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  • Methodology
  • Open Access

Rapid molecular genetic diagnosis of hypertrophic cardiomyopathy by semiconductor sequencing

†Contributed equally
Journal of Translational Medicine201412:173

https://doi.org/10.1186/1479-5876-12-173

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

  • Received: 17 December 2013
  • Accepted: 11 June 2014
  • Published:

Abstract

Background

Rapidly determining the complex genetic basis of Hypertrophic cardiomyopathy (HCM) is vital to better understanding and optimally managing this common polygenetic cardiovascular disease.

Methods

A rapid custom Ion-amplicon-resequencing assay, covering 30 commonly affected genes of HCM, was developed and validated in 120 unrelated patients with HCM to facilitate genetic diagnosis of this disease. With this HCM-specific panel and only 20 ng of input genomic DNA, physicians can, for the first time, go from blood samples to variants within a single day.

Results

On average, this approach gained 595628 mapped reads per sample, 95.51% reads on target (64.06 kb), 490-fold base coverage depth and 93.24% uniformity of base coverage in CDS regions of the 30 HCM genes. After validation, we detected underlying pathogenic variants in 87% (104 of 120) samples. Tested seven randomly selected HCM genes in eight samples by Sanger sequencing, the sensitivity and false-positive-rate of this HCM panel was 100% and 5%, respectively.

Conclusions

This Ion amplicon HCM resequencing assay provides a currently most rapid, comprehensive, cost-effective and reliable measure for genetic diagnosis of HCM in routinely obtained samples.

Keywords

  • Genetic diagnosis
  • Hypertrophic cardiomyopathy
  • Next-generation sequencing
  • Semiconductor sequencing

Background

Hypertrophic cardiomyopathy (HCM) is regarded as a most common inherited cardiac disorder (1/500) and the leading cause of sudden cardiac death in adolescents 0020[1–4]. So far, over 1000 mutations in at least 30 genes have been reported to responsible for HCM, which implied an highly genetic heterogeneity and hence resulting various clinical phenotypes, ranging from asymptomatic forms to sudden cardiac death in the young[3, 5–12]. Although disease-causing mutations in MYH7, MYBPC3 and TNNT2 had been considered to explain about half of HCM patients[8, 9], the frequency of each causal variant is relatively low and most rare mutations are unique in specific families[13]. Moreover, about 10% HCM patients harbored more than one mutation and thus suffering from an earlier onset or worse prognosis[2, 7]. Therefore, systemic genetic diagnosis for HCM patients was necessary and recommended by current clinical guidelines[14–17]. For instance, the identification of sudden-death-high-risk patients could benefit from an implantable cardioverter-defibrillator in primary prevention[18].

However, conventional Sanger sequencing was too laborious and expensive to content regular clinical practice[19]. Advancing high throughput next-generation sequencing (NGS) technologies have the potential to solve the problem by rapidly dissecting large regions at low cost[1, 20–22]. Nevertheless, the current NGS platforms have several weaknesses, including sample scalability, sequencing time and cost of entry, which need to be addressed if these technologies are going to service clinical routine genetic diagnosis[23]. With lowest-price, shortest running time, minimum start DNA amount and flexible sequencing-chip reagents, the recent flourishing semiconductor sequencing technique is notable[21, 24].

Our study provides, to our knowledge, a currently most rapid, comprehensive, cost-efficient and reliable assay for genetic diagnosis of hypertrophic cardiomyopathy in everyday clinical practice. Implementation of this method will change diagnosis and understanding of the molecular etiologies of HCM.

