Skip to main content
Download PDF

Copy number variant analysis for syndromic congenital heart disease in the Chinese population

Human Genomics volume 16, Article number: 51 (2022) Cite this article

Abstract

Background

Syndromic congenital heart disease (CHD) is among the most severe conditions in the pediatric population. Copy number variant (CNV) is an important cause of syndromic CHD, but few studies focused on CNVs related to these patients in China. The present study aimed to identify pathogenic CNVs associated with syndromic CHD in the Chinese population.

Methods

A total of 109 sporadic patients with syndromic CHD were applied chromosomal microarray analysis (CMA). Phenotype spectrum of pathogenic or likely pathogenic CNVs was analyzed. CHD-related genes were prioritized from genes within pathogenic or likely pathogenic CNVs by VarElect, OVA, AMELIE, and ToppGene.

Results

Using CMA, we identified 43 candidate CNVs in 37/109 patients. After filtering CNVs present in the general population, 29 pathogenic/likely pathogenic CNVs in 24 patients were identified. The diagnostic yield of CMA for pathogenic/likely pathogenic CNVs was 23.1% (24/104), excluding 5 cases with aneuploidies or gross chromosomal aberrations. The overlapping analysis of CHD-related gene lists from different prioritization tools highlighted 16 CHD candidate genes.

Conclusion

As the first study focused on CNVs in syndromic CHD from the Chinese population, this study reveals the importance of CMA in exploring the genetic etiology of syndromic CHD and expands our understanding of these complex diseases. The bioinformatic analysis of candidate genes suggests several CHD-related genes for further functional research.

Introduction

Syndromic congenital heart disease (CHD) accounts for approximately 20% of all patients with CHD [1], placing a heavy burden on the healthcare system. Extracardiac malformations in patients with CHD may influence their perioperative management, cardiac outcome, and mortality [2]. Chromosomal aberrations are common pathogenic causes in patients with syndromic CHD. Aneuploidies, including trisomy 21, trisomy 18, trisomy 13, and Turner syndrome, account for approximately 14% of all genetic causes of syndromic CHD. Copy number variants (CNVs), including 22q11 deletion, 1p36 deletion, 7q11.23 deletion, and other CNVs account for approximately 20% [1].

CNVs are crucial structural variants in the human genome caused by a deletion or duplication of genomic segments [3]. Identification of CNVs is a concern for children with congenital structural anomalies or multiple developmental disabilities. Chromosomal microarray analysis (CMA), including array comparative genomic hybridization (Array-CGH) and single-nucleotide polymorphism array, can identify chromosomal aberrations in an additional 12–15% of affected children compared with karyotyping [4]. Therefore, the American College of Medical Genetics (ACMG) standards and guidelines recommend CMA as a first-tier diagnostic strategy for patients with intellectual disabilities, autism spectrum disorders, and other multiple congenital anomalies [5]. In 2007, Thienpont et al. evaluated chromosomal aberration in 60 cases of syndromic CHD from Belgium with Array-CGH. They found 16.6% (10/60) of patients carrying causal CNVs [6]. Later, several studies evaluated the diagnostic yield of CMA from different countries or ethnic backgrounds [6,7,8,9,10,11,12,13,14,15]. Among these studies, the two cohorts with the largest sample sizes were the BCM1 (104 Hispanic/Latino Americans and 99 non-Hispanic patients of European descent) and BCH (260 cases from American) cohorts [9, 16]. The diagnostic yields of CMA in the two cohorts were 32.5% (66/203) and 18.1% (47/260), respectively. Although research on the relationship between CNVs and syndromic CHD is ongoing, no previous cohort studies have specifically reported CNVs in syndromic CHD from the Chinese population. In this study, we aimed to investigate the CNVs in syndromic CHD from the Chinese population and prioritize critical candidate genes.

Methods

Subjects and samples

A group of 109 sporadic patients with syndromic CHDs was recruited for this study. All patients were diagnosed with CHD and extracardiac malformations. Diagnoses were confirmed via imaging, clinical, and laboratory inspections. Patent ductus arteriosus (PDA) in children under one-year-old and patent foramen ovale were excluded. Peripheral blood samples were collected at the outpatient clinic and the inpatient ward of the Cardiothoracic Surgery Department. The Children’s Hospital of Fudan University ethics committee approved the study. The individuals’ parents signed the informed consent for the study, which follows the principles of the Declaration of Helsinki.

Chromosomal microarray analysis

Genomic DNA was extracted from peripheral blood using a QIAamp DNA Blood Kit (Qiagen). After enzyme cutting, labeling, hybridization, and purification, genomic DNA was submitted for CMA using the Agilent-CGX 60 K array or Affymetrix CytoScan 750 K microarray platforms. Details of the microarray technology and variant calling have been reported previously [17, 18]. Detected CNVs meeting the following criteria were excluded for further analysis: 1) gross chromosomal aberrations, including the size of CNV over 30 Mb; 2) CNVs with more than four occurrences in the Database of Genomic Variants (overlapping more than 50%). The remaining CNVs were interpreted using X-CNV (http://119.3.41.228/XCNV/index.php) [19], the DatabasE of genomiC varIation and Phenotype in Humans using Ensembl Resources (DECIPHER, https://www.deciphergenomics.org/) [20], and the Online Mendelian Inheritance in Man database (OMIM, https://www.omim.org/) [21]. CNVs were defined as pathogenic or likely pathogenic if any of the three web tools indicated pathogenicity or likely pathogenicity. X-CNV is a web tool to predict the pathogenicity of CNVs by integrating more than 30 informative features such as allele frequency, CNV length, CNV type, and some deleterious scores. In the development of X-CNV, Zhang et al. [22] reprocessed high-quality CNV data from multiple sources, including dbVar, DECIPHER, ClinGen, and the DGV databases. According to the meta-voting prediction (MVP) score generated by X-CNV, CNVs were divided into five categories: pathogenic, likely pathogenic, uncertain, likely benign, and benign. CNVs overlapped with regions interpreted by the DECIPHER database were defined as the corresponding pathogenicity. As for the OMIM database, CNVs were considered pathogenic when they presented genes associated with diseases. Moreover, CNVs were considered likely pathogenic when they presented genes associated with phenotypic alterations in the OMIM database [23]. The genome reference of X-CNV was GRCh37/hg19. When tracking CNVs in the DECIPHER database (GRCh38), NCBI-remap (https://www.ncbi.nlm.nih.gov/genome/tools/remap) was used for the genome conversion. The flowchart of this study is shown in Fig. 1.

