Changes

Yes, but not often. The --mle option outputs the most likely genotype configuration taking into account observed genotypes and integration over the most similar reference haplotypes. The original genotypes will be changed only if the underlying reference haplotypes strongly contradict the input genotype.

Yes, but not often. The --mle option outputs the most likely genotype configuration taking into account observed genotypes and integration over the most similar reference haplotypes. The original genotypes will be changed only if the underlying reference haplotypes strongly contradict the input genotype.

=== '''How do I get imputation quality estimates?''' ===

=== How do I get imputation quality estimates?  ===

A: A simple approach is to use --mask option. For example, --mask 0.02 masks 2% of the genotypes at random, impute them and compare with the masked original to estimate genotypic and allelic error rates. Messages like the following will be generated to stdout:  

A simple approach is to use --mask option. For example, --mask 0.02 masks 2% of the genotypes at random, impute them and compare with the masked original to estimate genotypic and allelic error rates. Messages like the following will be generated to stdout:  

   Comparing 948352 masked genotypes with MLE estimates ...

   Comparing 948352 masked genotypes with MLE estimates ...

   Estimated per allele error rate is 0.0293  

   Estimated per allele error rate is 0.0293  

&nbsp;&nbsp;&nbsp; A better approach is to mask a small proportion of SNPs (vs. genotypes in the above simple approach). One can generate a mask.dat from the original .dat file by simply changing the flag of a subset of markers from M to S2 without duplicating the .ped file. Post-imputation, one can use&nbsp;&nbsp; [http://www.sph.umich.edu/csg/ylwtx/CalcMatch.1.0.5.tgz CalcMatch ]and [http://www.sph.umich.edu/csg/ylwtx/doseR2.tgz doseR2.pl ]to estimate genotypic/allelic error rate and correlation respectively. Both programs can be downloaded from [http://www.sph.umich.edu/csg/ylwtx/software.html http://www.sph.umich.edu/csg/ylwtx/software.html]<br>

A better approach is to mask a small proportion of SNPs (vs. genotypes in the above simple approach). One can generate a mask.dat from the original .dat file by simply changing the flag of a subset of markers from M to S2 without duplicating the .ped file. Post-imputation, one can use&nbsp;&nbsp; [http://www.sph.umich.edu/csg/ylwtx/CalcMatch.1.0.5.tgz CalcMatch ]and [http://www.sph.umich.edu/csg/ylwtx/doseR2.tgz doseR2.pl ]to estimate genotypic/allelic error rate and correlation respectively. Both programs can be downloaded from [http://www.sph.umich.edu/csg/ylwtx/software.html http://www.sph.umich.edu/csg/ylwtx/software.html].

&nbsp;&nbsp;&nbsp; '''Warning''': Imputation involving masked datasets should be performed separately for imputation quality estimation. For production, one should use all available information.<br>

'''Warning''': Imputation involving masked datasets should be performed separately for imputation quality estimation. For production, one should use all available information.

<br>  

<br>

=== '''Shall I apply QC before or after imputation? If so, how? '''  ===

=== '''Shall I apply QC before or after imputation? If so, how? '''  ===