Genetic (co)variation and accuracy of selection for resistance to viral mosaic disease and production traits in an inter-ecotypic switchgrass breeding population

Authors: Edme, Serge; Sarath, Gautam; Palmer, Nathan - Nate; YUEN, GARY - University Of Nebraska; Muhle, Anthony; Mitchell, Robert - Rob; Tatineni, Satyanarayana - Ts; Tobias, Christian

Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/7/2020
Publication Date: 11/2/2020
Citation: Edme, S.J., Sarath, G., Palmer, N.A., Yuen, G., Muhle, A.A., Mitchell, R., Tatineni, S., Tobias, C.M. 2020. Genetic (co)variation and accuracy of selection for resistance to viral mosaic disease and production traits in an inter-ecotypic switchgrass breeding population. Crop Science. 61(3):1652-1665. https://doi.org/10.1002/csc2.20392.
DOI: https://doi.org/10.1002/csc2.20392

Interpretive Summary: Several methods exist to analyze data collected from plant breeding experiments. An efficient model tests the parameters according to the goals set by the breeder. Two models were compared to test their accuracy and reliability to predict heritability of the traits and the breeding potential of individuals in a switchgrass population derived by crossing Kanlow and Summer. The data were collected over 3 generations. The first model used the 3-generation dataset without including the pedigree relationship in the analysis. The second model used the same dataset but integrated the pedigree of the 1,622 individuals to understand how the parents transmitted their genetic potential (good genes) to subsequent generations. Our breeding objectives were to select for increased biomass and ethanol yields, decreased lignin content and improved resistance to mosaic disease. The two models revealed the same amount of genetic variation, estimated with the same efficiency in this population. The 3 most heritable traits were biomass yield, ethanol yield, and mosaic disease. Moreover, the model that integrated the pedigree data most accurately estimated breeding values for biomass, lignin content, ethanol, and disease resistance over the other model. Based on the relationships among traits, the results further indicated that increasing biomass yield also increased lignin content slightly (an unfavorable response) and that decreasing lignin content increased ethanol production (a favorable response) and would slightly decrease disease resistance (an unfavorable response). We conclude that some compromise needs to be made to select for high biomass yield, high ethanol yield, and improved disease resistance in switchgrass.

Technical Abstract: Obtaining good accuracy and reliability of estimated breeding values is essential to increase the efficiency of a plant breeding program. Genetic variation was assessed for mosaic (caused by Panicum mosaic virus and treated as categorical or Virc and binary or Virb), yield (dry matter or DMY and predicted ethanol or Etoh), and quality traits (lignin content as Klason or KL and acid detergent or ADL) in a Summer x Kanlow switchgrass (Panicum virgatum L.) population using restricted maximum likelihood under a phenotypic (PBLUP) and multivariate animal (ABLUP) models. The ABLUP traces the pedigree back through three generations comprising 1622 halfsibs to integrate the relationship matrix in the evaluation. The PBLUP model analyzed the complete dataset without the pedigree. The two models were compared in their precision in assessing the genetic parameters (standard errors) and in estimating the breeding values (accuracy and reliability). The two models were similar in most aspects, allocating the highest h2i values to DMY (0.38±0.035 vs 0.41±0.035), Etoh (0.46±0.031 vs 0.42±0.033), and Virc (0.43±0.046 vs 0.37±0.047) and the lowest values (0.17±0.032 to 0.30±0.044) to KL, ADL, and Virb. The genetic correlations were always larger than the non-significant residual and phenotypic correlations, though of the same sign. An instance of resource-allocation competition was depicted in the case of the DMY-KL covariation based on rG and rE being of opposing signs. All six traits were found to be under intermediate or strong additive genetic control, whereby selecting for high-biomass genotypes will slightly increase lignin content and simultaneously impart mosaic tolerance. Mitigating the increase in lignin content will require including Etoh in a selection index based on its much stronger negative correlation (rG=-0.63) with lignin. In this population, the accuracy values ranged from 0.06 to 0.94 (PBLUP) and from 0.26 to 0.92 (ABLUP) and corresponding reliability values from 0.004 to 0.89 (PBLUP) and from 0.07 to 0.87 (ABLUP). However, looking at each trait separately, the ABLUP model improved the average accuracy and reliability of DMY (h2i =0.41) and Etoh (h2i=0.42) by 11% and those of the remaining traits (h2i =0.17-0.37) by 4-5% over the PBLUP model. The ABLUP was evidenced as being a better model over PBLUP, which itself is a valid analysis in the absence of a pedigree.