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How to refine variant quality with GATK VQSR?

Learn how to refine variant quality using GATK VQSR with steps from data preparation to final validation, ensuring accurate filtered variants for analysis.

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How to refine variant quality with GATK VQSR?

 

Prepare Your Data

 

  • Ensure you have your reference genome, known sites of variation, and target variants ready.
  •  

  • Use the `HaplotypeCaller` to generate GVCF files for each sample. Merge these if you have multiple samples using `CombineGVCFs` and then run `GenotypeGVCFs` for joint genotyping.

 

Run BaseRecalibrator

 

  • Before variant recalibration, use the `BaseRecalibrator` tool to correct any systematic biases in base quality scores.
  •  

  • Provide it with known sites of variation to avoid skewing recalibration estimates.

 

Build the Recalibration Model

 

  • Use the `VariantRecalibrator` tool on your raw VCF file to build a model based on a set of highly-confident variants.
  •  

  • Choose appropriate truth and training datasets, like HapMap and 1000 Genomes Project, depending on your project specifications.

 

Apply the Recalibration Model

 

  • Run the `ApplyVQSR` command with the model generated, adjusting the level of stringency with the `--ts_filter_level` argument as needed (commonly 99.0 for SNPs and 90.0 for indels).
  •  

  • Separate SNPs and indels for recalibration, as they typically require different weighting of annotations.

 

Inspect Results

 

  • Examine your recalibrated VCF file to ensure the variant quality scores (VQSLOD) align with your expectations.
  •  

  • Visualize results using tools like IGV and summarize variant quality using `grep` or shell scripts to extract variants marked as PASS or filtered.

 

Fine-Tune Settings

 

  • If initial results are unsatisfactory, consider adjusting the set of annotations used in `VariantRecalibrator` or the `--ts_filter_level` to better suit your data's properties.
  •  

  • Review GATK documentation and community forums for insights on parameter adjustments specific to your dataset characteristics.

 

Final Validation

 

  • Perform a final validation step using independent, high-confidence datasets or experimental methods to ensure the integrity and accuracy of the filtered variants.
  •  

  • Document and save the final version of the recalibrated VCF file for downstream analysis and reporting.

 

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