Mapping Seed Phytic Acid Concentration and Iron Bioavailability in a Pea Recombinant Inbred Line Population
Bibliographic record
Abstract
ABSTRACT Phytate, the storage form of P in seeds, is not well digested by monogastrics, thereby contributing to micronutrient deficiency, decreased feed efficiency, and environmental pollution. This research was aimed at developing a single nucleotide polymorphism (SNP) based genetic linkage map and mapping genomic regions associated with phytic acid‐phosphorus (PA‐P) concentration using a recombinant inbred line (RIL) population (PR‐15) derived from a cross between a low phytate (low phytic acid [ lpa ]) mutant pea ( Pisum sativum L.) genotype, 1‐2347‐144, and a normal phytate cultivar CDC Meadow. A total of 163 RILs were genotyped using a 1536‐SNP Illumina GoldenGate array. Three hundred and sixty‐seven polymorphic SNP markers ordered into seven linkage groups (LGs) were used to generate a linkage map with a total length of 437.2 cM. PR‐15 lines were grown in replicated field trails in Saskatoon and Rosthern, SK, in 2012 and 2013. Chi‐square statistics confirmed the single gene inheritance of PA‐P concentration in these RILs. Phytic acid‐phosphorus (PA‐P) phenotype was mapped to LG5. Iron bioavailability (FEBIO) of PR‐15 lines estimated using the Caco‐2 cell culture bioassay was negatively correlated with PA‐P concentration. A quantitative trait locus (QTL) for FEBIO was mapped on to the same location on LG5 as phytic acid concentration. The QTL with a maximum LOD score of 15.1 explained 60.5% of the phenotypic variation in FEBIO. The markers flanking this QTL region can be employed in marker‐assisted selection to select pea lines with low phytate and greater Fe bioavailability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".