Supplementary Material for: Detection of Phenotype Modifier Genes Using Two-Locus Linkage Analysis in Complex Disorders Such as Major Psychosis
Bibliographic record
Abstract
Objective: To increase power to detect modifier loci conferring susceptibility to specific phenotypes such as disease diagnoses which are part of a broader disorder spectrum by jointly modeling a modifier and a broad susceptibility gene and to identify modifier loci conferring specific susceptibility to schizophrenia (SZ) or to bipolar disorder (BP) using the approach. Methods: We implemented a two-locus linkage analysis model where a gene 1 genotype increases the risk of a broad phenotype and a gene 2 genotype modifies the expression of gene 1 by conferring susceptibility to a specific phenotype. Results: Compared to a single-locus analysis within the broad phenotype, the proposed approach had greater power to detect the modifier gene 2 (0.96 vs. 0.54 under a simulation scenario including heterogeneity). In a sample of 12 mixed SZ and BP Eastern Quebec kindreds, D8S1110 at 8p22 showed the strongest evidence of linkage to a gene determining a specific phenotype (SZ or BP) among subjects susceptible to major psychosis because of putative genes at 10p13 (D10S245, conditional maximized LOD (cMOD) = 4.20, p = 0.0003) and 3q21–q23 (D3S2418, cMOD = 4.09, p = 0.0005). Conclusion: The proposed strategy is useful to detect modifier loci conferring susceptibility to a specific phenotype within a broader phenotype.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.792 | 0.223 |
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".