Dataset Information

Genome-wide CNV analysis in mouse induced pluripotent stem cells reveals dosage effect of pluripotent factors on genome integrity.

ABSTRACT:

Background

Induced pluripotent stem cells (iPSCs) derived from somatic cells have enormous potential for clinical applications. Notably, it was recently reported that reprogramming from somatic cells to iPSCs can induce genomic copy number variation (CNV), which is one of the major genetic causes of human diseases. However it was unclear if this genome instability is dependent on reprogramming methods and/or the genetic background of donor cells. Furthermore, genome-wide CNV analysis is technically challenging and CNV data need to be interpreted with care.

Results

In order to carefully investigate the possible CNV instability during somatic reprogramming, we performed genome-wide CNV analyses with 41 mouse iPSC lines generated from the same parental donor; therefore, the donor's genetic background can be controlled. Different reprogramming factor combinations and dosages were used for investigating potential method-dependent effects on genome integrity. We detected 63 iPSC CNVs using high-resolution comparative genomic hybridization. Intriguingly, CNV rates were negatively associated with the dosages of classic factor(s). Furthermore, the use of high-performance engineered factors led to less CNVs than the classic factor(s) of the same dosage.

Conclusion

Our observations suggest that sufficient reprogramming force can protect the genome from CNV instability during the reprogramming process.

SUBMITTER: Chen Y

PROVIDER: S-EPMC3912500 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Publications

Genome-wide CNV analysis in mouse induced pluripotent stem cells reveals dosage effect of pluripotent factors on genome integrity.

Chen Yulin Y Guo Lin L Chen Jiekai J Zhao Xiangjie X Zhou Weichen W Zhang Cheng C Wang Jiucun J Jin Li L Pei Duanqing D Zhang Feng F

BMC genomics 20140128

<h4>Background</h4>Induced pluripotent stem cells (iPSCs) derived from somatic cells have enormous potential for clinical applications. Notably, it was recently reported that reprogramming from somatic cells to iPSCs can induce genomic copy number variation (CNV), which is one of the major genetic causes of human diseases. However it was unclear if this genome instability is dependent on reprogramming methods and/or the genetic background of donor cells. Furthermore, genome-wide CNV analysis is ...[more]

PMID: 24472662

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Project description:BackgroundSNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP data using the marker intensities. However, these algorithms lack specificity to detect small CNVs owing to the high false positive rate when calling CNVs based on the intensity values. Therefore, the resulting association tests lack power even if the CNVs affecting disease risk are common. An alternative procedure called PennCNV uses information from both the marker intensities as well as the genotypes and therefore has increased sensitivity.ResultsBy using the hidden Markov model (HMM) implemented in PennCNV to derive the probabilities of different copy number states which we subsequently used in a logistic regression model, we developed a new genome-wide algorithm to detect CNV associations with diseases. We compared this new method with association test applied to the most probable copy number state for each individual that is provided by PennCNV after it performs an initial HMM analysis followed by application of the Viterbi algorithm, which removes information about copy number probabilities. In one of our simulation studies, we showed that for large CNVs (number of SNPs ? 10), the association tests based on PennCNV calls gave more significant results, but the new algorithm retained high power. For small CNVs (number of SNPs <10), the logistic algorithm provided smaller average p-values (e.g., p = 7.54e - 17 when relative risk RR = 3.0) in all the scenarios and could capture signals that PennCNV did not (e.g., p = 0.020 when RR = 3.0). From a second set of simulations, we showed that the new algorithm is more powerful in detecting disease associations with small CNVs (number of SNPs ranging from 3 to 5) under different penetrance models (e.g., when RR = 3.0, for relatively weak signals, power = 0.8030 comparing to 0.2879 obtained from the association tests based on PennCNV calls). The new method was implemented in software GWCNV. It is freely available at http://gwcnv.sourceforge.net, distributed under a GPL license.ConclusionsWe conclude that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than the existing HMM algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV.

Dataset Information

Genome-wide CNV analysis in mouse induced pluripotent stem cells reveals dosage effect of pluripotent factors on genome integrity.

Background

Results

Conclusion

Publications

Genome-wide CNV analysis in mouse induced pluripotent stem cells reveals dosage effect of pluripotent factors on genome integrity.

Similar Datasets

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