Materials and methods

HCM resequencing panel design

For the HCM resequencing panel targeted genes selection, recent ten years’ literatures, including prior genetic detection technique articles, reviews and case-reports of HCM, were carefully accessed. To recruit a maximum coverage of the mutation spectrum of this polygenetic disorder, we designed a currently most comprehensive HCM-specific resequencing panel including 30 causal genes that most frequently affected in patients with HCM (Table 1). Then, primers of overlapping amplicons covering the CDS-region and flanking sequences of each targeted gene were automated designed by Ion AmpliSeq™ Ready-to-Use custom designer platform following guide of the website (https://www.ampliseq.com/protected/dashboard.action) (Primers for Semiconductor sequencing are presented in Additional file1: Table S1). With the ability to perform ultrahigh-multiplex PCR reaction in one tube parallelly, the primers were mixed and provided (Life Technologies, Carlsbad, California, USA) in two primer-pools. Eventually, 97.96% of the targeted region (64.06 kb) was overlapped by 690 about 200 bp-length amplicons.
Table 1

Selected hypertrophic cardiomyopathy genes

Nr.

Gene

Ensembl number

Chromosome

CDS, n

Amplicons, n

Target, bp

Missed#, bp

Coverage*,%

1

MYBPC3

ENSG00000134571

chr11

32

46

3826

9

99.8

2

MYH7

ENSG00000092054

chr14

38

54

5808

127

97.8

3

TNNT2

ENSG00000118194

chr1

21

17

1286

0

100

4

ACTC1

ENSG00000159251

chr15

6

10

1134

0

100

5

TNNI3

ENSG00000129991

chr19

7

8

632

0

100

6

TPM1

ENSG00000140416

chr15

16

19

1429

0

100

7

MYL2

ENSG00000111245

chr12

7

7

501

0

100

8

MYL3

ENSG00000160808

chr3

6

8

588

71

87.9

9

TCAP

ENSG00000173991

chr17

2

4

504

0

100

10

PRKAG2

ENSG00000106617

chr7

18

22

1795

74

95.9

11

TNNC1

ENSG00000114854

chr3

6

7

486

10

97.9

12

CSRP3

ENSG00000129170

chr11

5

6

585

0

100

13

MYH6

ENSG00000197616

chr14

37

55

5820

216

96.3

14

PLN

ENSG00000198523

chr6

1

10

1503

364

75.8

15

MYOZ2

ENSG00000172399

chr4

5

10

795

0

100

16

ACTN2

ENSG00000077522

chr1

21

27

2685

0

100

17

JPH2

ENSG00000149596

chr20

6

17

2102

155

92.6

18

LAMP2

ENSG00000005893

chrX

11

19

1516

0

100

19

CAV3

ENSG00000182533

chr3

2

4

456

0

100

20

VCL

ENSG00000035403

chr10

22

37

3405

39

98.9

21

ANKRD1

ENSG00000148677

chr10

9

10

960

0

100

22

GLA

ENSG00000102393

chrX

7

13

1290

0

100

23

LDB3

ENSG00000122367

chr10

16

26

2508

0

100

24

CALR3

ENSG00000269058

chr19

9

13

1155

22

98.1

25

MYLK2

ENSG00000101306

chr20

12

22

1791

0

100

26

RYR2

ENSG00000198626

chr1

105

165

14904

74

99.5

27

CASQ2

ENSG00000118729

chr1

11

14

1200

0

100

28

FXN

ENSG00000165060

chr9

8

10

874

32

96.3

29

LMO4

ENSG00000143013

chr1

4

6

498

0

100

30

NEXN

ENSG00000162614

chr1

12

25

2028

111

94.5

Ensembl: June 2013 (GRCh37/hg19). CDS, coding sequence; *indicates in silico coverage of target sequences by multiplex PCR; #indicates in silico missed bases by multiplex PCR.

Patients and DNA sample preparation

With approval from the local ethics committee, 120 unrelated Chinese Han HCM patients confirmed by echocardiography during 2008 to 2013 with written informed consent were included in this study. Two of the 120 patients were from independent HCM pedigrees, carrying known pathogenic mutations rs121913641 and rs121913637 in the same loci (p.R719Q, p.R719W) in gene MYH7, respectively. Genomic DNA (gDNA) of each patient was extracted and RNase managed from peripheral leukocytes, using a DB-S kit (FUJIFILM Corporation, Tokyo, Japan) according to the manufacturer’s instructions. The purified gDNA was then checked with electrophoresis to avoid fragmental degradation and RNA pollution.

