Possible explanation

There are different reasons where you could get a low % of SNPs in the model used with your individual level data or summary stats file. Some of the issues include;

  1. Little overlap between the model SNPs and genotype/summary stats SNPs

  2. Incomplete/malformed models .db file especially when downloading

  3. Using only a few chromosomes to run prediXcan

Possible solution

  • Perform a “manual” inspection of the data. It is recommended you use an interactive environment like R or ipython - then load the model data and GWAS data and check the intersection of variants in model and GWAS.

  • When using variant ids check if the model variant ids and the genotype/summary stats variant ids are in the same genome build. If they are not use the liftover option to generate a variant id that matches the models variant id.

    The argument for the lift over is:
    –liftover path/to/hg19ToHg38.over.chain.gz
    –on_the_fly_mapping METADATA “chr{}

    On the fly mapping informs the tool how to recreate the variant name from the co ordinates to match the variant id format in the model.


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For attribution, please cite this work as

Festus (2021). Error low % of SNPs used. PredictDB. /post/2021/07/21/error-low-of-snps-used/

BibTeX citation

  title = "Error low % of SNPs used",
  author = "Festus",
  year = "2021",
  journal = "PredictDB",
  note = "/post/2021/07/21/error-low-of-snps-used/"