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Use of a targeted, combinatorial next-generation sequencing approach for the study of bicuspid aortic valve

  • Elizabeth M Bonachea1,
  • Gloria Zender2,
  • Peter White1, 3,
  • Don Corsmeier3,
  • David Newsom3,
  • Sara Fitzgerald-Butt1, 2,
  • Vidu Garg1, 2, 4 and
  • Kim L McBride1, 2Email author
BMC Medical Genomics20147:56

https://doi.org/10.1186/1755-8794-7-56

Received: 13 June 2014

Accepted: 24 September 2014

Published: 26 September 2014

Abstract

Background

Bicuspid aortic valve (BAV) is the most common type of congenital heart disease with a population prevalence of 1-2%. While BAV is known to be highly heritable, mutations in single genes (such as GATA5 and NOTCH1) have been reported in few human BAV cases. Traditional gene sequencing methods are time and labor intensive, while next-generation high throughput sequencing remains costly for large patient cohorts and requires extensive bioinformatics processing. Here we describe an approach to targeted multi-gene sequencing with combinatorial pooling of samples from BAV patients.

Methods

We studied a previously described cohort of 78 unrelated subjects with echocardiogram-identified BAV. Subjects were identified as having isolated BAV or BAV associated with coarctation of aorta (BAV-CoA). BAV cusp fusion morphology was defined as right-left cusp fusion, right non-coronary cusp fusion, or left non-coronary cusp fusion. Samples were combined into 19 pools using a uniquely overlapping combinatorial design; a given mutation could be attributed to a single individual on the basis of which pools contained the mutation. A custom gene capture of 97 candidate genes was sequenced on the Illumina HiSeq 2000. Multistep bioinformatics processing was performed for base calling, variant identification, and in-silico analysis of putative disease-causing variants.

Results

Targeted capture identified 42 rare, non-synonymous, exonic variants involving 35 of the 97 candidate genes. Among these variants, in-silico analysis classified 33 of these variants as putative disease-causing changes. Sanger sequencing confirmed thirty-one of these variants, found among 16 individuals. There were no significant differences in variant burden among BAV fusion phenotypes or isolated BAV versus BAV-CoA. Pathway analysis suggests a role for the WNT signaling pathway in human BAV.

Conclusion

We successfully developed a pooling and targeted capture strategy that enabled rapid and cost effective next generation sequencing of target genes in a large patient cohort. This approach identified a large number of putative disease-causing variants in a cohort of patients with BAV, including variants in 26 genes not previously associated with human BAV. The data suggest that BAV heritability is complex and polygenic. Our pooling approach saved over $39,350 compared to an unpooled, targeted capture sequencing strategy.

Keywords

Bicuspid aortic valve Genetics Next-generation sequencing Targeted capture Combinatorial pooling

Background

Congenital bicuspid aortic valve (BAV) is the most common type of cardiac malformation, with an estimated prevalence of 1-2% in the general population [1]. BAV, in which two of the three normal aortic cusps are fused together, encompasses a wide spectrum of clinical phenotypes. The valve abnormality may be isolated in some cases, whereas in others the aortic valve abnormality is present in conjunction with other cardiac malformations [2]. BAV may also be associated with varying degrees of aortic valve stenosis and/or insufficiency as well as with aortopathy. Among BAV patients, there is variability in cusp fusion phenotypes. Right coronary and left coronary (R-L) cusp fusion is more common than right coronary and non-coronary (R-NC) cusp fusion. Moreover, R-L cusp fusion is more often associated with additional cardiac malformations, whereas R-NC cusp fusion is more likely to be associated with aortic valve dysfunction [3]. The etiologies of these associations are unknown.

While multiple studies have demonstrated the high heritability of BAV, the underlying genetic causes remain poorly understood [47]. NOTCH1 and GATA5 are the only genes that have been linked to bicuspid aortic valve in humans, yet variants in these genes are present in only a minority of individuals with BAV [814]. Mice lacking Gata5 have partially penetrant BAV of the R-NC subtype, but human studies have not yet demonstrated a specific association between GATA5 variants and the R-NC subtype of BAV. Animal models of R-L BAV demonstrate excess fusion of the septal and parietal ridges of the outflow tract, whereas R-NC BAVs result from fusion of the septal ridge and posterior intercalated cushions [15]. These studies suggest that these two cusp fusion phenotypes may arise from distinct genetic perturbations in humans.

