├── .ipynb_checkpoints ├── tutorial_HLAQCImputation-checkpoint.ipynb └── tutorial_association-checkpoint.md ├── LICENSE ├── README.md ├── data ├── .DS_Store ├── AA_annotated.bim ├── Tutorial_1KGonly.bgl.phased.vcf.gz ├── genetic_map_chr6_combined_b37.txt ├── hgdp_chr6.bed ├── hgdp_chr6.bim └── hgdp_chr6.fam ├── data_assoc ├── HLA_DICTIONARY_AA.hg19.imgt3320.AA_tf.in_ref.rds ├── covariates.txt ├── phenotype.txt └── phenotype_multi.txt ├── images ├── .ipynb_checkpoints │ ├── hgdp_chr6.final.SNP2HLApy.imputed_allele_ids-checkpoint.png │ ├── hgdp_chr6.lmiss-checkpoint.png │ ├── tmpfile.hwe-checkpoint.png │ └── tmpfile.hwe.freq-checkpoint.png ├── SuppleFig2_MIS_usage.png ├── SuppleFig4_imputation_runtime.png ├── for_web_Overview_v4.png ├── hgdp_all_chr6.hg19.ba.only.GSA.1stSNPQC.1stSampQC.lmiss.png ├── hgdp_all_chr6.hg19.ba.only.GSA.1stSNPQC.1stSampQC.miss.frq.diff.plot.png ├── hgdp_all_chr6.hg19.ba.only.GSA.dedup.ambstrandrem.1stSNPQC.imiss.png ├── hgdp_all_chr6.hg19.ba.only.GSA.dedup.ambstrandrem.1stSNPQC.mind.het.png ├── hgdp_chr6.dedup.ambstrandrem.1stSNPQC.mind.het.genome.png ├── hgdp_chr6.final.SNP2HLApy.imputed.raw.png ├── hgdp_chr6.final.SNP2HLApy.imputed_allele_ids.png ├── hgdp_chr6.final.bim.png ├── hgdp_chr6.imiss.png ├── hgdp_chr6.lmiss.png ├── hgdp_dedup_bim_palindromic.png ├── plink.sexcheck.png ├── tmpfile.chr_pos_allele_freq.png ├── tmpfile.hwe.freq.png ├── tmpfile.hwe.png └── tmpfile.plot.png ├── script_assoc ├── convert_vcf_allele.py └── run_omnibus_AAtest.py ├── scripts ├── .DS_Store ├── SNP2HLA.py ├── beagle.jar ├── beagle2linkage.jar ├── beagle2vcf.jar ├── get_duprem_var.py ├── get_remID.py ├── rename_bim.py └── src │ ├── ParseDosage.csh │ └── merge_tables.pl ├── tutorial_HLAQCImputation.ipynb └── tutorial_association.md /.ipynb_checkpoints/tutorial_association-checkpoint.md: -------------------------------------------------------------------------------- 1 | # Tutorial for statistical test for HLA association with complex traits 2 | 3 | 4 | 5 | Author: Saori Sakaue (ssakaue@broadinstitute.org) 6 | 7 | Lastly updated: 07/28/2022 8 | 9 | 10 | 11 | ## HLA association and fine-mapping 12 | 13 | This tutorial corresponds to the section "*HLA association and fine-mapping*" in the manuscript. 14 | 15 | We use outputs from the HLA imputation tutorial. Other data are in `data_assoc` directory. Most scripts are in `script_assoc` directory. 16 | 17 | 18 | 19 | **Note!** 20 | 21 | If you use Minimac3 imputation in the above section, you should first convert the allele name (CHR:POS) to the original name (HLA_A*XX:XX) in the output VCF file. 22 | 23 | 24 | 25 | If you use `SNP2HLA.csh`, `SNP2HLA.py` or MIS, you do not have to do this procedure. 