mid-correlation (BM) formula to calculate correlation coefficients between CNVs and genes. Then, we used logistic regression with L1 penalty (LLR) function to detect genes related to disease. We added stability selection strategy, which can effectively reduce false positives, when using self-adaptive BM and LLR. Finally, a weighted path search algorithm was applied to find topDpath associations and important CNVs. The experimental results on both simulation and prostate cancer data show that IHI-BMLLR is significantly better than two state-of-the-art CNV detection methods (i.e., CCRET and DPtest) under false-positive control. Furthermore, we applied IHI-BMLLR to prostate cancer data and found significant path associations. Three new cancer-related genes were discovered in the paths, and these genes need to be verified by biological research in the future.

The next-generation sequencing technology offers a wealth of data resources for the detection of copy number variations (CNVs) at a high resolution. However, it is still challenging to correctly detect CNVs of different lengths. It is necessary to develop new CNV detection tools to meet this demand. In this work, we propose a new CNV detection method, called CBCNV, for the detection of CNVs of different lengths from whole genome sequencing data. CBCNV uses a clustering algorithm to divide the read depth segment profile, and assigns an abnormal score to each read depth segment. Based on the abnormal score profile, Tukey’s fences method is adopted in CBCNV to forecast CNVs. The performance of the proposed method is evaluated on simulated data sets, and is compared with those of several existing methods. The experimental results prove that the performance of CBCNV is better than those of several existing methods. The proposed method is further tested and verified on real data sets, and the experimental results are found to be consistent with the simulation results. Therefore, the proposed method can be expected to become a routine tool in the analysis of CNVs from tumor-normal matched samples.

Detection of CNVs (copy number variants) and ROH (runs of homozygosity) from SNP (single nucleotide polymorphism) genotyping data is often required in genomic studies. The post-analysis of CNV and ROH generally involves many steps, potentially across multiple computing platforms, which requires the researchers to be familiar with many different tools. In order to get around this problem and improve research efficiency, we present an R package that integrates the summarization, annotation, map conversion, comparison and visualization functions involved in studies of CNV and ROH. This one-stop post-analysis system is standardized, comprehensive, reproducible, timesaving, and user-friendly for researchers in humans and most diploid livestock species.

C3 Glomerulopathy (C3G) and Immune Complex-Mediated Membranoproliferative glomerulonephritis (IC-MPGN) are rare diseases characterized by glomerular deposition of C3 caused by dysregulation of the alternative pathway (AP) of complement. In approximately 20% of affected patients, dysregulation is driven by pathogenic variants in the two components of the AP C3 convertase, complement C3 (C3) and Factor B (CFB), or in complement Factor H (CFH) and Factor I (CFI), two genes that encode complement regulators. Copy number variations (CNVs) involving the CFH-related genes (CFHRs) that give rise to hybrid FHR proteins also have been described in a few C3G patients but not in IC-MPGN patients. In this study, we used multiplex ligation-dependent probe amplification (MLPA) to study the genomic architecture of the CFH-CFHR region and characterize CNVs in a large cohort of patients with C3G (n = 103) and IC-MPGN (n = 96) compared to healthy controls (n = 100). We identified new/rare CNVs resulting in structural variants (SVs) in 5 C3G and 2 IC-MPGN patients. Using long-read single molecule real-time sequencing (SMRT), we detected the breakpoints of three SVs. The identified SVs included: 1) a deletion of the entire CFH in one patient with IC-MPGN; 2) an increased number of CFHR4 copies in one IC-MPGN and three C3G patients; 3) a deletion from CFHR3-intron 3 to CFHR3-3′UTR (CFHR34–6Δ) that results in a FHR3-FHR1 hybrid protein in a C3G patient; and 4) a CFHR31–5-CFHR410 hybrid gene in a C3G patient. This work highlights the contribution of CFH-CFHR CNVs to the pathogenesis of both C3G and IC-MPGN.

