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At the Heart of a Complex Disease ‘Molecular Genetics of Congenital Heart Disease’

2017· other· en· W2587827908 on OpenAlexaff
Christoph Preuß, Florian Wünnemann, Grégor Andelfinger

Bibliographic record

VenueEncyclopedia of Life Sciences · 2017
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMendelian inheritanceGeneticsHeart diseaseBiologySingle-nucleotide polymorphismDiseaseMissense mutationPopulationPhenotypeGenetic heterogeneityGenotypeBioinformaticsGeneMedicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract Congenital heart disease (CHD) is the most common type of birth defect and affects almost 1% of the general population. Compared to other rare congenital disorders, CHD rarely shows strictly Mendelian inheritance patterns. Human genetic studies have revealed that multiple genes contribute to the disease in pathways, which affect early cardiac development. Despite recent large‐scale efforts to identify causal genes for CHD, the majority of cases remain enigmatic. The challenges in identifying genotype–phenotype relationships in CHD suggest a more complex pattern of inheritance, where structural as well as single nucleotide variants contribute to the disease and modifiers tune the spectrum of cardiac malformations expressed. Here, we review the current state of genetic research in CHD and discuss the challenges in moving variant identification in CHD into the personal genomics era. Key Concepts Congenital heart disease (CHD) is a complex developmental phenotype with many genes contributing to its etiology. Single nucleotide polymorphisms (SNPs) as well as structural variants contribute to the burden of CHD in the population. Loss of function variants (LOF) and missense mutations can have different impacts during cardiac development, thus leading to different CHD phenotypes. Genetic factors for congenital heart malformations can be inherited autosomal recessive, autosomal dominant, X‐linked or show non‐Mendelian patterns in families. Genetic background can alter the manifestation of CHD and lead to different CHD subtypes or buffer against 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 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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0310.013

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 head0.025
GPT teacher head0.319
Teacher spread0.294 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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