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, 16:15, 18 May 2010
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− | '''MaCH''' (MArkov Chain Haplotyping), mostly known as a software for genotype imputation, is a Hidden Markov Model (HMM) based haplotyper that reconstructs haplotypes from genotypes of unrelated individuals. Three primary utilities of MaCH are (1) to resolve haplotypes from diploid genotypes; (2) impute missing genotypes; and (3) perform disease mapping analysis. | + | [http://www.sph.umich.edu/csg/yli/mach/ '''MaCH'''] (MArkov Chain Haplotyping), mostly known as a software for genotype imputation, is a Hidden Markov Model (HMM) based haplotyper that reconstructs haplotypes from genotypes of unrelated individuals. Three primary utilities of MaCH are (1) to resolve haplotypes from diploid genotypes; (2) impute missing genotypes; and (3) perform disease mapping analysis. |
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| <br> | | <br> |
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| == FAQ == | | == FAQ == |
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− | '''Q: Why and how to perform a 2-step imputation?'''<br> | + | '''Q: Why and how to perform a 2-step imputation?'''<br> |
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− | A: When one has a large number of individuals (>1000), we recommend a 2-step imputation to speed up. <br> | + | A: When one has a large number of individuals (>1000), we recommend a 2-step imputation to speed up. <br> |
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− | A 2-step imputation contains the following 2 steps:<br> | + | A 2-step imputation contains the following 2 steps:<br> |
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− | (step 1) a representative subset of >= 200 unrelated individuals are used to calibrate model parameters; and<br> (step 2) actual genotype imputation is performed for every person using parameters inferred in step 1. <br> | + | (step 1) a representative subset of >= 200 unrelated individuals are used to calibrate model parameters; and<br> (step 2) actual genotype imputation is performed for every person using parameters inferred in step 1. <br> |
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| + | Example command lines for a 2-step imputation:<br> |
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− | Example command lines for a 2-step imputation:<br>
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| mach1 -d sample.dat -p subset.ped -s chr20.snps -h chr20.hap --compact --greedy --autoFlip -r 100 -o par_infer > mach.infer.log | | mach1 -d sample.dat -p subset.ped -s chr20.snps -h chr20.hap --compact --greedy --autoFlip -r 100 -o par_infer > mach.infer.log |
− | <br> | + | |
− | mach1 -d sample.dat -p sample.ped -s chr20.snps -h chr20.hap --compact --greedy --autoFlip <br> | + | <br> |
− | --errorMap par_infer.erate --crossoverMap par_infer.rec --mle --mldetails > mach.imp.log
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| + | mach1 -d sample.dat -p sample.ped -s chr20.snps -h chr20.hap --compact --greedy --autoFlip |
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| + | --errorMap par_infer.erate --crossoverMap par_infer.rec --mle --mldetails > mach.imp.log |
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| '''Q: Where can I find combined HapMap reference files? '''<br> A: http://www.sph.umich.edu/csg/yli/mach/download/HapMap-r21.html <br><br> | | '''Q: Where can I find combined HapMap reference files? '''<br> A: http://www.sph.umich.edu/csg/yli/mach/download/HapMap-r21.html <br><br> |
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| The combined files should look like:<br> '''comb.ped'''<br> r1 r1 0 0 1 A/C C/C G/G G/A A/A<br> 1 1 0 0 1 A/A ./. ./. G/G ./. | | The combined files should look like:<br> '''comb.ped'''<br> r1 r1 0 0 1 A/C C/C G/G G/A A/A<br> 1 1 0 0 1 A/A ./. ./. G/G ./. |
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− | '''comb.dat'''<br> M SNP1<br> M SNP2<br> M SNP3<br> M SNP4<br> M SNP5<br> | + | '''comb.dat'''<br> M SNP1<br> M SNP2<br> M SNP3<br> M SNP4<br> M SNP5<br> |
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| == Examples == | | == Examples == |