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Record W4389137487 · doi:10.1186/s13073-023-01258-4

Evaluating the association of biallelic OGDHL variants with significant phenotypic heterogeneity

2023· article· en· W4389137487 on OpenAlexaff
Sheng‐Jia Lin, Barbara Vona, Tracy Lau, Kevin Huang, Maha S. Zaki, Huda Shujaa Aldeen, Ehsan Ghayoor Karimiani, Clarissa Rocca, Mahmoud M. Noureldeen, Ahmed K. Saad, Cassidy Petree, Tobias Bartolomaeus, Rami Abou Jamra, Giovanni Zifarelli, Aditi Gotkhindikar, Ingrid M. Wentzensen, Mingjuan Liao, Emalyn Cork, Pratishtha Varshney, Narges Hashemi, Mohammad Hasan Mohammadi, Abolfazl Rad, Juanita Neira, Mehran Beiraghi Toosi, Cordula Knopp, Ingo Kurth, Thomas D. Challman, Rebecca Smith, Asmahan T. Abdalla, Thomas Haaf, Mohnish Suri, Manali Joshi, Wendy K. Chung, Andrés Moreno-De-Luca, Henry Houlden, Reza Maroofian, Gaurav K. Varshney

Bibliographic record

VenueGenome Medicine · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsKingston Health Sciences CentreQueen's University
FundersUniversity College London Hospitals Biomedical Research CentreMedical Research CouncilBrain Research UKDeutsche ForschungsgemeinschaftMinisterium für Wissenschaft, Forschung und Kunst Baden-WürttembergUniversity College LondonNational Institute for Health and Care ResearchGreat Ormond Street Hospital CharityMichael J. Fox Foundation for Parkinson's ResearchMuscular Dystrophy UKWellcome TrustOklahoma Medical Research FoundationEberhard Karls Universität TübingenMuscular Dystrophy Association
KeywordsGeneticsBiologyPhenotypeZebrafishHuman geneticsGenetic heterogeneityAlleleAllelic heterogeneityMendelian inheritanceGeneCRISPRComputational biologyGenotype

Abstract

fetched live from OpenAlex

BACKGROUND: Biallelic variants in OGDHL, encoding part of the α-ketoglutarate dehydrogenase complex, have been associated with highly heterogeneous neurological and neurodevelopmental disorders. However, the validity of this association remains to be confirmed. A second OGDHL patient cohort was recruited to carefully assess the gene-disease relationship. METHODS: Using an unbiased genotype-first approach, we screened large, multiethnic aggregated sequencing datasets worldwide for biallelic OGDHL variants. We used CRISPR/Cas9 to generate zebrafish knockouts of ogdhl, ogdh paralogs, and dhtkd1 to investigate functional relationships and impact during development. Functional complementation with patient variant transcripts was conducted to systematically assess protein functionality as a readout for pathogenicity. RESULTS: A cohort of 14 individuals from 12 unrelated families exhibited highly variable clinical phenotypes, with the majority of them presenting at least one additional variant, potentially accounting for a blended phenotype and complicating phenotypic understanding. We also uncovered extreme clinical heterogeneity and high allele frequencies, occasionally incompatible with a fully penetrant recessive disorder. Human cDNA of previously described and new variants were tested in an ogdhl zebrafish knockout model, adding functional evidence for variant reclassification. We disclosed evidence of hypomorphic alleles as well as a loss-of-function variant without deleterious effects in zebrafish variant testing also showing discordant familial segregation, challenging the relationship of OGDHL as a conventional Mendelian gene. Going further, we uncovered evidence for a complex compensatory relationship among OGDH, OGDHL, and DHTKD1 isoenzymes that are associated with neurodevelopmental disorders and exhibit complex transcriptional compensation patterns with partial functional redundancy. CONCLUSIONS: Based on the results of genetic, clinical, and functional studies, we formed three hypotheses in which to frame observations: biallelic OGDHL variants lead to a highly variable monogenic disorder, variants in OGDHL are following a complex pattern of inheritance, or they may not be causative at all. Our study further highlights the continuing challenges of assessing the validity of reported disease-gene associations and effects of variants identified in these genes. This is particularly more complicated in making genetic diagnoses based on identification of variants in genes presenting a highly heterogenous phenotype such as "OGDHL-related disorders".

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)0.000
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 score0.962
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.036
GPT teacher head0.313
Teacher spread0.277 · 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

Citations13
Published2023
Admission routes1
Has abstractyes

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