Gandal et al analyzed autism spectrum disorder, schizophrenia, and bipolar disorder across multiple levels of transcriptomic organization—gene expression, local splicing, transcript isoform expression, and coexpression networks for both protein-coding and noncoding genes to produce a quantitative, genome-wide resource. They performed TWAS based on 2,188 postmortem frontal and temporal cerebral cortex samples from 1,695 adults. RNA-sequencing reads were aligned to the GRCh37.p13 (hg19) reference genome. We generated a model using elastic-net weights released by Gandal et al. More info on the study: The TWAS is available at

Sabrina re-formatted the prediction models generated by Gandal et al into a sqlite database so that it’s compatible with with PrediXcan software. The script used for that is here The model weights were stored in Predictdb format and can be accessed from here.

Comparison of the different prediction models are compared here. Short conclusion: elastic net performance is just fine. No need to use other models. We also have seen that BSLMM and BLUP can increase LD contamination so I wouldn’t recommend those models even if prediction performance was better.

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Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The source code is licensed under MIT.

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For attribution, please cite this work as

Sabrina Mi, Michael Gandal (2020). PsychENCODE Brain Expression Models. PredictDB. /post/2020/07/19/psychencode-brain-expression-models/

BibTeX citation

  title = "PsychENCODE Brain Expression Models",
  author = "Sabrina Mi, Michael Gandal",
  year = "2020",
  journal = "PredictDB",
  note = "/post/2020/07/19/psychencode-brain-expression-models/"