Fig. 1
figure 1

Flowchart of this study

Full size image

Phenotype spectrum of syndromic CHD with pathogenic or likely pathogenic CNVs in this study

To further explore the distribution of the phenotype spectrum in CNV patients with syndromic CHD, the cardiac and non-cardiac phenotypes were analyzed. Each region of pathogenic or likely pathogenic CNVs in this study was searched in the DECIPHER database, and all overlapping CNVs were extracted for phenotype analysis.

Gene prioritization to identify CHD candidate genes

We developed a gene prioritization process to identify CHD candidate genes by integrating various web tools and databases (Additional file 1: Table S1), including phenotype-driven web tools (VarElect [24], OVA [25], and AMELIE [26]) and ToppGene [27]. For ToppGene, the training gene set was generated from RDDC (https://rddc.tsinghua-gd.org/), Phenopedia [28] (https://phgkb.cdc.gov/PHGKB/startPagePhenoPedia.action), and DisGeNET [29] (https://www.disgenet.org/), which contain genes related to CHD based on research articles and database mining (Additional file 2: Table S2). 1354 genes were finally defined as the training gene set [28, 29]. The 1249 input genes for all web tools were defined from protein-coding genes within pathogenic and likely pathogenic CNVs in this study by the UCSC genome browser (Human GRCh37/hg19) [30]. Then, we performed pathway analysis (Additional file 3: Additional methods) and analyzed the expression profile of the overlapping prioritized genes between four tools during murine cardiogenesis.

Statistical analysis

Statistical analyses were performed using GraphPad Prism (version 8.0).

Results

Clinical features and chromosomal imbalances in patients with syndromic CHD

A total of 109 patients with syndromic CHD underwent CMA analysis. The cases included 70 males and 39 females, with a mean age of 1.7 years (0–9.6 years). Among all cardiac phenotypes in this cohort (Table 1), septal defects were observed in 66.1% (72/109) of the patients, compound conotruncal defects in 10.1% (11/109), and obstruction of left ventricular outflow tract in 7.3% (8/109). The remaining 16.5% (18/109) of the patients presented septal defects with abnormities of valves, isolated abnormities of valves, isolated conotruncal defects, heterotaxy syndrome, and other cardiac defects. The main extracardiac comorbidities of all patients were neurodevelopmental disorders (37/109, 33.9%), craniofacial defects (13/109, 11.9%), genitourinary defects (12/109, 11.0%), digestive system defects (11/109, 10.1%), and musculoskeletal disorders (11/109, 10.1%).

Table 1 CHD and extracardiac phenotypes in patients with syndromic CHD
Full size table

We identified two patients with aneuploidies: one with trisomy 19 and one with trisomy 21 (Additional file 4: Table S3, cases 3 and 51). Three patients with gross chromosomal aberrations were found (Additional file 4: Table S3, cases 41, 74, and 95). The duplication of 3q26.1-q29 (34.8 Mb) existed in case 41. Case 74 carried the duplications of 18 CNVs, including 2q31.2-q35 (37.2 Mb), 3p26.3-p26.1 (3.6 Mb), 3p26.1-p25.3 (5.6 Mb), 3q28-q29 (6.4 Mb), 4p16.1-p15.32 (6.5 Mb), 4p15.1-p14 (5.6 Mb), 4q26-q31.3 (36.7 Mb), 5p15.1-p14.1 (11.0 Mb), 6q16.3-q21 (3.8 Mb), 8q21.13-q23.1 (26.2 Mb), 8q23.2-q23.3 (4.7 Mb), 10q22.3-q25.2 (32.7 Mb), 11p14.3-p11.2 (21.8 Mb), 11q12.1-q13.5 (20.1 Mb), 13q33.3-q34 (1.7 Mb), 17q22-q24.1 (7.9 Mb), 18q22.3-q23 (7.3 Mb), and 21q21.2-q22.11 (8.6 Mb). The duplications of 2p25.3-p11.2 (85.0 Mb), 2q11.1-q37.3 (143.8 Mb), and 20q11.21-q13.12 (12.8 Mb) were present in case 95. Apart from gross chromosomal aberrations, 37 patients carried 43 CNVs in this study. Five previously reported syndromes involving complex congenital malformations were also present in this cohort, including 1p36 microdeletion syndrome (case 11), DiGeorge syndrome (cases 32 and 66), Miller–Dieker syndrome (case 76), Cri du Chat syndrome (case 99), and Smith–Magenis syndrome (case 107). The overall rate of chromosomal imbalances in patients with syndromic CHD was 38.5% (42/109).

Pathogenic CNVs in patients with syndromic CHD

CNVs that appear more than four times in DGV (overlapping more than 50%) were regarded as common CNVs, as reported previously [12]. To find rare pathogenic CNVs, we filtered common CNVs and analyzed the remaining 34 CNVs by X-CNV, DECIPHER, and the OMIM database. 29 pathogenic or likely pathogenic CNVs in 24 patients were finally identified (Table 2). 22q11.2 was the only recurrent CNV. The 29 CNVs contained 1249 protein-coding genes. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt, http://www.webgestalt.org/) [31] was used for the gene ontology annotation of these genes (Additional file 5: Fig. S1).

Table 2 Pathogenic or likely pathogenic CNVs in 24 patients with syndromic CHD
Full size table

Then, we compared the characteristics of CNVs in studies of syndromic CHD from different countries or ethnicities (Table 3). Detailed information on CNVs for patients from Hungary [10], Greece [7], Brazil [12, 14], Belgium [6, 8], and the Caucasian population [15] was provided. As shown in Fig. 2A, among cases from Greece and Brazil, CNVs were mainly in chromosome 22. 22q11.2 was the most frequent region (Additional file 6: Table S4). In China, CNVs were more evenly distributed across chromosomes. We also compared the sizes of CNVs per individual (Fig. 2B). The CNV sizes of patients from Hungary were not provided, so we excluded these patients. Similar to patients from other countries or ethnicities, this study's most common size of CNVs in syndromic CHD was 1–5 Mb. In addition, we found a higher percentage of 20–40 Mb CNV sizes in Chinese patients.