Library preparation and sequencing

Ion Torrent adapter-ligated libraries were builded using Ion Ampliseq™ Library Kit 2.0 (Life Technologies) following the manufacturer’s protocol within about 5 hours. Briefly, 20 ng gDNA for every sample was quantitated by Qubit 2.0 fluorometer (Invitrogen, Carlsbad, CA, USA) for multiplex PCR amplification with each of the two primer-pools, respectively. The resulting amplicons of the two primer-pools were mixed together, and then ligated to barcodes and Ion Torrent adapters (Life Technologies). Subsequently libraries were purified with AMPure XP beads (Beckman Coulter, Brea, CA, USA) using 5-cycles of PCR amplification and further purification, followed by quantification by Qubit 2.0 fluorometer. In order to increase efficiency and reduce costs, sixteen uniquely barcoded libraries were combined together with equal molar ratios for one 318 chip. Subsequent emulsion PCR and enrichment of the sequencing beads of the pooled libraries was performed using the OneTouch system (Life Technologies) according to the manufacturer’s protocol within about 5 hours. Finally, 500 Flows (125 cycles) sequencing was done on the 318- chip using Ion PGM 200 Sequencing Kit (Life Technologies) on the Ion Torrent Personal Genome Machine (PGM) (Life Technologies) (Figure 1).
Figure 1
Figure 1

Semiconductor sequencing flow diagram. Amplicons of 30 HCM genes are amplified by using 20 ng input genomic DNA and two primer pools. The about 200 bp amplicons are end-polished, barcodes and adapters ligated, purified to become prepared libraries. Emulsion PCR is conducted by Ion One Touch to generate ISPs for semiconductor sequencing. ISP indicates Ion Sphere Particles.

Bioinformatic analysis

Raw data from 4.5 hours’ PGM runs were initially processed using the Ion Torrent platform-specific software Torrent Suite v3.6.2 to generate sequence reads, trim adapter sequences, align to the hg19 human reference genome, analyze coverage and call variants (Variants Caller parameter settings see Additional file1: Table S2). Then, all variants were annotated with an online-software Variant Effect Predictor (http://asia.ensembl.org/info/docs/variation/vep/index.html). To predict possible impact of detected non-synonymous variants in exons, all missense substitutions were scored and in-silico-function-predicted by SIFT (http://sift.jcvi.org/www/SIFT_BLink_submit.html) and PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/). The putative novel pathogenic variants were further confirmed that were reported neither in the 1000-Genome project database nor in the NCBI dbSNP database. To calculate the importance of novel mutations, conservation test was performed in patients and thirteen other species around the mutated position by COBALT algorithm (http://www.ncbi.nlm.nih.gov/tools/cobalt/cobalt.cgi).

Criteria for functional mutations

To determine a functional variant, the variant should be a Sanger sequencing validated coding variant. If the variant is a previously reported variant, it should be causal mutations in NCBI ClinVar database or HGMD database. If the variant is a novel variant, it should in accordance with none of the following database: the 1000-Genome project database, the NCBI dbSNP database, UCSC common SNP database and the 5000 Exmoes database in Exome Sequencing Project (ESP). If the variant is a missense substitution, it should be evolutionarily conserved and predicted functional damaged by either SIFT or Polyphen-2.

Sanger sequencing validation

All potential functional variants were validated with Sanger sequencing in an Applied Biosystems 3130 capillary sequencer using individual primers in both directions to obsolete false positive errors. The technically uncovered 1304 bp regions of the 13 targeted genes were carefully sequenced directly by Sanger sequencing. No more functional variant was detected in all 120 patients. To evaluate the sensitivity and false-positive-rate of this panel, we randomly selected seven genes (MYH7, MYBPC3, ACTC1, PRKAG2, MYOZ2, ACTN2, JPH2) and sequenced all exons and flanking regulation regions directly by Sanger sequencing in eight subjects from the 120 HCM patients.

