OBJECTIVES: Genomic-based diagnostic tests provide information with the potential to improve health and non-health outcomes for patients and families with rare inherited conditions. To date, no practical solutions to using cost-effectiveness analysis exist that take account of non-health benefits and recognise the existence of opportunity cost. This study aimed to identify all relevant non-health outcomes to define a taxonomy of value potentially deriving from genomic-based diagnostic tests. METHODS: Meta-ethnography was used to synthesize published qualitative evidence in an interpretive manner. Systematic bibliographic searches identified studies using electronic search strategies and terms relevant to genomic testing combined with synonyms for qualitative research in four databases (MEDLINE, Embase, PsychInfo and HAPI) from time of inception to April 2016. Two researchers identified studies for inclusion using pre-defined criteria. Data analysis and synthesis, using meta-ethnography, aimed to consolidate themes and concepts in existing qualitative studies to create a taxonomy of value grounded in empirical evidence. RESULTS: Thirty-seven studies were included and analysed in two stages concerned with: (i) multiple genetic conditions (12 studies); single inherited conditions (25 studies). Three types of value were identified and defined: (i) value of informed decision-making (ability of genomic-based diagnostic information to facilitate important health and life decisions); (ii) value of benefit to others (recognition of impact of information on others); (iii) value of knowing (value per se from genomic information). CONCLUSIONS: This study developed a taxonomy of value for genomic-based diagnostic tests. This is a necessary first step to move towards understanding how to take account of health and non-health effects in cost-effectiveness analyses and also consider opportunity cost within a constrained healthcare budget. A potential next step is to use stated preference methods to quantify how people trade between health and non-health outcomes to capture the value of genomic-based diagnostic tests.