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VQSR and Snpcall strategies for readgroups with different coverage distributions

Hi - I have a question on how best to do VQSR on my samples. One of the readgroups for my individuals are from genomic DNA and have very even coverage (around 10x) while the remaining 4-5 readgroups in the individuals are from Whole Genome Amplified (WGA) DNA. The WGA readgruops have very uneven coverage ranging from 0 to over a 1000 with a mean of around 30x (see attached image, blue is wga and turquoise is genomic, y-axis is depth and x-axis is sliding windows along a chromosome). So I have WGA and genomic libs for each individual and their coverage distributions are very different.

We tested different SNP calling (Unified Genotyper) and VSQR strategies and at the moment we think a strategy where we call and vqsr the genomic and wga libs separately and then combine them in the end works best. However I am interested on what the GATK team would have done in such a case. The reason we are doing it separately is that we think the vqsr on the combined libs would not be wise since there is such difference in the depth (and strand bias) between the WGA and genomic readgroups. If there was a way in the VQSR step to incorporate read group difference and include it in the algortihm it could maybe solve such a problem - but as far as I can see there is no such thing (we used the ReadGroupblacklist option when calling the RGs separately) - but for VQSR there is not a "include read group effects" kind of option. Or does it intrinsically include read group information in the machine learning step? By the way - we did the BQSR so the qualities would have been adjusted according to readgroup effects. But still there does seem to be a noticeable difference between the VQSR results we get from WGA vs genomic read groups (for instance WGA readgroups have consistently lower Hz than genomic readgroups calls - which we think is due to strand bias). From the VQSR plots it is clear that many SNPs are excluded in the WGA RGs due to strand bias and DP - however the bias is still visible after VQSR.

Sorry for the elaborate explanation - however - my question is how the GATK team would have handled SNPcalling and VQSR if the RG depth vary that much as in the attached image case.

Best Answer


  • OK - thank you. We do see some differences (regarding DP and FS ) in recalibration plots (and other stats) for the separate vs together recalibrations. So we decided to go with doing the recalibrations separately for the different RGs and then merge vcfs.

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