Hard-Filtering odd MQ distributions

anapaulamachado

Hello,
I'm working on filtering my snp calls from a non-model organism and thus going with hard-filtering instead of VQSR. I know this is always a bit of touch-and-go and there's no definite answer to which thresholds to employ (I've been through the documentation) but I'm hoping you can give me some more pointers.

I've started by using your indications first except for QD, and based on missing data and coverage. So right now I have filtered SNPs with: QD < 5 ¦¦ FS < 60 ¦¦ MQ < 40 ¦¦ MQRankSum < -12.5 ¦¦ ReadPosRankSum < -8 ¦¦ SOR < 3, maximum 30% missing calls, resulting in 16'397'726 SNPs (of 19'047'259 total unfiltered calls).
These the distributions before and after filtering (QUAL just for indication, didn't apply any filter to it).

It looks considerable better to me, but still far from what your example data looks like. In particular, I'm wondering about my MQ distribution that has these very high values (over 400, some come as "Inf" even) - have you seen this before?
Further, would it be "ok" to filter on the upper value of MQ as well as the lowest? Thanks !

Sheila

@anapaulamachado
Hi,

I know it is a pain, but there was a bug iirc in one of the GATK3 versions. If possible, can you try re-generating the VCFs with GATK 3.8-1 GenotypeGVCFs. Not sure if that will help or if you need to re-do the GVCFs as well, but it is worth a try with just GenotypeGVCFs at first.

-Sheila

anapaulamachado

@Sheila
Hi,
I've tried running GenotypeGVCFs with GATK3.8.1 but the distributions have not changed. Guess it must be from versions 3 to 4 or when making the g.vcf. Unfortunately I cannot recreate all my gvcf cause I got some of them from a colleague. I'll try to make the time in the next few weeks to recreate the gvcf for my own samples with 3.8.1 and at least let you know if that's it or not.

In any case, I selected these "too-high-MQ" calls and they look fine for all other indexes, no weird homo/heterozygosity, good QD/QUALS etc... I guess I'll just let it go and keep these variables, unless you see a problem with it.

Thanks !
Ana

Sheila

@anapaulamachado
Hi Ana,

I guess I'll just let it go and keep these variables, unless you see a problem with it.

Yes, I think that is totally fine and probably the easiest in your case.

-Sheila

Sheila

@anapaulamachado

Hi,

Which version of GATK are you using? I think those high MQ values were a bug in older versions.

-Sheila

niuguohao

Hi,
How to generate the plots you post?
Thanks!
Niugh

anapaulamachado

@Sheila sorry, forgot to post it. It's GATK3.7 so you might be on to something. I had to use g.vcf files generated with it so I decided to stick with the same version for the whole process.
Is there a way to handle these ?
Thanks, Ana

anapaulamachado

@niuguohao in the pdf attached there's a general tutorial to variant filtering. there's a section on these plots starting at page 11

Sheila

@anapaulamachado
Hi,

I know it is a pain, but there was a bug iirc in one of the GATK3 versions. If possible, can you try re-generating the VCFs with GATK 3.8-1 GenotypeGVCFs. Not sure if that will help or if you need to re-do the GVCFs as well, but it is worth a try with just GenotypeGVCFs at first.

-Sheila

anapaulamachado

@Sheila
Hi,
I've tried running GenotypeGVCFs with GATK3.8.1 but the distributions have not changed. Guess it must be from versions 3 to 4 or when making the g.vcf. Unfortunately I cannot recreate all my gvcf cause I got some of them from a colleague. I'll try to make the time in the next few weeks to recreate the gvcf for my own samples with 3.8.1 and at least let you know if that's it or not.

In any case, I selected these "too-high-MQ" calls and they look fine for all other indexes, no weird homo/heterozygosity, good QD/QUALS etc... I guess I'll just let it go and keep these variables, unless you see a problem with it.

Thanks !
Ana

Sheila

@anapaulamachado
Hi Ana,

I guess I'll just let it go and keep these variables, unless you see a problem with it.

Yes, I think that is totally fine and probably the easiest in your case.

-Sheila