Phenotypic characterization and genealogical tracing in an Afrikaner schizophrenia database
Bibliographic record
Abstract
Founder populations hold tremendous promise for mapping genes for complex traits, as they offer less genetic and environmental heterogeneity and greater potential for genealogical research. Not all founder populations are equally valuable, however. The Afrikaner population meets several criteria that make it an ideal population for mapping complex traits, including founding by a small number of initial founders that likely allowed for a relatively restricted set of mutations and a large current population size that allows identification of a sufficient number of cases. Here, we examine the potential to conduct genealogical research in this population and present initial results indicating that accurate genealogical tracing for up to 17 generations is feasible. We also examine the clinical similarities of schizophrenia cases diagnosed in South Africa and those diagnosed in other, heterogeneous populations, specifically the US. We find that, with regard to basic sample descriptors and cardinal symptoms of disease, the two populations are equivalent. It is, therefore, likely that results from our genetic study of schizophrenia will be applicable to other populations. Based on the results presented here, the history and current size of the population, as well as our previous analysis addressing the extent of background linkage disequilibrium (LD) in the Afrikaners, we conclude that the Afrikaner population is likely an appropriate founder population to map genes for schizophrenia using both linkage and LD approaches.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".