26 | 27 | 28 | 29 | ```bash 30 | refVCF="data/Tutorial_1KGonly" 31 | output="hgdp_chr6.final.EAGLE.phased.imputed" 32 | 33 | zcat ${refVCF}.vcf.gz | grep -v "#" | awk '{print $2,$3,$4,$5}' > ${refVCF}.converter 34 | 35 | python script_assoc/convert_vcf_allele.py ${output}.dose.vcf.gz ${refVCF}.converter data_assoc/converted.info | bgzip -c > data_assoc/converted.vcf.gz 36 | ``` 37 | 38 | 39 | 40 | `data_assoc/converted.info` provides R2 and AF information embedded in the VCF file. 41 | 42 | `data_assoc/converted.vcf.gz` is the output imputed VCF file with corrected variant names. 43 | 44 | 45 | 46 | ### Single-marker test 47 | 48 | We first convert imputed genotype to dosage txt file by `PLINK2`. 49 | 50 | ```bash 51 | imputed="data_assoc/converted" 52 | 53 | plink2 \ 54 | --vcf ${imputed}.vcf.gz dosage=DS \ 55 | --make-pgen --out ${imputed} 56 | 57 | # this will create ${imputed}.{pgen,psam,pvar} 58 | 59 | plink2 --pfile ${imputed} \ 60 | --pheno data_assoc/phenotype.txt \ 61 | --covar data_assoc/covariates.txt \ 62 | --pheno-name trait_name \ 63 | --glm omit-ref hide-covar cols=chrom,pos,ref,alt,test,nobs,beta,se,ci,tz,p,a1freqcc,a1freq \ 64 | --ci 0.95 \ 65 | --out single_marker_assoc \ 66 | --covar-variance-standardize 67 | 68 | ``` 69 | 70 | 71 | 72 | `single_marker_assoc.trait_name.glm.logistic.hybrid` is the output association statistics, and you can extract results for HLA alleles by `grep "HLA_"`, HLA amino acids by `grep "AA_"`, and HLA intragenic SNPs by `grep "SNPS_"`. 73 | 74 | 75 | 76 | You can also do this by using custom R script. 77 | 78 | ```bash 79 | # when you only test for HLA alleles and amino acids (modify as necessry) 80 | cat ${imputed}.pvar | grep -v "#" | grep -E 'HLA_|AA_' | cut -f3 > test_markers.txt 81 | cat ${imputed}.pvar | grep -v "#" | grep -E 'HLA_|AA_' | awk '{print $3,"T"}' > test_markers_alleles.txt # this is to make sure to output the dosages of the "presence" of the allele coded as T, but not the "absence" coded as A. 82 | 83 | plink2 --vcf ${imputed}.vcf.gz dosage=DS --export A --extract test_markers.txt --export-allele test_markers_alleles.txt --out ${imputed} 84 | ``` 85 | 86 | `${imputed}.raw` is the table of dosages for HLA alleles and amino acids. 87 | 88 | 89 | 90 | (In `R`) 91 | 92 | ```r 93 | d <- read.table("data_assoc/converted.raw", header=T) 94 | pheno <- read.table("data_assoc/phenotype.txt", header=T) 95 | cov <- read.table("data_assoc/covariates.txt", header=T) 96 | 97 | d <- merge(d, pheno, by = "IID") 98 | d <- merge(d, cov, by = "IID") 99 | d$trait_name <- d$trait_name - 1 # cases to be 1 and controls to be 0 100 | 101 | # if you want to test for HLA_A*01:01 102 | summary(glm(trait_name ~ HLA_A.01.01_T + sex + PC1 + PC2, data = d, family = binomial)) 103 | 104 | ``` 105 | 106 | 107 | 108 | Then, you can see the same statistics found in PLINK output. 109 | 110 | ```r 111 | Call: 112 | glm(formula = trait_name ~ HLA_A.01.01_T + sex + PC1 + PC2, family = binomial, data = d) 113 | 114 | Deviance Residuals: 115 | Min 1Q Median 3Q Max 116 | -1.0457 -0.8305 -0.7443 1.4218 1.9433 117 | 118 | Coefficients: 119 | Estimate Std. Error z value Pr(>|z|) 120 | (Intercept) -0.39449 0.23453 -1.682 0.0926 . 