Table 3 Diagnostic yield of CMA in studies of syndromic CHD from different countries or ethnicities
Full size table
Fig. 2
figure 2

Characteristics of CNVs in studies of syndromic CHD from different countries or ethnicities. A Distribution of CNV on different chromosomes. B Distribution of CNV sizes

Full size image

Phenotype spectrum of pathogenic or likely pathogenic CNVs in this study

Syndromic and isolated CHD prevalence in CNV patients from DECIPHER was analyzed. As shown in Fig. 3 and Table 4, ten of all CNVs were both related to syndromic and isolated CHD. The percentage of isolated CHD in each CNV was much lower than syndromic CHD. Then, we analyzed the detailed phenotype spectrum in CNV patients with syndromic and isolated CHD (Table 4). Septal defects and intellectual disabilities were the most common cardiac and non-cardiac phenotypes in CNV patients with syndromic CHD. For isolated CHD, complex conditions were more common, such as tetralogy of Fallot. Differential disease-associated genes (according to OMIM) between isolated CHD from DECIPHER and syndromic CHD in this study are also analyzed in Table 4. These genes may be candidate genes for non-cardiac phenotypes of CNV patients with syndromic CHD.

Fig. 3
figure 3

Percentage distribution of patients with syndromic and isolated CHD, across each pathogenic or likely pathogenic CNV type in this study. The percentage was summarized by searching the region of each CNV in DECIPHER database

Full size image
Table 4 The phenotypes of each pathogenic or likely pathogenic CNV in DECIPHER database
Full size table

Candidate gene prioritization

Next, we asked whether genes in these pathogenic or likely pathogenic CNVs were implicated in the cardiac phenotypes of patients with syndromic CHD. Among 1249 candidate genes, VarElect, OVA, and AMELIE prioritized 253, 200, and 169, respectively (Additional file 7: Table S5). With a ToppGene threshold of p-value < 0.05 and a ToppNet interaction count of ≥ 20, 236 genes were prioritized (Additional file 7: Table S5). The pathway enrichment analysis on prioritized genes by the four tools is listed in Table S6 (Additional file 8). We also analyzed the interaction networks of genes prioritized by the four tools using STRING (Additional file 9: Fig. S2). The genes prioritized by the four tools were similar to have interactions. There were 38/253 (15%) isolated genes (no connection to other genes) in the VarElect set, 18/200 (9%) in the OVA set, 19/169 (11%) in the AMELIE set, and 33/236 (14%) in the ToppGene set. Furthermore, an overlapping analysis of prioritized genes from the four tools was employed (Fig. S3A). Sixteen genes, including ACVR2B, B9D1, FLCN, AGO2, GLDC, MERTK, RHEB, NT5E, MPDZ, MNX1, SCN3B, THRB, TFAP2A, SUMF1, VHL, and TXNRD2, were found overlapping the four tools. We analyzed the expression pattern of the sixteen overlapping prioritized genes during the heart development of mice. The primary time window of heart development in mice is day 7.5–13.5 of embryonic development (E7.5-E13.5) [32]. As shown in Fig. S3, the mRNA expression of Acvr2b, Ago2, Mertk, Mpdz, and Vhl remained high during E7-E14 and decreased after maturation. These results suggested that these genes may be involved in heart development.

Discussion

Principal findings

Syndromic CHDs are linked to chromosomal abnormalities [33], CNVs [34], single gene defects, and undetermined causes. In 2010, the ACMG regarded CMA as a first-tier diagnostic method for developmental disabilities [35]. Then, several studies investigated the diagnostic yield of CMA in syndromic CHD. However, the sample sizes were small, and the contribution of CNVs in syndromic CHD from the Chinese cohort is not yet discussed. We used two CMA platforms to identify pathogenic or likely pathogenic CNVs in 109 subjects with syndromic CHD from the Chinese population. Whether a CNV contributes to a phenotype is according to various factors, including how it is inherited, the content of the genes, the copy number duplication or deletion, the array platform, and if it exists in the general population. In order to discuss submicroscopic structural changes of chromosomes, we removed patients with aneuploidies and gross chromosomal aberrations, filtered common CNVs in the general population database (DGV), and finally identified 34 CNVs in 28 patients.

Clinical characteristics in patients with previously reported syndromes

Five of the 28 patients presented previously reported syndromes. The 1.3 Mb 1p36.33 deletion in case 11 overlapped the distal critical region of 1p36. The related phenotypes of this distal region include anterior fontanel abnormalities, hypothyroidism, cleft palate, seizures, sensorineural hearing loss, congenital heart defects, and cardiomyopathy [36]. Case 11 presented ventricular septal defect (VSD) and mental retardation, commonly seen in patients with 1p36 distal region deletions. Frequent phenotypes of DiGeorge syndrome (22q11.2 deletion syndrome) include cardiovascular abnormalities, immunodeficiency, subtle but characteristic facial features, palatal abnormalities, endocrine abnormalities, gastrointestinal abnormalities, and genitourinary abnormalities [37]. With 22q11.21 deletion, case 32 manifested TOF, right aortic arch (RAA), athymism, and immunodeficiency, and case 66 exhibited VSD, abnormal facial features, and narrow glottis. Of these phenotypes, narrow glottis was less frequent in patients with DiGeorge syndrome. Miller–Dieker syndrome, or 17p13.3 deletion syndrome, is characterized by various dysmorphic features. Chen et al. summarized 29 cases with Miller–Dieker syndrome. They found that lissencephaly, corpus callosum dysgenesis/agenesis, and conotruncal heart defects were detected prenatally in 41% (12/29), 17% (5/29), and 14% (4/29) of the cases, respectively [38]. Several other studies have also observed lissencephaly, epilepsy, craniofacial dysmorphisms, and congenital anomalies in patients with Miller–Dieker syndrome [39]. In case 76 with 17p13.3 deletion, mental retardation and VSD were observed. However, central nervous system anomalies were not determined due to this patient's lack of magnetic resonance inspection. Cri du Chat syndrome (5p deletion) is characterized by the typical cry, severe mental and developmental retardation, and sensitive alterations. Less frequent characteristics, including cardiac, skeletal, genitourinary, metabolic, or immune abnormalities, may also be present [40]. In case 99 with 5p15.33-p15.31 deletion, we identified VSD, mental retardation, and motor retardation, matching the symptoms of patients with 5p deletion. Dysmorphism and visceral disorders (including congenital heart disease), neurocognitive impairment, and sleep–wake rhythm disorders are common phenotypes of Smith–Magenis syndrome (17p11.2 deletion) [41]. In this study, case 107 with 17p11.2 deletion presented VSD, developmental disorder of speech and language, and motor retardation. These phenotypes were within the phenotype spectrum of Smith–Magenis syndrome.

