Genetic dissection of seed composition traits in soybean using a MAGIC population (SoyMAGIC)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».