Results

Study population

One-hundred-and-twenty unrelated patients with HCM were studied. The mean age of male (85/120) was 45 ± 16 years (1, 78) and the maximal wall thickness was 18.34 ± 5.1 mm (14, 46). For female (35/120) the mean age was 51 ± 17 years (15, 86) and the maximal wall thickness was 19.73 ± 4.8 mm (13, 41). The mean left ventricular ejection fractions of male and female are 58 ± 14 and 62 ± 13, respectively. In the 120 HCM patients, 24.2% (29/120) are hypertrophic obstructive cardiomyopathy and 19.2% (23/120) have positive family history. (The detailed characteristics of HCM Patients were shown in Additional file1: Table S3).

Sequencing output and coverage

The sequencing of selected regions of 30 HCM-associated genes on the Ion torrent PGM achieved an average output of 595628 mapped reads and 95.51% on target per sample in the 120 HCM specimens. In summary, 99.55% of all target amplicons was covered at least once, 96.98% amplicons was covered at least 20 times, 91.95% amplicons was covered at least 100 times. The mean uniformity of base coverage is 93.24% in this panel. The average read depth in the 64.06 K target region across the 120 samples was ~490 folds (Figure 2). Moreover, chip-loading-rate was improved shortly and polyclonal-rate was reduced significantly after few trails in the beginning of experiments, which result in an increase in mean coverage.
Figure 2
Figure 2

Sequencing coverage overview of the 30 HCM genes for 120 samples. Blue graphs represent the distribution of coverage of the 30 HCM genes for 120 samples. The dashed line indicates the mean coverage (490X) over the enriched 30 HCM genes (color blocks).

Mutation detection and sanger sequencing validation

The Ion Torrent platform-specific software Torrent Suite v3.6.2 and online software Variant Effect Predictor were employed to align the reads sequences to the human reference genome build hg19, call variants and bioinformatical annotate. Criteria for variant identification were a read coverage of higher than 30-fold. All together, in the 120 patients, 458 known or novel variants were detected by Semiconductor sequencing and on average 80 variants per sample. After Sanger sequencing validation, except 25 variants, 433 variants were determined truly exist. Most of the 25 false-positive miscalls are insertions or deletions and detected in more than one sample. Of these 433 variants, 345 (80%) are predicted to be noncoding or synonymous, whereas 88 (20%) are non-synonymous, including missense mutation and small insertion/deletion, resulting in the change of amino acids (Table 2). Notably, we identified at least one functional variant in 104/120 (87%) HCM patients and found more than one functional variants in 12/120 (10%) HCM patients.Furthermore, the two known positive pathogenic mutations (rs121913641 and rs121913637) in the two probands were successfully identified.
Table 2

Sequencing summary

Measure

Value

No. of genes

30

No. of exons

462

No. of amplicons

690

Total no. of variants

433

   Deletions

11

   Insertions

7

   SNVs

415

Noncoding

240

   Intron variant

212

   Splice region variant

19

   3′ UTR variant

5

   5′ UTR variant

4

Synonymous

105

Nonsynonymous

88

   Missense variant

79

   Stop gain variant

6

   Frameshift variant

3

No. of known dbSNPs

293

No. of novel variants

140

SNVs, single-nucleotide variants.

Sensitivity and false-positive-rate evaluation

To further assess the sensitivity and specificity of this HCM panel, direct Sanger sequencing of seven randomly selected genes was performed in eight selected subjects. Finally, 38 variants were detected by semiconductor sequencing, including 35 known variants and 3 novel variants (Table 3). Compared with Sanger sequencing results, 2 variants were failed to be validated. Therefore, the sensitivity of this HCM panel was evaluated as 100% and the false-positive-rate was evaluated as 5%.
Table 3

Variants detected by semiconductor sequencing of the seven selected genes in eight samples