CNV pathogenicity prediction

Several approaches have been developed to predict CNV pathogenicity, including SVScore [42] (based on single-nucleotide polymorphism pathogenicity scores within CNV intervals), ACMG guidelines [22] (based on individual opinions on a series of scoring items), haploinsufficiency score [43], etc. X-CNV is a newly developed “one-stop” estimation tool that integrates diverse public data of CNVs and outperforms the SVScore, AnnotSV [44], and ClassifyCNV [45]. Therefore, X-CNV is a comprehensive approach to providing the pathogenic annotations of CNVs. Apart from X-CNV, we also used DECIPHER and the OMIM database to predict the pathogenicity of the 34 CNVs. Considering these three predicting methods, we determined 29 pathogenic or likely pathogenic CNVs in 24 patients. Among these CNVs, only del 22q11.21 was discovered recurrent in cases 32 and 66, indicating a high degree of heterogeneity of CNVs in syndromic CHD.

Diagnostic yield of CMA in syndromic CHD cohorts from different countries or ethnic backgrounds

We summarized the diagnostic yield of CMA in syndromic CHD cohorts from different countries or ethnic backgrounds (Table 4), and it varied from 10.3 to 67.3%. The difference in diagnostic yield may be associated with the populations included, the platforms used, and the criteria for pathogenic, likely pathogenic, or causal CNVs. In our study, the diagnostic yield of CMA was 23.1% (24/104), excluding 5 cases with aneuploidies and gross chromosomal aberrations. It is higher than 18.1% (47/260) in the BCH cohort but lower than 32.5% (66/203) in the BCM1 cohort. Then, recurrent CNVs were compared in our study and previously reported cohorts. Among the 11 reported cohorts summarized in Table 4, the causal CNVs of syndromic CHD in the BCH cohort were not listed. Thus, we compared the remaining 10 cohorts with ours to find recurrent CNVs (Additional file 10: Table S7). 31 recurrent CNVs were found among all cohorts, and the deletions of 22q11.21, 1p36.33, 17p13.3, 17p11.2, 17q25.3, 11q23.3-q25, 13q33.1-q34, and 5q35.3 were recurrent in our study and previously reported cohorts. The top two recurrent regions of all CNVs in our cohort and previously reported cohorts were 22q11 and 1p36 deletions, consistent with the EHRA/HRS/APHRS/LAHRS expert consensus statement [1]. Heterogeneous phenotypes of CHD and extracardiac malformations were observed in syndromic CHD from different countries and ethnicities. We summarized each study's top 3 cardiac and extracardiac malformations (Table 3). In patients carrying pathogenic or likely pathogenic CNVs from Greece, Brazil, China, and the Caucasian population, simple CHD, such as septal defects, was most common. In two studies that included patients from Belgium, we found that isolated conotruncal and septal defects were the most frequent cardiac phenotypes. Furthermore, neurodevelopmental disorders were the most common extracardiac comorbidities of patients from Greece and China. Craniofacial defects were the most frequent extracardiac comorbidities in cases from Belgium, Brazil, and the Caucasian population.

Of all CNVs non-recurrent between our cohort and previously reported cohorts, 3q25.33-q26.1 deletion (case 17), 8q24.21-q24.3 duplication (case 5), and 3p26.3-p24.2 duplication (case 102) were not published previously. Case 17 presented double outlet right ventricle (DORV), transposition of the great arteries (TGA), VSD, pulmonic stenosis (PS), PDA, abnormal right coronary artery branch, hydrocele, and genu valgum. Chang et al. [46] identified 3q25 deletion in 12 patients. They found that the CNV was associated with developmental delay, microcephaly, synophrys, epicanthus, ptosis, blepharophimosis, broad nasal bridge, ear abnormalities, and cardiac defects. Among these phenotypes, cardiac defects overlapped between patients with 3q25 deletion and case 17 with 3q25.33-q26.1 deletion. Case 5 carried two pathogenic CNVs, 13q33.1-q34 deletion, and 8q24.21-q24.3 duplication. He et al. discovered that patients carrying 13q33-q34 deletions had a high risk of developmental disability, facial deformity, CHD, and other malformations [47]. 8q24.21 is a hot spot associated with cancer, but the relationship between 8q24.21-q24.3 and CHD or other congenital malformations is not discussed yet. The phenotypes of case 102 were VSD, atrial septal defect (ASD), hypothyroidism, and developmental delay. Previous studies have discovered 3p26.3 microduplication in some patients with non-syndromic intellectual disability [48, 49]. Their CNV lengths were shorter than case 102, indicating that the inconsistent phenotypes of 3p26.3 duplication may be attributed to different lengths of CNV intervals.