Despite tremendous advances in gene sequencing technology, the genetic etiology of many common human conditions, including BAV, remains poorly understood. Candidate gene studies have long been used to detect variants in individual genes; such studies are easy to perform but require selection of genes with a proposed role in the disease process of interest. Genome-wide association studies allow investigators to compare multiple individuals with a given condition and identify common variants in a non-candidate driven approach [16]. However, because genome-wide association studies are predicated upon the common disease-common variant hypothesis, this approach is not ideal for the study of rare variants, particularly in complex conditions in which rare variants at multiple loci may be needed to produce a clinically recognizable phenotype [17, 18].

Next-generation sequencing (NGS) provides an opportunity for rapid, high-throughput sequencing of entire patient genomes and may overcome the limitation of genome-wide association studies in exploring the role of rare variants in complex diseases [19]. Whole genome sequencing remains at this time a costly technology, thus limiting its application to the sequencing of large cohorts of patients. It also produces a vast amount of data necessitating extensive bioinformatics processing. One option to overcome this issue is the design of targeted capture kits that allow for the rapid and accurate sequencing of only the genetic regions of interest. The two most common approaches to this technique have distinct limitations. Sequencing of a targeted set of genes can be done on individual samples, but this approach is very costly in larger cohorts. Alternatively, sequencing can be performed on pools of individual samples, wherein each sample is labeled with a unique genetic “barcode”; this approach is cost saving, but is quite labor intensive [20]. Combinatorial pooling schemes, wherein individuals are sampled in multiple pools, have been utilized to overcome these pitfalls and still permit identification of the individual sample contributing a given rare variant [21, 22].

Here, we present an approach using combinatorial pooling and targeted multi-gene sequencing to study a well-phenotyped cohort of individuals with BAV. We hypothesize that rare variants will be identified amongst a large proportion of the candidate genes, that multiple rare variants will be found in individual probands, and that such variants will segregate by cusp fusion phenotype.

Results

Identification of sequence variants

We studied a previously described cohort of 78 patients with echocardiogram-identified BAV [8]. Using a targeted capture approach, we sequenced 97 candidate genes selected by reviewing the literature for genes relevant to heart valve development.

The average depth of coverage for the targeted regions was 268X. Greater than 50X coverage was obtained for 99.04% of the bases sequenced (range: 94.19-99.62), with greater than 100X for 96.11% of bases covered. The percentage of sequencing on target was 71.81%.

Targeted capture identified 42 rare, non-synonymous, exonic variants involving 35 of the candidate genes (Additional file 1: Table S1). Among these variants, in-silico analysis classified 33 of these 42 variants as putative disease-causing changes; Sanger sequencing did not validate two of these 33 variants. The remaining 31 changes were identified in 16 individuals and involved 28 genes (Table 1). Each variant was identified in only one proband. There were no significant differences in variant burden among BAV fusion phenotypes or isolated BAV versus BAV-CoA, with p = 0.78 and p = 0.77, respectively (Additional file 2: Table S2). Only 2 of these variants (rs72541816 at APC and rs116164480 at GATA5) were de novo changes not present in either parent of the affected probands. These two variants were identified in the same individual with a family history of coarctation of the aorta. Of the 16 individuals in whom putative disease-causing variants were identified, two had variants in genes previously known to be involved in human BAV (NOTCH1, GATA5), one of whom we previously described [8]. Four of these 16 individuals had a family history of a left ventricular outflow tract malformation.
Table 1

Rare, non-synonymous, exonic variants in BAV cohort predicted damaging by in-silico analysis, confirmed by Sanger sequencing

