Methods for dose-response modeling of in vivo genotoxicity data are introduced and applied to a case study of acrylamide. Genetic toxicity results are typically summarized as being either positive or negative, with no further consideration of the dose-response patterns that can be estimated from such studies. This analysis explores the use of three modeling approaches: Poisson regression of counts of genetic effects per cell; dynamic modeling of the time-course of micronucleus production and loss as a function of exposure; and categorical regression of sets of genetic toxicity experiments, the results of which are recoded in terms of severities of response. Estimates derived from these models (benchmark doses and predictions of response rates for predetermined doses of interest) are then used to assess the relevance and role of the genetic toxicity results in a risk assessment. With respect to the acrylamide data base, the results suggest that the genetic damage studies do not appear to be consistent or congruent with the thyroid tumor endpoints observed in two long-term bioassays in rats. This suggests that acrylamide's mechanism of action with respect to production of such tumors may not be genotoxic, and that a cancer risk assessment that applied a linear, no-threshold approach to such endpoints might be inappropriate. Benchmark doses derived from the genetic toxicity data base do not appear to be the critical ones for acrylamide risk assessment. Dose metric and modeling issues associated with the proposed dose-response approach to evaluation of genetic toxicity data are explored, and it is recommended that further advancements of the methodology be developed and employed for optimal use of such data for risk assessment purposes.