FLT4 gene polymorphisms influence isolated ventricular septal defects predisposition in a Southwest China population

doi:10.21203/rs.3.rs-4342027/v1

Background

Ventricular septal defect (VSD) is the most common congenital heart disease. Although a small number of genes associated with VSD have been found, the genetic factors of VSD remain unclear. In this study, we evaluated the association of 10 candidate single nucleotide polymorphisms (SNPs) with isolated VSD in a population from Southwest China.

Methods

Based on the results of 34 congenital heart disease whole-exome sequencing and 1000 gene databases, 10 candidate SNPs were selected. A total of 618 samples were collected from the population of Southwest China, including 285 VSD samples and 333 normal samples. Ten SNPs in the case group and the control group were identified by SNaPshot genotyping. The χ² test was used to evaluate the relationship between VSD and each candidate SNP. The SNPs that had significant p values in the initial stage were further analysed using linkage disequilibrium, and haplotypes were assessed in 34 congenital heart disease whole-exome sequencing samples using Haploview software. The bins of SNPs that were in very strong linkage disequilibrium were further used to predict haplotypes by Arlequin software. ViennaRNA v2.5.1 predicted the haplotype mRNA secondary structure. We evaluated the correlation between mRNA secondary structure changes and ventricular septal defects.

Results

The χ² results showed that the allele frequency of FLT4 rs383985 (P = 0.040) was different between the control group and the case group (P < 0.05). FLT4 rs3736061 (r² = 1), rs3736062 (r² = 0.84), rs3736063 (r² = 0.84) and FLT4 rs383985 were in high linkage disequilibrium (r² > 0.8). Among them, rs3736061 and rs3736062 SNPs in the FLT4 gene led to synonymous mutations of amino acids, but predicting the secondary structure of mRNA might change the secondary structure of mRNA and reduce the free energy.

Conclusions

These findings suggest a possible molecular pathogenesis associated with isolated VSD, which warrants investigation in future studies.

Ventricular septal defect

FLT4

polymorphism

mRNA secondary structure

Congenital heart diseases (CHDs) are caused by structural abnormalities of the heart and vessels, and cardiovascular deformities are mainly due to the abnormal development of the heart during foetal development[1, 2]. CHDs are the most frequent birth defects[3]. Globally, the incidence is approximately 1 in 80 to 110 newborns and accounts for 30–50% of all foetal losses[4, 5]. Not all patients with CHDs can be diagnosed early, and the actual prevalence may be higher than estimated[6]. The underlying aetiology of CHDs remains poorly understood. Although it has long been thought that the development of CHDs is substantially influenced by the interaction and correlation between genetic and environmental factors, a large body of evidence indicates that genetic factors contribute to the majority of CHDs[2, 7, 8]. Therefore, it is increasingly important to discover the genetic pathogenesis of CHDs. VSD is the most common CHDs and may occur in isolation or in combination with other structural defects or can be part of more complex combinations, such as tetralogy of Fallot (TOF), double outlet right ventricle, transposition, or functionally univentricular hearts[9], which are characterized by a hole or defect in the septum between the right and left ventricles of the heart[10]. VSD occurs in approximately 1.5 to 3.5 per 1000 live births[11], and accounts for approximately 34% of all CHDs[12]. In Asia, it is estimated that 2.63 per 1000 children are born with VSD[13]. However, despite efforts to uncover the mechanism of VSD formation[14, 15], the details remain largely unknown.

Cardiac development is a complex process. The ontogeny of ventricular septation requires complex interactions among cells originating from different lineages along with coordination of apoptosis, specification, migration, differentiation, and proliferation of cells[16]. For example, dysregulated vascular endothelial growth factor (VEGF), which regulates cell proliferation, plays an important role in the pathogenesis of VSD. Studies have shown that it is associated with an increased risk for isolated VSD[17]. Additionally, the differential expression of genes involved in energy metabolism, the cell cycle and growth, the cytoskeleton, and cell adhesion plays an important role in the development of VSD[18, 19]. Taking the above findings into account, it is reasonable to consider the aetiology of VSD formation in a genetic context.