Gene name

Nucleotide change

Amino acid change

De novo

SIFT

PP2

EA EVS

All EVS

1000G MAF

dbSNP137 ID

APC

c.C7862G

p.S2621C

yes

0.03

0.641

0.005

0.003

0.058

rs72541816

AXIN1

c.G2522A

p.R841Q

no

0.4

1

0.012

0.008

0.01

rs34015754

AXIN2

c.C2051T

p.A684V

no

0.01

0.95

0.002

0.001

0

rs138287857

FLT1

c.C3092G

p.S1031C

no

0

1

0

0

0

N/A

GATA4

c.G1310C

p.G437A

no

0

0.787

0

0

0

N/A

GATA5

c.T698C

p.L233P

yes

0.05

0.723

0.001

0.001

0.003

rs116164480

GLI1

c.G3142A

p.D1048N

no

0

1

0

0

0

N/A

JAG1

c.G2810A

p.R937Q

no

0.47

0.093

0.002

0.001

0.001

rs145895196

MCTP2

c.C1634T

p.T545M

unknown

0

1

0

0

0

N/A

MCTP2

c.C2539T

p.L847F

no

0

1

0.0002

0.0002

0

rs150149342

MSX1

c.A581G

p.K194R

no

0

0.878

0.0003

0.0002

0

rs149092063

NFATC1

c.C230T

p.P77L

no

0

0.972

0

0

0

rs143045693

NFATC1

c.G628A

p.V210M

no

0.04

1

0

0

0

rs62096875

NOS1

c.G1975A

p.A659T

no

0

1

0

0

0

N/A

NOTCH1

c.C6481T

p.P2161S

unknown

0.02

0.975

0.0002

0.0002

0.001

rs201518848

NOTCH2

c.G6363C

p.K2121N

no

0.09

0.964

0.0008

0.0005

0

rs144047610

NOTCH3

c.A509G

p.H170R

no

0.01

0.974

0.002

0.001

0.001

rs147373451

PAX6

c.G1225A

p.G409R

no

0

1

0

0

0

N/A

PIGF

c.A370G

p.T124A

no

0.27

0.711

0.002

0.002

0.001

rs139098189

PPP3CA

c.C334T

p.R112C

no

0

1

0

0

0

N/A

PTCH1

c.G3487A

p.G1163S

no

0.06

1

0.0006

0.0006

0.001

rs113663584

PTCH2

c.C3139T

p.R1047W

no

0

0.998

0

0

0

N/A

SLC35B2

c.A1105G

p.I369V

no

0.04

0.891

0

0.00008

0

N/A

SNAI3

c.C488T

p.T163M

no

0.02

0.752

0

0

0.001

rs202205064

SOX9

c.G817C

p.V273L

no

0

0.719

0

0

0

rs201477430

TBX5

c.C1115T

p.S372L

no

0.65

0.861

0.0003

0.0002

0.001

rs143068551

TBX5

c.G787A

p.V263M

no

0.41

0.995

0

0.004

0.006

rs147405081

VEGFB

c.C286G

p.Q96E

no

0

0.596

0.002

0.002

0.002

rs111555072

VEGFC

c.A140T

p.E47V

no

0.01

0.985

0.005

0.004

0

rs55728985

WNT4

c.C129A

p.C43X

no

STOP

STOP

0

0

0

N/A

ZNF236

c.C4628T

p.P1543L

no

0.03

0.943

0

0

0

N/A

PP2; Polyphen 2.

EA, European American.

EVS, Exome Variant Server.

1000G, 1000 Genomes.

Pathway analysis

Pathway analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Pathway analysis was used to draw comparisons between the background of only those genes included in the targeted capture and the subset of genes in which rare, non-synonymous exonic variants predicted damaging by in silico analysis were identified. The pathway analysis revealed significant enrichment in genes involved in the WNT signaling pathway (p = 0.035).

Pooling design validation

All samples in the cohort underwent Sanger sequencing of the coding regions of GATA5 as previously reported by our group, used here as a test of the pooling design as well as the sensitivity and specificity of the variant calling algorithm. Four rare variants in GATA5 (each present in 1/78 individuals) were discovered by Sanger sequencing, of which three were identified by NGS [8]. All of the rare GATA5 variants identified by our NGS pooling design were attributed to the correct individual as confirmed by Sanger sequencing.

Sanger sequencing of GATA5 found a variant, p.Q3R, in one individual that was not identified through the pooling design [8]. No pool had this variant above our cut-off threshold of 2.5% (four pools had allele frequencies over 1% with a range of 1.06-1.36%). Coverage of this base was good, with average read depth of 460X.

