Bibliographic record
Abstract
Bipolar disorder is a common and serious mental illness. The occurrence of mania is central to the diagnosis, but affected individuals typically also suffer episodes of depression. The results of family, twin and adoption studies argue convincingly for genetic susceptibility to bipolar disorder. Linkage studies conducted at the Molecular Psychiatry Laboratory, UCL have previously implicated the regions 12q24, 21q22, lq42 and 11 pi4- 15 as harbouring susceptibly loci for bipolar disorder. In this thesis I report fine mapping of the 12q24, 21q22 and lq42 regions by linkage disequilibrium methods, employing a case-control design. For the llpl4-15 region association with the candidate gene BDNF was tested. I also present attempts to replicate findings of association at the genes DAOA and COMT, located in regions implicated by meta-analysis of the linkage data. I have attempted to put these investigations in context, necessitating consideration of the conceptual developmental of bipolar disorder, the classical techniques for assessing the genetic contribution to aetiology, and mapping strategies. Fine mapping of the UCL linkage regions implicated two novel susceptibility loci and provided support for two previously identified loci. Association of multiple markers within a 180 kb region of 12q24.3 was found, implicating Slynar and LOC387895. Association was also found with two markers in the more centromeric gene P2RX7, previously implicated in a Canadian sample. Multiple associated markers were found on 21q22.3. Two candidate genes - C21orf29 and TRPM2 - were identified from this region. Initial efforts to fine map the lq42 region suggested the involvement of the previously implicated DISCI gene. However association was only found with a single marker. Although haplotypic association was found with BDNF, the complex structure of the microsatellite marker hindered interpretation of the results. Partial replication of the association with DAOA was achieved but the involvement of COMT was not supported.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".