MarkerTrait Association in sugarcane

Association mapping studies in sugarcane need an effective method to account for population structure since it has a complex relationship with genotypes in a breeding population due to complex polyploid genomes. The use of accurate phenotypic data is essential to increase the power of detecting marker-trait associations (MTAs). In case that data was collected from multiple field trials (years and/or locations), data analysis need to include genotype by environment interaction (GEI) and  spatial variation for detecting MTAs over a wide range of environments.

Wei et al. (2010) reported the results of a study undertake to detect MTAs between genetic markers and the important sugarcane traits from three field trials. The group of researchers use ASReml-R to analyze these large and complex datasets by fitting eight different mixed models to examine the impact of three major factors: population structure, GEI and spatial variation. The results showed the differences in a number of significant markers (MTAs) detected from each model. It suggested that it is important to determine the effects of those factors which can be included or excluded in the models of analysis. Using the appropriate modeling may guarantee the success of association mapping.


Mixed model analysis in ASReml-R

  • A flexible syntax and a large scope for specifying variance models for the random effects
  • Present some of the useful modelling approaches
  • Efficiently analyse large and complex datasets



Wei X, Jackson PA, Hermann S, Kilian A, Uszynska KH, and Deomano E. 2010. Simultaneously accounting for population structure, genotype by environment interaction, and spatial variation in marker-trait associations in sugarcane. Genome. 53: 973-981.