46th European Mathematical Genetics Meeting (EMGM) 2018, Cagliari, Italy, April 18-20, 2018: Abstracts
Bibliographic record
Abstract
Hundreds of genetic variants have been identified as associated with a spectrum of diseases, but the fine-mapping of causal variants has been complicated by extended linkage disequilibrium (LD) and finite sample sizes.We propose to leverage information between diseases through joint analysis of data from related diseases in a novel Bayesian multinomial stochastic search framework, where prior model probabilities are formulated to favour combinations of models with a degree of sharing of causal variants between diseases.We use simulations and real data examples to illustrate the improved accuracy in comparison to a marginal analysis of each disease.That is, in simulations of two diseases that each have two causal variants, of which one is shared, we find that marginal disease analyses may fail to identify both causal variants for each disease.However, our multinomial framework tends to detect shared variants that are missed by marginal analyses.We jointly fine-map association signals for six diseases and of particular interest is IL2RA, which is known to be associated with several autoimmune diseases, including multiple sclerosis (MS), type 1 diabetes (T1D), autoimmune thyroid disease (AITD) and coeliac disease.Our proposed approach is computationally efficient and adds only five minutes overhead to the fine mapping of individual diseases.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.083 | 0.028 |
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".