Genetics of disc-related disorders: current findings and lessons from other complex diseases
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".