Population isolates: their special value for locating genes for bipolar disorder
Bibliographic record
Abstract
OBJECTIVES: Population isolates offer several advantages for those hoping to identify predisposition genes for bipolar disorder (BP). In this review article, the rationale for performing gene mapping studies in this type of population and the results of genetic mapping studies performed to date in population isolates are presented. METHODS: This article begins with a brief review of the concepts involved in mapping genes for BP. The concept of populations that show some degree of historical isolation and their special utility for certain types of gene mapping is presented. Methods of statistical analysis particularly relevant for gene mapping of complex diseases like BP are presented. Finally, several BP gene studies conducted to date in several population isolates are reviewed. RESULTS: Genetic mapping studies of BP have occurred thus far in several isolates or sub-isolates, including the Amish population, Costa Ricans, Finnish, and Canadians (in Quebec), and significant linkage scores have been identified in the latter three isolates. CONCLUSIONS: Possible greater homogeneity and greater consistency of diagnosis are factors that have been cited in several studies of BP done in isolates to date. Another special advantage of working in certain types of population isolate is their appropriateness for using certain types of association or linkage disequilibrium-based approaches at both the genome screening and fine mapping stages. These tests include mapping by linkage disequilibrium analyses, an approach that allows mapping to occur at the population, rather than the pedigree, level.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".