wiki:ImputationPipeline

Version 6 (modified by a.kanterakis, 8 years ago) (diff)

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Imputation pipeline

There are at the moment two collaborating initiatives:

  • VU: Mathijs Kattenberg and Joukejan Hottenga using IMPUTE
  • UMCG: Lude Franke, Harm-Jan Westra, George Byelas, Morris Swertz using Beagle

The objective is to bring these pipelines into the same space so they can be properly compared and optimized.

TODO: describe the protocols here;

Description from Harm-Jan

The imputation pipeline has changed, in such a way that it was reduced to only a few steps. To facilitate QC and conversion steps, I've bundled our conversion tools in one single program called ImputationTool.jar.

Here, I shortly describe the steps that need to be in the new pipeline, in placeholders I also describe what the commands could look like, if you would implement this in a shellscript (or java program). These examples can be the complete execution steps of the pipeline.

Commands to run locally:

  1. if the dataset is in binary plink format, use plink --recode to convert back to ped+map)
  2. convert dataset to trityper format, if it is in ped+map format.
    java -Xmx4g -jar ImputationTool.jar --mode pmtt --in $plinkLocation --out $trityperOutputLocation
    
  3. compare the dataset to be imputed to the reference dataset (for example HapMap2 release 24, also in TriTyper? format), and remove any snps for which the haplotypes are different, or do not correlate to the reference dataset. Also remove any SNP that is not in the reference. Save the output as Ped+Map
    java -Xmx4g -jar ImputationTool.jar ttpmh $trityperOutputLocation $referenceLocation $pedAndMapOutputLocation [$famFile] # supply a famfile, if you have any... it is not required
    
  4. split the ped files in batches of 300 samples
      * mkdir -p ".$datasetLocation."/batches/
      * split -a2 -l$batchSize $pedAndMapOutputLocation $batchOutputLocation
    
  5. run linkage2beagle to convert the ped and map files to beagle format
    for each batch 
    do
          java -Xmx7g -jar linkage2beagle.jar data=$batchOutputLocation/chr$chromosome.dat pedigree=$batchOutputLocation/chr$chromosome.ped.$batch  beagle=$beagleLocation/chr$chromosome.bgl.$batch
    done 
    

Commands to run in server:

  1. run the actual imputation on the batches on the cluster (needs hapmap to be recoded to beagle format as well, but I have these files for you)
    for each batch 
    do
    	java -Xmx11g -Djava.io.tmpdir=\$TMPDIR -jar beagle.jar unphased=$beagleLocation/chr$chromosome.bgl.$batch phased=$referenceLocation/HM2_Chr$chromosome-BEAGLE markers=$referenceLocation/markers_Chr$chromosome.txt missing=0 out=$outputLocation/Chr$chromosome/chr$chromosome-$batch
    done
    

Commands to run locally:

  1. convert the beagle imputed files into trityper format
    java -Xmx4g -jar ImputationTool.jar bttb $outputLocation Chr/ChrCHROMOSOME-BATCH $imputedTriTyperLocation $numSamples	
    
  2. correlate the imputed snps to the snps in the original dataset
    java -Xmx4g -jar ImputationTool.jar corr $trityperOutputLocation $datasetName $imputedTriTyperLocation $imputedDatasetName 
    
  3. (if needed) convert to other formats (plink dosage / ped+map))

That's basically it. A lot simpler than the previous version, don't you think? The required tool is attached to this e-mail, but might still be a bit buggish. Any recommendations are therefore more than welcome.

IMPUTE pipeline

TODO: paste shell script descriptions of each step.

BEABLE pipeline

TODO: paste shell script descriptions of each step

Discussion

Mixing platforms may influence imputation results

We into some troubles, resulting in our test statistic being highly inflated (which is indicative of false positive results). We thought of some possible causes which might explain this effect, although we should still test them:

  • SNPs with bad imputation quality: we should remove SNPs with an R2 value < 0.90 prior to GWAS. These values are stored alongside the beagle imputation output. Taking a more stringent cutoff seemed to decrease the inflation, although you lose half of the SNPs.
  • batch effects caused by overrepresentation of a certain haplotype within an imputation batch: for each batch of samples, beagle estimates a best fitting model to predict the genotypes of the missing SNPs, which is dependent upon both the input data as the reference dataset. Cases and controls should be therefore randomly distributed across the batches. Another option is to use impute, rather than beagle, since its batches are across parts of the genome, instead of samples.
  • difference in source platform: different platforms have different SNP content. When you impute datasets coming from different platforms, the resulting model which is based on the input data is also different. When associating traits in a GWAS meta-analysis, these differences may account for a platform specific effect. We should therefore remove the SNPs which are non-overlapping between such platforms, prior to imputation, and impute the samples after combining the datasets. This would remove such a platform-bias, although would also cause a huge loss of available SNPs, when the overlap between platforms is small. However, in my opinion, this problem is similar to the batch effect problem, and can possibly be resolved by randomizing the sample content of the batches: the model will then possibly be fitted to the data that is available. In any case the datasets that are used in a meta-analysis should be imputed together.