Documentation and scripts provided for calculating imputation accuracy. Tested using Minimac4, Beagle, Impute5 and our deep learning based imputation tool project (in progress). Should work with any imputed VCF file that has GT or GT+DS format fields.
- samtools/bcftools: used to calculate MAFs
- python v3: source code was implemented and tested on python 3.6
pandasnumpycyvcf2
The following inputs, in vcf.gz format, including its respective tabix tbi file, are required to run.
- ga: genotype array file used as input for imputation
- imputed: imputation results
- wgs: ground truth file, containing experimentally determined genotypes (i.e. Whole Genome Sequencing data)
IMPORTANT PREREQUISITE: All files provided must be in vcf.gz format (compressed, tabixed). Alleles must match, in other words: NO SWAPS, NO FLIPS, SAME BUILD!!! It is not necessary to provide allele frequencies, since the tool will calculate it internally using bcftools.
The help shows all the required arguments listed above, plus optional arguments.
python3 Compare_imputation_to_WGS.py -h
usage: Compare_imputation_to_WGS.py --ga <input_genotype_array.vcf.gz> --imputed <imputed_file.vcf.gz> --wgs <whole_genome_file.vcf.gz>
Use -h or --help to display help.
arguments:
-h, --help show this help message and exit
--ga GA optional, path to genotype array file in vcf.gz format, with tbi
--wgs WGS required, path to whole genome file in vcf.gz format, with tbi
--imputed IMPUTED required, path to imputed file in vcf.gz format, with tbi
--ref REF optional, path to reference panel file in vcf.gz
format, with tbi. Used for MAF calculation. WGS file
will be used if no reference file is provided.
--max_total_rows MAX_TOTAL_ROWS
optional, maximun number of rows or variants to be loaded
simultaneously, summing all chunks loaded by all cores
--max_per_core MAX_PER_CORE
optional, maximun number of variants per chunk per core, lower
it to avoid RAM overload
--min_per_core MIN_PER_CORE
optional, minimun number of variants per chunk per core,
increase to avoid interprocess communication overload
--sout SOUT optional, output file path/name per sample, default is
the same as the imputed file with
_per_sample_results.txt suffix
--vout VOUT optional, output file path/name per variant, default is
the same as the imputed file with
_per_variant_results.txt suffix
A detailed report with accuracy ratio, F1 score, Pearson correlation (r2) is generated and wrote to the output file (i.e accuracy_result.txt)
python3.6 Compare_imputation_to_WGS.py --ga aric_GT_ancestry-5_cad_190625.vcf.chr1.gz --imputed aric_intersectWGS_rpt-1_ancestry-5_phasing-eagle_seed-E_imputed-HRC_cad_190625.vcf.chr1.gz --wgs aric_WGS_ancestry-5_cad_190625.recode.vcf.chr1.gz
Processing 239 imputed samples
Processing chunk: 1 Max rows per chunk: 10000
1 Read imputed file time: 0.0004201233386993408
2 Chunking time: 8.239876478910446e-06
3 Calculation time: 0.16841873712837696
4 Merging calculations per sample time: 0.0037479093298316
Results per sample at: aric_intersectWGS_rpt-1_ancestry-5_phasing-eagle_seed-E_imputed-HRC_cad_190625.vcf.chr1_per_sample_results.txt
Results per variant at: aric_intersectWGS_rpt-1_ancestry-5_phasing-eagle_seed-E_imputed-HRC_cad_190625.vcf.chr1_per_variant_results.txt
Total run time (sec): 0.17389075085520744
The results will be displayed as the example bellow (per variant):
position SNP REF_MAF IMPUTED_MAF WGS_MAF F-score concordance_P0 IQS r2 precision recall TP TN FP FN TP_ratio TN_ratio FP_ratio FN_ratio RMSE
22:48742072 22:48742072_G_A 0.0003 0.0013 0.0093 0.992 0.984 0.246 0.143 1.0 0.984 754 738 0 12 1.0 0.984 0.0 0.016 0.126
22:48742498 22:48742498_G_A 0.0028 0.0 0.002 0.998 0.996 0.0 0.014 1.0 0.996 752 749 0 3 1.0 0.996 0.0 0.004 0.063
22:48743012 22:48743012_C_A 0.061 0.0578 0.0665 0.947 0.882 0.479 0.3 0.946 0.948 804 610 46 44 0.946 0.933 0.054 0.067 0.307
22:48742525 22:48742525_G_A 0.0041 0.0013 0.0033 0.998 0.996 0.57 0.36 1.0 0.996 754 747 0 3 1.0 0.996 0.0 0.004 0.066
22:48741511 22:48741511_C_T 0.0009 0.0007 0.0007 1.0 1.0 1.0 0.999 1.0 1.0 753 751 0 0 1.0 1.00.0 0.0 0.002
22:48741402 22:48741402_A_G 0.2096 0.0439 0.2646 0.836 0.621 0.233 0.185 0.879 0.796 814 370 112 208 0.879 0.640.121 0.36 0.591
22:48742178 22:48742178_G_A 0.0089 0.0007 0.0153 0.986 0.971 0.081 0.062 1.0 0.972 753 729 0 22 1.0 0.971 0.0 0.029 0.167
22:48741385 22:48741385_T_C 0.3718 0.113 0.4149 0.863 0.547 0.192 0.168 0.768 0.984 1078 83 325 18 0.768 0.822 0.232 0.178 0.656
22:48741702 22:48741702_C_T 0.001 0.0047 0.0293 0.976 0.956 0.287 0.165 0.995 0.958 755 712 4 33 0.995 0.956 0.005 0.044 0.238
