A simple, cost-effective high-throughput image analysis pipeline improves genomic prediction accuracy for days to maturity in wheat

BACKGROUND

High-throughput phenotyping and genomic selection accelerate genetic gain in breeding programs by advances in phenotyping and genotyping methods. This study developed a simple, cost-effective high-throughput image analysis pipeline to quantify digital images taken in a panel of 286 Iran bread wheat accessions under terminal drought stress and well-watered conditions. The color proportion of green to yellow (tolerance ratio) and the color proportion of yellow to green (stress ratio) was assessed for each canopy using the pipeline. The estimated tolerance and stress ratios were used as covariates in the genomic prediction models to evaluate the effect of change in canopy color on the improvement of the genomic prediction accuracy of different agronomic traits in wheat.

RESULTS

The reliability of the high-throughput image analysis pipeline was proved by three to four times of improvement in the accuracy of genomic predictions for days to maturity with the use of tolerance and stress ratios as covariates in the univariate genomic selection models. The higher prediction accuracies were attained for days to maturity when both tolerance and stress ratios were used as fixed effects in the univariate models. The results of this study indicated that the Bayesian ridge regression and ridge regression-best linear unbiased prediction methods were superior to other genomic prediction methods which were used in this study under terminal drought stress and well-watered conditions, respectively.

CONCLUSIONS

This study provided a robust, quick, and cost-effective machine learning-enabled image-phenotyping pipeline to improve the genomic prediction accuracy for days to maturity in wheat. The results encouraged the integration of phenomics and genomics in breeding programs.

Compositions of the seed oil of the Borago officinalis from Iran

In order to investigate the composition of borage (Borago officinalis L.) seed oil, this research was performed under the field conditions at Shahriyar and Garmsar zones, Iran during the 2012 planting year. The oil yield of borage was 31.46% and 33.7% at Shahriyar and Garmsar zone, respectively, and nine and eight fatty acids were identified in the seed oil of borage at Shahriyar and Garmsar, respectively - palmitic, linoleic, stearic and γ-linolenic acids were dominant in the seed oil of borage from both zones. Unsaturated fatty acid content was more than the saturated fatty acids in both zones. The ratio of linoleic acid and α-linolenic acid in the borage cultivated at Shahriyar and Garmsar zones was 2.13 and 2.29. The fatty acid profile of Garmsar borage, oleic and oleic/linoleic acid ratio, increased. Locations with different ecological conditions resulted in changes in both seed oil content and fatty acid profile of borage.

Enhanced production of silymarin by Ag+ elicitor in cell suspension cultures of Silybum marianum

Cell suspension cultures of Silybum marianum L. Gaertn (Compositae) produce silymarin, a mixture of flavonolignans. In an attempt to increase cell growth and silymarin production, we exposed cell cultures to various levels of Ag+ (0.2, 0.4, 0.8, 1, and 2 mM) for different exposure times (12, 24, 48, 72, 144, and 216 h). A dramatic increase in cell growth was observed after 12 h in media supplemented with 0.2, 0.4, 0.8, and 1 mM Ag+, and the value in medium treated by 1 mM Ag+ after 72 h was 7.21 g, which was about two-fold that of the control (3.32 g). The highest silymarin production reached about 56 microg g(-1) DW, 24 h after treatment with Ag+ (0.8 mM), which was 30-fold that of the control. Silybin, isosilybin, silychristin, silydianin, and taxifolin were abundant flavonolignans. Ag+ in low concentrations is a positive elicitor for cell growth and silymarin production in cell suspension cultures of Silybum marianum.

Evaluation of screening techniques for heat tolerance in wheat

Cell membrane thermal stability, antioxidant activity, phenolics content, Paraquat tolerance and kernel weight were compared for their ability to identify heat tolerant genotypes. Four wheat (Triticum aestivum L.) genotypes, Kauz, MTRWA116, Opata and W7984 were used in this study, Kauz and MTRWA116 being thermotolerant and thermosensitive, respectively. Plants were exposed to high temperatures of 39 and 35 degrees C and then their measurements form different techniques were compared to each other and to the controls. The experiments were run several times to measure the repeatability of the measurements. Although the amount of phenolic compounds increased under stress condition, there was no significant difference between tolerant and susceptible varieties. Membrane thermal stability, antioxidant activity, phenolics content and Paraquat tolerance did not provide repeatable data. Nor did they discriminate among tolerant and susceptible genotypes. Kernel weight, however, varied between tolerant and susceptible genotypes. The results indicate that kernel weight is more suited for heat stress screening than other physioloical techniques evaluated in this study.