Exploiting Causal Complexity in Aging

Gerontological genetics is a paradigm case of complexity, with many genes affecting the biological processes of aging, many environmental features influencing the same processes, and conglomerates of interactions within and between each of these sources of variance. To address this complexity, researchers have available increasingly powerful and precise tools. The relative stability and uniformity of genotype of inbred strains, the precision of localization of base pairs in DNA, the standardization of biomarker measures, and the specific-pathogen-free environments—these features, among others, make possible the repeatability of experimental circumstance from laboratory to laboratory that is a defining characteristic of experimental science. However, it is increasingly apparent that the results obtained from such tightly controlled experimental features may be severely limited in their generalizability. In this work, we seek to illustrate the complexity associated even within a set of controlled studies in mice. In these studies, a relatively simple phenotype, end-of-life, was used as a main outcome measure, with predictions based on particular genetic configurations previously shown to be associated with either increased or decreased median length of life. In addition, several other phenotypes were assessed to provide possible mechanistic insights into causal pathways. The results obtained provide salutary warning of the many variables involved in determining length of life and the great difficulty of controlling for all possible sources of variance. This complexity may be regarded as a major nuisance in the search for the dynamics of aging. The argument can be made, however, that specific examples of interaction “hot-spots” are research targets whose study may be particularly fruitful.