用于精神分裂症建模的具有极端多基因风险评分的公开可用 hiPSC 株系

Complex psychiatry Pub Date : 2021-02-01 Epub Date: 2020-11-02 DOI:10.1159/000512716
Kristina Dobrindt, Hanwen Zhang, Debamitra Das, Sara Abdollahi, Tim Prorok, Sulagna Ghosh, Sarah Weintraub, Giulio Genovese, Samuel K Powell, Anina Lund, Schahram Akbarian, Kevin Eggan, Steven McCarroll, Jubao Duan, Dimitrios Avramopoulos, Kristen J Brennand
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引用次数: 0

摘要

精神分裂症(SZ)是一种常见的使人衰弱的精神疾病,但有效的治疗方法却很有限。虽然这种多基因疾病具有高度遗传性,但其风险取决于数百种常见和罕见变异的复杂相互作用。将越来越多与疾病有显著相关性的基因位点转化为医学上可操作的信息仍是一项重要挑战。因此,建立平台以验证风险变异在细胞类型特异性和供体依赖性背景下的影响至关重要。为此,我们选择并鉴定了12个人类诱导多能干细胞(hiPSC)系,这些细胞系来自SZ多基因风险评分(PRS)极低和极高的对照供体。加利福尼亚再生医学研究所(CIRM)公开提供这些 hiPSC 细胞系。我们在 3 个独立实验室对这些极端 PRS hiPSCs 是否适合用于基于 CRISPR 的神经元和胶质细胞同源比较进行了评估,结果发现 12 个中有 9 个符合我们的标准。我们报告了一种标准化的可公开获得的 hiPSCs 资源,我们希望在这种资源上进行基因组工程并生成各种功能数据,同时通过使用一套共同的遗传背景来促进不同研究之间的比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Publicly Available hiPSC Lines with Extreme Polygenic Risk Scores for Modeling Schizophrenia.

Schizophrenia (SZ) is a common and debilitating psychiatric disorder with limited effective treatment options. Although highly heritable, risk for this polygenic disorder depends on the complex interplay of hundreds of common and rare variants. Translating the growing list of genetic loci significantly associated with disease into medically actionable information remains an important challenge. Thus, establishing platforms with which to validate the impact of risk variants in cell-type-specific and donor-dependent contexts is critical. Towards this, we selected and characterized a collection of 12 human induced pluripotent stem cell (hiPSC) lines derived from control donors with extremely low and high SZ polygenic risk scores (PRS). These hiPSC lines are publicly available at the California Institute for Regenerative Medicine (CIRM). The suitability of these extreme PRS hiPSCs for CRISPR-based isogenic comparisons of neurons and glia was evaluated across 3 independent laboratories, identifying 9 out of 12 meeting our criteria. We report a standardized resource of publicly available hiPSCs on which we hope to perform genome engineering and generate diverse kinds of functional data, with comparisons across studies facilitated by the use of a common set of genetic backgrounds.

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