Copy number variation (CNV) is a genomic mutation that plays an important role in tumor evolution and tumor genesis. Accurate detection of CNVs from next-generation sequencing (NGS) data is still a challenging task due to artifacts such as uneven mapped reads and unbalanced amplitudes of gains and losses. This study proposes a new approach called HBOS-CNV to detect CNVs from NGS data. The central point of HBOS-CNV is that it uses a new statistic, the histogram-based outlier score (HBOS), to evaluate the fluctuation of genome bins to determine those of changed copy numbers. In comparison with existing statistics in the evaluation of CNVs, HBOS is a non-linearly transformed value from the observed read depth (RD) value of each genome bin, having the potential ability to relieve the effects resulted from the above artifacts. In the calculation of HBOS values, a dynamic width histogram is utilized to depict the density of bins on the genome being analyzed, which can reduce the effects of noises partially contributed by mapping and sequencing errors. The evaluation of genome bins using such a new statistic can lead to less extremely significant CNVs having a high probability of detection. We evaluated this method using a large number of simulation datasets and compared it with four existing methods (CNVnator, CNV-IFTV, CNV-LOF, and iCopyDav). The results demonstrated that our proposed method outperforms the others in terms of sensitivity, precision, and F1-measure. Furthermore, we applied the proposed method to a set of real sequencing samples from the 1000 Genomes Project and determined a number of CNVs with biological meanings. Thus, the proposed method can be regarded as a routine approach in the field of genome mutation analysis for cancer samples.

Neurofibromatosis type 1 is a tumor predisposition syndrome inherited in autosomal dominant manner. Besides the intragenic loss-of-function mutations in NF1 gene, large deletions encompassing the NF1 gene and its flanking regions are responsible for the development of the variable clinical phenotype. These large deletions titled as NF1 microdeletions lead to a more severe clinical phenotype than those observed in patients with intragenic NF1 mutations. Around 5-10% of the cases harbor large deletion and four major types of NF1 microdeletions (type 1, 2, 3 and atypical) have been identified so far. They are distinguishable in term of their size and the location of the breakpoints, by the frequency of somatic mosaicism with normal cells not harboring the deletion and by the number of the affected genes within the deleted region. In our study genotype-phenotype analyses have been performed in 17 mostly pediatric patients with NF1 microdeletion syndrome identified by multiplex ligation-dependent probe amplification after systematic sequencing of the NF1 gene. Confirmation and classification of the NF1 large deletions were performed using array comparative genomic hybridization, where it was feasible. In our patient cohort 70% of the patients possess type-1 deletion, one patient harbors type-2 deletion and 23% of our cases have atypical NF1 deletion. All the atypical deletions identified in this study proved to be novel. One patient with atypical deletion displayed mosaicism. In our study NF1 microdeletion patients presented dysmorphic facial features, macrocephaly, large hands and feet, delayed cognitive development and/or learning difficulties, speech difficulties, overgrowth more often than patients with intragenic NF1 mutations. Moreover, neurobehavior problems, macrocephaly and overgrowth were less frequent in atypical cases compared to type-1 deletion. Proper diagnosis is challenging in certain patients since several clinical manifestations show age-dependency. Large tumor load exhibited more frequently in this type of disorder, therefore better understanding of genotype-phenotype correlations and progress of the disease is essential for individuals suffering from neurofibromatosis to improve the quality of their life. Our study presented additional clinical data related to NF1 microdeletion patients especially for pediatric cases and it contributes to the better understanding of this type of disorder.

Germline copy number variant (gCNV) has been studied as a genetic determinant for prognosis of several types of cancer, but little is known about how it affects non-small cell lung cancer (NSCLC) prognosis. We aimed to develop a prognostic nomogram for NSCLC based on gCNVs. Promising gCNVs that are associated with overall survival (OS) of NSCLC were sorted by analyzing the TCGA data and were validated in a small Chinese population. Then the successfully verified gCNVs were determined in a training cohort (n = 570) to develop a prognostic nomogram, and in a validation cohort (n = 465) to validate the nomogram. Thirty-five OS-related gCNVs were sorted and were reduced to 15 predictors by the Lasso regression analysis. Of them, only CNVR395.1 and CNVR2239.1 were confirmed to be associated with OS of NSCLC in the Chinese population. High polygenic risk score (PRS), which was calculated by the hazard effects of CNVR395.1 and CNVR2239.1, exerted a significantly higher death rate in the training cohort (HR = 1.41, 95%CI: 1.16–1.74) and validation cohort (HR = 1.42, 95%CI: 1.13–1.77) than low PRS. The nomogram incorporating PRS and surrounding factors, achieved admissible concordance indexes of 0.678 (95%CI: 0.664–0.693) and 0.686 (95%CI: 0.670–0.702) in predicting OS in the training and validation cohorts, respectively, and had well-fitted calibration curves. Moreover, an interaction between PRS and asbestos exposure was observed on affecting OS (Pinteraction = 0.042). Our analysis developed a nomogram that achieved an admissible prediction of NSCLC survival, which would be beneficial to the personalized intervention of NSCLC.

