Tagged with #haploid
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Call variants on a haploid genome with the UnifiedGenotyper, producing a raw (unfiltered) VCF.


  • TBD


  1. Determine the basic parameters of the analysis
  2. Call variants in your sequence data

1. Determine the basic parameters of the analysis

If you do not specify these parameters yourself, the program will use default values. However we recommend that you set them explicitly because it will help you understand how the results are bounded and how you can modify the program's behavior.

  • Ploidy (-ploidy)

In its basic use, this is the ploidy (number of chromosomes) per sample. By default it is set to 2, to process diploid organisms' genomes, but it can be set to any other desired value, starting at 1 for haploid organisms, and up for polyploids. This argument can also be used to handle pooled data. For that purpose, you'll need to set -ploidy to Number of samples in each pool * Sample Ploidy. There is no fixed upper limit, but keep in mind that high-level ploidy will increase processing times since the calculations involved are more complex. For full details on how to process pooled data, see Eran et al. (in preparation).

  • Genotype likelihood model (-glm)

This is the model that the program will use to calculate the genotype likelihoods. By default, it is set to SNP, but it can also be set to INDEL or BOTH. If set to BOTH, both SNPs and Indels will be called in the same run and be output to the same variants file.

  • Emission confidence threshold (–stand_emit_conf)

This is the minimum confidence threshold (phred-scaled) at which the program should emit sites that appear to be possibly variant.

  • Calling confidence threshold (–stand_call_conf)

This is the minimum confidence threshold (phred-scaled) at which the program should emit variant sites as called. If a site's associated genotype has a confidence score lower than the calling threshold, the program will emit the site as filtered and will annotate it as LowQual. This threshold separates high confidence calls from low confidence calls.

The terms called and filtered are tricky because they can mean different things depending on context. In ordinary language, people often say a site was called if it was emitted as variant. But in the GATK's technical language, saying a site was called means that that site passed the confidence threshold test. For filtered, it's even more confusing, because in ordinary language, when people say that sites were filtered, they usually mean that those sites successfully passed a filtering test. However, in the GATK's technical language, the same phrase (saying that sites were filtered) means that those sites failed the filtering test. In effect, it means that those would be filtered out if the filter was used to actually remove low-confidence calls from the callset, instead of just tagging them. In both cases, both usages are valid depending on the point of view of the person who is reporting the results. So it's always important to check what is the context when interpreting results that include these terms.

2. Call variants in your sequence data

Run the following GATK command:

java -jar GenomeAnalysisTK.jar \ 
    -T UnifiedGenotyper \ 
    -R haploid_reference.fa \ 
    -I haploid_reads.bam \ 
    -L 20 \ 
    -ploidy 1 
    --glm BOTH \ 
    --stand\_call\_conf 30 \ 
    --stand\_emit\_conf 10 \ 
    -o raw_haploid_variants.vcf 

This creates a VCF file called raw_haploid_variants.vcf, containing all the sites that the UnifiedGenotyper evaluated to be potentially variant.

Although you now have a nice fresh set of variant calls, the variant discovery stage is not over. The distinctions made by the caller itself between low-confidence calls and the rest is very primitive, and should not be taken as a definitive guide for filtering. The GATK callers are designed to be very lenient in calling variants, so it is extremely important to apply one of the recommended filtering methods (variant recalibration or hard-filtering), in order to move on to downstream analyses with the highest-quality call set possible.

Comments (14)

In general most GATK tools don't care about ploidy. The major exception is, of course, at the variant calling step: the variant callers need to know what ploidy is assumed for a given sample in order to perform the appropriate calculations.

Ploidy-related functionalities

As of version 3.3, the HaplotypeCaller and GenotypeGVCFs are able to deal with non-diploid organisms (whether haploid or exotically polyploid). In the case of HaplotypeCaller, you need to specify the ploidy of your non-diploid sample with the -ploidy argument. HC can only deal with one ploidy at a time, so if you want to process different chromosomes with different ploidies (e.g. to call X and Y in males) you need to run them separately. On the bright side, you can combine the resulting files afterward. In particular, if you’re running the -ERC GVCF workflow, you’ll find that both CombineGVCFs and GenotypeGVCFs are able to handle mixed ploidies (between locations and between samples). Both tools are able to correctly work out the ploidy of any given sample at a given site based on the composition of the GT field, so they don’t require you to specify the -ploidy argument.

For earlier versions (all the way to 2.0) the fallback option is UnifiedGenotyper, which also accepts the -ploidy argument.

Cases where ploidy needs to be specified

  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.

For normal organism ploidy, you just set the -ploidy argument to the 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).

Important limitations

Several variant annotations are not appropriate for use with non-diploid cases. In particular, InbreedingCoeff will not be annotated on non-diploid calls. Annotations that 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.

You 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.

Comments (10)

Until now, HaplotypeCaller was only capable of calling variants in diploid organisms due to some assumptions made in the underlying algorithms. I'm happy to announce that we now have a generalized version that is capable of handling any ploidy you specify at the command line!

