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Rare genomic copy number variants implicate new candidate genes for bicuspid aortic valve
Bicuspid aortic valve CNVs
https://orcid.org/0009-0001-0443-6093
Carlisle Steven G. Formal analysis Methodology Writing – original draft Writing – review & editing 1
Albasha Hasan Formal analysis Methodology Writing – original draft Writing – review & editing 2
Michelena Hector I. Data curation 3
https://orcid.org/0000-0002-3127-7673
Sabate-Rotes Anna Data curation 4
https://orcid.org/0009-0007-5307-4854
Bianco Lisa Data curation 4
https://orcid.org/0000-0001-8878-1507
De Backer Julie Data curation Writing – review & editing 5
Mosquera Laura Muiño Data curation Writing – review & editing 5
https://orcid.org/0000-0001-6853-578X
Yetman Anji T. Data curation Writing – review & editing 6
Bissell Malenka M. Data curation 7
Andreassi Maria Grazia Data curation 8
https://orcid.org/0000-0002-4604-0871
Foffa Ilenia Data curation 8
Hui Dawn S. Data curation 9
Caffarelli Anthony Data curation 10
Kim Yuli Y. Data curation 11
Guo Dongchuan Data curation Writing – review & editing 1
Citro Rodolfo Data curation 12
https://orcid.org/0000-0003-4134-1437
De Marco Margot Data curation 13
Tretter Justin T. Data curation 14
https://orcid.org/0000-0002-8407-8942
McBride Kim L. Data curation 15
Milewicz Dianna M. Data curation Project administration Resources Writing – review & editing 1
Body Simon C. Data curation Methodology Project administration Writing – review & editing 16
https://orcid.org/0000-0001-6341-9624
Prakash Siddharth K. Conceptualization Data curation Formal analysis Funding acquisition Methodology Project administration Supervision Writing – original draft Writing – review & editing 1 *
EBAV Investigators ¶
BAVCon Investigators ¶
1 University of Texas Health Science Center at Houston, Houston, Texas, United States of America
2 University College Dublin School of Medicine, Dublin, Ireland
3 Mayo Clinic, Rochester, Minnesota, United States of America
4 Vall d’Hebron University Hospital, Barcelona, Spain
5 Ghent University Hospital, Ghent, Belgium
6 University of Nebraska Medical Center, Omaha, Nebraska, United States of America
7 University of Leeds School of Medicine, Leeds, United Kingdom
8 Consiglio Nazionale delle Richerche (CNR), Instituto di Fisiologia Clinica, Pisa, Italy
9 University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America
10 Hoag Memorial Hospital Presbyterian, Newport Beach, California, United States of America
11 Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
12 University Hospital "San Giovanni di Dio e Ruggi d’Aragona," Salerno, Italy
13 Schola Medica Salernitana, University of Salerno, Baronissi, Italy
14 Cleveland Clinic, Cleveland, Ohio, United States of America
15 University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
16 Boston University School of Medicine, Boston, Massachusetts, United States of America
Mahdieh Nejat Editor
Shaheed Rajaei Hospital: Rajaie Cardiovascular Medical and Research Center, ISLAMIC REPUBLIC OF IRAN
Competing Interests: The authors have declared that no competing interests exist.

¶ Membership of the EBAV and BAVCon Investigators are provided in the Acknowledgments.

* E-mail: Siddharth.K.Prakash@uth.tmc.edu
6 9 2024
2024
19 9 e030451427 11 2023
14 5 2024
© 2024 Carlisle et al
2024
Carlisle et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Bicuspid aortic valve (BAV), the most common congenital heart defect, is a major cause of aortic valve disease requiring valve interventions and thoracic aortic aneurysms predisposing to acute aortic dissections. The spectrum of BAV ranges from early onset valve and aortic complications (EBAV) to sporadic late onset disease. Rare genomic copy number variants (CNVs) have previously been implicated in the development of BAV and thoracic aortic aneurysms. We determined the frequency and gene content of rare CNVs in EBAV probands (n = 272) using genome-wide SNP microarray analysis and three complementary CNV detection algorithms (cnvPartition, PennCNV, and QuantiSNP). Unselected control genotypes from the Database of Genotypes and Phenotypes were analyzed using identical methods. We filtered the data to select large genic CNVs that were detected by multiple algorithms. Findings were replicated in a BAV cohort with late onset sporadic disease (n = 5040). We identified 3 large and rare (< 1,1000 in controls) CNVs in EBAV probands. The burden of CNVs intersecting with genes known to cause BAV when mutated was increased in case-control analysis. CNVs intersecting with GATA4 and DSCAM were enriched in cases, recurrent in other datasets, and segregated with disease in families. In total, we identified potentially pathogenic CNVs in 9% of EBAV cases, implicating alterations of candidate genes at these loci in the pathogenesis of BAV.

http://dx.doi.org/10.13039/100000050 National Heart, Lung, and Blood Institute R01HL137028 https://orcid.org/0000-0001-6341-9624
Prakash Siddharth K. http://dx.doi.org/10.13039/100000050 National Heart, Lung, and Blood Institute R01HL114823 Body Simon C. http://dx.doi.org/10.13039/100000050 National Heart, Lung, and Blood Institute R21HL150373 Body Simon C. This study was supported in part by grants R01HL137028 (SP), R01HL114823 (SCB), and R21HL150373 (SCB) from the National Heart, Lung, and Blood Institute (NHLBI). The funder did not play any role in the study design, data collection, data analysis, decision to publish, or preparation of the manuscript. Data AvailabilityAll relevant data are included in the manuscript or Supporting Information files. All genotype and CNV call data were uploaded to zenodo (10.5281/zenodo.12655912) and will be available from the Database of Genotypes and Phenotypes.
Data Availability

All relevant data are included in the manuscript or Supporting Information files. All genotype and CNV call data were uploaded to zenodo (10.5281/zenodo.12655912) and will be available from the Database of Genotypes and Phenotypes.
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pmcIntroduction

Copy number variants (CNVs) have been implicated as causes or modifiers of many human diseases [1]. Specifically, large genomic CNVs are significantly enriched in cohorts with developmental delay or congenital abnormalities, and the severity of phenotypes has been correlated with the burden of rare CNVs [2]. These observations show that large, rare, de novo CNVs are likely to be pathogenic and can exert clinically relevant effects on disease pathogenesis [3, 4].