In the precision medicine of lung adenocarcinoma, the identification and prediction of tumor phenotypes for specific biomolecular events are still not studied in depth. Various earlier researches sheds light on the close correlation between genetic expression signatures and DNA copy number variations (CNVs), for which analysis of CNVs provides valuable information about molecular and phenotypic changes in tumorigenesis. In this study, we propose a comprehensive analysis combining genome-wide association analysis and an Elastic Net Regression predictive model, focus on predicting the levels of many gene expression signatures in lung adenocarcinoma, based upon DNA copy number features alone. Additionally, we predicted many other key phenotypes, including clinical features (pathological stage), gene mutations, and protein expressions. These Elastic Net prediction methods can also be applied to other gene sets, thereby facilitating their use as biomarkers in monitoring therapy.

Copy number variations (CNVs) are a major source of structural variation in mammalian genomes. Here, we characterized the genome-wide CNV in 2059 sheep from 67 populations all over the world using the Ovine Infinium HD (600K) SNP BeadChip. We tested their associations with distinct phenotypic traits by conducting multiple independent genome-wide tests. In total, we detected 7547 unique CNVs and 18,152 CNV events in 1217 non-redundant CNV regions (CNVRs), covering 245 Mb (∼10%) of the whole sheep genome. We identified seven CNVRs with frequencies correlating to geographical origins and 107 CNVRs overlapping 53 known quantitative trait loci (QTLs). Gene ontology and pathway enrichment analyses of CNV-overlapping genes revealed their common involvement in energy metabolism, endocrine regulation, nervous system development, cell proliferation, immune, and reproduction. For the phenotypic traits, we detected significantly associated (adjusted P < 0.05) CNVRs harboring functional candidate genes, such as SBNO2 for polycerate; PPP1R11 and GABBR1 for tail weight; AKT1 for supernumerary nipple; CSRP1, WNT7B, HMX1, and FGFR3 for ear size; and NOS3 and FILIP1 in Wadi sheep; SNRPD3, KHDRBS2, and SDCCAG3 in Hu sheep; NOS3, BMP1, and SLC19A1 in Icelandic; CDK2 in Finnsheep; MICA in Romanov; and REEP4 in Texel sheep for litter size. These CNVs and associated genes are important markers for molecular breeding of sheep and other livestock species.

We describe the clinical validation of a targeted DNA and RNA-based next-generation sequencing (NGS) assay at two clinical molecular diagnostic laboratories. This assay employs simultaneous DNA and RNA analysis of all coding exons to detect small variants (single-nucleotide variants, insertions, and deletions) in 148 genes, amplifications in 59 genes, and fusions and splice variants in 55 genes. During independent validations at two sites, 234 individual specimens were tested, including clinical formalin-fixed, paraffin-embedded (FFPE) tumor specimens, reference material, and cell lines. Samples were prepared using the Illumina TruSight Tumor 170 (TST170) kit, sequenced with Illumina sequencers, and the data were analyzed using the TST170 App. At both sites, TST170 had ≥98% success for ≥250× depth for ≥95% of covered positions. Variant calling was accurate and reproducible at allele frequencies ≥5%. Limit of detection studies determined that inputs of ≥50 ng of DNA (with ≥3.3 ng/μl) and ≥50 ng RNA (minimum of 7 copies/ng) were optimal for high analytical sensitivity. The TST170 assay results were highly concordant with prior results using different methods across all variant categories. Optimization of nucleic acid extraction and DNA shearing, and quality control following library preparation is recommended to maximize assay success rates. In summary, we describe the validation of comprehensive and simultaneous DNA and RNA-based NGS testing using TST170 at two clinical sites.

Objective: 1q44 microdeletion syndrome is difficult to diagnose due to the wide phenotypic spectrum and strong genetic heterogeneity. We explore the correlation between the chromosome microdeletions and phenotype in a child with 1q44 microdeletion syndrome, we collected the clinical features of the patient and combined them with adjacent copy number variation (CNV) regions previously reported.Methods: We collected the full medical history of the patient and summarized her clinical symptoms. Whole-exome sequencing (WES) and CapCNV analysis were performed with DNA extracted from both the patient's and her parents' peripheral blood samples. Fluorescent quantitative PCR (q-PCR) was performed for the use of verification to the CNV regions.Results: A 28.7 KB microdeletion was detected in the 1q44 region by whole-exome sequencing and low-depth whole-genome sequencing. The deleted region included the genes COX20 and HNRNPU. As verification, karyotype analysis showed no abnormality, and the results of qPCR were consistent with that of whole-exome sequencing and CapCNV analysis.Conclusion: The patient was diagnosed with 1q44 microdeletion syndrome with clinical and genetic analysis. Analyzing both whole-exome sequencing and CapCNV analysis can not only improve the diagnostic rate of clinically suspected syndromes that present with intellectual disability (ID) and multiple malformations but also support further study of the correlation between CNVs and clinical phenotypes. This study lays the foundation for the further study of the pathogenesis of complex diseases.

