Illumina DNA test panel-based genotyping of whole genome amplified-DNA extracted from hair samples: performance and agreement with genotyping results from genomic DNA from buccal cells
Bibliographic record
Abstract
BACKGROUND: Hair is a DNA source that can be collected easily and inexpensively from participants in epidemiological studies. However, there is concern about DNA quality and quantity. Therefore, we assessed genotyping performance of whole genome amplified (WGA)-DNA extracted from hair using the GenomePlex method and evaluated its agreement with genotyping results of buccal cell DNA from the same individuals, using the Illumina GoldenGate platform. METHODS: The Illumina DNA test panel includes 360 highly validated single nucleotide polymorphisms (SNPs) selected from the Linkage IV Panel that are distributed across the entire genome. DNA was extracted from both archived hair and buccal cell samples obtained from 44 randomly selected subjects participating in a large cohort study in Canada. RESULTS: The genotyping success rate was 97.7% for 44 paired samples. However, WGA-DNA from hair failed more during genotyping in comparison to buccal cell DNA. Hair samples with a pre-WGA-DNA>or=1 ng/microL quantified using the PicoGreen assay (n=33) showed an average genotyping completion rate of 98.8% and SNP concordance of 91.2% with genotyping performance of buccal cell DNA. In contrast, samples with a pre-WGA-DNA<1 ng/microL had lower genotyping completion rate (94%) and poor SNP concordance (49%). CONCLUSIONS: Results suggest that WGA-DNA obtained from hair can produce excellent genotyping call rates and show relatively good SNP concordance with results from buccal cell DNA using high-throughput technology. DNA quantity obtained from hair samples is a crucial determinant of genotyping performance. Larger studies are needed to examine the utility of hair DNA with different genotyping platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".