Plant & Animal Genomes XV Conference

January 13-17, 2007
Town & Country Convention Center
San Diego, CA

Linkage Disequilibrium And Association Mapping In Wheat Improvement

Mark E. Sorrells¹ , Shiaoman Chao²

¹  Department of Plant Breeding & Genetics, Cornell University, Ithaca, NY, USA

²  USDA-ARS Biosciences Research Laboratory, 1605 Albrecht Blvd, Fargo, ND, USA

Knowledge of the level of genetic diversity and historical relationships among elite germplasm can further the exploitation of genetic variation in wheat. Linkage disequilibrium (LD), or non-random association of alleles at adjacent loci within a population is the basis for association mapping strategies. The extent of LD can vary substantially between populations with different evolutionary histories. Association analysis can be used to integrate molecular tools and information into conventional breeding programs in a way that facilitates MAS of parents and segregating populations. Breeding programs are dynamic, complex genetic entities that require frequent evaluation of marker / phenotype relationships. In contrast to most QTL mapping studies involving biparental cross populations, association mapping can be conducted directly on the breeding material, thus permitting direct inference from data analysis to breeding materials. Phenotypic variation is observed for most traits of interest and marker polymorphism is higher than in biparental populations. Genotypic data can be combined with routine variety trial evaluations to provide phenotypic data for low heritability traits. Most importantly for breeding, novel alleles can be identified and their relative value can be assessed as often as necessary. Correction for population structure is critical in a collection of genotypes, especially in a breeding program, for which relationships are highly variable. A model to describe the association between markers and genes as conditional probabilities in synthetic populations under recurrent selection will be presented that is useful for predicting the potential of different populations for association analysis, and forecasting the response to marker-assisted selection.