The worldwide prevalence of congenital heart disease (CHD) is 8.2 per 1000 live births [5]. CNVs have been implicated in both syndromic and non-syndromic forms of CHD [6–10]. The pathogenicity and penetrance of CNVs was initially established for clinical syndromes such as velocardiofacial syndrome, Turner syndrome, or Williams–Beuren syndrome, which involve chromosomal or megabase scale duplications or deletions, but has since been expanded to include additional CHD subtypes [10]. CNVs contribute to 10% of all CHD cases and up to 25% of cases with extracardiac anomalies or other syndromic features [11]. The role of pathogenic CNVs affecting genes that are known to cause CHD when mutated, such as ELN and TBX1, has been established [12]. Furthermore, population-level analysis has consistently demonstrated an increased burden of CHD in carriers of CNVs at specific genomic hotspots compared to controls, displaying the pathogenic potential of rare or de novo CNVs [12–14].

Bicuspid aortic valve (BAV) is the most common congenital heart malformation with a population prevalence of 0.5–2% [15]. BAV predisposes to aortic valve stenosis and thoracic aortic aneurysms and is associated with other left ventricular outflow tract lesions such as mitral valve disease and coarctation [16]. The high heritability of BAV was demonstrated in first- and second-degree relatives, who are more than ten times more likely to be diagnosed with BAV compared to matched controls [17]. BAV can occur as an isolated congenital lesion or as part of a clinical syndrome. For example, the prevalence of BAV is increased in Velocardiofacial, Loeys-Dietz, Kabuki, and Turner syndromes. Pathogenic variants of several genes are implicated in familial non-syndromic BAV, which is typically inherited as an autosomal dominant trait with reduced penetrance and variable expressivity. There is strong cumulative evidence that pathogenic variants in GATA4, GATA6, MIB1, NOTCH1, ROBO4, SMAD4, and SMAD6 each contribute to a small percentage of non-syndromic BAV cases [18, 19]. Phenotypic expression of BAV disease ranges from incidental discovery in late adulthood to neonatal or childhood onset with severe manifestations requiring valve or aortic interventions. In comparison to patients with later disease onset, younger BAV cohorts tend to present with syndromic features or complex congenital malformations that are more likely to have a genetic cause, thereby increasing the power of association studies to discover clinically relevant CNVs [20]. Recently, we identified recurrent rare CNVs that were enriched for cardiac developmental genes in a young cohort with early-onset thoracic aortic aneurysms or acute aortic dissections [21].

We hypothesize that large rare genomic CNVs contribute to early onset complications of BAV. Consistent with previous observations, we predict that the burden and penetrance of rare CNVs will be increased in individuals with early onset disease when compared to elderly sporadic BAV cases and population controls. Identification of novel pathogenic CNVs can provide new insights into the genetic complexity of BAV and may be useful for personalized risk stratification or clinical guidance based on the specific recurrent CNV (Fig 1) [22]. Therefore, we set out to describe the burden and penetrance of rare CNVs in a young cohort with early onset complications of BAV disease (EBAV).

10.1371/journal.pone.0304514.g001 Fig 1 Specific rare genomic copy number variants may influence BAV disease severity.

Aortic events, thoracic aortic aneurysm, thoracic aortic dissection, or aortic valve stenosis or regurgitation requiring aortic valve repair or replacement.

Materials and methods

The study protocol was approved by the Committee for the Protection of Human Subjects at the University of Texas Health Science Center at Houston (HSC-MS-11-0185). Study recruitment began on July 1, 2017, and concluded on March 30, 2022. After written informed consent, we enrolled probands with early onset BAV disease (EBAV), which we defined as individuals with BAV who were under the age of 30 at the time of first clinical event. Clinical events were defined as aortic replacement, aortic valve surgery, aortic dissection, moderate or severe aortic stenosis or aortic regurgitation, large aneurysm (Z > 4.5), or intervention for BAV-related conditions. Those with hypoplastic left heart, known genetic mutations, genetic syndromes, or complex congenital heart disease were excluded. Samples were collected and genotyped as previously reported [23]. For comparison, we analyzed a cohort of older individuals of European ancestry with sporadic BAV disease selected from the International BAV Consortium (BAVGWAS) [24].

Phenotypes were derived from record review with confirmation of image data whenever possible [25, 26]. The computational pipeline for CNV analysis of Illumina single nucleotide polymorphism (SNP) array data included three independent CNV detection algorithms (Fig 2, S1 Appendix).

10.1371/journal.pone.0304514.g002 Fig 2 Overview of pipeline for CNV identification and validation.

SNP, single nucleotide polymorphism; QC, Quality control; CNV, copy number variant. Illumina B-allele frequency and signal intensity data was trimmed and exported using GenomeStudio. Three different algorithms (PennCNV [27], cnvPartition, and QuantiSNP [28]) were used to generate initial CNV calls and sample-level statistics. Sample-level quality control analysis was performed using PennCNV. PLINK [29] was used to define CNV regions for subsequent burden, enrichment, and replication tests. Raw CNV calls were individually screened for CNVs intersecting with genes implicated in BAV and enriched in case-control tests. CNVs were validated by examining the raw data in GenomeStudio.

GenomeStudio was used to exclude samples with indeterminate sex or more than 5% missing genotypes, and single nucleotide polymorphisms (SNPs) with GenTrain = 0. Principal component analysis was used to remove outliers that did not cluster with European ancestry. Prior to CNV analysis, each dataset was trimmed by selecting a common set of 650,000 SNPs that were genotyped on each of the microarrays used in this study.

Three independent algorithms (PennCNV, cnvPartition, and QuantiSNP) were used to generate CNV calls and sample-level quality statistics from SNP intensity data. PennCNV and QuantiSNP were run on Unix clusters and cnvPartition data were exported from GenomeStudio. The analysis was run using default configurations.

PennCNV was used to generate QC data and remove CNV calls that intersect with polymorphic genomic regions. Samples that met any of the following criteria were excluded, standard deviation of the LogR ratio (obtained from PennCNV) > 0.35 or number of CNVs > 2 standard deviations above the mean for each data set. CNV calls less than 20 Kilobases in length and/or spanned by fewer than 6 probes were excluded. The overlap function for rare CNVs in PLINK was used to construct CNV regions (CNVRs) after adjacent regions were merged using PennCNV.

LogR ratio (LRR) and B allele frequency (BAF) data at CNVRs and calls of interest were visualized in GenomeStudio for validation. For segregation analysis, GenomeStudio was used to determine the presence of CNVs in relatives.

A total of 22,014 unselected control Illumina Genotypes obtained from the Database of Genotypes and Phenotypes were analyzed using identical methods (S1 Table). The Wisconsin Longitudinal Study (WLS) includes data on a cohort of 10,300 individuals who graduated from Wisconsin high schools in 1957. The Health and Retirement Study (HRS) includes data on 37,000 individuals aged 50 above from 23,000 households across the United States. Principal component analysis was used to select European ancestry genotypes from these datasets for analysis. Datasets were paired for case-control analysis based on the concordance of log-transformed sample-level quality control statistics (number of CNV calls and standard deviation of logR ratios). Chi-squared or Fisher exact tests were used to compare CNV frequencies in cases and controls.

