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Record W804074734

Deciphering the genetic architecture of retinitis pigmentosa through a combination of panel and whole exome sequencing

2014· article· en· W804074734 on OpenAlexaff
Feng Wang, Li Zhao, Hui Wang, David Simpson, Stephen P. Daiger, Silvestri Giuliana, Kang Zhang, Robert K. Koenekoop, Ruifang Sui, Rui Chen

Bibliographic record

VenueResearch Portal (Queen's University Belfast) · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsRetinitis pigmentosaExome sequencingGenetic architectureExomeGeneticsBiologyComputational biologyGeneMutationPhenotype
DOInot available

Abstract

fetched live from OpenAlex

PurposeThe molecular basis of retinitis pigmentosa (RP) is a highly heterogeneous. Many novel pathogenic alleles, genotype-phenotype associations, and disease genes remain to be identified. In this study, we aim to dissect the complex genetic architecture of RP by characterizing a large cohort of RP patients. Methods552 RP patients from different ethnicity groups, including Caucasian and Han Chinese, were recruited. Genomic DNA was extracted from patients’ blood or saliva samples, and sequenced using our custom-designed panel, which includes around 200 retinal disease genes. Patients with negative results from our panel sequencing were further analyzed by whole exome sequencing. ResultsWe successfully identified putatively pathogenic variants in known retinal disease genes for 319 RP cases, achieving a solving rate of approximately 58%. Among the 176 solved simplex cases, multiple inheritance patterns were found, including autosomal recessive (73%), autosomal dominant (14%), x-linked (12%) and even digenic (2%). A total of 460 different pathogenic mutations were identified, 365 of which were novel. Interestingly, 58 mutations were recurrent in multiple solved cases, accounting for approximately 30% of total allele instances. USH2A was the most prevalent causative gene in our cohort, which accounts for about 15% of all the solved case. And EYS is significantly more prevalent (~10 fold) in RP patients from Han Chinese than those from Caucasian population. Surprisingly, around 20% of all the solved cases carried mutations in other retinal disease genes which had not been previously associated with RP. For those cases, where available, clinical reassessments were performed resulting in identification of novel genotype-phenotype correlations and clinical refinements. Finally, whole exome sequencing of unsolved cases revealed multiple candidate disease-causing genes which are currently in the process of further validation. ConclusionsSequencing-based comprehensive genetic testing of large patient cohort yield tremendous amount of new findings at multiple levels of the genetic architecture underlying RP. Information gained from this type of study will lay the foundation toward comprehensive and accurate molecular diagnosis of RP, which is critical for developing proper treatment of the disease.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.020
GPT teacher head0.252
Teacher spread0.232 · 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 designBench or experimental
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

Citations0
Published2014
Admission routes1
Has abstractyes

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