The potential for association mapping from historical trait data in wheat and barley (PhD)
About this project
Plant breeding continuously produces new cultivars (varieties) of agricultural crops, which are independently evaluated in field trials prior to commercialisation. Variety evaluation has been ongoing in England and Wales since the 1930s and has been a statutory requirement in the UK since 1963. The legacies are seed banks of historic cultivars and corresponding phenotype databases.
The hypothesis to be tested is that this resource, with some genotyping of seed, can be exploited in association mapping experiments. The chosen species represent those with the most complete historic phenotype databases and the least within-variety genetic variation.
The project examines the population structure and cryptic relatedness in the available panels with reference to their confounding effects on association mapping. Different analysis methods are considered and mixed effects modelling is identified as the most reliable. Association analysis of Mendelian and quantitative traits is conducted and the results compared with the success rate expected from simulation. Some of the potential QTL identified are shown to be co-incident on genetic maps with QTL identified by others in biparental mapping studies, the observed level of co-incidence is found to be significantly greater than would be expected by chance alone.
In conclusion the potential for association mapping of quantitative traits using this experimental model is constrained by the number of varieties for which sufficient phenotype data exists. In wheat the number of available mapped molecular markers is also limiting. Simply inherited traits, however, appear amenable to study in this way.
Larger genotyping arrays are becoming available but simulation shows that larger panels (2x for barley, 5x for wheat) of individuals will be needed if the power of the experiments is to be sufficient to discover, for example, 80% of loci contributing ≥10% of variation. The potential to achieve this panel size using advanced inter-crossed populations is briefly discussed.
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