The Intragenesis and Synthetic Biology Approach towards Accelerating Genetic Gains on Strawberry: Development of New Tools to Improve Fruit Quality and Resistance to Pathogens

Under climate change, the spread of pests and pathogens into new environments has a dramatic effect on crop protection control. Strawberry (Fragaria spp.) is one the most profitable crops of the Rosaceae family worldwide, but more than 50 different genera of pathogens affect this species. Therefore, accelerating the improvement of fruit quality and pathogen resistance in strawberry represents an important objective for breeding and reducing the usage of pesticides. New genome sequencing data and bioinformatics tools has provided important resources to expand the use of synthetic biology-assisted intragenesis strategies as a powerful tool to accelerate genetic gains in strawberry. In this paper, we took advantage of these innovative approaches to create four RNAi intragenic silencing cassettes by combining specific strawberry new promoters and pathogen defense-related candidate DNA sequences to increase strawberry fruit quality and resistance by silencing their corresponding endogenous genes, mainly during fruit ripening stages, thus avoiding any unwanted effect on plant growth and development. Using a fruit transient assay, GUS expression was detected by the two synthetic FvAAT2 and FvDOF2 promoters, both by histochemical assay and qPCR analysis of GUS transcript levels, thus ensuring the ability of the same to drive the expression of the silencing cassettes in this strawberry tissue. The approaches described here represent valuable new tools for the rapid development of improved strawberry lines.

Prediction of genetic gains for breeding objective traits and designing selection schemes for Washera and Gumuz indigenous sheep

In Ethiopia,there are 32.85 millions of sheep,more than 99 % of which are indigenous.However,the productivity of local sheep under traditional production system is low with high mortality of sheep.There are two ways of improving performance of sheep and goats,namely improving the enviroment of animals and/or improving there genetic potential.The aim of this study was to predict genetic gains of breedingobjective traits and select the best sheep selection scheme for Gumuz andWashera sheep. Body size(six month weight and yearling weight) and litter size were breeding objective traits identified by own flock animal ranking experiment and personal interview. Deterministic approach of ZPLAN computor program is used for modeling input parametres of Gumuz and Washera sheep and simulating breeding plans using gene flow method and selection index procedures. One-tier cooperative sheep breeding scheme were proposed whereby ram exchange between and within villages is the main means of genetic dissimination. Genetic gains predicted for six month weight of Gumuz and Washera sheep were 0.43 and 0.55 kg,respectively. Genetic gains predicted for yearling weight of Gumuz and Washera sheep were 0.55 and 0.60 kg,respectively. Genetic gains predicted for litter size of Gumuz and Washera sheep were 0.08 and 0.09 lambs,respectively. The lower rate of inbreeding, the higher monetary genetic gain for aggregate genotype,higher return to investmnet and higher profit/ewe/year were quality measures of breeding program considered to prefer scheme 4 for both Gumuz and Washera sheep.Hence,for both Gumuz and Washera sheep populations a sheep selection scheme designed with 15 % selection proportion and one year ram use for breeding was recommended. Special emphasis need to be given to yearling weight with higher predicted genetic response and higher percentage return to investment.

Kompetitive Allele Specific PCR (KASP) Markers for Potato: An Effective Tool for Increased Genetic Gains

Potato virus Y (PVY) and Phytophthora infestans (Mont.) de Bary that causes potato late blight (LB), pose serious constraints to cultivated potatoes due to significant yield reduction, and phenotyping for resistance remains challenging. Breeding operations for vegetatively propagated crops can lead to genotype mislabeling that, in turn, reduces genetic gains. Low-density and low-cost molecular marker assessment for phenotype prediction and quality control is a viable option for breeding programs. Here, we report on the development of kompetitive allele specific PCR (KASP) markers for LB and PVY resistance, and for routine quality control assessment of different breeding populations. Two KASP markers for LB resistance and two for PVY Ryadg were validated with an estimated assay power that ranged between 0.65 and 0.88. The developed QC KASP markers demonstrated the capability of discriminating tetraploid calls in breeding materials, including full-sibs and half-sibs. Routine implementation of the developed markers in a breeding program would assist with better allocation of resources and enable precise characterization of breeding material, thereby leading to increased genetic gains.

Assessment of Genetic Gain Trends for Yield in IRRI Rice Varieties in the Philippines Using “era” Trial Studies and Implications for Future Rice Breeding

Rice is a staple crop for 3.5 billion people in the world. To meet the challenges of the rice production for food security and demand due to population increase, yield improvement due to a rice variety’s genetic characteristics is imperative. Two studies presented in this paper were undertaken at the International Rice Research Institute (IRRI) in the Philippines, to assess genetic gains for yield in rice varieties bred over the past 50 years. These studies are called as “Era” studies as the varieties used for trials were released during long and distinct periods. Due to the differences in time periods of studies, varieties and locations, the studies were treated separately so as to not to compromise the data analyses. The studies demonstrated that IRRI developed varieties have achieved genetic gains and levels of genetic gains were dependent on correction or otherwise for maturities. In Study 1, the highest level of genetic gain was 0.70% at about a 23 kg ha-1 annual yield increase when not corrected for maturity followed by a genetic gain of 0.62% when corrected for maturity. In Study 2, the highest level of genetic gain was 0.74% at about a 19 kg ha-1 annual yield increase when corrected for maturity followed by 0.66% genetic gain when not corrected for maturity. Implications for breeding programs are discussed, however, the studies were not intended to compare genetic gains achieved through different breeding methods nor to compare genetic gains achieved using plot trials versus realized genetic gains for crops grown under farmers’ management.

