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BMC Medical Genomics

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Genome-wide copy number variant analysis for congenital ventricular septal defects in Chinese Han population

  • Yu An1, 2, 4,
  • Wenyuan Duan3,
  • Guoying Huang4,
  • Xiaoli Chen5,
  • Li Li5,
  • Chenxia Nie6,
  • Jia Hou4,
  • Yonghao Gui4,
  • Yiming Wu1,
  • Feng Zhang2,
  • Yiping Shen7,
  • Bailin Wu1, 4, 7Email author and
  • Hongyan Wang8Email author
BMC Medical Genomics20169:2

https://doi.org/10.1186/s12920-015-0163-4

Received: 8 September 2015

Accepted: 18 December 2015

Published: 8 January 2016

Abstract

Background

Ventricular septal defects (VSDs) constitute the most prevalent congenital heart disease (CHD), occurs either in isolation (isolated VSD) or in combination with other cardiac defects (complex VSD). Copy number variation (CNV) has been highlighted as a possible contributing factor to the etiology of many congenital diseases. However, little is known concerning the involvement of CNVs in either isolated or complex VSDs.

Methods

We analyzed 154 unrelated Chinese individuals with VSD by chromosomal microarray analysis. The subjects were recruited from four hospitals across China. Each case underwent clinical assessment to define the type of VSD, either isolated or complex VSD. CNVs detected were categorized into syndrom related CNVs, recurrent CNVs and rare CNVs. Genes encompassed by the CNVs were analyzed using enrichment and pathway analysis.

Results

Among 154 probands, we identified 29 rare CNVs in 26 VSD patients (16.9 %, 26/154) and 8 syndrome-related CNVs in 8 VSD patients (5.2 %, 8/154). 12 of the detected 29 rare CNVs (41.3 %) were recurrently reported in DECIPHER or ISCA database as associated with either VSD or general heart disease. Fifteen genes (5 %, 15/285) within CNVs were associated with a broad spectrum of complicated CHD. Among these15 genes, 7 genes were in “abnormal interventricular septum morphology” derived from the MGI (mouse genome informatics) database, and nine genes were associated with cardiovascular system development (GO:0072538).We also found that these VSD-related candidate genes are enriched in chromatin binding and transcription regulation, which are the biological processes underlying heart development.

Conclusions

Our study demonstrates the potential clinical diagnostic utility of genomic imbalance profiling in VSD patients. Additionally, gene enrichment and pathway analysis helped us to implicate VSD related candidate genes.

Keywords

Ventricular septal defectaCGHCongenital heart defectCopy number variants

Background

Congenital heart defects (CHDs) are the most prominent birth defects, with a prevalence of 4 to 10 per 1000 live births [1]. A ventricular septal defect (VSD) occurs in more than 1 in 300 live births and is the most common CHD identified to date [2]. Although nearly 40 % of infants with VSDs can survive without treatment up to the age of 15 years, VSD patients diagnosed in adulthood may experience potentially serious clinical and hemodynamic problems [3]. Early detection and diagnosis lead to improved prognosis for patients with CHD.

Genomic imbalances detected by karyotype or FISH explain 9 % to 18 % of neonatal CHD cases [4]. CHD-related CNVs, identified by chromosomal microarray analysis (CMA), have been reported on almost every human chromosome [59] and numerical chromosomal abnormalities such as trisomy 21, trisomy 18 and trisomy 13 and also CNVs such as 22q11.2 deletion are causally related to CHD. Although the causal relationship between CNVs within the size range of 100 kb-1 Mb and CHD is incompletely investigated, rare de novo CNVs were revealed up to 5 % of CHD trios [10].

Some CNV studies focus on one type of CHD such as syndromic CHD[5], tetralogy of Fallot[8], double outlet right ventricle[11], thoracic aortic aneurysms and dissections[12] and isolated congenital heart disease[9]. Aproximately 10 % of Tetralogy of Fallot CHD patients (TOF) display an increased genome-wide CNV burden [8, 10]. Hence,while Studies focusing on the involvement of CNV in CHD development have been reported [5, 7, 8, 12], the complex and heterogeneous phenotypic and genetic nature of CHD suggest the need for further investigation of their genetic basis, particularly for certain category of CHD.

