|Agrama, H - ARKANSAS RREC|
|Lee, Fleet - ARKANSAS RREC|
Submitted to: Rice Technical Working Group Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: December 15, 2005
Publication Date: November 2, 2006
Citation: Agrama, H.A., Eizenga, G.C., Lee, F.N. 2006. Linkage disequilibrium and mapping association of yield and disease traits in rice. Rice Technical Working Group Meeting Proceedings, February 29-March 1, 2006, Houston, TX. CDROM. Technical Abstract: Rice (Oryza sativa L.) genetic mapping often involves the development, genotyping, and phenotyping of doubled haploid, recombinant inbred or advanced backcross populations derived from a cross between somewhat diverse parents. This type of mapping population shows extreme disequilibrium between linked loci. Population-based genetic association studies are another approach available for mapping the relevant genes and identifying the variants that control economically important traits. The objective of this research was to use blast resistant rice accessions to determine the utility of population structure analysis, linkage disequilibrium (LD), and mapping association of yield and blast [Magnaporthe grisea (T.T. Herbert) Yaegashi & Udagawa] traits in evaluating rice germplasm. One hundred twenty simple sequence repeat (SSR) markers located across the twelve rice chromosomes were selected from the core set of 189 markers for use in this study. Associations between SSR markers and phenotypic traits were investigated in a collection of 103 rice accessions. Ninety-two of these accessions were introduced from seven countries, including five regions of China and the remaining eleven were U.S. cultivars. All accessions were evaluated for the complex traits yield, kernel width, kernel length, kernel length/width ratio, 1000-kernel weight and blast in replicated trials. Regression of these traits on individual marker data using TASSEL software disclosed marker-trait associations. The SSR markers were highly polymorphic across all germplasm accessions. To infer population structure and assign ancestries, a mode-based clustering algorithm implemented in the program Structure v2.1 was used. Population structure analysis identified eight main clusters at K = 7 and these clusters generally corresponded to the major geographic regions that the accessions originated from, including the U.S. cultivars. The accessions were classified by UPGMA and algorithm neighbor joining tree based on the genetic similarity matrix. Diversity clustering of all genotypes generally agreed with the population structure classification. In the structure of several populations, individuals had partial membership in multiple clusters. These accessions might have a complex breeding history involving intercrossing and introgression between germplasm groups, overlaid with strong selection pressure for agronomic and quality characteristics. LD patterns and distributions are of fundamental importance for genome-wide mapping associations. LD between pairs of SSR loci was estimated using the squared allele-frequency correlation (r2). Between linked markers, LD decreased in centiMorgans (cM) with decreased distance between loci. A considerable drop in LD decay values between 15 to 20 cM was observed, suggesting that it should be possible to achieve resolution down to the 15 cM level. Association between markers and traits was examined using significance of marker-trait correlations in comparison with associations found in other QTL studies. Many of the associated markers were located in regions where earlier QTL were found. The decline of LD within relatively short distances in the genome, makes fine mapping yield and blast traits possible. Population structure and diversity analyses will enhance rice breeding programs by allowing breeders to choose parents from a wide variety of backgrounds for incorporation into their breeding programs. These results indicate association mapping approaches in rice are a viable alternative to classical QTL approaches using mapping populations. The application of association mapping to rice breeding appears to be a promising approach to overcome the limitations of conventional linkage mapping.