Genetic dissection of seed composition traits in soybean using a MAGIC population (SoyMAGIC)
Bibliographic record
Abstract
Multi-parent advanced generation inter-cross (MAGIC) populations serve as effective genetic platforms for precise mapping of quantitative traits, such as agronomic and seed composition traits in soybean (Glycine max). Through this study, an eight-parent MAGIC population (SoyMAGIC) consisting of 721 recombinant inbred lines (RILs) has been established through inter-mating of eight soybean founders. The founders comprised genetically diverse elite cultivars exhibiting a wide range of agronomic and seed composition characteristics. This study aimed to: 1) establish and introduce the SoyMAGIC population as a novel platform for investigating genotypic and phenotypic traits related to soybean agronomic and seed quality, and 2) capitalize on this unprecedented opportunity provided by SoyMAGIC, along with advances in the DNA sequencing technology, to discover quantitative trait loci (QTL) associated with the target traits through Genome Wide Association Studies (GWAS). The RILs were evaluated for important seed composition traits across four different locations in Ontario (Ridgetown, Palmyra, Elora and Ottawa) in 2020 and 2021 using randomized complete block designs with two replications. Seed composition traits were assessed using near-infrared reflectance (NIR). The genotyping-by-sequencing (GBS) method was employed to identify polymorphic SNP markers among the RILs, which were subsequently used for genetic studies and detecting QTL associated with the target traits. We constructed a high-density linkage map using inclusive composite interval mapping (ICIM) method, which resulted in a map with a length of 3,770.75 cM and 12,007 SNP markers. Compared to parental lines, the RILs displayed transgressive segregation for the selected traits, as well as a higher recombination frequency across the genome, which confirm SoyMAGIC’s ability to increase recombination frequency among the RILs. The assessment of haplotype blocks indicated an uneven distribution of the parental genomes in RILs, implying that certain parental genomes had a greater or lesser influence on the population. The RILs were used to calculate the decay distance of genome- and chromosome-wide linkage disequilibrium (LD). Afterward, GWAS was performed along with candidate gene prediction for seed composition traits using 122,747 SNPs for both the entire SoyMAGIC population and a subset of 200 early maturing RILs. Altogether, 212 markers, which were divided into 196 QTL that showed significant associations with the seed composition traits. By providing valuable genetic information, SoyMAGIC enhances our understanding of the genetic structure of seed composition traits in soybean.Multi-parent advanced generation inter-cross (MAGIC) populations serve as effective genetic platforms for precise mapping of quantitative traits, such as agronomic and seed composition traits in soybean (Glycine max). Through this study, an eight-parent MAGIC population (SoyMAGIC) consisting of 721 recombinant inbred lines (RILs) has been established through inter-mating of eight soybean founders. The founders comprised genetically diverse elite cultivars exhibiting a wide range of agronomic and seed composition characteristics. This study aimed to: 1) establish and introduce the SoyMAGIC population as a novel platform for investigating genotypic and phenotypic traits related to soybean agronomic and seed quality, and 2) capitalize on this unprecedented opportunity provided by SoyMAGIC, along with advances in the DNA sequencing technology, to discover quantitative trait loci (QTL) associated with the target traits through Genome Wide Association Studies (GWAS). The RILs were evaluated for important seed composition traits across four different locations in Ontario (Ridgetown, Palmyra, Elora and Ottawa) in 2020 and 2021 using randomized complete block designs with two replications. Seed composition traits were assessed using near-infrared reflectance (NIR). The genotyping-by-sequencing (GBS) method was employed to identify polymorphic SNP markers among the RILs, which were subsequently used for genetic studies and detecting QTL associated with the target traits. We constructed a high-density linkage map using inclusive composite interval mapping (ICIM) method, which resulted in a map with a length of 3,770.75 cM and 12,007 SNP markers. Compared to parental lines, the RILs displayed transgressive segregation for the selected traits, as well as a higher recombination frequency across the genome, which confirm SoyMAGIC’s ability to increase recombination frequency among the RILs. The assessment of haplotype blocks indicated an uneven distribution of the parental genomes in RILs, implying that certain parental genomes had a greater or lesser influence on the population. The RILs were used to calculate the decay distance of genome- and chromosome-wide linkage disequilibrium (LD). Afterward, GWAS was performed along with candidate gene prediction for seed composition traits using 122,747 SNPs for both the entire SoyMAGIC population and a subset of 200 early maturing RILs. Altogether, 212 markers, which were divided into 196 QTL that showed significant associations with the seed composition traits. By providing valuable genetic information, SoyMAGIC enhances our understanding of the genetic structure of seed composition traits in soybean.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".