Rare CNV functions in PLINK (v1.7) were used to perform permutation-based burden tests or gene set-based enrichment tests as previously described [29, 30]. Case control burden tests were restricted to CNVs that were longer than 110 Kb and less than 0.1% in frequency. CNV overlap functions in PLINK were used to identify rare CNVs that intersect between datasets or involve specific BAV or CHD genes (S2 Table). The list of candidate genes included 190 CHD genes that have strong cumulative evidence to cause BAV or related congenital malformations from human or animal model data [31–33]. Genome Reference Consortium Human Build 37 was used for CNV annotation [34].

Results

The EBAV cohort included a total of 544 samples, 272 EBAV probands, 21 relatives with BAV, and 251 apparently unaffected family members (26 trios and 15 multiplex families). The BAVGWAS sample contained 5,040 genotypes with associated demographic and clinical data. After exclusions due to data quality control or missing phenotypic data, 499 EBAV genotypes and 4,216 BAVGWAS were included in the final analysis. In phenotypic comparisons, EBAV probands were significantly younger at diagnosis, had more frequent co-existing congenital heart and vascular lesions, and underwent more frequent valve or aortic interventions (Table 1).

10.1371/journal.pone.0304514.t001 Table 1 Characteristics of EBAV and BAVGWAS probands.

	EBAV (n = 272)	BAVGWAS (n = 3141)	
Female (%)	35	29	
Age at diagnosis (years)	36 ± 21	52 ± 16	
AA (%)	13	37	
Predominant AR (%)	14	40	
Predominant AS (%)	18	37	
Other Lesions (%)	22	1	
Aortic Valve Surgery (%)	33	16	
n, number of cases; ±, standard deviation; AA, aortic aneurysm; AR, aortic regurgitation; AS, aortic stenosis; Other Lesions, other congenital heart malformations (primarily coarctation or ventricular septal defect).

In comparisons between EBAV and WLS data, the rate of large CNVs was increased in EBAV cases compared to WLS controls, driven primarily by enrichment of large rare genomic deletions (P<0.001, Table 2, S3 Table).

10.1371/journal.pone.0304514.t002 Table 2 Burden analysis of EBAV CNVs.

	RATE	P	PROP	P	TOT	P	AVG	P	
Large	0.51	<0.001	0.17	1	2648	<0.001	690	<0.001	
Rare	0.36	0.79	0.21	1	426	<0.001	288	0.04	
Duplications	0.07	0.96	0.07	0.96	648	0.25	615	0.18	
Deletions	0.11	<0.001	0.05	0.02	1477	0.001	608	0.23	
Large, CNV regions between 250 Kb and 5 Mb in length; Rare, occur in fewer than 1 in 1000 individuals; RATE, number of CNVs per individual; PROP, proportion of samples with one or more CNVs; TOT, total length of all CNVs in kilobases; AVG, mean CNV length; P, permuted P-value. Tests are 1-sided with 100,000 permutations.

In BAVGWAS cases, there were no significant differences in CNV rates or the proportions of individuals with large rare CNVs in comparison to HRS controls (Table 3).

10.1371/journal.pone.0304514.t003 Table 3 Burden analysis of BAVGWAS CNVs.

	RATE	P	PROP	P	TOT	P	AVG	P	
Large	0.23	1	0.20	1	688	0.02	581	<0.001	
Rare	0.28	1	0.23	1	306	0.87	253	0.6	
Duplications	0.18	1	0.15	1	309	0.98	266	0.98	
Deletions	0.11	1	0.10	1	244	0.04	226	0.02	
Large, CNV regions between 250 Kb and 5 Mb in length; Rare, occur in fewer than 1 in 1000 individuals; RATE, number of CNVs per individual; PROP, proportion of samples with one or more CNVs; TOT, total length of all CNVs in kilobases; AVG, mean CNV length; P, permuted P value. Tests are 1-sided with 100,000 permutations.

We discovered 32 large (>250 kb) and rare (<1:1000) CNVs in EBAV probands, and 26 of these CNVs included protein-coding genes (S4 Table). The overall burden of large rare genic CNVs was not different between EBAV cases and WLS controls. However, the burden of large rare genic CNVs intersecting with genes known to cause BAV when mutated or implicated in syndromic BAV was significantly increased in EBAV cases (Table 4). EBAV probands with large rare CNVs were more likely to have concomitant congenital heart lesions and have other family members who were diagnosed with BAV (S5 Table).

10.1371/journal.pone.0304514.t004 Table 4 Burden of rare EBAV CNVs.

	EBAV	WLS			
	Calls	Rate	Calls	Rate	RR	P	
Genic	26	0.84	1151	0.65	1.2	0.18	
Deletions	10	0.04	439	0.05	0.75	0.85	
BAV	4	0.01	0	0.00	296	1x10-5	
Calls, total number of CNVs that met the specified criteria; Rate, number of CNVs per individual; RR, relative risk; P, P value; Genic, CNVs that intersect with genes; BAV, CNVs that intersect with genes that are known to cause bicuspid aortic valve (BAV) when mutated or implicated in syndromic BAV; Total, total number of large, rare CNVs. P values were calculated using 100,000 permutations.

In region-based association tests, 55 genes spanned by 26 CNVs were enriched in the EBAV cohort compared to WLS controls (S6 and S7 Tables). The largest CNVs involved DSCAM in 21q22 and GATA4 in 8p23. Large duplications involving the Velocardiofacial (VCFS) region in 22q11.2 and a recurrent CHD-associated CNV region in 1q21.1 were also enriched in EBAV probands. In BAVGWAS cases, common CNVs involving NANOG in 12p13.31 and rare CNVs involving NIBPL in 5p13.2, which are essential for early heart development, are enriched in comparison to HRS controls (S8 and S9 Tables). NIBPL mutations cause Cornelia-de Lange syndrome with a spectrum of congenital heart malformations including BAV.

We also scrutinized candidate genomic regions that are implicated in CHD by analyzing data from individual CNV algorithms to detect copy number alterations that may have been filtered out due to strict quality control criteria prior to enrichment studies. We identified additional rare EBAV CNVs that intersect with CHD candidate genes CELSR1, GJA5, RAF1, LTBP1, KIF1A, MYH11, TTN, and the VCFS region in 22q11.2. We detected additional GATA4 and DSCAM CNVs in multiplex families. These CNVs were enriched in EBAV cases compared to WLS controls (Table 5, S10 Table). In total, 9% of EBAV probands had CNVs that are likely to contribute to the development of BAV (S11 Table).

10.1371/journal.pone.0304514.t005 Table 5 CNVs affecting congenital heart disease genes in EBAV.

