Dissecting the Genetic Architecture of Biofuel-Related Traits in a Sorghum Breeding Population. Issue 12 (1st December 2020)

• Record Type: Journal Article
• Authors: Ishimori, Motoyuki; Takanashi, Hideki; Hamazaki, Kosuke; Atagi, Yamato; Kajiya-Kanegae, Hiromi; Fujimoto, Masaru; Yoneda, Junichi; Tokunaga, Tsuyoshi; Tsutsumi, Nobuhiro; Iwata, Hiroyoshi
• Is Part Of: G3. Volume 10:Issue 12(2020)
• Page Start: 4565
• Page End: 4577
• Publication Date: 2020-12-01
• Subjects: sorghum -- genomic prediction -- Bayesian alphabet -- breeding population -- GWAS -- GenPred -- Shared data resources; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1534/g3.120.401582
• Languages: English
• ISSNs: 2160-1836

Abstract

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 3 rd generation S0 individuals. Of over 200 ancestral inbred accessions in the base population, only 13 founders contributed to the 3 rd 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 .