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Both the promises and pitfalls of the cell reprogramming research platform rest on human genetic variation, making the measurement of its impact one of the most urgent issues in the field. Harnessing large transcriptomics datasets of induced pluripotent stem cells (iPSC), we investigate the implications of this variability for iPSC-based disease modeling. In particular, we show that the widespread use of more than one clone per individual in combination with current analytical practices is detrimental to the robustness of the findings. We then proceed to identify methods to address this challenge and leverage multiple clones per individual. Finally, we evaluate the specificity and sensitivity of different sample sizes and experimental designs, presenting computational tools for power analysis. These findings and tools reframe the nature of replicates used in disease modeling and provide important resources for the design, analysis, and interpretation of iPSC-based studies.

Taming Human Genetic Variability: Transcriptomic Meta-Analysis Guides the Experimental Design and Interpretation of iPSC-Based Disease Modeling