Lihua Liu^1,2 , Lixin Wang¹ , Ji Yao² , Yonglian Zheng² , Changping Zhao¹

1.Beijing Engineering and Technique Research Center of Hybrid Wheat, Beijing Academy of Agricultural and Forestry Sciences, Beijing, 100097, P.R. China
2.National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, P.R. China
Author    Correspondence author
Molecular Plant Breeding, 2010, Vol. 1, No. 5   doi: 10.5376/mpb.2010.01.0005
Received: 26 Jul., 2010    Accepted: 20 Oct., 2010    Published: 30 Oct., 2010

Liu et al., 2010, Association mapping of six agronomic traits on chromosome 4A of wheat (Triticum aestivum L.), Molecular Plant Breeding Vol.1 No.5 (doi: 10.5376/mpb.2010.01.0005)

Association mapping is a powerful approach to identify associations between traits of interest and genetic markers. In this study, 103 wheat germplasm accessions from China were genotyped using 76 SSR markers and 40 EST-SSR markers. The phenotyping of plant height, spike length, spikelets per spike, spikelets density, grains per spike and thousand-kernel weight were carried out in three locations for three years. Six subpopulations were identified among these accessions by population structure analysis based on 49 SSR and 40 EST-SSR markers. Linkage disequilibrium (LD) on chromosome 4A extended up to ~3 cM with r2=0.054. Based on the mixed linear model considering population structure and relative kinship, a total of 10 SSR markers (p<0.01) on chromosome 4A were significantly associated with six agronomic traits, and six of them were associated with multiple traits. Some of the associated markers were in agreement with previous quantitative trait loci (QTL) analysis. This study demonstrated that association mapping can be successfully applied in wheat breeding context for detection of marker-traits associations. Furthermore, association mapping can enhance previous QTL information and provide additional QTL information for marker-assisted selection.

Association mapping; Population structure; Linkage disequilibrium; Mixed linear model