Clustering of Pedigrees Using Marker Allele Frequencies: Impact on Linkage Analysis
Bibliographic record
Abstract
Ethnicity may form the basis for locus heterogeneity at certain susceptibility loci for complex diseases. Classification of pedigrees into ethnic groups is usually based upon self-report, but this may not be sensitive or specific. We investigated whether it is possible to cluster families from an admixed population using pedigree-specific marker allele frequencies. We used 323 autosomal microsatellite markers from 216 pedigrees who described themselves as either Caucasian or African American. First, we compared the stated ethnicity of pedigrees with clusters using pedigree-specific marker allele frequencies as input for a self-organizing map, a type of neural network. Using data from different chromosomes, nine pedigrees which were self-reported as African American were clustered with the Caucasian pedigrees. Removal of these nine pedigrees from the African American group did not markedly affect linkage results. We then proceeded to determine whether there was further heterogeneity between pedigrees using 1 x 3 nodes. Forty-four pedigrees were clustered in a group intermediate to the African American or Caucasian clusters. This group was composed of 36 and 8 pedigrees that described themselves as African American and Caucasian, respectively. Linkage analysis was performed in this group and results compared with the groups based upon self-reported ethnicity. Linkage to a region on chromosome 3 was observed in this intermediate group, which was more significant than any of the results obtained when pedigrees were grouped using self-reported ethnicity. Use of marker data may assist in clustering pedigrees with similar, ethnic backgrounds and may increase the power for genetic linkage studies.
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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 | 0.022 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".