BackgroundThe lack of understanding of molecular pathologies of the solitary functioning kidney makes improving and strengthening the continuity of care between pediatric and adult nephrological patients difficult. Copy number variations (CNVs) account for a molecular cause of solitary functioning kidney, but characterization of the pathogenic genes remains challenging.MethodsIn our prospective cohort study, 99 fetuses clinically diagnosed with a solitary functioning kidney were enrolled and evaluated using chromosomal microarray analysis (CMA). The genetic drivers for the pathogenic CNVs were analyzed. We characterized QPRT localization in fetal kidneys using immunohistochemistry and its expression in adult kidneys using quantitative RT-PCR. Further, QPRT was knocked down using siRNA in human embryonic kidney (HEK293T) cells, and the cell cycle and proliferation were tested.ResultsBesides one Triple X syndrome and one Down syndrome, we identified a total of 45 CNVs out of 34 subjects. Among the 14 pathogenic CNVs, CNV 16p11.2 reached the highest number of records with the phenotype of kidney anomalies in the Decipher database. Among the 26 genes within the 16p11.2 region, as a key enzyme for nicotinamide adenine dinucleotide (NAD+) biosynthesis, QPRT was distinctly localized in renal tubules but was barely observed in renal interstitial and glomeruli in fetal kidneys. The loss of QPRT prevented cells’ efficient transition into S phase, affected cell-cycle progression, and abrogated proliferation of human embryonic kidney cells.ConclusionOur data suggest that QPRT is a candidate gene associated with susceptibility for solitary functioning kidney. The CNVs discovered in our study exhibit great potential for future applications in genetic counseling and pregnancy management.

Copy number aberrations (CNA) are one of the most important classes of genomic mutations related to oncogenetic effects. In the past three decades, a vast amount of CNA data has been generated by molecular-cytogenetic and genome sequencing based methods. While this data has been instrumental in the identification of cancer-related genes and promoted research into the relation between CNA and histo-pathologically defined cancer types, the heterogeneity of source data and derived CNV profiles pose great challenges for data integration and comparative analysis. Furthermore, a majority of existing studies have been focused on the association of CNA to pre-selected “driver” genes with limited application to rare drivers and other genomic elements.In this study, we developed a bioinformatics pipeline to integrate a collection of 44,988 high-quality CNA profiles of high diversity. Using a hybrid model of neural networks and attention algorithm, we generated the CNA signatures of 31 cancer subtypes, depicting the uniqueness of their respective CNA landscapes. Finally, we constructed a multi-label classifier to identify the cancer type and the organ of origin from copy number profiling data. The investigation of the signatures suggested common patterns, not only of physiologically related cancer types but also of clinico-pathologically distant cancer types such as different cancers originating from the neural crest. Further experiments of classification models confirmed the effectiveness of the signatures in distinguishing different cancer types and demonstrated their potential in tumor classification.

Purpose: Exon deletions make up to 80% of mutations in the DMD gene, which cause Duchenne and Becker muscular dystrophy. Exon 45-55 regions were reported as deletion hotspots and intron 44 harbored more than 25% of deletion start points. We aimed to investigate the fine structures of breakpoints in intron 44 to find potential mechanisms of large deletions in intron 44.Methods: Twenty-two dystrophinopathy patients whose deletion started in intron 44 were sequenced using long-read sequencing of a DMD gene capture panel. Sequence homology, palindromic sequences, and polypyrimidine sequences were searched at the breakpoint junctions. RepeatMasker was used to analyze repetitive elements and Mfold was applied to predict secondary DNA structure.Results: With a designed DMD capture panel, 22 samples achieved 2.25 gigabases and 1.28 million reads on average. Average depth was 308× and 99.98% bases were covered at least 1×. The deletion breakpoints in intron 44 were scattered and no breakpoints clustered in any region less than 500 bp. A total of 72.7% of breakpoints located in distal 100 kb of intron 44 and more repetitive elements were found in this region. Microhomologies of 0–1 bp were found in 36.4% (8/22) of patients, which corresponded with non-homologous end-joining. Microhomologies of 2–20 bp were found in 59.1% (13/22) of patients, which corresponded with microhomology-mediated end-joining. Moreover, a 7 bp insertion was found in one patient, which might be evidence of aberrant replication origin firing. Palindromic sequences, polypyrimidine sequences, and small hairpin loops were found near several breakpoint junctions. No evidence of large hairpin loop formation in deletion root sequences was observed.Conclusion: This study was the first to explore possible mechanisms underlying exon deletions starting from intron 44 of the DMD gene based on long-read sequencing. Diverse mechanisms might be associated with deletions in the DMD gene.