Gene

Position

Type

Zygosity

Nucleotide substitution

Variant frequency

P-value#

Coverage

Consequence

Novelty

No. of patients

Validated by Sanger sequencing

ACTC1

15:35084215

SNP

Het

C/G

47.94

6.3E-06

413

intron

rs3729755

5

Yes

ACTN2

1:236899042

SNP

Het

G/A

44.09

2.5E-05

254

intron

rs2288600

2

Yes

1:236902865

SNP

Het

A/C

55.93

1.0E-10

59

intron

rs2288602

8

Yes

1:236910983

SNP

Het

G/A

50.83

5.0E-05

303

missense

rs80257412

1

Yes

1:236902594

SNP

Het

C/G

49.1

6.3E-05

387

splice region

rs2288601

8

Yes

1:236882303

SNP

Hom

T/C

100

2.5E-07

299

synonymous

rs1341864

8

Yes

1:236883421

SNP

Hom

C/T

100

2.5E-07

301

synonymous

rs1341863

8

Yes

1:236898942

SNP

Het

G/C

44.86

5.0E-05

185

synonymous

rs2288599

1

Yes

1:236925844

SNP

Het

G/A

47.51

1.6E-05

402

synonymous

rs12063382

1

Yes

JPH2

20:42745062

SNP

Het

C/G

51.85

1.6E-05

542

intron

rs184801349

1

Yes

20:42814931

SNP

Hom

T/C

100

2.0E-04

68

intron

rs6031442

2

Yes

20:42747247

SNP

Het

C/T

51.85

6.3E-06

675

missense

rs3810510

6

Yes

20:42743454

SNP

Het

A/G

61.57

5.0E-05

216

synonymous

rs6093935

2

Yes

20:42815190

SNP

Het

G/A

48.17

4.0E-05

764

synonymous

rs1883790

8

Yes

MYBPC3

11:47361084

SNP

Hom

T/C

99.59

1.3E-06

245

intron

rs2856653

8

Yes

11:47364248

SNP

Het

C/T

48.39

6.3E-06

839

missense

CM981325

1

Yes

11:47370041

SNP

Het

T/C

48.2

4.0E-05

639

missense

rs3729989

1

Yes

11:47354782

SNP

Het

C/T

62.19

2.0E-05

283

stop gained

Novel

2

Yes

11:47354787

SNP

Het

C/T

50.92

6.3E-05

218

synonymous

rs1052373

8

Yes

MYH7

14:23882144

SNP

Hom

T/C

95.41

4.0E-07

283

intron

rs2284651

2

Yes

14:23883195

SNP

Het

G/A

52.63

3.2E-05

779

intron

rs3729499

1

Yes

14:23900093

SNP

Hom

C/T

99.66

1.6E-08

594

intron

rs45580436

1

Yes

14:23902974

SNP

Het

C/A

55.58

2.0E-05

403

intron

rs3729992

1

Yes

14:23895179

SNP

Het

C/T

47.07

5.0E-06

871

missense

rs121913641

1

Yes

14:23895180

SNP

Het

G/A

48.93

7.9E-06

750

missense

rs121913637

1

Yes

14:23902806

SNP

Het

A/G

45.93

2.0E-05

405

missense

Novel

8

No

14:23892888

SNP

Hom

A/G

100

1.6E-08

582

synonymous

rs7157716

2

Yes

14:23899060

SNP

Het

G/A

52.54

5.0E-05

788

synonymous

rs735712

1

Yes

14:23900794

SNP

Het

G/A

45.06

5.0E-05

466

synonymous

rs2069542

1

Yes

14:23902753

SNP

Het

G/A

34.09

6.3E-06

44

synonymous

rs2069540

4

Yes

MYOZ2

4:120079159

SNP

Hom

A/G

98.14

2.0E-07

322

intron

rs11721566

8

Yes

4:120106982

SNP

Hom

T/C

99.54

1.6E-06

217

intron

rs7661020

7

Yes

4:120057709

SNP

Het

A/C

46.36

7.9E-05

302

missense

rs76757102

2

Yes

PRKAG2

7:151265714

SNP

Hom

C/T

100

2.0E-06

177

intron

rs2241053

5

Yes

7:151292395

INS

Hom

–/T

84.34

1.0E-10

281

intron

Novel

8

No

7:151292395

SNP

Het

A/T

30.58

3.2E-05

121

intron

rs35348247

6

Yes

7:151478406

SNP

Het

C/T

51.55

1.6E-05

419

missense

rs79474211

1

Yes

7:151483612

SNP

Het

C/T

47.25

4.0E-05

946

missense

rs144857453

1

Yes

# P-value was generated automatically by the Ion Torrent platform-specific software Torrent Suite v3.6.2, detail refer to Supplementary materials.

Discussion