Discussion

This NGS design utilizing targeted sequencing of pooled BAV patient samples identified 33 rare, non-synonymous exonic variants predicted damaging by in silico analysis. Traditional Sanger sequencing methods confirmed 31 of these 33 changes (94%). Analysis of the GATA5 comparison dataset indicated that the pooling scheme allowed for accurate subject identification. This investigation identified rare variants in 26 genes not previously known to be involved in human BAV; such variants are considered hypothesis-generating and merit further testing in replication cohorts.

Animal models of BAV suggest a possible genotype-phenotype correlation related to cusp fusion phenotypes. However, our data does not support such a correlation in regards to cusp fusion, nor was there a correlation for isolated BAV versus BAV associated with coarctation of the aorta. Sample size and low incidence of familial BAV may limit our ability to detect such an association, but other groups have had similar findings. Rare, non-synonymous exonic variants in GATA5 have not been shown to correlate with cusp fusion [8, 13]. Investigations of familial BAV in large cohorts have demonstrated that cusp fusion morphologies were inherited interchangeably within families [23, 24]. Taken together, these studies suggest that differing BAV phenotypes may derive from a common genetic pathway influenced by downstream modifying elements. Thorough testing of genotype-phenotype correlations would require larger cohorts with significant representation of cusp fusion phenotypes, associated congenital cardiac malformations, aortopathy, and aortic valve insufficiency/stenosis.

Prior to this study, only GATA5 and NOTCH1 variants had been associated with isolated human BAV. Our data identified variants in 26 additional genes not previously identified in human BAV patients. Interestingly, all of these variants are reported in less than 1% of the Exome Variant Server controls and half are absent in this control population. Nonetheless, only 2 of the 31 putative disease-causing changes confirmed by traditional sequencing methods were de novo, in that they were not identified in either parent of the affected proband. We speculate that these 31 variants may be susceptibility alleles, with additional factors (genetic or environmental) required for full phenotype expression [25]. Our finding of multiple variants in the same proband further supports this hypothesis. Among the 16 individuals in whom putative disease-causing variants were identified, the mean variant burden was 1.8 with a range of 1 to 5.

Pathway analysis provides an opportunity to ascribe further meaning to the large number of candidate genes that may be identified in high-throughput approaches such as the one described here. Bioinformatics analysis via DAVID identified significant enrichment of WNT pathway genes including WNT4, PPP3CA, NFATC1, APC, AXIN1 and AXIN 2. DAVID pathway analysis can compare a subset of variants to any background of an investigator’s choosing; by utilizing a background of only the genes included in the targeted capture as opposed to the whole genome, the pathway analysis is not biased by overrepresentation of WNT pathway genes in the targeted capture design. WNT pathway genes display variable expression at various stages in valvulogenesis and have also been implicated in calcific valvular degeneration [26, 27]. Coupling of NGS with pathway analysis allows for the development of more targeted sequencing approaches for subsequent studies. Further investigation into this and similar BAV cohorts could include an enhanced focus on the WNT signaling pathway. A more narrow scope of investigation would then facilitate advanced functional investigations of identified variants.

Several methods are now available for combining multiple individuals into a single sequencing run. Sample-specific indexing uses a short barcode sequence that is unique to each individual in a pool. This barcode is attached to the adapter sequence during library preparation. Commercially available kits now allow up to 96 individuals to be combined in a single run, with deconvolution allowing identification of the individual. Some problems remain in identifying correctly which sequence reads belong to the individual tagged, particularly if single (one end) indexing is used. The pooling method used here does not allow direct deconvolution, but it is not difficult to identify the individual possessing the identified variant. However, the pooling method offers the advantage of error mitigation through use of biological replicates, reducing the false positive rate due to the high frequency of sequencing errors in NGS [28]. Pooling will also overcome problems inherent in the indexing technique itself (including double indexing) that lead to sequencing errors [29].

