ABSTRACT
The initiation, progression, and severity of human cancer are complex processes that are
dependent on many genes, many environmental factors, and chance events that are
perhaps not measurable with current technology or are simply unknowable. Success in
the design and execution of population-based association studies to identify those genetic
and environmental factors that play an important role in cancer biology will depend on
our ability to embrace, rather than ignore, complexity in the genotype-to-phenotype
mapping relationship for any given human ecology. We review here several novel
analytical strategies that assume complexity and thus complement traditional parametric
statistical strategies such as those based on logistic regression that often make simplifying
assumptions. The rapid advances in the speed and affordability of computing along with
the availability of powerful open-source software have made novel analytical strategies
accessible to epidemiologists and geneticists.