Region	Genes	Case	Control	OR	P	95% CI	
22:46261909–51187440	CELSR1	1	1	33	0.07	2.1 to 530	
1:146326373–147340734	GJA5	1	2	17	0.17	1.5 to 183	
3:12599717–12803792	RAF1	1	2	17	0.16	1.5 to 183	
22:41278694–41813285	DSCAM	4	2	67	<0.001	12 to 367	
8:11495032–11856903	GATA4	4	0	301	<0.001	16 to 5599	
22:19000000–22000000	TBX1, CRKL	4	10	13	<0.001	4.2 to 43	
16:15484868–16295863	MYH11	2	22	3.0	0.34	0.70 to 13	
2:241652252–241678528	KIF1A	3	22	4.5	0.04	1.3 to 15	
2:32775984–33331219	LTBP1	2	26	2.5	0.45	0.60 to 11	
Region, hg38 coordinates corresponding to the minimum overlap region of CNVs; Genes, candidate genes in region; Case, number of rare CNVs in EBAV cases that intersect with region; Control, number of CNVs in WLS controls that intersect with region; OR, odds ratio; P, chi-squared P-value; 95% CI, 95% confidence interval. Inherited CNVs were only counted once.

We similarly scrutinized BAVGWAS data for additional CNVs that intersect with CHD genes. We found that large duplications involving SOX7 and GATA4 in 8p23 and the VCFS region in 22q11.2 were also significantly enriched in BAVGWAS cases compared to HRS controls (Table 6, S12 Table).

10.1371/journal.pone.0304514.t006 Table 6 CNVs affecting congenital heart disease genes in BAVGWAS.

Region	Genes	Case	Control	OR	P	95% CI	
3:29993977–31273870	TGFBR2	1	0	5.6	0.75	0.23 to 138	
9:101861767–102092282	TGFBR1	1	0	5.6	0.75	0.23 to 138	
21:41577819–41842252	DSCAM	2	1	3.7	0.58	0.34 to 41	
22:46924254–46931077	CELSR1	3	1	5.6	0.25	0.58 to 54	
2:111404636–11310378	TMEM87B, FBLN7	3	2	2.8	0.48	0.47 to 17	
8:11385469–11821835	GATA4	8	1	15	0.002	1.9 to 120	
2:147166377–147308112	GJA5	4	10	0.75	0.83	0.23 to 2.4	
16:29664753–30199713	MAPK3	3	15	0.37	0.17	0.11 to 1.3	
22:19000000–22000000	TBX1, CRKL	18	11	3.1	0.004	1.4 to 6.5	
2:32689829–33299434	LTBP1	9	22	0.76	0.62	0.35 to 1.7	
16:15240816–16281154	MYH11	13	27	0.90	0.89	0.46 to 1.7	
2:241640262–241689833	KIF1A	13	30	0.81	0.64	0.42 to 1.6	
Region, hg38 coordinates corresponding to the minimum overlap region of CNVs; Genes, candidate genes in region; Case, number of rare CNVs in EBAV cases that intersect with region; Control, number of CNVs in WLS controls that intersect with region; OR, odds ratio; P, chi-squared P-value; 95% CI, 95% confidence interval. Inherited CNVs were only counted once.

Next, we attempted to replicate our observations by identifying CNVs in the BAVGWAS dataset that overlapped with EBAV CNVs. Large rare CNVs intersecting with GATA4, DSCAM, CELSR1, GJA5, MYH11, KIF1A, and TBX1 overlapped between the EBAV and BAVGWAS datasets (S13 Table). However, only CNVs intersecting with GATA4 and DSCAM were significantly enriched in both datasets (Fig 3).

10.1371/journal.pone.0304514.g003 Fig 3 UCSC genome browser plots of GATA4 and DSCAM variants.

Each bar represents a copy number variant (CNV). Blue, EBAV CNVs; Red: CNVs BAVGWAS CNVs. (a) Ideogram of Chromosome 8 with CNV region highlighted red; (b) Plot of GATA4 CNVs; (c) Ideogram of Chromosome 21 with CNV region highlighted red; (d) Plot of DSCAM CNVs. Figures were constructed using the UCSC Genome Browser (http://genome.ucsc.edu) [35].

We also identified 21 very large genomic CNVs > 5 Mb in length in the BAVGWAS dataset. Analysis of GenomeStudio data showed that most of these were mosaic loss of heterozygosity regions or duplications. Nine were large germline chromosome-scale aberrations, including two cases of trisomy 21 (S14 Table). We did not identify any large X chromosome copy variants that may be consistent with Turner syndrome. There were no megabase-scale copy number variants in the EBAV dataset.

Pedigree analysis showed that CNVs involving CELSR1, LTBP1, KIF1A, GATA4, and DSCAM segregate with BAV in EBAV families (S1 Fig). Most of these CNVs occurred de novo in probands and were not found in unaffected family members. CNV carriers tended to present due to moderate or severe aortic regurgitation requiring valvular surgery. One proband had aortic coarctation. The age at presentation or sex of individuals with rare CNVs was not significantly different from the rest of the EBAV cohort.

Discussion

We identified large, rare, and likely pathogenic CNVs in almost 10% of EBAV probands that are enriched in genes that cause BAV when mutated. The percentage of EBAV cases with likely pathogenic CNVs is similar to our previous observations in a cohort with early onset TAD [36]. Enrichment of CNVs involving GATA4 and DSCAM in EBAV cases replicated in two additional BAV datasets and thousands of unselected control genotypes. This analysis provides compelling evidence that rare CNVs collectively contribute to more BAV cases than any single mutated gene.

GATA-binding protein 4 (GATA4) is a transcription factor that is required for cardiac and neuronal differentiation during embryogenesis [37]. Mutations of GATA4 and its homologs GATA5 and GATA6 cause congenital heart lesions [38]. Mutations in the GATA4 gene have been linked to a range of congenital heart diseases in humans, such as cardiac septal defects, tetralogy of Fallot, and patent ductus arteriosus [39]. Patients with BAV who have rare functional variants in the GATA family exhibit varying degrees of aortopathy expression, including aortic aneurysm, dissection, and/or aortic stenosis. Alonso‐Montes et al. described 4 predicted deleterious GATA4 mutations in 122 non-syndromic BAV probands who did not have affected relatives [40]. Rare GATA4 deletions and putative loss of function mutations are also implicated in CHD with distinctive features, underlining the importance of GATA4 dosage to cardiac development [41, 42]. Glessner et al. discovered large de novo duplications involving GATA4 in CHD trios with conotruncal defects or left ventricular outflow tract obstructive lesions [43]. Some duplications were inherited from apparently unaffected parents. Zogopoulos and Yu described similar genomic duplications in unaffected individuals and in unselected control genotypes [44, 45].

