OBJECTIVES:  The objectives of this study were (1) to design and implement a stated-preference study to quantify the value of whole-genome sequencing (WGS) and (2) to evaluate the limitations of current stated-preference study-design methods for evaluating such complex health-care technologies. METHODS:  Following evaluations of early drafts in respondent interviews, the final study design incorporated solutions to three study-design challenges. First: multiple kinds and levels of uncertainty about the likelihood of numerous kinds of gene variants required simplifying the decision problem to evaluating a single gene variant with associated health consequences of varying severity. Second: the probability of getting WGS information about elevated health risks for which there are possible risk-reducing interventions, but have their own uncertain effects, required a simplified discrete-choice experiment (DCE) for the assumed health problem using surgery, medication, and watchful-waiting labeled alternatives. Third: valuing multiple kinds of test results required constructing contingent-valuation questions for a report containing only actionable information versus a report containing both actionable and non-actionable information. RESULTS:  The online survey was administered to a US general-population sample of 410 respondents. Differences in DCE preferences for watchful waiting versus surgery or medication for a 20% chance of mild symptoms and a 60% chance of severe symptoms were statistically significant. A majority of respondents had no interest in non-actionable genomic information (55%, 95%CI: 50- 60%). Respondents with a positive value of information were willing to pay $299 (SD: $86, p<0.01) for actionable findings and $180 (SD: $83, p<0.05) for non-actionable findings. CONCLUSIONS:  Our study complies with the ISPOR checklist for good stated-preference research practices and the results have reasonable face validity. However, necessary simplifications of the problem that departed substantially more than usual from the actual decision context suggest that current stated-reference methods have significant limitations for quantifying policy-relevant preferences for such complex technologies as WGS.