Bibliographic record
Abstract
Understanding the complexities involved in identifying disease causing genes is \nstill a monumental task. As we know, genetic variants and environmental factors can \ninfluence the risk of disease outcomes. Epidemiological studies have identified that \nage is one of a number of environmental risk factors for Familial Pulmonary Fibrosis \n(FPF), but the genetic risk factors involved identification of disease causing genes still \nare a problem largely unsolved. An inherited disease-causing locus occurs in the same \ngenomic position as an ancestor who has the disease trait, and the disease genotype \nmay be associated with a marker genotype. A joint modeling of genetic linkage and \nassociation within families having a remote common ancestor or at population level is \npresented in this thesis. This joint modeling uses a likelihood approach that allows the \ninclusion of other covariates into the model for quantitative traits and binary traits with \nmultivariate random effects. Power studies via simulation compare the new proposed \nprocedure with standard linkage or association procedures. The joint test is more powerful \nthan linkage or association test alone where both sources of variation of linkage or \nassociation are present. Furthermore, the proposed method also allows testing against \nspecific alternatives - for example, against the significance of linkage where there is \nno association, significance of association where there is no linkage, and significance \nof both linkage and association. By utilizing data from five FPF families in Newfoundland, \nfour candidate loci were identified for the linkage or/and association with \nage-at-onset gene and FPF (rs4605929 in chromosome 6, rs11078200 in chromosome \n7, rs1941686 in chromosome 18 and rs114682 in chromosome 22).
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".