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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Plant Sci. | doi: 10.3389/fpls.2019.01129

Enhancing genomic selection with quantitative trait loci and nonadditive effects revealed by empirical evidence in maize

 Xiaogang Liu1, Hongwu Wang1, Xiaojiao Hu1, Kun Li1, Zhifang Liu1, Yujin Wu1 and Changling Huang1*
  • 1Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, China

Genomic selection (GS), a tool developed for molecular breeding, is used by plant breeders to improve breeding efficacy by shortening the breeding cycle and to facilitate the selection of candidate lines for creating hybrids without phenotyping in various environments. Association and linkage mapping have been widely used to explore and detect candidate genes in order to understand the genetic mechanisms of quantitative traits. In the current study, phenotypic and genotypic data from three experimental populations, including data on six agronomic traits (e.g., plant height, ear height, ear length, ear diameter, grain yield per plant, and hundred-kernel weight), were used to evaluate the effect of trait-relevant markers (TRMs) on prediction accuracy estimation. Integrating information from mapping into a statistical model can efficiently improve prediction performance compared with using stochastically selected markers to perform GS. The prediction accuracy can reach plateau when a total of 500 to 1000 TRMs are utilized in GS, regardless of whether association and linkage mapping are performed using data derived from the whole population or from only the training set. The prediction accuracy can be significantly enhanced by including nonadditive effects and TRMs in the GS model when genotypic data with high proportions of heterozygous alleles and complex agronomic traits with low heritability are used to perform GS. In addition, taking information on population structure into account can slightly improve prediction performance when the genetic relationship between the training and testing sets is influenced by population stratification due to different allele frequencies. In conclusion, GS is a useful approach for prescreening candidate lines, and the empirical evidence provided by the current study for TRMs and nonadditive effects can inform plant breeding and in turn contribute to the improvement of selection efficiency in practical GS-assisted breeding programs.

Keywords: Maize, genomic selection, association and linkage mapping, trait-relevant marker, nonadditive effect, population structure

Received: 06 Jun 2019; Accepted: 15 Aug 2019.

Copyright: © 2019 Liu, Wang, Hu, Li, Liu, Wu and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Changling Huang, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China,