The aim of the present study was to detect CHD-associated CNVs in Chinese patients with VSD. Although several studies had examined the occurrence of CNVs in Chinese CHD patients [13, 14], the CNVs in the Chinese patients with VSD have not been particularly investigated. Detecting the CNVs in patients with VSD may reveal VSD specific candidate genes and associated pathways.

Methods

Subjects

The subjects were recruited from multi-center hospital-based CHD cohort between 2000 and 2009. We randomly enrolled 166 unrelated patients (Subject details in Additional file 1: Table S1). All patients except seven had VSD phenotype. Every subject underwent complete cardiac evaluation. Congenital cardiac malformations were diagnosed by echocardiography and subsequently confirmed during surgery when performed. We categorized cases into two large groups: Isolated VSD (patients with VSD as the only cardiac defect) and complex VSD (patients with more than two additional cardiac phenotypes besides VSD). The additional phenotype besides cardiac phenotype such as mental defect or developmental disability was not discussed due to lack of clinical evaluation. The ethics committee of Fudan University approved the study. Documented consents were obtained from all participating patients or their legal guardians.

CNV callings and rare CNVs identification

The Agilent Human Genome CGH microarray 244 k kit was used for CMA analysis (Agilent Technologies). Sample-specific CNV regions were identified using two software packages, Agilent DNA Analytics 4.0 CH3 Module (Agilent Technologies) and Nexus Copy Number v5.0 (BioDiscovery). Copy number gains or losses identified by both software packages were further manually inspected and confirmed.

We interpreted the CNVs hierarchically as shown in Figure 1. Common CNVs were removed based upon their frequency in DGV (Database of Genomic Variants) [15, 16] and Chinese control data sets which were compiled from four published data sets including 10 individuals from Park et al. [17], 779 individuals from Lin et al. [18], 99 individuals established by SGVP (Singapore Genome Variation Project) [19] and 80 Han Chinese by Lou et al. [20]. CNVs with >70 % overlap with the ones reported in DGV were considered as common CNVs; CNVs partially (< 30 %) overlapped or with no overlap with the DGV dataset or other data sets were considered as rare CNVs. For the rare CNVs, we consulted the DECIPHER (https://decipher.sanger.ac.uk/) and ISCA (now as Clingene, https://www.clinicalgenome.org/) databases for evidence of clinical relevance [21]. The Refseq genes which included in CNVs were identified by UCSC browser (Human NCBI36/hg18 Assembly).
Fig. 1

Workflow of CNV analysis and candidate genes discovery. CNV calls by DNA Analytics were performed by using the ADM2 algorithm, with a sensitivity threshold of 6.0 and a minimum of 5 probes. The QC metrics table was used to check signal intensity and background noise. Above 0.22 of DLR Score (Derivative Log Ratio) was set as the cutoff to avoid false CNVs. 6 cases were removed because of bad quality of data during the QC filter.6 cases with aneuploid abnormals (Trisomy X and Down syndrome) were not put into further analysis

Validation of small rare CNVs

CNVs with marginal QC values or of small size (< 80 kb) were selected to be confirmed by multiplex ligation-dependent probe amplification analysis (MLPA) (MLPA probes are listed in Additional file 1: Table S2). We also performed parental testing for 16 probands as listed in Additional file 1: Tables S3–S4.

Statistical analyses

Statistical analysis was performed using SPSS 17. Two-side Fisher’s exact test and Student’s t-test were performed for qualitative and quantitative variables respectively.

Identifying CHD-associated genes

In order to identify VSD related genes, we compared the genes located in our rare CNVs with known CHD candidate genes. The Mouse Genome Informatics resource (MGI, http://www.informatics.jax.org/) can be very informative for studying disease-related genes in the human. We used “abnormal interventricular septum morphology” as the MP term to search for VSD related genes listed in MGI (MP: 0000281 as shown in the Additional file 1: Figure S1; http://www.informatics.jax.org/) and identified 147 genes with 375 genotypes and 416 annotations from MGI. In addition, 202 CHD-related genes were compiled from other resources: 104 genes from UCSC with the Human Genome Build 19 (cardiac gene: 76, cardiac transcription factor gene: 28), 51 genes from published literature (non-syndromic and syndromic CHD) and 47 genes from the CHD wiki. We also collected gene sets from the term “cardiovascular system development” (GO: 0072358) and candidate pathways involved in cardiac development such as Wnt, Notch, Hedgehog and FGF by KEGG and Netpath (http://www.netpath.org/). The CHD-related pathway selection processes are listed in Additional file 1: Figure S2. In total, there are a total of 1957 collected genes involved in cardiac related pathways which were combined as a potentially CHD- related dataset for further analysis. We compared the above combined data sets with genes mapping to CNVs detected in VSD patients.

