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 / P. Germain, G. Testa. - In: STEM CELL REPORTS. - ISSN 2213-6711. - 8:6(2017 Jun 06), pp. 1784-1796. [10.1016/j.stemcr.2017.05.012]

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

P. Germain
;
G. Testa
2017

Abstract

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.
disease modeling; human genetic variation; iPSC; induced pluripotent stem cells; mixed models; power analysis; sample size; spurious; transcriptomics
Settore BIO/11 - Biologia Molecolare
Settore BIO/13 - Biologia Applicata
Settore BIO/18 - Genetica
Settore MED/03 - Genetica Medica
Settore MED/04 - Patologia Generale
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/504316
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