Determination of sample size for testing associations between genetic markers and quantitative traits in trait-based analysis
Bibliographic record
Abstract
A general formula for computing the required sample size for DNA genotyping was developed for between-population sampling schemes (control vs. selected lines in one-way selection) and within-population sampling schemes (two-tail sampling, tri-sampling or multi-sampling). In DNA fingerprinting (presence or absence of a band), the minimum sample size required for detection of marker-trait association depends upon three factors: 1) the level of significance (α = 0.05 or 0.01) and degrees of freedom for χ 2 values: a higher level of significance and a greater d.f. needs a greater sample size; 2) the sum of squares in marker frequencies between groups: a greater sum of squares requires a smaller sample size; and 3) the product of [Formula: see text], where [Formula: see text] is the average of marker genotypic frequencies among groups. The product is maximum when [Formula: see text]. A larger product requirse a greater sample size. The proposed tri-sampling allows for the detection of gene action of the linked QTL, but requires a larger sample size than two-tail sampling. Detection of non-additive gene action requires a smaller sample size than the detection of additive gene action in tri-sampling scheme. The required sample size increases rapidly with increasing number of groups sampled in trait-based analysis. The required sample size is also derived for RFLP genotyping of a diallelic locus (three marker genotypes: +/+, +/−, and −/−) and a multiallelic locus. The restriction fragment length polymorphism (RFLP) genotyping requires a smaller sample size than DNA fingerprinting for detection of marker-QTL association. Key words: Sample size, genetic markers, trait-based analysis
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 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 | 0.055 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".