Copy number variation (CNV), is defined as repetitions or deletions of genomic segments of 1 Kb to 5 Mb, and is a major trigger for human disease. The high-throughput and low-cost characteristics of next-generation sequencing technology provide the possibility of the detection of CNVs in the whole genome, and also greatly improve the clinical practicability of next-generation sequencing (NGS) testing. However, current methods for the detection of CNVs are easily affected by sequencing and mapping errors, and uneven distribution of reads. In this paper, we propose an improved approach, CNV-MEANN, for the detection of CNVs, involving changing the structure of the neural network used in the MFCNV method. This method has three differences relative to the MFCNV method: (1) it utilizes a new feature, mapping quality, to replace two features in MFCNV, (2) it considers the influence of the loss categories of CNV on disease prediction, and refines the output structure, and (3) it uses a mind evolutionary algorithm to optimize the backpropagation (neural network) neural network model, and calculates individual scores for each genome bin to predict CNVs. Using both simulated and real datasets, we tested the performance of CNV-MEANN and compared its performance with those of seven widely used CNV detection methods. Experimental results demonstrated that the CNV-MEANN approach outperformed other methods with respect to sensitivity, precision, and F1-score. The proposed method was able to detect many CNVs that other approaches could not, and it reduced the boundary bias. CNV-MEANN is expected to be an effective method for the analysis of changes in CNVs in the genome.

BackgroundSubmicroscopic segmental imbalances detected by array-comparative genomic hybridization (array-CGH) were discovered to be common in chronic myeloid leukemia (CML) patients with t(9;22) as the sole chromosomal anomaly. To confirm the findings of the previous study and expand the investigation, additional CML patients with t(9;22) as the sole chromosomal anomaly were recruited and copy number variants (CNVs) were searched for.MethodsKaryotyping tests were performed on 106 CML patients during January 2010–September 2019 in our Genetics Laboratory. Eighty-four (79.2%) patients had the Philadelphia (Ph) chromosome as the sole chromosomal anomaly. Only 49(58.3%) of these 84 patients had sufficient marrow or leukemia blood materials to additionally be included in the array-CGH analysis. Fluorescence in situ hybridization (FISH) was carried out to confirm the genes covered by the deleted or duplicated regions of the CNVs.Results11(22.4%) out of the 49 patients were found to have one to three somatic segmental somatic segmental (CNVs), including fourteen deletions and three duplications. The common region associated with deletions was on 9q33.3-34.12. Identified in five (45.5%) of the 11 positive patients with segmental CNVs, the deletions ranged from 106 kb to 4.1 Mb in size. Two (18.2%) cases had a deletion in the ABL1-BCR fusion gene on der (9), while three (27.3%) cases had a deletion in the ASS1 gene. The remaining CNVs were randomly distributed on different autosomes.ConclusionSubtle genomic CNVs are relatively common in CML patients without cytogenetically visible additional chromosomal aberrations (ACAs). Long-term studies investigating the potential impact on patient prognosis and treatment outcome is underway.

Among the diseases with X-linked inheritance and intellectual disability, duplication of the Xp11.23p11.22 region is indeed a rare phenomenon, with less than 90 cases known in the literature. Most of them have been recognized with the routine application of array techniques, as these copy number variations (CNVs) are highly variable in size, occurring in recurrent and non-recurrent forms. Its pathogenic role is not debated anymore, but the information available about the pathomechanism, especially in affected females, is still very limited. It has been observed that the phenotype in females varies from normal to severe, which does not correlate with the size of the duplication or the genes involved, and which makes it very difficult to give an individual prognosis. Among the patients studied by the authors because of intellectual disability, epilepsy, and minor anomalies, overlapping duplications affecting the Xp11.23p11.22 region were detected in three females. Based on our detailed phenotype analysis, we concluded that Xp11.23p11.22 duplication is a neurodevelopmental disorder.