More precise estimates of the pooling strategy false negative rates and investigation into the causes of these false negatives are necessary to improve the technique. The GATA5 p.Q3R variant may have been missed for a variety of reasons including, but not limited to: error in DNA concentration measurement of the individual possessing the variant, volume measurement variability during pooling, or stochastic events during sequencing. One potential solution may be utilizing different DNA quantification methods for more accurate concentration prior to pooling. Additionally, a combinatorial design wherein each individual is represented in exactly three rather than two pools would potentially reduce false positive and negative rates.

A cost analysis of our approach showed significant savings. Targeted capture used in conjunction with the pooling scheme herein described resulted in a total sequencing cost of $15,950 for the entire 78 proband cohort. Targeted capture without pooling would have a total cost of $54,300 for a cohort of the same sample number, representing a cost savings of $39,350 from pooling alone. Moreover, assuming a cost of $1200 per sample for whole exome sequencing, the pooled and targeted approach would produce a relative cost saving of $77,650 for this cohort as compared to whole exome sequencing without pooling. Compared to whole genome sequencing without pooling (assumed to cost $5950 per sample), the pooled and targeted technique would realize a savings of $448,150.

Conclusions

This unique approach to targeted gene sequencing identified a large number of putative disease-causing variants in a cohort of patients with BAV, including variants in 26 genes not previously associated with human BAV. Pathway analysis supported a role for WNT pathway genes in human BAV. The data as a whole further underscore the complex, polygenic nature of BAV. This technique provides a method for sample multiplexing that lowers costs and reduces sequencing errors.

Methods

Study population

The study cohort, previously described by our group, included 78 unrelated individuals (59 male, 19 female) with BAV [8]. Subjects were prospectively recruited from June 2004 to June 2011 as part of a larger study involving genetic testing in patients with congenital left ventricular tract outflow defects. Informed consent was obtained from study subjects or parents of subjects less than 18 years of age (assent was obtained from subjects 9–17 years of age) under protocols approved by the Institutional Review Board (IRB) at Nationwide Children’s Hospital. Subjects with known chromosomal abnormalities were excluded from the analysis. The majority of individuals were of Caucasian ethnicity, with 1 African-American, 1 Asian, and 3 Hispanic individuals. Each subject had undergone clinical echocardiography with images sufficient to identify associated cardiac malformations and aortic valve cusp fusion morphology (Table 2). Fifty of the 78 subjects (64%) had isolated BAV while the remainder had BAV-CoA. Forty-six subjects (59%) had R-L cusp fusion, 39% had R-NC fusion, and 2% had L-NC fusion. Eighteen of the 78 subjects had a family history of a left ventricular outflow tract defect. For the majority of subjects, parent samples were also obtained under the same IRB protocol. Genomic DNA was isolated from blood or saliva samples using the 5 PRIME DNA extraction kit (Thermo Fisher Scientific, Pittsburgh, PA).
Table 2

Cardiac phenotype of study population

 

BAV

BAV-CoA

Overall

R-L

27(34.5%)

20(25.5%)

47(60%)

R-NC

22(28%)

7(9%)

30(38.5%)

L-NC

1(1%)

1(1%)

2(2.5%)

Overall

50(64%)

28(36%)

 

BAV, bicuspid aortic valve (isolated).

BAV-CoA, bicuspid aortic valve with coarctation of the aorta.

R-L, fusion of right coronary cusp and left coronary cusp.

R-NC, fusion of right coronary cusp and non-coronary cusp.

L-NC, fusion of left coronary cusp and non-coronary cusp.

Pooling scheme

Proband genomic DNA was combined into 19 unique pools each representing 9 or 10 individuals. The pools were constructed using overlapping design such that each individual was represented in exactly two pools, and a given rare variant could be uniquely attributed to a single individual on the basis of which two pools contained the variant. Individual genomic DNA samples were quantified by Nanodrop (Thermo Fisher Scientific), diluted to a concentration of 200 ng/microliter, and then requantified by Qubit fluorometer (Invitrogen Life Technologies, Carlsbad, CA). Quality of the DNA was assessed by SYBR Gold agarose gel (Life Technologies). Samples were then pooled, with the total amount of DNA for each pool consisting of 5 micrograms in 50 microliters (i.e. 500 ng per sample for a pool of 10 individuals and 550 ng per sample for a pool of 9 individuals).

