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Publicly Available hiPSC Lines with Extreme Polygenic Risk Scores for Modeling Schizophrenia

Kristina Dobrindt, Hanwen Zhang, Debamitra Das, Sara Abdollahi, Tim Prorok, Sulagna Ghosh, Sarah J. Weintraub, Giulio Genovese, Samuel K. Powell, Anina Lund, Schahram Akbarian, Kevin Eggan, Steven A. McCarroll, Jubao Duan, Dimitrios Avramopoulos, Kristen Brennand

2020Complex Psychiatry33 citationsDOIOpen Access PDF

Abstract

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.

Topics & Concepts

Polygenic risk scoreInduced pluripotent stem cellSchizophrenia (object-oriented programming)CRISPRComputational biologyBiologyPrecision medicineGeneticsMedicinePsychiatryGeneSingle-nucleotide polymorphismGenotypeEmbryonic stem cellCRISPR and Genetic EngineeringPluripotent Stem Cells ResearchSingle-cell and spatial transcriptomics