Tagged with #inbreedingcoeff
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A new tool has been released!

Check out the documentation at InbreedingCoeff.

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Workflow

To call variants with the GATK using pedigree information, you should base your workflow on the Best Practices recommendations -- the principles detailed there all apply to pedigree analysis.

But there is one crucial addition: you should make sure to pass a pedigree file (PED file) to all GATK walkers that you use in your workflow. Some will deliver better results if they see the pedigree data.

At the moment there are two of the standard annotations affected by pedigree:

  • Allele Frequency (computed on founders only)
  • Inbreeding coefficient (computed on founders only)

Note that you will need at least 10 founders to compute the inbreeding coefficient.

Trio Analysis

In the specific case of trios, an additional GATK walker, PhaseByTransmission, should be used to obtain trio-aware genotypes as well as phase by descent.

Important note

The annotations mentioned above have been adapted for PED files starting with GATK v.1.6. If you already have VCF files generated by an older version of the GATK or have not passed a PED file while running the UnifiedGenotyper or VariantAnnotator, you should do the following:

  • Run the latest version of the VariantAnnotator to re-annotate your variants.
  • Re-annotate all the standard annotations by passing the argument -G StandardAnnotation to VariantAnnotator. Make sure you pass your PED file to the VariantAnnotator as well!
  • If you are using Variant Quality Score Recalibration (VQSR) with the InbreedingCoefficient as an annotation in your model, you should re-run VQSR once the InbreedingCoefficient is updated.

PED files

The PED files used as input for these tools are based on PLINK pedigree files. The general description can be found here.

For these tools, the PED files must contain only the first 6 columns from the PLINK format PED file, and no alleles, like a FAM file in PLINK.

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I'm curious about the experience of the community at large with VQSR, and specifically with which sets of annotations people have found to work well. The GATK team's recommendations are valuable, but my impression is that they have fairly homogenous data types - I'd like to know if anyone has found it useful to deviate from their recommendations.

For instance, I no longer include InbreedingCoefficient with my exome runs. This was spurred by a case where previously validated variants were getting discarded by VQSR. It turned out that these particular variants were homozygous alternate in the diseased samples and homozygous reference in the controls, yielding an InbreedingCoefficient very close to 1. We decided that the all-homozygous case was far more likely to be genuinely interesting than a sequencing/variant calling artifact, so we removed the annotation from VQSR. In order to catch the all-heterozygous case (which is more likely to be an error), we add a VariantFiltration pass for 'InbreedingCoefficient < -0.8' following ApplyRecalibration.

In my case, I think InbreedingCoefficient isn't as useful because my UG/VQSR cohorts tend to be smaller and less diverse than what the GATK team typically runs (and to be honest, I'm still not sure we're doing the best thing). Has anyone else found it useful to modify these annotations? It would be helpful if we could build a more complete picture of these metrics in a diverse set of experiments.