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Record W2888721862

Genetics of disc-related disorders: current findings and lessons from other complex diseases

2014· article· en· W2888721862 on OpenAlexaff
Annu Näkki, Michele C. Battié, Jaakko Kaprio

Bibliographic record

VenueSTM:n Hallinnonalan avoin julkaisuarkisto (Julkari) · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and fungal interactions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeneticsBiologyContext (archaeology)HeritabilityGenome-wide association studyCandidate geneGenetic associationGeneDiseaseEvolutionary biologyGenotypeMedicineSingle-nucleotide polymorphismPathology
DOInot available

Abstract

fetched live from OpenAlex

Disc-related disorders are highly genetic conditions with heritability estimates of up to 75 % and yet, few genomic locations have been moderately associated with the disorders. Candidate gene studies have shown possible disease associations on loci and genes of 1p21.1 (COL11A1), 6q27 (THBS2), 9q22.31 (ASPN), 10p12.31 (SKT), 20q11.2 (GDF5) and 20q13.12 (MMP9). More recently, in 2012, the first genome-wide association study revealed variants on loci and genes of 3p26.2, 6p21.32 (HLA region) and 6q26 (PARK2) that associate with disc-related disorders. In many other complex diseases, large meta-analyses of hundreds of thousands of study subjects and loci have revealed remarkable pathways. As methodology is evolving rapidly, we have already stepped into the era of routinely sequencing all bases in all human exons and we are approaching the era of sequencing the entire genome of study subjects with common diseases. The past decade has taught us that the common variants seen throughout populations seem to have low effects in many common diseases, explain relatively little of the overall heritability of the diseases and demand thousands of study subjects to identify associations. It seems that familial rare variants play an important role in many common diseases leading us back to valuing studies with large families and isolated populations. Moreover, careful characterization of environmental conditions are needed to explore and determine gene–environment interactions as genes that increase disease risk in one context may not do so under another context.

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)0.007
metaresearch head score (Gemma)0.010
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: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.002
Science and technology studies0.0010.005
Scholarly communication0.0030.007
Open science0.0020.002
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0030.001

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 head0.034
GPT teacher head0.273
Teacher spread0.239 · 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
GenreReview

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

Citations1
Published2014
Admission routes1
Has abstractyes

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