Targeted capture

A custom, targeted gene capture was designed using the Agilent SureSelect Target Enrichment kit (Table 3). Candidate genes were selected on the basis of relevance to cardiac development and/or congenital heart defects in humans and animal models. Reference sequences were obtained from the Ensembl database. Probes were designed using paired, double-end, 75 base pair reads with centered design and 2x tiling frequency. A total of 97 candidate genes were probed using a whole gene interval approach, representing 7.6 Mb of DNA. Analysis was subsequently confined to exonic regions.
Table 3

Targeted capture gene list

Ensembl gene ID

Gene name

Chromosome

Gene start (bp)

Gene end (bp)

Size

ENSG00000107796

ACTA2

10

90694831

90751147

56316

ENSG00000115170

ACVR1

2

158592958

158732374

139416

ENSG00000134982

APC

5

112043195

112181936

138741

ENSG00000081181

ARG2

14

68086515

68118437

31922

ENSG00000103126

AXIN1

16

337440

402673

65233

ENSG00000168646

AXIN2

17

63524681

63557765

33084

ENSG00000149541

B3GAT3

11

62382768

62389647

6879

ENSG00000242252

BGLAP

1

156211753

156213112

1359

ENSG00000125845

BMP2

20

6748311

6760910

12599

ENSG00000125378

BMP4

14

54416454

54425479

9025

ENSG00000107779

BMPR1A

10

88516396

88684945

168549

ENSG00000138696

BMPR1B

4

95679119

96079599

400480

ENSG00000204217

BMPR2

2

203241659

203432474

190815

ENSG00000134072

CAMK1

3

9799026

9811676

12650

ENSG00000105974

CAV1

7

116164839

116201233

36394

ENSG00000179776

CDH5

16

66400533

66438686

38153

ENSG00000132535

DLG4

17

7093209

7123369

30160

ENSG00000198719

DLL1

6

170591294

170599561

8267

ENSG00000090932

DLL3

19

39989557

39999118

9561

ENSG00000128917

DLL4

15

41221538

41231237

9699

ENSG00000106991

ENG

9

130577291

130617035

39744

ENSG00000138685

FGF2

4

123747863

123819391

71528

ENSG00000107831

FGF8

10

103530081

103535827

5746

ENSG00000102755

FLT1

13

28874489

29069265

194776

ENSG00000136574

GATA4

8

11534468

11617511

83043

ENSG00000130700

GATA5

20

61038553

61051026

12473

ENSG00000141448

GATA6

18

19749404

19782491

33087

ENSG00000111087

GLI1

12

57853918

57866045

12127

ENSG00000074047

GLI2

2

121493199

121750229

257030

ENSG00000106571

GLI3

7

42000548

42277469

276921

ENSG00000105464

GRIN2D

19

48898132

48948187

50055

ENSG00000164116

GUCY1A3

4

156587863

156653501

65638

ENSG00000061918

GUCY1B3

4

156680144

156728743

48599

ENSG00000164683

HEY1

8

80676245

80680098

3853

ENSG00000135547

HEY2

6

126068810

126082415

13605

ENSG00000163909

HEYL

1

40089825

40105617

15792

ENSG00000080824

HSP90AA1

14

102547106

102606036

58930

ENSG00000096384

HSP90AB1

6

44214824

44221620

6796

ENSG00000166598

HSP90B1

12

104323885

104347423

23538

ENSG00000101384

JAG1

20

10618332

10654608

36276

ENSG00000184916

JAG2

14

105607318

105635161

27843

ENSG00000123700

KCNJ2

17

68164814

68176160

11346

ENSG00000127528

KLF2

19

16435651

16438337

2686

ENSG00000140563

MCTP2

15

94774767

95023632

248865

ENSG00000087245

MMP2

16

55423612

55540603

116991

ENSG00000163132

MSX1

4

4861393

4865663

4270

ENSG00000120149

MSX2

5

174151536

174158144

6608

ENSG00000131196

NFATC1

18

77155772

77289325

133553

ENSG00000183072

NKX2-5

5

172659112

172662360

3248

ENSG00000089250

NOS1

12

117645947

117889975

244028

ENSG00000007171

NOS2

17

26083792

26127525

43733

ENSG00000164867

NOS3

7

150688083

150711676

23593

ENSG00000148400

NOTCH1

9

139388896

139440314

51418

ENSG00000134250

NOTCH2

1

120454176

120612240

158064

ENSG00000074181