The association between genetics and fetuses with ventriculomegaly (VM) is unknown. This study aimed to classify and evaluate abnormal copy number variations (CNVs) in fetuses with VM. From December 2016 to September 2020, amniotic fluid or umbilical cord blood from 293 pregnant women carrying fetuses with VM was extracted for single-nucleotide polymorphism microarray (SNP array). Among 293 fetuses with VM, 31 were detected with abnormal CNVs, including 22 with pathogenic CNVs (7.51%) and nine with variation of uncertain clinical significance (VUS) CNVs (3.07%). Of the 22 fetuses with pathogenic CNVs, 13 had known disease syndromes. Among the 293 fetuses, 133 had mild isolated VM [pathogenic CNVs, 7/133 (5.26%)]; 142 had mild non-isolated VM [pathogenic CNVs, 13/142 (9.15%)]; 12 had severe isolated VM [pathogenic CNVs, 2/12 (16.67%)]; and six had severe non-isolated VM (no abnormal CNVs was detected). There was no statistical significance in the rate of pathogenic CNVs among the four groups (P= 0.326,P> 0.05). Among the 267 fetuses with successful follow-up, 38 were terminated (of these, 21 had pathogenic CNVs). Of the 229 fetuses, two had developmental delay and the remaining 227 had a good prognosis after birth. Overall, the results are useful for the detection of fetal microdeletion/microduplication syndrome and for the accurate assessment of fetal prognosis in prenatal consultation.

Psychosis is a highly heritable and heterogeneous psychiatric condition. Its genetic architecture is thought to be the result of the joint effect of common and rare variants. Families with high prevalence are an interesting approach to shed light on the rare variant’s contribution without the need of collecting large cohorts. To unravel the genomic architecture of a family enriched for psychosis, with four affected individuals, we applied a system genomic approach based on karyotyping, genotyping by whole-exome sequencing to search for rare single nucleotide variants (SNVs) and SNP array to search for copy-number variants (CNVs). We identified a rare non-synonymous variant, g.39914279 C > G, in the MACF1 gene, segregating with psychosis. Rare variants in the MACF1 gene have been previously detected in SCZ patients. Besides, two rare CNVs, DUP3p26.3 and DUP16q23.3, were also identified in the family affecting relevant genes (CNTN6 and CDH13, respectively). We hypothesize that the co-segregation of these duplications with the rare variant g.39914279 C > G of MACF1 gene precipitated with schizophrenia and schizoaffective disorder.

Echogenic intracardiac focus (EIF) is one of the most common ultrasound soft markers (USMs) in prenatal screening. However, the association of EIF with chromosomal abnormalities is still controversial. From January 2018 to April 2020, a total of 571 fetuses with USMs in our center were enrolled, among which 150 (26.27%) presented EIFs. We analyzed the karyotype anomalies and copy number variations (CNVs) in fetuses who presented EIFs by comparing their ultrasound indications, maternal ages and gestational stages. There were no statistically significant differences in the incidence of chromosomal abnormalities between fetuses with EIFs and the fetuses with USMs (4.00 vs. 7.71%, p = 0.112). Additionally, the incidence of chromosomal abnormalities was not related to maternal age (4.10% in maternal age below 35 yeas vs. 3.57% in maternal age above 35, p = 1.000). Interestingly, after 28 weeks of gestation, fetuses with EIFs showed more chromosomal abnormalities (20.00%) than that in the group before 28 weeks of gestation (2.22%, p = 0.014), and this result was attributed to the detection of pathogenic CNVs. After birth, 25 of children conducted cardiac development re-examination. Among them, 9 (36%, 9/25) were diagnosed with congenital heart disease, primarily patent foramen oval and ventricular septal defects (7/9, 77.77%). We concluded that the appearance of EIFs in early or mid-trimester would not indicate an increased risk of fetal chromosomal abnormalities. However, the persistence of EIFs in late trimester was associated with a higher risk of pathology-related CNVs and its persistent appearance may indicate heart development defects after birth. Thus, our results suggest that CNV detection has its advantages in prenatal diagnosis, especially for those with EIFs that persist in the third trimester.

Hemophilia A is an X-linked recessive bleeding disorder caused by various types of pathological defects in the factor VIII gene (F8/FVIII). Preimplantation genetic testing for monogenic disease (PGT-M) is a powerful tool to tackle the transmission of monogenic inherited disorders from generation to generation. In our case, a mutation in F8 had passed through female carriers in a hemophilia A family and resulted in two male patients with hemophilia A. To identify the etiological genetic variants of F8, next-generation sequencing (NGS) was used for chromosome copy number variation detection, Sanger sequencing to verify mutation sites, single nucleotide polymorphism (SNP) for site amplification, and sequencing to validate the genetic linkage. Finally, a novel missense mutation, p. (Phe690Leu)/c.2070C > A, occurring in exon 13 of F8, was screened out as a pathogenic mutation. Following this, an F8 normal euploid blastocyst was transferred. At the 18th week, the pregnant mother underwent amniocentesis, NGS, Sanger sequencing, and SNP typing that further confirmed that the fetus had a healthy genotype. After delivery, a neonatal blood sample was sent for FVIII concentration detection, and the result established that the FVIII protein was rescued to a nearly average level. We first identified a new type of pathogenic mutation in F8, which has not been previously reported, selected a genetically healthy progeny for an affected family, and provided valuable knowledge of the diagnosis and treatment of hemophilia A.