This new feature, which we're calling "omniploidy", is technically still under development, but we think it's mature enough for the more adventurous to try out as a beta test ahead of the next official release. We'd especially love to get some feedback from people who work with non-diploids on a regular basis, so we're hoping that some of you microbiologists and assorted plant scientists will take it out for a spin and let us know how it behaves in your hands.

It's available in the latest nightly builds; just use the -ploidy argument to give it a whirl. If you have any questions or feedback, please post a comment on this article in the forum.

Caveat: the downstream tools involved in the new GVCF-based workflow (GenotypeGVCFs and CombineGVCFs) are not yet capable of handling non-diploid calls correctly -- but we're working on it.


We have added omniploidy support to the GVCF handling tools, with the following limitations:

  • When running, you need to indicate the sample ploidy that was used to generate the GVCFs with -ploidy. As usual 2 is the default ploidy.

  • The system does not support mixed ploidy across samples nor positions. An error message will be thrown if you attempt to genotype GVCFs that have a mixture, or that have some genotype whose ploidy does not match the -ploidy argument.


As of GATK version 3.3-0, the GVCF tools are capable of ad-hoc ploidy detection, and can handle mixed ploidies. See the release highlights for details.

Comments (1)

I have been trying to use CombinegVCFs on gVCF file produce by HaplotypeCaller in GVCF mode. The output VCF file doesn't seem to have any data in the genotype field: (just a dot)

chr1 95849 . T . . END=95850 GT:DP:GQ:MIN_DP:PL .:48:89:47:0,90 .:100:99:3:0,106 chr1 95851 . T . . END=95856 GT:DP:GQ:MIN_DP:PL .:48:44:47:0,45 .:100:99:3:0,106 chr1 95857 . GAA G,GA, . . DP=50;MQ=60.23;MQ0=0 GT:AD:DP:MIN_DP:PL:SB .:0,6,41,0:47:.:1150,935,0,1090:0,0,18,23 .:.:100:3:0,106,106,106 chr1 95858 . A . . END=95859 GT:DP:GQ:MIN_DP:PL . .:100:99:3:0,106 chr1 95860 . A . . END=96535 GT:DP:GQ:MIN_DP:PL .:44:99:27:0,1038 .:100:99:3:0,106 chr1 96536 . A G, . . DP=95;MQ=57.24;MQ0=0 GT:AD:DP:MIN_DP:PL:SB .:0,25,0:25:.:792,0,792:0,0,13,12 .:.:70:70:0,0,0 chr1 96537 . G . . END=96823 GT:DP:GQ:MIN_DP:PL .:37:99:23:0,380 .:68:99:51:0,374 chr1 96824 . C . . . GT:DP:GQ:MIN_DP:PL .:31:25:31:0,26 .:51:57:51:0,58

This is the command I used:

java -Xmx45g -Djava.io.tmpdir=/home/LANPARK/mboursnell/javatempdir -jar /opt/gatk/GenomeAnalysisTK.jar -R /home/genetics/strep_equi/strep_equi.fasta -T CombineGVCFs -V 17-1-2-5_NL_S12_L001_R1_001.gVCF -V 17-1-2-6_NL_S9_L001_R1_001.gVCF -o combined_1a.vcf -S STRICT

Comments (3)


Just wanted to confirm.. I have a data from 4 spores of a yeast (haploid) tetrad.. If I want to call out variants using all 4 spores (4 bam files), do I need to set -ploidy as 1 or as 4 (Number of samples in each pool * Sample Ploidy) ??

This is the code I am using:

java -d64 -Xms1g -Xmx4g -jar GenomeAnalysisTK.jar -glm SNP -nt 52 -R genome.fasta -T UnifiedGenotyper -I $basename"_A.realigned.bam" -I $basename"_B.realigned.bam" -I $basename"_C.realigned.bam" -I $basename"_D.realigned.bam" -ploidy 4 -o $basename.snps.vcf -stand_call_conf 25.0 -stand_emit_conf 10.0

Thank you.

Comments (2)


I'm working with SNP calling in a bacterium - I don't have a set of known SNPs, so prior to recalibration, so generate a mask file from all data. My question is, what should I put for the following two options:

--snpHets --indelHets

because the bacterium is haploid (and I specify --ploidy 1) it seems like these options should be set to zero, as there are no heterozygous loci, but I worry that if I set them to zero, that it won't work as expected. Any advice? I was just setting them to 0.001

Thanks, Gavin

Comments (1)


Does the GATK team have any recommendations for filtering SNP data for haploid genomes? Our team works with microbial eukaryotes, both haploid and diploid and we have used the GATK v3 best practices for filtering for the latter. [VQSR was not possible, since we do not have access to a truth/high confidence SNP set.]

Thanks, Mika

Comments (16)

Dear GATK team,

I know that in the past GATK was not suitable for haploid genomes. I wanted to ask if this possibly changed since then - and whether it is possible to use GATK for haploid genomes.

Thanks a lot, Gilgi