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Mortality in humans is primarily due to diseases with complex causes such as heart disease, cancer, and diabetes. Finding the genetic underpinnings of such diseases could lead to advances in medicine. Reviewing studies that try to locate causal variants in diseases has revealed several problems. With the utilization of the tools available in the model system Drosophila melanogaster and the complex phenotype of the wing, I attempt to improve mapping techniques of a complex phenotype in order to investigate the genetic structure of a quantitative trait including number of causal loci, gene ontology, and gametic disequilibrium. This is done firstly by determining the number of dimensions of the phenotype containing significant additive genetic variation, and secondly by mapping the phenotype to the genotype using multivariate statistical tests and vector projections. The base population in this study is the Drosophila genetic reference panel (DGRP) which is a collection of inbred lines derived from the same natural population. The modeled genetic variance-covariance (G) matrix, from 185 of the DGRP lines, show that all measured traits (21) from the phenotype have significant genetic variation. The eigenvalues of each successive dimension decrease in importance by 26.8%, indicating a slow linear regression on a log scale. In an outbred population simulated by a series of diallel crosses, the modeled G matrix indicated 16 dimensions held significant genetic variation. The decrease in dimensionality is due to the loss of homozygozity due to dominance and epistasis. Mapping the inbred females from 165 of the DGRP lines to 85,000 SNPs resulted in 1,795 significant sites with a false discovery rate of 25%. The top mapped sites have a wide range of effect sizes, and most fall outside the coded protein. The mapping results suggest that there is a large amount of quantitative inputs from regulatory regions of the genome as well as protein coding regions.