121 | HLA_A.01.01_T -0.03740 0.21231 -0.176 0.8602 122 | sex -0.38172 0.15030 -2.540 0.0111 * 123 | PC1 0.05466 0.07488 0.730 0.4654 124 | PC2 -0.17217 0.07520 -2.289 0.0221 * 125 | --- 126 | Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 127 | 128 | (Dispersion parameter for binomial family taken to be 1) 129 | 130 | Null deviance: 1068.1 on 906 degrees of freedom 131 | Residual deviance: 1056.0 on 902 degrees of freedom 132 | AIC: 1066 133 | 134 | Number of Fisher Scoring iterations: 4 135 | ``` 136 | 137 | 138 | 139 | ### Omnibus test 140 | 141 | First, we extract amino acid polymorphisms from the dosage output and creat `*.raw` file by using `PLINK2`. 142 | 143 | In this example, we also apply QC to extract any amino acid polymorphisms with *Rsq* > 0.7. 144 | 145 | ```bash 146 | sed 1d data_assoc/converted.info | grep "^AA_" | awk '{if($5>0.7)print $1}' > QCed_AA_variants.txt 147 | sed 1d data_assoc/converted.info | grep "^AA_" | awk '{if($5>0.7)print $1,"T"}' > QCed_AA_variants_alleles.txt 148 | 149 | plink2 --pfile ${imputed} \ 150 | --extract QCed_AA_variants.txt \ 151 | --export-allele QCed_AA_variants_alleles.txt \ 152 | --export A \ 153 | --out ${imputed}.QCed_AA 154 | 155 | # output amino acid names without "_T" and by converting "-" with "minus" as a workaround in R 156 | head -n1 ${imputed}.QCed_AA.raw | cut -f7- | tr "\t" "\n" | sed -e "s/_T$//" | sed 's/-/minus/' > ${imputed}.QCed_AA.allele_name.txt 157 | 158 | 159 | # run omnibus test by using those input files! 160 | python3 script_assoc/run_omnibus_AAtest.py \ 161 | --aaraw ${imputed}.QCed_AA.raw \ 162 | --out test_output \ 163 | --allele ${imputed}.QCed_AA.allele_name.txt \ 164 | --pheno data_assoc/phenotype.txt \ 165 | --cov data_assoc/covariates.txt \ 166 | --phenoname trait_name \ 167 | --covname sex PC1 PC2 168 | 169 | ``` 170 | 171 | 172 | 173 | In this example, `test_output_omnibus_result.txt` is the output statistics with the tested amino acid positions, deviance explained by including the tested amino acid position into the model, and p value from the ANOVA. 174 | 175 | ```bash 176 | $ head test_output_omnibus_result.txt 177 | 178 | ALLELE_NAME OMNIBUS_DEVIANCE OMNIBUS_PVALUE 179 | AA_A_minus22_29910338_exon1 1.25834923435173 0.533031574597542 180 | AA_A_minus15_29910359_exon1 3.52122377743922 0.171939623712512 181 | AA_A_minus11_29910371_exon1 2.15789903921791 0.339952451524512 182 | AA_A_minus2_29910398_exon1 4.53914553021627 0.103356328081297 183 | AA_A_9_29910558_exon2 5.54515225056707 0.593743388681586 184 | AA_A_12_29910567_exon2 0.0109563791947949 0.916635505963766 185 | AA_A_17_29910582_exon2 0.0188695939268655 0.890740998058756 186 | AA_A_19_29910588_exon2 0.000823349387019334 0.977108589575259 187 | AA_A_43_29910660_exon2 0.523234132611833 0.769805751833586 188 | ``` 189 | 190 | 191 | 192 | 193 | 194 | ### Conditional haplotype test 195 | 196 | First, we extract two-field allele dosages from the imputed genotype after QC. 197 | 198 | ```bash 199 | sed 1d data_assoc/converted.info | grep "^HLA_" | cut -f1,5 | grep ":" | awk '{if($2>0.7)print $1}' > QCed_HLA_tf.txt 200 | sed 1d data_assoc/converted.info | grep "^HLA_" | cut -f1,5 | grep ":" | awk '{if($2>0.7)print $1,"T"}' > QCed_HLA_tf_alleles.txt 201 | 202 | plink2 --pfile ${imputed} \ 203 | --extract QCed_HLA_tf.txt \ 204 | --export-allele QCed_HLA_tf_alleles.txt \ 205 | --export A \ 206 | --out ${imputed}.QCed_HLA_tf 207 | 208 | # output HLA names without "_T", "*", and ":" as a workaround in R 209 | head -n1 ${imputed}.QCed_HLA_tf.raw | cut -f7- | tr "\t" "\n" | sed -e "s/_T$//" | sed 's/*/_/' | sed 's/:/_/' > ${imputed}.QCed_HLA_tf.allele_name.txt 210 | 211 | 212 | ``` 213 | 214 | 215 | 216 | We first perform the same omnibus test for single AA position but using the two-fiedl allele information. 217 | 218 | Let's do this for HLA-DRB1 as an example. 219 | 220 | ```r 221 | library(dplyr) 222 | library(data.table) 223 | 224 | # info file summarizes all information on the correspondence between two-field alleles and amino acid residues 225 | info <- readRDS("data_assoc/HLA_DICTIONARY_AA.hg19.imgt3320.AA_tf.in_ref.rds") 226 | 227 | HLA="DRB1" 228 | info <- info[info$gene==HLA,] 229 | 230 | info$tag <- paste0(info$pos,":",info$AA) 231 | 232 | allpos <- sort( unique(info$pos) ) 233 | 234 | res <- data.frame() 235 | for( pos in allpos ){ 236 | y <- info[ info$pos %in% c(pos), ] 237 | for( k in 1:length( unique( y$tag )) ){ 238 | ytag <- unique( y$tag )[ k ] 239 | hap <- ytag 240 | y_4d <- subset(y, tag == ytag )$hla 241 | hap_4d <- y_4d 242 | if( length(hap_4d) > 0 ){ 243 | out <- data.frame(hap, hla = hap_4d, pos ) 244 | res <- rbind(res, out) 245 | }}} 246 | 247 | # res will be used to group two-field alleles based on amino acid residues at each position. 248 | 249 | 250 | # read imputed two field alleles 251 | dose <- read.table("data_assoc/converted.QCed_HLA_tf.raw", header=T) 252 | dose <- dose[,c(-1,-3:-6)] 253 | allelenames <- as.character(read.table("data_assoc/converted.QCed_HLA_tf.allele_name.txt")$V1) 254 | colnames(dose) <- c("IID",allelenames) 255 | 256 | # read phenotype and covariates 257 | pheno <- read.table("data_assoc/phenotype.txt", header=T) 258 | if(max(pheno[,2])==2){pheno[,2]<-pheno[,2] - 1} 259 | cov <- read.table("data_assoc/covariates.txt", header=T) 260 | dat <- merge(dose, pheno, by = "IID") 261 | dat <- merge(dat, cov, by = "IID") 262 | 263 | pval_list<-NULL 264 | deviance_list<-NULL 265 | 266 | for(thispos in allpos){ 267 | thishaps<-as.character(unique(subset(res,pos==thispos)$hap)) 268 | adopted<-NULL 269 | for(thishap in thishaps){ 270 | hlas<-as.character(subset(res,hap==thishap)$hla) # extracting all two-field alleles explained by this position-AA residue