Package: HIBAG

Version: 1.20.0

Command: /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD check --install=check:HIBAG.install-out.txt --library=/Library/Frameworks/R.framework/Versions/Current/Resources/library --no-vignettes --timings HIBAG_1.20.0.tar.gz

StartedAt: 2019-10-16 03:36:52 -0400 (Wed, 16 Oct 2019)

EndedAt: 2019-10-16 03:38:52 -0400 (Wed, 16 Oct 2019)

EllapsedTime: 119.4 seconds

RetCode: 0

Status: OK

CheckDir: HIBAG.Rcheck

Warnings: 0

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### Running command:
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###   /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD check --install=check:HIBAG.install-out.txt --library=/Library/Frameworks/R.framework/Versions/Current/Resources/library --no-vignettes --timings HIBAG_1.20.0.tar.gz
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* using log directory ‘/Users/biocbuild/bbs-3.9-bioc/meat/HIBAG.Rcheck’
* using R version 3.6.1 (2019-07-05)
* using platform: x86_64-apple-darwin15.6.0 (64-bit)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘HIBAG/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HIBAG’ version ‘1.20.0’
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘HIBAG’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking compiled code ... NOTE
Note: information on .o files is not available
* checking installed files from ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU or elapsed time > 5s
                 user system elapsed
hlaConvSequence 5.431   0.49   5.928
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘runTests.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in ‘inst/doc’ ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 1 NOTE
See
  ‘/Users/biocbuild/bbs-3.9-bioc/meat/HIBAG.Rcheck/00check.log’
for details.

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### Running command:
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###   /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD INSTALL HIBAG
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* installing to library ‘/Library/Frameworks/R.framework/Versions/3.6/Resources/library’
* installing *source* package ‘HIBAG’ ...
** using staged installation
** libs
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX.sdk -I/usr/local/include  -fPIC  -Wall -g -O2  -c HIBAG.cpp -o HIBAG.o
clang++ -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX.sdk -I/usr/local/include  -fPIC  -Wall -g -O2  -c LibHLA.cpp -o LibHLA.o
clang++ -std=gnu++11 -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -single_module -multiply_defined suppress -L/Library/Frameworks/R.framework/Resources/lib -L/usr/local/lib -o HIBAG.so HIBAG.o LibHLA.o -F/Library/Frameworks/R.framework/.. -framework R -Wl,-framework -Wl,CoreFoundation
installing to /Library/Frameworks/R.framework/Versions/3.6/Resources/library/00LOCK-HIBAG/00new/HIBAG/libs
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
   ‘HIBAG_Tutorial.Rmd’ 
** testing if installed package can be loaded from temporary location
** checking absolute paths in shared objects and dynamic libraries
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (HIBAG)

