Genetic Risk for Obesity Predicts Nucleus Accumbens Size and Responsivity to Real-World Food Cues

Obesity is a major public health concern that involves an interaction between genetic susceptibility and exposure to environmental cues (e.g., food marketing); however, the mechanisms that link these factors and contribute to unhealthy eating are unclear. Using a well-known obesity risk polymorphism (FTO rs9939609) in a sample of 78 children (ages 9-12 y), we observed that children at risk for obesity exhibited stronger responses to food commercials in the nucleus accumbens (NAcc) than children not at risk. Similarly, children at a higher genetic risk for obesity demonstrated larger NAcc volumes. Although a recessive model of this polymorphism best predicted body mass and adiposity, a dominant model was most predictive of NAcc size and responsivity to food cues. These findings suggest that children genetically at risk for obesity are predisposed to represent reward signals more strongly, which, in turn, may contribute to unhealthy eating behaviors later in life

The role of visualization and 3-D printing in biological data mining

BACKGROUND: Biological data mining is a powerful tool that can provide a wealth of information about patterns of genetic and genomic biomarkers of health and disease. A potential disadvantage of data mining is volume and complexity of the results that can often be overwhelming. It is our working hypothesis that visualization methods can greatly enhance our ability to make sense of data mining results. More specifically, we propose that 3-D printing has an important role to play as a visualization technology in biological data mining. We provide here a brief review of 3-D printing along with a case study to illustrate how it might be used in a research setting. RESULTS: We present as a case study a genetic interaction network associated with grey matter density, an endophenotype for late onset Alzheimer's disease, as a physical model constructed with a 3-D printer. The synergy or interaction effects of multiple genetic variants were represented through a color gradient of the physical connections between nodes. The digital gene-gene interaction network was then 3-D printed to generate a physical network model. CONCLUSIONS: The physical 3-D gene-gene interaction network provided an easily manipulated, intuitive and creative way to visualize the synergistic relationships between the genetic variants and grey matter density in patients with late onset Alzheimer's disease. We discuss the advantages and disadvantages of this novel method of biological data mining visualization