These observations are consistent with low-penetrance CHD in GATA4 duplication carriers. Similar to other complex and multifactorial disorders, CHD pathogenesis is likely caused by the cumulative impact of multiple CNVs or mutations, each exerting small to moderate effects to collectively disrupt cardiac development. For example, the frequency of congenital heart lesions is increased in individuals who have both 22q11.2 deletions and a common 12p13.31 duplication involving the SLC2A3 gene. The SLC2A3 CNV likely functions as a modifier of the cardiac phenotype associated with 22q11 deletion syndrome, exemplifying a “two-hit” model [46].

More than half of patients with Down syndrome have congenital heart malformations due to the interaction of multiple dosage-sensitive CHD genes on chromosome 21 [47–49]. Down syndrome cell adhesion molecule (DSCAM), previously shown to play a critical role in neurogenesis, has also been implicated in the pathophysiology of CHD [50]. Analysis of rare segmental trisomies of chromosome 21 suggested that duplication of DSCAM and the contiguous COL6A1 and COL6A2 genes may cause septal abnormalities and other Down Syndrome-related CHD lesions, including BAV. Overexpression of DSCAM and COL6A2 causes cardiac malformations in mice [51]. Our findings suggest that rare CNVs involving DSCAM may contribute to some non-syndromic BAV cases.

GATA4 and DSCAM CNVs segregated with disease in multiple families but are not fully penetrant and were detected in some unaffected relatives. Intriguingly, large 22q11.2, GATA4 and DSCAM CNVs were more highly enriched in EBAV than in BAVGWAS cases, suggesting that these CNVs may drive early onset BAV disease. These results are consistent with our observation that pathogenic CNVs involving candidate BAV genes are also enriched in EBAV compared to BAVGWAS cases. Our data suggests that pathogenic CNVs at these loci may predict accelerated disease onset or more severe complications.

We also identified recurrent rare CNVs involving specific dosage-sensitive cardiac developmental genes that are implicated in non-syndromic CHD. Recurrent 1q21.1 distal deletions encompassing GJA5, the gene encoding Connexin-40, are associated with CHD lesions including BAV. Enrichment of small genomic duplications spanning the GJA5 gene in cohorts with tetralogy of Fallot and cardiac abnormalities in mice with a targeted GJA5 deletion imply that dosage variations of GJA5 contribute to CHD [52]. CELSR1, a cadherin superfamily member, is mutated in families with BAV and hypoplastic left heart syndrome [53]. LTBP1 encodes an extracellular matrix protein that regulates TGF-β and fibrillin and has been implicated in congenital heart lesions [54]. KIF1A, encoding a kinesin microtubule transporter, was implicated in a dominant multisystem syndromic disorder with valvular and cardiac defects [55]. Mutation of MYH11 causes familial thoracic aortic aneurysms and dissections with an increased prevalence of BAV [56]. TTN mutations cause dilated cardiomyopathy and are associated with other left-sided congenital lesions [57]. Mutations or copy number changes involving these genes all cause a wide spectrum of penetrance and phenotypic severity, consistent with sensitivity to genetic or clinical modifiers.

Our combinatorial analysis method eliminated many CNVs that were detected by single algorithms or did not meet quality control benchmarks. Therefore, our analysis likely underestimated the contribution of rare pathogenic CNVs to BAV. We also recognize that cardiac development involves the complex interaction of many genes. We selectively validated individual CNVs at loci of interest but may have underrepresented CNVs that had no a priori relationship with CHD. The apparent penetrance of some CNVs may be less than expected due to missing phenotypic information. The available clinical data was not sufficiently detailed to permit genotype-phenotype correlations with specific CHD clinical features.

In conclusion, we identified large rare CNVs in a significant proportion of BAV cases, including a subset of CNVs that may predict early onset complications of BAV disease. These observations add to the evidence that rare CNVs may eventually have clinical utility for risk stratification and personalized disease management (Fig 1).

Supporting information

S1 Appendix Computational pipeline for CNV analysis.

(DOCX)

S1 Table Summary of control cohorts.

WLS, Wisconsin Longitudinal Study on Aging; HRS, Health and Retirement Study; Accession, accession number in the Database of Genotypes Phenotypes. WLS includes data on a cohort of 10,300 individuals who graduated from Wisconsin high schools in 1957. HRS includes data on 37,000 individuals aged 50 above from 23,000 households across the United States.

(DOCX)

S2 Table List of genes implicated in BAV or CHD.

Used for intersection studies.

(DOCX)

S3 Table Summary of CNV data.

EBAV, early onset bicuspid aortic valve cohort; BAVGWAS, Genome-wide Association Study from the International BAV Consortium; WLS, Wisconsin Longitudinal Study on Aging; HRS, Health Retirement Study; PennCNV, number of CNV calls detected by PennCNV algorithm after quality control; cnvPartition, number of CNV calls detected by cnvPartition algorithm after quality control; QuantiSNP, number of CNVs detected by QuantiSNP algorithm after quality control; Merged, number of CNV regions after merging adjacent calls; >5 MB, number of CNV regions that are larger than 5 megabases; Rare, number of CNVs that occur in less than 1 in 1000 samples of the combined datasets; Rare Deletions, number of large (> 250 Kb) rare deletions.

(DOCX)

S4 Table Large rare copy number variants in the EBAV probands.

Gene(s), genes intersected by CNV; Chr, chromosome; Start BP, start base pair of CNV; Stop BP, stop base pair of CNV; DUP, duplication; DEL, deletion. All CNVs were validated by direct inspection in GenomeStudio.

(DOCX)

S5 Table Phenotype comparison of EBAV samples with without large, rare CNVs.

CNV, samples with large rare CNVs; n, number of samples; TAA, thoracic aortic aneurysm; AR, aortic regurgitation; AS, aortic stenosis; Other lesions, other congenital heart malformations; >1 Affected, number of families with more than one affected individual. Percentages are in parentheses. *Significantly increased.

(DOCX)

S6 Table Top hits in region-based association tests of EBAV CNVs compared to WLS controls.

Chr, Chromosome; EMP1, permutation-based empiric P-value; EMP2, after genome-wide correction. *Top candidate genes.

(DOCX)

S7 Table Rare CNVs enriched in EBAV cohort.

Gene(s), genes intersected by CNV; Chr, chromosome; Start, start base pair of CNV; Stop, stop base pair of CNV; DUP, duplication; DEL, deletion. *Call in apparently unaffected family member. **Call in affected family member belonging to multiplex family.

(DOCX)

S8 Table Top hits in region-based association tests of BAVGWAS CNVs compared to HRS controls.

Chr, Chromosome; EMP1, permutation-based empiric P-value; EMP2, after genome-wide correction. *Top candidate genes.

(DOCX)

S9 Table Rare CNVs enriched in BAVGWAS cohort.