R version 3.6.1 (2019-07-05) -- "Action of the Toes" Copyright (C) 2019 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin15.6.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ############################################################# > # > # DESCRIPTION: Unit tests in the HIBAG package > # > > # load the HIBAG package > library(HIBAG) HIBAG (HLA Genotype Imputation with Attribute Bagging) Kernel Version: v1.4 Supported by Streaming SIMD Extensions (SSE2) [64-bit] > > > ############################################################# > > # a list of HLA genes > hla.list <- c("A", "B", "C", "DQA1", "DQB1", "DRB1") > > # pre-defined lower bound of prediction accuracy > hla.acc <- c(0.9, 0.8, 0.8, 0.8, 0.8, 0.7) > > > for (hla.idx in seq_len(length(hla.list))) + { + hla.id <- hla.list[hla.idx] + + # make a "hlaAlleleClass" object + hla <- hlaAllele(HLA_Type_Table$sample.id, + H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")], + H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")], + locus=hla.id, assembly="hg19") + + # divide HLA types randomly + set.seed(100) + hlatab <- hlaSplitAllele(hla, train.prop=0.5) + + # SNP predictors within the flanking region on each side + region <- 500 # kb + snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id, + HapMap_CEU_Geno$snp.position, + hla.id, region*1000, assembly="hg19") + + # training and validation genotypes + train.geno <- hlaGenoSubset(HapMap_CEU_Geno, + snp.sel=match(snpid, HapMap_CEU_Geno$snp.id), + samp.sel=match(hlatab$training$value$sample.id, + HapMap_CEU_Geno$sample.id)) + test.geno <- hlaGenoSubset(HapMap_CEU_Geno, + samp.sel=match(hlatab$validation$value$sample.id, + HapMap_CEU_Geno$sample.id)) + + + # train a HIBAG model + set.seed(100) + model <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=10) + summary(model) + + # validation + pred <- predict(model, test.geno) + summary(pred) + + # compare + comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model, + call.threshold=0) + print(comp$overall) + + # check + if (comp$overall$acc.haplo < hla.acc[hla.idx]) + { + stop("HLA - ", hla.id, ", 'acc.haplo' should be >= ", + hla.acc[hla.idx], ".") + } + + cat("\n\n") + } Exclude 11 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 03:38:02 [1] 2019-10-16 03:38:02, OOB Acc: 77.27%, # of SNPs: 13, # of Haplo: 23 [2] 2019-10-16 03:38:03, OOB Acc: 88.46%, # of SNPs: 12, # of Haplo: 48 [3] 2019-10-16 03:38:03, OOB Acc: 85.71%, # of SNPs: 11, # of Haplo: 14 [4] 2019-10-16 03:38:03, OOB Acc: 75.00%, # of SNPs: 13, # of Haplo: 23 [5] 2019-10-16 03:38:03, OOB Acc: 79.41%, # of SNPs: 13, # of Haplo: 34 [6] 2019-10-16 03:38:03, OOB Acc: 100.00%, # of SNPs: 19, # of Haplo: 72 [7] 2019-10-16 03:38:03, OOB Acc: 100.00%, # of SNPs: 17, # of Haplo: 37 [8] 2019-10-16 03:38:03, OOB Acc: 84.62%, # of SNPs: 14, # of Haplo: 58 [9] 2019-10-16 03:38:03, OOB Acc: 89.29%, # of SNPs: 13, # of Haplo: 34 [10] 2019-10-16 03:38:03, OOB Acc: 80.77%, # of SNPs: 14, # of Haplo: 24 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004109150 0.0004156612 0.0004583766 0.0050417085 0.0096582452 0.0232052136 Max. Mean SD 0.5364225707 0.0500771527 0.1262815588 Accuracy with training data: 98.5% Out-of-bag accuracy: 86.1% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 10 total # of SNPs used: 93 avg. # of SNPs in an individual classifier: 13.90 (sd: 2.38, min: 11, max: 19, median: 13.00) avg. # of haplotypes in an individual classifier: 36.70 (sd: 17.93, min: 14, max: 72, median: 34.00) avg. out-of-bag accuracy: 86.05% (sd: 8.68%, min: 75.00%, max: 100.00%, median: 85.16%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004109150 0.0004156612 0.0004583766 0.0050417085 0.0096582452 0.0232052136 Max. Mean SD 0.5364225707 0.0500771527 0.1262815588 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 03:38:03) 0% Predicting (2019-10-16 03:38:03) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 1 (3.8%) 3 (11.5%) 4 (15.4%) 18 (69.2%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000000 0.002746 0.006607 0.033131 0.023928 0.536423 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 26 25 51 0.9615385 0.9807692 0 n.call call.rate 1 26 1 Exclude 1 monomorphic SNP Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 19 # of SNPs: 340, # of samples: 28 # of unique HLA alleles: 22 [-] 2019-10-16 03:38:03 [1] 2019-10-16 03:38:04, OOB Acc: 58.33%, # of SNPs: 17, # of Haplo: 52 [2] 2019-10-16 03:38:04, OOB Acc: 63.64%, # of SNPs: 18, # of Haplo: 51 [3] 2019-10-16 03:38:04, OOB Acc: 50.00%, # of SNPs: 15, # of Haplo: 29 [4] 2019-10-16 03:38:04, OOB Acc: 59.09%, # of SNPs: 12, # of Haplo: 57 [5] 2019-10-16 03:38:04, OOB Acc: 63.64%, # of SNPs: 15, # of Haplo: 86 [6] 2019-10-16 03:38:04, OOB Acc: 79.17%, # of SNPs: 18, # of Haplo: 66 [7] 2019-10-16 03:38:05, OOB Acc: 70.83%, # of SNPs: 15, # of Haplo: 86 [8] 2019-10-16 03:38:05, OOB Acc: 77.78%, # of SNPs: 16, # of Haplo: 117 [9] 2019-10-16 03:38:05, OOB Acc: 77.78%, # of SNPs: 18, # of Haplo: 92 [10] 2019-10-16 03:38:06, OOB Acc: 61.11%, # of SNPs: 15, # of Haplo: 72 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 9.567411e-05 9.631975e-05 1.021306e-04 3.122433e-03 7.264975e-03 1.191013e-02 Max. Mean SD 2.325640e-01 1.687210e-02 4.316862e-02 Accuracy with training data: 100.0% Out-of-bag accuracy: 66.1% Gene: B Training dataset: 28 samples X 340 SNPs # of HLA alleles: 22 # of individual classifiers: 10 total # of SNPs used: 118 avg. # of SNPs in an individual classifier: 15.90 (sd: 1.91, min: 12, max: 18, median: 15.50) avg. # of haplotypes in an individual classifier: 70.80 (sd: 25.28, min: 29, max: 117, median: 69.00) avg. out-of-bag accuracy: 66.14% (sd: 9.84%, min: 50.00%, max: 79.17%, median: 63.64%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 9.567411e-05 9.631975e-05 1.021306e-04 3.122433e-03 7.264975e-03 1.191013e-02 Max. Mean SD 2.325640e-01 1.687210e-02 4.316862e-02 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 340 SNPs, 22 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 15 Predicting (2019-10-16 03:38:06) 0% Predicting (2019-10-16 03:38:06) 100% Gene: B Range: [31321649bp, 31324989bp] on hg19 # of samples: 15 # of unique HLA alleles: 9 # of unique HLA genotypes: 12 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 3 (20.0%) 5 (33.3%) 3 (20.0%) 4 (26.7%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 2.000e-08 4.068e-05 2.934e-03 3.316e-02 6.112e-03 2.412e-01 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 15 11 25 0.7333333 0.8333333 0 n.call call.rate 1 15 1 Exclude 2 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 19 # of SNPs: 354, # of samples: 36 # of unique HLA alleles: 17 [-] 2019-10-16 03:38:06 [1] 2019-10-16 03:38:06, OOB Acc: 80.77%, # of SNPs: 19, # of Haplo: 40 [2] 2019-10-16 03:38:06, OOB Acc: 90.91%, # of SNPs: 32, # of Haplo: 32 [3] 2019-10-16 03:38:06, OOB Acc: 89.29%, # of SNPs: 19, # of Haplo: 43 [4] 2019-10-16 03:38:07, OOB Acc: 84.62%, # of SNPs: 19, # of Haplo: 72 [5] 2019-10-16 03:38:07, OOB Acc: 90.00%, # of SNPs: 19, # of Haplo: 66 [6] 2019-10-16 03:38:07, OOB Acc: 95.00%, # of SNPs: 21, # of Haplo: 59 [7] 2019-10-16 03:38:07, OOB Acc: 90.62%, # of SNPs: 18, # of Haplo: 25 [8] 2019-10-16 03:38:07, OOB Acc: 89.29%, # of SNPs: 23, # of Haplo: 57 [9] 2019-10-16 03:38:08, OOB Acc: 84.62%, # of SNPs: 18, # of Haplo: 39 [10] 2019-10-16 03:38:08, OOB Acc: 89.29%, # of SNPs: 35, # of Haplo: 62 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0007716165 0.0007743171 0.0007986223 0.0017413935 0.0044732464 0.0093175730 Max. Mean SD 0.0723014524 0.0089787922 0.0135525777 Accuracy with training data: 100.0% Out-of-bag accuracy: 88.4% Gene: C Training dataset: 36 samples X 354 SNPs # of HLA alleles: 17 # of individual classifiers: 10 total # of SNPs used: 135 avg. # of SNPs in an individual classifier: 22.30 (sd: 6.13, min: 18, max: 35, median: 19.00) avg. # of haplotypes in an individual classifier: 49.50 (sd: 15.74, min: 25, max: 72, median: 50.00) avg. out-of-bag accuracy: 88.44% (sd: 4.04%, min: 80.77%, max: 95.00%, median: 89.29%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0007716165 0.0007743171 0.0007986223 0.0017413935 0.0044732464 0.0093175730 Max. Mean SD 0.0723014524 0.0089787922 0.0135525777 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 354 SNPs, 17 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 24 Predicting (2019-10-16 03:38:08) 0% Predicting (2019-10-16 03:38:08) 100% Gene: C Range: [31236526bp, 31239913bp] on hg19 # of samples: 24 # of unique HLA alleles: 14 # of unique HLA genotypes: 19 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 2 (8.3%) 3 (12.5%) 6 (25.0%) 13 (54.2%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000000 0.0000000 0.0002058 0.0060220 0.0035911 0.0497611 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 24 16 39 0.6666667 0.8125 0 n.call call.rate 1 24 1 Exclude 4 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 19 # of SNPs: 345, # of samples: 31 # of unique HLA alleles: 7 [-] 2019-10-16 03:38:08 [1] 2019-10-16 03:38:08, OOB Acc: 95.45%, # of SNPs: 11, # of Haplo: 22 [2] 2019-10-16 03:38:08, OOB Acc: 100.00%, # of SNPs: 13, # of Haplo: 22 [3] 2019-10-16 03:38:08, OOB Acc: 83.33%, # of SNPs: 15, # of Haplo: 23 [4] 2019-10-16 03:38:08, OOB Acc: 82.14%, # of SNPs: 8, # of Haplo: 14 [5] 2019-10-16 03:38:08, OOB Acc: 88.46%, # of SNPs: 11, # of Haplo: 34 [6] 2019-10-16 03:38:08, OOB Acc: 90.00%, # of SNPs: 11, # of Haplo: 21 [7] 2019-10-16 03:38:08, OOB Acc: 92.31%, # of SNPs: 14, # of Haplo: 23 [8] 2019-10-16 03:38:08, OOB Acc: 96.15%, # of SNPs: 11, # of Haplo: 16 [9] 2019-10-16 03:38:08, OOB Acc: 89.29%, # of SNPs: 12, # of Haplo: 19 [10] 2019-10-16 03:38:08, OOB Acc: 86.36%, # of SNPs: 8, # of Haplo: 13 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.001972961 0.001998819 0.002231547 0.005363515 0.008831104 0.018431530 Max. Mean SD 0.557800372 0.029648068 0.098391249 Accuracy with training data: 96.8% Out-of-bag accuracy: 90.4% Gene: DQA1 Training dataset: 31 samples X 345 SNPs # of HLA alleles: 7 # of individual classifiers: 10 total # of SNPs used: 80 avg. # of SNPs in an individual classifier: 11.40 (sd: 2.27, min: 8, max: 15, median: 11.00) avg. # of haplotypes in an individual classifier: 20.70 (sd: 5.96, min: 13, max: 34, median: 21.50) avg. out-of-bag accuracy: 90.35% (sd: 5.72%, min: 82.14%, max: 100.00%, median: 89.64%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.001972961 0.001998819 0.002231547 0.005363515 0.008831104 0.018431530 Max. Mean SD 0.557800372 0.029648068 0.098391249 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 345 SNPs, 7 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 29 Predicting (2019-10-16 03:38:08) 0% Predicting (2019-10-16 03:38:08) 100% Gene: DQA1 Range: [32605169bp, 32612152bp] on hg19 # of samples: 29 # of unique HLA alleles: 6 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 5 (17.2%) 5 (17.2%) 2 (6.9%) 17 (58.6%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000001 0.0019253 0.0069908 0.0562248 0.0167536 0.5649087 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 29 21 49 0.7241379 0.8448276 0 n.call call.rate 1 29 1 Exclude 6 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 19 # of SNPs: 350, # of samples: 34 # of unique HLA alleles: 12 [-] 2019-10-16 03:38:09 [1] 2019-10-16 03:38:09, OOB Acc: 86.36%, # of SNPs: 13, # of Haplo: 34 [2] 2019-10-16 03:38:09, OOB Acc: 76.92%, # of SNPs: 21, # of Haplo: 42 [3] 2019-10-16 03:38:09, OOB Acc: 80.77%, # of SNPs: 10, # of Haplo: 17 [4] 2019-10-16 03:38:09, OOB Acc: 92.31%, # of SNPs: 22, # of Haplo: 78 [5] 2019-10-16 03:38:09, OOB Acc: 92.31%, # of SNPs: 11, # of Haplo: 40 [6] 2019-10-16 03:38:09, OOB Acc: 71.43%, # of SNPs: 8, # of Haplo: 22 [7] 2019-10-16 03:38:09, OOB Acc: 71.43%, # of SNPs: 14, # of Haplo: 53 [8] 2019-10-16 03:38:09, OOB Acc: 86.36%, # of SNPs: 14, # of Haplo: 40 [9] 2019-10-16 03:38:10, OOB Acc: 100.00%, # of SNPs: 16, # of Haplo: 56 [10] 2019-10-16 03:38:10, OOB Acc: 88.46%, # of SNPs: 14, # of Haplo: 34 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0003282346 0.0003687353 0.0007332412 0.0039826881 0.0073528147 0.0148594626 Max. Mean SD 0.2905810098 0.0221828682 0.0553213757 Accuracy with training data: 98.5% Out-of-bag accuracy: 84.6% Gene: DQB1 Training dataset: 34 samples X 350 SNPs # of HLA alleles: 12 # of individual classifiers: 10 total # of SNPs used: 99 avg. # of SNPs in an individual classifier: 14.30 (sd: 4.45, min: 8, max: 22, median: 14.00) avg. # of haplotypes in an individual classifier: 41.60 (sd: 17.55, min: 17, max: 78, median: 40.00) avg. out-of-bag accuracy: 84.64% (sd: 9.41%, min: 71.43%, max: 100.00%, median: 86.36%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0003282346 0.0003687353 0.0007332412 0.0039826881 0.0073528147 0.0148594626 Max. Mean SD 0.2905810098 0.0221828682 0.0553213757 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 350 SNPs, 12 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 03:38:10) 0% Predicting (2019-10-16 03:38:10) 100% Gene: DQB1 Range: [32627241bp, 32634466bp] on hg19 # of samples: 26 # of unique HLA alleles: 10 # of unique HLA genotypes: 17 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 3 (11.5%) 7 (26.9%) 5 (19.2%) 11 (42.3%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000000 0.0002253 0.0018486 0.0289671 0.0099906 0.3724278 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 26 21 46 0.8076923 0.8846154 0 n.call call.rate 1 26 1 Exclude 5 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 18 # of SNPs: 322, # of samples: 35 # of unique HLA alleles: 20 [-] 2019-10-16 03:38:10 [1] 2019-10-16 03:38:10, OOB Acc: 70.00%, # of SNPs: 17, # of Haplo: 77 [2] 2019-10-16 03:38:11, OOB Acc: 68.75%, # of SNPs: 22, # of Haplo: 119 [3] 2019-10-16 03:38:11, OOB Acc: 73.33%, # of SNPs: 19, # of Haplo: 33 [4] 2019-10-16 03:38:11, OOB Acc: 84.62%, # of SNPs: 18, # of Haplo: 67 [5] 2019-10-16 03:38:12, OOB Acc: 86.36%, # of SNPs: 24, # of Haplo: 127 [6] 2019-10-16 03:38:12, OOB Acc: 66.67%, # of SNPs: 18, # of Haplo: 102 [7] 2019-10-16 03:38:13, OOB Acc: 75.00%, # of SNPs: 15, # of Haplo: 71 [8] 2019-10-16 03:38:13, OOB Acc: 70.00%, # of SNPs: 15, # of Haplo: 32 [9] 2019-10-16 03:38:13, OOB Acc: 91.67%, # of SNPs: 20, # of Haplo: 93 [10] 2019-10-16 03:38:14, OOB Acc: 66.67%, # of SNPs: 15, # of Haplo: 57 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.041155e-05 3.565321e-05 1.728281e-04 1.616937e-03 2.836751e-03 7.180633e-03 Max. Mean SD 5.019208e-01 4.576117e-02 1.379081e-01 Accuracy with training data: 94.3% Out-of-bag accuracy: 75.3% Gene: DRB1 Training dataset: 35 samples X 322 SNPs # of HLA alleles: 20 # of individual classifiers: 10 total # of SNPs used: 129 avg. # of SNPs in an individual classifier: 18.30 (sd: 3.06, min: 15, max: 24, median: 18.00) avg. # of haplotypes in an individual classifier: 77.80 (sd: 32.72, min: 32, max: 127, median: 74.00) avg. out-of-bag accuracy: 75.31% (sd: 9.00%, min: 66.67%, max: 91.67%, median: 71.67%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.041155e-05 3.565321e-05 1.728281e-04 1.616937e-03 2.836751e-03 7.180633e-03 Max. Mean SD 5.019208e-01 4.576117e-02 1.379081e-01 Genome assembly: hg19 HIBAG model: 10 individual classifiers, 322 SNPs, 20 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 25 Predicting (2019-10-16 03:38:14) 0% Predicting (2019-10-16 03:38:14) 100% Gene: DRB1 Range: [32546546bp, 32557613bp] on hg19 # of samples: 25 # of unique HLA alleles: 10 # of unique HLA genotypes: 17 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 4 (16.0%) 5 (20.0%) 9 (36.0%) 7 (28.0%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000000 0.0001451 0.0007388 0.0111563 0.0028980 0.2302289 total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold 1 25 16 40 0.64 0.8 0 n.call call.rate 1 25 1 > > > > ############################################################# > > { + function.list <- readRDS( + system.file("Meta", "Rd.rds", package="HIBAG"))$Name + + sapply(function.list, FUN = function(func.name) + { + args <- list( + topic = func.name, + package = "HIBAG", + echo = FALSE, + verbose = FALSE, + ask = FALSE + ) + suppressWarnings(do.call(example, args)) + NULL + }) + invisible() + } SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 23 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 14 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 03:38:14 1, SNP: 211, Loss: 196.4, OOB Acc: 54.55%, # of Haplo: 13 2, SNP: 66, Loss: 173.548, OOB Acc: 63.64%, # of Haplo: 13 3, SNP: 177, Loss: 136.352, OOB Acc: 68.18%, # of Haplo: 13 4, SNP: 108, Loss: 95.8359, OOB Acc: 72.73%, # of Haplo: 13 5, SNP: 127, Loss: 67.3216, OOB Acc: 77.27%, # of Haplo: 13 6, SNP: 95, Loss: 47.5888, OOB Acc: 77.27%, # of Haplo: 13 7, SNP: 33, Loss: 37.2631, OOB Acc: 77.27%, # of Haplo: 16 8, SNP: 6, Loss: 29.7419, OOB Acc: 77.27%, # of Haplo: 18 9, SNP: 208, Loss: 25.6913, OOB Acc: 77.27%, # of Haplo: 19 10, SNP: 225, Loss: 25.3087, OOB Acc: 77.27%, # of Haplo: 21 11, SNP: 11, Loss: 24.8356, OOB Acc: 77.27%, # of Haplo: 23 12, SNP: 151, Loss: 19.4134, OOB Acc: 77.27%, # of Haplo: 23 13, SNP: 199, Loss: 17.011, OOB Acc: 77.27%, # of Haplo: 23 [1] 2019-10-16 03:38:14, OOB Acc: 77.27%, # of SNPs: 13, # of Haplo: 23 1, SNP: 160, Loss: 221.236, OOB Acc: 76.92%, # of Haplo: 17 2, SNP: 145, Loss: 173.538, OOB Acc: 80.77%, # of Haplo: 23 3, SNP: 177, Loss: 128.58, OOB Acc: 84.62%, # of Haplo: 31 4, SNP: 111, Loss: 79.6877, OOB Acc: 84.62%, # of Haplo: 31 5, SNP: 207, Loss: 52.5557, OOB Acc: 88.46%, # of Haplo: 32 6, SNP: 245, Loss: 41.8731, OOB Acc: 88.46%, # of Haplo: 34 7, SNP: 230, Loss: 31.7937, OOB Acc: 88.46%, # of Haplo: 38 8, SNP: 151, Loss: 20.4566, OOB Acc: 88.46%, # of Haplo: 36 9, SNP: 14, Loss: 19.5805, OOB Acc: 88.46%, # of Haplo: 42 10, SNP: 132, Loss: 19.5101, OOB Acc: 88.46%, # of Haplo: 42 11, SNP: 221, Loss: 19.485, OOB Acc: 88.46%, # of Haplo: 44 12, SNP: 251, Loss: 18.5695, OOB Acc: 88.46%, # of Haplo: 48 [2] 2019-10-16 03:38:14, OOB Acc: 88.46%, # of SNPs: 12, # of Haplo: 48 1, SNP: 191, Loss: 193.067, OOB Acc: 57.14%, # of Haplo: 11 2, SNP: 264, Loss: 150.427, OOB Acc: 64.29%, # of Haplo: 12 3, SNP: 132, Loss: 93.4067, OOB Acc: 67.86%, # of Haplo: 12 4, SNP: 128, Loss: 39.8353, OOB Acc: 71.43%, # of Haplo: 12 5, SNP: 160, Loss: 28.2998, OOB Acc: 75.00%, # of Haplo: 12 6, SNP: 144, Loss: 13.635, OOB Acc: 75.00%, # of Haplo: 12 7, SNP: 111, Loss: 6.04609, OOB Acc: 75.00%, # of Haplo: 12 8, SNP: 40, Loss: 6.04583, OOB Acc: 82.14%, # of Haplo: 14 9, SNP: 141, Loss: 6.04583, OOB Acc: 85.71%, # of Haplo: 14 10, SNP: 73, Loss: 2.9038, OOB Acc: 85.71%, # of Haplo: 14 11, SNP: 199, Loss: 2.20025, OOB Acc: 85.71%, # of Haplo: 14 [3] 2019-10-16 03:38:14, OOB Acc: 85.71%, # of SNPs: 11, # of Haplo: 14 1, SNP: 147, Loss: 158.631, OOB Acc: 50.00%, # of Haplo: 12 2, SNP: 152, Loss: 140.375, OOB Acc: 55.00%, # of Haplo: 13 3, SNP: 78, Loss: 115.887, OOB Acc: 60.00%, # of Haplo: 16 4, SNP: 115, Loss: 77.8082, OOB Acc: 60.00%, # of Haplo: 18 5, SNP: 148, Loss: 62.6831, OOB Acc: 65.00%, # of Haplo: 18 6, SNP: 13, Loss: 46.5657, OOB Acc: 75.00%, # of Haplo: 20 7, SNP: 109, Loss: 31.0312, OOB Acc: 75.00%, # of Haplo: 20 8, SNP: 176, Loss: 22.5073, OOB Acc: 75.00%, # of Haplo: 21 9, SNP: 145, Loss: 20.9122, OOB Acc: 75.00%, # of Haplo: 21 10, SNP: 128, Loss: 20.6728, OOB Acc: 75.00%, # of Haplo: 21 11, SNP: 73, Loss: 14.6217, OOB Acc: 75.00%, # of Haplo: 22 12, SNP: 151, Loss: 10.2879, OOB Acc: 75.00%, # of Haplo: 23 13, SNP: 199, Loss: 8.74645, OOB Acc: 75.00%, # of Haplo: 23 [4] 2019-10-16 03:38:14, OOB Acc: 75.00%, # of SNPs: 13, # of Haplo: 23 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 Max. Mean SD 0.5261716555 0.0476729895 0.1180875414 Accuracy with training data: 97.1% Out-of-bag accuracy: 81.6% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 38 avg. # of SNPs in an individual classifier: 12.25 (sd: 0.96, min: 11, max: 13, median: 12.50) avg. # of haplotypes in an individual classifier: 27.00 (sd: 14.63, min: 14, max: 48, median: 23.00) avg. out-of-bag accuracy: 81.61% (sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 Max. Mean SD 0.5261716555 0.0476729895 0.1180875414 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 03:38:14) 0% Predicting (2019-10-16 03:38:14) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 11 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 1 (3.8%) 4 (15.4%) 4 (15.4%) 17 (65.4%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000000 0.002097 0.007711 0.034403 0.028032 0.526172 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 03:38:14) 0% Predicting (2019-10-16 03:38:14) 100% Open "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode. Open "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/HIBAG/extdata/HapMap_CEU.fam". Open "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/HIBAG/extdata/HapMap_CEU.bim". Import 3932 SNPs within the xMHC region on chromosome 6. HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 90 Predicting (2019-10-16 03:38:14) 0% Predicting (2019-10-16 03:38:14) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 using the default genome assembly (assembly="hg19") Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 12 # of unique HLA genotypes: 28 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 100 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 dominant model: [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p 24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* ----- 01:01 36 24 50.0 50.0 0.0000 1.000 1.000 02:01 25 35 52.0 48.6 0.0000 1.000 1.000 02:06 59 1 50.8 0.0 0.0000 1.000 1.000 03:01 51 9 49.0 55.6 0.0000 1.000 1.000 11:01 55 5 50.9 40.0 0.0000 1.000 1.000 23:01 58 2 50.0 50.0 0.0000 1.000 1.000 24:03 59 1 50.8 0.0 0.0000 1.000 1.000 25:01 55 5 52.7 20.0 0.8727 0.350 0.353 26:01 57 3 52.6 0.0 1.4035 0.236 0.237 29:02 56 4 51.8 25.0 0.2679 0.605 0.612 31:01 57 3 49.1 66.7 0.0000 1.000 1.000 32:01 56 4 46.4 100.0 2.4107 0.121 0.112 68:01 57 3 52.6 0.0 1.4035 0.236 0.237 additive model: [-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p 01:01 95 25 50.5 48.0 0.0000 1.000 1.000 02:01 77 43 48.1 53.5 0.1450 0.703 0.704 02:06 119 1 50.4 0.0 0.0000 1.000 1.000 03:01 111 9 49.5 55.6 0.0000 1.000 1.000 11:01 115 5 50.4 40.0 0.0000 1.000 1.000 23:01 117 3 50.4 33.3 0.0000 1.000 1.000 24:02 109 11 46.8 81.8 3.6030 0.058 0.053 24:03 119 1 50.4 0.0 0.0000 1.000 1.000 25:01 115 5 51.3 20.0 0.8348 0.361 0.364 26:01 117 3 51.3 0.0 1.3675 0.242 0.244 29:02 116 4 50.9 25.0 0.2586 0.611 0.619 31:01 117 3 49.6 66.7 0.0000 1.000 1.000 32:01 116 4 48.3 100.0 2.3276 0.127 0.119 68:01 117 3 51.3 0.0 1.3675 0.242 0.244 recessive model: [-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p 01:01 59 1 50.8 0 0.000 1.000 1.000 02:01 52 8 46.2 75 1.298 0.255 0.254 02:06 60 0 50.0 . . . . 03:01 60 0 50.0 . . . . 11:01 60 0 50.0 . . . . 23:01 59 1 50.8 0 0.000 1.000 1.000 24:02 60 0 50.0 . . . . 24:03 60 0 50.0 . . . . 25:01 60 0 50.0 . . . . 26:01 60 0 50.0 . . . . 29:02 60 0 50.0 . . . . 31:01 60 0 50.0 . . . . 32:01 60 0 50.0 . . . . 