Calling non-diploid organisms with UnifiedGenotyper

delangel

Calling non-diploid organisms with UnifiedGenotyper

New in GATK 2.0 is the capability of UnifiedGenotyper to natively call non-diploid organisms. Three use cases are currently supported:

1. Native variant calling in haploid or polyploid organisms.
2. Pooled calling where many pooled organisms share a single barcode and hence are treated as a single "sample".
3. Pooled validation/genotyping at known sites.

In order to enable this feature, users need to set the -ploidy argument to desired number of chromosomes per organism. In the case of pooled sequencing experiments, this argument should be set to the number of chromosomes per barcoded sample, i.e. (Ploidy per individual) * (Individuals in pool). Note that all other UnifiedGenotyper arguments work in the same way.

A full minimal command line would look for example like

java -jar GenomeAnalysisTK.jar \
-R reference.fasta \
-I myReads.bam \
-T UnifiedGenotyper \
-ploidy 3 

The glm argument works in the same way as in the diploid case - set to [INDEL|SNP|BOTH] to specify which variants to discover and/or genotype.

Current Limitations

Many of these limitations will be gradually removed in the following weeks as we iron out details and fix issues in the GATK 2.0 beta.

• Fragment-aware calling like the one provided by default for diploid organisms is not present for the non-diploid case.

• Some annotations do not work in non-diploid cases. In particular, current InbreedingCoeff is omitted. Annotations which do work and are supported in non-diploid use cases are the following: QUAL, QD, SB, FS, AC, AF and Genotype annotations such as PL, AD, GT, etc.

• The interaction between non-diploid calling and other experimental tools like HaplotypeCaller or ReduceReads is currently not supported.

• Whereas it's entirely possible to use VQSR to filter non-diploid calls, we currently have no experience with this and can hence offer no support nor best practices for this.

• Only a maximum of 4 alleles can be genotyped. This is not relevant for the SNP case, but discovering or genotyping more than this number of indel alleles will not work and an arbitrary set of 4 alleles will be chosen at a site.

Users should also be aware of the fundamental accuracy limitations of high ploidy calling. Calling low-frequency variants in a pool or in an organism with high ploidy is hard because these rare variants become almost indistinguishable from sequencing errors.

marcustuke

As GATK2 can handle Mitochondrial DNA, is there a recommended ploidy setting for human Mitochondria? I understand that mtDNA can vary dramatically in how many copies are present in a cell, but is there some sort of consensus value? (e.g. some sort of function of mean coverage)

Thank-you very much!

delangel

We've experimented with 50 to 100 but we make no optimality claims on that - probably a better number would be the ratio of (mean coverage in the MT contig) / (mean coverage in somatic chromosomes)

mike_lyons

@delangel are there any other recommended settings for MT with GATK?

sahiilseth

@delangel How does UG use this ploidy information for calling variants in MT? For SNPs at any position we dont expect more than 4 alleles (ATGC). In our low-pass data we have 5-7X coverage overall, and ~700X in case of mitochondria.

delangel

It's internal machinery needs to know the organism ploidy (i.e. number of chromosomes inside) to work well (btw number of possible different alleles is different than ploidy). Given your coverage I'd start with -ploidy 100 or so