Discovering novel CHD candidate genes by CNV detection

Previous studies have demonstrated that the number of candidate genes of different prioritization tools varied significantly. Qiao et al. used five prioritization web tools to identify candidate genes of subjects with intellectual disabilities and found a discrepancy in candidate gene sets of different web tools [50]. Jayaraman et al. used the software ENDEAVOUR, ToppGene, and DIR to rank candidate genes of leukemogenesis [51]. They found that the top 100 ranked genes from each tool differed, and only 54 genes overlapped in priority gene lists from these prediction approaches. As prioritization web tools using various databases and algorithms, many recent studies have recommended combining multiple web tools to identify critical candidate genes [52,53,54]. In this study, we used four gene prioritization tools to prioritize candidate genes of CHD within pathogenic or likely pathogenic CNVs. Our data also showed discrepancies in different priority lists (Additional file 11: Fig. S3A). The pathway enrichment analysis showed that the priority lists were enriched in different pathways associated with heart development. Thus, the combination of multiple web tools is necessary to identify phenotype-related genes and find critical candidate genes comprehensively. The overlapping analysis between priority lists suggested 16 genes as candidate genes associated with CHD. Furthermore, 31.3% (5/16) of the overlapping prioritized genes between four tools showed a high mRNA expression during the critical time window of heart development in mice. Cardiac phenotypes were observed in the targeted homozygous null allele mice of 87.5% (14/16) of the prioritized genes according to the MGI database, indicating that the prioritization process can highlight CHD-related genes. Of note, mice homozygotes for the targeted null alleles of Acvr2b, B9d1, and Gldc exhibit septal defects, which can be observed in the corresponding patients.

The sixteen prioritized genes were associated with eleven cases, and four carried abnormal copy numbers of at least two prioritized genes (cases 77, 102, 103, and 107). Previous studies have discovered that genetic disturbance in CHD is a multi-factorial, polygenic etiology [55, 56]. Single-nucleotide variants analyses in patients with CHD have also demonstrated that oligogenic or polygenic variants may contribute together to the pathogenesis of CHD [57, 58]. As there are dosage alterations of multiple genes in each CNV, it highlights efforts to understand the roles of multiple genes in the phenotypes. Morrow et al. summarized the molecular genetics of 22q11.2 deletion syndrome and highlighted the combined roles of the loss of TBX1, CRKL, and DGCR8 in 22q11.2-caused congenital malformations. Other genes mapped to this region, such as COMT, PRODH, and PIK4CA, may contribute to cognitive and behavioral problems in patients with 22q11.2 deletion [59]. In this study, case 102 carried duplication of VHL, SUMF1, and THRB, which were prioritized. Other genes, including CAV3, COLQ, CRELD1, RAB5A, RAF1, RARB, SLC6A6, CRBN, PPARG, and WNT7A, were also associated with cardiovascular system phenotypes according to the MGI database. Although the prioritization process identified several CHD-related genes, the consideration of the possibility that multiple genes on each CNV may contribute to the phenotypes together is needed. Further model organism research should focus on this issue and comprehensively uncover the polygenic etiology of syndromic CHD.

Another issue is that certain ethnic or racial groups tend to have more CHD-susceptible variants and influence the prevalence and outcomes of CHD [60]. For example, a meta-analysis revealed that MTHFR gene 677 T polymorphism was a genetic risk factor in the development of CHD in the Chinese population [61]. Lahm et al. [62] also identified multiple risk loci for all major CHD subgroups in patients of German ethnicity. In this study, we detected several CNVs from the Chinese population and provided a unique source for identifying novel CHD candidate genes. For each CNV, we listed CHD-related genes for the reference of future functional studies. And the sixteen overlapping genes are considered to be the most likely candidate CHD genes.

Strengths and limitations

Our study focused on patients with syndromic CHD in the Chinese population, which enabled us to discuss the role of CNVs in both CHD and multiple extracardiac abnormalities. However, there are some limitations in our study. Firstly, we only included sporadic cases, and the parents of all cases were not included. Secondly, as the extracardiac phenotypes were variable in our study, finding the relationship between CNVs and a specific extracardiac phenotype was not easy. Therefore, we only described the phenotype spectrum of each pathogenic or likely pathogenic CNV in syndromic CHD from DECIPHER database and this study. Moreover, the gene prioritization process was only performed for CHD-related genes. In the future, syndromic CHD involving a specific subtype of extracardiac malformations with larger sample size is needed further to delineate the correlation between CNV and syndromic CHD.

Conclusions

This study firstly applied CMA and bioinformatic analysis to explore syndromic CHD-related CNVs and genes from the perspective of the Chinese population. The pathogenic or likely pathogenic CNVs found in this study extended our understanding of the chromosomal aberrations in syndromic CHD. The combination of prioritization tools was essential in prioritizing CHD candidate genes and helping discover the pathogenesis of syndromic CHD.

Availability of data and materials

The datasets are available from the corresponding author upon request.

Abbreviations

CHD:

Congenital heart disease

CNV:

Copy number variant

CMA:

Chromosomal microarray analysis

MVP:

Meta-voting prediction

STRING:

Search Tool for Retrieval of Interacting Proteins

MGI:

Mouse genome informatics

ASD:

Atrial septal defect

VSD:

Ventricular septal defect

AVSD:

Atrioventricular septal defect

TR:

Tricuspid regurgitation

MR:

Mitral regurgitation

PS:

Pulmonic stenosis

MS:

Mitral stenosis

AS:

Aortic stenosis

CoA:

Coarctation of the aorta

TOF:

Tetralogy of Fallot

DORV:

Double outlet right ventricle

TGA:

Transposition of the great arteries

PA:

Pulmonary atresia

PAPVC:

Partial anomalous pulmonary venous connection

PAS:

Pulmonary artery sling

SV:

Single ventricle

PDA:

Patent ductus arteriosus

F:

Female

M:

Male

NM:

Not mentioned

RAA:

Right aortic arch

PVS:

Pulmonary valve stenosis

References

  1. Wilde AAM, Semsarian C, Márquez MF, Sepehri Shamloo A, Ackerman MJ, Ashley EA, et al. European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) expert consensus statement on the state of genetic testing for cardiac diseases. Heart Rhythm. 2022;19(7):e1–e60.

  2. Gonzalez JH, Shirali GS, Atz AM, Taylor SN, Forbus GA, Zyblewski SC, et al. Universal screening for extracardiac abnormalities in neonates with congenital heart disease. Pediatr Cardiol. 2009;30(3):269–73.