68:01 60 0 50.0 . . . . genotype model: [-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p 24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042* ----- 01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000 02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271 02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000 03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000 11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000 23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000 24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000 25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353 26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237 29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612 31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000 32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112 68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237 dominant model: [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p 01:01 36 24 -0.14684 -0.117427 0.909 02:01 25 35 -0.32331 -0.000618 0.190 02:06 59 1 -0.14024 0.170057 . 03:01 51 9 -0.05600 -0.583178 0.147 11:01 55 5 -0.19188 0.489815 0.287 23:01 58 2 -0.15400 0.413687 0.281 24:02 49 11 -0.10486 -0.269664 0.537 24:03 59 1 -0.11409 -1.373118 . 25:01 55 5 -0.12237 -0.274749 0.742 26:01 57 3 -0.12473 -0.331558 0.690 29:02 56 4 -0.13044 -0.199941 0.789 31:01 57 3 -0.10097 -0.783003 0.607 32:01 56 4 -0.07702 -0.947791 0.092 68:01 57 3 -0.16915 0.512457 0.196 genotype model: [-/-] [-/h] [h/h] avg.[-/-] avg.[-/h] avg.[h/h] anova.p 01:01 36 23 1 -0.14684 -0.08833 -0.78655 0.784 02:01 25 27 8 -0.32331 -0.02341 0.07631 0.446 02:06 59 1 0 -0.14024 0.17006 . 0.756 03:01 51 9 0 -0.05600 -0.58318 . 0.138 11:01 55 5 0 -0.19188 0.48981 . 0.137 23:01 58 1 1 -0.15400 0.10762 0.71975 0.663 24:02 49 11 0 -0.10486 -0.26966 . 0.618 24:03 59 1 0 -0.11409 -1.37312 . 0.205 25:01 55 5 0 -0.12237 -0.27475 . 0.742 26:01 57 3 0 -0.12473 -0.33156 . 0.725 29:02 56 4 0 -0.13044 -0.19994 . 0.892 31:01 57 3 0 -0.10097 -0.78300 . 0.243 32:01 56 4 0 -0.07702 -0.94779 . 0.086 68:01 57 3 0 -0.16915 0.51246 . 0.243 Logistic regression (dominant model) with 60 individuals: glm(case ~ h, family = binomial, data = data) [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est 24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.792e+00 ----- 01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -8.777e-16 02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.372e-01 02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01 03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.624e-01 11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.418e-01 23:01 58 2 50.0 50.0 0.0000 1.000 1.000 2.874e-15 24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01 25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.495e+00 26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01 29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.170e+00 31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.282e-01 32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.771e+01 68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01 h.2.5% h.97.5% h.pval 24:02 0.1585 3.4251 0.032* ----- 01:01 -1.0330 1.0330 1.000 02:01 -1.1643 0.8899 0.793 02:06 -2868.1268 2836.9268 0.991 03:01 -1.1624 1.6872 0.718 11:01 -2.3074 1.4237 0.643 23:01 -2.8192 2.8192 1.000 24:03 -2868.1268 2836.9268 0.991 25:01 -3.7498 0.7588 0.194 26:01 -2731.9621 2698.6192 0.990 29:02 -3.4931 1.1530 0.324 31:01 -1.7277 3.1842 0.561 32:01 -3859.2763 3894.6947 0.993 68:01 -2731.9621 2698.6192 0.990 Logistic regression (dominant model) with 60 individuals: glm(case ~ h + pc1, family = binomial, data = data) [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est 24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.793e+00 ----- 01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -2.268e-04 02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.370e-01 02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.562e+01 03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.686e-01 11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.451e-01 23:01 58 2 50.0 50.0 0.0000 1.000 1.000 -3.062e-03 24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01 25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.501e+00 26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01 29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.189e+00 31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.289e-01 32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.781e+01 68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.673e+01 h.2.5% h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval 24:02 0.1587 3.4264 0.032* 0.011111 -0.5249 0.5471 0.968 ----- 01:01 -1.0334 1.0330 1.000 -0.005807 -0.5126 0.5010 0.982 02:01 -1.1652 0.8913 0.794 -0.002618 -0.5102 0.5049 0.992 02:06 -2868.1460 2836.9076 0.991 -0.028534 -0.5374 0.4803 0.912 03:01 -1.1813 1.7185 0.717 0.011958 -0.5044 0.5283 0.964 11:01 -2.3225 1.4322 0.642 0.008025 -0.5026 0.5186 0.975 23:01 -2.8348 2.8287 0.998 -0.005857 -0.5148 0.5031 0.982 24:03 -2868.1286 2836.9250 0.991 -0.011249 -0.5182 0.4957 0.965 25:01 -3.7579 0.7568 0.193 -0.025685 -0.5490 0.4976 0.923 26:01 -2731.8901 2698.5450 0.990 -0.014069 -0.5297 0.5015 0.957 29:02 -3.5309 1.1526 0.320 0.033234 -0.4796 0.5461 0.899 31:01 -1.7274 3.1851 0.561 -0.008320 -0.5153 0.4987 0.974 32:01 -3845.6317 3881.2510 0.993 -0.125426 -0.6671 0.4162 0.650 68:01 -2721.2124 2687.7497 0.990 -0.086589 -0.6512 0.4781 0.764 Logistic regression (dominant model) with 60 individuals: glm(case ~ h + pc1, family = binomial, data = data) [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est_OR 24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 6.005e+00 ----- 01:01 36 24 50.0 50.0 0.0000 1.000 1.000 9.998e-01 02:01 25 35 52.0 48.6 0.0000 1.000 1.000 8.720e-01 02:06 59 1 50.8 0.0 0.0000 1.000 1.000 1.647e-07 03:01 51 9 49.0 55.6 0.0000 1.000 1.000 1.308e+00 11:01 55 5 50.9 40.0 0.0000 1.000 1.000 6.407e-01 23:01 58 2 50.0 50.0 0.0000 1.000 1.000 9.969e-01 24:03 59 1 50.8 0.0 0.0000 1.000 1.000 1.676e-07 25:01 55 5 52.7 20.0 0.8727 0.350 0.353 2.230e-01 26:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.744e-08 29:02 56 4 51.8 25.0 0.2679 0.605 0.612 3.045e-01 31:01 57 3 49.1 66.7 0.0000 1.000 1.000 2.073e+00 32:01 56 4 46.4 100.0 2.4107 0.121 0.112 5.428e+07 68:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.416e-08 h.2.5%_OR h.97.5%_OR h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval 24:02 1.17200 30.766 0.032* 0.011111 -0.5249 0.5471 0.968 ----- 01:01 0.35579 2.809 1.000 -0.005807 -0.5126 0.5010 0.982 02:01 0.31185 2.438 0.794 -0.002618 -0.5102 0.5049 0.992 02:06 0.00000 Inf 0.991 -0.028534 -0.5374 0.4803 0.912 03:01 0.30687 5.576 0.717 0.011958 -0.5044 0.5283 0.964 11:01 0.09803 4.188 0.642 0.008025 -0.5026 0.5186 0.975 23:01 0.05873 16.923 0.998 -0.005857 -0.5148 0.5031 0.982 24:03 0.00000 Inf 0.991 -0.011249 -0.5182 0.4957 0.965 25:01 0.02333 2.131 0.193 -0.025685 -0.5490 0.4976 0.923 26:01 0.00000 Inf 0.990 -0.014069 -0.5297 0.5015 0.957 29:02 0.02928 3.167 0.320 0.033234 -0.4796 0.5461 0.899 31:01 0.17774 24.171 0.561 -0.008320 -0.5153 0.4987 0.974 32:01 0.00000 Inf 0.993 -0.125426 -0.6671 0.4162 0.650 68:01 0.00000 Inf 0.990 -0.086589 -0.6512 0.4781 0.764 Linear regression (dominant model) with 60 individuals: glm(y ~ h, data = data) [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% h.97.5% 01:01 36 24 -0.14684 -0.117427 0.909 0.02941 -0.4805 0.5393 02:01 25 35 -0.32331 -0.000618 0.190 0.32269 -0.1772 0.8226 02:06 59 1 -0.14024 0.170057 . 0.31030 -1.6397 2.2603 03:01 51 9 -0.05600 -0.583178 0.147 -0.52718 -1.2136 0.1592 11:01 55 5 -0.19188 0.489815 0.287 0.68170 -0.2051 1.5685 23:01 58 2 -0.15400 0.413687 0.281 0.56768 -0.8165 1.9518 24:02 49 11 -0.10486 -0.269664 0.537 -0.16481 -0.8091 0.4795 24:03 59 1 -0.11409 -1.373118 . -1.25903 -3.1835 0.6655 25:01 55 5 -0.12237 -0.274749 0.742 -0.15237 -1.0555 0.7507 26:01 57 3 -0.12473 -0.331558 0.690 -0.20683 -1.3519 0.9383 29:02 56 4 -0.13044 -0.199941 0.789 -0.06950 -1.0709 0.9319 31:01 57 3 -0.10097 -0.783003 0.607 -0.68203 -1.8149 0.4508 32:01 56 4 -0.07702 -0.947791 0.092 -0.87077 -1.8470 0.1054 68:01 57 3 -0.16915 0.512457 0.196 0.68161 -0.4512 1.8145 h.pval 01:01 0.910 02:01 0.211 02:06 0.756 03:01 0.138 11:01 0.137 23:01 0.425 24:02 0.618 24:03 0.205 25:01 0.742 26:01 0.725 29:02 0.892 31:01 0.243 32:01 0.086 68:01 0.243 Linear regression (dominant model) with 60 individuals: glm(y ~ h + pc1, data = data) [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% 01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773 02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891 02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722 03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884 11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520 23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598 24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034 24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708 25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388 26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460 29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496 31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254 32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862 68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749 h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval 01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386 02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414 02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371 03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539 11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474 23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343 24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393 24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395 25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396 26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390 29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372 31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371 32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210 68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294 Linear regression (dominant model) with 60 individuals: glm(y ~ h + pc1, data = data) [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% 01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773 02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891 02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722 03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884 11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520 23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598 24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034 24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708 25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388 26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460 29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496 31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254 32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862 68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749 h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval 01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386 02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414 02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371 03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539 11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474 23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343 24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393 24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395 25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396 26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390 29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372 31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371 32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210 68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294 Logistic regression (additive model) with 60 individuals: glm(case ~ h, family = binomial, data = data) [-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p h.est h.2.5% 24:02 109 11 46.8 81.8 3.6030 0.058 0.053 1.7918 0.1585 ----- 01:01 95 25 50.5 48.0 0.0000 1.000 1.000 -0.1207 -1.0843 02:01 77 43 48.1 53.5 0.1450 0.703 0.704 0.2137 -0.5289 02:06 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268 03:01 111 9 49.5 55.6 0.0000 1.000 1.000 0.2624 -1.1624 11:01 115 5 50.4 40.0 0.0000 1.000 1.000 -0.4418 -2.3074 23:01 117 3 50.4 33.3 0.0000 1.000 1.000 -0.4323 -2.3435 24:03 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268 25:01 115 5 51.3 20.0 0.8348 0.361 0.364 -1.4955 -3.7498 26:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621 29:02 116 4 50.9 25.0 0.2586 0.611 0.619 -1.1701 -3.4931 31:01 117 3 49.6 66.7 0.0000 1.000 1.000 0.7282 -1.7277 32:01 116 4 48.3 100.0 2.3276 0.127 0.119 17.7092 -3859.2763 68:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621 h.97.5% h.pval 24:02 3.4251 0.032* ----- 01:01 0.8430 0.806 02:01 0.9563 0.573 02:06 2836.9268 0.991 03:01 1.6872 0.718 11:01 1.4237 0.643 23:01 1.4789 0.658 24:03 2836.9268 0.991 25:01 0.7588 0.194 26:01 2698.6192 0.990 29:02 1.1530 0.324 31:01 3.1842 0.561 32:01 3894.6947 0.993 68:01 2698.6192 0.990 Logistic regression (recessive model) with 60 individuals: glm(case ~ h, family = binomial, data = data) [-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p h.est 01:01 59 1 50.8 0 0.000 1.000 1.000 -15.600 02:01 52 8 46.2 75 1.298 0.255 0.254 1.253 02:06 60 0 50.0 . . . . . 03:01 60 0 50.0 . . . . . 11:01 60 0 50.0 . . . . . 23:01 59 1 50.8 0 0.000 1.000 1.000 -15.600 24:02 60 0 50.0 . . . . . 24:03 60 0 50.0 . . . . . 25:01 60 0 50.0 . . . . . 26:01 60 0 50.0 . . . . . 29:02 60 0 50.0 . . . . . 31:01 60 0 50.0 . . . . . 32:01 60 0 50.0 . . . . . 68:01 60 0 50.0 . . . . . h.2.5% h.97.5% h.pval 01:01 -2868.1268 2836.927 0.991 02:01 -0.4379 2.943 0.146 02:06 . . . 03:01 . . . 11:01 . . . 23:01 -2868.1268 2836.927 0.991 24:02 . . . 24:03 . . . 25:01 . . . 26:01 . . . 29:02 . . . 31:01 . . . 32:01 . . . 68:01 . . . Logistic regression (genotype model) with 60 individuals: glm(case ~ h, family = binomial, data = data) [-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p 24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042* ----- 01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000 02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271 02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000 03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000 11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000 23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000 24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000 25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353 26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237 29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612 31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000 32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112 68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237 h1.est h1.2.5% h1.97.5% h1.pval h2.est h2.2.5% h2.97.5% 24:02 1.79176 0.1585 3.4251 0.032* . . . ----- 01:01 0.08701 -0.9600 1.1340 0.871 -15.566 -2868.0929 2836.961 02:01 -0.45474 -1.5524 0.6430 0.417 1.019 -0.7637 2.801 02:06 -15.59997 -2868.1268 2836.9268 0.991 . . . 03:01 0.26236 -1.1624 1.6872 0.718 . . . 11:01 -0.44183 -2.3074 1.4237 0.643 . . . 23:01 16.56607 -4686.4552 4719.5873 0.994 -16.566 -4719.5873 4686.455 24:03 -15.59997 -2868.1268 2836.9268 0.991 . . . 25:01 -1.49549 -3.7498 0.7588 0.194 . . . 26:01 -16.67143 -2731.9621 2698.6192 0.990 . . . 29:02 -1.17007 -3.4931 1.1530 0.324 . . . 31:01 0.72824 -1.7277 3.1842 0.561 . . . 32:01 17.70917 -3859.2763 3894.6947 0.993 . . . 68:01 -16.67143 -2731.9621 2698.6192 0.990 . . . h2.pval 24:02 . ----- 01:01 0.991 02:01 0.263 02:06 . 03:01 . 11:01 . 23:01 0.994 24:03 . 25:01 . 26:01 . 29:02 . 31:01 . 32:01 . 68:01 . Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 23 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 14 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 03:38:16 1, SNP: 211, Loss: 196.4, OOB Acc: 54.55%, # of Haplo: 13 2, SNP: 66, Loss: 173.548, OOB Acc: 63.64%, # of Haplo: 13 3, SNP: 177, Loss: 136.352, OOB Acc: 68.18%, # of Haplo: 13 4, SNP: 108, Loss: 95.8359, OOB Acc: 72.73%, # of Haplo: 13 5, SNP: 127, Loss: 67.3216, OOB Acc: 77.27%, # of Haplo: 13 6, SNP: 95, Loss: 47.5888, OOB Acc: 77.27%, # of Haplo: 13 7, SNP: 33, Loss: 37.2631, OOB Acc: 77.27%, # of Haplo: 16 8, SNP: 6, Loss: 29.7419, OOB Acc: 77.27%, # of Haplo: 18 9, SNP: 208, Loss: 25.6913, OOB Acc: 77.27%, # of Haplo: 19 10, SNP: 225, Loss: 25.3087, OOB Acc: 77.27%, # of Haplo: 21 11, SNP: 11, Loss: 24.8356, OOB Acc: 77.27%, # of Haplo: 23 12, SNP: 151, Loss: 19.4134, OOB Acc: 77.27%, # of Haplo: 23 13, SNP: 199, Loss: 17.011, OOB Acc: 77.27%, # of Haplo: 23 [1] 2019-10-16 03:38:16, OOB Acc: 77.27%, # of SNPs: 13, # of Haplo: 23 1, SNP: 160, Loss: 221.236, OOB Acc: 76.92%, # of Haplo: 17 2, SNP: 145, Loss: 173.538, OOB Acc: 80.77%, # of Haplo: 23 3, SNP: 177, Loss: 128.58, OOB Acc: 84.62%, # of Haplo: 31 4, SNP: 111, Loss: 79.6877, OOB Acc: 84.62%, # of Haplo: 31 5, SNP: 207, Loss: 52.5557, OOB Acc: 88.46%, # of Haplo: 32 6, SNP: 245, Loss: 41.8731, OOB Acc: 88.46%, # of Haplo: 34 7, SNP: 230, Loss: 31.7937, OOB Acc: 88.46%, # of Haplo: 38 8, SNP: 151, Loss: 20.4566, OOB Acc: 88.46%, # of Haplo: 36 9, SNP: 14, Loss: 19.5805, OOB Acc: 88.46%, # of Haplo: 42 10, SNP: 132, Loss: 19.5101, OOB Acc: 88.46%, # of Haplo: 42 11, SNP: 221, Loss: 19.485, OOB Acc: 88.46%, # of Haplo: 44 12, SNP: 251, Loss: 18.5695, OOB Acc: 88.46%, # of Haplo: 48 [2] 2019-10-16 03:38:16, OOB Acc: 88.46%, # of SNPs: 12, # of Haplo: 48 1, SNP: 191, Loss: 193.067, OOB Acc: 57.14%, # of Haplo: 11 2, SNP: 264, Loss: 150.427, OOB Acc: 64.29%, # of Haplo: 12 3, SNP: 132, Loss: 93.4067, OOB Acc: 67.86%, # of Haplo: 12 4, SNP: 128, Loss: 39.8353, OOB Acc: 71.43%, # of Haplo: 12 5, SNP: 160, Loss: 28.2998, OOB Acc: 75.00%, # of Haplo: 12 6, SNP: 144, Loss: 13.635, OOB Acc: 75.00%, # of Haplo: 12 7, SNP: 111, Loss: 6.04609, OOB Acc: 75.00%, # of Haplo: 12 8, SNP: 40, Loss: 6.04583, OOB Acc: 82.14%, # of Haplo: 14 9, SNP: 141, Loss: 6.04583, OOB Acc: 85.71%, # of Haplo: 14 10, SNP: 73, Loss: 2.9038, OOB Acc: 85.71%, # of Haplo: 14 11, SNP: 199, Loss: 2.20025, OOB Acc: 85.71%, # of Haplo: 14 [3] 2019-10-16 03:38:16, OOB Acc: 85.71%, # of SNPs: 11, # of Haplo: 14 1, SNP: 147, Loss: 158.631, OOB Acc: 50.00%, # of Haplo: 12 2, SNP: 152, Loss: 140.375, OOB Acc: 55.00%, # of Haplo: 13 3, SNP: 78, Loss: 115.887, OOB Acc: 60.00%, # of Haplo: 16 4, SNP: 115, Loss: 77.8082, OOB Acc: 60.00%, # of Haplo: 18 5, SNP: 148, Loss: 62.6831, OOB Acc: 65.00%, # of Haplo: 18 6, SNP: 13, Loss: 46.5657, OOB Acc: 75.00%, # of Haplo: 20 7, SNP: 109, Loss: 31.0312, OOB Acc: 75.00%, # of Haplo: 20 8, SNP: 176, Loss: 22.5073, OOB Acc: 75.00%, # of Haplo: 21 9, SNP: 145, Loss: 20.9122, OOB Acc: 75.00%, # of Haplo: 21 10, SNP: 128, Loss: 20.6728, OOB Acc: 75.00%, # of Haplo: 21 11, SNP: 73, Loss: 14.6217, OOB Acc: 75.00%, # of Haplo: 22 12, SNP: 151, Loss: 10.2879, OOB Acc: 75.00%, # of Haplo: 23 13, SNP: 199, Loss: 8.74645, OOB Acc: 75.00%, # of Haplo: 23 [4] 2019-10-16 03:38:16, OOB Acc: 75.00%, # of SNPs: 13, # of Haplo: 23 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 Max. Mean SD 0.5261716555 0.0476729895 0.1180875414 Accuracy with training data: 97.1% Out-of-bag accuracy: 81.6% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 38 avg. # of SNPs in an individual classifier: 12.25 (sd: 0.96, min: 11, max: 13, median: 12.50) avg. # of haplotypes in an individual classifier: 27.00 (sd: 14.63, min: 14, max: 48, median: 23.00) avg. out-of-bag accuracy: 81.61% (sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 Max. Mean SD 0.5261716555 0.0476729895 0.1180875414 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 03:38:16) 0% Predicting (2019-10-16 03:38:16) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 11 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 1 (3.8%) 4 (15.4%) 4 (15.4%) 17 (65.4%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000000 0.002097 0.007711 0.034403 0.028032 0.526172 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 03:38:16) 0% Predicting (2019-10-16 03:38:16) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 11 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 1 (3.8%) 4 (15.4%) 4 (15.4%) 17 (65.4%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000000 0.002097 0.007711 0.034403 0.028032 0.526172 Open "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode. Open "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/HIBAG/extdata/HapMap_CEU.fam". Open "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/HIBAG/extdata/HapMap_CEU.bim". Import 3932 SNPs within the xMHC region on chromosome 6. SNP genotypes: 90 samples X 3932 SNPs SNPs range from 28694391bp to 33426848bp on hg19 Missing rate per SNP: min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489 Missing rate per sample: min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554 Minor allele frequency: min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144 Allelic information: A/G C/T G/T A/C C/G A/T 1567 1510 348 332 111 64 Open "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode. Open "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/HIBAG/extdata/HapMap_CEU.fam". Open "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/HIBAG/extdata/HapMap_CEU.bim". Import 5316 SNPs from chromosome 6. SNP genotypes: 90 samples X 5316 SNPs SNPs range from 25651262bp to 33426848bp on hg19 Missing rate per SNP: min: 0, max: 0.1, mean: 0.0882054, median: 0.1, sd: 0.030674 Missing rate per sample: min: 0, max: 0.863619, mean: 0.0882054, median: 0.00131678, sd: 0.259735 Minor allele frequency: min: 0, max: 0.5, mean: 0.201867, median: 0.179012, sd: 0.155475 Allelic information: A/G C/T G/T A/C C/G A/T 2102 2046 480 471 134 83 Exclude 1 monomorphic SNP Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 9 # of SNPs: 77, # of samples: 60 # of unique HLA alleles: 12 [-] 2019-10-16 03:38:17 [1] 2019-10-16 03:38:17, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20 [2] 2019-10-16 03:38:17, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 Max. Mean SD 4.728168e-01 4.409445e-02 1.068815e-01 Accuracy with training data: 95.0% Out-of-bag accuracy: 94.5% Gene: DQB1 Training dataset: 60 samples X 77 SNPs # of HLA alleles: 12 # of individual classifiers: 2 total # of SNPs used: 20 avg. # of SNPs in an individual classifier: 14.00 (sd: 1.41, min: 13, max: 15, median: 14.00) avg. # of haplotypes in an individual classifier: 20.50 (sd: 0.71, min: 20, max: 21, median: 20.50) avg. out-of-bag accuracy: 94.45% (sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 Max. Mean SD 4.728168e-01 4.409445e-02 1.068815e-01 Genome assembly: hg19 The HIBAG model: There are 77 SNP predictors in total. There are 2 individual classifiers. Summarize the missing fractions of SNP predictors per classifier: Min. 1st Qu. Median Mean 3rd Qu. Max. 0 0 0 0 0 0 Gene: C Range: [31236526bp, 31239913bp] on hg19 # of samples: 60 # of unique HLA alleles: 17 # of unique HLA genotypes: 35 Gene: C Range: [31236526bp, 31239913bp] on hg19 # of samples: 100 # of unique HLA alleles: 17 # of unique HLA genotypes: 35 Gene: C Range: [31236526bp, 31239913bp] on hg19 # of samples: 100 # of unique HLA alleles: 0 # of unique HLA genotypes: 0 Gene: C Range: [31236526bp, 31239913bp] on hg19 # of samples: 200 # of unique HLA alleles: 17 # of unique HLA genotypes: 35 Exclude 9 monomorphic SNPs Build a HIBAG model with 1 individual