Gene(s), genes intersected by CNV; Chr, chromosome; Start, start base pair of CNV; Stop, stop base pair of CNV; DUP, duplication; DEL, deletion.

(DOCX)

S10 Table EBAV CNVs intersecting with congenital heart disease genes.

Chr, chromosome; Start, start base pair of CNV; Stop, stop base pair of CNV; DUP, duplication; DEL, deletion. *Call in unaffected family member, **Call in affected family member from a multiplex family.

(DOCX)

S11 Table Phenotype information of EBAV probands with candidate rare CNVs.

Gene, principal gene/region intersected by CNV; TAA, thoracic aortic aneurysm, TAD, thoracic aortic dissection; AS, aortic stenosis; AR, aortic regurgitation; AVR, aortic valve replacement; Aortic Repair, open or endovascular aortic procedure.

(DOCX)

S12 Table BAVGWAS CNVs intersecting with congenital heart disease genes.

Chr, chromosome; Start, start base pair of CNV; Stop, stop base pair of CNV; DUP, duplication; DEL, deletion.

(DOCX)

S13 Table CNV overlaps between EBAV and BAVGWAS cohorts.

Chr, chromosome; Start, start base pair of CNV; Stop, stop base pair of CNV; DUP, duplication; DEL, deletion. Some GATA4 CNVs were not identified in the overlap analysis because they exceeded the size threshold (5 Mb).

(DOCX)

S14 Table Large genomic events in the BAVGWAS dataset.

Chr, chromosome; Start, start base pair of CNV; Stop, stop base pair of CNV; DUP, duplication; DEL, deletion; LOH, loss of heterozygosity.

(DOCX)

S1 Fig Segregation of candidate BAV CNVs.

(A) GATA4 CNV; (B) DSCAM CNV; (C) CELSR1 CNV; (D, E) KIF1A CNVs; (F) LTBP1 CNVs. Dot, apparently unaffected CNV carrier; Shaded, affected CNV carrier; U, no genotype was available.

(TIFF)

S1 File (PNG)

We thank Joana Castillo and Jacqueline Jennings for sample preparation, William J. Allen for computational support, and Gladys Zapata, Nitesh Mehta, and the Laboratory for Translational Genomics at Baylor College of Medicine for microarray genotyping. Figs 1 and 2 were created using BioRender.com. S1 Fig was created using CeGaT Pedigree Chart Designer. The Texas Advanced Computing Center (TACC) at The University of Texas at Austin (http://www.tacc.utexas.edu) provided high-performance computing resources for data analysis.

The EBAV Investigators are: Siddharth K. Prakash, Dianna M. Milewicz, Shaine A. Morris, Rita Milewski, Giuseppe Limongelli, Allesandro Della Corte, Laura Perrone, Yuli Y. Kim, Hector I. Michelena, Maria G. Andreassi, Arturo Evangelista, Denver Sallee, Angela Yetman, Kim McBride, Eduardo Bossone, Rodolfo Citro, Dawn S. Hui, Malenka M. Bissell, Andrea Ballotti, Ilenia Foffa, Margot De Marco, Anthony Caffarelli, Rita Weise, Julie DeBacker, Laura Muino Mosquera, Robbin Cohen, Laura Dos Subira, Justin T. Tretter, Anna Sabe Rotes, Martina Caiazza, Lamia Ait Ali, Francesca Pluchinotta, Simon C. Body.

Lead author: Siddharth K. Prakash

Siddharth.K.Prakash@uth.tmc.edu

The BAVCon Investigators are: Simon C. Body, Alessandro Della Corte, Rodolfo Citro, Yohan Bossé, Alessandro Frigiola, Andrea Ballotta, Arturo Evangelista, Evaldas Girdsaukas, Betti Giusti, Bo Yang, Carlo de Vincentiis, Dan Gilon, Thoralf M. Sundt, David Messika Zeitoun, Dianna M. Milewicz, Siddharth K. Prakash, Eduardo Bossone, Eric Eisselbacher, Vicenza Stefano Nistri, Francesca R. Pluchinotta, Giuseppe Limongelli, Gordon S. Huggins, Joshua C. Denny, Patrick M. McCarthy, S. Chris Malaisrie, Aakash Bavishi, Hector I. Michelena, J. Daniel Muehlschelgel, Kim Eagle, Lasse Folkersen, Malenka M. Bissell, Patrick Mathieu, Per Eriksson, Peter Lichtner, Ronen Durst, Sébastien Thériault.

Lead author: Simon C. Body

scbody@bu.edu

10.1371/journal.pone.0304514.r001
Decision Letter 0
Brusgaard Klaus Academic Editor
© 2024 Klaus Brusgaard
2024
Klaus Brusgaard
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

25 Jan 2024

PONE-D-23-37920Rare Genomic Copy Number Variants Implicate New Candidate Genes for Bicuspid Aortic ValvePLOS ONE

Dear Dr. Prakash, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Nitesh Mehta, and the Laboratory for Translational Genomics at Baylor College of Medicine for 

microarray genotyping. This study was supported in part by R01HL137028 (SP). Fig 1 created 

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Additional Editor Comments:

==============================

The study is relevant and of interest. The reviewers are in agreement that the mauscript at a number of points needs clarification and generally needs some word processing. Plieas pay attention to reviewer comments and adhere to these.

==============================

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

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Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

**********

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Reviewer #1: No

Reviewer #2: I Don't Know

Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The paper by Carlisle et al describes rare copy number variants in two sets of patients with bicuspid aortic valves. While this type of investigation has been described before, the paper is of interest as it has two different cohorts, one early onset and one late onset, and describes the differences and overlap between these findings. However, because of the two patient cohorts, multiple control cohorts and the differences between these cohorts, the paper could do with some textual cleaning up, as at times it is confusing.

The use of control cohorts to compare against needs more detail. WLS and HRS and Illumina Genotypes are used to compare CNVs against. What are these cohorts? Basic information on this is needed. Why are certain control cohorts used to compare against certain patient cohorts? Why not use one cohort, etc etc. How sure are the authors that these cohorts do not also contain patients with BAV (as BAV is a rather frequent phenotype), and would this be a problem?

The authors do three comparisons, EBAV alone, BAVGWAS alone and overlap between the two. However this is not quite clear from the results and the way it is written. I would like a clear separation between these comparisons in both text and tables, as that makes it much easier to follow. Is there a table in which the overlap between the two sets is described?

Table S9 is unclear. How can there be segregation when none of the family have the CNV? Eg BAV787. I would suggest including pedigrees, as that makes this type of information much easier to parse.

On line 191/193 prevalence is discussed, but I don’t see any statistics here to compare between the two groups.

On line 98 references are missing to the various papers that investigated the genes mentioned.

In the discussion, before line 335 there is a paragraph on GJA5 deletions , after this there is just a summary genes, each on their own sentence. This reads rather jarring in comparison to the previous part. I would put a connecting or explanatory sentence here, to prevent it reading like a summary.

