High-density genetic linkage map construction and QTL mapping of grain shape and size in the wheat population Yanda1817 × Beinong6.

Qiuhong Wu ,Chen Dang,Yong Liang,Dong Zhang,Ling Wang,Yu Cui,Hao Wang,Yan Zhang,Jie Li,Yumei Xiao ,Xin Zhao ,Shuai Ouyang ,Minsheng You ,Chunlong Yuan ,Ping Lü ,Lixin Wang ,Huiru Peng ,Lili Wang ,Deyun Zhang ,Meihua Yu ,Joan C Han ,Yongxing Chen ,Shumin Zhou ,Yinlian Huang ,Jiaojiao Chen ,Guoxin Wang ,Fei Lin ,Jirui Wang ,Jingzhong Xie ,Ming‐Cheng Luo ,Zhenzhong Wang ,Delin Li ,Jinxia Qin ,Qixin Sun ,Zhiyong Liu

doi:10.1371/journal.pone.0118144

Abstract

High-density genetic linkage maps are necessary for precisely mapping quantitative trait loci (QTLs) controlling grain shape and size in wheat. By applying the Infinium iSelect 9K SNP assay, we have constructed a high-density genetic linkage map with 269 F 8 recombinant inbred lines (RILs) developed between a Chinese cornerstone wheat breeding parental line Yanda1817 and a high-yielding line Beinong6. The map contains 2431 SNPs and 128 SSR & EST-SSR markers in a total coverage of 3213.2 cM with an average interval of 1.26 cM per marker. Eighty-eight QTLs for thousand-grain weight (TGW), grain length (GL), grain width (GW) and grain thickness (GT) were detected in nine ecological environments (Beijing, Shijiazhuang and Kaifeng) during five years between 2010–2014 by inclusive composite interval mapping (ICIM) (LOD≥2.5). Among which, 17 QTLs for TGW were mapped on chromosomes 1A, 1B, 2A, 2B, 3A, 3B, 3D, 4A, 4D, 5A, 5B and 6B with phenotypic variations ranging from 2.62% to 12.08%. Four stable QTLs for TGW could be detected in five and seven environments, respectively. Thirty-two QTLs for GL were mapped on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6B, 7A and 7B, with phenotypic variations ranging from 2.62% to 44.39%. QGl.cau-2A.2 can be detected in all the environments with the largest phenotypic variations, indicating that it is a major and stable QTL. For GW, 12 QTLs were identified with phenotypic variations range from 3.69% to 12.30%. We found 27 QTLs for GT with phenotypic variations ranged from 2.55% to 36.42%. In particular, QTL QGt.cau-5A.1 with phenotypic variations of 6.82–23.59% was detected in all the nine environments. Moreover, pleiotropic effects were detected for several QTL loci responsible for grain shape and size that could serve as target regions for fine mapping and marker assisted selection in wheat breeding programs.

Highlights

Wheat is the third highest producing cereal crop after maize and rice, and is the leading sources of vegetable protein in human food
Field trials were conducted at Beijing, Shijiazhuang and Kaifeng under different agro-climatic conditions for five continuous years (2010–2014 in 9 total environments) to evaluate thousand grain weight (TGW), grain length (GL), grain width (GW) and grain thickness (GT) variation amongst the two parents (Yanda1817 and Beinong6) and the recombinant inbred lines (RILs) populations
The frequency distributions of the investigated traits reveled continuous variations and transgressive segregation in the RIL populations, suggesting that the phenotypic data of TGW, GL, GW and GT are normally distributed and that the traits are controlled by multiple loci

Summary

Introduction

Wheat is the third highest producing cereal crop after maize and rice, and is the leading sources of vegetable protein in human food. Wheat is a staple food used to make flour for different kinds of products. Grain weight and size are the targets for breeding, not merely because they are the major components of grain yield, and due to their impacts on milling and baking quality [1]. Grain weight and size are complex quantitative traits controlled by a number of genes and significantly influenced by the environment. The grain weight and size can be divided into a number of components including thousand grain weight (TGW), grain length (GL), grain width (GW), and grain thickness (GT), etc. Previous studies have proved that TGW has high heritability values and is phenotypically the most stable yield component [8]

Methods

Results

Conclusion