classifier: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 266, # of samples: 60 # of unique HLA alleles: 14 [-] 2019-10-16 03:38:17 [1] 2019-10-16 03:38:17, OOB Acc: 86.96%, # of SNPs: 12, # of Haplo: 32 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 4.166789e-14 4.261245e-14 5.111347e-14 2.589270e-03 1.608934e-02 5.868848e-02 Max. Mean SD 6.267394e-01 6.664806e-02 1.405453e-01 Accuracy with training data: 94.2% Out-of-bag accuracy: 87.0% Exclude 9 monomorphic SNPs Build a HIBAG model with 1 individual classifier: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 266, # of samples: 60 # of unique HLA alleles: 14 [-] 2019-10-16 03:38:17 [1] 2019-10-16 03:38:17, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 40 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 7.894066e-24 9.219565e-20 9.218854e-19 2.189685e-03 7.704546e-03 2.406258e-02 Max. Mean SD 2.755151e-01 2.949891e-02 6.162169e-02 Accuracy with training data: 95.0% Out-of-bag accuracy: 87.5% Gene: A Training dataset: 60 samples X 266 SNPs # of HLA alleles: 14 # of individual classifiers: 2 total # of SNPs used: 24 avg. # of SNPs in an individual classifier: 13.50 (sd: 2.12, min: 12, max: 15, median: 13.50) avg. # of haplotypes in an individual classifier: 36.00 (sd: 5.66, min: 32, max: 40, median: 36.00) avg. out-of-bag accuracy: 87.23% (sd: 0.38%, min: 86.96%, max: 87.50%, median: 87.23%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 9.233104e-13 5.204084e-10 5.195775e-09 2.309655e-03 1.448839e-02 3.746431e-02 Max. Mean SD 4.511273e-01 4.807348e-02 1.006148e-01 Genome assembly: hg19 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 23 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 14 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 03:38:17 1, SNP: 211, Loss: 196.4, OOB Acc: 54.55%, # of Haplo: 13 2, SNP: 66, Loss: 173.548, OOB Acc: 63.64%, # of Haplo: 13 3, SNP: 177, Loss: 136.352, OOB Acc: 68.18%, # of Haplo: 13 4, SNP: 108, Loss: 95.8359, OOB Acc: 72.73%, # of Haplo: 13 5, SNP: 127, Loss: 67.3216, OOB Acc: 77.27%, # of Haplo: 13 6, SNP: 95, Loss: 47.5888, OOB Acc: 77.27%, # of Haplo: 13 7, SNP: 33, Loss: 37.2631, OOB Acc: 77.27%, # of Haplo: 16 8, SNP: 6, Loss: 29.7419, OOB Acc: 77.27%, # of Haplo: 18 9, SNP: 208, Loss: 25.6913, OOB Acc: 77.27%, # of Haplo: 19 10, SNP: 225, Loss: 25.3087, OOB Acc: 77.27%, # of Haplo: 21 11, SNP: 11, Loss: 24.8356, OOB Acc: 77.27%, # of Haplo: 23 12, SNP: 151, Loss: 19.4134, OOB Acc: 77.27%, # of Haplo: 23 13, SNP: 199, Loss: 17.011, OOB Acc: 77.27%, # of Haplo: 23 [1] 2019-10-16 03:38:17, OOB Acc: 77.27%, # of SNPs: 13, # of Haplo: 23 1, SNP: 160, Loss: 221.236, OOB Acc: 76.92%, # of Haplo: 17 2, SNP: 145, Loss: 173.538, OOB Acc: 80.77%, # of Haplo: 23 3, SNP: 177, Loss: 128.58, OOB Acc: 84.62%, # of Haplo: 31 4, SNP: 111, Loss: 79.6877, OOB Acc: 84.62%, # of Haplo: 31 5, SNP: 207, Loss: 52.5557, OOB Acc: 88.46%, # of Haplo: 32 6, SNP: 245, Loss: 41.8731, OOB Acc: 88.46%, # of Haplo: 34 7, SNP: 230, Loss: 31.7937, OOB Acc: 88.46%, # of Haplo: 38 8, SNP: 151, Loss: 20.4566, OOB Acc: 88.46%, # of Haplo: 36 9, SNP: 14, Loss: 19.5805, OOB Acc: 88.46%, # of Haplo: 42 10, SNP: 132, Loss: 19.5101, OOB Acc: 88.46%, # of Haplo: 42 11, SNP: 221, Loss: 19.485, OOB Acc: 88.46%, # of Haplo: 44 12, SNP: 251, Loss: 18.5695, OOB Acc: 88.46%, # of Haplo: 48 [2] 2019-10-16 03:38:17, OOB Acc: 88.46%, # of SNPs: 12, # of Haplo: 48 1, SNP: 191, Loss: 193.067, OOB Acc: 57.14%, # of Haplo: 11 2, SNP: 264, Loss: 150.427, OOB Acc: 64.29%, # of Haplo: 12 3, SNP: 132, Loss: 93.4067, OOB Acc: 67.86%, # of Haplo: 12 4, SNP: 128, Loss: 39.8353, OOB Acc: 71.43%, # of Haplo: 12 5, SNP: 160, Loss: 28.2998, OOB Acc: 75.00%, # of Haplo: 12 6, SNP: 144, Loss: 13.635, OOB Acc: 75.00%, # of Haplo: 12 7, SNP: 111, Loss: 6.04609, OOB Acc: 75.00%, # of Haplo: 12 8, SNP: 40, Loss: 6.04583, OOB Acc: 82.14%, # of Haplo: 14 9, SNP: 141, Loss: 6.04583, OOB Acc: 85.71%, # of Haplo: 14 10, SNP: 73, Loss: 2.9038, OOB Acc: 85.71%, # of Haplo: 14 11, SNP: 199, Loss: 2.20025, OOB Acc: 85.71%, # of Haplo: 14 [3] 2019-10-16 03:38:17, OOB Acc: 85.71%, # of SNPs: 11, # of Haplo: 14 1, SNP: 147, Loss: 158.631, OOB Acc: 50.00%, # of Haplo: 12 2, SNP: 152, Loss: 140.375, OOB Acc: 55.00%, # of Haplo: 13 3, SNP: 78, Loss: 115.887, OOB Acc: 60.00%, # of Haplo: 16 4, SNP: 115, Loss: 77.8082, OOB Acc: 60.00%, # of Haplo: 18 5, SNP: 148, Loss: 62.6831, OOB Acc: 65.00%, # of Haplo: 18 6, SNP: 13, Loss: 46.5657, OOB Acc: 75.00%, # of Haplo: 20 7, SNP: 109, Loss: 31.0312, OOB Acc: 75.00%, # of Haplo: 20 8, SNP: 176, Loss: 22.5073, OOB Acc: 75.00%, # of Haplo: 21 9, SNP: 145, Loss: 20.9122, OOB Acc: 75.00%, # of Haplo: 21 10, SNP: 128, Loss: 20.6728, OOB Acc: 75.00%, # of Haplo: 21 11, SNP: 73, Loss: 14.6217, OOB Acc: 75.00%, # of Haplo: 22 12, SNP: 151, Loss: 10.2879, OOB Acc: 75.00%, # of Haplo: 23 13, SNP: 199, Loss: 8.74645, OOB Acc: 75.00%, # of Haplo: 23 [4] 2019-10-16 03:38:17, OOB Acc: 75.00%, # of SNPs: 13, # of Haplo: 23 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 Max. Mean SD 0.5261716555 0.0476729895 0.1180875414 Accuracy with training data: 97.1% Out-of-bag accuracy: 81.6% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 38 avg. # of SNPs in an individual classifier: 12.25 (sd: 0.96, min: 11, max: 13, median: 12.50) avg. # of haplotypes in an individual classifier: 27.00 (sd: 14.63, min: 14, max: 48, median: 23.00) avg. out-of-bag accuracy: 81.61% (sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0267068470 Max. Mean SD 0.5261716555 0.0476729895 0.1180875414 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 03:38:18) 0% Predicting (2019-10-16 03:38:18) 100% Allelic ambiguity: 01:01, 02:02 Allelic ambiguity: 01:01, 02:02 Allelic ambiguity: 09:01 Allelic ambiguity: 09:01 Allelic ambiguity: 05:01, 06:01 Allelic ambiguity: 05:01, 06:01 Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01 Pos Num * - A D E F G H I K L M N Q R S T V W Y 1 120 120 . . . . . . . . . . . . . . . . . . . 9 120 . 81 . . . . . . . . . . . . . 15 7 . . 17 44 120 . 25 . . . . . . . . . . . . 95 . . . . . 56 120 . 117 . . . . . . . . . . . . 3 . . . . . 62 120 . 46 . . 15 . 44 . . . 4 . . . 11 . . . . . 63 120 . 105 . . . . . . . . . . 11 4 . . . . . . 65 120 . 105 . . . . 15 . . . . . . . . . . . . . 66 120 . 61 . . . . . . . 59 . . . . . . . . . . 67 120 . 25 . . . . . . . . . . . . . . . 95 . . 70 120 . 99 . . . . . . . . . . . 21 . . . . . . 73 120 . 117 . . . . . . 3 . . . . . . . . . . . 74 120 . 76 . . . . . 44 . . . . . . . . . . . . 76 120 . 32 . . 24 . . . . . . . . . . . . 64 . . 77 120 . 47 . 64 . . . . . . . . . . . 9 . . . . 79 120 . 96 . . . . . . . . . . . . 24 . . . . . 80 120 . 96 . . . . . . 24 . . . . . . . . . . . 81 120 . 96 24 . . . . . . . . . . . . . . . . . 82 120 . 96 . . . . . . . . 24 . . . . . . . . . 83 120 . 96 . . . . . . . . . . . . 24 . . . . . 90 120 . 38 82 . . . . . . . . . . . . . . . . . 95 120 . 61 . . . . . . . . 15 . . . . . . 44 . . 97 120 . 39 . . . . . . . . . 29 . . 52 . . . . . 99 120 . 105 . . . 15 . . . . . . . . . . . . . . 105 120 . 42 . . . . . . . . . . . . . 78 . . . . 107 120 . 76 . . . . . . . . . . . . . . . . 44 . 109 120 . 116 . . . . . . . . 4 . . . . . . . . . 114 120 . 46 . . . . . 59 . . . . . 15 . . . . . . 116 120 . 61 . . . . . . . . . . . . . . . . . 59 127 120 . 58 . . . . . . . 62 . . . . . . . . . . 142 120 . 73 . . . . . . . . . . . . . . 47 . . . 144 120 . 98 . . . . . . . . . . . 22 . . . . . . 145 120 . 73 . . . . . 47 . . . . . . . . . . . . 149 120 . 112 . . . . . . . . . . . . . . 8 . . . 150 120 . 25 95 . . . . . . . . . . . . . . . . . 151 120 . 106 . . . . . . . . . . . . 14 . . . . . 152 120 . 30 . . 17 . . . . . . . . . . . . 73 . . 156 120 . 25 . . . . . . . . 67 . . 17 . . . . 11 . 158 120 . 25 95 . . . . . . . . . . . . . . . . . 161 120 . 111 . 9 . . . . . . . . . . . . . . . . 163 120 . 38 . . . . . . . . . . . . . . 82 . . . 166 120 . 39 . . 81 . . . . . . . . . . . . . . . 167 120 . 39 . . . . . . . . . . . . . . . . 81 . 183 120 120 . . . . . . . . . . . . . . . . . . . Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01 Pos Num * - A D E F G H I K L M N Q R S T V W Y -23 120 120 . . . . . . . . . . . . . . . . . . . -22 120 120 . . . . . . . . . . . . . . . . . . . -21 120 120 . . . . . . . . . . . . . . . . . . . -20 120 120 . . . . . . . . . . . . . . . . . . . -19 120 120 . . . . . . . . . . . . . . . . . . . -18 120 120 . . . . . . . . . . . . . . . . . . . -17 120 120 . . . . . . . . . . . . . . . . . . . -16 120 120 . . . . . . . . . . . . . . . . . . . -15 120 120 . . . . . . . . . . . . . . . . . . . -14 120 120 . . . . . . . . . . . . . . . . . . . -13 120 120 . . . . . . . . . . . . . . . . . . . -12 120 120 . . . . . . . . . . . . . . . . . . . -11 120 120 . . . . . . . . . . . . . . . . . . . -10 120 120 . . . . . . . . . . . . . . . . . . . -9 120 120 . . . . . . . . . . . . . . . . . . . -8 120 120 . . . . . . . . . . . . . . . . . . . -7 120 120 . . . . . . . . . . . . . . . . . . . -6 120 120 . . . . . . . . . . . . . . . . . . . -5 120 120 . . . . . . . . . . . . . . . . . . . -4 120 120 . . . . . . . . . . . . . . . . . . . -3 120 120 . . . . . . . . . . . . . . . . . . . -2 120 120 . . . . . . . . . . . . . . . . . . . -1 120 120 . . . . . . . . . . . . . . . . . . . . 120 120 . . . . . . . . . . . . . . . . . . . 1 120 120 . . . . . . . . . . . . . . . . . . . 9 120 . 81 . . . . . . . . . . . . . 15 7 . . 17 44 120 . 25 . . . . . . . . . . . . 95 . . . . . 56 120 . 117 . . . . . . . . . . . . 3 . . . . . 62 120 . 46 . . 15 . 44 . . . 4 . . . 11 . . . . . 63 120 . 105 . . . . . . . . . . 11 4 . . . . . . 65 120 . 105 . . . . 15 . . . . . . . . . . . . . 66 120 . 61 . . . . . . . 59 . . . . . . . . . . 67 120 . 25 . . . . . . . . . . . . . . . 95 . . 70 120 . 99 . . . . . . . . . . . 21 . . . . . . 73 120 . 117 . . . . . . 3 . . . . . . . . . . . 74 120 . 76 . . . . . 44 . . . . . . . . . . . . 76 120 . 32 . . 24 . . . . . . . . . . . . 64 . . 77 120 . 47 . 64 . . . . . . . . . . . 9 . . . . 79 120 . 96 . . . . . . . . . . . . 24 . . . . . 80 120 . 96 . . . . . . 24 . . . . . . . . . . . 81 120 . 96 24 . . . . . . . . . . . . . . . . . 82 120 . 96 . . . . . . . . 24 . . . . . . . . . 83 120 . 96 . . . . . . . . . . . . 24 . . . . . 90 120 . 38 82 . . . . . . . . . . . . . . . . . 95 120 . 61 . . . . . . . . 15 . . . . . . 44 . . 97 120 . 39 . . . . . . . . . 29 . . 52 . . . . . 99 120 . 105 . . . 15 . . . . . . . . . . . . . . 105 120 . 42 . . . . . . . . . . . . . 78 . . . . 107 120 . 76 . . . . . . . . . . . . . . . . 44 . 109 120 . 116 . . . . . . . . 4 . . . . . . . . . 114 120 . 46 . . . . . 59 . . . . . 15 . . . . . . 116 120 . 61 . . . . . . . . . . . . . . . . . 59 127 120 . 58 . . . . . . . 62 . . . . . . . . . . 142 120 . 73 . . . . . . . . . . . . . . 47 . . . 144 120 . 98 . . . . . . . . . . . 22 . . . . . . 145 120 . 73 . . . . . 47 . . . . . . . . . . . . 149 120 . 112 . . . . . . . . . . . . . . 8 . . . 150 120 . 25 95 . . . . . . . . . . . . . . . . . 151 120 . 106 . . . . . . . . . . . . 14 . . . . . 152 120 . 30 . . 17 . . . . . . . . . . . . 73 . . 156 120 . 25 . . . . . . . . 67 . . 17 . . . . 11 . 158 120 . 25 95 . . . . . . . . . . . . . . . . . 161 120 . 111 . 9 . . . . . . . . . . . . . . . . 163 120 . 38 . . . . . . . . . . . . . . 82 . . . 166 120 . 39 . . 81 . . . . . . . . . . . . . . . 167 120 . 39 . . . . . . . . . . . . . . . . 81 . 183 120 120 . . . . . . . . . . . . . . . . . . . 184 120 120 . . . . . . . . . . . . . . . . . . . 185 120 120 . . . . . . . . . . . . . . . . . . . 186 120 120 . . . . . . . . . . . . . . . . . . . 187 120 120 . . . . . . . . . . . . . . . . . . . 188 120 120 . . . . . . . . . . . . . . . . . . . 189 120 120 . . . . . . . . . . . . . . . . . . . 190 120 120 . . . . . . . . . . . . . . . . . . . 191 120 120 . . . . . . . . . . . . . . . . . . . 192 120 120 . . . . . . . . . . . . . . . . . . . 193 120 120 . . . . . . . . . . . . . . . . . . . 194 120 120 . . . . . . . . . . . . . . . . . . . 195 120 120 . . . . . . . . . . . . . . . . . . . 196 120 120 . . . . . . . . . . . . . . . . . . . 197 120 120 . . . . . . . . . . . . . . . . . . . 198 120 120 . . . . . . . . . . . . . . . . . . . 199 120 120 . . . . . . . . . . . . . . . . . . . 200 120 120 . . . . . . . . . . . . . . . . . . . 201 120 120 . . . . . . . . . . . . . . . . . . . 202 120 120 . . . . . . . . . . . . . . . . . . . 203 120 120 . . . . . . . . . . . . . . . . . . . 204 120 120 . . . . . . . . . . . . . . . . . . . 205 120 120 . . . . . . . . . . . . . . . . . . . 206 120 120 . . . . . . . . . . . . . . . . . . . 207 120 120 . . . . . . . . . . . . . . . . . . . 208 120 120 . . . . . . . . . . . . . . . . . . . 209 120 120 . . . . . . . . . . . . . . . . . . . 210 120 120 . . . . . . . . . . . . . . . . . . . 211 120 120 . . . . . . . . . . . . . . . . . . . 212 120 120 . . . . . . . . . . . . . . . . . . . 213 120 120 . . . . . . . . . . . . . . . . . . . 214 120 120 . . . . . . . . . . . . . . . . . . . 215 120 120 . . . . . . . . . . . . . . . . . . . 216 120 120 . . . . . . . . . . . . . . . . . . . 217 120 120 . . . . . . . . . . . . . . . . . . . 218 120 120 . . . . . . . . . . . . . . . . . . . 219 120 120 . . . . . . . . . . . . . . . . . . . 220 120 120 . . . . . . . . . . . . . . . . . . . 221 120 120 . . . . . . . . . . . . . . . . . . . 222 120 120 . . . . . . . . . . . . . . . . . . . 223 120 120 . . . . . . . . . . . . . . . . . . . 224 120 120 . . . . . . . . . . . . . . . . . . . 225 120 120 . . . . . . . . . . . . . . . . . . . 226 120 120 . . . . . . . . . . . . . . . . . . . 227 120 120 . . . . . . . . . . . . . . . . . . . 228 120 120 . . . . . . . . . . . . . . . . . . . 229 120 120 . . . . . . . . . . . . . . . . . . . 230 120 120 . . . . . . . . . . . . . . . . . . . 231 120 120 . . . . . . . . . . . . . . . . . . . 232 120 120 . . . . . . . . . . . . . . . . . . . 233 120 120 . . . . . . . . . . . . . . . . . . . 234 120 120 . . . . . . . . . . . . . . . . . . . 235 120 120 . . . . . . . . . . . . . . . . . . . 236 120 120 . . . . . . . . . . . . . . . . . . . 237 120 120 . . . . . . . . . . . . . . . . . . . 238 120 120 . . . . . . . . . . . . . . . . . . . 239 120 120 . . . . . . . . . . . . . . . . . . . 240 120 120 . . . . . . . . . . . . . . . . . . . 241 120 120 . . . . . . . . . . . . . . . . . . . 242 120 120 . . . . . . . . . . . . . . . . . . . 243 120 120 . . . . . . . . . . . . . . . . . . . 244 120 120 . . . . . . . . . . . . . . . . . . . 245 120 120 . . . . . . . . . . . . . . . . . . . 246 120 120 . . . . . . . . . . . . . . . . . . . 247 120 120 . . . . . . . . . . . . . . . . . . . 248 120 120 . . . . . . . . . . . . . . . . . . . 249 120 120 . . . . . . . . . . . . . . . . . . . 250 120 120 . . . . . . . . . . . . . . . . . . . 251 120 120 . . . . . . . . . . . . . . . . . . . 252 120 120 . . . . . . . . . . . . . . . . . . . 253 120 120 . . . . . . . . . . . . . . . . . . . 254 120 120 . . . . . . . . . . . . . . . . . . . 255 120 120 . . . . . . . . . . . . . . . . . . . 256 120 120 . . . . . . . . . . . . . . . . . . . 257 120 120 . . . . . . . . . . . . . . . . . . . 258 120 120 . . . . . . . . . . . . . . . . . . . 259 120 120 . . . . . . . . . . . . . . . . . . . 260 120 120 . . . . . . . . . . . . . . . . . . . 261 120 120 . . . . . . . . . . . . . . . . . . . 262 120 120 . . . . . . . . . . . . . . . . . . . 263 120 120 . . . . . . . . . . . . . . . . . . . 264 120 120 . . . . . . . . . . . . . . . . . . . 265 120 120 . . . . . . . . . . . . . . . . . . . 266 120 120 . . . . . . . . . . . . . . . . . . . 267 120 120 . . . . . . . . . . . . . . . . . . . 268 120 120 . . . . . . . . . . . . . . . . . . . 269 120 120 . . . . . . . . . . . . . . . . . . . 270 120 120 . . . . . . . . . . . . . . . . . . . 271 120 120 . . . . . . . . . . . . . . . . . . . 272 120 120 . . . . . . . . . . . . . . . . . . . 273 120 120 . . . . . . . . . . . . . . . . . . . 274 120 120 . . . . . . . . . . . . . . . . . . . 275 120 120 . . . . . . . . . . . . . . . . . . . 276 120 120 . . . . . . . . . . . . . . . . . . . 277 120 120 . . . . . . . . . . . . . . . . . . . 278 120 120 . . . . . . . . . . . . . . . . . . . 279 120 120 . . . . . . . . . . . . . . . . . . . 280 120 120 . . . . . . . . . . . . . . . . . . . 281 120 120 . . . . . . . . . . . . . . . . . . . 282 120 120 . . . . . . . . . . . . . . . . . . . 283 120 120 . . . . . . . . . . . . . . . . . . . 284 120 120 . . . . . . . . . . . . . . . . . . . 285 120 120 . . . . . . . . . . . . . . . . . . . 286 120 120 . . . . . . . . . . . . . . . . . . . 287 120 120 . . . . . . . . . . . . . . . . . . . 288 120 120 . . . . . . . . . . . . . . . . . . . 289 120 120 . . . . . . . . . . . . . . . . . . . 290 120 120 . . . . . . . . . . . . . . . . . . . 291 120 120 . . . . . . . . . . . . . . . . . . . 292 120 120 . . . . . . . . . . . . . . . . . . . 293 120 120 . . . . . . . . . . . . . . . . . . . 294 120 120 . . . . . . . . . . . . . . . . . . . 295 120 120 . . . . . . . . . . . . . . . . . . . 296 120 120 . . . . . . . . . . . . . . . . . . . 297 120 120 . . . . . . . . . . . . . . . . . . . 298 120 120 . . . . . . . . . . . . . . . . . . . 299 120 120 . . . . . . . . . . . . . . . . . . . 300 120 120 . . . . . . . . . . . . . . . . . . . 301 120 120 . . . . . . . . . . . . . . . . . . . 302 120 120 . . . . . . . . . . . . . . . . . . . 303 120 120 . . . . . . . . . . . . . . . . . . . 304 120 120 . . . . . . . . . . . . . . . . . . . 305 120 120 . . . . . . . . . . . . . . . . . . . 306 120 120 . . . . . . . . . . . . . . . . . . . 307 120 120 . . . . . . . . . . . . . . . . . . . 308 120 120 . . . . . . . . . . . . . . . . . . . 309 120 120 . . . . . . . . . . . . . . . . . . . 310 120 120 . . . . . . . . . . . . . . . . . . . 311 120 120 . . . . . . . . . . . . . . . . . . . 312 120 120 . . . . . . . . . . . . . . . . . . . 313 120 120 . . . . . . . . . . . . . . . . . . . 314 120 120 . . . . . . . . . . . . . . . . . . . 315 120 120 . . . . . . . . . . . . . . . . . . . 316 120 120 . . . . . . . . . . . . . . . . . . . 317 120 120 . . . . . . . . . . . . . . . . . . . 318 120 120 . . . . . . . . . . . . . . . . . . . 319 120 120 . . . . . . . . . . . . . . . . . . . 320 120 120 . . . . . . . . . . . . . . . . . . . 321 120 120 . . . . . . . . . . . . . . . . . . . 322 120 120 . . . . . . . . . . . . . . . . . . . 323 120 120 . . . . . . . . . . . . . . . . . . . 324 120 120 . . . . . . . . . . . . . . . . . . . 325 120 120 . . . . . . . . . . . . . . . . . . . 326 120 120 . . . . . . . . . . . . . . . . . . . 327 120 120 . . . . . . . . . . . . . . . . . . . 328 120 120 . . . . . . . . . . . . . . . . . . . 329 120 120 . . . . . . . . . . . . . . . . . . . 330 120 120 . . . . . . . . . . . . . . . . . . . 331 120 120 . . . . . . . . . . . . . . . . . . . 332 120 120 . . . . . . . . . . . . . . . . . . . 333 120 120 . . . . . . . . . . . . . . . . . . . 334 120 120 . . . . . . . . . . . . . . . . . . . 335 120 120 . . . . . . . . . . . . . . . . . . . 336 120 120 . . . . . . . . . . . . . . . . . . . 337 120 120 . . . . . . . . . . . . . . . . . . . 338 120 120 . . . . . . . . . . . . . . . . . . . 339 120 120 . . . . . . . . . . . . . . . . . . . 340 120 120 . . . . . . . . . . . . . . . . . . . 341 120 120 . . . . . . . . . . . . . . . . . . . Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02 Pos Num * - A D E F G I K L M N P Q R S T Y 5 120 112 . . . . . . . . . . 8 . . . . . . 6 120 20 92 8 . . . . . . . . . . . . . . . 7 112 20 92 . . . . . . . . . . . . . . . . 8 112 20 92 . . . . . . . . . . . . . . . . 9 112 3 76 . . . 33 . . . . . . . . . . . . 10 112 3 109 . . . . . . . . . . . . . . . . 11 112 3 109 . . . . . . . . . . . . . . . . 12 112 3 109 . . . . . . . . . . . . . . . . 13 112 3 93 16 . . . . . . . . . . . . . . . 14 112 3 14 . . . . . . . . 95 . . . . . . . 15 112 3 109 . . . . . . . . . . . . . . . . 16 112 3 109 . . . . . . . . . . . . . . . . 17 112 3 109 . . . . . . . . . . . . . . . . 18 112 3 109 . . . . . . . . . . . . . . . . 19 112 3 109 . . . . . . . . . . . . . . . . 20 112 3 109 . . . . . . . . . . . . . . . . 26 112 . 20 . . . . . . . 76 . . . . . . . 16 28 112 . 100 . . . . . . . . . . . . . 12 . . 30 112 . 24 . . . . . . . . . . . . . 12 . 76 37 112 . 100 . . . . . 12 . . . . . . . . . . 38 112 . 29 83 . . . . . . . . . . . . . . . 45 112 . 96 . . 16 . . . . . . . . . . . . . 46 112 . 100 . . 12 . . . . . . . . . . . . . 47 112 . 100 . . . 12 . . . . . . . . . . . . 52 112 . 100 . . . . . . . 12 . . . . . . . . 53 112 . 54 . . . . . . . 58 . . . . . . . . 55 112 . 57 . . . . . . . 12 . . 43 . . . . . 56 112 . 109 . . . . . . . 3 . . . . . . . . 57 112 . 14 33 64 . . . . . . . . . . . 1 . . 66 112 . 97 . 15 . . . . . . . . . . . . . . 67 112 . 97 . . . . . 15 . . . . . . . . . . 70 112 3 50 . . 3 . . . . . . . . . 56 . . . 71 112 3 14 . 3 . . . . 12 . . . . . . . 80 . 72 112 3 109 . . . . . . . . . . . . . . . . 73 112 3 109 . . . . . . . . . . . . . . . . 74 112 3 17 12 . 80 . . . . . . . . . . . . . 75 112 3 29 . . . . . . . 80 . . . . . . . . 76 112 3 109 . . . . . . . . . . . . . . . . 77 112 3 26 . . . . . . . . . . . . . . 83 . 78 112 3 109 . . . . . . . . . . . . . . . . 79 112 3 109 . . . . . . . . . . . . . . . . 80 112 3 109 . . . . . . . . . . . . . . . . 81 112 3 109 . . . . . . . . . . . . . . . . 82 112 3 109 . . . . . . . . . . . . . . . . 83 112 3 109 . . . . . . . . . . . . . . . . 84 112 3 51 . . . . . . . . . . . 58 . . . . 85 112 3 51 . . . . . . . 58 . . . . . . . . 86 112 3 50 . . 58 . 1 . . . . . . . . . . . 87 112 3 15 . . . 36 . . . 58 . . . . . . . . 88 112 3 109 . . . . . . . . . . . . . . . . 89 112 3 51 . . . . . . . . . . . . . . 58 . 90 112 3 51 . . . . . . . . . . . . . . 58 . 91 112 3 109 . . . . . . . . . . . . . . . . 92 112 3 109 . . . . . . . . . . . . . . . . 93 112 3 109 . . . . . . . . . . . . . . . . 94 112 17 95 . . . . . . . . . . . . . . . . 95 112 112 . . . . . . . . . . . . . . . . . Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02 Pos Num * - A D E F G I K L M N P Q R S T Y -31 120 112 . . . . . . . . . . 8 . . . . . . -30 120 112 . 8 . . . . . . . . . . . . . . . -29 112 112 . . . . . . . . . . . . . . . . . -28 112 112 . . . . . . . . . . . . . . . . . -27 112 112 . . . . . . . . . . . . . . . . . -26 112 112 . . . . . . . . . . . . . . . . . -25 112 112 . . . . . . . . . . . . . . . . . -24 112 112 . . . . . . . . . . . . . . . . . -23 112 112 . . . . . . . . . . . . . . . . . -22 112 112 . . . . . . . . . . . . . . . . . -21 112 112 . . . . . . . . . . . . . . . . . -20 112 112 . . . . . . . . . . . . . . . . . -19 112 112 . . . . . . . . . . . . . . . . . -18 112 112 . . . . . . . . . . . . . . . . . -17 112 112 . . . . . . . . . . . . . . . . . -16 112 112 . . . . . . . . . . . . . . . . . -15 112 112 . . . . . . . . . . . . . . . . . -14 112 112 . . . . . . . . . . . . . . . . . -13 112 112 . . . . . . . . . . . . . . . . . -12 112 112 . . . . . . . . . . . . . . . . . -11 112 112 . . . . . . . . . . . . . . . . . -10 112 112 . . . . . . . . . . . . . . . . . -9 112 112 . . . . . . . . . . . . . . . . . -8 112 112 . . . . . . . . . . . . . . . . . -7 112 112 . . . . . . . . . . . . . . . . . -6 112 112 . . . . . . . . . . . . . . . . . -5 112 112 . . . . . . . . . . . . . . . . . -4 112 112 . . . . . . . . . . . . . . . . . -3 112 112 . . . . . . . . . . . . . . . . . -2 112 112 . . . . . . . . . . . . . . . . . -1 112 112 . . . . . . . . . . . . . . . . . . 