Reviewer #2: The manuscript reports the association of copy number variants with bicuspid aortic valve disease. The authors report that CNVs could explain a notable number of cases. This is a very interesting study and advances genetic discovery efforts for BAV. I did however find some areas of the manuscript difficult to interpret. I believe the manuscript could be improved with a clearer description of findings. I list several points below.

Cohort numbers are confusingly presented. For EBAV 272 probands are mentioned in the abstract, 293 families on Page 6, a cohort size of 544 (comprising both affected and unaffected samples) presented in Table 1 and 279 probands in Table 2. For BAVGWAS the text refers to 5040, but Table 2 summarises just 3141. It is therefore unclear how many samples have contributed to the analyses. Can the authors please explain and address this so that numbers analysed are clearly presented.

I don’t see the need for Table 1 with the information included.

Page 9, paragraph 2. Presentation of Tables 3 and S2 in relation to the text could be improved. Is the “prevalence of large and rare CNVs”, referring to the PE for number of CNVs per individual?

Table S3 could include a column listing affected genes.

Where is the data supporting the page 5 statement “Seven of these genic CNVs were

205 enriched in EBAV cases compared to WLS controls with a genome-wide adjusted empiric P < 0.05.”

Which BAV genes are in the CNVs assessed in Table 4?

Page 10. “Large duplications involving the 208  Velocardiofacial (VCFS) region in 22q11.2 and 1q21.1 microduplications were also enriched in 209  EBAV cases (Table S4)”.

The table does not present evidence for enrichment.

Page 10. Which CHD genes were scrutinized? A supplementary table listing genes assessed would be helpful. I think important to show CHD associated genes where no CNV was identified.

Page 10, Table 5 and table S6. I believe “subtle” should be properly defined to clarify criteria of the CNVs analysed in this section. Relationship of the two tables is unclear – for some genes (e.g., MYH11) the CNVs described in Table S6 appear to correspond to the numbers presented in Table 5, however, this is not true for others (e.g., GATA4). Please explain.

Burden testing P-values should be presented to the data in Table 5 and Table 6.

Burden testing method is not properly described.

Abstract and Page 13, opening paragraph of discussion. The authors report potentially pathogenic variants for 8% of BAV cases in their EBAV cohort. It is not immediately clear to me which CNVs were considered as likely pathogenic. A table summarising the cases where CNVs have been interpreted in this way, with a clinical description of proband/family could be included.

The methods should include a section clearly describing the statistical tests performed.

Reviewer #3: The authors analyzed the frequency and genetic relevance of large and rare CNVs in a cohort of early onset bicuspid aortic valve disease. BAV is the most common congenital heart defect and a major cause of severe cardiac complications (early) or later in life. As the genetic causes of the disease are still incompletely understood and especially risk stratification is unclear the study is of great clinical interest. The genetic methods are appropriate, and the manuscript is well written.

However, I have some comments:

1. Did the authors check whether large and rare CNVs tend to occur more often in patients with BAV developing an aortic aneurysm or is the distribution equal?

2. Where the CNVs more common in families with more than 1 affected individual?

3. Interestingly, CNVs only affected the TGFB family in late onset BAV. How do the authors explain this finding?

3, The average clinical reader may not be used to CNVs. I therefore recommend including a simple graphical abstract.

**********

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Reviewer #3: No

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10.1371/journal.pone.0304514.r002
Author response to Decision Letter 0
Submission Version1
1 Mar 2024

Reviewer 1

1. The use of control cohorts to compare against needs more detail. WLS and HRS and Illumina Genotypes are used to compare CNVs against. What are these cohorts? Basic information on this is needed. Why are certain control cohorts used to compare against certain patient cohorts? Why not use one cohort, etc etc. How sure are the authors that these cohorts do not also contain patients with BAV (as BAV is a rather frequent phenotype), and would this be a problem?

We added a description of the control cohorts to S1 Table and lines 150-155: “The Wisconsin Longitudinal Study (WLS) includes data on a cohort of 10,300 individuals who graduated from Wisconsin high schools in 1957. The Health and Retirement Study (HRS) includes data on 37,000 individuals aged 50 above from 23,000 households across the United States. Principal component analysis was used to select European ancestry genotypes from these datasets for analysis.” We also added the rationale for selection of comparison cohorts to lines 155-157: “Datasets were paired for case-control analysis based on the concordance of log-transformed sample-level quality control statistics (number of CNV calls and standard deviation of the LogR Ratio).” While we acknowledge that unselected population cohorts may contain rare individuals with BAV, the effect, if present, would decrease the strength of reported associations (type II error).

2. The authors do three comparisons, EBAV alone, BAVGWAS alone and overlap between the two. However, this is not quite clear from the results and the way it is written. I would like a clear separation between these comparisons in both text and tables, as that makes it much easier to follow. Is there a table in which the overlap between the two sets is described?

We revised the Results section to separate EBAV and BAVGWAS results as requested. Burden analysis of the entire EBAV cohort is described in lines 181-184, followed by Table 2. Burden analysis of the BAVGWAS cohort is described in lines 192-193, followed by Table 3. Identification and enrichment analysis of large rare CNVs in the EBAV dataset is described in lines 201-218. Enrichment analysis of the BAVGWAS dataset is described in lines 218-222. We describe the overlap of large rare EBAV and BAVGWAS CNVs in lines 247-252. All overlapping rare CNVs are listed in Table S12.

3. Table S9 is unclear. How can there be segregation when none of the other family have the CNV? e.g. BAV787. I would suggest including pedigrees, as that makes this type of information much easier to parse.

We added a sentence in lines 266-267 to explain that these CNVs occurred as de novo in EBAV families. We replaced the supplemental table by S1 Figure that illustrates the pedigrees.

4. On line 191/193 prevalence is discussed, but I don’t see any statistics here to compare between the two groups.

We changed lines 181-183 as follows: “In comparisons between EBAV and WLS data, the rate of large CNVs was increased in EBAV cases compared to WLS controls, driven primarily by enrichment of large rare genomic deletions (P<0.001, Table 2).”

5. On line 98 references are missing to the various papers that investigated the genes mentioned.

We added two citations to line 81 that provide essential summary data about the genes in question.

6. In the discussion, before line 335 there is a paragraph on GJA5 deletions, after this there is just a summary gene, each on their own sentence. This reads rather jarring in comparison to the previous part. I would put a connecting or explanatory sentence here, to prevent it reading like a summary.

We edited this paragraph (lines 318-332) to improve readability as requested.

