Biology subjects

Sabag, I.

Publications and source records attributed to Sabag, I..

3 recordsLinked to original sources

bioRxiv2024

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame

Sesame (Sesamum indicum) is an important oilseed crop with rising demand due to its high oil quality. To meet these future demands, there is an urgent need to develop and integrate new breeding strategies. While genomic resources have advanced genetic research in sesame, implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11 conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.

plant biology↗

bioRxiv2022

Multi-environment analysis enhances genomic prediction accuracy of agronomic traits in sesame

Sesame is an ancient oilseed crop containing many valuable nutritional components. Recently, the demand for sesame seeds and their products has increased worldwide, making it necessary to enhance the development of high-yielding cultivars. One approach to enhance genetic gain in breeding programs is genomic selection. However, studies on genomic selection and genomic prediction in sesame are limited. In this study, we performed genomic prediction for agronomic traits using the phenotypes and genotypes of a sesame diversity panel grown under Mediterranean climatic conditions over two growing seasons. We aimed to assess the accuracy of prediction for nine important agronomic traits in sesame using single- and multi-environment analyses. In single-environment analysis, genomic best linear unbiased prediction, BayesB, BayesC, and reproducing kernel Hilbert spaces models showed no substantial differences. The average prediction accuracy of the nine traits across these models ranged from 0.39-0.79 for both growing seasons. In the multi-environment analysis, the marker-by-environment interaction model, which decomposed the marker effects into components shared across environments and environment-specific deviations, improved the prediction accuracies for all traits by 15%-58% compared to the single-environment model, particularly when borrowing information from other environments was made possible. Our results showed that single-environment analysis produced moderate-to-high genomic prediction accuracy for agronomic traits in sesame. The multi-environment analysis further enhanced this accuracy by exploiting marker-by-environment interaction. We concluded that genomic prediction using multi-environmental trial data could improve efforts for breeding cultivars adapted to the semi-arid Mediterranean climate.

genetics↗

bioRxiv2021

Genome-wide association uncovers the genetic architecture of tradeoff between flowering date and yield components in sesame

Unrevealing the genetic makeup of crop morpho-agronomic traits is essential for improving yield quality and sustainability. Sesame (Sesamum indicum L.), one of the oldest oil-crops in the world, which despite its economical and agricultural importance, is an orphan crop-plant that undergone limited modern selection, thus, preserving wide genetic diversity. Here we harnessed this natural variation in a newly developed sesame panel (SCHUJI) to perform genome-wide association studies for morpho-agronomic traits under the Mediterranean climate conditions. Field-based phenotyping of the SCHUJI panel across two seasons exposed wide phenotypic variation for all traits. Using 20,294 single-nucleotide polymorphism markers, we detected 50 genomic signals associated with these traits. Major genomic region on LG2 was associated with flowering date and yield-related traits, exemplified the key role of the flowering date on productivity. Our results shed light on the genetic architecture of flowering date and its interaction with yield components in sesame and may serve as a basis for future sesame breeding programs in the Mediterranean basin.

plant biology↗