Previous studies have shown that genetic variations related to cell growth coordination, skeleton construction and cell adhesion might affect cardiac development, and many candidate genes responsible for susceptibility to VSD are involved in these pathways[16, 20, 21]. Based on 34 congenital heart disease whole-exome sequencing and gene function candidate strategies, we selected 10 SNPs in 9 genes for genotyping. FN1 encodes fibronectin, a protein involved in cell adhesion and migration processes, including embryogenesis. DNAH5 encodes dynein, which is part of the microtubule-associated dynein complex. FLT4 encodes tyrosine kinase receptors for vascular endothelial growth factors, and mutations in FLT4 are associated with TOF. LAMC3 belongs to laminin, which is involved in cell adhesion, differentiation, migration, signal transduction, neurite growth and metastasis. IQGAP1 encodes scaffold proteins and is involved in cytoskeletal rearrangement, cell adhesion, cell proliferation gene transcription and cell polarization. HYDIN encodes an axonal and cilial protein found primarily in the foetal heart and bronchial ciliated epithelium. B9D1 is involved in cilia formation. Subsequently, 10 selected SNPs were validated in 618 samples (285 VSD patients and 333 normal controls) from Southwest China to identify the genetic association with VSD.

2.1. Subjects

Two hundred eighty-five isolated VSD patients recruited from Fuwai Yunnan Cardiovascular Hospital between 2017 and 2021 were included in the case-control. The clinical diagnosis was performed by a cardiologist based on the clinical and echocardiography findings with the surgical notes. The control group comprised 333 healthy subjects with no history of congenital heart disease. All subjects were from Yunnan Province of southwestern China. We excluded patients with other CHDs, hypertension, coronary heart disease, cardiac valve disease, tachyarrhythmia, Alzheimer’s disease, acute viral myocarditis, or systemic disease. The study complied with the Declaration of Helsinki and was approved by the Ethics Committee of Fuwai Yunnan Cardiovascular Hospital. Written informed consent was obtained from the subjects before participation in the trial.

2.2. SNP selection and genotyping

This study prescreened candidate SNPs in the population of Southwest China based on whole-exome sequencing of 34 congenital heart disease patients (novogene, Beijing City, China) and gene functional candidate strategies. The 1000-gene database was used as a control. The differences in the case and control groups were compared using the χ² test. False discovery rate (FDR) was used for p value correction. SNPs with minor allele frequencies (MAF) > 10% and corrected P < 0.05 were selected. A total of 10 SNPs from the nine genes (rs6707530 in FN1, rs12659700 in DNAH5, rs383985 in FLT4, rs710074 in LAMC3, rs3124309 in COL5A1, rs598893 in COL4A1, rs2589941 in IQGAP1, rs7198975 and rs1774266 in HYDIN, rs11650112 in B9D1) were selected. Subsequently, individual genotyping was performed on VSD patients and control groups to confirm the association with VSD. Ten candidate SNPs were genotyped with SNaPshot[22, 23].

After written informed consent was obtained from the subjects, 3 ml of peripheral venous blood was collected. DNA preparation: Genomic DNA was extracted from peripheral venous blood following the manufacturer’s instructions of the AxyPrep Blood Genomic DNA MiniPrep Kit (Axygen, Hangzhou City, China) and stored at − 80°C until use. PCR primer design: Primer Premier 5 software (Premier Biosoft Ltd., USA) was used to design specific multiple primers for specific amplification of candidate SNPs. The 15 µl reaction system included 1 µl gDNA, 0.3 µl F primer, 0.3 µl R primer, 7.5 µl PCR Mix, and 5.9 µl ddH2O. The reaction conditions were 95°C, 5 min; 94°C, 20 sec; 55°C, 20 sec; 72°C, 40 sec; 35 cycles; and 72°C, 10 min. Supplementary Table 1 shows the primer sequences. The purified amplified product was subjected to single base extension (SBE). The reaction system consisted of 2 µl PCR purified product, 1 µl SNaPshot mix (NBE, USA), 0.2 µl extension primer, and 2.8 µl ddH₂O, for a total of 6 µl. SBE products were separated by capillary electrophoresis, sequenced using a 3730XL gene sequencer (ABI, USA) and analysed by GeneMarker software (version 2.6.4).