112 112 . . . . . . . . . . . . . . . . . 1 112 112 . . . . . . . . . . . . . . . . . 2 112 112 . . . . . . . . . . . . . . . . . 3 112 112 . . . . . . . . . . . . . . . . . 4 112 112 . . . . . . . . . . . . . . . . . 5 112 112 . . . . . . . . . . . . . . . . . 6 112 20 92 . . . . . . . . . . . . . . . . 7 112 20 92 . . . . . . . . . . . . . . . . 8 112 20 92 . . . . . . . . . . . . . . . . 9 112 3 76 . . . 33 . . . . . . . . . . . . 10 112 3 109 . . . . . . . . . . . . . . . . 11 112 3 109 . . . . . . . . . . . . . . . . 12 112 3 109 . . . . . . . . . . . . . . . . 13 112 3 93 16 . . . . . . . . . . . . . . . 14 112 3 14 . . . . . . . . 95 . . . . . . . 15 112 3 109 . . . . . . . . . . . . . . . . 16 112 3 109 . . . . . . . . . . . . . . . . 17 112 3 109 . . . . . . . . . . . . . . . . 18 112 3 109 . . . . . . . . . . . . . . . . 19 112 3 109 . . . . . . . . . . . . . . . . 20 112 3 109 . . . . . . . . . . . . . . . . 26 112 . 20 . . . . . . . 76 . . . . . . . 16 28 112 . 100 . . . . . . . . . . . . . 12 . . 30 112 . 24 . . . . . . . . . . . . . 12 . 76 37 112 . 100 . . . . . 12 . . . . . . . . . . 38 112 . 29 83 . . . . . . . . . . . . . . . 45 112 . 96 . . 16 . . . . . . . . . . . . . 46 112 . 100 . . 12 . . . . . . . . . . . . . 47 112 . 100 . . . 12 . . . . . . . . . . . . 52 112 . 100 . . . . . . . 12 . . . . . . . . 53 112 . 54 . . . . . . . 58 . . . . . . . . 55 112 . 57 . . . . . . . 12 . . 43 . . . . . 56 112 . 109 . . . . . . . 3 . . . . . . . . 57 112 . 14 33 64 . . . . . . . . . . . 1 . . 66 112 . 97 . 15 . . . . . . . . . . . . . . 67 112 . 97 . . . . . 15 . . . . . . . . . . 70 112 3 50 . . 3 . . . . . . . . . 56 . . . 71 112 3 14 . 3 . . . . 12 . . . . . . . 80 . 72 112 3 109 . . . . . . . . . . . . . . . . 73 112 3 109 . . . . . . . . . . . . . . . . 74 112 3 17 12 . 80 . . . . . . . . . . . . . 75 112 3 29 . . . . . . . 80 . . . . . . . . 76 112 3 109 . . . . . . . . . . . . . . . . 77 112 3 26 . . . . . . . . . . . . . . 83 . 78 112 3 109 . . . . . . . . . . . . . . . . 79 112 3 109 . . . . . . . . . . . . . . . . 80 112 3 109 . . . . . . . . . . . . . . . . 81 112 3 109 . . . . . . . . . . . . . . . . 82 112 3 109 . . . . . . . . . . . . . . . . 83 112 3 109 . . . . . . . . . . . . . . . . 84 112 3 51 . . . . . . . . . . . 58 . . . . 85 112 3 51 . . . . . . . 58 . . . . . . . . 86 112 3 50 . . 58 . 1 . . . . . . . . . . . 87 112 3 15 . . . 36 . . . 58 . . . . . . . . 88 112 3 109 . . . . . . . . . . . . . . . . 89 112 3 51 . . . . . . . . . . . . . . 58 . 90 112 3 51 . . . . . . . . . . . . . . 58 . 91 112 3 109 . . . . . . . . . . . . . . . . 92 112 3 109 . . . . . . . . . . . . . . . . 93 112 3 109 . . . . . . . . . . . . . . . . 94 112 17 95 . . . . . . . . . . . . . . . . 95 112 112 . . . . . . . . . . . . . . . . . 96 112 112 . . . . . . . . . . . . . . . . . 97 112 112 . . . . . . . . . . . . . . . . . 98 112 112 . . . . . . . . . . . . . . . . . 99 112 112 . . . . . . . . . . . . . . . . . 100 112 112 . . . . . . . . . . . . . . . . . 101 112 112 . . . . . . . . . . . . . . . . . 102 112 112 . . . . . . . . . . . . . . . . . 103 112 112 . . . . . . . . . . . . . . . . . 104 112 112 . . . . . . . . . . . . . . . . . 105 112 112 . . . . . . . . . . . . . . . . . 106 112 112 . . . . . . . . . . . . . . . . . 107 112 112 . . . . . . . . . . . . . . . . . 108 112 112 . . . . . . . . . . . . . . . . . 109 112 112 . . . . . . . . . . . . . . . . . 110 112 112 . . . . . . . . . . . . . . . . . 111 112 112 . . . . . . . . . . . . . . . . . 112 112 112 . . . . . . . . . . . . . . . . . 113 112 112 . . . . . . . . . . . . . . . . . 114 112 112 . . . . . . . . . . . . . . . . . 115 112 112 . . . . . . . . . . . . . . . . . 116 112 112 . . . . . . . . . . . . . . . . . 117 112 112 . . . . . . . . . . . . . . . . . 118 112 112 . . . . . . . . . . . . . . . . . 119 112 112 . . . . . . . . . . . . . . . . . 120 112 112 . . . . . . . . . . . . . . . . . 121 112 112 . . . . . . . . . . . . . . . . . 122 112 112 . . . . . . . . . . . . . . . . . 123 112 112 . . . . . . . . . . . . . . . . . 124 112 112 . . . . . . . . . . . . . . . . . 125 112 112 . . . . . . . . . . . . . . . . . 126 112 112 . . . . . . . . . . . . . . . . . 127 112 112 . . . . . . . . . . . . . . . . . 128 112 112 . . . . . . . . . . . . . . . . . 129 112 112 . . . . . . . . . . . . . . . . . 130 112 112 . . . . . . . . . . . . . . . . . 131 112 112 . . . . . . . . . . . . . . . . . 132 112 112 . . . . . . . . . . . . . . . . . 133 112 112 . . . . . . . . . . . . . . . . . 134 112 112 . . . . . . . . . . . . . . . . . 135 112 112 . . . . . . . . . . . . . . . . . 136 112 112 . . . . . . . . . . . . . . . . . 137 112 112 . . . . . . . . . . . . . . . . . 138 112 112 . . . . . . . . . . . . . . . . . 139 112 112 . . . . . . . . . . . . . . . . . 140 112 112 . . . . . . . . . . . . . . . . . 141 112 112 . . . . . . . . . . . . . . . . . 142 112 112 . . . . . . . . . . . . . . . . . 143 112 112 . . . . . . . . . . . . . . . . . 144 112 112 . . . . . . . . . . . . . . . . . 145 112 112 . . . . . . . . . . . . . . . . . 146 112 112 . . . . . . . . . . . . . . . . . 147 112 112 . . . . . . . . . . . . . . . . . 148 112 112 . . . . . . . . . . . . . . . . . 149 112 112 . . . . . . . . . . . . . . . . . 150 112 112 . . . . . . . . . . . . . . . . . 151 112 112 . . . . . . . . . . . . . . . . . 152 112 112 . . . . . . . . . . . . . . . . . 153 112 112 . . . . . . . . . . . . . . . . . 154 112 112 . . . . . . . . . . . . . . . . . 155 112 112 . . . . . . . . . . . . . . . . . 156 112 112 . . . . . . . . . . . . . . . . . 157 112 112 . . . . . . . . . . . . . . . . . 158 112 112 . . . . . . . . . . . . . . . . . 159 112 112 . . . . . . . . . . . . . . . . . 160 112 112 . . . . . . . . . . . . . . . . . 161 112 112 . . . . . . . . . . . . . . . . . 162 112 112 . . . . . . . . . . . . . . . . . 163 112 112 . . . . . . . . . . . . . . . . . 164 112 112 . . . . . . . . . . . . . . . . . 165 112 112 . . . . . . . . . . . . . . . . . 166 112 112 . . . . . . . . . . . . . . . . . 167 112 112 . . . . . . . . . . . . . . . . . 168 112 112 . . . . . . . . . . . . . . . . . 169 112 112 . . . . . . . . . . . . . . . . . 170 112 112 . . . . . . . . . . . . . . . . . 171 112 112 . . . . . . . . . . . . . . . . . 172 112 112 . . . . . . . . . . . . . . . . . 173 112 112 . . . . . . . . . . . . . . . . . 174 112 112 . . . . . . . . . . . . . . . . . 175 112 112 . . . . . . . . . . . . . . . . . 176 112 112 . . . . . . . . . . . . . . . . . 177 112 112 . . . . . . . . . . . . . . . . . 178 112 112 . . . . . . . . . . . . . . . . . 179 112 112 . . . . . . . . . . . . . . . . . 180 112 112 . . . . . . . . . . . . . . . . . 181 112 112 . . . . . . . . . . . . . . . . . 182 112 112 . . . . . . . . . . . . . . . . . 183 112 112 . . . . . . . . . . . . . . . . . 184 112 112 . . . . . . . . . . . . . . . . . 185 112 112 . . . . . . . . . . . . . . . . . 186 112 112 . . . . . . . . . . . . . . . . . 187 112 112 . . . . . . . . . . . . . . . . . 188 112 112 . . . . . . . . . . . . . . . . . 189 112 112 . . . . . . . . . . . . . . . . . 190 112 112 . . . . . . . . . . . . . . . . . 191 112 112 . . . . . . . . . . . . . . . . . 192 112 112 . . . . . . . . . . . . . . . . . 193 112 112 . . . . . . . . . . . . . . . . . 194 112 112 . . . . . . . . . . . . . . . . . 195 112 112 . . . . . . . . . . . . . . . . . 196 112 112 . . . . . . . . . . . . . . . . . 197 112 112 . . . . . . . . . . . . . . . . . 198 112 112 . . . . . . . . . . . . . . . . . 199 112 112 . . . . . . . . . . . . . . . . . 200 112 112 . . . . . . . . . . . . . . . . . 201 112 112 . . . . . . . . . . . . . . . . . 202 112 112 . . . . . . . . . . . . . . . . . 203 112 112 . . . . . . . . . . . . . . . . . 204 112 112 . . . . . . . . . . . . . . . . . 205 112 112 . . . . . . . . . . . . . . . . . 206 112 112 . . . . . . . . . . . . . . . . . 207 112 112 . . . . . . . . . . . . . . . . . 208 112 112 . . . . . . . . . . . . . . . . . 209 112 112 . . . . . . . . . . . . . . . . . 210 112 112 . . . . . . . . . . . . . . . . . 211 112 112 . . . . . . . . . . . . . . . . . 212 112 112 . . . . . . . . . . . . . . . . . 213 112 112 . . . . . . . . . . . . . . . . . 214 112 112 . . . . . . . . . . . . . . . . . 215 112 112 . . . . . . . . . . . . . . . . . 216 112 112 . . . . . . . . . . . . . . . . . 217 112 112 . . . . . . . . . . . . . . . . . 218 112 112 . . . . . . . . . . . . . . . . . 219 112 112 . . . . . . . . . . . . . . . . . 220 112 112 . . . . . . . . . . . . . . . . . 221 112 112 . . . . . . . . . . . . . . . . . 222 112 112 . . . . . . . . . . . . . . . . . 223 112 112 . . . . . . . . . . . . . . . . . 224 112 112 . . . . . . . . . . . . . . . . . 225 112 112 . . . . . . . . . . . . . . . . . 226 112 112 . . . . . . . . . . . . . . . . . 227 112 112 . . . . . . . . . . . . . . . . . 228 112 112 . . . . . . . . . . . . . . . . . 229 112 112 . . . . . . . . . . . . . . . . . 230 112 112 . . . . . . . . . . . . . . . . . 231 112 112 . . . . . . . . . . . . . . . . . 232 112 112 . . . . . . . . . . . . . . . . . 233 112 112 . . . . . . . . . . . . . . . . . 234 112 112 . . . . . . . . . . . . . . . . . 235 112 112 . . . . . . . . . . . . . . . . . 236 112 112 . . . . . . . . . . . . . . . . . 237 112 112 . . . . . . . . . . . . . . . . . SNP genotypes: 60 samples X 275 SNPs SNPs range from 29417816bp to 30410205bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558 Missing rate per sample: min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066 Minor allele frequency: min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271 Allelic information: C/T A/G G/T A/C 125 97 32 21 Exclude 9 monomorphic SNPs Build a HIBAG model with 10 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 266, # of samples: 60 # of unique HLA alleles: 14 [-] 2019-10-16 03:38:23 [1] 2019-10-16 03:38:23, OOB Acc: 86.96%, # of SNPs: 12, # of Haplo: 32 [2] 2019-10-16 03:38:23, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 40 [3] 2019-10-16 03:38:23, OOB Acc: 97.92%, # of SNPs: 14, # of Haplo: 21 [4] 2019-10-16 03:38:23, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 25 [5] 2019-10-16 03:38:23, OOB Acc: 78.95%, # of SNPs: 14, # of Haplo: 21 [6] 2019-10-16 03:38:23, OOB Acc: 93.75%, # of SNPs: 16, # of Haplo: 22 [7] 2019-10-16 03:38:24, OOB Acc: 93.75%, # of SNPs: 24, # of Haplo: 81 [8] 2019-10-16 03:38:24, OOB Acc: 92.86%, # of SNPs: 20, # of Haplo: 45 [9] 2019-10-16 03:38:25, OOB Acc: 94.74%, # of SNPs: 16, # of Haplo: 45 [10] 2019-10-16 03:38:25, OOB Acc: 97.37%, # of SNPs: 15, # of Haplo: 40 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0001493079 0.0001617044 0.0002732730 0.0039571951 0.0150902995 0.0320853535 Max. Mean SD 0.3770420992 0.0416903408 0.0825371577 Accuracy with training data: 98.3% Out-of-bag accuracy: 91.9% Gene: A Training dataset: 60 samples X 266 SNPs # of HLA alleles: 14 # of individual classifiers: 10 total # of SNPs used: 95 avg. # of SNPs in an individual classifier: 16.00 (sd: 3.50, min: 12, max: 24, median: 15.00) avg. # of haplotypes in an individual classifier: 37.20 (sd: 18.22, min: 21, max: 81, median: 36.00) avg. out-of-bag accuracy: 91.92% (sd: 5.83%, min: 78.95%, max: 97.92%, median: 93.75%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0001493079 0.0001617044 0.0002732730 0.0039571951 0.0150902995 0.0320853535 Max. Mean SD 0.3770420992 0.0416903408 0.0825371577 Genome assembly: hg19 SNP genotypes: 60 samples X 275 SNPs SNPs range from 29417816bp to 30410205bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558 Missing rate per sample: min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066 Minor allele frequency: min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271 Allelic information: C/T A/G G/T A/C 125 97 32 21 SNP genotypes: 60 samples X 275 SNPs SNPs range from 29417816bp to 30410205bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558 Missing rate per sample: min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066 Minor allele frequency: min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271 Allelic information: C/T A/G G/T A/C 125 97 32 21 Loading required namespace: gdsfmt Loading required namespace: SNPRelate Import 2348 SNPs from chromosome 6. 3 SNPs with invalid alleles have been removed. SNP genotypes: 165 samples X 2345 SNPs SNPs range from 24652946bp to 33524089bp on hg18 Missing rate per SNP: min: 0, max: 0.0484848, mean: 0.001698, median: 0, sd: 0.00504919 Missing rate per sample: min: 0, max: 0.0110874, mean: 0.001698, median: 0.000852878, sd: 0.00195105 Minor allele frequency: min: 0, max: 0.5, mean: 0.194167, median: 0.172727, sd: 0.149319 Allelic information: A/G T/C G/A C/T T/G A/C C/A G/T C/G G/C A/T T/A 556 467 410 400 116 109 103 81 30 29 28 16 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 Open "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode. Open "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/HIBAG/extdata/HapMap_CEU.fam". Open "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/HIBAG/extdata/HapMap_CEU.bim". Import 3932 SNPs within the xMHC region on chromosome 6. No allelic strand orders are switched. SNP genotypes: 150 samples X 1214 SNPs SNPs range from 28695148bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0866667, mean: 0.0844646, median: 0.0866667, sd: 0.0128841 Missing rate per sample: min: 0, max: 0.968699, mean: 0.0844646, median: 0.000823723, sd: 0.273119 Minor allele frequency: min: 0, max: 0.5, mean: 0.234168, median: 0.218978, sd: 0.137855 Allelic information: A/G C/T G/T A/C 505 496 109 104 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 SNP genotypes: 60 samples X 1197 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0657059, median: 0.0666667, sd: 0.00757446 Missing rate per sample: min: 0, max: 0.978279, mean: 0.0657059, median: 0.000835422, sd: 0.245786 Minor allele frequency: min: 0.101695, max: 0.5, mean: 0.278734, median: 0.267857, sd: 0.117338 Allelic information: A/G C/T A/C G/T 511 476 105 105 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 Open "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode. Open "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/HIBAG/extdata/HapMap_CEU.fam". Open "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/HIBAG/extdata/HapMap_CEU.bim". Import 3932 SNPs within the xMHC region on chromosome 6. SNP genotypes: 90 samples X 3932 SNPs SNPs range from 28694391bp to 33426848bp on hg19 Missing rate per SNP: min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489 Missing rate per sample: min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554 Minor allele frequency: min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144 Allelic information: A/G C/T G/T A/C C/G A/T 1567 1510 348 332 111 64 No allelic strand orders are switched. SNP genotypes: 60 samples X 1214 SNPs SNPs range from 28695148bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0650879, median: 0.0666667, sd: 0.0097381 Missing rate per sample: min: 0, max: 0.968699, mean: 0.0650879, median: 0.000823723, sd: 0.243373 Minor allele frequency: min: 0, max: 0.5, mean: 0.234476, median: 0.223214, sd: 0.13833 Allelic information: A/G C/T G/T A/C 505 496 109 104 SNP genotypes: 60 samples X 275 SNPs SNPs range from 29417816bp to 30410205bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558 Missing rate per sample: min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066 Minor allele frequency: min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271 Allelic information: C/T A/G G/T A/C 125 97 32 21 MAF filter (>=0.01), excluding 9 SNP(s) using the default genome assembly (assembly="hg19") SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 Build a HIBAG model with 1 individual classifier: # of SNPs randomly sampled as candidates for each selection: 10 # of SNPs: 83, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 03:38:28 [1] 2019-10-16 03:38:28, OOB Acc: 92.00%, # of SNPs: 24, # of Haplo: 29 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.222247e-28 1.128571e-24 1.128371e-23 6.944660e-04 8.333349e-03 3.673611e-02 Max. Mean SD 9.105734e-02 2.054649e-02 2.598603e-02 Accuracy with training data: 96.7% Out-of-bag accuracy: 92.0% Build a HIBAG model with 1 individual classifier: # of SNPs randomly sampled as candidates for each selection: 10 # of SNPs: 83, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 03:38:28 [1] 2019-10-16 03:38:28, OOB Acc: 97.50%, # of SNPs: 18, # of Haplo: 34 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 5.014366e-13 4.671716e-10 4.667203e-09 1.640727e-03 7.504546e-03 2.126745e-02 Max. Mean SD 9.784316e-02 1.490504e-02 1.947399e-02 Accuracy with training data: 97.5% Out-of-bag accuracy: 97.5% Build a HIBAG model with 1 individual classifier: # of SNPs randomly sampled as candidates for each selection: 10 # of SNPs: 83, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 03:38:28 [1] 2019-10-16 03:38:28, OOB Acc: 88.89%, # of SNPs: 14, # of Haplo: 38 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.222223e-18 6.603163e-16 6.583163e-15 1.944468e-03 1.020834e-02 4.122739e-02 Max. Mean SD 1.808372e-01 2.422083e-02 3.699146e-02 Accuracy with training data: 95.8% Out-of-bag accuracy: 88.9% Gene: C Training dataset: 60 samples X 83 SNPs # of HLA alleles: 17 # of individual classifiers: 3 total # of SNPs used: 40 avg. # of SNPs in an individual classifier: 18.67 (sd: 5.03, min: 14, max: 24, median: 18.00) avg. # of haplotypes in an individual classifier: 33.67 (sd: 4.51, min: 29, max: 38, median: 34.00) avg. out-of-bag accuracy: 92.80% (sd: 4.36%, min: 88.89%, max: 97.50%, median: 92.00%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 1.708707e-13 1.229313e-05 1.229313e-04 1.860746e-03 9.050936e-03 3.332722e-02 Max. Mean SD 1.210500e-01 1.989079e-02 2.507466e-02 Genome assembly: hg19 Exclude 1 monomorphic SNP Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 9 # of SNPs: 77, # of samples: 60 # of unique HLA alleles: 12 [-] 2019-10-16 03:38:28 [1] 2019-10-16 03:38:29, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20 [2] 2019-10-16 03:38:29, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 Max. Mean SD 4.728168e-01 4.409445e-02 1.068815e-01 Accuracy with training data: 95.0% Out-of-bag accuracy: 94.5% Gene: DQB1 Training dataset: 60 samples X 77 SNPs # of HLA alleles: 12 # of individual classifiers: 2 total # of SNPs used: 20 avg. # of SNPs in an individual classifier: 14.00 (sd: 1.41, min: 13, max: 15, median: 14.00) avg. # of haplotypes in an individual classifier: 20.50 (sd: 0.71, min: 20, max: 21, median: 20.50) avg. out-of-bag accuracy: 94.45% (sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 Max. Mean SD 4.728168e-01 4.409445e-02 1.068815e-01 Genome assembly: hg19 Exclude 9 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 266, # of samples: 60 # of unique HLA alleles: 14 [-] 2019-10-16 03:38:29 [1] 2019-10-16 03:38:29, OOB Acc: 86.96%, # of SNPs: 12, # of Haplo: 32 [2] 2019-10-16 03:38:29, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 40 [3] 2019-10-16 03:38:29, OOB Acc: 97.92%, # of SNPs: 14, # of Haplo: 21 [4] 2019-10-16 03:38:29, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 25 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777 Max. Mean SD 0.3714064677 0.0407831671 0.0808783377 Accuracy with training data: 99.2% Out-of-bag accuracy: 92.0% Gene: A Training dataset: 60 samples X 266 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 42 avg. # of SNPs in an individual classifier: 13.75 (sd: 1.26, min: 12, max: 15, median: 14.00) avg. # of haplotypes in an individual classifier: 29.50 (sd: 8.35, min: 21, max: 40, median: 28.50) avg. out-of-bag accuracy: 91.96% (sd: 5.56%, min: 86.96%, max: 97.92%, median: 91.48%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777 Max. Mean SD 0.3714064677 0.0407831671 0.0808783377 Genome assembly: hg19 Gene: A Training dataset: 60 samples X 266 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 42 avg. # of SNPs in an individual classifier: 13.75 (sd: 1.26, min: 12, max: 15, median: 14.00) avg. # of haplotypes in an individual classifier: 29.50 (sd: 8.35, min: 21, max: 40, median: 28.50) avg. out-of-bag accuracy: 91.96% (sd: 5.56%, min: 86.96%, max: 97.92%, median: 91.48%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777 Max. Mean SD 0.3714064677 0.0407831671 0.0808783377 Genome assembly: hg19 Wed Oct 16 03:38:29 2019, passing the 1/4 classifiers. Wed Oct 16 03:38:29 2019, passing the 2/4 classifiers. Wed Oct 16 03:38:29 2019, passing the 3/4 classifiers. Wed Oct 16 03:38:29 2019, passing the 4/4 classifiers. Allele Num. Freq. CR ACC SEN SPE PPV NPV Miscall Valid. Valid. (%) (%) (%) (%) (%) (%) (%) ---- Overall accuracy: 92.0%, Call rate: 100.0% 01:01 25 0.2083 100.0 100.0 100.0 100.0 100.0 100.0 -- 02:01 43 0.3583 100.0 96.7 100.0 95.1 92.5 100.0 -- 02:06 1 0.0083 25.0 97.7 0.0 100.0 -- 97.7 02:01 (100) 03:01 9 0.0750 100.0 100.0 100.0 100.0 100.0 100.0 -- 11:01 5 0.0417 100.0 100.0 100.0 100.0 100.0 100.0 -- 23:01 3 0.0250 100.0 98.4 75.0 100.0 100.0 98.4 24:02 (100) 24:02 11 0.0917 100.0 97.3 100.0 97.1 76.2 100.0 -- 24:03 1 0.0083 100.0 97.8 0.0 100.0 -- 97.8 24:02 (75) 25:01 5 0.0417 100.0 98.4 100.0 98.3 84.7 100.0 -- 26:01 3 0.0250 100.0 98.4 62.5 100.0 100.0 98.4 25:01 (83) 29:02 4 0.0333 100.0 97.8 75.0 100.0 100.0 97.8 02:01 (75) 31:01 3 0.0250 75.0 100.0 100.0 100.0 100.0 100.0 -- 32:01 4 0.0333 100.0 100.0 100.0 100.0 100.0 100.0 -- 68:01 3 0.0250 100.0 100.0 100.0 100.0 100.0 100.0 -- \title{Imputation Evaluation} \documentclass[12pt]{article} \usepackage{fullpage} \usepackage{longtable} \begin{document} \maketitle \setlength{\LTcapwidth}{6.5in} % -------- BEGIN TABLE -------- \begin{longtable}{rrr | rrrrrrl} \caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).} \label{tab:accuracy} \\ Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\ & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\ \hline\hline \endfirsthead \multicolumn{10}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\ Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\ & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\ \hline\hline \endhead \hline \multicolumn{10}{r}{Continued on next page ...} \\ \hline \endfoot \hline\hline \endlastfoot \multicolumn{10}{l}{\it Overall accuracy: 92.0\%, Call rate: 100.0\%} \\ 01:01 & 25 & 0.2083 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 02:01 & 43 & 0.3583 & 100.0 & 96.7 & 100.0 & 95.1 & 92.5 & 100.0 & -- \\ 02:06 & 1 & 0.0083 & 25.0 & 97.7 & 0.0 & 100.0 & -- & 97.7 & 02:01 (100) \\ 03:01 & 9 & 0.0750 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 11:01 & 5 & 0.0417 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 23:01 & 3 & 0.0250 & 100.0 & 98.4 & 75.0 & 100.0 & 100.0 & 98.4 & 24:02 (100) \\ 24:02 & 11 & 0.0917 & 100.0 & 97.3 & 100.0 & 97.1 & 76.2 & 100.0 & -- \\ 24:03 & 1 & 0.0083 & 100.0 & 97.8 & 0.0 & 100.0 & -- & 97.8 & 24:02 (75) \\ 25:01 & 5 & 0.0417 & 100.0 & 98.4 & 100.0 & 98.3 & 84.7 & 100.0 & -- \\ 26:01 & 3 & 0.0250 & 100.0 & 98.4 & 62.5 & 100.0 & 100.0 & 98.4 & 25:01 (83) \\ 29:02 & 4 & 0.0333 & 100.0 & 97.8 & 75.0 & 100.0 & 100.0 & 97.8 & 02:01 (75) \\ 31:01 & 3 & 0.0250 & 75.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 32:01 & 4 & 0.0333 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 68:01 & 3 & 0.0250 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ \end{longtable} % -------- END TABLE -------- \end{document} Imputation Evaluation