Traits that define yield and genetic gain in East African highland banana breeding

East African highland bananas (Musa spp. AAA group) are an important staple in the Great Lakes region of East Africa. Their production has declined due to pests and diseases. Breeding for host plant resistance is a sustainable option for addressing this challenge. Understanding the relationships between growth parameters and bunch weight (i.e., yield) is crucial to guide breeding efforts for this crop. We investigated cause-effect relationships, through path analysis, in bunch weight of East African highland banana derived hybrids, their parents and grandparents. These family structures were planted in a 7 × 8 rectangular lattice design, replicated twice. Genetic gains for bunch weight (kg plant−1) and yield potential (t ha−1 year−1) were estimated. Significant increases of bunch weight and yield potential were noted from the landrace triploid germplasm, their derived primary tetraploid hybrids and secondary triploid bred-germplasm. Path analysis revealed that fruit length, circumference and number, number of hands and plant cycle number had a direct positive effect on the bunch weight. Days to fruit filling, days to maturity and index of non-spotted leaves had indirect effects on bunch weight. The average genetic gains for bunch weight and yield potential were 1.4% and 1.3% per year, respectively. This is the first report about genetic gains in banana breeding. Our findings may be useful for assessing progress and directing future breeding efforts in banana breeding.

Genetic Trends Estimation in IRRIs Rice Drought Breeding Program and Identification of High Yielding Drought-Tolerant Lines

BackgroundEstimation of genetic trends using historical data is an important parameter to check the success of the breeding programs. The estimated genetic trends can act as a guideline to target the appropriate breeding strategies and optimize the breeding program for improved genetic gains. In this study, 17 years of historical data from IRRI’s rice drought breeding program was used to estimate the genetic trends and assess the success of the breeding program. We also identified top-performing lines based on grain yield breeding values as an elite panel for implementing future population improvement-based breeding schemes.ResultsA two-stage approach of pedigree-based mixed model analysis was used to analyze the data and extract the breeding values and estimate the genetic trends for grain yield under non-stress, drought, and in combined data of non-stress and drought. Lower grain yield values were observed in all the drought trials. Heritability for grain yield estimates ranged between 0.20-0.94 under the drought trials, and 0.43-0.83 under non-stress trials. Under non-stress conditions the genetic gain of 0.44% (21.20 kg/ha/year) for genotypes and 0.17 % (7.90 kg/ha/year) for checks was observed. The genetic trend under the drought conditions exhibited a positive trend with the genetic gain of 0.11% (1.98kg/ha/year) for genotypes and 0.55% (9.52kg/ha/year) for checks. For combined analysis showed a genetic gain of 0.39% (12.13 kg/ha/year) for genotypes and 0.60% (13.69 kg/ha/year) for checks was observed. For elite panel selection, 200 promising lines were selected based on higher breeding values for grain yield and prediction accuracy of >0.40. The breeding values of the 200 genotypes formulating the core panel ranged between 2366.17 and 4622.59 (kg/ha).ConclusionsA positive genetic rate was observed under all the three conditions; however, the rate of increase was lower than the required rate of 1.5% genetic gain. We propose a recurrent selection breeding strategy within the elite population with the integration of modern tools and technologies to boost the genetic gains in IRRI’s drought breeding program. The elite breeding panel identified in this study forms an easily available and highly enriched genetic resource for future recurrent selection programs to boost the genetic gains.

Using UAV-based hyperspectral imaging and functional regression to assist in predicting grain yield and related traits in wheat under heat-related stress environments for the purpose of stable yielding genotypes

Quantifying certain physiological traits under heat-stress is crucial for maximizing genetic gain for wheat yield and yield-related components. In-season estimation of different physiological traits related to heat stress tolerance can ensure the finding of germplasm, which could help in making effective genetic gains in yield. However, estimation of those complex traits is time- and labor-intensive. Unmanned aerial vehicle (UAV) based hyperspectral imaging could be a powerful tool to estimate indirectly in-season genetic variation for different complex physiological traits in plant breeding that could improve genetic gains for different important economic traits, like grain yield. This study aims to predict in-season genetic variations for cellular membrane thermostability (CMT), yield and yield related traits based on spectral data collected from UAVs; particularly, in cases where there is a small sample size to collect data from and a large range of features collected per sample. In these cases, traditional methods of yield-prediction modeling become less robust. To handle this, a functional regression approach was employed that addresses limitations of previous techniques to create a model for predicting CMT, grain yield and other traits in wheat under heat stress environmental conditions and when data availability is constrained. The results preliminarily indicate that the overall models of each trait studied presented a good accuracy compared to their data’s standard deviation. The yield prediction model presented an average error of 13.42%, showing the function-on-function algorithm chosen for the model as reliable for small datasets with high dimensionality.