Association mapping reveals gene action and interactions in the determination of flowering time in barley

The interaction between members of a gene network has an important impact on the variation of quantitative traits, and can influence the outcome of phenotype/genotype association studies. Three genes (Ppd-H1, HvCO1, HvFT1) known to play an essential role in the regulation of flowering time under long days in barley were subjected to an analysis of nucleotide diversity in a collection of 220 spring barley accessions. The coding region of Ppd-H1 was highly diverse, while both HvCO1 and HvFT1 showed a rather limited level of diversity. Within all three genes, the extent of linkage disequilibrium was variable, but on average only moderate. Ppd-H1 is strongly associated with flowering time across four environments, showing a difference of five to ten days between the most extreme haplotypes. The association between flowering time and the variation at HvFT1 and HvCO1 was strongly dependent on the haplotype present at Ppd-H1. The interaction between HvCO1 and Ppd-H1 was statistically significant, but this association disappeared when the analysis was corrected for the geographical origin of the accessions. No association existed between flowering time and allelic variation at HvFT1. In contrast to Ppd-H1, functional variation at both HvCO1 and HvFT1 is limited in cultivated barley

Data visualization in yield component analysis: an expert study

Even though data visualization is a common analytical tool in numerous disciplines, it has rarely been used in agricultural sciences, particularly in agronomy. In this paper, we discuss a study on employing data visualization to analyze a multiplicative model. This model is often used by agronomists, for example in the so-called yield component analysis. The multiplicative model in agronomy is normally analyzed by statistical or related methods. In practice, unfortunately, usefulness of these methods is limited since they help to answer only a few questions, not allowing for a complex view of the phenomena studied. We believe that data visualization could be used for such complex analysis and presentation of the multiplicative model. To that end, we conducted an expert survey. It showed that visualization methods could indeed be useful for analysis and presentation of the multiplicative model