SPrediXcan Harmonization Errors

Author

Sabrina Mi

Published

January 7, 2021

The error message– INFO - 0 % of model's snps used– can typically be traced to inconsistencies between variant IDs in prediction models and input GWAS files. Our GTEx v8 mashr and elastic net models are defined in hg38. For GWAS defined in hg19, we recommend first processing the GWAS with our harmonization tool (tutorial)

Here, we focus on harmonization issues for prediction models and summary statistics in the same build. By harmonization, we mean that the same SNP can be identified with different names in the prediction model and GWAS, so we want to ensure they match. Our prediction models identify SNPs by their RSID and “varID”, a naming format with chromosome, position, allele, and build information. For example, the variant ‘rs12551220’ has varID ‘chr9_137032730_G_A_b38’. In our example, we focus on a GWAS that does not provide RSIDs for variants, so we requires some processing in order to run SPrediXcan.

The raw GWAS file:

zless /Users/sabrinami/Desktop/psychencode_test_data/clozuk_pgc2.meta.sumstats.txt.gz | head
SNP Freq.A1 CHR BP  A1  A2  OR  SE  P
10:100968448:T:AA   0.3519  10  100968448   t   aa  1.0024  0.01    0.812
10:101574552:A:ATG  0.4493  10  101574552   a   atg 0.989060.0097   0.2585
10:10222597:AT:A    0   10  10222597    a   at  0.9997  0.01    0.9777

Our prediction models follow the format:

sqlite> SELECT * FROM weights LIMIT 5;
ENSG00000000457|rs6703487|chr1_169550749_C_T_b37|C|T|0.0010487355157559
ENSG00000000457|rs9332476|chr1_169556756_A_G_b37|A|G|0.0037017916840068
ENSG00000000457|rs2142759|chr1_169618820_G_A_b37|G|A|0.00806408251476846

The variants identified in the SNP column of the GWAS file match neither the rsid or varID columns of the model. If we run SPrediXcan without harmonizing, we get the INFO - 0 % of model's snps used error message:

python3 $METAXCAN/SPrediXcan.py --gwas_file $DATA/clozuk_pgc2.meta.sumstats.out.txt.gz \
> --model_db_path $MODEL/psychencode_model/psychencode.db \
> --covariance $MODEL/psychencode_model/psychencode.txt.gz \
> --or_column OR \
> --pvalue_column P \
> --snp_column SNP \
> --non_effect_allele_column A2 \
> --effect_allele_column A1 \
> --throw \
> --output_file $RESULTS/clozuk_pgc2_imputeformat.csv

INFO - Processing GWAS command line parameters
INFO - Building beta for /Users/sabrinami/Desktop/psychencode_test_data/clozuk_pgc2.meta.sumstats.out.txt.gz and /Users/sabrinami/Github/psychencode/models/psychencode_model/psychencode.db
INFO - Reading input gwas with special handling: /Users/sabrinami/Desktop/psychencode_test_data/clozuk_pgc2.meta.sumstats.out.txt.gz
INFO - Processing input gwas
INFO - Aligning GWAS to models
INFO - Trimming output
INFO - Successfully parsed input gwas in 18.136219276 seconds
INFO - Started metaxcan process
INFO - Loading model from: /Users/sabrinami/Github/psychencode/models/psychencode_model/psychencode.db
INFO - Loading covariance data from: /Users/sabrinami/Github/psychencode/models/psychencode_model/psychencode.txt.gz
INFO - Processing loaded gwas
INFO - Started metaxcan association
INFO - 0 % of model's snps used
INFO - Sucessfully processed metaxcan association in 132.015579446 seconds

To harmonize, we add a varID column in the GWAS file with the same format of as the model using information in the CHR, BP, A1, A2 columns. The modified GWAS looks like:

zless /Users/sabrinami/Desktop/psychencode_test_data/clozuk_pgc2.meta.sumstats.out.txt.gz | head
SNP Freq.A1 CHR BP  A1  A2  OR  SE  P   varID   
10:100968448:T:AA   0.3519  10  100968448   T   AA  1.0024  0.01    0.812   chr10_100968448_T_AA_b37    
10:101574552:A:ATG  0.4493  10  101574552   A   ATG 0.989060.0097   0.2585  chr10_101574552_A_ATG_b37
10:10222597:AT:A    0   10  10222597    A   AT  0.9997  0.01    0.9777  chr10_10222597_A_AT_b37