pairs 271 | hlas<-hlas[hlas %in% allelenames] # restrict two-field alleles to those in our QCed data 272 | if(length(hlas)>0){ 273 | dat$thishap <- rowSums(dat[hlas]) 274 | colnames(dat)[ncol(dat)] <- thishap 275 | adopted<-c(adopted,thishap) 276 | } 277 | } 278 | obj1<-glm(trait_name ~ sex+PC1+PC2,data=dat,family=binomial(link="logit")) # model with covariates 279 | obj2<-glm(trait_name~as.matrix(dat[adopted])+sex+PC1+PC2,data=dat,family=binomial(link="logit")) # model with this AA position 280 | Chisqtest <- anova(obj1,obj2,test="Chisq") 281 | pval<-Chisqtest$`Pr(>Chi)`[2] 282 | deviance<-Chisqtest$Deviance[2] 283 | pval_list<-c(pval_list,pval) 284 | deviance_list<-c(deviance_list,deviance) 285 | } 286 | 287 | summary<-data.frame(POSITION=allpos,OMNIBUS_DEVIANCE=deviance_list,OMNIBUS_PVALUE = pval_list) 288 | 289 | # this summary is a summary for the first round of conditional haplotype test. 290 | head(summary) 291 | 292 | # POSITION OMNIBUS_DEVIANCE OMNIBUS_PVALUE 293 | #1 -25 0.1598153 0.9232016 294 | #2 -24 0.1573953 0.9243193 295 | #3 -17 2.5568379 0.2784772 296 | #4 -16 0.1598153 0.9232016 297 | #5 -1 0.1670549 0.9198658 298 | #6 4 4.4417129 0.1085161 299 | ``` 300 | 301 | 302 | 303 | If the strongest association among all the positions was at position 11, we next run similar analyses conditioned on the position 11. 304 | 305 | ```r 306 | # This script is continued from the above R workspace. 307 | 308 | # 309 | res.prev <- res # transfer res information from the previous round into res.prev 310 | 311 | condpos = "11" # this is a position we want to condition on 312 | thishaps<-as.character(unique(subset(res.prev, pos==condpos)$hap)) 313 | 314 | thishaps 315 | 316 | adopted.prev<-NULL 317 | for(thishap in thishaps){ 318 | hlas<-as.character(subset(res,hap==thishap)$hla) 319 | hlas<-hlas[hlas %in% allelenames] 320 | if(length(hlas)>0){ 321 | dat$thishap<-rowSums(dat[hlas]) 322 | colnames(dat)[ncol(dat)]<-thishap 323 | adopted.prev<-c(adopted.prev,thishap) 324 | } 325 | } 326 | 327 | adopted.prev 328 | # [1] "11:L" "11:S" "11:V" "11:G" "11:D" "11:P" 329 | # These are the amino acid residues obverved in the data in the previous round at position 11. 330 | 331 | # Let's move on to the next round 332 | allpos <- setdiff(allpos, condpos) # all the other positions to analyse 333 | 334 | res <- data.frame() 335 | for( pos in allpos ){ 336 | x <- info[ info$pos %in% c(condpos), ] # haplotype information at the position I want to condition on 337 | y <- info[ info$pos %in% c(pos), ] # haplotype information at the position I want to analyze 338 | for( i in 1:length( unique( x$tag )) ){ 339 | for( k in 1:length( unique( y$tag )) ){ 340 | xtag <- unique( x$tag )[ i ] 341 | ytag <- unique( y$tag )[ k ] 342 | hap <- paste0(xtag,"_",ytag) 343 | x_4d <- subset(x, tag == xtag )$hla 344 | y_4d <- subset(y, tag == ytag )$hla 345 | hap_4d <- intersect( x_4d, y_4d ) 