Novel West syndrome candidate genes in a Chinese cohort

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

<div class="section"> <a class="named-anchor" id="cns12860-sec-0001">  </a> <h5 class="section-title" id="d1112817e373">Aims</h5> <p id="d1112817e375">West syndrome (WS) is a classic form of early infantile epileptic encephalopathy (EIEE) characterized by tonic spasms with clustering, arrest of psychomotor development, and hypsarrhythmia on electroencephalography. Genetic defects play a critical role in the pathology of WS, and 54 EIEE genes have been identified till date. This study was designed to uncover new candidate genes for West syndrome. </p> </div><div class="section"> <a class="named-anchor" id="cns12860-sec-0002">  </a> <h5 class="section-title" id="d1112817e378">Methods</h5> <p id="d1112817e380">In this study, we recruited 56 Chinese families with WS of unknown etiology. Whole exome sequencing (WES) was performed to identify Mendelian inheritance rare or novel variants. The association between candidate genes and WS was analyzed from many aspects, including recurrent genes in patients, predicted variant effect on genes, human tolerance to deficient genes, gene expression in the nervous system, coexpression with EIEE genes, mutual interaction with known EIEE proteins, genes related to ion channel or fragile X mental retardation protein function, and mouse models with manifestation of seizures. Genes with supporting evidence from those aspects were defined as highlight candidate genes. </p> </div><div class="section"> <a class="named-anchor" id="cns12860-sec-0003">  </a> <h5 class="section-title" id="d1112817e383">Results</h5> <p id="d1112817e385">Whole exome sequencing identified 112 candidate variants in 89 genes. Among the candidate genes, 33 were autosomal dominant, 22 were autosomal recessive, and 34 were X‐linked. Complex bioinformatic analysis revealed 17 highlight candidate genes: <i>ATP2A2</i>, <i> CD99L2</i>, <i> CLCN6</i>, <i> CYFIP1</i>, <i> CYFIP2</i>, <i> GNB1</i>, <i> GPT2</i>, <i> HUWE1</i>, <i> KMT2D</i>, <i> MYO18A</i>, <i> NOS3</i>, <i> RYR1</i>, <i> RYR2</i>, <i> RYR3</i>, <i> TAF1</i>, <i> TECTA</i>, and <i>UBA1</i>. The majority of highlight candidate genes are calcium‐signaling pathway and mental retardation genes. </p> </div><div class="section"> <a class="named-anchor" id="cns12860-sec-0004">  </a> <h5 class="section-title" id="d1112817e441">Conclusions</h5> <p id="d1112817e443">This is the first WES study of Chinese WS patients with unknown etiology. This combination of phenotypic and genomic data will enable further testing to elucidate mechanisms underlying the pathogenesis of WS. </p> </div>

Related collections

Most cited references 58

Record: found
Abstract: found
Article: found

Is Open Access

Fast and accurate short read alignment with Burrows–Wheeler transform

Heng Li, Richard Durbin (2009)

Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk

0 comments Cited 10900 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

Sue Richards, Nazneen Aziz, Sherri Bale … (2015)

The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.

0 comments Cited 7493 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

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/ .