    Article  PubMed  Google Scholar 

  3. Itsara A, Cooper GM, Baker C, Girirajan S, Li J, Absher D, et al. Population analysis of large copy number variants and hotspots of human genetic disease. Am J Hum Genet. 2009;84(2):148–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Wapner RJ, Martin CL, Levy B, Ballif BC, Eng CM, Zachary JM, et al. Chromosomal microarray versus karyotyping for prenatal diagnosis. N Engl J Med. 2012;367(23):2175–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Kearney HM, Thorland EC, Brown KK, Quintero-Rivera F, South ST. American College of Medical Genetics standards and guidelines for interpretation and reporting of postnatal constitutional copy number variants. Genet Med Off J Am Coll Med Genet. 2011;13(7):680–5.

    Google Scholar 

  6. Thienpont B, Mertens L, de Ravel T, Eyskens B, Boshoff D, Maas N, et al. Submicroscopic chromosomal imbalances detected by array-CGH are a frequent cause of congenital heart defects in selected patients. Eur Heart J. 2007;28(22):2778–84.

    Article  CAS  PubMed  Google Scholar 

  7. Syrmou A, Tzetis M, Fryssira H, Kosma K, Oikonomakis V, Giannikou K, et al. Array comparative genomic hybridization as a clinical diagnostic tool in syndromic and nonsyndromic congenital heart disease. Pediatr Res. 2013;73(6):772–6.

    Article  CAS  PubMed  Google Scholar 

  8. Breckpot J, Thienpont B, Peeters H, de Ravel T, Singer A, Rayyan M, et al. Array comparative genomic hybridization as a diagnostic tool for syndromic heart defects. J Pediatr. 2010;156(5):810–7.

    Article  CAS  PubMed  Google Scholar 

  9. Geng J, Picker J, Zheng Z, Zhang X, Wang J, Hisama F, et al. Chromosome microarray testing for patients with congenital heart defects reveals novel disease causing loci and high diagnostic yield. BMC Genomics. 2014;15(1):1127.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Nagy O, Szakszon K, Biró BO, Mogyorósy G, Nagy D, Nagy B, et al. Copy number variants detection by microarray and multiplex ligation-dependent probe amplification in congenital heart diseases. J Biotechnol. 2019;299:86–95.

    Article  CAS  PubMed  Google Scholar 

  11. Richards AA, Santos LJ, Nichols HA, Crider BP, Elder FF, Hauser NS, et al. Cryptic chromosomal abnormalities identified in children with congenital heart disease. Pediatr Res. 2008;64(4):358–63.

    Article  PubMed  Google Scholar 

  12. Molck MC, Simioni M, Paiva Vieira T, Sgardioli IC, Paoli Monteiro F, Souza J, et al. Genomic imbalances in syndromic congenital heart disease. J Pediatr. 2017;93(5):497–507.

    Article  Google Scholar 

  13. Hussein IR, Bader RS, Chaudhary AG, Bassiouni R, Alquaiti M, Ashgan F, et al. Identification of de novo and rare inherited copy number variants in children with syndromic congenital heart defects. Pediatr Cardiol. 2018;39(5):924–40.

    Article  PubMed  Google Scholar 

  14. Monteiro RAC, de Freitas ML, Vianna GS, de Oliveira VT, Pietra RX, Ferreira LCA, et al. Major contribution of genomic copy number variation in syndromic congenital heart disease: the use of MLPA as the first genetic test. Mol Syndromol. 2017;8(5):227–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Goldmuntz E, Paluru P, Glessner J, Hakonarson H, Biegel JA, White PS, et al. Microdeletions and microduplications in patients with congenital heart disease and multiple congenital anomalies. Congenit Heart Dis. 2011;6(6):592–602.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Lalani SR, Shaw C, Wang X, Patel A, Patterson LW, Kolodziejska K, et al. Rare DNA copy number variants in cardiovascular malformations with extracardiac abnormalities. Eur J Hum Genet EJHG. 2013;21(2):173–81.

    Article  CAS  PubMed  Google Scholar 

  17. Mak ASL, Chiu ATG, Leung GKC, Mak CCY, Chu YWY, Mok GTK, et al. Use of clinical chromosomal microarray in Chinese patients with autism spectrum disorder-implications of a copy number variation involving DPP10. Mol Autism. 2017;8:31.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Singh V, Bala R, Chakraborty A, Rajender S, Trivedi S, Singh K. Duplications in 19p13.3 are associated with male infertility. J Assist Reprod Genet. 2019;36(10):2171–9.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Zhang L, Shi J, Ouyang J, Zhang R, Tao Y, Yuan D, et al. X-CNV: genome-wide prediction of the pathogenicity of copy number variations. Genome Med. 2021;13(1):132.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Firth HV, Richards SM, Bevan AP, Clayton S, Corpas M, Rajan D, et al. DECIPHER: database of chromosomal imbalance and phenotype in humans using ensembl resources. Am J Hum Genet. 2009;84(4):524–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Amberger JS, Hamosh A. Searching online mendelian inheritance in man (OMIM): a knowledgebase of human genes and genetic phenotypes. Curr Protoc Bioinform. 2017;58:1–2.

    Article  Google Scholar 

  22. Riggs ER, Andersen EF, Cherry AM, Kantarci S, Kearney H, Patel A, et al. 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). Genet Med Off J Am Coll Med Genet. 2020;22(2):245–57.

    Google Scholar 

  23. Godoy V, Bellucco FT, Colovati M, Oliveira-Junior HR, Moysés-Oliveira M, Melaragno MI. Copy number variation (CNV) identification, interpretation, and database from Brazilian patients. Genet Mol Biol. 2020;43(4):e20190218.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Stelzer G, Plaschkes I, Oz-Levi D, Alkelai A, Olender T, Zimmerman S, et al. VarElect: the phenotype-based variation prioritizer of the GeneCards Suite. BMC Genomics. 2016;17(Suppl 2):444.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Antanaviciute A, Watson CM, Harrison SM, Lascelles C, Crinnion L, Markham AF, et al. OVA: integrating molecular and physical phenotype data from multiple biomedical domain ontologies with variant filtering for enhanced variant prioritization. Bioinformatics (Oxford, England). 2015;31(23):3822–9.

