Genome wide analysis of flowering time trait in multiple environments via high-throughput genotyping technique in Brassica napus L.

Lun Li,Longjiang Yu,Yan Long,Jacqueline Batley,Jinling Meng,Libin Zhang,Maoteng Li,Jessica Dalton-Morgan,Paul Hohenlohe

doi:10.1371/journal.pone.0119425

Abstract

The prediction of the flowering time (FT) trait in Brassica napus based on genome-wide markers and the detection of underlying genetic factors is important not only for oilseed producers around the world but also for the other crop industry in the rotation system in China. In previous studies the low density and mixture of biomarkers used obstructed genomic selection in B. napus and comprehensive mapping of FT related loci. In this study, a high-density genome-wide SNP set was genotyped from a double-haploid population of B. napus. We first performed genomic prediction of FT traits in B. napus using SNPs across the genome under ten environments of three geographic regions via eight existing genomic predictive models. The results showed that all the models achieved comparably high accuracies, verifying the feasibility of genomic prediction in B. napus. Next, we performed a large-scale mapping of FT related loci among three regions, and found 437 associated SNPs, some of which represented known FT genes, such as AP1 and PHYE. The genes tagged by the associated SNPs were enriched in biological processes involved in the formation of flowers. Epistasis analysis showed that significant interactions were found between detected loci, even among some known FT related genes. All the results showed that our large scale and high-density genotype data are of great practical and scientific values for B. napus. To our best knowledge, this is the first evaluation of genomic selection models in B. napus based on a high-density SNP dataset and large-scale mapping of FT loci.

Highlights

Rapeseed (Brassica napus), as one of the leading sources of livestock feed, vegetable oil and biofuel, is the second most prominent oil seed crop in the world, supplying approximately 62.4 million tonnes of oilseed production per year
To verify that our genotype data is sufficient for genomic prediction of the flowering time (FT) trait in B. napus and to evaluate the genomic prediction models two conventional genomic prediction models regression best linear unbiased prediction (RR-BLUP) and reproducing kernel Hilbert spaces (RKHS) were first applied to SNP chip and FT trait data collected across ten environments
High accuracies were achieved for both methods (0.737 and 0.760 respectively), and the better performance of RKHS indicated that non-additive effects exist

Summary

Introduction

Rapeseed (Brassica napus), as one of the leading sources of livestock feed, vegetable oil and biofuel, is the second most prominent oil seed crop in the world, supplying approximately 62.4 million tonnes of oilseed production per year. China is the top rapeseed oil producer in the world, yielding about 4.8 milion tonnes of oil each year (2009–2011, http://faostat.fao.org/). Rapeseed was planted mainly as a rotational crop with rice, maize, cotton or some vegetables in China [1]. Recent efforts have been made in mapping genomic locations related with agronomic traits (including FT) in B. napus [2,3,4,5,6,7,8], which allows the breeders for the potential of marker-assisted selection (MAS) in crop breeding. A comprehensive and unbiased scan of the genome is imperative

Methods

Results

Conclusion