Genetic complexity in hypertrophic cardiomyopathy revealed by high-throughput sequencing

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Abstract

Background

Clinical interpretation of the large number of rare variants identified by high throughput sequencing (HTS) technologies is challenging. The aim of this study was to explore the clinical implications of a HTS strategy for patients with hypertrophic cardiomyopathy (HCM) using a targeted HTS methodology and workflow developed for patients with a range of inherited cardiovascular diseases. By comparing the sequencing results with published findings and with sequence data from a large-scale exome sequencing screen of UK individuals, we sought to quantify the strength of the evidence supporting causality for detected candidate variants.

Methods and results

223 unrelated patients with HCM (46±15 years at diagnosis, 74% males) were studied. In order to analyse coding, intronic and regulatory regions of 41 cardiovascular genes, we used solution-based sequence capture followed by massive parallel resequencing on Illumina GAIIx. Average read-depth in the 2.1 Mb target region was 120. Rare (frequency<0.5%) non-synonymous, loss-of-function and splice-site variants were defined as candidates. Excluding titin, we identified 152 distinct candidate variants in sarcomeric or associated genes (89 novel) in 143 patients (64%). Four sarcomeric genes ( MYH7, MYBPC3, TNNI3, TNNT2) showed an excess of rare single non-synonymous single-nucleotide polymorphisms (nsSNPs) in cases compared to controls. The estimated probability that a nsSNP in these genes is pathogenic varied between 57% and near certainty depending on the location. We detected an additional 94 candidate variants (73 novel) in desmosomal, and ion-channel genes in 96 patients (43%).

Conclusions

This study provides the first large-scale quantitative analysis of the prevalence of sarcomere protein gene variants in patients with HCM using HTS technology. Inclusion of other genes implicated in inherited cardiac disease identifies a large number of non-synonymous rare variants of unknown clinical significance.

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Most cited references 94

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The Sequence Alignment/Map format and SAMtools

Heng Li, Bob Handsaker, Alec Wysoker … (2009)

Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk

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ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

Kai Wang, Mingyao Li, Hakon Hakonarson (2010)

High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .

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A method and server for predicting damaging missense mutations

Ivan Adzhubei, Steffen Schmidt, Leonid Peshkin … (2010)