VSD candidate gene identification and pathway analysis

To define the most promising candidate genes from above defined gene list, ToppGENE was used as a gene prioritization and enrichment tool [22]. We used Ingenuity Pathway Analysis (IPA) to annotate genes encompassed within VSD-related CNVs for their molecular and cellular functions and associated pathways. Network scores were calculated based on the hypergeometric distribution and Fisher’s exact test.

Results

Chromosomal imbalances in VSD patients

We identified six aneuploid abnormalities: two cases of trisomy X (47, XXX) and four of trisomy 21. Up to 70 % of Down syndrome [23]subjects but only 1.3 % of trisomy X subjects have been reported to display CHD. CHD features in the trisomy X patients included VSD, ASD (Atrial septal defect), pulmonic and aortic stenosis coarctation [24].

Rare CNVs in VSD patients

There were 1575 CNVs detected in our 154 patient cohort, with a median size of 310.5 kb (max 33.4 Mb, min 6.7 kb), compared with a median size was 52.9 kb (max 2.1 Mb, min 1.1 kb) in 965 controls. We identified 29 rare genic CNVs (CNV with at leaset one gene mapping to the dosage altered region ) in 25 of 153 VSD cases (16.3 %). Nighteen duplication CNVs involved 172 genes while 10 deletion CNVs affected 113 genes (Fig. 1). We also detected 32 intergenic CNVs but these were excluded from further analysis (Additional file 1: Table S9). 24.1 % (7/29) of the genic CNVs were less than 100 kb, 34.4 % (10/29) were from 100 kb to 500 kb and 41.3 % (12/29) were larger than 500 kb as shown in Additional file 1: Figure S3. Parental testing revealed that six CNVs were inherited from unaffected parents, reducing the likelihood that these are clinically significant. Three CNVs were confirmed as de novo: one deletion of 57.9 kb at Xp22.2 involving EGFL6 gene (Additional file 1: Figure S4) and two duplications of 156.0 kb at 14q32.12, and of 117.8 kb in 7p14.2, which were experimentally confirmed; the two CNV gains were found in same subject.

CNVs larger than 1 Mb

Five VSD cases revealed CNVs larger than 1 Mb (as shown Additional file 1: Table S5). Case 1 and 2 (NS255 and NS7783) had deletion at 22q11.2 (DiGeorge Syndrome). Case 3 (NB245) carried an 11.9 Mb duplication at 16p13.11-p11.2 involving 105 genes. This duplication was detected in a neonate with VSD, ASD and unexplained seizure. Case 4 (NA082) had a 10 Mb deletion at 4q34.3-q35.1 and a 18 Mb duplication at 3q26.32-q29. Both termed 4q loss and 4q syndrome are known to be associated with cardiovascular abnormalities [25]. Case 5 (NA252) had a 1 Mb loss at Xq21.1 and deletion at Xq21.1-q21.31 has been reported in patients with cardiac and renal anomalies [26]. The Xq21.1 deletion region encompasses the genes CYSLTR1, GPR174, LPAR4, MIR4328, P2RY10 and ZCCHC5.

CNVs putatively associated with VSD

All of the identified 29 rare CNVs, putatively causally associated with VSDs were placed on the chromosomal map of the genome (Additional file 1: Figure S5). These CNVs comprised mostly subtelomeric or centromeric imbalances and distributed on chromosomes such as 2p, 2q, 3p, 4q, 6p, 15q, 16q, 21q and 22q and most of these CNVs located on chromosomes 2, 3, 4, 7, 16 and X. The CNVs sizes identified in our VSD study are much smaller than those deposited in CHD wiki, which reports three regions (4q-ter, 15q26.2, 16q22) and one gene (TBX1) related to CHD. Twelve of the 29 CNVs (41.3 %) affect regions known to be ASD, VSD or general heart disease-related in DECIPHER and ISCA (Table 1).
Table 1

