An analysis of identical single-nucleotide polymorphisms genotyped by two different platforms
Bibliographic record
Abstract
The overlap of 94 single-nucleotide polymorphisms (SNP) among the 4,720 and 11,120 SNPs contained in the linkage panels of Illumina and Affymetrix, respectively, allows an assessment of the discrepancy rate produced by these two platforms. Although the no-call rate for the Affymetrix platform is approximately 8.6 times greater than for the Illumina platform, when both platforms make a genotypic call, the agreement is an impressive 99.85%. To determine if disputed genotypes can be resolved without sequencing, we studied recombination in the region of the discrepancy for the most discrepant SNP rs958883 (typed by Illumina) and tsc02060848 (typed by Affymetrix). We find that the number of inferred recombinants is substantially higher for the Affymetrix genotypes compared to the Illumina genotypes. We illustrate this with pedigree 10043, in which 3 of 7 versus 0 of 7 offspring must be double recombinants using the genotypes from the Affymetrix and the Illumina platforms, respectively. Of the 36 SNPs with one or more discrepancies, we identified a subset that appears to cluster in families. Some of this clustering may be due to the presence of a second segregating SNP that obliterates a XbaI site (the restriction enzyme used in the Affymetrix platform), resulting in a fragment too long (>1,000 bp) to be amplified.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".