Rutkoski, J. | Search bioRxiv

bioRxiv2018

Estimation of realized rates of genetic gain and indicators for breeding program assessment

Routine estimation of the rate of genetic gain ({Delta}Gt) realized by a breeding program has been proposed as a means to monitor its effectiveness. Several methods of realized{Delta} Gt estimation have been utilized in other studies, but none have been objectively evaluated in a plant breeding context. Stochastic simulations of 80 rice (Oryza sativa) breeding programs over 28 years were done to generate data used to evaluate five methods of realized{Delta} Gt estimation in terms of error, precision, efficiency and correlation between true and predicted annual mean breeding values. Two indicators of{Delta} Gt, the expected{Delta} Gt and the average number of equivalent complete generations (EqCg), were described and evaluated. At best, estimates of realized{Delta} Gt were over or underestimated by 15% and 27% when considering all 28 years and the past 15 years of breeding respectively. The best methods were the control population, estimated breeding value, and ERA trial methods. Among these, correlations between true and estimated{Delta} Gt were at best 0.59, indicating that these methods cannot very accurately rank breeding programs in terms of realized{Delta} Gt. The expected{Delta} Gt and the average EqCg were shown to be useful indicators for determining if a non-zero genetic gain is expected. Determining which of the three best realized{Delta} Gt estimation methods evaluated, if any, would be appropriate for any given breeding program should be done with careful consideration of the objectives, resources, seed stocks, and structure of the data available.

genetics↗

bioRxiv2018

Use of Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype x environment (G x E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program, International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregon, Mexico over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker-and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker-and pedigree-based models were marginal when correcting for days to heading. Our results demonstrates the potential of hyperspectral imaging in predicting grain yield within a multi-environment context, it also supports further studies on integration of hyperspectral reflectance phenotyping in breeding programs.

genomics↗