The Application of Pittsburgh-Style Learning Classifier Systems to Address Genetic Heterogeneity and Epistasis in Association Studies

Despite the growing abundance and quality of genetic data, genetic epidemiologists continue to struggle with connecting the phenotype of common complex disease to underlying genetic markers and etiologies. In the context of gene association studies, this process is greatly complicated by phenomena such as genetic heterogeneity (GH) and epistasis (gene-gene interactions), which constitute difficult, but accessible challenges for bioinformatisists. While previous work has demonstrated the potential of using Michigan-style Learning Classifier Systems (LCSs) as a direct approach to this problem, the present study examines Pittsburgh-style LCSs, an architecturally and functionally distinct class of algorithm, linked by the common goal of evolving a solution comprised of multiple rules as opposed to a single "best" rule. This study highlights the strengths and weaknesses of the Pittsburgh-style LCS architectures (GALE and GAssist) as they are applied to the GH/epistasis problem.

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