Whole-Genome Amplification Enables Accurate Genotyping for Microarray-Based High-Density Single Nucleotide Polymorphism Array
Bibliographic record
Abstract
In large-scale genome-wide association studies based on high-density single nucleotide polymorphism (SNP) genotyping array, the quantity and quality of available genomic DNA (gDNA) is a practical problem. We examined the feasibility of using the Multiple Displacement Amplification (MDA) method of whole-genome amplification (WGA) for such a platform. The Affymetrix Early Access Mendel Nsp 250K GeneChip was used for genotyping 224,940 SNPs per sample for 28 DNA samples. We compared the call concordance using 14 gDNA samples and their corresponding 14 WGA samples. The overall mean genotype call rates in gDNA and the corresponding WGA samples were comparable at 97.07% [95% confidence interval (CI), 96.17-97.97] versus 97.77% (95% CI, 97.26-98.28; P = 0.154), respectively. Reproducibility of the platform, calculated as concordance in duplicate samples, was 99.45%. Overall genotypes for 97.74% (95% CI, 97.03-98.44) of SNPs were concordant between gDNA and WGA samples. When the analysis was restricted to well-performing SNPs (successful genotyping in gDNA and WGA in >90% of samples), 99.11% (95% CI, 98.80-99.42) of the SNPs, on average, were concordant, and overall a SNP showed a discordant call in 0.92% (95% CI, 0.90-0.94) of paired samples. In a pair of gDNA and WGA DNA, similar concordance was reproducible on Illumina's Infinium 610 Quad platform as well. Although copy number analysis revealed a total of seven small telomeric regions in six chromosomes with loss of copy number, the estimated genome representation was 99.29%. In conclusion, our study confirms that high-density oligonucleotide array-based genotyping can yield reproducible data and MDA-WGA DNA products can be effectively used for genome-wide SNP genotyping analysis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".