Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants

Zhang, Siwei; Zhang, Hanwen; Zhou, Yifan; Qiao, Min; Zhao, Siming; Kozlova, Alena; Shi, Jianxin; Sanders, Alan R.; Wang, Gao; Luo, Kaixuan; Sengupta, Subhajit; West, Siobhan; Qian, Sheng; Streit, Michael; Avramopoulos, Dimitrios; Cowan, Chad A.; Chen, Mengjie; Pang, Zhiping P.; Gejman, Pablo V.; He, Xin; Duan, Jubao

doi:10.1126/science.aay3983

Cited by 90 publications

(143 citation statements)

References 84 publications

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“…Second, we applied dCas9 to retarget the SZ risk SNP rs7148456 associated with allele-specific open chromatin at the BAG5 locus [61]. In 2 hiPSC lines, we transfected a customized ABEmax vector system [72] to express gRNA, dCas9, and ABEmax in a single vector.…”

Section: Resultsmentioning

confidence: 99%

“…For testing bulk DNA base editing efficiency, we used a customized pβactin-ABEmax-puro vector system for which we cloned the dCas9-adenine base editor part from the ABEmax vector (Addgene, #112095) [59] into the pSpCas9(BB)-2A-Puro (PX459) V2.0 vector (Addgene, #62988) [60]. We edited a common SNP rs7148456 that showed allele-specific open chromatin in BAG5 [61] in 2 hiPSC lines: CW70372 (low PRS) and CW30525 (high PRS). Cells for editing were plated on 4-well dishes (Thermo Scientific) coated with Matrigel (Corning).…”

Section: Methodsmentioning

confidence: 99%

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

Dobrindt¹,

Zhang²,

Das³

et al. 2020

Complex Psychiatry

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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 characterize a collection of twelve 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 three independent laboratories, identifying 9 out of 12 meeting our criteria. We report a standardized resource of publicly available hiPSCs, with which we collectively commit to conducting future CRISPR-engineering, in order to facilitate comparison and integration of functional validation studies across the field of psychiatric genetics.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Dobrindt¹,

Zhang²,

Das³

et al. 2020

Complex Psychiatry

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“…In our work, we overcome these limitations by taking advantage of a large collection of ASC variants in iPSC-derived neuronal cells. Unlike eQTLs, ASC variants are inside or very close to open chromatin regions, and most of them are expected to be functional [90]. Using this dataset, we demonstrated that our MetaChrom predicted variants are much more likely to be ASC variants than methods that do not take into account specific biological contexts (Figure 4).…”

Section: Discussionmentioning

confidence: 91%

“…One limitation is that we do not yet have a single quantitative metric that combines statistical associations with deep learning based functional predictions. In our recent study [90], we show that it is possible to use ASC variants as functional information as prior in Bayesian fine-mapping. It would be interesting to extend such strategy to MetaChrom predictions.…”

Section: Discussionmentioning

confidence: 93%

Predicting Epigenomic Functions of Genetic Variants in the Context of Neurodevelopment via Deep Transfer Learning

Qian

Zhang

et al. 2021

Preprint

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Decoding the regulatory effects of non-coding variants is a key challenge in understanding the mechanisms of gene regulation as well as the genetics of common diseases. Recently, deep learning models have been introduced to predict genome-wide epigenomic profiles and effects of DNA variants, in various cellular contexts, but they were often trained in cell lines or bulk tissues that may not be related to phenotypes of interest. This is particularly a challenge for neuropsychiatric disorders, since the most relevant cell and tissue types are often missing in the training data of such models. To address this issue, we introduce a deep transfer learning framework termed MetaChrom that takes advantage of both a reference dataset - an extensive compendium of publicly available epigenomic data, and epigenomic profiles of cell types related to specific phenotypes of interest. We trained and evaluated our model on a comprehensive set of epigenomic profiles from fetal and adult brain, and cellular models representing early neurodevelopment. MetaChrom predicts these epigenomic features with much higher accuracy than previous methods, and than models without the use of reference epigenomic data for transfer learning. Using experimentally determined regulatory variants from iPS cell-derived neurons, we show that MetaChrom predicts functional variants more accurately than existing non-coding variant scoring tools. By combining genome-wide association study (GWAS) data with MetaChrom predictions, we prioritized 31 SNPs for Schizophrenia (SCZ). These candidate SNPs suggest potential risk genes of SCZ and the biological contexts where they act. In summary, MetaChrom is a general transfer learning framework that can be applied to the study of regulatory functions of DNA sequences and variants in any disease-related cell or tissue types. The software tool is available at https://github.com/bl-2633/MetaChrom and a prediction web server is accessible at https://metachrom.ttic.edu.

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“…To systematically identify the SLE genetic risk variants that contribute to transcriptional dysregulation in the EBV-transformed B cell line GM12878, we designed and applied a massively parallel reporter assay (MPRA) [18][19][20][21][22][23][24][25][26][27] (Figure 1, Supplemental Figure 1). MPRA extends standard reporter assays, replacing low-throughput luciferase with high-throughput mRNA expression detection.…”

Section: Introductionmentioning

confidence: 99%

Genome-wide discovery of SLE genetic risk variant allelic enhancer activity

Lü

Forney

et al. 2020

Preprint

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Genome-wide association studies of Systemic Lupus Erythematosus (SLE) nominate 3,073 genetic variants at 91 risk loci. To systematically screen these variants for allelic transcriptional enhancer activity, we constructed a massively parallel reporter assay (MPRA) library comprising 12,396 DNA oligonucleotides containing the genomic context around every allele of each SLE variant. Transfection into EBV-infected B cells revealed 482 variants with enhancer activity, with 51 variants showing genotype-dependent (allelic) enhancer activity at 27 risk loci. In-depth analysis of allelic transcription factor (TF) binding at and around these 51 variants identified one class of TFs whose DNA-binding motif tends to be directly altered by the risk variant and a second, larger class of TFs that also bind allelically but do not have their motifs directly altered by the variant. Collectively, our approach provides a blueprint for the discovery of allelic gene regulation at risk loci for any disease and offers insight into the transcriptional regulatory mechanisms underlying SLE.

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Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants

Cited by 90 publications

References 84 publications

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

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

Predicting Epigenomic Functions of Genetic Variants in the Context of Neurodevelopment via Deep Transfer Learning

Genome-wide discovery of SLE genetic risk variant allelic enhancer activity

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