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Developed in the Data Sciences Platform at the Broad Institute, the toolkit offers a wide variety of tools with a primary focus on variant discovery and genotyping. Its powerful processing engine and high-performance computing features make it capable of taking on projects of any size. Learn more

Question about handling genotypes during VCF liftOver from hg38 to hg19 using LiftoverVCF command



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    Laura Gauthier

    Hi user105689,

    The picard code says 

    If this interval is in the opposite orientation, all alleles and genotypes will be reverse complemented and indels will be left-aligned.

    I don't see any explicit mention of phasing, but based on the code I would expect the phasing to be maintained.

    I have run this tool myself on real data and I do remember it being "memory hungry", but I don't remember how much I actually required.  Sorry.


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    Hi Laura Gauthier,

    Thank you for your response.

    I had a concern about whether Picard's LiftoverVcf tool can successfully modify genotypes in situations where REF/ALT allele swaps occur across different reference genomes. In my original post, I mentioned that Reverse Complemented Alleles might need modification of genotypes, but I may have been mistaken.

    After successfully running the tool, I can confirm that GATK is able to modify genotypes when REF/ALT allele swaps occur (e.g. 0|0 -> 1|1, 0|1 -> 1|0, 1|0 -> 0|1, 1|1 -> 0|0). It's worth noting that REF/ALT allele swaps are only recovered if the "RECOVER_SWAPPED_REF_ALT" argument is used.

    Here are a few additional comments that might be helpful: Please note that these comments are accurate as of April 2023, and may not be applicable in the future.

    • I encountered a memory error when attempting to install Picard via conda, so I ended up using CrossMap (v0.6.4) instead, as it required less memory for VCF liftOver. However, I discovered that the genotypes were not successfully modified as expected with CrossMap (v0.6.4).
    • I ultimately installed Picard v2.27.5 binaries ( The job took approximately 45 minutes to run for 1,230,000 variants x 4200 samples. Please check end of this post for the code that was used to run it.
    • Unfortunately, I cannot determine the amount of memory actually used, as multiple jobs were run in parallel on the HPC cluster.

    Code that was used to run:

    java -jar /path/to/picard_v2.27.5/picard.jar \
    LiftoverVcf \
    I=${INPUT_VCF} \
    O=${OUTPUT_VCF} \
    CHAIN=${HG38toHG19_CHAIN} \
    MAX_RECORDS_IN_RAM=50000 \

    Hope this helps!

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    Laura Gauthier

    Great, that memory benchmarking will likely be helpful to other users in the future -- thanks!

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