Genetic analysis of wheat ear architecture in F2 hybrid of tetraploid wheats Triticum aethiopicum and T. carthlicum and its computer phenotyping

Abstract

A comprehensive description of plant phenotypes of certain taxa is an important task when describing genera and species, as well as when setting their natural taxonomies. The development of modern technologies of effective phenotyping makes it possible to obtain a large amount of data with a quantitative and/or qualitative description of various traits in plants, mainly based on the analysis of their digital images. The study compared the results of the F2 hybrids assessment - visually and using machine learning methods - of two endemic tetraploid (2n = 4x = 28) wheat species which are Ethiopian wheat (Triticum aethiopicum Jakubz.) and Kartalian or Dika wheat (T. carthlicum Nevski). In the latter case, it is proposed to use the method of a mixture of Gaussian (normal) distributions in plant morphometry in order to identify groups that differ in character values. Most taxonomically important (species-specific) traits are controlled oligogenically and have a clear phenotypic manifestation, so hybridological analysis was an indispensable and basic type of analysis for subsequent detailed phenotyping of wheat spikes using machine-learning methods. According to a number of criteria, the estimates of patterns of inheritance obtained by different methods coincide. Based on the conducted research, we can state that the trait "tetraaristatum" (the presence of awns on both flower and spike glumes) is species-specific (taxonomically important) for T. carthlicum and it can be effectively used for taxonomic purposes both in carrying out hybridological analysis and in experiments using machine learning. Such a species-specific character is the "character (type) of awnedness" for T. aethiopicum. Our study demonstrates that a combination of automatic phenotyping methods and a model of a mixture of Gaussian distributions can, in principle, lead to an automatic analysis of the allocation of classes in F2 hybrids. It allows, in turn, to detect the presence of genes associated with species-specific traits of wheat plants. Further, the improvement of the applied artificial intelligence (AI) algorithms is required.

Plant phenotyping relevance

コムギ穂の形態形質を対象に、画像に基づく機械学習フェノタイピングとガウス混合モデルを提案・適用しており、表現型の自動抽出・分類が研究の中心である。

Code and data availability

The paper's supplementary materials (Supplementary Tables S1–S3 and Figure S1) contain the paper-specific phenotyping data: species-specific trait descriptions, the 19 spike morphometric characters per projection, and the Gaussian mixture model splitting results (means, variances, group sizes, χ² values). The full text