## 4 Discussion  
This work demonstrates, for the first time, the relationship between asymmetry indices in winter wheat grain cuts and yield components. Index 2 is inversely related to grain vitreousness, i.e., with an increase in index 2, the endosperm loses strong bonds of starch grains with protein and becomes starchier, which means that the flour will no longer be of first-class quality. A change in index 5, which is responsible for the lower part of the grain cut, is associated with a direct strong change in gluten. However, for [PERSON]'s correlation analysis we used the averaged data of eight and up to one value for each feature, so a two-sided test of the reliability of the correlation coefficients showed significant relationships mainly at the 0.05 level (95% probability). It is recommended to increase the number of replicates of feature measurements describing yield components in order to check for reliable relationships at the 0.01 significance level. This study showed that our closely related accessions differed significantly in the length and width of the kernel. This could be explained by the differential expression of the GS3 gene, which significantly affects the length and weight of caryopsis (kernel), and to a lesser extent, their width [24,25]. The GW2 gene was found to alter the width and weight of caryopsis, and, to a lesser extent, increase their length by changing the width of the glume [26]. Similarly, the SW5 gene was reported to affect the width of caryopsis by regulating the size of the outer glume [27]. Our accessions also significantly differed in the parameters associated with yield components, which can be assumed to involve genes that are divided into several groups: transcription factors regulating spike development, inflorescence structure, and grain number; genes involved in the metabolism and signaling of growth regulators that affect plant structure; genes involved in carbohydrate metabolism that affect plant structure and grain yield [28,29]. Our study, although limited to a single growing season, uncovered significant correlations between kernel shape parameters and economically valuable traits in wheat. The \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Plant Trait**} & **Equation Quality R2;** & \multirow{2}{*}{**Linear Regression Equation**} & \multirow{2}{*}{**Deciphering the Terms of the Equation**} \\  & **SEE** & & & \\ \hline Plant height & 0.776; 3.6 & y = 926.745a \(-\) 920.030b + 72.919 & y—plant height, a—index 1, b—index 3, constant \\ \hline Plant height & 0.840; 3.2 & y = 1156.286a \(-\) 737.155b \(-\) 577.281c + 154.056 & y—plant height, a—index 1, b—index 3, c—index 2, constant \\ \hline Tillering rate & 0.843; 0.077 & y = 39.467a \(-\) 20.247b \(-\) 8.738 & y—tilling rate, a—index 3, b—index 2, constant \\ \hline Tillering rate & 0.923; 0.056 & y = 43.665a \(-\) 40.681b + 2.714c \(-\) 1.892 & y—tilling rate, a—index 3, b—index 2, c—roundness index, constant \\ \hline Gluten content & 0.795; 0.658 & y = 256.846a \(-\) 157.628b \(-\) 380.710c + 155.778 & y—gluten content, a—index 1, b—index 4, c—asymmetry index, constant \\ \hline \end{tabular}
\end{table} 
Table 2: Regression equations for predicting some wheat traits by 1-5: grain cut index, roundness index and grain asymmetry index. The quality of the equation is shown by the coefficient of determination (R2) and the standard error of estimation (SEE), and the criterion for including the index in the equation at F \(<\) 0.05 are indicated. indices showed low coefficients of variation (up to 10%), so it can be assumed that these traits are stable in resistant lines and cultivars [20; 21]. We assume that the application of these traits will be useful for variety identification (variety passport). The creation of such a card index of kernel shape indices would probably reduce the cost of variety identification by means of grain protein electrophoresis.