Elucidation of gene action and combining ability for productive tillering in spring barley
Notice bibliographique
Résumé
The purpose of the present study is to identify breeding and genetic peculiarities for productive tillering in spring barley genotypes of different origin, purposes of usage and botanical affiliation, as well as to identify effective genetic sources to further improving of the trait. There were created two complete (6 × 6) diallel crossing schemes. Into the Scheme I elite Ukrainian (MIP Tytul and Avhur) and Western European (Datcha, Quench, Gladys, and Beatrix) malting spring barley varieties were involved. Scheme II included awnless covered barley varieties Kozyr and Vitrazh bred at the Plant Production Institute named after V. Y. Yuriev of NAAS of Ukraine, naked barley varieties Condor and CDC Rattan from Canada, as well as awned feed barley variety MIP Myroslav created at MIW and malting barley variety Sebastian from Denmark. For more reliable and informative characterization of barley varieties and their progeny for productive tillering in terms of inheritance, parameters of genetic variation and general combining ability (GCA) statistical analyses of experimental data from different (2019 and 2020) growing seasons were conducted. Accordingly to the indicator of phenotypic dominance all possible modes of inheritance were detected, except for negative dominance in the Scheme I in 2020. The degree of phenotypic dominance significantly varied depending on both varieties involved in crossing schemes and conditions of the years of trials. There was overdominance in loci in both schemes in both years. The other parameters of genetic variation showed significant differences in gene action for productive tillering between crossing Schemes. In Scheme I in both years the dominance was mainly unidirectional and due to dominant effects. In the Scheme II in both years there was multidirectional dominance. In Scheme I compliance with the additive-dominant system was revealed in 2019, but in 2020 there was a strong epistasis. In Scheme II in both years non-allelic interaction was identified. In general, the mode of gene action showed a very complex gene action for productive tillering in barley and a significant role of non-genetic factors in phenotypic manifestation of the trait. Despite this, the level of heritability in the narrow sense in both Schemes pointed to the possibility of the successful selection of individuals with genetically determined increased productive tillering in the splitting generations. In Scheme I the final selection for productive tillering will be more effective in later generations, when dominant alleles become homozygous. In Scheme II it is theoretically possible to select plants with high productive tillering on both recessive and dominant basis. In both schemes the non-allelic interaction should be taken into consideration. Spring barley varieties Beatrix, Datcha, MIP Myroslav and Kozyr can be used as effective genetic sources for involvement in crossings aimed at improving the productive tillering. The results of present study contribute to further development of studies devoted to evaluation of gene action for yield-related traits in spring barley, as well as identification of new genetic sources for plant improvement.
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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 | 0,000 | 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) | 0,001 | 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 ».