To the Editor: Applications of rapidly advancing sequencing technologies exacerbate the need to interpret individual sequence variants. Sequencing of phenotyped clinical subjects will soon become a method of choice in studies of the genetic causes of Mendelian and complex diseases. New exon capture techniques will direct sequencing efforts towards the most informative and easily interpretable protein-coding fraction of the genome. Thus, the demand for computational predictions of the impact of protein sequence variants will continue to grow. Here we present a new method and the corresponding software tool, PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/), which is different from the early tool PolyPhen1 in the set of predictive features, alignment pipeline, and the method of classification (Fig. 1a). PolyPhen-2 uses eight sequence-based and three structure-based predictive features (Supplementary Table 1) which were selected automatically by an iterative greedy algorithm (Supplementary Methods). Majority of these features involve comparison of a property of the wild-type (ancestral, normal) allele and the corresponding property of the mutant (derived, disease-causing) allele, which together define an amino acid replacement. Most informative features characterize how well the two human alleles fit into the pattern of amino acid replacements within the multiple sequence alignment of homologous proteins, how distant the protein harboring the first deviation from the human wild-type allele is from the human protein, and whether the mutant allele originated at a hypermutable site2. The alignment pipeline selects the set of homologous sequences for the analysis using a clustering algorithm and then constructs and refines their multiple alignment (Supplementary Fig. 1). The functional significance of an allele replacement is predicted from its individual features (Supplementary Figs. 2–4) by Naïve Bayes classifier (Supplementary Methods). We used two pairs of datasets to train and test PolyPhen-2. We compiled the first pair, HumDiv, from all 3,155 damaging alleles with known effects on the molecular function causing human Mendelian diseases, present in the UniProt database, together with 6,321 differences between human proteins and their closely related mammalian homologs, assumed to be non-damaging (Supplementary Methods). The second pair, HumVar3, consists of all the 13,032 human disease-causing mutations from UniProt, together with 8,946 human nsSNPs without annotated involvement in disease, which were treated as non-damaging. We found that PolyPhen-2 performance, as presented by its receiver operating characteristic curves, was consistently superior compared to PolyPhen (Fig. 1b) and it also compared favorably with the three other popular prediction tools4–6 (Fig. 1c). For a false positive rate of 20%, PolyPhen-2 achieves the rate of true positive predictions of 92% and 73% on HumDiv and HumVar, respectively (Supplementary Table 2). One reason for a lower accuracy of predictions on HumVar is that nsSNPs assumed to be non-damaging in HumVar contain a sizable fraction of mildly deleterious alleles. In contrast, most of amino acid replacements assumed non-damaging in HumDiv must be close to selective neutrality. Because alleles that are even mildly but unconditionally deleterious cannot be fixed in the evolving lineage, no method based on comparative sequence analysis is ideal for discriminating between drastically and mildly deleterious mutations, which are assigned to the opposite categories in HumVar. Another reason is that HumDiv uses an extra criterion to avoid possible erroneous annotations of damaging mutations. For a mutation, PolyPhen-2 calculates Naïve Bayes posterior probability that this mutation is damaging and reports estimates of false positive (the chance that the mutation is classified as damaging when it is in fact non-damaging) and true positive (the chance that the mutation is classified as damaging when it is indeed damaging) rates. A mutation is also appraised qualitatively, as benign, possibly damaging, or probably damaging (Supplementary Methods). The user can choose between HumDiv- and HumVar-trained PolyPhen-2. Diagnostics of Mendelian diseases requires distinguishing mutations with drastic effects from all the remaining human variation, including abundant mildly deleterious alleles. Thus, HumVar-trained PolyPhen-2 should be used for this task. In contrast, HumDiv-trained PolyPhen-2 should be used for evaluating rare alleles at loci potentially involved in complex phenotypes, dense mapping of regions identified by genome-wide association studies, and analysis of natural selection from sequence data, where even mildly deleterious alleles must be treated as damaging. Supplementary Material 1

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Author and article information

Journal

Journal ID (nlm-ta): J Med Genet

Journal ID (iso-abbrev): J. Med. Genet

Journal ID (hwp): jmedgenet

Journal ID (publisher-id): jmg

Title: Journal of Medical Genetics

Publisher: BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )

ISSN (Print): 0022-2593

ISSN (Electronic): 1468-6244

Publication date (Print): April 2013

Publication date (Electronic): 8 February 2013

Volume: 50

Issue: 4

Pages: 228-239

Affiliations

[1 ]UCL Institute of Cardiovascular Science , London, UK

[2 ]Department of Molecular Haematology and Cancer Biology, UCL Genomics , UCL Institute of Child Health , London, UK

[3 ]UCL Genetics Institute , London, UK

[4 ]www.uk10.k.org

Author notes

[Correspondence to ] Prof. Perry Elliott, The Heart Hospital, 16-18 Westmoreland Street, London W1G 8PH, UK; perry.elliott@ 123456ucl.ac.uk

LRL and AZ contributed equally to this paper and are join first authors

Article

Publisher ID: jmedgenet-2012-101270

DOI: 10.1136/jmedgenet-2012-101270

PMC ID: 3607113

PubMed ID: 23396983

SO-VID: 10655766-32f6-4f5e-bb49-550dfef6d29d

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/3.0/ and http://creativecommons.org/licenses/by-nc/3.0/legalcode

History

Date received : 29 August 2012

Date revision received : 6 January 2013

Date accepted : 9 January 2013

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ScienceOpen disciplines: Genetics

Keywords: genetics,high-throughput sequencing,hypertrophic cardiomyopathy

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ScienceOpen disciplines: Genetics

Keywords: genetics, high-throughput sequencing, hypertrophic cardiomyopathy

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Genetic complexity in hypertrophic cardiomyopathy revealed by high-throughput sequencing

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Abstract

Background

Methods and results

Conclusions

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UCL: UN SDG 03 Good Health and Well-Being

Most cited references 94

The Sequence Alignment/Map format and SAMtools

ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

A method and server for predicting damaging missense mutations

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Cited by 65

Most referenced authors 2,738