Imputation Evaluation

Table 1L: The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).

Allele	Num. Valid.	Freq. Valid.	CR (%)	ACC (%)	SEN (%)	SPE (%)	PPV (%)	NPV (%)	Miscall (%)
Overall accuracy: 92.0%, Call rate: 100.0%
01:01	25	0.2083	100.0	100.0	100.0	100.0	100.0	100.0	--
02:01	43	0.3583	100.0	96.7	100.0	95.1	92.5	100.0	--
02:06	1	0.0083	25.0	97.7	0.0	100.0	--	97.7	02:01 (100)
03:01	9	0.0750	100.0	100.0	100.0	100.0	100.0	100.0	--
11:01	5	0.0417	100.0	100.0	100.0	100.0	100.0	100.0	--
23:01	3	0.0250	100.0	98.4	75.0	100.0	100.0	98.4	24:02 (100)
24:02	11	0.0917	100.0	97.3	100.0	97.1	76.2	100.0	--
24:03	1	0.0083	100.0	97.8	0.0	100.0	--	97.8	24:02 (75)
25:01	5	0.0417	100.0	98.4	100.0	98.3	84.7	100.0	--
26:01	3	0.0250	100.0	98.4	62.5	100.0	100.0	98.4	25:01 (83)
29:02	4	0.0333	100.0	97.8	75.0	100.0	100.0	97.8	02:01 (75)
31:01	3	0.0250	75.0	100.0	100.0	100.0	100.0	100.0	--
32:01	4	0.0333	100.0	100.0	100.0	100.0	100.0	100.0	--
68:01	3	0.0250	100.0	100.0	100.0	100.0	100.0	100.0	--

Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 23 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 14 Build a HIBAG model of 4 individual classifiers in parallel with 2 compute nodes: 2019-10-16 03:38:31, 1, job 2, # of SNPs: 12, # of haplo: 53, acc: 90.9% -- avg out-of-bag acc: 90.91%, sd: NA%, min: 90.91%, max: 90.91% 2019-10-16 03:38:31, 2, job 2, # of SNPs: 14, # of haplo: 26, acc: 90.9% -- avg out-of-bag acc: 90.91%, sd: 0.00%, min: 90.91%, max: 90.91% 2019-10-16 03:38:31, 3, job 1, # of SNPs: 14, # of haplo: 70, acc: 90.9% -- avg out-of-bag acc: 90.91%, sd: 0.00%, min: 90.91%, max: 90.91% 2019-10-16 03:38:31, 4, job 2, # of SNPs: 15, # of haplo: 51, acc: 70.8% -- avg out-of-bag acc: 85.89%, sd: 10.04%, min: 70.83%, max: 90.91% Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0001081400 0.0001260082 0.0002868224 0.0024431008 0.0080031409 0.0325892348 Max. Mean SD 0.4701164424 0.0507868945 0.1188983069 Accuracy with training data: 98.5% Out-of-bag accuracy: 85.9% Build a HIBAG model of 4 individual classifiers in parallel with 2 compute nodes: The model is autosaved in 'tmp_model.RData'. 2019-10-16 03:38:32, 1, job 1, # of SNPs: 11, # of haplo: 25, acc: 87.5% -- avg out-of-bag acc: 87.50%, sd: NA%, min: 87.50%, max: 87.50% 2019-10-16 03:38:33, 2, job 2, # of SNPs: 15, # of haplo: 38, acc: 83.3% -- avg out-of-bag acc: 85.42%, sd: 2.95%, min: 83.33%, max: 87.50% 2019-10-16 03:38:33, 3, job 2, # of SNPs: 12, # of haplo: 54, acc: 79.2% Stop "job 2". -- avg out-of-bag acc: 83.33%, sd: 4.17%, min: 79.17%, max: 87.50% 2019-10-16 03:38:33, 4, job 1, # of SNPs: 19, # of haplo: 57, acc: 93.8% Stop "job 1". -- avg out-of-bag acc: 85.94%, sd: 6.22%, min: 79.17%, max: 93.75% Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0006401993 0.0006512536 0.0007507428 0.0037575807 0.0077551837 0.0190314220 Max. Mean SD 0.4576709956 0.0416266235 0.1076618487 Accuracy with training data: 97.1% Out-of-bag accuracy: 85.9% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 47 avg. # of SNPs in an individual classifier: 14.25 (sd: 3.59, min: 11, max: 19, median: 13.50) avg. # of haplotypes in an individual classifier: 43.50 (sd: 14.89, min: 25, max: 57, median: 46.00) avg. out-of-bag accuracy: 85.94% (sd: 6.22%, min: 79.17%, max: 93.75%, median: 85.42%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0006401993 0.0006512536 0.0007507428 0.0037575807 0.0077551837 0.0190314220 Max. Mean SD 0.4576709956 0.0416266235 0.1076618487 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 03:38:33) 0% Predicting (2019-10-16 03:38:33) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 11 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 2 (7.7%) 3 (11.5%) 7 (26.9%) 14 (53.8%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000000 0.001220 0.004518 0.028600 0.016672 0.457671 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Run in parallel with 2 compute nodes. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 11 # of unique HLA genotypes: 14 Posterior probability: [0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1] 2 (7.7%) 3 (11.5%) 7 (26.9%) 14 (53.8%) Matching proportion of SNP haplotype: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.000000 0.001220 0.004518 0.028600 0.016672 0.457671 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 21 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 17 Exclude 11 monomorphic SNPs Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 03:38:34 1, SNP: 235, Loss: 208.912, OOB Acc: 59.09%, # of Haplo: 14 2, SNP: 136, Loss: 139.316, OOB Acc: 63.64%, # of Haplo: 14 3, SNP: 126, Loss: 96.148, OOB Acc: 72.73%, # of Haplo: 14 4, SNP: 89, Loss: 76.2917, OOB Acc: 77.27%, # of Haplo: 15 5, SNP: 102, Loss: 61.3783, OOB Acc: 86.36%, # of Haplo: 15 6, SNP: 105, Loss: 49.1567, OOB Acc: 90.91%, # of Haplo: 15 7, SNP: 117, Loss: 43.0927, OOB Acc: 95.45%, # of Haplo: 15 8, SNP: 259, Loss: 26.6243, OOB Acc: 95.45%, # of Haplo: 17 9, SNP: 60, Loss: 17.6253, OOB Acc: 95.45%, # of Haplo: 19 10, SNP: 236, Loss: 9.50329, OOB Acc: 95.45%, # of Haplo: 20 11, SNP: 94, Loss: 7.27191, OOB Acc: 95.45%, # of Haplo: 21 12, SNP: 58, Loss: 6.70503, OOB Acc: 95.45%, # of Haplo: 27 13, SNP: 243, Loss: 2.87079, OOB Acc: 95.45%, # of Haplo: 30 14, SNP: 5, Loss: 2.77321, OOB Acc: 95.45%, # of Haplo: 31 [1] 2019-10-16 03:38:34, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 31 1, SNP: 149, Loss: 171.797, OOB Acc: 66.67%, # of Haplo: 13 2, SNP: 176, Loss: 120.459, OOB Acc: 72.22%, # of Haplo: 14 3, SNP: 97, Loss: 80.1731, OOB Acc: 83.33%, # of Haplo: 14 4, SNP: 56, Loss: 51.5193, OOB Acc: 94.44%, # of Haplo: 16 5, SNP: 182, Loss: 34.5643, OOB Acc: 94.44%, # of Haplo: 18 6, SNP: 121, Loss: 23.0259, OOB Acc: 94.44%, # of Haplo: 18 7, SNP: 234, Loss: 15.0596, OOB Acc: 94.44%, # of Haplo: 20 8, SNP: 148, Loss: 9.66757, OOB Acc: 94.44%, # of Haplo: 20 9, SNP: 19, Loss: 4.29975, OOB Acc: 94.44%, # of Haplo: 27 10, SNP: 226, Loss: 0.481093, OOB Acc: 94.44%, # of Haplo: 27 11, SNP: 64, Loss: 0.447483, OOB Acc: 94.44%, # of Haplo: 28 12, SNP: 240, Loss: 0.365545, OOB Acc: 94.44%, # of Haplo: 37 13, SNP: 57, Loss: 0.365132, OOB Acc: 94.44%, # of Haplo: 38 [2] 2019-10-16 03:38:34, OOB Acc: 94.44%, # of SNPs: 13, # of Haplo: 38 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.000000e+00 4.210645e-09 4.210645e-08 2.324904e-03 8.109951e-03 1.989621e-02 Max. Mean SD 5.990492e-02 1.420983e-02 1.652472e-02 Accuracy with training data: 100.0% Out-of-bag accuracy: 94.9% Exclude 11 monomorphic SNPs Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 03:38:34 1, SNP: 118, Loss: 190.304, OOB Acc: 53.57%, # of Haplo: 13 2, SNP: 175, Loss: 157.208, OOB Acc: 60.71%, # of Haplo: 15 3, SNP: 103, Loss: 128.429, OOB Acc: 64.29%, # of Haplo: 15 4, SNP: 182, Loss: 66.6054, OOB Acc: 71.43%, # of Haplo: 15 5, SNP: 152, Loss: 58.8041, OOB Acc: 78.57%, # of Haplo: 15 6, SNP: 111, Loss: 30.086, OOB Acc: 82.14%, # of Haplo: 15 7, SNP: 130, Loss: 15.3177, OOB Acc: 89.29%, # of Haplo: 19 8, SNP: 229, Loss: 9.99758, OOB Acc: 89.29%, # of Haplo: 28 9, SNP: 185, Loss: 7.40712, OOB Acc: 89.29%, # of Haplo: 29 10, SNP: 199, Loss: 6.21341, OOB Acc: 89.29%, # of Haplo: 29 11, SNP: 217, Loss: 1.38739, OOB Acc: 89.29%, # of Haplo: 30 [1] 2019-10-16 03:38:34, OOB Acc: 89.29%, # of SNPs: 11, # of Haplo: 30 1, SNP: 101, Loss: 154.355, OOB Acc: 46.15%, # of Haplo: 16 2, SNP: 102, Loss: 139.148, OOB Acc: 61.54%, # of Haplo: 22 3, SNP: 132, Loss: 95.2502, OOB Acc: 73.08%, # of Haplo: 23 4, SNP: 147, Loss: 76.9692, OOB Acc: 76.92%, # of Haplo: 34 5, SNP: 53, Loss: 68.3851, OOB Acc: 88.46%, # of Haplo: 51 6, SNP: 186, Loss: 41.8787, OOB Acc: 88.46%, # of Haplo: 53 7, SNP: 128, Loss: 33.5437, OOB Acc: 92.31%, # of Haplo: 53 8, SNP: 14, Loss: 23.3103, OOB Acc: 92.31%, # of Haplo: 55 9, SNP: 219, Loss: 18.3628, OOB Acc: 92.31%, # of Haplo: 57 10, SNP: 149, Loss: 17.9413, OOB Acc: 92.31%, # of Haplo: 89 11, SNP: 73, Loss: 16.3172, OOB Acc: 92.31%, # of Haplo: 90 12, SNP: 70, Loss: 16.1056, OOB Acc: 92.31%, # of Haplo: 90 13, SNP: 199, Loss: 12.3057, OOB Acc: 92.31%, # of Haplo: 90 14, SNP: 203, Loss: 12.2013, OOB Acc: 92.31%, # of Haplo: 90 15, SNP: 151, Loss: 11.1795, OOB Acc: 92.31%, # of Haplo: 90 [2] 2019-10-16 03:38:35, OOB Acc: 92.31%, # of SNPs: 15, # of Haplo: 90 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0002703521 0.0002971139 0.0005379705 0.0035684045 0.0131584084 0.0413920204 Max. Mean SD 0.5762364122 0.0439769026 0.0999476134 Accuracy with training data: 97.1% Out-of-bag accuracy: 90.8% HIBAG model: 2 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting by voting from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 03:38:35) 0% Predicting (2019-10-16 03:38:35) 100% HIBAG model: 2 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting by voting from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 03:38:35) 0% Predicting (2019-10-16 03:38:35) 100% Exclude 1 monomorphic SNP Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 13 # of SNPs: 158, # of samples: 60 # of unique HLA alleles: 14 [-] 2019-10-16 03:38:35 1, SNP: 141, Loss: 378.06, OOB Acc: 52.08%, # of Haplo: 14 2, SNP: 74, Loss: 262.497, OOB Acc: 58.33%, # of Haplo: 15 3, SNP: 78, Loss: 162.497, OOB Acc: 68.75%, # of Haplo: 19 4, SNP: 118, Loss: 70.0426, OOB Acc: 72.92%, # of Haplo: 23 5, SNP: 82, Loss: 45.8279, OOB Acc: 83.33%, # of Haplo: 23 6, SNP: 95, Loss: 35.4414, OOB Acc: 89.58%, # of Haplo: 27 7, SNP: 89, Loss: 32.6134, OOB Acc: 89.58%, # of Haplo: 35 8, SNP: 83, Loss: 31.7921, OOB Acc: 89.58%, # of Haplo: 51 9, SNP: 151, Loss: 31.0653, OOB Acc: 89.58%, # of Haplo: 55 10, SNP: 94, Loss: 31.0246, OOB Acc: 89.58%, # of Haplo: 55 11, SNP: 111, Loss: 18.9027, OOB Acc: 89.58%, # of Haplo: 56 12, SNP: 139, Loss: 18.4248, OOB Acc: 89.58%, # of Haplo: 59 13, SNP: 93, Loss: 17.0195, OOB Acc: 91.67%, # of Haplo: 58 14, SNP: 15, Loss: 14.1692, OOB Acc: 91.67%, # of Haplo: 60 [1] 2019-10-16 03:38:35, OOB Acc: 91.67%, # of SNPs: 14, # of Haplo: 60 1, SNP: 94, Loss: 403.365, OOB Acc: 63.16%, # of Haplo: 15 2, SNP: 82, Loss: 294.053, OOB Acc: 71.05%, # of Haplo: 18 3, SNP: 57, Loss: 226.142, OOB Acc: 76.32%, # of Haplo: 23 4, SNP: 155, Loss: 197.199, OOB Acc: 84.21%, # of Haplo: 29 5, SNP: 44, Loss: 132.804, OOB Acc: 86.84%, # of Haplo: 40 6, SNP: 30, Loss: 122.507, OOB Acc: 92.11%, # of Haplo: 40 7, SNP: 109, Loss: 72.0179, OOB Acc: 92.11%, # of Haplo: 41 8, SNP: 72, Loss: 59.3281, OOB Acc: 92.11%, # of Haplo: 41 9, SNP: 36, Loss: 54.939, OOB Acc: 94.74%, # of Haplo: 43 10, SNP: 127, Loss: 48.1392, OOB Acc: 94.74%, # of Haplo: 43 11, SNP: 53, Loss: 44.7676, OOB Acc: 94.74%, # of Haplo: 43 12, SNP: 148, Loss: 43.047, OOB Acc: 94.74%, # of Haplo: 44 13, SNP: 152, Loss: 40.2104, OOB Acc: 94.74%, # of Haplo: 45 14, SNP: 125, Loss: 39.8862, OOB Acc: 94.74%, # of Haplo: 45 15, SNP: 78, Loss: 39.5652, OOB Acc: 94.74%, # of Haplo: 45 16, SNP: 3, Loss: 39.0621, OOB Acc: 94.74%, # of Haplo: 47 17, SNP: 141, Loss: 37.6822, OOB Acc: 94.74%, # of Haplo: 47 18, SNP: 1, Loss: 36.5022, OOB Acc: 94.74%, # of Haplo: 50 [2] 2019-10-16 03:38:36, OOB Acc: 94.74%, # of SNPs: 18, # of Haplo: 50 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 Max. Mean SD 5.948644e-01 6.324378e-02 1.404915e-01 Accuracy with training data: 96.7% Out-of-bag accuracy: 93.2% Gene: A Training dataset: 60 samples X 158 SNPs # of HLA alleles: 14 # of individual classifiers: 2 total # of SNPs used: 28 avg. # of SNPs in an individual classifier: 16.00 (sd: 2.83, min: 14, max: 18, median: 16.00) avg. # of haplotypes in an individual classifier: 55.00 (sd: 7.07, min: 50, max: 60, median: 55.00) avg. out-of-bag accuracy: 93.20% (sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 Max. Mean SD 5.948644e-01 6.324378e-02 1.404915e-01 Genome assembly: hg19 Remove 130 unused SNPs. Gene: A Training dataset: 60 samples X 28 SNPs # of HLA alleles: 14 # of individual classifiers: 2 total # of SNPs used: 28 avg. # of SNPs in an individual classifier: 16.00 (sd: 2.83, min: 14, max: 18, median: 16.00) avg. # of haplotypes in an individual classifier: 55.00 (sd: 7.07, min: 50, max: 60, median: 55.00) avg. out-of-bag accuracy: 93.20% (sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 Max. Mean SD 5.948644e-01 6.324378e-02 1.404915e-01 Genome assembly: hg19 Platform: Illumina 1M Duo Information: Training set -- HapMap Phase II HIBAG model: 2 individual classifiers, 158 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 60 Predicting (2019-10-16 03:38:36) 0% Predicting (2019-10-16 03:38:36) 100% HIBAG model: 2 individual classifiers, 28 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 60 Predicting (2019-10-16 03:38:36) 0% Predicting (2019-10-16 03:38:36) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 21 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 17 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 03:38:36 1, SNP: 235, Loss: 208.912, OOB Acc: 59.09%, # of Haplo: 14 2, SNP: 136, Loss: 139.316, OOB Acc: 63.64%, # of Haplo: 14 3, SNP: 126, Loss: 96.148, OOB Acc: 72.73%, # of Haplo: 14 4, SNP: 89, Loss: 76.2917, OOB Acc: 77.27%, # of Haplo: 15 5, SNP: 102, Loss: 61.3783, OOB Acc: 86.36%, # of Haplo: 15 6, SNP: 105, Loss: 49.1567, OOB Acc: 90.91%, # of Haplo: 15 7, SNP: 117, Loss: 43.0927, OOB Acc: 95.45%, # of Haplo: 15 8, SNP: 259, Loss: 26.6243, OOB Acc: 95.45%, # of Haplo: 17 9, SNP: 60, Loss: 17.6253, OOB Acc: 95.45%, # of Haplo: 19 10, SNP: 236, Loss: 9.50329, OOB Acc: 95.45%, # of Haplo: 20 11, SNP: 94, Loss: 7.27191, OOB Acc: 95.45%, # of Haplo: 21 12, SNP: 58, Loss: 6.70503, OOB Acc: 95.45%, # of Haplo: 27 13, SNP: 243, Loss: 2.87079, OOB Acc: 95.45%, # of Haplo: 30 14, SNP: 5, Loss: 2.77321, OOB Acc: 95.45%, # of Haplo: 31 [1] 2019-10-16 03:38:36, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 31 1, SNP: 149, Loss: 171.797, OOB Acc: 66.67%, # of Haplo: 13 2, SNP: 176, Loss: 120.459, OOB Acc: 72.22%, # of Haplo: 14 3, SNP: 97, Loss: 80.1731, OOB Acc: 83.33%, # of Haplo: 14 4, SNP: 56, Loss: 51.5193, OOB Acc: 94.44%, # of Haplo: 16 5, SNP: 182, Loss: 34.5643, OOB Acc: 94.44%, # of Haplo: 18 6, SNP: 121, Loss: 23.0259, OOB Acc: 94.44%, # of Haplo: 18 7, SNP: 234, Loss: 15.0596, OOB Acc: 94.44%, # of Haplo: 20 8, SNP: 148, Loss: 9.66757, OOB Acc: 94.44%, # of Haplo: 20 9, SNP: 19, Loss: 4.29975, OOB Acc: 94.44%, # of Haplo: 27 10, SNP: 226, Loss: 0.481093, OOB Acc: 94.44%, # of Haplo: 27 11, SNP: 64, Loss: 0.447483, OOB Acc: 94.44%, # of Haplo: 28 12, SNP: 240, Loss: 0.365545, OOB Acc: 94.44%, # of Haplo: 37 13, SNP: 57, Loss: 0.365132, OOB Acc: 94.44%, # of Haplo: 38 [2] 2019-10-16 03:38:36, OOB Acc: 94.44%, # of SNPs: 13, # of Haplo: 38 1, SNP: 118, Loss: 190.304, OOB Acc: 53.57%, # of Haplo: 13 2, SNP: 175, Loss: 157.208, OOB Acc: 60.71%, # of Haplo: 15 3, SNP: 103, Loss: 128.429, OOB Acc: 64.29%, # of Haplo: 15 4, SNP: 182, Loss: 66.6054, OOB Acc: 71.43%, # of Haplo: 15 5, SNP: 152, Loss: 58.8041, OOB Acc: 78.57%, # of Haplo: 15 6, SNP: 111, Loss: 30.086, OOB Acc: 82.14%, # of Haplo: 15 7, SNP: 130, Loss: 15.3177, OOB Acc: 89.29%, # of Haplo: 19 8, SNP: 229, Loss: 9.99758, OOB Acc: 89.29%, # of Haplo: 28 9, SNP: 185, Loss: 7.40712, OOB Acc: 89.29%, # of Haplo: 29 10, SNP: 199, Loss: 6.21341, OOB Acc: 89.29%, # of Haplo: 29 11, SNP: 217, Loss: 1.38739, OOB Acc: 89.29%, # of Haplo: 30 [3] 2019-10-16 03:38:36, OOB Acc: 89.29%, # of SNPs: 11, # of Haplo: 30 1, SNP: 101, Loss: 154.355, OOB Acc: 46.15%, # of Haplo: 16 2, SNP: 102, Loss: 139.148, OOB Acc: 61.54%, # of Haplo: 22 3, SNP: 132, Loss: 95.2502, OOB Acc: 73.08%, # of Haplo: 23 4, SNP: 147, Loss: 76.9692, OOB Acc: 76.92%, # of Haplo: 34 5, SNP: 53, Loss: 68.3851, OOB Acc: 88.46%, # of Haplo: 51 6, SNP: 186, Loss: 41.8787, OOB Acc: 88.46%, # of Haplo: 53 7, SNP: 128, Loss: 33.5437, OOB Acc: 92.31%, # of Haplo: 53 8, SNP: 14, Loss: 23.3103, OOB Acc: 92.31%, # of Haplo: 55 9, SNP: 219, Loss: 18.3628, OOB Acc: 92.31%, # of Haplo: 57 10, SNP: 149, Loss: 17.9413, OOB Acc: 92.31%, # of Haplo: 89 11, SNP: 73, Loss: 16.3172, OOB Acc: 92.31%, # of Haplo: 90 12, SNP: 70, Loss: 16.1056, OOB Acc: 92.31%, # of Haplo: 90 13, SNP: 199, Loss: 12.3057, OOB Acc: 92.31%, # of Haplo: 90 14, SNP: 203, Loss: 12.2013, OOB Acc: 92.31%, # of Haplo: 90 15, SNP: 151, Loss: 11.1795, OOB Acc: 92.31%, # of Haplo: 90 [4] 2019-10-16 03:38:36, OOB Acc: 92.31%, # of SNPs: 15, # of Haplo: 90 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Accuracy with training data: 97.1% Out-of-bag accuracy: 92.9% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 49 avg. # of SNPs in an individual classifier: 13.25 (sd: 1.71, min: 11, max: 15, median: 13.50) avg. # of haplotypes in an individual classifier: 47.25 (sd: 28.72, min: 30, max: 90, median: 34.50) avg. out-of-bag accuracy: 92.87% (sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 03:38:36) 0% Predicting (2019-10-16 03:38:36) 100% Allele Num. Freq. Num. Freq. CR ACC SEN SPE PPV NPV Miscall Train Train Valid. Valid. (%) (%) (%) (%) (%) (%) (%) ---- Overall accuracy: 88.5%, Call rate: 100.0% 01:01 13 0.1912 12 0.2308 100.0 96.2 100.0 95.0 85.7 100.0 -- 02:01 25 0.3676 18 0.3462 100.0 98.1 94.4 100.0 100.0 97.1 01:01 (100) 02:06 1 0.0147 0 0 -- -- -- -- -- -- -- 03:01 4 0.0588 5 0.0962 100.0 98.1 100.0 97.9 83.3 100.0 -- 11:01 2 0.0294 3 0.0577 100.0 100.0 100.0 100.0 100.0 100.0 -- 23:01 1 0.0147 2 0.0385 100.0 96.2 0.0 100.0 -- 96.2 24:02 (100) 24:02 6 0.0882 5 0.0962 100.0 92.3 60.0 95.7 60.0 95.7 01:01 (50) 24:03 1 0.0147 0 0 -- -- -- -- -- -- -- 25:01 4 0.0588 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 -- 26:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 -- 29:02 3 0.0441 1 0.0192 100.0 98.1 0.0 100.0 -- 98.1 03:01 (50) 31:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 -- 32:01 2 0.0294 2 0.0385 100.0 100.0 100.0 100.0 100.0 100.0 -- 68:01 2 0.0294 1 0.0192 100.0 98.1 100.0 98.0 50.0 100.0 -- \title{Imputation Evaluation} \documentclass[12pt]{article} \usepackage{fullpage} \usepackage{longtable} \begin{document} \maketitle \setlength{\LTcapwidth}{6.5in} % -------- BEGIN TABLE -------- \begin{longtable}{rrrrr | rrrrrrl} \caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).} \label{tab:accuracy} \\ Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\ & Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\ \hline\hline \endfirsthead \multicolumn{12}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\ Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\ & Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\ \hline\hline \endhead \hline \multicolumn{12}{r}{Continued on next page ...} \\ \hline \endfoot \hline\hline \endlastfoot \multicolumn{12}{l}{\it Overall accuracy: 88.5\%, Call rate: 100.0\%} \\ 01:01 & 13 & 0.1912 & 12 & 0.2308 & 100.0 & 96.2 & 100.0 & 95.0 & 85.7 & 100.0 & -- \\ 02:01 & 25 & 0.3676 & 18 & 0.3462 & 100.0 & 98.1 & 94.4 & 100.0 & 100.0 & 97.1 & 01:01 (100) \\ 02:06 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\ 03:01 & 4 & 0.0588 & 5 & 0.0962 & 100.0 & 98.1 & 100.0 & 97.9 & 83.3 & 100.0 & -- \\ 11:01 & 2 & 0.0294 & 3 & 0.0577 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 23:01 & 1 & 0.0147 & 2 & 0.0385 & 100.0 & 96.2 & 0.0 & 100.0 & -- & 96.2 & 24:02 (100) \\ 24:02 & 6 & 0.0882 & 5 & 0.0962 & 100.0 & 92.3 & 60.0 & 95.7 & 60.0 & 95.7 & 01:01 (50) \\ 24:03 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\ 25:01 & 4 & 0.0588 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 26:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 29:02 & 3 & 0.0441 & 1 & 0.0192 & 100.0 & 98.1 & 0.0 & 100.0 & -- & 98.1 & 03:01 (50) \\ 31:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 32:01 & 2 & 0.0294 & 2 & 0.0385 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\ 68:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 100.0 & 98.0 & 50.0 & 100.0 & -- \\ \end{longtable} % -------- END TABLE -------- \end{document} Imputation Evaluation