2.3. Linkage disequilibrium and haplotype blocks

On the basis of these genotype results, SNPs of positive genes were selected from the 34 congenital heart disease whole-exome sequencing results, and SNP information was retrieved from the NCBI dbSNP database (https://www.ncbi.nlm.nih.gov/snp/). The selected SNPs were analysed using linkage disequilibrium (LD). The haplotype reconstruction results were calculated by Haploview v3.32. Haploview used the confidence interval method to perform LD assessment. We used an r² threshold of 0.8. SNPs were selected as the markers for our study and further plotted using a bioinformatics online tool (http://www.bioinformatics.com.cn). The ELB algorithm of Arlequin V3.5.2.2 predicted haplotypes of the bins of SNPs that were in very strong linkage disequilibrium with a specified r² threshold. ViennaRNA v2.5.1[24] performed mRNA secondary structure prediction for these haplotypes.

Statistical Package for Social Sciences (IBM Corporation, Armonk, NY) was used for statistical analysis. Quantitative data were expressed as the mean ± SD, and comparisons between the two groups were performed using Student’s t test. Qualitative data and allele frequency were compared using the χ² test, and the genetic analysis model (dominant, recessive) was used to calculate the association between candidate SNPs and the risk of congenital heart disease. The relative risk of disease was expressed by odds ratio (OR) and 95% confidence interval (95% CI). All statistical analyses were two-tailed and were performed by Plink 1.9. P < 0.05 was defined as statistically significant.

4.1. Basic characteristics of the study subjects

A total of 617 subjects were recruited, including 285 VSD individuals and 333 healthy individuals. VSD was diagnosed by cardiologists according to echocardiographic results and surgical records. There were 148 males and 137 females with VSD, with an average age of 9.78 ± 11.44 years, and 121 males and 212 females in the control group, with an average age of 50.16 ± 14.78 years.

4.2. Typing and analyses of SNPs

All subjects were genotyped with SNaPshot, and the call rate of genotyping was 100%. In the control group and the case group, all 10 candidate SNPs conformed to Hardy–Weinberg proportions, and the frequency of minor alleles was greater than 0.05. The χ² test compared the frequency of 10 SNP alleles between the case group and the control group, and P < 0.05 was defined as statistically significant. The results are given in Table 1. FLT4 rs383985 showed differences between the VSD and control groups. In the general population, the low-frequency alleles are considered to be mutations, so FLT4 rs383985 (C, T, G) was analysed for C, T combined[25], and the MAF was statistically lower than that in the control group. Although the difference disappeared after Bonferroni correction, we still believe that this mutation is likely to be associated with VSD.

Table 1

Comparison of the gene frequency of 10 SNPs in the VSD population and normal population.
Gene	SNP	Minor/Major	MAF (VSD)	MAF (control)	Alle	HWE-P
		Minor/Major			P value
FN1	rs6707530	T/G	0.21	0.26	0.060	0.249
DNAH5	rs12659700	T/C	0.11	0.13	0.271	0.742
FLT4	rs383985	(C + T)/G	0.19	0.24	0.040	0.444
LAMC3	rs710074	C/A	0.35	0.36	0.515	0.730
COL5A1	rs3124309	T/C	0.50	0.49	0.778	0.872
COL4A1	rs598893	C/T	0.20	0.21	0.812	0.310
IQGAP1	rs2589941	C/T	0.17	0.15	0.342	0.305
HYDIN	rs1774266	A/G	0.43	0.42	0.686	0.265
	rs7198975	A/G	0.43	0.42	0.726	0.237
B9D1	rs11650112	T/C	0.17	0.13	0.087	0.103
P value in boldface indicates statistical significance, SNP, single nucleotide polymorphism; MAF(VSD), Minor allele frequency in VSD patients; MAF (control), Minor allele frequency in normal controls; HWE-P, P value of Hardy–Weinberg equilibrium.

