Not sure where to start? If you are new to PrediXcan and MetaXcan, we recommend starting your analyses with the DGN-HapMap Whole Blood model or one of the various GTEx-HapMap tissue models. See our FAQ and README file for more info.
We are releasing prediction models trained on GTEx Version 7 data. We have updated our processing pipeline, and restricted to individuals of European ancestry to obtain more reliable LD data. This reduces false positive associations in the Summary Version of PrediXcan. Because of this choice, the gain in sample size relative to V6p is modest (ranging from -18 to 89), with whole blood, LCLs and fibroblasts experiencing reduced sample size. We developed a new criteria to assess model performance. We have also decided to include prediction models for both pseudogenes and lincRNAs. For your convenience, we also include a SNP annotation file with information on the SNPs used to train the models.
While preparing GTEx V7 prediction models, we identified a few issues in the way prediction performance was estimated in the previous release (2016-09-08 release). In aggregate, these caused the prediction model performance to be overestimated. Reassuringly, predicted expression levels and the downstream associations with phenotypes remain mostly unchanged, even though prediction weights vary slightly. However, some gene/tissue pairs are no longer considered reliable. See the flag file here .
Detailed description of the pipeline update and the effect in prediction models performance and downstream S-PrediXcan association can be found here .
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The Genotype-Tissue Expression (GTEx; Sample size ) project was supported by the Common Fund of the Office of the Director of the National Institutes of Health. All GTEx Data was downloaded from The database of Genotypes and Phenotypes (dbGaP).
Depression Genes and Networks (DGN; 922 whole-blood samples) Data was provided by Dr. Douglas F. Levinson. We gratefully acknowledge the resources were supported by National Institutes of Health/National Institute of Mental Health grants 5RC2MH089916 (PI: Douglas F. Levinson, M.D.; Coinvestigators: Myrna M. Weissman, Ph.D., James B. Potash, M.D., MPH, Daphne Koller, Ph.D., and Alexander E. Urban, Ph.D.) and 3R01MH090941 (Co-investigator: Daphne Koller, Ph.D.).
This work is supported by R01MH107666 (H.K.I.), K12 CA139160 (H.K.I.), R01 MH101820 (GTEx), P30 DK20595, P60 DK20595 (Diabetes Research and Training Center), P50 DA037844 (Rat Genomics), and P50 MH094267 (Conte).
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