We also change our SPrediXcan parameters to indicate the GWAS SNP names follow the varID format, with the argument --model_db_snp_key varID. It runs correctly because it messages INFO - 63 % of model's snps used:

python3 $METAXCAN/SPrediXcan.py --gwas_file $DATA/clozuk_pgc2.meta.sumstats.out.txt.gz \
> --model_db_path $MODEL/psychencode_model/psychencode.db \
> --covariance $MODEL/psychencode_model/psychencode_varID.txt.gz \
> --keep_non_rsid --model_db_snp_key varID \
> --or_column OR \
> --pvalue_column P \
> --snp_column varID \
> --non_effect_allele_column A2 \
> --effect_allele_column A1 \
> --throw \
> --output_file $RESULTS/clozuk_pgc2_psychencode.csv

INFO - Processing GWAS command line parameters
INFO - Building beta for /Users/sabrinami/Desktop/psychencode_test_data/clozuk_pgc2.meta.sumstats.out.txt.gz and /Users/sabrinami/Github/psychencode/models/psychencode_model/psychencode.db
INFO - Reading input gwas with special handling: /Users/sabrinami/Desktop/psychencode_test_data/clozuk_pgc2.meta.sumstats.out.txt.gz
INFO - Processing input gwas
INFO - Aligning GWAS to models
INFO - Trimming output
INFO - Successfully parsed input gwas in 22.284876615 seconds
INFO - Started metaxcan process
INFO - Loading model from: /Users/sabrinami/Github/psychencode/models/psychencode_model/psychencode.db
INFO - Loading covariance data from: /Users/sabrinami/Github/psychencode/models/psychencode_model/psychencode_varID.txt.gz
INFO - Processing loaded gwas
INFO - Started metaxcan association
INFO - 10 % of model's snps found so far in the gwas study
INFO - 20 % of model's snps found so far in the gwas study
INFO - 30 % of model's snps found so far in the gwas study
INFO - 40 % of model's snps found so far in the gwas study
INFO - 50 % of model's snps found so far in the gwas study
INFO - 60 % of model's snps found so far in the gwas study
INFO - 63 % of model's snps used
INFO - Sucessfully processed metaxcan association in 153.879826246 seconds

If we still see the INFO - 0 % of model's snps used message, there may still be inconsistencies between the GWAS and covariance file.

In the example, SPrediXcan runs using the varID format names from both the GWAS file and model. However, the covariance file may still identify variants by their RSID:

zless /Users/sabrinami/Github/psychencode/models/psychencode_model/psychencode.txt.gz | head
GENE RSID1 RSID2 VALUE
ENSG00000003249 rs140580708 rs140580708 0.013030848604677675
ENSG00000003249 rs140580708 rs164739 -0.008913106189505495
ENSG00000003249 rs140580708 rs1991508 -0.0017680061881014297

Creating a new covariance file with varID format variant names replacing the RSID1 and RSID2 columns should fix the error. The prediction model file provides a dictionary to update IDs. We have example code to do so.

The following covariance file (with the same column names) gives the correct output:

zless /Users/sabrinami/Github/psychencode/models/psychencode_model/psychencode_varID.txt.gz | head
GENE RSID1 RSID2 VALUE
ENSG00000003249 chr16_89594100_C_G_b37 chr16_89594100_C_G_b37 0.0130308486046777
ENSG00000003249 chr16_89594100_C_G_b37 chr16_89667699_C_G_b37 -0.0089131061895055
ENSG00000003249 chr16_89594100_C_G_b37 chr16_89672155_C_T_b37 -0.00176800618810143

