The aim of this project is to use the ninespine stickleback as a model to understand the relative importance of natural selection and random demographic events in shaping the evolution of ecologically relevant complex traits; harness the demographic history of the stickleback to detect complex genetic interactions affecting complex traits; improve the empirical understanding of the genetics of complex traits, and explore the effect of copy number variation (CNV) on complex traits. I will employ the latest genomic technologies to produce a panel of Single Nucleotide Polymorphisms (SNPs) to map Quantitative Trait Loci (QTL). I will test the effects of selection and demographic history with Qst-Fst analysis, to determine which genomic regions are subject to selection across populations and which ones have population specific effects. Identifying population specific QTL will allow improving our empirical understanding of how QTL interact with their genetic background, providing novel insight to understand context-dependent genetic effects. I will use the data generated for QTL mapping to detect the presence of CNV, and I will test the relative importance of this kind of genetic variability in the establishment of quantitative traits variability. These results are expected to significantly impact on our current understanding of the distribution of adaptive variation in the wild, and lead to significant advancements in the theoretical framework of adaptive quantitative genetics. In addition, these results will have a direct impact in informing selective and breeding practices of domesticated animals and plants, to balance the need of preserving genetic variability while allowing for an efficient genetic and phenotypic response to artificial selection. It will also provide us greater knowledge to effectively manage endangered species fragmented in disjointed populations which are simultaneously facing both genetic diversity loss and anthropic and climate change selection.