Diversity, Structure, and Marker-Trait Association Analysis of the Maize Recombinant Inbred Line Population

CHEN Jing-tang; HU Li-zong; ZHU Li-ying; GUO Jin-jie; ZHAO Yong-feng and HUANG Ya-qun

Diversity, Structure, and Marker-Trait Association Analysis of the Maize Recombinant Inbred Line Population

2011

CHEN Jing-tang; HU Li-zong; ZHU Li-ying; GUO Jin-jie; ZHAO Yong-feng and HUANG Ya-qun (College of Agronomy, Agricultural University of Hebei/Hebei Sub-Center of Chinese National Maize Improvement Center/Northern China Key Laboratory for Crop Germplasm Resources, Ministry of Education)

Association mapping has emerged as a new tool to elucidate complex quantitative trait loci in maize, but there are fewreports about systematic association analysis for the specific SSR markers with agronomic traits of interest in China.We investigated the morphological and genetic diversity and population structure for 76 maize recombinant inbredlines, and then association analysis were further performed between 48 simple sequence repeat loci and 17 morphologicaltraits, consisting of nine ear-related traits and eight other traits. The 48 SSR markers were screened out and furtherclassified into two groups including a group of loci in regions harboring reported quantitative trait loci that affect earshape and a group of markers distributing on the whole genome randomly. The result indicated that the population ofrecombinant inbred lines was structured, showing five subpopulations. Our association results revealed that therewere 82, 59, and 40 significant associations detected by K-test, logistic regression, and both analysis, respectively.When the 17 traits were considered separately, the significant associations between Q-SSRs and E-traits were raised to27.8%, whereas the other groups of combinations ranged between 2.3 and 6.3%. As the proportion of significantassociations is higher among the Q-SSR subset of markers and the subset of traits related to ear shape than those forall of the other combinations, we conclude that this approach is valid for establishing true positive marker-traitrelationships. Our results also demonstrated that association mapping could complement and enhance previous QTLinformation for marker-assisted selection.

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