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Record W2070357564 · doi:10.1186/s12920-014-0067-8

High throughput exome coverage of clinically relevant cardiac genes

2014· article· en· W2070357564 on OpenAlexafffund
Dorin Manase, Lisa C.A. D’Alessandro, Ashok Kumar Manickaraj, Saeed Al Turki, Matthew E. Hurles, Seema Mital

Bibliographic record

VenueBMC Medical Genomics · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersHospital for Sick ChildrenOntario Ministry of Research and InnovationHeart and Stroke Foundation of Canada
KeywordsExome sequencingGeneExomeHuman geneticsBiologyGeneticsComputational biologyEnsemblGenomicsHuman genomeDNA sequencingCandidate geneMedical geneticsDNA microarrayBioinformaticsGenomeMutationGene expression

Abstract

fetched live from OpenAlex

BACKGROUND: Given the growing use of whole-exome sequencing (WES) for clinical diagnostics of complex human disorders, we evaluated coverage of clinically relevant cardiac genes on WES and factors influencing uniformity and depth of coverage of exonic regions. METHODS: Two hundred and thirteen human DNA samples were exome sequenced via Illumina HiSeq using different versions of the Agilent SureSelect capture kit. 50 cardiac genes were further analyzed including 31 genes from the American College of Medical Genetics (ACMG) list for reporting of incidental findings and 19 genes associated with congenital heart disease for which clinical testing is available. Gene coordinates were obtained from two databases, CCDS and Known Gene and compared. Read depth for each region was extracted from the exomes and used to assess capture variability between kits for individual genes, and for overall coverage. GC content, gene size, and inter-sample variability were also tested as potential contributors to variability in gene coverage. RESULTS: All versions of capture kits (designed based on Consensus coding sequence) included only 55% of known genomic regions for the cardiac genes. Although newer versions of each Agilent kit showed improvement in capture of CCDS regions to 99%, only 64% of Known Gene regions were captured even with newer capture kits. There was considerable variability in coverage of the cardiac genes. 10 of the 50 genes including 6 on the ACMG list had less than the optimal coverage of 30X. Within each gene, only 32 of the 50 genes had the majority of their bases covered at an interquartile range ≥30X. Heterogeneity in gene coverage was modestly associated with gene size and significantly associated with GC content. CONCLUSIONS: Despite improvement in overall coverage across the exome with newer capture kit versions and higher sequencing depths, only 50% of known genomic regions of clinical cardiac genes are targeted and individual gene coverage is non-uniform. This may contribute to a bias with greater attribution of disease causation to mutations in well-represented and well-covered genes. Improvements in WES technology are needed before widespread clinical application.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.254
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2014
Admission routes2
Has abstractyes

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