4.3. Genetic model analysis of the correlation between candidate SNPs and VSD

We evaluated the correlation between the above positive SNPs and the risk of VSD through two inheritance models (M is a low-frequency allele, assuming dominant model: MM + MW vs. WW, and recessive model: MM vs. WW + MW). The results are shown in Table 2. Under our dominance model hypothesis, FLT4 rs383985 was still different from VSD (p value = 0.029), and the odds ratio of FLT4 rs383985 was 0.69 (95% CI: 0.50–0.90), which suggests a protective effect relative to the rare allele with regard to susceptibility to VSD.

Table 2

Genetic model analyses of the candidate SNPs in VSD and normal populations.
Gene (SNP)	Genotype	VSD Freq	Control Freq	P value	OR (95% CI)
FLT4(rs383985)
Dominant	MM + MW	0.34	0.43	0.029	0.69(0.50–0.90)
	WW	0.66	0.57
Recessive	MM	0.04	0.05	0.531
	MW + WW	0.96	0.95
P value in boldface indicates statistical significance. SNP, single nucleotide polymorphism; Freq, frequency; OR, odds ratio; CI, confidence interval.

4.4. Linkage disequilibrium and haplotype blocks

A total of 75 SNPs in the FLT4 gene were selected from 34 congenital heart disease whole-exome sequencing results as the markers for our study. The detailed haplotype block information and linkage disequilibrium plot are shown in Fig. 1A. FLT4 rs383985 was strongly linked with rs3736061, rs3736062 and rs3736063 (r² > 0.8). A total of 4 SNPs were selected as the markers for our study. Figure 1B and Table 3 depict the characteristics of the SNPs in FLT4, including dbSNP ID, genomic position and genomic function. The FLT4 rs3736061 and rs3736062, which cause a synonymous change in the amino acid, were not in the protein domain structures (Fig. 1C).

Table 3

The SNP markers selected in the 34 CHD samples.
Gene Symbol	dbSNP ID	Genomic Position (GRCh37)	Function
FLT4	rs3736061	Chr5:180057231	Exon 4
FLT4	rs383985	Chr5:180055862	Intron 8
FLT4	rs3736062	Chr5:180052946	Exon 10
FLT4	rs3736063	Chr5:180052817	Intron 11

4.5. mRNA analysis

Based on 34 congenital heart disease whole-exome sequencing results, FLT4 rs383985 and its strongly linked loci were selected for haplotype analysis. In the haplotype analysis, which was predicted using the ELB algorithm based on Arlequin V3.5.2.2, the four-locus haplotype consisted of FLT4 rs383985, rs3736063, rs3736062, and rs3736061. The four-locus haplotypes in FLT4 were obtained: CGGG, ACAA, ACGG, and CTGG. Since only rs3736061 and rs3736062 are located on the exon, only the different sites on the mRNA can be obtained. Three two-locus haplotypes consisted of rs3736061 and rs3736062: CG (carrier frequency = 89.7%), AA (carrier frequency = 8.8%), and AG (carrier frequency = 1.5%). Among them, the CG haplotype with the highest frequency is consistent with the reference sequence in the database and carries FLT4-positive mutation linkage sites in the AG haplotype and AA haplotype. The first 1600 nucleotides of FLT4 were selected, and the secondary structure of mRNA was predicted using ViennaRNA V2.5.1 software. Detailed secondary structure information of mRNA (the centroid secondary structure) is shown in Fig. 2. The secondary structure of mRNA carrying the highest frequency of the CG haplotype (Fig. 2A) was used as the control, with a minimum free energy of -550.44 kcal/mol. In the AG haplotype with only one allele changed, the mRNA secondary structure was changed (Fig. 2B), and the minimum free energy decreased to -655.14 kcal/mol. In the AA haplotype with two changed alleles, the mRNA secondary structure changed even more (Fig. 2C) with a minimum free energy of -675.10 kcal/mol, and the decrease was more obvious. Compared with the reference haplotype (Fig. 2A), the more allele changes there are, the greater the change in the mRNA secondary structure of the corresponding haplotype, the more obvious the free energy decrease, and the more stable the mRNA structure.

