Applying genotypic principal component scores as latent phenotypes in genome-wide and epistatic analyses of soybean agronomic traits
Bibliographic record
Abstract
Identification of marker trait associations (MTAs) for agronomic traits of soybean ( Glycine max L. Merr.) can often be limited by confounding genotype by environment interactions. In this study, phenotypic data was derived from the calculation of genotypic principal component scores (gPCs) by GGEbiplot from a multiple year and location agronomic dataset to assess the validity and feasibility of using gPC scores in genome-wide association analysis (GWAS) in comparison with traditional phenotypes. Important quantitative trait loci (QTL) were discovered for maturity, seed oil content, yield, and plant height that were not detected using the traditional phenotypes. MTAs were detected by GWAS analysis with PC1, PC2, and PC4 phenotypes. QTL for maturity associated with the E1 and E3 soybean maturity loci demonstrate the validity of this approach by detecting these well studied regions. Epistatic analysis revealed QTL controlling both oil and protein content but did not uncover significant interactions associated with other traits. This result further contributes to the understanding of complex gene networks controlling pleiotropic traits such as seed oil and seed protein content. QTL for the studied traits are reported across six Glycine max chromosomes with 15 genes and one gene cluster proposed as candidates controlling agronomic traits.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".