Enhancing genomic selection by fitting large-effect SNPs as fixed effects and a genotype-by-environment effect using a maize BC1F3:4 population


Autoři: Dongdong Li aff001;  Zhenxiang Xu aff002;  Riliang Gu aff002;  Pingxi Wang aff002;  Demar Lyle aff002;  Jialiang Xu aff001;  Hongwei Zhang aff001;  Guogying Wang aff001
Působiště autorů: National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, P. R. China aff001;  Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing, P. R. China aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223898

Souhrn

The popularity of genomic selection (GS) has increased owing to its prospects in commercial breeding. It is necessary to enhance GS to increase its efficiency. In this study, a maize BC1F3:4 population, consisting of 481 families, was evaluated for days to anthesis in four environments, and genotyped with DNA chips including 55,000 single nucleotide polymorphisms (SNPs). This population was used to investigate whether GS could be enhanced by borrowing information from the genetic basis and genotype-by-environment (G × E) interaction. The results showed that: 1) fitting the top four large-effect SNPs as fixed effects could increase prediction accuracy, including three minor-effect SNPs explaining less than 10% phenotypic variance; 2) the increase of prediction accuracy when fitting large-effect SNPs as fixed effects was related to the decrease of genetic variance; 3) generally, the GS model fitting large-effect SNPs as fixed effects and G × E component enhanced GS. Therefore, we propose fitting large-effect markers as fixed effects and G × E effect for crop breeding projects in order to obtain accurately predicted phenotypic data and conduct efficient selection of desired plants.

Klíčová slova:

Genetic loci – Genome-wide association studies – Heredity – Maize – Plant breeding – Plant genomics – Quantitative trait loci – Variant genotypes


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2019 Číslo 10