Ventricular septal defects are a common type of congenital heart disease, and it has been reported that genetic variation, genetic polymorphisms and environmental factors may be involved in the incidence of VSD[2, 16]. The present study aims to identify the genetic variation and aetiology of genetic polymorphisms involved in VSD. In our study, FLT4 rs383985 was found to be associated with VSD in the population of southwest China. The human FLT4 gene contains 34 exons and is located on chromosome 5q35.3. The FLT4 gene encodes the vascular endothelial growth Factor 3 receptor (VEGFR3), which is part of the VEGF signalling pathway[26]. Vascular endothelial growth factor receptors (VEGFRs) are critical in coordinating the development and maintenance of the cardiovascular and lymphatic vascular systems. Their aberrant expression or dysfunction is associated with a range of human diseases[27–29]. The expression of Vegfr3 protein in early embryonic murine hearts has been observed in the endocardium at E9.5 and throughout the heart at E12.5[30]. Moreover, mice with complete knockout of VEGFR3 exhibited cardiovascular failure at E9.5. Considering the occurrence of this severe cardiovascular phenotype, there is a distinct role for the receptor in early cardiovascular development[31]. VEGFR3 can also play a physiological role in controlling the expression of the major angiogenesis regulator vascular endothelial growth Factor 2 receptor (VEGFR2)[32]. VEGFR2 has a role in cardiomyocyte hypertrophy through paracrine signalling between endothelial cells and cardiomyocytes during physiological myocardial growth. There is now robust evidence that rare deleterious variants in FLT4 are a predisposing factor for sporadic, nonsyndromic TOF[33], the majority of FLT4 variants predisposing to TOF result in truncation of the protein coding sequence, either by the introduction of stop codons, frame shift mutations or disruption of the conserved splice site regions that dictate the removal of intronic sequences from transcripts before translation [29]. Jin et al.[34] found that dominant FLT4 mutations accounted for 2.3% of TOF, which is consistent with Donna's finding that FLT4 is one of the main susceptibility genes for TOF[29]. Xie et al.[35] also found FLT4 copy number variants (CNVs) in pulmonary atresia in a VSD cohort. These findings suggest that FLT4 plays an important role in the pathogenesis of VSD.

In this study, we tested the hypothesis that polymorphisms of the FLT4 gene contribute to susceptibility to isolated VSD. In the general population, the low-frequency alleles are considered to be mutations, FLT4 rs383985 is associated with susceptibility to VSD in the southwest region, and carrying the low-frequency C or T allele is a protective factor for VSD (OR = 0.69, P = 0.029). FLT4 rs383985 is located between exon 8 and exon 9 and close to exon 8. LD analysis shows that this site is strongly linked with FLT4 rs3736061, rs3736062 and rs3736063, in which FLT4 rs3736061 and rs3736062 are located in exon 4 and exon 10, although both cause amino acid synonymous mutations. Recent studies have shown that even the synonymous mutation does not change the protein structure and affects protein expression by changing mRNA levels. Synonymous mutations play nearly the same role in causing disease as nonsynonymous mutations[36]. Therefore, the two-locus haplotypes in FLT4 (rs3736061 and rs3736062) predicted the mRNA secondary structure. The AG haplotype, which accounted for approximately 1.5% of our sample, had altered mRNA secondary structure and decreased the minimum free energy relative to the reference haplotype CG. The AA haplotype had an estimated frequency of 8.8%, suggesting that the mRNA secondary structure changed more and that the minimum free energy decreased even more relative to the reference haplotype, CG. As the allele changes increased, the mRNA secondary structure changes increased, the minimum free energy decreased more, and the mRNA structure was more stable. This suggests that the synonymous mutation of FLT4 may make mRNA difficult to degrade and allows it to exist for a longer time, which plays a certain role in the prevention of VSD.