346 | if( length(hap_4d) > 0 ){ 347 | out <- data.frame(hap, hla = hap_4d, pos ) 348 | res <- rbind(res, out) 349 | }}}} 350 | 351 | head(res) 352 | 353 | # hap hla pos 354 | #1 11:L_-25:K HLA_DRB1_01_01 -25 355 | #2 11:L_-25:K HLA_DRB1_01_02 -25 356 | #3 11:L_-25:K HLA_DRB1_01_03 -25 357 | #4 11:S_-25:R HLA_DRB1_03_01 -25 358 | #5 11:S_-25:R HLA_DRB1_03_02 -25 359 | #6 11:S_-25:R HLA_DRB1_08_01 -25 360 | 361 | # Haplotypes defined by two amino acid positions and their correspondence to the two-field alleles 362 | 363 | pval_list<-NULL 364 | deviance_list<-NULL 365 | 366 | for(thispos in allpos){ 367 | thishaps<-as.character(unique(subset(res,pos==thispos)$hap)) 368 | adopted<-NULL 369 | for(thishap in thishaps){ 370 | hlas<-as.character(subset(res,hap==thishap)$hla) 371 | hlas<-hlas[hlas %in% allelenames] 372 | if(length(hlas)>0){ 373 | dat$thishap<-rowSums(dat[hlas]) 374 | colnames(dat)[ncol(dat)]<-thishap 375 | adopted<-c(adopted,thishap) 376 | } 377 | } 378 | obj1<-glm(trait_name ~ as.matrix(dat[adopted.prev])+sex+PC1+PC2,data=dat,family=binomial(link="logit")) # a model including groups defined by the previous round (single position) 379 | obj2<-glm(trait_name ~ as.matrix(dat[adopted])+sex+PC1+PC2,data=dat,family=binomial(link="logit")) # a model including groups defined by this round (two positions) 380 | Chisqtest <- anova(obj1,obj2,test="Chisq") 381 | pval<-Chisqtest$`Pr(>Chi)`[2] 382 | deviance<-Chisqtest$Deviance[2] 383 | pval_list<-c(pval_list,pval) 384 | deviance_list<-c(deviance_list,deviance) 385 | } 386 | 387 | summary<-data.frame(POSITION=allpos,OMNIBUS_DEVIANCE=deviance_list,OMNIBUS_PVALUE = pval_list) 388 | 389 | 390 | # this "summary" is a summary for the second round of conditional haplotype test. 391 | head(summary) 392 | 393 | # POSITION OMNIBUS_DEVIANCE OMNIBUS_PVALUE 394 | #1 -25 2.6065100 0.1064257 395 | #2 -24 2.6065100 0.1064257 396 | #3 -17 0.0000000 NA 397 | #4 -16 2.6065100 0.1064257 398 | #5 -1 0.8824553 0.3475301 399 | #6 4 0.0000000 NA 400 | 401 | ``` 402 | 403 | 404 | 405 | We continue these procedures until we do not get any significant results (`OMNIBUS_PVALUE`). 406 | 407 | 408 | 409 | - Non-additive association test 410 | 411 | We use the same data to test non-additive effect. 412 | 413 | In this vignette, we generate and use best-guess genotype from the imputation result. 414 | 415 | ```bash 416 | plink2 --vcf ${imputed}.vcf.gz \ 417 | --extract QCed_HLA_tf.txt \ 418 | --export-allele QCed_HLA_tf_alleles.txt \ 419 | --export A \ 420 | --out ${imputed}.QCed_HLA_tf.best_guess 421 | 422 | # Here we do not specify dosage and export the data to a amtrix 423 | 424 | # output HLA names without "_T", "*", and ":" as a workaround in R 425 | head -n1 ${imputed}.QCed_HLA_tf.best_guess.raw | cut -f7- | tr "\t" "\n" | sed -e "s/_T$//" | sed 's/*/_/' | sed 's/:/_/' > ${imputed}.QCed_HLA_tf.best_guess.allele_name.txt 426 | 427 | ``` 