Objectives:  The clinical value of genomic tests is not always apparent, and very few have demonstrated cost-effectiveness.  The objective of this conceptual paper is to understand whether the principles and methods of cost-effectiveness analysis (CEA) are appropriate for the evaluation of genomic-based diagnostic tests, such as whole genome sequencing. Methods: Literature on CEA methods to evaluate genomic tests was systematically searched using ‘pearl growing’ methods. Data were extracted to identify challenges and solutions to conducting CEA in this context. The key characteristics of genomic tests from an economic perspective were summarized and used to distinguish further challenges. Results: Our review highlights two main differences between CEA of genomic tests and that of other technologies:  the complexities of evaluating tests for multiple disorders and the potential for genomic information to have consequences for future generations requiring infinite time horizons. Another common feature, not unique to genomic-based diagnostic tests but commonly identified in the literature, was the valuation of non-health benefits.  Alternatives to evaluate the diagnosis of multiple disorders are discussed: an iterative approach assessing each diagnosis independently; an aggregate approach combining the cost and benefits from all disorders into a single evaluation; a pragmatic approach that identifies the most important disorders combined with a qualitative assessment of the direction of bias for disorders not included in the full analysis. Consideration of the potential for infinite time horizons suggests CEA should focus on systems that could store, and share, genomic information between generations. Conclusions: The challenges shared with other health technologies, particularly diagnostic tests, suggest that the general principles and methods of CEA are appropriate for genomic tests. Further methodological research would be valuable on approaches for assessing the value of sharing genomic information across generations, approaches to evaluate tests for multiple disorders and trading-off health and non-health benefits.