NOTCH3

19

15270445

15311792

41347

ENSG00000204301

NOTCH4

6

32162620

32191844

29224

ENSG00000151665

PIGF

2

46808076

46844258

36182

ENSG00000076356

PLXNA2

1

208195587

208417665

222078

ENSG00000132170

PPARG

3

12328867

12475855

146988

ENSG00000138814

PPP3CA

4

101944566

102269435

324869

ENSG00000188191

PRKAR1B

7

588834

767287

178453

ENSG00000154229

PRKCA

17

64298754

64806861

508107

ENSG00000080815

PSEN1

14

73603126

73690399

87273

ENSG00000143801

PSEN2

1

227057885

227083806

25921

ENSG00000185920

PTCH1

9

98205262

98279339

74077

ENSG00000117425

PTCH2

1

45285516

45308735

23219

ENSG00000131759

RARA

17

38465444

38513094

47650

ENSG00000077092

RARB

3

25215823

25639423

423600

ENSG00000172819

RARG

12

53604354

53626764

22410

ENSG00000124813

RUNX2

6

45295894

45632086

336192

ENSG00000186350

RXRA

9

137208944

137332431

123487

ENSG00000204231

RXRB

6

33161365

33168630

7265

ENSG00000143171

RXRG

1

165370159

165414433

44274

ENSG00000162572

SCNN1D

1

1214447

1227409

12962

ENSG00000075223

SEMA3C

7

80371854

80551675

179821

ENSG00000164690

SHH

7

155592680

155604967

12287

ENSG00000128602

SMO

7

128828713

128853386

24673

ENSG00000124216

SNAI1

20

48599536

48605423

5887

ENSG00000019549

SNAI2

8

49830249

49834299

4050

ENSG00000185669

SNAI3

16

88744090

88752901

8811

ENSG00000125398

SOX9

17

70117161

70122561

5400

ENSG00000184058

TBX1

22

19744226

19771116

26890

ENSG00000121068

TBX2

17

59477257

59486827

9570

ENSG00000164532

TBX20

7

35242042

35293758

51716

ENSG00000089225

TBX5

12

114791736

114846247

54511

ENSG00000105329

TGFB1

19

41836813

41859831

23018

ENSG00000106799

TGFBR1

9

101866320

101916474

50154

ENSG00000163513

TGFBR2

3

30647994

30735634

87640

ENSG00000122691

TWIST1

7

19060614

19157295

96681

ENSG00000070010

UFD1L

22

19437464

19466738

29274

ENSG00000112715

VEGFA

6

43737921

43754224

16303

ENSG00000173511

VEGFB

11

64002010

64006259

4249

ENSG00000150630

VEGFC

4

177604689

177713881

109192

ENSG00000105989

WNT2

7

116916685

116963343

46658

ENSG00000162552

WNT4

1

22446461

22470462

24001

ENSG00000184937

WT1

11

32409321

32457176

47855

ENSG00000130856

ZNF236

18

74534563

74682683

148120

    

CAPTURE SIZE

7567444

Sequencing

Sequencing of the pooled target captured proband genomic DNA was performed on the Illumina HiSeq 2000. Variants considered potentially pathogenic identified by NGS were subsequently confirmed by Sanger sequencing. Where available, parent samples were also sequenced for these potentially pathogenic variants. Sequencing primers are available upon request.

Bioinformatics algorithms

Bioinformatics analysis was performed using Churchill, our laboratory’s pipeline for the discovery of human genetic variation. Churchill utilizes the Burrows Wheeler Aligner (BWA) for the alignment of sequence data to the reference genome, hg19. Further refinement steps were performed on the aligned sequence data using Genome Analysis ToolKit (GATK) following the Broad Institute’s guidelines for best practices (https://www.broadinstitute.org/gatk/guide/best-practices). We utilized the GATK’s (version 2.4-9) UnifiedGenotyper (UG) to call variants in the pooled samples. In order to properly handle the pooled data, we amended the recommended UG settings by including the –sample_ploidy configuration parameter and giving it a value of 20, reflecting the potential for 20 individual alleles in a pooled sample of 10 individuals. The threshold for calling was set to 2.5% alternate allele frequency on the basis of the pooling scheme.

