Microarrays allow measurements of the quantity of every mRNA transcript in a subject and of the particular versions of their genes. Understanding the relationship between a particular genetic location and its expression is fundamental to elucidating the relationships among genes, other genes' transcripts, and proteins translated from those transcripts. Currently, few statistical labs have developed models that use all available biological information. This research helps develop the knowledge base used by the 21st century's pioneering researchers in oncology, metabolic engineering and pharmacogenetics. To strengthen the available models, I introduced a biological distance based covariance matrix. Using simulated data, I examined the incorporation of biological distance in statistical genetics, specifically into expression quantitative trait loci mapping. I used receiver operator characteristic curves to compare these approaches, and generated recommendations for when it is advantageous to include annotation information into gene mapping. The greatest benefit arises in pleiotropic relationships where each transcript has low heritability, although using excessively noisy annotations is disadvantageous. These tools fill a small part of the gap in our understanding of the complex dynamical system that is molecular or systems biology.