Summary of rare CNVs identified in CHD patients include genes

sample ID

Gender

age

Cytoband

Chromosome Region

Event

Interval(kb)

Count of Gene

Major candidate genesa

CHD phenotype

DECIPHER/ISCA/OMIM (no. of clinical features)a

NA012

F

1y8m

6p12.1

Chr6:55,823,840-55,949,133

Gain

125

1

BMP5

VSD

/

NA027

M

1y3m

2q35

Chr2:216,757,058-216,797,948

Gain

41

1

XRCC5

VSD

/

NA067

M

7 m

4q12

Chr4:57,627,423-57,940,932

Gain

314

2

IGFBP7, LOC255130

VSD

/

NA068

F

5y5 m

16q22.1

chr16:67,868,480-68,086,257

Loss

218

8

COG8, CYB5B, NIP7, PDF, SNTB2, TERF2, TMED6, VPS4A

TGA

VSD (20)

NA079

F

3y1m

10q24.31-q24.32

Chr10:102,953,588-103,040,185

Gain

87

2

LBX1

VSD

VSD (10)

NA008

F

2y8m

3q12.1- q12.2

Chr3:101,403,767-101,519,268

Gain

116

1

TBC1D23

VSD

/

NA082

F

3y1m

3p22.2

Chr3:37377757-37577933

Gain

201

3

GOLGA4, C3ORF35, ITGA9

ASD

/

   

3q26.33-29

Chr3:180469228-198475603

Gain

18006

234

SOX2, MAP3K13, BCL6, TP63, FGF12

ASD

/

   

4q34.3-q35.2

Chr4:181,026,511-190,784,769

Loss

9694

76

CASP3, ING2, PDLIM3, SLC25A4, F11

ASD

VSD (5)

NA084

F

1y2m

2q36.1

Chr2:222,835,872-223,511,548

Gain

676

6

PAX3

VSD

/

NA252

M

4 m

Xq13.3

ChrX:73900661-74462483

Gain

562

2

KIAA2022,ABCB7

VSD

/

   

Xq21.1

ChrX:77,425,233-78,428,001

Loss

1003

11

CYSLTR1, GPR174, LPAR4, P2RY10

VSD

/

NA380

F

4y7m

16p13.11

Chr16:15,406,764-16,170,797

Gain

764

9

MYH11, NDE1

VSD

ASD (15), ASD (3)

NA423

M

5 m

16q24.1

Chr16:16,574,972-28,505,961

Loss

100

2

ATP2C2, WFDC1

VSD

/

NB1264

M

27d

4p16.1

Chr4:8,270,586-8,498,212

Loss

228

4

ACOX3, C4orf23, HTRA3, SH3TC1

VSD,ASD

/

NB245

M

1 m

16p13.11 - p11.2

Chr16:16,574,972-28,505,961

Gain

11931

135

CACNG3, CHP2, PLK1, PRKCB, XYLT1

VSD,ASD

ASD (15)

NB887

F

4 m

Xq22.1

ChrX:100,039,582-100,068,017

Gain

28

1

XKRX

VSD,ASD,PDA,PH

/

NB910

M

11d

13q13.3

Chr13:35,777,130-35,835,221

Gain

58

1

NBEA

VSD,PDA,PFO

/

   

Xq27.2

ChrX:140,727,218-141,583,235

Gain

856

3

MAGEC1, MAGEC2, MAGEC3

VSD,PDA,PFO

/

NC15

M

5y

20p12.1

Chr20:16,574,972-28,505,961

Gain

120

1

KIF16B

VSD

ASD,VSD (15)

NC27

F

4y

15q13.1

Chr15:25,833,244-25,871,572

Loss

38

1

OCA2

VSD

/

NC28

M

3y

15q26.2

Chr15:92,616,792-92,673,355

Loss

57

1

MCTP2

VSD

AVSD, CHD (4), ASD

NS176

M

7y1m

7p14.2

Chr7:36657642-36756092

Gain

118

1

AOAH

VSD

ASD,VSD

  

7y1m

14q32.12

Chr14:91069401-91230897

Gain

160

2

C14orf184, CATSPERB

VSD

ASD

NS480

M

4y

1q31.2

Chr1:190,543,305-190,707,353

Gain

164

1

RGS21

VSD

ASD (35)