Results per sample:
imputed_ids WGS_ids F-score concordance_P0 r2 precision recall TP TN FP FN TP_ratio TN_ratio FP_ratio FN_ratio RMSE
A00003_A00003 A00003_A00003 0.976 0.95 0.497 0.972 0.981 103.0 94.0 3.0 2.0 0.972 0.979 0.028 0.021 0.152
A00018_A00018 A00018_A00018 0.932 0.851 0.696 0.972 0.896 103.0 84.0 3.0 12.0 0.972 0.875 0.028 0.125 0.296
A00056_A00056 A00056_A00056 0.99 0.98 0.613 0.981 1.0 104.0 96.0 2.0 0.0 0.981 1.0 0.019 0.0 0.162
A00080_A00080 A00080_A00080 0.986 0.97 0.671 0.981 0.991 106.0 93.0 2.0 1.0 0.981 0.989 0.019 0.011 0.141
A00083_A00083 A00083_A00083 0.99 0.98 0.56 0.981 1.0 104.0 96.0 2.0 0.0 0.981 1.0 0.019 0.0 0.128
A00099_A00099 A00099_A00099 0.99 0.98 0.139 0.99 0.99 102.0 98.0 1.0 1.0 0.99 0.99 0.01 0.01 0.138
A00120_A00120 A00120_A00120 0.976 0.96 0.5 0.962 0.99 101.0 96.0 4.0 1.0 0.962 0.99 0.038 0.01 0.21
A00146_A00146 A00146_A00146 0.972 0.941 0.64 0.963 0.981 103.0 93.0 4.0 2.0 0.963 0.979 0.037 0.021 0.176
A00152_A00152 A00152_A00152 0.986 0.97 0.403 0.981 0.99 103.0 96.0 2.0 1.0 0.981 0.99 0.019 0.01 0.17
The results can be interpreted as follows.
Metrics per variant:
- REF_MAF: Reference Panel MAF (if reference panel is provided)
- IMPUTED_MAF: Imputed MAF
- WGS_MAF: Whole Genome MAF
- F-score: macro F-score (not weighted),
- concordance_P0: accuracy ratio (concordance, from the article cited bellow [1]),
- IQS: imputation quality score (from the same article [1])
- precision: precision
- recall: recall
- TP: true positives
- TN: true negatives
- FP: false positives
- FN: false negatives
- TP_ratio: true positives ratio
- TN_ratio: true negatives ratio
- FP_ratio: false positives ratio
- FN_ratio: false negatives ratio
- RMSE: root mean squared error
Metrics per sample:
- F-core: F-score per sample
- concordance_P0: accuracy ratio
- r2: r-squared
- precision: precision
- recall: recall
- TP: true positives
- TN: true negatives
- FP: false positives
- FN: false negatives
- TP_ratio: true positives ratio
- TN_ratio: true negatives ratio
- FP_ratio: false positives ratio
- FN_ratio: false negatives ratio
- RMSE: root mean squared error
Check sample_data folder for real sample of inputs/outputs data and test run:
cd sample_data
python3 ../Compare_imputation_to_WGS.py --ga input_genotype_array_100samples.vcf.gz --wgs whole_genome_file_100samples.vcf.gz --imputed imputed_file_100samples.vcf.gz
This message will show up if everything worked:
Processing chunk: 1 Max rows per chunk: 10000
...
Processing chunk: 52 Max rows per chunk: 10000
1 Read imputed file time: 5.211546601727605
2 Chunking time: 0.0284710843116045
3 Calculation time: 90.96355835348368
4 Merging calculations per sample time: 20.620813813060522
Results per sample at: imputed_file_100samples.vcf_per_sample_results.txt
Results per variant at: imputed_file_100samples.vcf_per_variant_results.txt
Total run time (sec): 117.97938374243677
After running this example, you can visualize the results for the test sample data in:
- imputed_file_100samples.vcf_per_sample_results.txt
- imputed_file_100samples.vcf_per_variant_results.txt
For example:
head imputed_file_100samples.vcf_per_variant_results.txt
position SNP IMPUTED_MAF WGS_MAF F-score concordance_P0 IQS r2 precision recall TP TN FP FN TP_ratio TN_ratio FP_ratio FN_ratio RMSE
22:16050783 22:16050783_A_G 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 100 100 0 0 1.0 1.00.0 0.0 0.001
22:16050922 22:16050922_T_G 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 100 100 0 0 1.0 1.00.0 0.0 0.0
22:16050984 22:16050984_C_G 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 100 100 0 0 1.0 1.00.0 0.0 0.0
22:16051269 22:16051269_G_T 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 100 100 0 0 1.0 1.00.0 0.0 0.0
22:16051477 22:16051477_C_A 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 100 100 0 0 1.0 1.00.0 0.0 0.006
22:16052240 22:16052240_C_G 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 100 100 0 0 1.0 1.00.0 0.0 0.0
22:16052271 22:16052271_G_A 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 100 100 0 0 1.0 1.00.0 0.0 0.001
22:16052428 22:16052428_G_A 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 100 100 0 0 1.0 1.00.0 0.0 0.0
22:16052639 22:16052639_C_T 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 100 100 0 0 1.0 1.00.0 0.0 0.0
[1] Ramnarine S, Zhang J, Chen LS, Culverhouse R, Duan W, Hancock DB, Hartz SM, Johnson EO, Olfson E, Schwantes-An TH, Saccone NL. When does choice of accuracy measure alter imputation accuracy assessments?. PloS one. 2015 Oct 12;10(10):e0137601.
