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

Four-way data analysis within the linear mixed modelling framework

Cultivars have to be evaluated under different crop management systems across agro-ecosystems and years using multi-environment trials (MET) before releasing them to the market. Frequently, data collected in METs are arranged according to cultivar (G), management (M), location, (L) and year (Y) combinations in a four-way G x M x L x Y data table that is highly unbalanced for cultivars across locations and time. Therefore, we present the restricted maximum likelihood method (REML) for linear mixed models (LMM) with a factor analytic variance-covariance matrix for assessing cultivar adaptation to crop management systems and environments based on unbalanced datasets. Such a multi-environmental trial system has been in operation in Poland for winter wheat (Triticum aestivum L.) in the form of the Post-registration Variety Testing System (PVTS). This study aimed to illustrate the use of LMM in the analysis of unbalanced four-way G x M x L x Y data. LMM analysis provided adjusted means of grain yield for 51 winter wheat cultivars bred in different regions in Europe, tested across 18 trial locations and seven consecutive cropping seasons in two crop management intensities. The application of the four-way LMM with a factor analytic variance-covariance matrix is a complementary and effective tool for evaluating the unbalanced G x M x L x Y table. Cultivars tested had different adaptive responses to the Polish agro-ecosystems separately for each of the crop management intensities. Wide adaptation in both crop management systems was exhibited by cultivars Mulan and Jenga bred in Germany

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