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Record W2789976276 · doi:10.1161/circgen.118.002109

Noncoding Genetic Variation and Gene Expression

2018· letter· en· W2789976276 on OpenAlexaff
Jason D. Roberts

Bibliographic record

VenueCirculation Genomic and Precision Medicine · 2018
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineGeneGene expressionGeneticsGenetic variationVariation (astronomy)Computational biologyBiology

Abstract

fetched live from OpenAlex

2] Although considerable progress has been made since the first genetic culprit for AF was identified in 2003, translating our improved genetic understanding into clinically actionable treatment strategies remains a vision rather than a reality. Leveraging insights gleaned from rare variants identified in familial AF cases may be limited by their generalizability to the greater AF population, whereas a major challenge faced with common variants identified through genome-wide association studies (GWAS) has been clarifying their functional relevance. Single nucleotide polymorphisms (SNPs) identified through GWAS have been predominantly nonprotein coding, leaving experts to hypothesize their mechanism of action. s the list of AF GWAS SNPs progressively expands, there is a mounting need to clarify their functional effects to translate their identification into clinically actionable tools. The predominant belief has been that these SNPs reside within regulatory regions that modulate expression of nearby (and potentially remote) genes. Genomic loci that associate with altered mRNA expression levels are referred to as expression quantitative trait loci (eQTLs), with the terms cis and trans indicating regulation of nearby and distant genes, respectively. 12 Notably, regulation of gene expression varies across cell types, and hence eQTL values are tissue-specific, a notion highlighting the importance of evaluating disease-associated SNPs in a disease-relevant tissue context. Given the limited availability of many human tissue types, a large-scale National Institutes of Health-funded initiative termed the Genotype-Tissue Expression project was launched in 2010 to serve as a publically available resource to facilitate investigation into the relationship between genetic variation and gene expression. ince its initial launch, the Genotype-Tissue Expression project has accumulated 11 688 samples across 53 tissue types from a total of 714 donors. On entry into the database, tissues undergo massively parallel RNA sequencing to quantitate genome-wide mRNA expression levels, whereas SNP genotyping is performed on DNA from peripheral blood. 14 Relevant to cardiac disease, tissues samples have been collected from the right atrial appendage (264 with donor genotype), the left ventricle, and the coronary arteries. Although right atrial appendage samples have been accrued, left atrial tissue has not been collected, which may limit the ability to leverage Genotype-Tissue Expression project data for AF research given that AF pathophysiology is felt to predominantly reside within the left atrium.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score1.000

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 integrity0.0010.000
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 head0.021
GPT teacher head0.267
Teacher spread0.246 · 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.

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

Citations2
Published2018
Admission routes1
Has abstractyes

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