Design and implementation of a custom next generation sequencing panel for selected vitamin D associated genes
Bibliographic record
Abstract
BACKGROUND: Biologically active vitamin D has an important regulatory role within the genome. It binds the vitamin D receptor (VDR) in order to control the expression of a wide range of genes as well as interacting with the epigenome to modify chromatin and methylation status. Vitamin D deficiency is associated with several human diseases including end-stage renal disease. METHODS: This article describes the design and testing of a custom, targeted next generation sequencing (NGS) panel for selected vitamin D associated genes. Sequencing runs were used to determine the effectiveness of the panel for variant calling, to compare efficiency and data across different sequencers, and to perform representative, proof of principle association analyses. These analyses were underpowered for significance testing. Amplicons were designed in two pools (163 and 166 fragments respectively) and used to sequence two cohorts of renal transplant recipients on the Ion Personal Genome Machine (PGM)™ and Ion S5™ XL desktop sequencers. RESULTS: Coverage was provided for 43.8 kilobases across seven vitamin D associated genes (CYP24A1, CUBN, VDR, GC, NADSYN1, CYP27B1, CYP2R1) as well as 38 prioritised SNPs. Sequencing runs provided sufficient sequencing quality, data output and validated the effective library preparation and panel design. CONCLUSIONS: This novel, custom-designed, validated panel provides a fast, cost effective, and specific approach for the analysis of vitamin D associated genes in a wide range of patient cohorts. This article does not report results from a controlled health-care intervention.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".