Genome-wide association study of seed protein and amino acid contents in cultivated lentils as determined by near-infrared reflectance spectroscopy
Notice bibliographique
Résumé
Lentil (Lens culinaris Medik.) is an important legume crop and is considered as a plant-based protein food to fight protein malnutrition and bring health benefits. The current study focused on the development and evaluation of near-infrared reflectance spectroscopy (NIRS) models to predict the protein and amino acid contents in lentils by two NIRS spectrometers: PerkinElmer DA7250 and FT 9700. In total, 361 lentil samples grown in Saskatchewan, Canada, were selected as a calibration set. NIRS calibration models developed by partial least squares (PLS) equation had a satisfactory performance for measuring protein and most amino acids (except for histidine, tyrosine, methionine and cysteine) in lentils with R2C ＞0.65. The sample status, type of spectrometer, and amino acid/protein correlation could influence the NIRS models’ predictive abilities. NIRS models from DA 7250 achieved similar accuracy for the determination of crude protein and amino acids in whole and ground lentils. In the current study, the predictive ability of DA 7250 models and FT 9700 models was not significantly different for all compositions (p<0.05). For amino acids highly correlated to crude protein, NIRS generally predicted them with a higher accuracy. The protein and 18 amino acid contents of 1290 whole lentil samples predicted from DA 7250 models on a dry basis were used as the phenotypic data in the later genome-wide association study (GWAS). GWAS was conducted using phenotypic data from four environments in Saskatchewan, Canada and 266,164 single nucleotide polymorphism (SNP) markers for 324 lentil accessions to identify significantly associated markers. A total of 85 SNP markers were identified to have significant associations with protein and/or 18 amino acids. Only one identical SNP marker (SLCU.2RBY.CHR7_524204079) significantly associated with Val was identified in two environments, and other SNPs were identified only in one environment. These identified SNPs could be studied further to find potential genomic regions or candidate genes. In summary, NIRS could be regarded as a highly promising method for rapid prediction of lentil seed protein and most amino acid contents, and GWAS had a great potential to dissect the genetic basis of these traits in cultivated lentils. Both NIRS technology and GWAS can facilitate breeding programs to enhance lentil seed protein content and quality.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 | 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) | 0,000 | 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 tête enseignante, 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 ».