Pigmentation, or the coloring of the skin, hair and mucous membranes, has been shown to influence melanoma risk. Most, if not all traits and diseases, have a genetic component that influences development or susceptibility. These genetic components, or loci, can be determined using genome-wide association studies (GWAS). GWAS studies associate millions of relatively common genetic modifications between a healthy population and a population containing a trait or disease of interest. The assignment and interpretation of these genetic modifications has also proved challenging. In this study, we aim to utilize a series of computational approaches to determine whether GWAS associations between pigmentation and melanoma are truly causal rather than due to coincidence. We then use a machine learning algorithm to identify and assign a likelihood of gene involvement for each trait. This information will enable us to further examine the shared biological mechanisms between pigmentation phenotype and melanoma.