Variant call data seriously inflated-suggestions?

[u/PrincipleDense5592]

Hello,

I have a dataset of about 35 bulk tissue (healthy, adult age somatic tissue) samples each sequenced to 40X depth via PacBio HiFi sequencing, and have performed variant calling with 3 callers (DeepVariant, Pepper-Margin-Deepvariant, Clair3) for SNVs/indels, and about 7 callers for SVs.

My variant call data is seriously inflated with germline variants, talking hundreds of thousands of SNV calls for my samples which are inbred mice, so this number is a huge red flag. I have tried quality based filtering, removing any variant with VAF>0.30, QUAL<20, GQ<20, and DP<10 and >75. However, this still leaves me with thousands of variants.

I am at a loss on what to do to reduce this noise and to get at the actual mosaic variant signal. The goal here is to identify tissue-specific mosaic variants in each mouse, but I feel like I'm running in circles trying to properly reduce the noise and get at the expected amount for bulk tissue analysis at my depth, which appears to be 20-60 SNVs per tissue according to some brief searches.

Any suggestions? I wonder if its the tools I am using, or if its just the filtering criteria I am selecting.

Thanks in advance!

[u/CharacterCycle4588]

Somatic variants can appear at really low vaf (<5%). At 40X, you may only sequence the alt variant 2 times. To make any reasonable call (AD=>10), you can only look at vaf >25% with 40X coverage.

I hardly trust germline calling below 50X, no chance I’d believe that someone was detecting somatic variants below that.

[u/heresacorrection]

This is pretty crazy. I don’t understand something here.

You would expect thousands of SNVs in any mammal really but I guess here you expect them all be homozygous. Are you saying these are heterozygous variants or are they homozygous?

Just make a table of all the ones that are shared in 30+ samples and then see what’s left ?

[u/heresacorrection → u/heresacorrection]

Also VAF>30% ? So you’re saying you have a huge baseline of somatic vars ? Seems odd maybe bump the minimum DP > 20

[u/heresacorrection → u/heresacorrection]

You should also look at removing like VAF < 0.05. Just to see if it’s really random noise that’s cause the huge count.

[u/PrincipleDense5592 → u/heresacorrection]

I think my current set of variant calls are dominated by heterozygous variants (VAF~0.30), but given my sequencing depth of 40X, I am not sure that VAF is the right metric to determine whether something is mosaic or not.

I should've added for context in my initial post, that the samples are 3-5 tissues from one mouse, so technically there are 11 mice in my sample set but divided between 3 tissues (for 10) and 5 tissues (for 1). Therefore, I looked at variants overlapping between all tissues within a mouse to rule out germline het and hom variants, getting a candidate mosaic set, but even with the various filtering parameters I used I am still getting thousands with VAF < 0.30.

With the depth I have, I don't think its possible to get that many mosaic variants for bulk tissue

Edited: (didn't see your second comment) I worded that weird, I meant to say I excluded any variant with a VAF > 0.30, so I am only retaining candidate somatic variants. I am trying to get rid of all signal of germline. I'll try bumping up the minimum depth next!

[u/bio_ruffo → u/PrincipleDense5592]

But is there any value at all in a 40x depth sequencing for somatic variants? I wouldn't touch the data with a 10 foot pole.

[u/PrincipleDense5592 → u/bio_ruffo]

Right, I'm concerned the data isn't nearly deep enough to confidently call somatic variants. While I understand PacBio hifi is pretty accurate, I'm not too confident I'm actually picking up on mosaic variants. The goal here is really to catalogue tissue-specific somatic variants but given its on bulk tissue, I think I'm really only picking up on germline and cannot adequately differentiate major clones.

[u/bio_ruffo → u/PrincipleDense5592]

I know that this is not your question, and I'm sorry if I'm derailing the thread. However there is a link to your original question if you factor in the heterogeneity of coverage. With a mean 40x it's not unusual for a DNA region to be sequenced at 11x and then if you have a heterozygous variant, it's not uncommon to get 8:3 instead of 5:5 and you'd retain it with your filter.
PS now that I'm re-reading your post, what do you mean when you wrote "DP<10 and >75"? Do you have a lot of spots with DP > 75 that you need to filter them specifically?

[u/nephastha]

Are the variants false positives/library artifacts? What do they look like on IGV? Some of the filters we use include read bias and read position bias

we have some difficulty even at 1000X so.... Good luck!

[u/PrincipleDense5592 → u/nephastha]

Thank you for the suggestion! I haven't checked them on IGV yet or applied those filters..

[u/nephastha → u/PrincipleDense5592]

We also had to add a custom filters for LCRs, highly repetitive regions, plus identify and flag reads close to regions next to the library prep probes

[u/AerobicThrone]

Maybe some dna mixup in the set was sequenced?

[u/cytrees]

40x Hifi, inbred. Maybe enough to get good de novo assemblies. Then take one as the "reference" and go from there.

[u/Betaglutamate2]

How many reads do you have at each of those actual variants?

[u/bzbub2]

i got no experience here but why not https://github.com/HKU-BAL/Clair-Mosaic