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Complex traits represent valuable research targets as many highly desirable properties of plants and animals (such as growth rate and height) fall into this group. However, associating biological markers with these traits is incredibly challenging, in part due to their small effect sizes. For the two species at the core of our research, European aspen (Populus tremula) and Norway spruce (Picea abies), association studies are even more challenging, primarily due to the fragmented state of their genome assemblies. These assemblies represent the gene space well, but poorly represented inter-genic regions hinder variant discovery and large scale association studies.

In this thesis, I present my work to improve association discovery of complex traits in forest trees. Firstly, to overcome the issues with assembly fragmentation, I have created an updated version of the P. tremula genome, which is highly contiguous and anchored in full chromosomes. To calculate the dense linkage map required to order and orient the aspen assembly, I developed "BatchMap", a parallel implementation of linkage mapping software. BatchMap has been successfully applied to several dense linkage maps, including aspen and Norway spruce, and was essential to the progress in improving the aspen genome assembly. Further, I developed seidr, which represents a starting point in multi-layer, network-based systems biology, an analysis technique with promising prospects for complex trait association analysis. As a case study, I applied some of the methods developed to the analysis of leaf shape in natural populations of European aspen, a complex, omnigenic trait.

The multi-layer model of systems biology and related analysis techniques offer promise in the analysis of complex traits, and this thesis represents a starting point toward an intricate, holistic model of systems biology that may help to unravel the overwhelmingly complicated nature of complex traits.