Page Banner

United States Department of Agriculture

Agricultural Research Service


item Hyten, David
item Song, Qijiang
item Costa, Jose
item Shoemaker, Randy
item Cregan, Perry

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 1/2/2005
Publication Date: 1/2/2005
Citation: Hyten, D.L., Song, Q., Costa, J., Shoemaker, R.C., Cregan, P.B. 2005. Different patterns of linkage disequilibrium around three disease loci in four soybean populations. Meeting Abstract. Plant & Animal Genome XIII Abstract. P. 432.

Interpretive Summary:

Technical Abstract: Linkage disequilibrium (LD) is the "non-random association of alleles" and can be utilized through association analysis to discover quantitative trait loci (QTL). If a population has extensive LD few markers will be needed to scan the whole genome for QTL but the positions of these QTL will not be well defined. Conversely, in a population with limited LD, genetic factors can be fine mapped. Our goal was to provide an initial assessment of LD around three disease loci in four distinct soybean populations: Glycine soja, the wild soybean; Asian G. max; N. Am. cultivar ancestors; and N. Am. public cultivars released in the 1980s. Multiple fragments throughout three 300+kb regions were sequenced for common SNPs (freq. >0.10) in the four soybean populations. The three regions were located on the soybean linkage groups A2, G, and J surrounding the Rhg4, rhg1, and Rps2 disease resistant loci, respectively. The extent and structure of LD surrounding the three disease resistant loci were similar in the Glycine soja population while the other three populations exhibited different patterns of LD with extensive LD throughout the Rhg4 region and very little LD throughout the Rps2 region in the other three populations. The variability of LD structure and extent around these three disease loci complicates the prospects of applying whole genome genetic association analysis without first assaying the genome for LD structure. Such an analysis will assist in determining which regions need low marker density and which regions need high marker density for a thorough genome scan.

Last Modified: 10/19/2017
Footer Content Back to Top of Page