428 | 429 | 430 | 431 | Then, in `R`, 432 | 433 | ```r 434 | dose <- read.table("data_assoc/converted.QCed_HLA_tf.best_guess.raw", header=T) 435 | dose <- dose[,c(-1,-3:-6)] 436 | allelenames <- as.character(read.table("data_assoc/converted.QCed_HLA_tf.best_guess.allele_name.txt")$V1) 437 | colnames(dose) <- c("IID",allelenames) 438 | 439 | # read phenotype and covariates 440 | pheno <- read.table("data_assoc/phenotype.txt", header=T) 441 | if(max(pheno[,2])==2){pheno[,2]<-pheno[,2] - 1} # cases to be 1 and controls to be 0 442 | 443 | cov <- read.table("data_assoc/covariates.txt", header=T) 444 | dat <- merge(dose, pheno, by = "IID") 445 | dat <- merge(dat, cov, by = "IID") 446 | 447 | # if you want to test for HLA_A*01:01's non-additive effect 448 | 449 | tested_allele = "HLA_A_01_01" 450 | dat$non_add <- 0 451 | dat[dat[,tested_allele]==1,]$non_add <- 1 # define non-additive term to have 1 if and only if the genotype is heterozygous. 452 | 453 | model.0 <- glm(trait_name ~ as.matrix(dat[,tested_allele]) + sex + PC1 + PC2, data = dat, family = binomial) 454 | model.1 <- glm(trait_name ~ as.matrix(dat[,tested_allele]) + non_add + sex + PC1 + PC2, data = dat, family = binomial) 455 | 456 | Chisqtest <- anova(model.0,model.1,test="Chisq") # assess the improvement of the model by including non-additive term 457 | model.pval<-Chisqtest$`Pr(>Chi)`[2] 458 | model.deviance<-Chisqtest$Deviance[2] 459 | SUMC<-summary(model.1)$coefficients 460 | main.beta<-SUMC[2,1] # we get coefficients of the additive term 461 | main.se<-SUMC[2,2] 462 | main.p<-SUMC[2,4] 463 | non_add.beta<-SUMC[3,1] # we get coefficients of the non-additive term 464 | non_add.se<-SUMC[3,2] 465 | non_add.p<-SUMC[3,4] 466 | out<-data.frame(allele=tested_allele, anova.deviance=model.deviance, anova.p=model.pval, main.beta=main.beta, main.se=main.se, main.p=main.p, non_add.beta=non_add.beta, non_add.se=non_add.se, non_add.p=non_add.p) 467 | 468 | # this "out" summarizes the statistics at this allele (HLA_A*01:01), both additive and non-additive 469 | out 470 | 471 | # allele anova.deviance anova.p main.beta main.se main.p 472 | #1 HLA_A_01_01 0.1587487 0.6903112 -0.2378009 0.5629893 0.6727405 473 | # non_add.beta non_add.se non_add.p 474 | #1 0.2325363 0.6026695 0.6996124 475 | ``` 476 | 477 | 478 | 479 | 480 | 481 | ### Interaction test 482 | 483 | Let's test interaction between two HLA alleles of the same gene. 484 | 485 | As we explained in the manuscript, it is sometimes important to restrict the analyses to common alleles. The rare * rare allele combination could yield inflated statistics due to the noisy estimate of the effect sizes for both alleles. 