Taming Human Genetic Variability: Transcriptomic Meta-Analysis Guides the Experimental Design and Interpretation of iPSC-Based Disease Modeling
| Autor: | Giuseppe Testa, Pierre-Luc Germain |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Resource mixed models induced pluripotent stem cells human genetic variation Disease Computational biology Human genetic variation power analysis Biology Biochemistry Models Biological 03 medical and health sciences transcriptomics disease modeling Genetics Humans Genetic variability Induced pluripotent stem cell lcsh:QH301-705.5 lcsh:R5-920 iPSC business.industry Gene Expression Profiling Robustness (evolution) Genetic Variation Cell Biology sample size 3. Good health Biotechnology 030104 developmental biology lcsh:Biology (General) Sample size determination Research Design Meta-analysis spurious business lcsh:Medicine (General) Reprogramming Developmental Biology |
| Zdroj: | Stem Cell Reports, Vol 8, Iss 6, Pp 1784-1796 (2017) Stem Cell Reports |
| ISSN: | 2213-6711 |
| Popis: | Summary 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. Highlights • Differences between individuals dominate transcriptional variance • Using multiple clones per individual can be detrimental to differential analysis • We provide resources for the design, analysis, and interpretation of iPSC studies Germain and Testa harness large public induced pluripotent stem cells (iPSC) transcriptomic datasets to assess the impact of human variability on iPSC-based disease modeling. They evaluate different experimental designs, showing among other things that using multiple clones per individual can be detrimental to the robustness of the results, establishing standards and informing decisions on the design of iPSC-based studies. |
| Databáze: | OpenAIRE |
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