Dataset Information

Analysis of the ABCA4 gene by next-generation sequencing.

ABSTRACT: To find all possible disease-associated variants in coding sequences of the ABCA4 gene in a large cohort of patients diagnosed with ABCA4-associated diseases.One hundred sixty-eight patients who had been clinically diagnosed with Stargardt disease, cone-rod dystrophy, and other ABCA4-associated phenotypes were prescreened for mutations in ABCA4 with the ABCA4 microarray, resulting in finding 1 of 2 expected mutations in 111 patients and 0 of 2 mutations in 57 patients. The next-generation sequencing (NGS) strategy was applied to these patients to sequence the entire coding region and the splice sites of the ABCA4 gene. Identified new variants were confirmed or rejected by Sanger sequencing and analyzed for possible pathogenicity by in silico programs and, where possible, by segregation analyses.Sequencing was successful in 159 of 168 patients and identified the second disease-associated allele in 49 of 103 (~48%) of patients with one previously identified mutation. Among those with no mutations, both disease-associated alleles were detected in 4 of 56 patients, and one mutation was detected in 10 of 56 patients. The authors detected a total of 57 previously unknown, possibly pathogenic, variants: 29 missense, 4 nonsense, 9 small deletions and 15 splice-site-altering variants. Of these, 55 variants were deemed pathogenic by a combination of predictive methods and segregation analyses.Many mutations in the coding sequences of the ABCA4 gene are still unknown, and many possibly reside in noncoding regions of the ABCA4 locus. Although the ABCA4 array remains a good first-pass screening option, the NGS platform is a time- and cost-efficient tool for screening large cohorts.

SUBMITTER: Zernant J

PROVIDER: S-EPMC3208189 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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Analysis of the ABCA4 gene by next-generation sequencing.

Zernant Jana J Schubert Carl C Im Kate M KM Burke Tomas T Brown Carolyn M CM Fishman Gerald A GA Tsang Stephen H SH Gouras Peter P Dean Michael M Allikmets Rando R

Investigative ophthalmology & visual science 20111031 11

<h4>Purpose</h4>To find all possible disease-associated variants in coding sequences of the ABCA4 gene in a large cohort of patients diagnosed with ABCA4-associated diseases.<h4>Methods</h4>One hundred sixty-eight patients who had been clinically diagnosed with Stargardt disease, cone-rod dystrophy, and other ABCA4-associated phenotypes were prescreened for mutations in ABCA4 with the ABCA4 microarray, resulting in finding 1 of 2 expected mutations in 111 patients and 0 of 2 mutations in 57 pati ...[more]

PMID: 21911583

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Project description:Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from "data-to-knowledge-to-innovation," a crucial missing step in the current era is, however, our limited understanding of biological and clinical contexts associated with data. Prominent among the emerging remedies to this challenge are the gene set enrichment tools. This study reports on GeneAnalytics™ ( geneanalytics.genecards.org ), a comprehensive and easy-to-apply gene set analysis tool for rapid contextualization of expression patterns and functional signatures embedded in the postgenomics Big Data domains, such as Next Generation Sequencing (NGS), RNAseq, and microarray experiments. GeneAnalytics' differentiating features include in-depth evidence-based scoring algorithms, an intuitive user interface and proprietary unified data. GeneAnalytics employs the LifeMap Science's GeneCards suite, including the GeneCards®--the human gene database; the MalaCards-the human diseases database; and the PathCards--the biological pathways database. Expression-based analysis in GeneAnalytics relies on the LifeMap Discovery®--the embryonic development and stem cells database, which includes manually curated expression data for normal and diseased tissues, enabling advanced matching algorithm for gene-tissue association. This assists in evaluating differentiation protocols and discovering biomarkers for tissues and cells. Results are directly linked to gene, disease, or cell "cards" in the GeneCards suite. Future developments aim to enhance the GeneAnalytics algorithm as well as visualizations, employing varied graphical display items. Such attributes make GeneAnalytics a broadly applicable postgenomics data analyses and interpretation tool for translation of data to knowledge-based innovation in various Big Data fields such as precision medicine, ecogenomics, nutrigenomics, pharmacogenomics, vaccinomics, and others yet to emerge on the postgenomics horizon.

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Analysis of the ABCA4 gene by next-generation sequencing.

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Analysis of the ABCA4 gene by next-generation sequencing.

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