Background: The efficiency of immune checkpoint inhibitors (ICIs) in bladder cancer (BLCA) treatment has been widely validated; however, the tumor response to ICIs was generally low. It is critical and urgent to find biomarkers that can predict tumor response to ICIs. The tumor microenvironment (TME), which may play important roles to either dampen or enhance immune responses, has been widely concerned.Methods: The cancer genome atlas BLCA (TCGA-BLCA) cohort (n = 400) was used in this study. Based on the proportions of 22 types of immune cells calculated by CIBERSORT, TME was classified by K-means Clustering and differentially expressed genes (DEGs) were determined. Based on DEGs, patients were classified into three groups, and cluster signature genes were identified after reducing redundant genes. Then TMEscore was calculated based on cluster signature genes, and the samples were classified to two subtypes. We performed somatic mutation and copy number variation analysis to identify the genetic characteristics of the two subtypes. Correlation analysis was performed to explore the correlation between TMEscore and the tumor response to ICIs as well as the prognosis of BLCA.Results: According to the proportions of immune cells, two TME clusters were determined, and 1,144 DEGs and 138 cluster signature genes were identified. Based on cluster signature genes, samples were classified into TMEscore-high (n = 199) and TMEscore-low (n = 201) subtypes. Survival analysis showed patients with TMEscore-high phenotype had better prognosis. Among the 45 differentially expressed micro-RNAs (miRNAs) and 1,033 differentially expressed messenger RNAs (mRNAs) between the two subtypes, 16 miRNAs and 287 mRNAs had statistically significant impact on the prognosis of BLCA. Furthermore, there were 94 genes with significant differences between the two subtypes, and they were enriched in RTK-RAS, NOTCH, WNT, Hippo, and PI3K pathways. The Tumor Immune Dysfunction and Exclusion (TIDE) score of TMEscore-high BLCA was statistically lower than that of TMEscore-low BLCA. Receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) of TMEscore and tumor mutation burden (TMB) is 0.6918 and 0.5374, respectively.Conclusion: We developed a method to classify BLCA patients to two TME subtypes, TMEscore-high and TMEscore-low, and we found TMEscore-high subtype of BLCA had a good prognosis and a good response to ICIs.

Genomic structural variants (SVs) are a major source of genetic and phenotypic variation but have not been investigated systematically in rainbow trout (Oncorhynchus mykiss), an important aquaculture species of cold freshwater. The objectives of this study were 1) to identify and validate high-confidence SVs in rainbow trout using whole-genome re-sequencing; and 2) to examine the contribution of transposable elements (TEs) to SVs in rainbow trout. A total of 96 rainbow trout, including 11 homozygous lines and 85 outbred fish from three breeding populations, were whole-genome sequenced with an average genome coverage of 17.2×. Putative SVs were identified using the program Smoove which integrates LUMPY and other associated tools into one package. After rigorous filtering, 13,863 high-confidence SVs were identified. Pacific Biosciences long-reads of Arlee, one of the homozygous lines used for SV detection, validated 98% (3,948 of 4,030) of the high-confidence SVs identified in the Arlee homozygous line. Based on principal component analysis, the 85 outbred fish clustered into three groups consistent with their populations of origin, further indicating that the high-confidence SVs identified in this study are robust. The repetitive DNA content of the high-confidence SV sequences was 86.5%, which is much higher than the 57.1% repetitive DNA content of the reference genome, and is also higher than the repetitive DNA content of Atlantic salmon SVs reported previously. TEs thus contribute substantially to SVs in rainbow trout as TEs make up the majority of repetitive sequences. Hundreds of the high-confidence SVs were annotated as exon-loss or gene-fusion variants, and may have phenotypic effects. The high-confidence SVs reported in this study provide a foundation for further rainbow trout SV studies.

