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Accurate dissection of highly polygenic traits is difficult, in part due to the power required to identify and characterise minor loci, but also due to the potential nonadditive interactions between the contributing genetic variations within a population. This is often further complicated by the genetic features of natural or agricultural populations, where a good understanding of the genetic architecture of a quantitative trait, e.g. the risk to develop a disorder, or growth-traits in farm animals, would be beneficial. The aim of this thesis is to contribute to a better understanding of the genetic architecture of quantitative traits. In order to do this, the three studies in this thesis make use of a large 18-generation intercross population created from a long running selection experiment on 56-day bodyweight in chicken, the Virginia weight lines.. Combining this population with a new, cost efficient approach to genotyping, we created a large, powerful dataset to explore multiple aspects of the quantitative trait in question, and how its genetic architecture has been shaped by artificial selection.

The first study describes the approach used to generate the dataset and uses the increased power and resolution for a comprehensive genome wide QTL scan, identifying multiple novel loci and mapping others at better resolution.

The second study leverages the same dataset to study the contribution of capacitating epistasis to the selection response. We identify multiple capacitors that explain a modest amount of the selection response, as well as dissect a previous interaction between two QTL into a larger epistatic network with multiple within and across chromosome interactions that explains a large fraction of the phenotypic variance and selection response. 

In the third study, we make use of the outbred nature of the founders to investigate the contribution of still segregating variants to the selection response by adding a GWAS approach to the QTL mapping. We identify multiple novel loci that have not been identified by the QTL approach before, many of which likely still contribute to the selection response due to only segregating in one of the two founding lines. Overall, this thesis showcases the complexity of quantitative trait genetic architecture under selection, by identifying multiple novel loci and epistatic networks that contribute to the selection response in different ways, as well as highlights some of the benefits of combining multiple approaches with different assumptions.