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Record W4308132893 · doi:10.1210/jendso/bvac150.286

RF33 | PSAT69 A Combined Candidate Gene/Whole Exome Sequencing Approach Permits a Rapid Genetic Diagnosis for >81% Individuals with Primary Adrenal Insufficiency.

2022· article· en· W4308132893 on OpenAlexaboutno aff
Li Chong Chan, Chris Smith, Jordan S. Read, Charlotte Hall, Avinaash Maharaj, Lucia Marroquin Ramirez, Younus Qamar, Claire Hughes, Adrian Clark, Salwa Musa, Rathi Prasad, Louise Metherell

Bibliographic record

VenueJournal of the Endocrine Society · 2022
Typearticle
Languageen
FieldMedicine
TopicAdrenal Hormones and Disorders
Canadian institutionsnot available
FundersMedical Research Council
KeywordsSanger sequencingExome sequencingPrimary Adrenal InsufficiencyGenetic testingAdrenal insufficiencyBiologyGeneticsCandidate geneExomeWhole genome sequencingGeneDNA sequencingBioinformaticsMedicineMutationGenome

Abstract

fetched live from OpenAlex

Abstract Introduction Mutations in MC2R causing familial glucocorticoid deficiency (FGD), a rare form of primary, isolated adrenal insufficiency, were first published in 1993, discovered by candidate gene sequencing (CGS). Through advances in genetic techniques from homozygosity mapping to whole exome sequencing (WES) we have linked a further five genes to adrenal insufficiency and, in conjunction with other groups, this has expanded the number of monogenic causes for isolated or syndromic adrenal insufficiency to >25. Patients and Methods Over the last 30 years over 400 individuals with suspected FGD, from 31 different countries, have been referred to our centre for genetic testing, ranging in age from neonates to individuals in their eighties. All cases had low or undetectable serum cortisol usually paired with an elevated plasma ACTH level. Our strategy for sequencing has evolved over time and we now routinely sequence the small, frequently mutated, candidate genes by Sanger sequencing (MC2R, MRAP, STAR and CYP11A1) before proceeding to WES as the most cost-effective route to a genetic diagnosis. In total, 369of the 400 individuals have been fully characterised either by CGS or WES. For CGS, sequences were analysed by alignment to reference sequences using BioEdit software and for WES variant call files were initially analysed using Ingenuity Variant Analysis package and/or examination of BAM files, using the Integrative Genomics Viewer, to detect exonic deletions. Rare, synonymous or predicted benign variants were further tested by an in vitro splicing assay using the pET01 vector (MoBiTec) Results In 308/ 369 individuals we found a definitive diagnosis in a gene known to be causal for adrenal insufficiency, giving a success rate of >81%, and identified 15 novel mutations. The aetiologies of diagnosed cases were as follows; MC2R (22%), MRAP (17%), NNT (15%), STAR (9%), CYP11A1 (7%), with the remaining 30% due to a further 13 genes. Founder effects were clear for variants in some genes/populations, whereas in others hotspot mutations were present in multiple ethnicities. Examples include the previously described S74I in MC2R and rs6161 in CYP11A1 in the UK population, P24Rfs*4 in MCM4 in Ireland, R188C in STAR in Canada, and newly discovered associations with T731= in NNT in Sudan and R222Q in SGPL1 in Saudi Arabia. Whereas MRAP splice mutations commonly seen at the exon 3/intron 3 junction were present in individuals from many countries. The work has also highlighted a number of causal synonymous and predicted benign variants that result in splicing defects. Conclusion The use of CGS/WES now permits a rapid genetic diagnosis for >83% individuals with PAI improving tailored patient management. For the remaining patients it is unclear whether they have unconventional mutations in known genes or if there are further gene defects to be discovered. Presentation: Saturday, June 11, 2022 1:00 p.m. - 3:00 p.m., Monday, June 13, 2022 1:24 p.m. - 1:29 p.m.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0140.005

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 head0.249
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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