Genetically-Informed Patient Selection for iPSC Studies of Complex Diseases May Aid in Reducing Cellular Heterogeneity

Hoekstra, Stephanie D.; Stringer, Sven; Heine, Vivi M.; Posthuma, Daniëlle

doi:10.3389/fncel.2017.00164

Cited by 41 publications

(29 citation statements)

References 29 publications

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“…3d) 37,42,56,70,79,84,92 . Interestingly, it has been estimated that sample sizes of 10-30 individuals per hiPSC study may be required to achieve a statistical power of 80%, assuming that the cellular readouts variance is high and the disease effect is small (>0.7 relative heterogeneity, which is the ratio between within-group standard deviation and mean group difference) 189 . In this situation, most iPSC studies of PD may fall below the suggested sample size requirements.…”

Section: Studies Of Pd With Patient-derived Ipscsmentioning

confidence: 99%

Genetic predispositions of Parkinson’s disease revealed in patient-derived brain cells

Tran

Anastacio

Bardy

2020

npj Parkinsons Dis.

111

103

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Parkinson's disease (PD) is the second most prevalent neurological disorder and has been the focus of intense investigations to understand its etiology and progression, but it still lacks a cure. Modeling diseases of the central nervous system in vitro with human induced pluripotent stem cells (hiPSC) is still in its infancy but has the potential to expedite the discovery and validation of new treatments. Here, we discuss the interplay between genetic predispositions and midbrain neuronal impairments in people living with PD. We first summarize the prevalence of causal Parkinson's genes and risk factors reported in 74 epidemiological and genomic studies. We then present a meta-analysis of 385 hiPSC-derived neuronal lines from 67 recent independent original research articles, which point towards specific impairments in neurons from Parkinson's patients, within the context of genetic predispositions. Despite the heterogeneous nature of the disease, current iPSC models reveal converging molecular pathways underlying neurodegeneration in a range of familial and sporadic forms of Parkinson's disease. Altogether, consolidating our understanding of robust cellular phenotypes across genetic cohorts of Parkinson's patients may guide future personalized drug screens in preclinical research.

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Section: Studies Of Pd With Patient-derived Ipscsmentioning

confidence: 99%

Genetic predispositions of Parkinson’s disease revealed in patient-derived brain cells

Tran

Anastacio

Bardy

2020

npj Parkinsons Dis.

111

103

View full text Add to dashboard Cite

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“…The larger the variance in the system, the bigger the needed sample size. Greater variance is typically associated with idiopathic versus genetically stratified cohorts, thus, in general, one would expect a larger sample size to be necessary for these studies [61]. For example, large, penetrant CNVs such as the 22q11.2 deletion often affect the expression of many genes within the CNV boundary [62].…”

Section: An Ideal Scenariomentioning

confidence: 99%

“…First, studies using related controls such as trio or quad designs that minimize genetic variation between ASD probands and controls may require smaller sample numbers compared with studies using unrelated controls in order to detect significant signal above background. Indeed, the selection of control individuals is equally as important as the selection of ASD patients in study design, and can influence the degree to which phenotypes are detected (or not detected) in a given assay [61]. As discussed below, this will require greater access to family-based ASD hPSC collections which are already widely utilized in genetic studies, to achieve statistical power with a more tenable number of samples.…”

Section: Strategies To Reduce Sample Sizementioning

confidence: 99%

Using human pluripotent stem cell models to study autism in the era of big data

Nehme

Barrett

2020

Molecular Autism

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Advances in human pluripotent stem cell (hPSC) biology coupled with protocols to generate diverse brain cell types in vitro have provided neuroscientists with opportunities to dissect basic and disease mechanisms in increasingly relevant cellular substrates. At the same time, large data collections and analyses have facilitated unprecedented insights into autism genetics, normal human genetic variation, and the molecular landscape of the developing human brain. While such insights have enabled the investigation of key mechanistic questions in autism, they also highlight important limitations associated with the use of existing hPSC models. In this review, we discuss four such issues which influence the efficacy of hPSC models for studying autism, including (i) sources of variance, (ii) scale and format of study design, (iii) divergence from the human brain in vivo, and (iv) regulatory policies and compliance governing the use of hPSCs. Moreover, we advocate for a set of immediate and long-term priorities to address these issues and to accelerate the generation and reproducibility of data in order to facilitate future fundamental as well as therapeutic discoveries.

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“…B. Untersuchungen intermediärer Phänotypen für den genetischen Beitrag stratifiziert werden [13]. Aber auch im experimentellen Modell gibt es erste erfolgreiche Ansätze zur funktionellenAnalyse despolygenenBeitrags [20].…”

Section: Ausblickunclassified