Imputation Evaluation

Table 1L: The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).

Allele	Num. Train	Freq. Train	Num. Valid.	Freq. Valid.	CR (%)	ACC (%)	SEN (%)	SPE (%)	PPV (%)	NPV (%)	Miscall (%)
Overall accuracy: 88.5%, Call rate: 100.0%
01:01	13	0.1912	12	0.2308	100.0	96.2	100.0	95.0	85.7	100.0	--
02:01	25	0.3676	18	0.3462	100.0	98.1	94.4	100.0	100.0	97.1	01:01 (100)
02:06	1	0.0147	0	0	--	--	--	--	--	--	--
03:01	4	0.0588	5	0.0962	100.0	98.1	100.0	97.9	83.3	100.0	--
11:01	2	0.0294	3	0.0577	100.0	100.0	100.0	100.0	100.0	100.0	--
23:01	1	0.0147	2	0.0385	100.0	96.2	0.0	100.0	--	96.2	24:02 (100)
24:02	6	0.0882	5	0.0962	100.0	92.3	60.0	95.7	60.0	95.7	01:01 (50)
24:03	1	0.0147	0	0	--	--	--	--	--	--	--
25:01	4	0.0588	1	0.0192	100.0	100.0	100.0	100.0	100.0	100.0	--
26:01	2	0.0294	1	0.0192	100.0	100.0	100.0	100.0	100.0	100.0	--
29:02	3	0.0441	1	0.0192	100.0	98.1	0.0	100.0	--	98.1	03:01 (50)
31:01	2	0.0294	1	0.0192	100.0	100.0	100.0	100.0	100.0	100.0	--
32:01	2	0.0294	2	0.0385	100.0	100.0	100.0	100.0	100.0	100.0	--
68:01	2	0.0294	1	0.0192	100.0	98.1	100.0	98.0	50.0	100.0	--

