Performance of Amplified DNA in an Illumina GoldenGate BeadArray Assay
Bibliographic record
Abstract
Whole genome amplification (WGA) offers a means to enrich DNA quantities for epidemiologic studies. We used an ovarian cancer study of 1,536 single nucleotide polymorphisms (SNPs) and 2,368 samples to assess performance of multiple displacement amplification (MDA) WGA using an Illumina GoldenGate BeadArray. Initial screening revealed successful genotyping for 93.4% of WGA samples and 99.3% of genomic samples, and 93.2% of SNPs for WGA samples and 96.3% of SNPs for genomic samples. SNP failure was predicted by Illumina-provided designability rank, %GC (P < or = 0.002), and for WGA only, distance to telomere and Illumina-provided SNP score (P < or = 0.002). Distance to telomere and %GC were highly correlated; adjustment for %GC removed the association between distance to telomere and SNP failure. Although universally high, per-SNP call rates were related to designability rank, SNP score, %GC, minor allele frequency, distance to telomere (P < or = 0.01), and, for WGA only, Illumina-provided validation class (P < 0.001). We found excellent concordance generally (>99.0%) among 124 WGA:genomic replicates, 15 WGA replicates, 88 replicate aliquots of the same WGA preparation, and 25 genomic replicates. Where there was discordance, it was across WGA:genomic replicates but limited to only a few samples among other replicates suggesting the introduction of error. Designability rank and SNP score correlated with WGA:genomic concordance (P < 0.001). In summary, use of MDA WGA DNA is feasible; however, caution is warranted regarding SNP selection and analysis. We recommend that biological SNP characteristics, notably distance to telomere and GC content (<50% GC recommended), as well as Illumina-provided metrics be considered in the creation of GoldenGate assays using MDA WGA DNA.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".