NS494

M

2 m

2q14.2

Chr2:119,275,149-119,375,870

Gain

101

1

EN1

VSD,PFO,PH

ASD (5)

NS548

M

3y

7q11.22

Chr7:70,953,860-71,032,938

Loss

79

1

CALN1

VSD

VSD (15)

NS584

F

8 m

9q21.32

Chr9:84,859,691-85,387,778

Loss

528

2

FRMD3, RASEF

ASD,PS

/

NS659

F

12y5 m

7q31.32

Chr7:121,449,591-122,397,323

Gain

948

7

AASS, CADPS2, FEZF1, PTPRZ1

VSD

/

NS667

F

1y9m

21q22.3

Chr21:41639464-41733339

Gain

94

3

FAM3B,MX2, MX1

VSD

ASD (15)

NS8343

M

5 m

Xp22.2

ChrX:13,472,898-13,530,787

Loss

58

1

EGFL6

VSD,PDA,ASD,PFO

/

aMajor genes means that they are not included all genes involved in the CNVs and the genes in bold are the candidate genes which have evidences derived from previous studies

bReported phenotype in DECIPHER/ISCA/OMIM. Number in parenthesis is the number of features that the patient was affected. ASD, Atrial septal defect; VSD, Ventricular septal defect; CHD, congenital heart disease

CNV comparison in isolated and complex VSD

We compared CNVs within the 100 isolated VSD patients with those 44 complex VSD patients (Additional file 1: Table S6). There was a trend towards increased CNV size in patients with complex VSD, but the difference did not reach statistical significance. There was no significant difference in rare CNV numbers (average CNV count for each case) for either deletions or duplications.

Enrichment of CHD related genes

Several lines of evidence support the enrichment of CHD related genes within the CNVs detected in VSD patients. First, we found that PAX3 and LBX1 (in duplications) and CRKL, GP1BB, PDLIM3, TBX1, TXNRD2 (in deletions) were annotated in the MGI database and CHD wiki as associated with CHD. Evidence from the literature and from GO signal pathway analysis further supported this notion (Tables 2 and 3). Second, the enrichment analysis revealed 25 genes of 285 genes within both duplication and deletion CNVs detected in this study enriched in transcription factor, chromatin binding and three of five biological processes associated with heart development or cardiovascular system development are the main functions for candidate genes (Table 4). Third, the top two networks constructed by IPA analysis for the 285 genes include networks of cardiovascular disease and network of herediary disorder (Score 46: 25 genes) (Fisher's exact test, P = 3.42E-08 to 3.79E-02) (Additional file 1: Table S7). Top transcription regulators (NANOG, TP53, SOX2, POU5F1, IRF1) inferred by IPA analysis were listed in the Additional file 1: Table S8 and Additional file 1: Figure S6C. As a homeobox, NANOG regulates several transcription factors [27] such as EN1, SOX2, LBX1 and ZFP42 in our dataset (P = 4.91E-03), which controls cellular growth, organic growth and development.
Table 2

Genes involved in CNVs related to in heart development

Data resource

Gene number

Hit no./total gene

Gene list for gain (172 genes) in our study

Gene list for loss (113 genes) in our study

MGI database

147

7/285

PAX3, LBX1

CRKL b, GP1BB b, PDLIM3, TBX1 b, TXNRD2 b

Candidate genes§

202

9/285

PAX3, LBX1, MYH11, FGF12

CASP3, CRKL b, PDLIM3, TBX1 b, TXNRD2 b

Genes derived from GO:0072538

(cardiovascular system development)

1957

9/285

PAX3, LBX1, MYH11, PRKCB, IGFBP7

CYSLTR1, LPAR4, CRKL b, TBX1 b, TXNRD2 b

§CHD wiki (47 genes), UCSC Genome Browser (104 genes), literatures (51genes); the overlapping genes between different datasets were merged. bgenes which were included in CNVs related to DiGeorge syndrome

Table 3

Genes involved in CNVs related to cell surface receptor signaling pathway and heart development

Known signal pathwaya

Gene number

Hit no./total gene

Gene list for gain (172genes) in this study

Gene list for loss (113 genes) in this study

AKT pathway (VEGF, Insulin, MAPK, ErbB) (KEGG)