Congenital heart disease is a complex polygenic genetic disease, and the identification of its pathogenic genes has always been a difficult problem. Generally, gene association analysis is used for the genetic variation of the risk of complex diseases. Due to genetic background heterogeneity, pathogenic genes or loci of different races or regions may be difficult to replicate in all populations. Therefore, it is meaningful to conduct association analysis of VSD susceptibility in populations with different genetic backgrounds. This is the first reported association between FLT4 SNPs and isolated VSD. It is important to identify the aetiology associated with genetic polymorphisms, and improving the understanding of its pathogenesis and supporting genetic causes may be important for designing prenatal screening and genetic counselling for high-risk families. In addition, this study will contribute to the development of new diagnostic and treatment strategies.

In this study, FLT4 rs383985 and its strongly linked synonymous mutations were found to be associated with the occurrence of isolated VSD. Therefore, further studies are needed to prove the functional association between gene variants and VSD susceptibility, which may be helpful for the diagnosis and prevention of congenital heart disease.

Ethics Approval and Consent to Participate

The study was approved by the Research Ethics Committee of Fuwai Yunnan Cardiovascular Hospital. All the patients enrolled in the study signed written informed consent. No animal studies were carried out by the authors for this article.

Consent for publication

Not applicable.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding

This work was supported by Yunnan Province Science and Technology Department Kunming Medical University basic research joint special project [Grant number 202401AY070001-019], the Research and Application of Epidemiology, Pathogenesis, Diagnosis and Treatment of Cardiovascular Diseases in High Altitude of Yunnan Province Project [Grant number 202103AC100004], the major science and technology special plan project of Yunnan Province "Research and Development of Key Technologies for Innovative Diagnosis and Treatment of Structural Heart Disease in Southwest Plateau"[Grant number 202302AA310045]. CAMS Innovation Fund for Medical Sciences (CIFMS, 2021-I2M-1-024) and Yunnan Science and Technology Talents and Platform Project, Special Funds of Advanced Scientific and Technological Talents and Innovation Teams [Grant number 202005AC160011].

Availability of data and materials

Data relevant to this study are available from the corresponding authors upon reasonable request.

Author Contribution

Hao Sun conceived the study, Yunhan Zhang, Xiaoli Dong and Jun Zhang analysed the data and wrote the manuscript, Miao Zhao and Jiang Wang diagnosed the patients, Jiayou Chu, Zhaoqing Yang and Shaohui Ma carried out the experiments. Keqin Lin and Zhiling Luo provided financial support and supervised the manuscript. All authors read and approved the final manuscript.

Acknowledgments

The authors are thankful for the experimental platform provided by the Department of Medical Genetics, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College.