Reviewer 2

1. Cohort numbers are confusingly presented. For EBAV 272 probands are mentioned in the abstract, 293 families on Page 6, a cohort size of 544 (comprising both affected and unaffected samples) presented in Table 1 and 279 probands in Table 2. For BAVGWAS the text refers to 5040, but Table 2 summarizes just 3141. It is therefore unclear how many samples have contributed to the analyses. Can the authors please explain and address this so that numbers analyzed are clearly presented?

We updated lines 168-171 to summarize the EBAV and BAGWAS cohorts as follows: “The EBAV cohort included a total of 544 samples: 272 EBAV probands, 21 relatives with BAV, and 251 apparently unaffected family members (26 trios and 15 multiplex families). The BAVGWAS sample contained 5,040 genotypes with associated demographic and clinical data.” We edited Table 1 (formerly Table 2) to reflect the total number of unrelated individuals that were included in the final analysis, rather than the total numbers in each dataset. We clarified this in lines 171-172: “After exclusions due to data quality control or missing phenotypic data, 499 EBAV genotypes and 4216 BAVGWAS genoytpes were included in the final analysis.”

2. I don’t see the need for Table 1 with the information included.

We deleted Table 1.

3. Page 9, paragraph 2. Presentation of Tables 3 and S2 in relation to the text could be improved. Is the “prevalence of large and rare CNVs”, referring to the P-value for number of CNVs per individual?

We edited this paragraph (lines 181-184) as follows: “In comparisons between EBAV and WLS data, the rate of large CNVs was increased in EBAV cases compared to WLS controls, driven primarily by enrichment of large rare genomic deletions (P<0.001, Table 2 [formerly Table 3).”

4. Table S3 could include a column listing affected genes.

We added a column listing affected genes to Table S4 as requested.

5. Where is the data supporting the page 5 statement “Seven of these genic CNVs were

enriched in EBAV cases compared to WLS controls with a genome-wide adjusted empiric P < 0.05.”

We added specific data about these enriched CNVs with empiric P-values to Tables S6 and S8.

6. Which BAV genes are in the CNVs assessed in Table 4?

We added a list of genes known to cause BAV when mutated to Table S4.

7. Page 10. “Large duplications involving the Velocardiofacial (VCFS) region in 22q11.2 and 1q21.1 microduplications were also enriched in EBAV cases (Table S4)”. The table does not present evidence for enrichment.

We added enrichment data for these rare EBAV CNVs to Table S6.

8. Which CHD genes were scrutinized? A supplementary table listing genes assessed would be helpful. I think important to show CHD associated genes where no CNV was identified.

We revised lines 162-165 in the Methods section as follows: “CNV overlap functions in PLINK were used to identify rare CNVs that intersect between datasets or involve specific BAV or CHD genes (S2 Table). The list of candidate genes included 190 CHD genes that have strong cumulative evidence to cause BAV or related congenital malformations from human or animal model data.” The specific CHD candidate genes are included in Table S2.

9. Page 10, Table 5 and table S6. I believe “subtle” should be properly defined to clarify criteria of the CNVs analysed in this section. Relationship of the two tables is unclear – for some genes (e.g., MYH11) the CNVs described in Table S6 appear to correspond to the numbers presented in Table 5, however, this is not true for others (e.g., GATA4). Please explain.

We edited lines 222-224 as follows, removing ‘subtle’ from the description: “We also scrutinized candidate genomic regions that are implicated in CHD by analyzing data from individual CNV algorithms to detect copy number alterations that may have been filtered out due to strict quality control criteria prior to enrichment studies.” The top candidate genes that are mentioned in the text and in Table 5 are marked with asterisks in Tables S6 and S8.

10. Burden testing P-values should be presented to the data in Table 5 and Table 6.

We added P-values to Table 5 and Table 6 as requested.

11. Burden testing method is not properly described.

We edited lines 159-160 as follows to describe the burden testing method: “Rare CNV functions in PLINK (v1.7) were used to perform permutation-based burden tests or gene set-based enrichment tests as previously described [28, 29].”

12. Abstract and Page 13, opening paragraph of discussion. The authors report potentially pathogenic variants for 8% of BAV cases in their EBAV cohort. It is not immediately clear to me which CNVs were considered as likely pathogenic. A table summarizing the cases where CNVs have been interpreted in this way, with a clinical description of proband/family could be included.

We edited lines 224-229 in the Results section on page 13 as follows: “We identified additional rare EBAV CNVs that intersect with CHD candidate genes CELSR1, GJA5, RAF1, LTBP1, KIF1A, MYH11, TTN, and the VCFS region in 22q11.2. We detected additional GATA4 and DSCAM CNVs in multiplex families. These CNVs were enriched in EBAV cases compared to WLS controls (Table 5, S10 Table). In total, 9% of EBAV probands had CNVs that are likely to contribute to the development of BAV (S11 Table).” Table S11 contains phenotypic data on these individuals.

13. The methods should include a section clearly describing the statistical tests performed.

We edited the appropriate section of the Methods (lines 157-160) to include descriptions of statistical methods. We edited the legends of Tables 5 and 6 to state how P values are calculated.

Reviewer 3

1. Did the authors check whether large and rare CNVs tend to occur more often in patients with BAV developing an aortic aneurysm or is the distribution equal?

In lines 204-206, we added: “EBAV probands with large rare CNVs were more likely to have concomitant congenital heart lesions and have other family members who were diagnosed with BAV (S5 Table).” Table S5 summarizes these phenotypic differences. There were no differences in the prevalence of aneurysms, which are common in EBAV cases.

2. Where the CNVs more common in families with more than 1 affected individual?

As stated in #1, EBAV probands with rare CNVs were more likely to have a relative with BAV.

3. Interestingly, CNVs only affected the TGF-beta family in late onset BAV. How do the authors explain this finding?

There were only two rare CNVs involving TGFBR1 and TGFBR2 in the BAVGWAS dataset and they were not significantly enriched compared to controls. Deletions or duplications of TGFBR1 and TGFBR2 may cause different phenotypes than amino acid substitutions that cause Loeys-Dietz syndrome predisposing to TAD and BAV. Without additional data, it is not possible to evaluate the significance of these observations.

4. The average clinical reader may not be used to CNVs. I therefore recommend including a simple graphical abstract.

We inserted a graphical abstract (Figure 1) into the manuscript as requested.

Attachment Submitted filename: Carlisle.Albasha.Prakash.Response.to.Reviewers.docx

10.1371/journal.pone.0304514.r003
Decision Letter 1
Mahdieh Nejat Academic Editor
© 2024 Nejat Mahdieh
2024
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https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
14 May 2024

Rare Genomic Copy Number Variants Implicate New Candidate Genes for Bicuspid Aortic Valve

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10.1371/journal.pone.0304514.r004
Acceptance letter
Mahdieh Nejat Academic Editor
© 2024 Nejat Mahdieh
2024
Nejat Mahdieh
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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