423

5

CACNG3, CHP2, PRKCB

CASP3, CRKL b

FGF pathway (regulation of actin cytoskeleton) (KEGG)

212

1

-

CRKL b

Hedgehog-Bmp pathway (KEGG)

56

1

BMP5

-

Notch pathway (Netpath)

100

2

EN1

CASP3

Notch pathway (KEGG)

44

0

-

-

TGF-BMP pathway (KEGG)

84

1

BMP5

-

Wnt pathway (Netpath)

121

1

PRKCB

-

Wnt pathway (KEGG)

264

3

CACNG3,CHP2,PRKCB

-

total

1304

14/285

  

aCHD-related pathway from KEGG and Netpath, the overlapping genes between different datasets were merged

bgenes which were included in CNVs related to DiGeorge syndrome

Table 4

Significantly enriched gene ontology (GO) terms from the genes involved in CNVs of VSD patients

 

ID

Name

Genes

Genes input

Genes in Annotation

P-value

Molecular Function

1

GO:0003682

chromatin binding

HIRA,SOX2, PRKCB, ING2,TP63, BCL6,PAX3

7

394

1.12E-04

2

GO:0043565

sequence-specific DNA binding

PAX3, EN1, SOX2, TBX1, TP63, LBX1, BCL6

7

741

7.04E-03

3

GO:0003700

sequence-specific DNA binding transcription factor activity

HIRA, EN1, SOX2, PAX3, TBX1, TP63, LBX1, BCL6

8

1052

8.35E-03

4

GO:0001071

nucleic acid binding transcription factor activity

HIRA, EN1, SOX2, PAX3, TBX1, TP63, LBX1, BCL6

8

1053

8.41E-03

Biological Process

1

GO:0007507

heart development

MYH11, TXNRD2, CRKL, FGF12, PDLIM3, TBX1, LBX1,CASP3,PAX3

9

466

9.36E-06

2

GO:0072358

cardiovascular system development

MYH11,TXNRD2,CRKL,FGF12,PDLIM3,TBX1,LBX1,CASP3, PRKCB,PAX3

10

889

1.85E-04

3

GO:0072359

circulatory system development

MYH11,TXNRD2,CRKL,FGF12,PDLIM3,TBX1,LBX1,CASP3, PRKCB,PAX3

10

889

1.85E-04

4

GO:0042127

regulation of cell proliferation

SOX2,CASP3,PAX3,BCL6,COMT,IGFBP7,LBX1,IL4R,TP63,TBX1,CHP2

11

1338

1.21E-03

5

GO:0045596

negative regulation of cell differentiation

SOX2, MED15, TBX1, TP63, LBX1, IL4R,BCL6

7

527

7.52E-03

Cellular Component

1

GO:0005667

transcription factor complex

SOX2, PAX3, LBX1, ING2,TP63

5

343

7.40E-03

2

GO:0044427

chromosomal part

BCL6, PLK1, NDE1, ING1, TP63, HIRA

6

596

1.00E-02

Mouse Phenotype

1

MP:0003421

abnormal thyroid gland development

PAX3, TBX1,CRKL

3

14

1.14E-02

2

MP:0020135

abnormal heart ventricle thickness

MYH11, TXNRD2, PAX3, LBX1, BCL6

5

126

3.50E-02

3

MP:0006284

absent hypaxial muscle

PAX3, LBX1

2

3

3.71E-02

4

MP:0004914

absent ultimobranchial body

PAX3, TBX1

2

3

3.71E-02

P-value: Corrected by Bonferroni and cutoff is 0.05

Discussion

Genomic imbalance, including known genomic disorders, contribute to the genetic etiology of congenital malformations such as CHD. In previous studies, syndromic chromosome abnormalities explained 6-9 % of CHD [28]. We found that Down syndrome (4 cases, 2.5 %), DiGeorge syndrome (2 cases, 1.2 %) and Trisomy X syndrome (2 cases, 1.2 %) contributed to up to 5 % of cases of VSD, consistent with the previous report [23, 24]. In addition, we identified large CNVs (> 1 Mb) (3/161, 1.9 %) including 4q34.3-q35.1, 3q26.32-q29 and 16p13.11-p11.2, which are associated with CHD as reported by DECIPHER and ISCA. Other CNV regions identified in our study such as 4q-ter, 15q26.2, and 16q22 had also been reported in the CHD wiki. We did not identify any significant difference in size, number or genic content of rare CNVs between complex VSDs and isolated VSDs. Some previous reports had reported a higher rate of CNVs carried in patients with CHD plus extracardiac or developmental abnormalities[5], but some claimed no significant increase[29]. We believe it likely that the genes affected by the CNVs are more important to cause VSD than CNV size or number, but the sample size might be too small to identify differences between isolated and complex VSDs.

