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PGG.MHC

Database related codes
The codes here are mainly used to set up the analysis part of the database to run

HLA Typing

WGS_summary

Tools to use can find on our PGG.MHC database web. After making inferences about each individual using the software described above
WGS_merge_hla.py can combine the results to get the final result. Please note that the selection method is used, so it is possible that your replicating process will produce some different results than the ones we provide. The file in WGS_summary have test files.

WES_summary

Tools to use can find on our PGG.MHC database web. After making inferences about each individual using the software described above
WES_merge_hla.py can combine the results to get the final result. Please note that the selection method is used, so it is possible that your replicating process will produce some different results than the ones we provide. The file in WES_summary have test files.

HLA Imputation

SNP2HLA_workflow.py use SNP2HLA to do the imputation of HLA alleles from genotyping data.

usage: SNP2HLA_workflow.py [-h] -f VCF -o OUT -p PREFIX [-x THREADS]
                           [-w WINDOWSIZE] -v {38,19,18} -r REFERENCE_PANEL

optional arguments:
  -h, --help            show this help message and exit
  -f VCF, --vcf VCF     vcf file input bgzip and tabix
  -o OUT, --out OUT     output dir
  -p PREFIX, --prefix PREFIX
                        output prefix
  -x THREADS, --threads THREADS
                        threads
  -w WINDOWSIZE, --windowsize WINDOWSIZE
                        SNP2HLA window_size
  -v {38,19,18}, --version {38,19,18}
                        SNP versions
  -r REFERENCE_PANEL, --reference_panel REFERENCE_PANEL
                        reference_panel

dosage_result_to_hla_type.py was used to sort out the HLA alleles from the dosage result.

usage: dosage_result_to_hla_type.py [-h] -d DOSAGE -o OUT -f FAM

optional arguments:
  -h, --help            show this help message and exit
  -d DOSAGE, --dosage DOSAGE
                        dosage results
  -o OUT, --out OUT     output dir
  -f FAM, --fam FAM     fam file


For more information ,you can visit: HLA Imputation Instructions

HLA Association

After install PyHLA,The following parameters were used to analyze the association between different controls:

PyHLA.py 
 -i INPUT, --input INPUT
 -o OUT, --out OUT
 -d {2,4,6}, --digit {2,4,6},default 4
 -a, --assoc association analysis
 -m {allelic,dom,rec,additive}, --model {allelic,dom,rec,additive} genetic model, default allelic
 -t {fisher,chisq,logistic,linear}, statistical test method,default fisher
 -f FREQ, --freq FREQ  minimal frequency, default 0
 -j {FDR,FDR_BY,Bonferroni,Holm}, --adjust ,p value correction, default FDR
--digit 4               [Default]
--test fisher           [Default]
--model allelic         [Default]
--freq 0                [Default]
--adjust FDR            [Default]
--out output.txt        [Default]

For more information ,you can visit: HLA Association Instructions

Reference

SNP2HLA: Xiaoming Jia*, Buhm Han*, Suna Onengut-Gumuscu, Wei-Min Chen, Patrick J. Concannon, Stephen S. Rich, Soumya Raychaudhuri, Paul I.W. de Bakker. "Imputing Amino Acid Polymorphisms in Human Leukocyte Antigenes." PLoS One. 8(6):e64683. 2013.

PyHLA:Yanhui Fan, You-Qiang Song. (2016) PyHLA: tests for association between HLA alleles and diseases. BMC Bioinformatics. 2017. 18:90

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