Single-cross prediction with imputed multiomic data: a case study in rapeseed
Bibliographic record
Abstract
Advancements in sequencing technologies enabled the assembly and characterization of plant genomes with high resolution. In breeding programs, these data are combined with phenotypic information in genomic prediction to select genotypes based on their genetic profiles. Although single nucleotide polymorphism (SNP) arrays are commonly used for genotyping, they capture only a fraction of the genomewide diversity. To address this, one approach involves genotyping the entire population with arrays, sequence a subset using whole-genome sequencing (WGS), or assessing gene expression profiles, followed by imputing the data across the entire population. This study evaluates the effect of imputed WGS markers (SNPs and structural variants) and expression data on genomic prediction in a rapeseed hybrid breeding population. A combination of SNP arrays, WGS, and RNA sequencing was employed, followed by imputation of marker and expression data. Genomic prediction was utilized to estimate general and specific combining ability effects in untested hybrids. However, while adding imputed whole-genome and expression data increased marker density and linkage disequilibrium, it did not enhance prediction accuracy compared to SNP array data. This is attributed to redundancy in relationship, imputation errors, or environmental influences on gene expressions. This suggests that SNP arrays continue to be reliable for genomic prediction in rapeseed hybrid breeding.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".