Our interpretation suggests that critical genes contribute to the development of CHD by altered expression due to duplication or deletion CNVs. The genes identified in both de novo and recurrent CNVs were likely to be CHD-related genes. For example, we found a de novo deletion at Xp22.2 including EGFL6. EGFL6 involved in the regulation of cell cycle, proliferation and developmental processes has been previously reported as a candidate gene for human developmental disorders and is expressed during embryonic development [30]. 16p13.11 duplication is recurrent in our cohort, it had also been reported to be significantly associated with CHD recently [31]. MYH11 is the proposed candidate gene at this interval as defects in this gene underlie aortic aneurysm familial thoracic type 4 (AAT4) [MIM: 132900] and also contribute to familial thoracic aortic aneurysm and dissection (TAAD) and patent arterial duct (PDA). Our study suggests that EGFL6 and MYH11 may be dosage sensitive genes involved in embryonic heart development. Furthermore, we specifically evaluated genes involved in CNVs detected in patients with VSD. We identified 15 genes previously known to be associated with CHD or in CHD-related signal pathways (Tables 2 and 3). Among them, CRKL, TBX1, TXNRD2, GP1BB were known to be involved in DiGeorge syndrome. MYH11, TXNRD2, PAX3, LBX1 and BCL6 were associated with abnormal heart ventricle thickness (MP: 0020135). BMP5, EN1, PRKCB, CACNG3 and CHP2 were clustered in CHD related signaling pathways. Importantly, CASP3, CRKL, FGF12, LBX1, MYH11, PDLIM3, TXNRD2 and TBX1 are related to heart development (GO: 0007507) and also cardiovascular system development (GO: 0072358). It was inferred that these candidate genes might have effects on a wide range of cardiac tissues and regulate heart development at different stages.

Two types of molecular functions including chromatin binding and transcription factor complex were revealed through unbiased gene priority and enrichment analysis for all genes within CNVs of VSD patients and 5 biological processes via GO annotations, which indicated to be related to VSD. Transcription factors including LBX1, PAX3, EN1, SOX2 and TBX1 with confirmed effects on cardiogenesis were detected in our data set. LBX1 is a homeodomian-containing transcription factor required for the diversification of heart precursor cells in Drosophila and its expression had been described in cardiac neural cells and in migrating muscle precursor cells [32]. The overexpression of Lbx1 mRNA resulted in enlarged somites, an increase in cell proliferation by upregulating MyoD and lack of differentiated muscle [33]. PAX3, as a key regulatory factor in controlling the migrating of myogenic precursor cells, genetically acted in the upstream pathways of Lbx1 and Msx1. Pax3 also directly activate MyoD expression. The rising levels of Pax3 and Lbx1 result in enlarged muscle precursor cell population and then increase the bias for myogenic differentiation [34]. Additionally, a transcription regulation loop (NANOG-SOX2-OTC4) associated with downstream cascade regulation on GATA4, NKX2.5, MESP to modulate heart development (Additional file 1: Figure S6C). As the first formed organ, the genesis of heart involves a very complex series of morphogenetic interactions [35] and the transcription factors are essential for cardiogenesis at different embryonic stages.

As reported in the recent exome sequencing of CHD, de novo mutations in chromatin markers played a vital role in regulating cardiac development genes [36]. Seven genes (HIRA, SOX2, PRKCB, ING2, TP63, BCL6 and PAX3) in this study were enriched in chromatin binding pathway (GO: 0003682) (P =1.12E-04)which are worthy of being investigated in more detail in future studies.

Based on our cohort, chromosomal imbalances account for 5.2 % (8/154) and rare CNVs account for 16.9 % (26/154) of the cases. No significant difference was detected in terms of CNV diagnostic yield between complex and isolated VSD patients, indicating that both populations should be tested for genomic imbalances. Although the VSD-related candidate genes (as shown in Table 5) need further studies to confirm their involvement in VSD pathogenesis, our findings demonstrated that high-density microarray analysis is a useful tool to uncover potential underline genomic causes for VSDs and extended enrichment and pathway analysis indicate possible convergence on pathways during cardiogenesis.