This study provides the first comprehensive HCM-specific semiconductor sequencing assay, attempting to facilitate the clinical diagnosis and optimally manage HCM patients. Compared with other NGS platform, semiconductor sequencing has the highest throughput and shortest run time[20, 21]. From DNA extraction to data analysis, within only one day, 64.06 kb targeted CDS and flanking regulating regions of 30 genes in up to sixteen samples can be parallelly scanned using one Ion torrent 318-chip. As described in this article, our workflow leads to a mean coverage of 490X, allowing the reliable detection of sequence variants with high accuracy. On the whole, we identified 140 novel sequence variants, which are not listed in the NCBI dbSNP or 1000-Gemome project databases. By bioinformatical prediction of SIFT and Polyphen-2, we revealed potential functional mutations in known disease genes in 104 (87%) of the 120 patients with HCM. This detection rate is in the expected range and provides much better performance compared with previous studies[7, 19, 25].

To evaluate the capability of our Ion amplicon HCM-specific panel, we carried out assessments of experiments in several aspects. By Sanger sequencing, we dissected the panel technically uncovered 1304 bp regions of the 13 targeted genes in all patients and identified no more potential functional variants. Besides, the panel presented satisfactory results with high sensitivity (100%) and low false-positive-rate (5%) in the following validating tests. Thus, it is reasonable to believe that our panel has enough power to detect potential functional variants in HCM patients.

Ion torrent PGM is considered to have weakness in producing long-homopolymer-associated insertion/deletion errors[21]. Hence, by carefully dissecting the sequences after validation, we found that this kind of primary error type was the most miscall reason in this HCM-panel. Besides, a heterozygous-substitution-miscall (c. T136C, p. F46L) in gene MYH7 was detected in 58 of 120 subjects after Sanger sequencing filtration. Since this miscall exists in high proportion of participants and with high coverage, we suspected that it is due to mistake during multiplex PCR. Although this HCM-panel could generate above false positive mistakes, the following Sanger sequencing verification can easily eliminate them.

Although there were some other HCM-relevant genes reported sporadically, such as TTN, MYPN, CRYAB, MTTL1, RAF1 and FHL1, the connection between them and HCM pathogenesis were not ascertained[26]. Our panel was designed for clinical genetic diagnosis, hence, selected only causal genes. But we will pay attention to these and other candidate genes constantly, and update our panels once they are ascertained to be pathogenesis in the future.

Conclusions

This study established a currently most comprehensive and reliable semiconductor HCM-specific resequencing assay and provided a useful, rapid and cost-effective measure for clinical routine genetic diagnosis of HCM. Implementation of this method will significantly improve routine diagnosis of HCM and change understanding of the molecular etiologies of this disease.

Notes

Declarations

Acknowledgements

We thank the patients for consenting to participate in this study and publication. This work was funded by grants of the National “973” projects (No. 2012AA02A510; No. 2013CB531105) and National Natural Science Foundation of China, Project (No. 81170159).