Reuse

--- title: SPrediXcan Harmonization Errors author: Sabrina Mi date: '2021-01-07' slug: spredixcan-harmonization-errors categories: [] tags: [] --- The error message-- `INFO - 0 % of model's snps used`-- can typically be traced to inconsistencies between variant IDs in prediction models and input GWAS files. Our GTEx v8 mashr and elastic net models are defined in hg38. For GWAS defined in hg19, we recommend first processing the GWAS with our [harmonization tool](https://github.com/hakyimlab/summary-gwas-imputation/wiki/GWAS-Harmonization-And-Imputation) ([tutorial](https://github.com/hakyimlab/MetaXcan/wiki/Tutorial:-GTEx-v8-MASH-models-integration-with-a-Coronary-Artery-Disease-GWAS)) Here, we focus on harmonization issues for prediction models and summary statistics in the same build. By harmonization, we mean that the same SNP can be identified with different names in the prediction model and GWAS, so we want to ensure they match. Our prediction models identify SNPs by their RSID and "varID", a naming format with chromosome, position, allele, and build information. For example, the variant 'rs12551220' has varID 'chr9_137032730_G_A_b38'. In our example, we focus on a GWAS that does not provide RSIDs for variants, so we requires some processing in order to run SPrediXcan. The raw GWAS file: ``` zless /Users/sabrinami/Desktop/psychencode_test_data/clozuk_pgc2.meta.sumstats.txt.gz | head SNP Freq.A1 CHR BP A1 A2 OR SE P 10:100968448:T:AA 0.3519 10 100968448 t aa 1.0024 0.01 0.812 10:101574552:A:ATG 0.4493 10 101574552 a atg 0.989060.0097 0.2585 10:10222597:AT:A 0 10 10222597 a at 0.9997 0.01 0.9777 ``` Our prediction models follow the format: ``` sqlite> SELECT * FROM weights LIMIT 5; ENSG00000000457|rs6703487|chr1_169550749_C_T_b37|C|T|0.0010487355157559 ENSG00000000457|rs9332476|chr1_169556756_A_G_b37|A|G|0.0037017916840068 ENSG00000000457|rs2142759|chr1_169618820_G_A_b37|G|A|0.00806408251476846 ``` The variants identified in the `SNP` column of the GWAS file match neither the `rsid` or `varID` columns of the model. If we run SPrediXcan without harmonizing, we get the `INFO - 0 % of model's snps used` error message: ``` python3 $METAXCAN/SPrediXcan.py --gwas_file $DATA/clozuk_pgc2.meta.sumstats.out.txt.gz \ > --model_db_path $MODEL/psychencode_model/psychencode.db \ > --covariance $MODEL/psychencode_model/psychencode.txt.gz \ > --or_column OR \ > --pvalue_column P \ > --snp_column SNP \ > --non_effect_allele_column A2 \ > --effect_allele_column A1 \ > --throw \ > --output_file $RESULTS/clozuk_pgc2_imputeformat.csv ``` ``` INFO - Processing GWAS command line parameters INFO - Building beta for /Users/sabrinami/Desktop/psychencode_test_data/clozuk_pgc2.meta.sumstats.out.txt.gz and /Users/sabrinami/Github/psychencode/models/psychencode_model/psychencode.db INFO - Reading input gwas with special handling: /Users/sabrinami/Desktop/psychencode_test_data/clozuk_pgc2.meta.sumstats.out.txt.gz INFO - Processing input gwas INFO - Aligning GWAS to models INFO - Trimming output INFO - Successfully parsed input gwas in 18.136219276 seconds INFO - Started metaxcan process INFO - Loading model from: /Users/sabrinami/Github/psychencode/models/psychencode_model/psychencode.db INFO - Loading covariance data from: /Users/sabrinami/Github/psychencode/models/psychencode_model/psychencode.txt.gz INFO - Processing loaded gwas INFO - Started metaxcan association INFO - 0 % of model's snps used INFO - Sucessfully processed metaxcan association in 132.015579446 seconds ``` To harmonize, we add a `varID` column in the GWAS file with the same format of as the model using information in the `CHR`, `BP`, `A1`, `A2` columns. The modified GWAS looks like: ``` zless /Users/sabrinami/Desktop/psychencode_test_data/clozuk_pgc2.meta.sumstats.out.txt.gz | head SNP Freq.A1 CHR BP A1 A2 OR SE P varID 10:100968448:T:AA 0.3519 10 100968448 T AA 1.0024 0.01 0.812 chr10_100968448_T_AA_b37 10:101574552:A:ATG 0.4493 10 101574552 A ATG 0.989060.0097 0.2585 chr10_101574552_A_ATG_b37 10:10222597:AT:A 0 10 10222597 A AT 0.9997 0.01 0.9777 chr10_10222597_A_AT_b37 ``` We also change our SPrediXcan parameters to indicate the GWAS SNP names follow the `varID` format, with the argument `--model_db_snp_key varID`. It runs correctly because it messages `INFO - 63 % of model's snps used`: ``` python3 $METAXCAN/SPrediXcan.py --gwas_file $DATA/clozuk_pgc2.meta.sumstats.out.txt.gz \ > --model_db_path $MODEL/psychencode_model/psychencode.db \ > --covariance $MODEL/psychencode_model/psychencode_varID.txt.gz \ > --keep_non_rsid --model_db_snp_key varID \ > --or_column OR \ > --pvalue_column P \ > --snp_column varID \ > --non_effect_allele_column A2 \ > --effect_allele_column A1 \ > --throw \ > --output_file $RESULTS/clozuk_pgc2_psychencode.csv ``` ``` INFO - Processing GWAS command line parameters INFO - Building beta for /Users/sabrinami/Desktop/psychencode_test_data/clozuk_pgc2.meta.sumstats.out.txt.gz and /Users/sabrinami/Github/psychencode/models/psychencode_model/psychencode.db INFO - Reading input gwas with special handling: /Users/sabrinami/Desktop/psychencode_test_data/clozuk_pgc2.meta.sumstats.out.txt.gz INFO - Processing input gwas INFO - Aligning GWAS to models INFO - Trimming output INFO - Successfully parsed input gwas in 22.284876615 seconds INFO - Started metaxcan process INFO - Loading model from: /Users/sabrinami/Github/psychencode/models/psychencode_model/psychencode.db INFO - Loading covariance data from: /Users/sabrinami/Github/psychencode/models/psychencode_model/psychencode_varID.txt.gz INFO - Processing loaded gwas INFO - Started metaxcan association INFO - 10 % of model's snps found so far in the gwas study INFO - 20 % of model's snps found so far in the gwas study INFO - 30 % of model's snps found so far in the gwas study INFO - 40 % of model's snps found so far in the gwas study INFO - 50 % of model's snps found so far in the gwas study INFO - 60 % of model's snps found so far in the gwas study INFO - 63 % of model's snps used INFO - Sucessfully processed metaxcan association in 153.879826246 seconds ``` If we still see the `INFO - 0 % of model's snps used` message, there may still be inconsistencies between the GWAS and covariance file. In the example, SPrediXcan runs using the `varID` format names from both the GWAS file and model. However, the covariance file may still identify variants by their RSID: ``` zless /Users/sabrinami/Github/psychencode/models/psychencode_model/psychencode.txt.gz | head GENE RSID1 RSID2 VALUE ENSG00000003249 rs140580708 rs140580708 0.013030848604677675 ENSG00000003249 rs140580708 rs164739 -0.008913106189505495 ENSG00000003249 rs140580708 rs1991508 -0.0017680061881014297 ``` Creating a new covariance file with `varID` format variant names replacing the `RSID1` and `RSID2` columns should fix the error. The prediction model file provides a dictionary to update IDs. We have [example code](file:///Users/sabrinami/Github/psychencode/docs/test_scz_clozuk_pgc.html#reformat_model_covariance) to do so. The following covariance file (with the same column names) gives the correct output: ``` zless /Users/sabrinami/Github/psychencode/models/psychencode_model/psychencode_varID.txt.gz | head GENE RSID1 RSID2 VALUE ENSG00000003249 chr16_89594100_C_G_b37 chr16_89594100_C_G_b37 0.0130308486046777 ENSG00000003249 chr16_89594100_C_G_b37 chr16_89667699_C_G_b37 -0.0089131061895055 ENSG00000003249 chr16_89594100_C_G_b37 chr16_89672155_C_T_b37 -0.00176800618810143 ```