Background: Cervical cancer became the third most common cancer among women, and genome characterization of cervical cancer patients has revealed the extensive complexity of molecular alterations. However, identifying driver mutation and depicting molecular classification in cervical cancer remain a challenge.Methods: We performed an integrative multi-platform analysis of a cervical cancer cohort from The Cancer Genome Atlas (TCGA) based on 284 clinical cases and identified the driver genes and possible molecular classification of cervical cancer.Results: Multi-platform integration showed that cervical cancer exhibited a wide range of mutation. The top 10 mutated genes were TTN, PIK3CA, MUC4, KMT2C, MUC16, KMT2D, SYNE1, FLG, DST, and EP300, with a mutation rate from 12 to 33%. Applying GISTIC to detect copy number variation (CNV), the most frequent chromosome arm-level CNVs included losses in 4p, 11p, and 11q and gains in 20q, 3q, and 1q. Then, we performed unsupervised consensus clustering of tumor CNV profiles and methylation profiles and detected four statistically significant expression subtypes. Finally, by combining the multidimensional datasets, we identified 10 potential driver genes, including GPR107, CHRNA5, ZBTB20, Rb1, NCAPH2, SCA1, SLC25A5, RBPMS, DDX3X, and H2BFM.Conclusions: This comprehensive analysis described the genetic characteristic of cervical cancer and identified novel driver genes in cervical cancer. These results provide insight into developing precision treatment in cervical cancer.

Background:Polled intersex syndrome (PIS) leads to reproductive disorders in goats and exerts a heavy influence on goat breeding. Since 2001, the core variant of an 11.7 kb deletion at ~129 Mb on chromosome 1 (CHI1) has been widely used as a genetic diagnostic criterion. In 2020, a ~0.48 Mb insertion within the PIS deletion was identified by sequencing in XX intersex goats. However, the suitability of this variation for the diagnosis of intersex goats worldwide and its further molecular genetic mechanism need to be clarified.Results:The whole-genome selective sweep of intersex goats from China was performed with whole-genome next-generation sequencing technology for large sample populations and a case–control study on interbreeds. A series of candidate genes related to the goat intersexuality phenotype were found. We further confirmed that a ~0.48 Mb duplicated fragment (includingERGandKCNJ15) downstream of the ~20 Mb PIS region was reversely inserted into the PIS locus in intersex Chinese goats and was consistent with that in European Saanen and Valais black-necked goats. High-throughput chromosome conformation capture (Hi-C) technology was then used to compare the 3D structures of the PIS variant neighborhood in CHI1 between intersex and non-intersex goats. A newly found structure was validated as an intrachromosomal rearrangement. This inserted duplication changed the original spatial structure of goat CHI1 and caused the appearance of several specific loop structures in the adjacent ~20 kb downstream region ofFOXL2.Conclusions:Results suggested that the novel complex PIS variant genome was sufficient as a broad-spectrum clinical diagnostic marker of XX intersexuality in goats from Europe and China. A series of private dense loop structures caused by segment insertion into the PIS deletion might affect the expression ofFOXL2or other neighboring novel candidate genes. However, these structures require further in-depth molecular biological experimental verification. In general, this study provided new insights for future research on the molecular genetic mechanism underlying female-to-male sex reversal in goats.

Breast cancer is the most common malignancy in women, and because it has a high mortality rate, it is urgent to develop computational methods to increase the accuracy of breast cancer survival predictive models. Although multi-omics data such as gene expression have been extensively used in recent studies, the accurate prognosis of breast cancer remains a challenge. Somatic mutations are another important and promising data source for studying cancer development, and its effect on the prognosis of breast cancer remains to be further explored. Meanwhile, these omics datasets are high-dimensional and redundant. Therefore, we adopted multiple kernel learning (MKL) to efficiently integrate somatic mutation to currently molecular data including gene expression, copy number variation (CNV), methylation, and protein expression data for the prediction of breast cancer survival. Before integration, the maximum relevance minimum redundancy (mRMR) feature selection method was utilized to select features that present high relevance to survival and low redundancy among themselves for each type of data. The experimental results demonstrated that the proposed method achieved the most optimal performance and there was a remarkable improvement in the prediction performance when somatic mutations were included, indicating that somatic mutations are critical for improving breast cancer survival predictions. Moreover, mRMR was superior to other feature selection methods used in previous studies. Furthermore, MKL outperformed the other traditional classifiers in multi-omics data integration. Our analysis indicated that through employing promising omics data such as somatic mutations and harnessing the power of proper feature selection methods and effective integration frameworks, the breast cancer survival predictive accuracy can be further increased, thereby providing a more optimal clinical diagnosis and more effective treatment for breast cancer patients.