Authors’ Affiliations

(1)
Departments of Internal Medicine and Gene Therapy Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095# Jiefang Ave, Wuhan, 430030, China
(2)
Division of Cardiology, the First Affiliated Hospital, Nanjing Medical University, Nanjing, China

References

  1. Fokstuen S, Munoz A, Melacini P, Iliceto S, Perrot A, Ozcelik C, Jeanrenaud X, Rieubland C, Farr M, Faber L, Sigwart U, Mach F, Lerch R, Antonarakis SE, Blouin JL: Rapid detection of genetic variants in hypertrophic cardiomyopathy by custom DNA resequencing array in clinical practice. J Med Genet. 2011, 48: 572-576.View ArticlePubMedGoogle Scholar
  2. Zou Y, Wang J, Liu X, Wang Y, Chen Y, Sun K, Gao S, Zhang C, Wang Z, Zhang Y, Feng X, Song Y, Wu Y, Zhang H, Jia L, Wang H, Wang D, Yan C, Lu M, Zhou X, Song L, Hui R: Multiple gene mutations, not the type of mutation, are the modifier of left ventricle hypertrophy in patients with hypertrophic cardiomyopathy. Mol Biol Rep. 2013, 40: 3969-3976.View ArticlePubMedGoogle Scholar
  3. Maron BJ, Maron MS, Semsarian C: Genetics of hypertrophic cardiomyopathy after 20 years: clinical perspectives. J Am Coll Cardiol. 2012, 60: 705-715.View ArticlePubMedGoogle Scholar
  4. Ashrafian H, McKenna WJ, Watkins H: Disease pathways and novel therapeutic targets in hypertrophic cardiomyopathy. Circ Res. 2011, 109: 86-96.View ArticlePubMedGoogle Scholar
  5. Bos JM, Ommen SR, Ackerman MJ: Genetics of hypertrophic cardiomyopathy: one, two, or more diseases?. Curr Opin Cardiol. 2007, 22: 193-199.View ArticlePubMedGoogle Scholar
  6. Ho CY: Hypertrophic cardiomyopathy in 2012. Circulation. 2012, 125: 1432-1438.PubMed CentralView ArticlePubMedGoogle Scholar
  7. Meder B, Haas J, Keller A, Heid C, Just S, Borries A, Boisguerin V, Scharfenberger-Schmeer M, Stahler P, Beier M, Weichenhan D, Strom TM, Pfeufer A, Korn B, Katus HA, Rottbauer W: Targeted next-generation sequencing for the molecular genetic diagnostics of cardiomyopathies. Circ Cardiovasc Genet. 2011, 4: 110-122.View ArticlePubMedGoogle Scholar
  8. Watkins H, Ashrafian H, Redwood C: Inherited cardiomyopathies. N Engl J Med. 2011, 364: 1643-1656.View ArticlePubMedGoogle Scholar
  9. Liu W, Hu D, Zhu T, Ma Z, Yang J, Xie W, Li C, Li L, Li T, Bian H, Tong Q: Mutation spectrum in a large cohort of unrelated chinese patients with hypertrophic cardiomyopathy. Am J Cardiol. 2013, 112: 585-589.View ArticlePubMedGoogle Scholar
  10. Ho E, Bhindi R, Ashley EA, Figtree GA: Genetic analysis in cardiovascular disease: a clinical perspective. Cardiol Rev. 2011, 19: 81-89.View ArticlePubMedGoogle Scholar
  11. Charron P, Komajda M: Molecular genetics in hypertrophic cardiomyopathy: towards individualized management of the disease. Expert Rev Mol Diagn. 2006, 6: 65-78.View ArticlePubMedGoogle Scholar
  12. Seidman JG, Seidman C: The genetic basis for cardiomyopathy: from mutation identification to mechanistic paradigms. Cell. 2001, 104: 557-567.View ArticlePubMedGoogle Scholar
  13. Marian AJ: Hypertrophic cardiomyopathy: from genetics to treatment. Eur J Clin Invest. 2010, 40: 360-369.PubMed CentralView ArticlePubMedGoogle Scholar