Natraj Setty HSS, Gouda Patil SS, Ramegowda RT, V V, Vijayalakshmi IB: Comprehensive Approach to Congenital Heart Defects. Journal of Cardiovascular Disease Research 2017, 8(1):01-05.
Chen HX, Yang ZY, Hou HT, Wang J, Wang XL, Yang Q, Liu L, He GW: Novel mutations of TCTN3/LTBP2 with cellular function changes in congenital heart disease associated with polydactyly. J Cell Mol Med 2020, 24(23):13751-13762.
van der Bom T, Zomer AC, Zwinderman AH, Meijboom FJ, Bouma BJ, Mulder BJ: The changing epidemiology of congenital heart disease. Nat Rev Cardiol 2011, 8(1):50-60.
Gilboa SM, Salemi JL, Nembhard WN, Fixler DE, Correa A: Mortality resulting from congenital heart disease among children and adults in the United States, 1999 to 2006. Circulation 2010, 122(22):2254-2263.
Zaidi S, Brueckner M: Genetics and Genomics of Congenital Heart Disease. Circ Res 2017, 120(6):923-940.
Pierpont ME, Brueckner M, Chung WK, Garg V, Lacro RV, McGuire AL, Mital S, Priest JR, Pu WT, Roberts A et al: Genetic Basis for Congenital Heart Disease: Revisited: A Scientific Statement From the American Heart Association. Circulation 2018, 138(21):e653-e711.
Andelfinger G: Genetic factors in congenital heart malformation. Clin Genet 2008, 73(6):516-527.
Tremblay N, Yang SW, Hitz MP, Asselin G, Ginns J, Riopel K, Gendron R, Montpetit A, Duhig E, Dubé MP et al: Familial ventricular aneurysms and septal defects map to chromosome 10p15. European heart journal 2011, 32(5):568-573.
Spicer DE, Hsu HH, Co-Vu J, Anderson RH, Fricker FJ: Ventricular septal defect. Orphanet journal of rare diseases 2014, 9:144.
Morray BH: Ventricular Septal Defect Closure Devices, Techniques, and Outcomes. Interv Cardiol Clin 2019, 8(1):1-10.
García-Montes JA, Calderón-Colmenero J, Casanova M, Zarco E, Fernández de la Reguera G, Buendía A: Risk factors for prolonged mechanical ventilation after surgical repair of congenital heart disease. Arch Cardiol Mex 2005, 75(4):402-407.
van der Linde D, Konings EE, Slager MA, Witsenburg M, Helbing WA, Takkenberg JJ, Roos-Hesselink JW: Birth prevalence of congenital heart disease worldwide: a systematic review and meta-analysis. Journal of the American College of Cardiology 2011, 58(21):2241-2247.
Tatum GH, Barker P: Epidemiology of Heart Defects: Visual Guide to Neonatal Cardiology; 2018.
Chen HX, Zhang X, Hou HT, Wang J, Yang Q, Wang XL, He GW: Identification of a novel and functional mutation in the TBX5 gene in a patient by screening from 354 patients with isolated ventricular septal defect. European journal of medical genetics 2017, 60(7):385-390.
Xuan C, Jia KG, Wang BB, Bai XY, Gao G, Yang Q, Wang XL, Liu XC, Ma X, He GW: Identification of two novel mutations of the HOMEZ gene in Chinese patients with isolated ventricular septal defect. Genetic testing and molecular biomarkers 2013, 17(5):390-394.
Kaynak B, von Heydebreck A, Mebus S, Seelow D, Hennig S, Vogel J, Sperling HP, Pregla R, Alexi-Meskishvili V, Hetzer R et al: Genome-wide array analysis of normal and malformed human hearts. Circulation 2003, 107(19):2467-2474.
Xie J, Yi L, Xu ZF, Mo XM, Hu YL, Wang DJ, Ren HZ, Han B, Wang Y, Yang C et al: VEGF C-634G polymorphism is associated with protection from isolated ventricular septal defect: case-control and TDT studies. European journal of human genetics : EJHG 2007, 15(12):1246-1251.
Zhang H, Zhou L, Yang R, Sheng Y, Sun W, Kong X, Cao K: Identification of differentially expressed genes in human heart with ventricular septal defect using suppression subtractive hybridization. Biochem Biophys Res Commun 2006, 342(1):135-144.
Penny DJ, Vick GW, 3rd: Ventricular septal defect. Lancet (London, England) 2011, 377(9771):1103-1112.
Soheili F, Jalili Z, Rahbar M, Khatooni Z, Mashayekhi A, Jafari H: Novel mutation of GATA4 gene in Kurdish population of Iran with nonsyndromic congenital heart septals defects. Congenit Heart Dis 2018, 13(2):295-304.
