Winfried Rief presents the results of a study evaluating the impact of presenting genetic risk information to obese patients. Guided by Leventhal's Common-Sense Model, Linda Cameron presents the results of an internet-based experiment assessing whether information regarding links between physiological processes underlying genetic risk, disease development, and behavior enhances beliefs motivating protective behavior and alters willingness to buy tests. Finally, Theresa Marteau presents the results of a randomized controlled trial of using genotype to tailor nicotine replacement therapy in smokers attempting to stop, testing the hypothesis based on Leventhal's Common-Sense Model of Self-Regulation of Health and Illness that genetic risk information increases the use of medication as a coping response. The discussion will focus upon the understanding that these four studies contribute, both theoretically and empirically, to explaining and changing behaviour in the context of genetic risk information. Background: Public understanding of the relationship between health behaviors and genes is likely to affect the motivational impact of learning information about one's own genes. Extant research has featured difficulty measuring public understandings of this relationship. This essay explores public understanding of the relationship between genes and behavior, especially with regard to the mathematical relationships to risk contribution. It also contributes a psychometrically valid scale for measuring beliefs about gene-behavior relationships. Methods: Three population representative surveys (N=633, 658, 1218) were conducted using the Knowledge Networks panel platform. Results: Interpretations of risk vary depending on whether genes and behavior are conceived of as health-damaging (loss frame) or health-protecting (gain frame). In the loss frame, the majority of the population adopts an additive model of the relationship with approximately one third adopting an amplificative model. Scores on the nonmathematically based scale indicate higher belief in the existence of interaction than scores on the more concrete question format. Conclusions: The existence of different interpretations of genebehavior relationships based on gain/loss frame and abstract/ concrete modes indicates the need to select frame and mode carefully in both teaching and research. Research is needed to identify optimal configurations for teaching and presenting this relatively complex material. There is strong evidence for high heredity for obesity. Moreover, if obesity has developed, it is difficult to control weight, and unsuccessful and disappointing weight reduction programs are frequent. In this study we wanted to investigate whether information about the genetic background of obesity is helpful for affected people to develop realistic goals and reduce negative self-perceptions. After genetic screenings of 1000 obese people, we included 320 for a randomized clinical trial evaluating the effect of genetic information versus general information (without genetic content) in obesity. A subgroup of 16 participants was confirmed for functionally relevant MC4R mutations, and they received consultation with genetic information. Their results were analyzed in detail. The acceptance of a consultation including genetic information was high, and those people with a family history of obesity appreciated to receive genetic information in the consultation. People with MC4R mutations reported less feelings of shame and guilt and more realistic weight loss goals after the intervention. We conclude that consultation for obese people should include information about genetic backgrounds, especially if obesity-relevant genetic mutations are obvious, or if people have a family history of obesity. Increasingly, individuals can buy genetic tests to identify disease risks that can be reduced through behavior change. Given the challenges in understanding how behavior can reduce geneticallyconferred risk, there is a need to identify how to present information to optimize comprehension and adaptive behavior. Guided by the Common-Sense Model, we tested whether information about links between physiological processes underly-ing genetic risk, disease development, and behavior enhances beliefs motivating protective action and alters willingness to buy tests. We tested the impact of this information for genetic tests versus tests of a non-genetic biomarker, at higher versus lower risk levels, and when presenting risk information in graphic versus numeric formats. In an internet-based factorial design experiment, 782 adults responded to messages about a test for colorectal cancer risk. We varied messages by risk-action link information (provision or no provision of information describing how a low-fat diet reduces cancer risk given a positive result), risk increment (20%, 50%, or 80% risk given a positive result), risk format (numeric or graphic presentation of risk increments), and test type (genetic or enzyme test). ANOVAs revealed that providing risk-action link information enhanced beliefs of response efficacy (that low-fat diets reduce disease risk) and coherence (understanding how low-fat diets reduce risk), and lowered appraisals of anticipated risk with a low-fat diet (p's<.0001). These effects held across risk increments, risk formats, and test types. For genetic tests, provision of riskaction link information reduced the amount individuals were willing to pay for the test (p<.0001). Brief explanations of how action can reduce biomarker-detected risks promoted beliefs motivating protective action; paradoxically, by enhancing understanding of behavioral control over the risk, they reduced the value of genetic risk information. Despite growing interest in pharmacogenetics to tailor cessation treatment, the behavioural impact of genetic feedback is untested.