    CAS  Google Scholar 

  26. Birgmeier J, Haeussler M, Deisseroth CA, Steinberg EH, Jagadeesh KA, Ratner AJ, et al. AMELIE speeds mendelian diagnosis by matching patient phenotype and genotype to primary literature. Sci Transl Med. 2020;12(544):eaau9113.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009;37:W305–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Yu W, Clyne M, Khoury MJ, Gwinn M. Phenopedia and Genopedia: disease-centered and gene-centered views of the evolving knowledge of human genetic associations. Bioinformatics (Oxford, England). 2010;26(1):145–6.

    Article  CAS  Google Scholar 

  29. Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J, Ronzano F, Centeno E, Sanz F, et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2020;48(D1):D845–55.

    PubMed  Google Scholar 

  30. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The human genome browser at UCSC. Genome Res. 2002;12(6):996–1006.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 2019;47(W1):W199-w205.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Li Y, Lv Z, He L, Huang X, Zhang S, Zhao H, et al. Genetic tracing identifies early segregation of the cardiomyocyte and nonmyocyte lineages. Circ Res. 2019;125(3):343–55.

    Article  CAS  PubMed  Google Scholar 

  33. Hartman RJ, Rasmussen SA, Botto LD, Riehle-Colarusso T, Martin CL, Cragan JD, et al. The contribution of chromosomal abnormalities to congenital heart defects: a population-based study. Pediatr Cardiol. 2011;32(8):1147–57.

    Article  PubMed  Google Scholar 

  34. Soemedi R, Wilson IJ, Bentham J, Darlay R, Töpf A, Zelenika D, et al. Contribution of global rare copy-number variants to the risk of sporadic congenital heart disease. Am J Hum Genet. 2012;91(3):489–501.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Miller DT, Adam MP, Aradhya S, Biesecker LG, Brothman AR, Carter NP, et al. Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. Am J Hum Genet. 2010;86(5):749–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Jordan VK, Zaveri HP, Scott DA. 1p36 deletion syndrome: an update. Appl Clin Genet. 2015;8:189–200.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. McDonald-McGinn DM, Sullivan KE, Marino B, Philip N, Swillen A, Vorstman JA, et al. 22q11.2 deletion syndrome. Nat Rev Dis Primers. 2015;1:15071.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Chen CP, Chang TY, Guo WY, Wu PC, Wang LK, Chern SR, et al. Chromosome 17p13.3 deletion syndrome: aCGH characterization, prenatal findings and diagnosis, and literature review. Gene. 2013;532(1):152–9.

    Article  CAS  PubMed  Google Scholar 

  39. Blazejewski SM, Bennison SA, Smith TH, Toyo-Oka K. Neurodevelopmental genetic diseases associated with microdeletions and microduplications of chromosome 17p13.3. Front Genet. 2018;9:80.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Rodríguez-Caballero A, Torres-Lagares D, Rodríguez-Pérez A, Serrera-Figallo MA, Hernández-Guisado JM, Machuca-Portillo G. Cri du chat syndrome: a critical review. Medicina oral, patologia oral y cirugia bucal. 2010;15(3):e473–8.

    Article  PubMed  Google Scholar 

  41. Poisson A, Nicolas A, Bousquet I, Raverot V, Gronfier C, Demily C. Smith-magenis syndrome: molecular basis of a genetic-driven melatonin circadian secretion disorder. Int J Mol Sci. 2019;20(14):3533.

    Article  CAS  PubMed Central  Google Scholar 

  42. Ganel L, Abel HJ, Hall IM. SVScore: an impact prediction tool for structural variation. Bioinformatics (Oxford, England). 2017;33(7):1083–5.

    CAS  Google Scholar 

  43. Rice AM, McLysaght A. Dosage sensitivity is a major determinant of human copy number variant pathogenicity. Nat Commun. 2017;8:14366.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Geoffroy V, Herenger Y, Kress A, Stoetzel C, Piton A, Dollfus H, et al. AnnotSV: an integrated tool for structural variations annotation. Bioinformatics (Oxford, England). 2018;34(20):3572–4.

    Article  CAS  Google Scholar 

  45. Gurbich TA, Ilinsky VV. ClassifyCNV: a tool for clinical annotation of copy-number variants. Sci Rep. 2020;10(1):20375.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chang YT, Wang CH, Chou IC, Lin WD, Chee SY, Kuo HT, et al. Case report of Chromosome 3q25 deletion syndrome or Mucopolysaccharidosis IIIB. Biomedicine. 2014;4(1):7.

    Article  PubMed  PubMed Central  Google Scholar 

  47. He X, Shen H, Fu H, Feng C, Liu Z, Jin Y, et al. Reduced anogenital distance, hematuria and left renal hypoplasia in a patient with 13q33.1-34 deletion: case report and literature review. BMC Pediatr. 2020;20(1):327.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kashevarova AA, Nazarenko LP, Schultz-Pedersen S, Skryabin NA, Salyukova OA, Chechetkina NN, et al. Single gene microdeletions and microduplication of 3p26.3 in three unrelated families: CNTN6 as a new candidate gene for intellectual disability. Mol Cytogenet. 2014;7(1):97.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Shoukier M, Fuchs S, Schwaibold E, Lingen M, Gärtner J, Brockmann K, et al. Microduplication of 3p26.3 in nonsyndromic intellectual disability indicates an important role of CHL1 for normal cognitive function. Neuropediatrics. 2013;44(5):268–71.

    Article  PubMed  Google Scholar 

  50. Qiao Y, Harvard C, Tyson C, Liu X, Fawcett C, Pavlidis P, et al. Outcome of array CGH analysis for 255 subjects with intellectual disability and search for candidate genes using bioinformatics. Hum Genet. 2010;128(2):179–94.

    Article  CAS  PubMed  Google Scholar 

  51. Jayaraman A, Jamil K, Khan HA. Identifying new targets in leukemogenesis using computational approaches. Saudi J Biol Sci. 2015;22(5):610–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Freytag S, Burgess R, Oliver KL, Bahlo M. brain-coX: investigating and visualising gene co-expression in seven human brain transcriptomic datasets. Genome Med. 2017;9(1):55.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Zhan Y, Zhang R, Lv H, Song X, Xu X, Chai L, et al. Prioritization of candidate genes for periodontitis using multiple computational tools. J Periodontol. 2014;85(8):1059–69.

