Assessment of complement C4 gene copy number using the paralog ratio test
Bibliographic record
Abstract
The complement C4 locus is in the class III region of the MHC, and exhibits copy number variation. Complement C4 null alleles have shown association with a number of diseases including systemic lupus erythematosus (SLE). However, most studies to date have used protein immunophenotyping and not direct interrogation of the genome to determine C4 null allele status. Moreover, a lack of accurate C4 gene copy number (GCN) estimation and tight linkage disequilibrium across the disease-associated MHC haplotypes has confounded attempts to establish whether or not these associations are causal. We have therefore developed a high throughput paralog ratio test (PRT) in association with two restriction enzyme digest variant ratio tests (REDVRs) to determine total C4 GCN, C4A GCN, and C4B GCN. In the densely genotyped CEU cohort we show that this method is accurate and reproducible when compared to gold standard Southern blot copy number estimation with a discrepancy rate of 9%. We find a broad range of C4 GCNs in the CEU and the 1958 British Birth Cohort populations under study. In addition, SNP-C4 CNV analyses show only moderate levels of correlation and therefore do not support the use of SNP genotypes as proxies for complement C4 GCN.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".