In-silico analysis

Rare, non-synonymous, exonic variants were analyzed using the Polyphen 2 and SIFT algorithms. Reference populations from the 1000 Genomes Project and Exome Variant Server were utilized as control populations [30, 31]. Pathway analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) with cutoffs of p-value less than 0.05 [32, 33].

Availability of supporting data

This project has been registered with the National Center for Biotechnology Information (NCBI) BioProject database, identifier PRJNA260036, and can be accessed at: http://www.ncbi.nlm.nih.gov/bioproject/260036.

Supporting sequence data for this project has been deposited with the NCBI Sequence Read Archive. The study accession is SRP045998, available at the following link: http://www.ncbi.nlm.nih.gov/sra/?term=SRP045998 Biosample IDs for the pools, with their corresponding URLs are:

3015266: http://www.ncbi.nlm.nih.gov/biosample/3015266

3015267: http://www.ncbi.nlm.nih.gov/biosample/3015267

3015268: http://www.ncbi.nlm.nih.gov/biosample/3015268

3015269: http://www.ncbi.nlm.nih.gov/biosample/3015269

3015270: http://www.ncbi.nlm.nih.gov/biosample/3015270

3015271: http://www.ncbi.nlm.nih.gov/biosample/3015271

3015272: http://www.ncbi.nlm.nih.gov/biosample/3015272

3015273: http://www.ncbi.nlm.nih.gov/biosample/3015273

3015274: http://www.ncbi.nlm.nih.gov/biosample/3015274

3015275: http://www.ncbi.nlm.nih.gov/biosample/3015275

3015276: http://www.ncbi.nlm.nih.gov/biosample/3015276

3015277: http://www.ncbi.nlm.nih.gov/biosample/3015277

3015278: http://www.ncbi.nlm.nih.gov/biosample/3015278

3015279: http://www.ncbi.nlm.nih.gov/biosample/3015279

3015280: http://www.ncbi.nlm.nih.gov/biosample/3015280

3015281: http://www.ncbi.nlm.nih.gov/biosample/3015281

3015282: http://www.ncbi.nlm.nih.gov/biosample/3015282

3015283: http://www.ncbi.nlm.nih.gov/biosample/3015283

3015284: http://www.ncbi.nlm.nih.gov/biosample/3015284

3015285: http://www.ncbi.nlm.nih.gov/biosample/3015285

Abbreviations

BAV: 

Bicuspid aortic valve

BAV-CoA: 

Bicuspid aortic valve associated with coarctation of the aorta

R-L: 

Right coronary and left coronary

R-NC: 

Right coronary and non-coronary

NGS: 

Next-generation sequencing

DAVID: 

Database for annotation, visualization and integrated discovery

GATK: 

Genome analysis toolkit

UG: 

UnifiedGenotyper.

Declarations

Acknowledgements

This work was supported by funding from the National Institutes of Health/National Heart, Lung, and Blood Institute and The Research Institute at Nationwide Children’s Hospital (grant R01HL109758). Recruitment was conducted under approved IRB protocol #0405HS134. We thank the participants and their families for their involvement in this study. The authors would like to thank the NHLBI GO Exome Sequencing Project and its ongoing studies which produced and provided exome variant calls for comparison: the Lung GO Sequencing Project (HL-102923), the WHI Sequencing Project (HL-102924), the Broad GO Sequencing Project (HL-102925), the Seattle GO Sequencing Project (HL-102926) and the Heart GO Sequencing Project (HL-103010).

Authors’ Affiliations

(1)
Department of Pediatrics, The Ohio State University
(2)
Center for Cardiovascular and Pulmonary Research and The Heart Center, Nationwide Children’s Hospital
(3)
Biomedical Genomics Core, The Research Institute at Nationwide Children’s Hospital
(4)
Department of Molecular Genetics, The Ohio State University

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  34. Pre-publication history

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Copyright

© Bonachea et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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.

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