Integrative Network Biology: Graph Prototyping for Co-Expression Cancer Networks

Network-based analysis has been proven useful in biologically-oriented areas, e.g., to explore the dynamics and complexity of biological networks. Investigating a set of networks allows deriving general knowledge about the underlying topological and functional properties. The integrative analysis of networks typically combines networks from different studies that investigate the same or similar research questions. In order to perform an integrative analysis it is often necessary to compare the properties of matching edges across the data set. This identification of common edges is often burdensome and computational intensive. Here, we present an approach that is different from inferring a new network based on common features. Instead, we select one network as a graph prototype, which then represents a set of comparable network objects, as it has the least average distance to all other networks in the same set. We demonstrate the usefulness of the graph prototyping approach on a set of prostate cancer networks and a set of corresponding benign networks. We further show that the distances within the cancer group and the benign group are statistically different depending on the utilized distance measure

Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology?

Establishing causal relationships between environmental exposures and common diseases is beset with problems of unresolved confounding, reverse causation and selection bias that may result in spurious inferences. Mendelian randomization, in which a functional genetic variant acts as a proxy for an environmental exposure, provides a means of overcoming these problems as the inheritance of genetic variants is independent of-that is randomized with respect to-the inheritance of other traits, according to Mendel's law of independent assortment. Examples drawn from exposures and outcomes as diverse as milk and osteoporosis, alcohol and coronary heart disease, sheep dip and farm workers' compensation neurosis, folate and neural tube defects are used to illustrate the applications of Mendelian randomization approaches in assessing potential environmental causes of disease. As with all genetic epidemiology studies there are problems associated with the need for large sample sizes, the non-replication of findings, and the lack of relevant functional genetic variants. In addition to these problems, Mendelian randomization findings may be confounded by other genetic variants in linkage disequilibrium with the variant under study, or by population stratification. Furthermore, pleiotropy of effect of a genetic variant may result in null associations, as may canalisation of genetic effects. If correctly conducted and carefully interpreted, Mendelian randomization studies can provide useful evidence to support or reject causal hypotheses linking environmental exposures to common diseases