Muntean I, Togănel R, Benedek T: Genetics of Congenital Heart Disease: Past and Present. Biochem Genet 2017, 55(2):105-123.
Pontes ML, Fondevila M, Laréu MV, Medeiros R: SNP Markers as Additional Information to Resolve Complex Kinship Cases. Transfus Med Hemother 2015, 42(6):385-388.
Sobrino B, Brión M, Carracedo A: SNPs in forensic genetics: a review on SNP typing methodologies. Forensic science international 2005, 154(2-3):181-194.
Lorenz R, Bernhart SH, Höner Zu Siederdissen C, Tafer H, Flamm C, Stadler PF, Hofacker IL: ViennaRNA Package 2.0. Algorithms Mol Biol 2011, 6:26.
Hüebner C, Petermann I, Browning BL, Shelling AN, Ferguson LR: Triallelic single nucleotide polymorphisms and genotyping error in genetic epidemiology studies: MDR1 (ABCB1) G2677/T/A as an example. Cancer Epidemiol Biomarkers Prev 2007, 16(6):1185-1192.
Reuter MS, Jobling R, Chaturvedi RR, Manshaei R, Costain G, Heung T, Curtis M, Hosseini SM, Liston E, Lowther C et al: Haploinsufficiency of vascular endothelial growth factor related signaling genes is associated with tetralogy of Fallot. Genet Med 2019, 21(4):1001-1007.
Monaghan RM, Page DJ, Ostergaard P, Keavney BD: The physiological and pathological functions of VEGFR3 in cardiac and lymphatic development and related diseases. Cardiovascular research 2021, 117(8):1877-1890.
Olsson AK, Dimberg A, Kreuger J, Claesson-Welsh L: VEGF receptor signalling - in control of vascular function. Nat Rev Mol Cell Biol 2006, 7(5):359-371.
Page DJ, Miossec MJ, Williams SG, Monaghan RM, Fotiou E, Cordell HJ, Sutcliffe L, Topf A, Bourgey M, Bourque G et al: Whole Exome Sequencing Reveals the Major Genetic Contributors to Nonsyndromic Tetralogy of Fallot. Circ Res 2019, 124(4):553-563.
Klotz L, Norman S, Vieira JM, Masters M, Rohling M, Dubé KN, Bollini S, Matsuzaki F, Carr CA, Riley PR: Cardiac lymphatics are heterogeneous in origin and respond to injury. Nature 2015, 522(7554):62-67.
Dumont DJ, Jussila L, Taipale J, Lymboussaki A, Mustonen T, Pajusola K, Breitman M, Alitalo K: Cardiovascular failure in mouse embryos deficient in VEGF receptor-3. Science 1998, 282(5390):946-949.
Heinolainen K, Karaman S, D'Amico G, Tammela T, Sormunen R, Eklund L, Alitalo K, Zarkada G: VEGFR3 Modulates Vascular Permeability by Controlling VEGF/VEGFR2 Signaling. Circ Res 2017, 120(9):1414-1425.
Sevim Bayrak C, Zhang P, Tristani-Firouzi M, Gelb BD, Itan Y: De novo variants in exomes of congenital heart disease patients identify risk genes and pathways. Genome Med 2020, 12(1):9.
Jin SC, Homsy J, Zaidi S, Lu Q, Morton S, DePalma SR, Zeng X, Qi H, Chang W, Sierant MC et al: Contribution of rare inherited and de novo variants in 2,871 congenital heart disease probands. Nature genetics 2017, 49(11):1593-1601.
Xie H, Hong N, Zhang E, Li F, Sun K, Yu Y: Identification of Rare Copy Number Variants Associated With Pulmonary Atresia With Ventricular Septal Defect. Frontiers in genetics 2019, 10:15.
Shen X, Song S, Li C, Zhang J: Synonymous mutations in representative yeast genes are mostly strongly non-neutral. Nature 2022.

No competing interests reported.

SupplementaryTable1..docx

FLT4 gene polymorphisms influence isolated ventricular septal defects predisposition in a Southwest China population

Status:

Journal Publication

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

1. Introduction

2. Methods and Materials

2.1. Subjects

2.2. SNP selection and genotyping

2.3. Linkage disequilibrium and haplotype blocks

3. Statistical analysis

4. Results

4.1. Basic characteristics of the study subjects

4.2. Typing and analyses of SNPs

4.3. Genetic model analysis of the correlation between candidate SNPs and VSD

4.4. Linkage disequilibrium and haplotype blocks

4.5. mRNA analysis

5. Discussion

6. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1