  14. Charron P, Arad M, Arbustini E, Basso C, Bilinska Z, Elliott P, Helio T, Keren A, McKenna WJ, Monserrat L, Pankuweit S, Perrot A, Rapezzi C, Ristic A, Seggewiss H, van Langen I, Tavazzi L: Genetic counselling and testing in cardiomyopathies: a position statement of the European society of cardiology working group on myocardial and pericardial diseases. Eur Heart J. 2010, 31: 2715-2726.View ArticlePubMedGoogle Scholar
  15. Hershberger RE, Lindenfeld J, Mestroni L, Seidman CE, Taylor MR, Towbin JA: Genetic evaluation of cardiomyopathy–a Heart Failure Society of America practice guideline. J Card Fail. 2009, 15: 83-97.View ArticlePubMedGoogle Scholar
  16. Ho CY: Genetic considerations in hypertrophic cardiomyopathy. Prog Cardiovasc Dis. 2012, 54: 456-460.PubMed CentralView ArticlePubMedGoogle Scholar
  17. Ho CY: Genetics and clinical destiny: improving care in hypertrophic cardiomyopathy. Circulation. 2010, 122: 2430-2440. discussion 2440PubMed CentralView ArticlePubMedGoogle Scholar
  18. Giannakopoulos G, Frangos C: [Hypertrophic cardiomyopathy in 2013]. Rev Med Suisse. 2013, 9 (502): 504-507.Google Scholar
  19. Waldmuller S, Muller M, Rackebrandt K, Binner P, Poths S, Bonin M, Scheffold T: Array-based resequencing assay for mutations causing hypertrophic cardiomyopathy. Clin Chem. 2008, 54: 682-687.View ArticlePubMedGoogle Scholar
  20. Voelkerding KV, Dames S, Durtschi JD: Next generation sequencing for clinical diagnostics-principles and application to targeted resequencing for hypertrophic cardiomyopathy: a paper from the 2009 William Beaumont Hospital Symposium on Molecular Pathology. J Mol Diagn. 2010, 12: 539-551.PubMed CentralView ArticlePubMedGoogle Scholar
  21. Churko JM, Mantalas GL, Snyder MP, Wu JC: Overview of high throughput sequencing technologies to elucidate molecular pathways in cardiovascular diseases. Circ Res. 2013, 112: 1613-1623.View ArticlePubMedGoogle Scholar
  22. Carvill GL, Heavin SB, Yendle SC, McMahon JM, O’Roak BJ, Cook J, Khan A, Dorschner MO, Weaver M, Calvert S, Malone S, Wallace G, Stanley T, Bye AM, Bleasel A, Howell KB, Kivity S, Mackay MT, Rodriguez-Casero V, Webster R, Korczyn A, Afawi Z, Zelnick N, Lerman-Sagie T, Lev D, Moller RS, Gill D, Andrade DM, Freeman JL: Targeted resequencing in epileptic encephalopathies identifies de novo mutations in CHD2 and SYNGAP1. Nat Genet. 2013, 45: 825-830.View ArticlePubMedGoogle Scholar
  23. Elliott AM, Radecki J, Moghis B, Li X, Kammesheidt A: Rapid detection of the ACMG/ACOG-recommended 23 CFTR disease-causing mutations using ion torrent semiconductor sequencing. J Biomol Tech. 2012, 23: 24-30.PubMed CentralView ArticlePubMedGoogle Scholar
  24. Loman NJ, Misra RV, Dallman TJ, Constantinidou C, Gharbia SE, Wain J, Pallen MJ: Performance comparison of benchtop high-throughput sequencing platforms. Nat Biotechnol. 2012, 30: 434-439.View ArticlePubMedGoogle Scholar
  25. Lopes LR, Zekavati A, Syrris P, Hubank M, Giambartolomei C, Dalageorgou C, Jenkins S, McKenna W, Plagnol V, Elliott PM: Genetic complexity in hypertrophic cardiomyopathy revealed by high-throughput sequencing. J Med Genet. 2013, 50: 228-239.PubMed CentralView ArticlePubMedGoogle Scholar
  26. Wilde AA, Behr ER: Genetic testing for inherited cardiac disease. Nat Rev Cardiol. 2013, 10: 571-583.View ArticlePubMedGoogle Scholar

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© Li 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/2.0), 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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