**Overall accuracy: 88.5%, Call rate: 100.0%** | Allele | # Train | Freq. Train | # Valid. | Freq. Valid. | CR (%) | ACC (%) | SEN (%) | SPE (%) | PPV (%) | NPV (%) | Miscall (%) | |:--|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|:--| | 01:01 | 13 | 0.1912 | 12 | 0.2308 | 100.0 | 96.2 | 100.0 | 95.0 | 85.7 | 100.0 | -- | | 02:01 | 25 | 0.3676 | 18 | 0.3462 | 100.0 | 98.1 | 94.4 | 100.0 | 100.0 | 97.1 | 01:01 (100) | | 02:06 | 1 | 0.0147 | 0 | 0 | -- | -- | -- | -- | -- | -- | -- | | 03:01 | 4 | 0.0588 | 5 | 0.0962 | 100.0 | 98.1 | 100.0 | 97.9 | 83.3 | 100.0 | -- | | 11:01 | 2 | 0.0294 | 3 | 0.0577 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- | | 23:01 | 1 | 0.0147 | 2 | 0.0385 | 100.0 | 96.2 | 0.0 | 100.0 | -- | 96.2 | 24:02 (100) | | 24:02 | 6 | 0.0882 | 5 | 0.0962 | 100.0 | 92.3 | 60.0 | 95.7 | 60.0 | 95.7 | 01:01 (50) | | 24:03 | 1 | 0.0147 | 0 | 0 | -- | -- | -- | -- | -- | -- | -- | | 25:01 | 4 | 0.0588 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- | | 26:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- | | 29:02 | 3 | 0.0441 | 1 | 0.0192 | 100.0 | 98.1 | 0.0 | 100.0 | -- | 98.1 | 03:01 (50) | | 31:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- | | 32:01 | 2 | 0.0294 | 2 | 0.0385 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- | | 68:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 98.1 | 100.0 | 98.0 | 50.0 | 100.0 | -- | Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 21 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 17 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 03:38:37 1, SNP: 235, Loss: 208.912, OOB Acc: 59.09%, # of Haplo: 14 2, SNP: 136, Loss: 139.316, OOB Acc: 63.64%, # of Haplo: 14 3, SNP: 126, Loss: 96.148, OOB Acc: 72.73%, # of Haplo: 14 4, SNP: 89, Loss: 76.2917, OOB Acc: 77.27%, # of Haplo: 15 5, SNP: 102, Loss: 61.3783, OOB Acc: 86.36%, # of Haplo: 15 6, SNP: 105, Loss: 49.1567, OOB Acc: 90.91%, # of Haplo: 15 7, SNP: 117, Loss: 43.0927, OOB Acc: 95.45%, # of Haplo: 15 8, SNP: 259, Loss: 26.6243, OOB Acc: 95.45%, # of Haplo: 17 9, SNP: 60, Loss: 17.6253, OOB Acc: 95.45%, # of Haplo: 19 10, SNP: 236, Loss: 9.50329, OOB Acc: 95.45%, # of Haplo: 20 11, SNP: 94, Loss: 7.27191, OOB Acc: 95.45%, # of Haplo: 21 12, SNP: 58, Loss: 6.70503, OOB Acc: 95.45%, # of Haplo: 27 13, SNP: 243, Loss: 2.87079, OOB Acc: 95.45%, # of Haplo: 30 14, SNP: 5, Loss: 2.77321, OOB Acc: 95.45%, # of Haplo: 31 [1] 2019-10-16 03:38:37, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 31 1, SNP: 149, Loss: 171.797, OOB Acc: 66.67%, # of Haplo: 13 2, SNP: 176, Loss: 120.459, OOB Acc: 72.22%, # of Haplo: 14 3, SNP: 97, Loss: 80.1731, OOB Acc: 83.33%, # of Haplo: 14 4, SNP: 56, Loss: 51.5193, OOB Acc: 94.44%, # of Haplo: 16 5, SNP: 182, Loss: 34.5643, OOB Acc: 94.44%, # of Haplo: 18 6, SNP: 121, Loss: 23.0259, OOB Acc: 94.44%, # of Haplo: 18 7, SNP: 234, Loss: 15.0596, OOB Acc: 94.44%, # of Haplo: 20 8, SNP: 148, Loss: 9.66757, OOB Acc: 94.44%, # of Haplo: 20 9, SNP: 19, Loss: 4.29975, OOB Acc: 94.44%, # of Haplo: 27 10, SNP: 226, Loss: 0.481093, OOB Acc: 94.44%, # of Haplo: 27 11, SNP: 64, Loss: 0.447483, OOB Acc: 94.44%, # of Haplo: 28 12, SNP: 240, Loss: 0.365545, OOB Acc: 94.44%, # of Haplo: 37 13, SNP: 57, Loss: 0.365132, OOB Acc: 94.44%, # of Haplo: 38 [2] 2019-10-16 03:38:37, OOB Acc: 94.44%, # of SNPs: 13, # of Haplo: 38 1, SNP: 118, Loss: 190.304, OOB Acc: 53.57%, # of Haplo: 13 2, SNP: 175, Loss: 157.208, OOB Acc: 60.71%, # of Haplo: 15 3, SNP: 103, Loss: 128.429, OOB Acc: 64.29%, # of Haplo: 15 4, SNP: 182, Loss: 66.6054, OOB Acc: 71.43%, # of Haplo: 15 5, SNP: 152, Loss: 58.8041, OOB Acc: 78.57%, # of Haplo: 15 6, SNP: 111, Loss: 30.086, OOB Acc: 82.14%, # of Haplo: 15 7, SNP: 130, Loss: 15.3177, OOB Acc: 89.29%, # of Haplo: 19 8, SNP: 229, Loss: 9.99758, OOB Acc: 89.29%, # of Haplo: 28 9, SNP: 185, Loss: 7.40712, OOB Acc: 89.29%, # of Haplo: 29 10, SNP: 199, Loss: 6.21341, OOB Acc: 89.29%, # of Haplo: 29 11, SNP: 217, Loss: 1.38739, OOB Acc: 89.29%, # of Haplo: 30 [3] 2019-10-16 03:38:37, OOB Acc: 89.29%, # of SNPs: 11, # of Haplo: 30 1, SNP: 101, Loss: 154.355, OOB Acc: 46.15%, # of Haplo: 16 2, SNP: 102, Loss: 139.148, OOB Acc: 61.54%, # of Haplo: 22 3, SNP: 132, Loss: 95.2502, OOB Acc: 73.08%, # of Haplo: 23 4, SNP: 147, Loss: 76.9692, OOB Acc: 76.92%, # of Haplo: 34 5, SNP: 53, Loss: 68.3851, OOB Acc: 88.46%, # of Haplo: 51 6, SNP: 186, Loss: 41.8787, OOB Acc: 88.46%, # of Haplo: 53 7, SNP: 128, Loss: 33.5437, OOB Acc: 92.31%, # of Haplo: 53 8, SNP: 14, Loss: 23.3103, OOB Acc: 92.31%, # of Haplo: 55 9, SNP: 219, Loss: 18.3628, OOB Acc: 92.31%, # of Haplo: 57 10, SNP: 149, Loss: 17.9413, OOB Acc: 92.31%, # of Haplo: 89 11, SNP: 73, Loss: 16.3172, OOB Acc: 92.31%, # of Haplo: 90 12, SNP: 70, Loss: 16.1056, OOB Acc: 92.31%, # of Haplo: 90 13, SNP: 199, Loss: 12.3057, OOB Acc: 92.31%, # of Haplo: 90 14, SNP: 203, Loss: 12.2013, OOB Acc: 92.31%, # of Haplo: 90 15, SNP: 151, Loss: 11.1795, OOB Acc: 92.31%, # of Haplo: 90 [4] 2019-10-16 03:38:37, OOB Acc: 92.31%, # of SNPs: 15, # of Haplo: 90 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Accuracy with training data: 97.1% Out-of-bag accuracy: 92.9% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 49 avg. # of SNPs in an individual classifier: 13.25 (sd: 1.71, min: 11, max: 15, median: 13.50) avg. # of haplotypes in an individual classifier: 47.25 (sd: 28.72, min: 30, max: 90, median: 34.50) avg. out-of-bag accuracy: 92.87% (sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 03:38:37) 0% Predicting (2019-10-16 03:38:37) 100% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 21 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 17 Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 8 # of SNPs: 51, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 03:38:38 1, SNP: 13, Loss: 391.274, OOB Acc: 41.67%, # of Haplo: 17 2, SNP: 2, Loss: 321.685, OOB Acc: 52.08%, # of Haplo: 18 3, SNP: 36, Loss: 232.846, OOB Acc: 58.33%, # of Haplo: 19 4, SNP: 28, Loss: 178.077, OOB Acc: 62.50%, # of Haplo: 20 5, SNP: 35, Loss: 107.151, OOB Acc: 68.75%, # of Haplo: 20 6, SNP: 3, Loss: 72.2736, OOB Acc: 72.92%, # of Haplo: 23 7, SNP: 19, Loss: 50.8439, OOB Acc: 77.08%, # of Haplo: 25 8, SNP: 4, Loss: 47.2744, OOB Acc: 83.33%, # of Haplo: 29 9, SNP: 42, Loss: 47.0092, OOB Acc: 85.42%, # of Haplo: 37 10, SNP: 33, Loss: 41.5486, OOB Acc: 85.42%, # of Haplo: 41 11, SNP: 5, Loss: 39.769, OOB Acc: 85.42%, # of Haplo: 51 12, SNP: 10, Loss: 34.0977, OOB Acc: 85.42%, # of Haplo: 51 13, SNP: 37, Loss: 32.3969, OOB Acc: 85.42%, # of Haplo: 52 14, SNP: 7, Loss: 28.1492, OOB Acc: 85.42%, # of Haplo: 52 15, SNP: 15, Loss: 27.2163, OOB Acc: 85.42%, # of Haplo: 55 [1] 2019-10-16 03:38:38, OOB Acc: 85.42%, # of SNPs: 15, # of Haplo: 55 1, SNP: 18, Loss: 453.852, OOB Acc: 44.12%, # of Haplo: 17 2, SNP: 4, Loss: 358.517, OOB Acc: 50.00%, # of Haplo: 18 3, SNP: 49, Loss: 258.495, OOB Acc: 52.94%, # of Haplo: 18 4, SNP: 5, Loss: 172.555, OOB Acc: 67.65%, # of Haplo: 21 5, SNP: 42, Loss: 144.905, OOB Acc: 76.47%, # of Haplo: 21 6, SNP: 38, Loss: 98.7462, OOB Acc: 79.41%, # of Haplo: 21 7, SNP: 36, Loss: 83.4743, OOB Acc: 82.35%, # of Haplo: 24 8, SNP: 19, Loss: 60.2385, OOB Acc: 88.24%, # of Haplo: 24 9, SNP: 46, Loss: 49.1775, OOB Acc: 88.24%, # of Haplo: 24 10, SNP: 20, Loss: 42.3205, OOB Acc: 88.24%, # of Haplo: 24 11, SNP: 12, Loss: 41.1299, OOB Acc: 91.18%, # of Haplo: 25 12, SNP: 1, Loss: 33.8332, OOB Acc: 91.18%, # of Haplo: 25 13, SNP: 37, Loss: 32.8313, OOB Acc: 91.18%, # of Haplo: 26 14, SNP: 7, Loss: 38.8398, OOB Acc: 94.12%, # of Haplo: 27 15, SNP: 15, Loss: 35.0817, OOB Acc: 94.12%, # of Haplo: 32 16, SNP: 39, Loss: 33.7063, OOB Acc: 94.12%, # of Haplo: 30 [2] 2019-10-16 03:38:38, OOB Acc: 94.12%, # of SNPs: 16, # of Haplo: 30 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 3.479253e-10 4.700783e-10 1.569456e-09 3.197938e-03 1.266674e-02 3.773631e-02 Max. Mean SD 9.805734e-02 2.430678e-02 2.697855e-02 Accuracy with training data: 95.8% Out-of-bag accuracy: 89.8% Gene: C Training dataset: 60 samples X 51 SNPs # of HLA alleles: 17 # of individual classifiers: 2 total # of SNPs used: 23 avg. # of SNPs in an individual classifier: 15.50 (sd: 0.71, min: 15, max: 16, median: 15.50) avg. # of haplotypes in an individual classifier: 42.50 (sd: 17.68, min: 30, max: 55, median: 42.50) avg. out-of-bag accuracy: 89.77% (sd: 6.15%, min: 85.42%, max: 94.12%, median: 89.77%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 3.479253e-10 4.700783e-10 1.569456e-09 3.197938e-03 1.266674e-02 3.773631e-02 Max. Mean SD 9.805734e-02 2.430678e-02 2.697855e-02 Genome assembly: hg19 Gene: C Training dataset: 60 samples X 51 SNPs # of HLA alleles: 17 # of individual classifiers: 1 total # of SNPs used: 15 avg. # of SNPs in an individual classifier: 15.00 (sd: NA, min: 15, max: 15, median: 15.00) avg. # of haplotypes in an individual classifier: 55.00 (sd: NA, min: 55, max: 55, median: 55.00) avg. out-of-bag accuracy: 85.42% (sd: NA%, min: 85.42%, max: 85.42%, median: 85.42%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 3.479253e-10 4.700783e-10 1.569456e-09 3.197938e-03 1.266674e-02 3.773631e-02 Max. Mean SD 9.805734e-02 2.430678e-02 2.697855e-02 Genome assembly: hg19 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 60 # of unique HLA alleles: 14 # of unique HLA genotypes: 29 Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 10 # of SNPs: 83, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 03:38:39 1, SNP: 44, Loss: 396.506, OOB Acc: 41.67%, # of Haplo: 17 2, SNP: 58, Loss: 291.148, OOB Acc: 50.00%, # of Haplo: 17 3, SNP: 80, Loss: 211.469, OOB Acc: 56.25%, # of Haplo: 20 4, SNP: 18, Loss: 138.615, OOB Acc: 60.42%, # of Haplo: 20 5, SNP: 29, Loss: 111.977, OOB Acc: 62.50%, # of Haplo: 22 6, SNP: 62, Loss: 90.976, OOB Acc: 68.75%, # of Haplo: 24 7, SNP: 13, Loss: 70.2962, OOB Acc: 72.92%, # of Haplo: 24 8, SNP: 14, Loss: 54.5685, OOB Acc: 77.08%, # of Haplo: 22 9, SNP: 72, Loss: 35.1951, OOB Acc: 77.08%, # of Haplo: 24 10, SNP: 3, Loss: 23.2868, OOB Acc: 79.17%, # of Haplo: 24 11, SNP: 70, Loss: 21.089, OOB Acc: 79.17%, # of Haplo: 28 12, SNP: 5, Loss: 20.9664, OOB Acc: 79.17%, # of Haplo: 29 13, SNP: 40, Loss: 20.9662, OOB Acc: 83.33%, # of Haplo: 37 14, SNP: 24, Loss: 20.2385, OOB Acc: 85.42%, # of Haplo: 38 15, SNP: 2, Loss: 20.2383, OOB Acc: 87.50%, # of Haplo: 39 16, SNP: 74, Loss: 20.0601, OOB Acc: 87.50%, # of Haplo: 40 17, SNP: 57, Loss: 17.8846, OOB Acc: 87.50%, # of Haplo: 41 18, SNP: 56, Loss: 14.7377, OOB Acc: 89.58%, # of Haplo: 43 19, SNP: 27, Loss: 10.7709, OOB Acc: 89.58%, # of Haplo: 43 [1] 2019-10-16 03:38:39, OOB Acc: 89.58%, # of SNPs: 19, # of Haplo: 43 1, SNP: 66, Loss: 434.369, OOB Acc: 44.12%, # of Haplo: 19 2, SNP: 28, Loss: 337.451, OOB Acc: 58.82%, # of Haplo: 21 3, SNP: 30, Loss: 302.194, OOB Acc: 73.53%, # of Haplo: 21 4, SNP: 59, Loss: 209.932, OOB Acc: 73.53%, # of Haplo: 21 5, SNP: 69, Loss: 146.631, OOB Acc: 82.35%, # of Haplo: 21 6, SNP: 73, Loss: 96.4111, OOB Acc: 91.18%, # of Haplo: 21 7, SNP: 70, Loss: 81.5466, OOB Acc: 91.18%, # of Haplo: 21 8, SNP: 3, Loss: 71.8294, OOB Acc: 91.18%, # of Haplo: 22 9, SNP: 79, Loss: 63.7214, OOB Acc: 94.12%, # of Haplo: 26 10, SNP: 5, Loss: 59.4963, OOB Acc: 94.12%, # of Haplo: 27 11, SNP: 27, Loss: 39.6101, OOB Acc: 94.12%, # of Haplo: 27 12, SNP: 15, Loss: 39.0565, OOB Acc: 94.12%, # of Haplo: 27 13, SNP: 56, Loss: 32.0611, OOB Acc: 94.12%, # of Haplo: 30 14, SNP: 2, Loss: 28.065, OOB Acc: 94.12%, # of Haplo: 33 15, SNP: 6, Loss: 26.52, OOB Acc: 94.12%, # of Haplo: 37 16, SNP: 81, Loss: 26.4461, OOB Acc: 94.12%, # of Haplo: 43 17, SNP: 82, Loss: 25.6218, OOB Acc: 94.12%, # of Haplo: 51 18, SNP: 32, Loss: 20.8498, OOB Acc: 94.12%, # of Haplo: 52 [2] 2019-10-16 03:38:39, OOB Acc: 94.12%, # of SNPs: 18, # of Haplo: 52 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.361139e-08 7.194017e-06 7.172766e-05 1.579874e-03 9.379945e-03 3.397539e-02 Max. Mean SD 7.489335e-02 1.870792e-02 2.126647e-02 Accuracy with training data: 96.7% Out-of-bag accuracy: 91.9% Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 34 # of unique HLA alleles: 14 # of unique HLA genotypes: 21 Gene: A Range: [29910247bp, 29913661bp] on hg19 # of samples: 26 # of unique HLA alleles: 12 # of unique HLA genotypes: 17 Exclude 11 monomorphic SNPs Build a HIBAG model with 4 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 17 # of SNPs: 264, # of samples: 34 # of unique HLA alleles: 14 [-] 2019-10-16 03:38:39 1, SNP: 235, Loss: 208.912, OOB Acc: 59.09%, # of Haplo: 14 2, SNP: 136, Loss: 139.316, OOB Acc: 63.64%, # of Haplo: 14 3, SNP: 126, Loss: 96.148, OOB Acc: 72.73%, # of Haplo: 14 4, SNP: 89, Loss: 76.2917, OOB Acc: 77.27%, # of Haplo: 15 5, SNP: 102, Loss: 61.3783, OOB Acc: 86.36%, # of Haplo: 15 6, SNP: 105, Loss: 49.1567, OOB Acc: 90.91%, # of Haplo: 15 7, SNP: 117, Loss: 43.0927, OOB Acc: 95.45%, # of Haplo: 15 8, SNP: 259, Loss: 26.6243, OOB Acc: 95.45%, # of Haplo: 17 9, SNP: 60, Loss: 17.6253, OOB Acc: 95.45%, # of Haplo: 19 10, SNP: 236, Loss: 9.50329, OOB Acc: 95.45%, # of Haplo: 20 11, SNP: 94, Loss: 7.27191, OOB Acc: 95.45%, # of Haplo: 21 12, SNP: 58, Loss: 6.70503, OOB Acc: 95.45%, # of Haplo: 27 13, SNP: 243, Loss: 2.87079, OOB Acc: 95.45%, # of Haplo: 30 14, SNP: 5, Loss: 2.77321, OOB Acc: 95.45%, # of Haplo: 31 [1] 2019-10-16 03:38:39, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 31 1, SNP: 149, Loss: 171.797, OOB Acc: 66.67%, # of Haplo: 13 2, SNP: 176, Loss: 120.459, OOB Acc: 72.22%, # of Haplo: 14 3, SNP: 97, Loss: 80.1731, OOB Acc: 83.33%, # of Haplo: 14 4, SNP: 56, Loss: 51.5193, OOB Acc: 94.44%, # of Haplo: 16 5, SNP: 182, Loss: 34.5643, OOB Acc: 94.44%, # of Haplo: 18 6, SNP: 121, Loss: 23.0259, OOB Acc: 94.44%, # of Haplo: 18 7, SNP: 234, Loss: 15.0596, OOB Acc: 94.44%, # of Haplo: 20 8, SNP: 148, Loss: 9.66757, OOB Acc: 94.44%, # of Haplo: 20 9, SNP: 19, Loss: 4.29975, OOB Acc: 94.44%, # of Haplo: 27 10, SNP: 226, Loss: 0.481093, OOB Acc: 94.44%, # of Haplo: 27 11, SNP: 64, Loss: 0.447483, OOB Acc: 94.44%, # of Haplo: 28 12, SNP: 240, Loss: 0.365545, OOB Acc: 94.44%, # of Haplo: 37 13, SNP: 57, Loss: 0.365132, OOB Acc: 94.44%, # of Haplo: 38 [2] 2019-10-16 03:38:39, OOB Acc: 94.44%, # of SNPs: 13, # of Haplo: 38 1, SNP: 118, Loss: 190.304, OOB Acc: 53.57%, # of Haplo: 13 2, SNP: 175, Loss: 157.208, OOB Acc: 60.71%, # of Haplo: 15 3, SNP: 103, Loss: 128.429, OOB Acc: 64.29%, # of Haplo: 15 4, SNP: 182, Loss: 66.6054, OOB Acc: 71.43%, # of Haplo: 15 5, SNP: 152, Loss: 58.8041, OOB Acc: 78.57%, # of Haplo: 15 6, SNP: 111, Loss: 30.086, OOB Acc: 82.14%, # of Haplo: 15 7, SNP: 130, Loss: 15.3177, OOB Acc: 89.29%, # of Haplo: 19 8, SNP: 229, Loss: 9.99758, OOB Acc: 89.29%, # of Haplo: 28 9, SNP: 185, Loss: 7.40712, OOB Acc: 89.29%, # of Haplo: 29 10, SNP: 199, Loss: 6.21341, OOB Acc: 89.29%, # of Haplo: 29 11, SNP: 217, Loss: 1.38739, OOB Acc: 89.29%, # of Haplo: 30 [3] 2019-10-16 03:38:39, OOB Acc: 89.29%, # of SNPs: 11, # of Haplo: 30 1, SNP: 101, Loss: 154.355, OOB Acc: 46.15%, # of Haplo: 16 2, SNP: 102, Loss: 139.148, OOB Acc: 61.54%, # of Haplo: 22 3, SNP: 132, Loss: 95.2502, OOB Acc: 73.08%, # of Haplo: 23 4, SNP: 147, Loss: 76.9692, OOB Acc: 76.92%, # of Haplo: 34 5, SNP: 53, Loss: 68.3851, OOB Acc: 88.46%, # of Haplo: 51 6, SNP: 186, Loss: 41.8787, OOB Acc: 88.46%, # of Haplo: 53 7, SNP: 128, Loss: 33.5437, OOB Acc: 92.31%, # of Haplo: 53 8, SNP: 14, Loss: 23.3103, OOB Acc: 92.31%, # of Haplo: 55 9, SNP: 219, Loss: 18.3628, OOB Acc: 92.31%, # of Haplo: 57 10, SNP: 149, Loss: 17.9413, OOB Acc: 92.31%, # of Haplo: 89 11, SNP: 73, Loss: 16.3172, OOB Acc: 92.31%, # of Haplo: 90 12, SNP: 70, Loss: 16.1056, OOB Acc: 92.31%, # of Haplo: 90 13, SNP: 199, Loss: 12.3057, OOB Acc: 92.31%, # of Haplo: 90 14, SNP: 203, Loss: 12.2013, OOB Acc: 92.31%, # of Haplo: 90 15, SNP: 151, Loss: 11.1795, OOB Acc: 92.31%, # of Haplo: 90 [4] 2019-10-16 03:38:40, OOB Acc: 92.31%, # of SNPs: 15, # of Haplo: 90 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Accuracy with training data: 97.1% Out-of-bag accuracy: 92.9% Gene: A Training dataset: 34 samples X 264 SNPs # of HLA alleles: 14 # of individual classifiers: 4 total # of SNPs used: 49 avg. # of SNPs in an individual classifier: 13.25 (sd: 1.71, min: 11, max: 15, median: 13.50) avg. # of haplotypes in an individual classifier: 47.25 (sd: 28.72, min: 30, max: 90, median: 34.50) avg. out-of-bag accuracy: 92.87% (sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0313228563 Max. Mean SD 0.2881182061 0.0290933670 0.0516709455 Genome assembly: hg19 HIBAG model: 4 individual classifiers, 264 SNPs, 14 unique HLA alleles. Predicting based on the averaged posterior probabilities from all individual classifiers. Model assembly: hg19, SNP assembly: hg19 No allelic strand orders are switched. Number of samples: 26 Predicting (2019-10-16 03:38:40) 0% Predicting (2019-10-16 03:38:40) 100% Build a HIBAG model with 2 individual classifiers: # of SNPs randomly sampled as candidates for each selection: 10 # of SNPs: 83, # of samples: 60 # of unique HLA alleles: 17 [-] 2019-10-16 03:38:40 1, SNP: 44, Loss: 396.506, OOB Acc: 41.67%, # of Haplo: 17 2, SNP: 58, Loss: 291.148, OOB Acc: 50.00%, # of Haplo: 17 3, SNP: 80, Loss: 211.469, OOB Acc: 56.25%, # of Haplo: 20 4, SNP: 18, Loss: 138.615, OOB Acc: 60.42%, # of Haplo: 20 5, SNP: 29, Loss: 111.977, OOB Acc: 62.50%, # of Haplo: 22 6, SNP: 62, Loss: 90.976, OOB Acc: 68.75%, # of Haplo: 24 7, SNP: 13, Loss: 70.2962, OOB Acc: 72.92%, # of Haplo: 24 8, SNP: 14, Loss: 54.5685, OOB Acc: 77.08%, # of Haplo: 22 9, SNP: 72, Loss: 35.1951, OOB Acc: 77.08%, # of Haplo: 24 10, SNP: 3, Loss: 23.2868, OOB Acc: 79.17%, # of Haplo: 24 11, SNP: 70, Loss: 21.089, OOB Acc: 79.17%, # of Haplo: 28 12, SNP: 5, Loss: 20.9664, OOB Acc: 79.17%, # of Haplo: 29 13, SNP: 40, Loss: 20.9662, OOB Acc: 83.33%, # of Haplo: 37 14, SNP: 24, Loss: 20.2385, OOB Acc: 85.42%, # of Haplo: 38 15, SNP: 2, Loss: 20.2383, OOB Acc: 87.50%, # of Haplo: 39 16, SNP: 74, Loss: 20.0601, OOB Acc: 87.50%, # of Haplo: 40 17, SNP: 57, Loss: 17.8846, OOB Acc: 87.50%, # of Haplo: 41 18, SNP: 56, Loss: 14.7377, OOB Acc: 89.58%, # of Haplo: 43 19, SNP: 27, Loss: 10.7709, OOB Acc: 89.58%, # of Haplo: 43 [1] 2019-10-16 03:38:40, OOB Acc: 89.58%, # of SNPs: 19, # of Haplo: 43 1, SNP: 66, Loss: 434.369, OOB Acc: 44.12%, # of Haplo: 19 2, SNP: 28, Loss: 337.451, OOB Acc: 58.82%, # of Haplo: 21 3, SNP: 30, Loss: 302.194, OOB Acc: 73.53%, # of Haplo: 21 4, SNP: 59, Loss: 209.932, OOB Acc: 73.53%, # of Haplo: 21 5, SNP: 69, Loss: 146.631, OOB Acc: 82.35%, # of Haplo: 21 6, SNP: 73, Loss: 96.4111, OOB Acc: 91.18%, # of Haplo: 21 7, SNP: 70, Loss: 81.5466, OOB Acc: 91.18%, # of Haplo: 21 8, SNP: 3, Loss: 71.8294, OOB Acc: 91.18%, # of Haplo: 22 9, SNP: 79, Loss: 63.7214, OOB Acc: 94.12%, # of Haplo: 26 10, SNP: 5, Loss: 59.4963, OOB Acc: 94.12%, # of Haplo: 27 11, SNP: 27, Loss: 39.6101, OOB Acc: 94.12%, # of Haplo: 27 12, SNP: 15, Loss: 39.0565, OOB Acc: 94.12%, # of Haplo: 27 13, SNP: 56, Loss: 32.0611, OOB Acc: 94.12%, # of Haplo: 30 14, SNP: 2, Loss: 28.065, OOB Acc: 94.12%, # of Haplo: 33 15, SNP: 6, Loss: 26.52, OOB Acc: 94.12%, # of Haplo: 37 16, SNP: 81, Loss: 26.4461, OOB Acc: 94.12%, # of Haplo: 43 17, SNP: 82, Loss: 25.6218, OOB Acc: 94.12%, # of Haplo: 51 18, SNP: 32, Loss: 20.8498, OOB Acc: 94.12%, # of Haplo: 52 [2] 2019-10-16 03:38:40, OOB Acc: 94.12%, # of SNPs: 18, # of Haplo: 52 Calculating matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.361139e-08 7.194017e-06 7.172766e-05 1.579874e-03 9.379945e-03 3.397539e-02 Max. Mean SD 7.489335e-02 1.870792e-02 2.126647e-02 Accuracy with training data: 96.7% Out-of-bag accuracy: 91.9% Gene: C Training dataset: 60 samples X 83 SNPs # of HLA alleles: 17 # of individual classifiers: 2 total # of SNPs used: 31 avg. # of SNPs in an individual classifier: 18.50 (sd: 0.71, min: 18, max: 19, median: 18.50) avg. # of haplotypes in an individual classifier: 47.50 (sd: 6.36, min: 43, max: 52, median: 47.50) avg. out-of-bag accuracy: 91.85% (sd: 3.21%, min: 89.58%, max: 94.12%, median: 91.85%) Matching proportion: Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu. 2.361139e-08 7.194017e-06 7.172766e-05 1.579874e-03 9.379945e-03 3.397539e-02 Max. Mean SD 7.489335e-02 1.870792e-02 2.126647e-02 Genome assembly: hg19 SNP genotypes: 60 samples X 1564 SNPs SNPs range from 25769023bp to 33421576bp on hg19 Missing rate per SNP: min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287 Missing rate per sample: min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737 Minor allele frequency: min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389 Allelic information: A/G C/T G/T A/C 655 632 141 136 > > proc.time() user system elapsed 32.121 1.286 38.305

name	user	system	elapsed
HIBAG-package	0.516	0.040	0.564
hlaAllele	0.020	0.003	0.023
hlaAlleleDigit	0.022	0.003	0.024
hlaAlleleSubset	0.021	0.003	0.023
hlaAssocTest	1.343	0.051	1.394
hlaAttrBagging	0.474	0.026	0.499
hlaBED2Geno	0.144	0.027	0.171
hlaCheckAllele	0.000	0.000	0.001
hlaCheckSNPs	0.107	0.004	0.112
hlaCombineAllele	0.037	0.003	0.039
hlaCombineModelObj	0.379	0.004	0.383
hlaCompareAllele	0.379	0.013	0.392
hlaConvSequence	5.431	0.490	5.928
hlaDistance	2.103	0.016	2.119
hlaErrMsg	0.001	0.000	0.000
hlaFlankingSNP	0.018	0.003	0.021
hlaGDS2Geno	0.109	0.017	0.127
hlaGeno2PED	0.050	0.004	0.053
hlaGenoAFreq	0.006	0.001	0.008
hlaGenoCombine	0.075	0.010	0.085
hlaGenoLD	1.218	0.012	1.230
hlaGenoMFreq	0.007	0.001	0.007
hlaGenoMRate	0.007	0.001	0.008
hlaGenoMRate_Samp	0.006	0.001	0.009
hlaGenoSubset	0.010	0.003	0.014
hlaGenoSwitchStrand	0.062	0.007	0.068
hlaLDMatrix	2.483	0.302	2.786
hlaLociInfo	0.007	0.002	0.011
hlaMakeSNPGeno	0.029	0.002	0.031
hlaModelFiles	0.281	0.008	0.289
hlaModelFromObj	0.080	0.003	0.082
hlaOutOfBag	0.627	0.011	0.637
hlaParallelAttrBagging	0.288	0.066	4.567
hlaPredMerge	0.397	0.008	0.406
hlaPublish	0.535	0.009	0.544
hlaReport	0.387	0.013	0.400
hlaReportPlot	2.489	0.046	2.574
hlaSNPID	0.006	0.000	0.007
hlaSampleAllele	0.008	0.002	0.010
hlaSplitAllele	0.057	0.001	0.059
hlaSubModelObj	0.103	0.003	0.106
hlaUniqueAllele	0.009	0.001	0.010
plot.hlaAttrBagObj	0.406	0.004	0.409
predict.hlaAttrBagClass	0.354	0.012	0.366
print.hlaAttrBagClass	0.195	0.005	0.199
summary.hlaSNPGenoClass	0.005	0.001	0.005

CHECK report for HIBAG on celaya2

Summary

Command output

Installation output

Tests output

Imputation Evaluation

Table 1L: The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).

Imputation Evaluation

Table 1L: The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).

Example timings