    Article  CAS  PubMed  Google Scholar 

  54. Saik OV, Nimaev VV, Usmonov DB, Demenkov PS, Ivanisenko TV, Lavrik IN, et al. Prioritization of genes involved in endothelial cell apoptosis by their implication in lymphedema using an analysis of associative gene networks with ANDSystem. BMC Med Genomics. 2019;12(Suppl 2):47.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Pavan M, Ruiz VF, Silva FA, Sobreira TJ, Cravo RM, Vasconcelos M, et al. ALDH1A2 (RALDH2) genetic variation in human congenital heart disease. BMC Med Genet. 2009;10:113.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Santos-Ledo A, Washer S, Dhanaseelan T, Eley L, Alqatani A, Chrystal PW, et al. Alternative splicing of jnk1a in zebrafish determines first heart field ventricular cardiomyocyte numbers through modulation of hand2 expression. PLoS Genet. 2020;16(5):e1008782.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Priest JR, Osoegawa K, Mohammed N, Nanda V, Kundu R, Schultz K, et al. De novo and rare variants at multiple loci support the oligogenic origins of atrioventricular septal heart defects. PLoS Genet. 2016;12(4):e1005963.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Gifford CA, Ranade SS, Samarakoon R, Salunga HT, de Soysa TY, Huang Y, et al. Oligogenic inheritance of a human heart disease involving a genetic modifier. Science (New York, NY). 2019;364(6443):865–70.

    Article  CAS  Google Scholar 

  59. Morrow BE, McDonald-McGinn DM, Emanuel BS, Vermeesch JR, Scambler PJ. Molecular genetics of 22q11.2 deletion syndrome. Am J Med Genet Part A. 2018;176(10):2070–81.

    Article  CAS  PubMed  Google Scholar 

  60. Mullen M, Zhang A, Lui GK, Romfh AW, Rhee JW, Wu JC. Race and genetics in congenital heart disease: application of iPSCs, omics, and machine learning technologies. Front Cardiovasc Med. 2021;8:635280.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Yuan Y, Yu X, Niu F, Lu N. Genetic polymorphism of methylenetetrahydrofolate reductase as a potential risk factor for congenital heart disease: a meta-analysis in Chinese pediatric population. Medicine. 2017;96(23):e7057.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Lahm H, Jia M, Dreßen M, Wirth F, Puluca N, Gilsbach R, et al. Congenital heart disease risk loci identified by genome-wide association study in European patients. J Clin Investig. 2021. https://doi.org/10.1172/JCI141837.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the bioinformatics platform of Institutes of Biomedical Sciences, Fudan University, for commenting on the bioinformatic analysis.

Funding

This work was supported by the National Key Research and Development Program of China (2021YFC2701000, 2016YFC1000500), the National Natural Science Foundation of China (81873482, 81873483), Shanghai Basic Research Project of Science and Technology Innovation Action Plan (20JC1418300), Chinese Academy of Medical Sciences Research Unit (2018RU002), and Shanghai Natural Science Foundation of Science and Technology Innovation Action Plan (21ZR1409900).

Author information

Author notes

  1. Ping Li and Weicheng Chen authors have contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Pediatric Heart Center, Children’s Hospital of Fudan University, No. 399 Wanyuan Road, Shanghai, 201102, People’s Republic of China

    Ping Li, Weicheng Chen, Mengru Li, Zhengshan Zhao, Zhiyu Feng, Han Gao, Meijiao Suo, Ziqing Xu, Guixiang Tian, Feizhen Wu, Sheng Wei & Guoying Huang

  2. Shanghai Key Laboratory of Birth Defects, Shanghai, 201102, People’s Republic of China

    Weicheng Chen, Feizhen Wu, Sheng Wei & Guoying Huang

  3. Laboratory of Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, People’s Republic of China

    Feizhen Wu

Authors
  1. Ping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weicheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mengru Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhengshan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhiyu Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Han Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Meijiao Suo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ziqing Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Guixiang Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Feizhen Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Sheng Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Guoying Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

PL and WC drafted the manuscript, and they contributed equally to this work. GH and WS revised the manuscript critically for important intellectual content, and they are correspondent authors of this work. ML, ZZ, ZF, HG, MS, ZX, and GT participated in the bioinformatic analysis, sample collection, and literature summary. FW reviewed the bioinformatic analysis of this manuscript.

Corresponding authors

Correspondence to Sheng Wei or Guoying Huang.

Ethics declarations

Ethics approval and consent to participate

The ethics committee of the Children’s Hospital of Fudan University approved the study. The individuals’ parents signed the informed consent for the study, which follows the principles of the Declaration of Helsinki.

Consent for publication

The individuals’ parents signed the informed consent for the study.

Competing interests

The authors declare that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1

. Table S1. Summary of tools for candidate gene prioritization, including VarElect, OVA, AMELIE, and ToppGene.

Additional file 2

. Table S2. Training gene set generated from RDDC, Phenopedia, and DisGeNET.

Additional file 3

. Additional methods. Pathway analysis of the prioritized genes, the databases, and the dataset.

Additional file 4

. Table S3. The remaining 85 patients with syndromic CHD.

Additional file 5

. Fig. S1. Gene ontology analysis of the 1249 candidate genes (1129 matched).

Additional file 6

. Table S4. CNVs in studies of syndromic CHD from different countries or ethnicities.

Additional file 7

. Table S5. Prioritized genes of four tools.

Additional file 8

. Table S6. Pathway enrichment analysis on prioritized genes by different tools.

Additional file 9

. Fig. S2. Interaction analyses of prioritized genes by different tools.

Additional file 10

. Table S7. Recurrent CNVs in previously published syndromic CHD cohorts and our study.

Additional file 11

. Fig. S3. The prioritized genes overlapping between the four tools.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, P., Chen, W., Li, M. et al. Copy number variant analysis for syndromic congenital heart disease in the Chinese population. Hum Genomics 16, 51 (2022). https://doi.org/10.1186/s40246-022-00426-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40246-022-00426-8

Keywords

  • Copy number variant
  • Syndromic congenital heart disease
  • Chromosomal microarray analysis
  • Candidate gene

Human Genomics

ISSN: 1479-7364