Conclusions

In this pilot study, we identified genomic imbalances had an important contribution to the genetic burden of patient with VSD, which was consistent with the previous report in CHD. The rare CNVs VSD patients carried were interpreted and classified for clinical utility by comparing the population CNV database and patient-derived CNV database. CNV analysis of VSD patient in this study firstly showed genetic status of VSD on copy number variant and no significant difference between isolated VSD and complex VSD indicated that both populations need equal CNV tests. Furthermore, we applied gene enrichment and pathway analysis for understanding the relevant genes involved and the potential relevance of CNV with heart development, which may delineate the genetic etiology and pathways of VSDs.
Table 5

The 18 candidate genes for VSD identified in this study

Gene

Gene Annotation

CNV type

Hits into gene set

Phenotype

(individual number)

CRKL

v-crk avian sarcoma virus CT10 oncogene homolog-like

loss

iVSD (2) cVSD(1)

LBX1

ladybird homeobox 1

gain

ο

iVSD

PAX3

paired box 3

gain

ο

iVSD

TBX1

T-box 1

loss

iVSD (2) cVSD(1)

PDLIM3

PDZ and LIM domain 3

loss

ASD

TXNRD2

thioredoxin reductase 2

loss

iVSD (2) cVSD(1)

GP1BB

glycoprotein Ib (platelet), beta polypeptide

loss

iVSD (2) cVSD(1)

CASP3

caspase 3, apoptosis-related cysteine peptidase

loss

ASD

MYH11

myosin, heavy chain 11, smooth muscle

gain

ο

iVSD

BMP5

bone morphogenetic protein 5

gain

ο

iVSD

EN1

engrailed homeobox 1

gain

ο

cVSD

PRKCB

protein kinase C, beta

gain

ο

cVSD

FGF12

fibroblast growth factor 12

gain

ο

ASD

HIRA

histone cell cycle regulator

loss

ο

iVSD (2) cVSD(1)

SOX2

SRY (sex determining region Y)-box 2

gain

ο

ASD

DGCR2

DiGeorge syndrome critical region gene 2

loss

ο

iVSD (2) cVSD(1)

PLK1

polo-like kinase 1

gain

ο

cVSD

EGFL6

EGF-like-domain, multiple 6

loss

de novo

cVSD

Note: ▪ MGI database, Genes within Geneset reported in literature and CHD wiki

Prioritized by Gene set from literature and CHD wiki

Genes within GO and KEGG pathway

Prioritized by Gene set from GO and KEGG pathway

ο Genes significantly enriched by IPA

iVSD: isolated VSD; cVSD: complex VSD

Declarations

Acknowledgements

We want to thank the families of patients for their participation. We appreciate the contribution of Children’s Hospital of Fudan University, Institute of Cardiovascular Disease in General Hospital of Jinan Military Region, Capital Institute of Pediatrics in Beijing and Changzhi Medical College for sample enrollment. We especially appreciated revising manuscript by Dr. James F. Gusella and Dr. Michael Talkowski from Center for Human Genetic Research of Massachusetts General Hospital, Harvard Medical School.

Funding

This study was supported by the 973 National Basic Research Program of China (2013CB945400) to Drs HY Wang, BL Wu and Y An; the National Natural Science Foundation of China (81430005, 81270232), the Ministry of Education of China (313016), and the Commission for Science and Technology of Shanghai Municipality (13JC1407600) to Dr HY Wang.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

Authors’ Affiliations

(1)
Institutes of Biomedical Sciences and Children’s Hospital, Fudan University
(2)
The State Key Laboratory of Genetic Engineering, Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, and Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University
(3)
Jinan Military General Hospital
(4)
Children’s Hospital, Fudan University
(5)
Capital Institute of Pediatrics
(6)
Department of Biology, Changzhi MedicalCollege
(7)
Department of Laboratory Medicine, Children’s Hospital Boston, Harvard Medical School
(8)
School of Life Sciences, Obstetrics & Gynecology Hospital, Institute of Reproduction & Development, Fudan University

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© An et al. 2016

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