Dissecting the Genetic Architecture of Biofuel-Related Traits in a Sorghum Breeding Population

Abstract

In sorghum [Sorghum bicolor (L.) Moench], hybrid cultivars for the biofuel industry are desired. Along with selection based on testcross performance, evaluation of the breeding population per se is also important for the success of hybrid breeding. In addition to additive genetic effects, non-additive (i.e., dominance and epistatic) effects are expected to contribute to the performance of early generations. Unfortunately, studies on early generations in sorghum breeding programs are limited. In this study, we analyzed a breeding population for bioenergy sorghum, which was previously developed based on testcross performance, to compare genomic selection models both trained on and evaluated for the per se performance of the 3rd generation S0 individuals. Of over 200 ancestral inbred accessions in the base population, only 13 founders contributed to the 3rd generation as progenitors. Compared to the founders, the performances of the population per se were improved for target traits. The total genetic variance within the S0 generation progenies themselves for all traits was mainly additive, although non-additive variances contributed to each trait to some extent. For genomic selection, linear regression models explicitly considering all genetic components showed a higher predictive ability than other linear and non-linear models. Although the number and effect distribution of underlying loci was different among the traits, the influence of priors for marker effects was relatively small. These results indicate the importance of considering non-additive effects for dissecting the genetic architecture of early breeding generations and predicting the performance per se.

Genomic‐assisted breeding for climate‐smart coffee

“…For the posterior density, we ran the Markov chain for 100,000 time steps, with a burn‐in of 10,000. For estimating genetic variance components, we calculated genotypic value in each MCMC sample after burn‐in (Alves et al., 2019; Ishimori et al., 2020; Lehermeier et al., 2017) as: $${\hat g_m} = \mathop \sum \limits_{z = 1}^L {m_{iz}}{\hat u_z}\;$$, where $${\hat g_m}$$is the estimated genetic value of the i th individual for the additive, dominant, or epistatic effects, $${\hat u_z}$$ is the estimated marker effect for a given genetic parametrization (additive or nonadditive) for the marker z , and $${m_{iz}}$$ is the marker score. The total genetic variance and variance components ($$\sigma _A^2,\;\sigma _D^2,\sigma _{AA}^2,\sigma _{AD}^2$$) were calculated as the variance of estimated values across all genotypes in each MCMC sample.…”