486 | 487 | If we decide to QC based on MAF > 0.05 for this interaction analysis, 488 | 489 | ```bash 490 | plink2 --pfile ${imputed} \ 491 | --extract QCed_HLA_tf.txt \ 492 | --export-allele QCed_HLA_tf_alleles.txt \ 493 | --export A \ 494 | --maf 0.05 \ 495 | --out ${imputed}.QCed_HLA_tf.MAF 496 | 497 | # output HLA names without "_T", "*", and ":" as a workaround in R 498 | head -n1 ${imputed}.QCed_HLA_tf.MAF.raw | cut -f7- | tr "\t" "\n" | sed -e "s/_T$//" | sed 's/*/_/' | sed 's/:/_/' > ${imputed}.QCed_HLA_tf.MAF.allele_name.txt 499 | 500 | 501 | ``` 502 | 503 | 504 | 505 | And in `R` 506 | 507 | ```r 508 | dose <- read.table("data_assoc/converted.QCed_HLA_tf.MAF.raw", header=T) 509 | dose <- dose[,c(-1,-3:-6)] 510 | allelenames <- as.character(read.table("data_assoc/converted.QCed_HLA_tf.MAF.allele_name.txt")$V1) 511 | colnames(dose) <- c("IID",allelenames) 512 | 513 | # read phenotype and covariates 514 | pheno <- read.table("data_assoc/phenotype.txt", header=T) 515 | if(max(pheno[,2])==2){pheno[,2]<-pheno[,2] - 1} # cases to be 1 and controls to be 0 516 | 517 | cov <- read.table("data_assoc/covariates.txt", header=T) 518 | dat <- merge(dose, pheno, by = "IID") 519 | dat <- merge(dat, cov, by = "IID") 520 | 521 | 522 | # For example, if you want to test for HLA_DRB1*03:01 and HLA_DRB1*15:01 523 | 524 | first_allele = "HLA_DRB1_03_01" 525 | seconde_allele = "HLA_DRB1_15_01" 526 | 527 | # It is always nice to check the distribution of samples based on these two alleles. 528 | 529 | table(round(dat[,c(first_allele, seconde_allele)])) 530 | 531 | # HLA_DRB1_15_01 532 | #HLA_DRB1_03_01 0 1 2 533 | # 0 624 89 4 534 | # 1 156 14 0 535 | # 2 20 0 0 536 | 537 | # We confirmed that the number of samples based on allelic combination of HLA-DRB1. 538 | 539 | summary(glm(trait_name ~ HLA_DRB1_03_01 + HLA_DRB1_15_01 + HLA_DRB1_03_01*HLA_DRB1_15_01 + sex + PC1 + PC2, data = dat, family = binomial)) 540 | 541 | 542 | ################################ 543 | Call: 544 | glm(formula = trait_name ~ HLA_DRB1_03_01 + HLA_DRB1_15_01 + 545 | HLA_DRB1_03_01 * HLA_DRB1_15_01 + sex + PC1 + PC2, family = binomial, 546 | data = dat) 547 | 548 | Deviance Residuals: 549 | Min 1Q Median 3Q Max 550 | -1.0787 -0.8305 -0.7422 1.3944 1.9409 551 | 552 | Coefficients: 553 | Estimate Std. Error z value Pr(>|z|) 554 | (Intercept) -0.39243 0.23787 -1.650 0.0990 . 555 | HLA_DRB1_03_01 0.11719 0.16267 0.720 0.4713 556 | HLA_DRB1_15_01 -0.40575 0.27059 -1.500 0.1337 557 | sex -0.37822 0.15060 -2.511 0.0120 * 558 | PC1 0.05676 0.07515 0.755 0.4501 559 | PC2 -0.17298 0.07519 -2.300 0.0214 * 560 | HLA_DRB1_03_01:HLA_DRB1_15_01 0.33678 0.70759 0.476 0.6341 561 | --- 562 | Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 563 | 564 | (Dispersion parameter for binomial family taken to be 1) 565 | 566 | Null deviance: 1068.1 on 906 degrees of freedom 567 | Residual deviance: 1052.7 on 900 degrees of freedom 568 | AIC: 1066.7 569 | 570 | Number of Fisher Scoring iterations: 4 571 | ################################ 572 | 573 | # "HLA_DRB1_03_01:HLA_DRB1_15_01" is showing the effect of interaction between two alleles. 574 | 575 | model.0 <- glm(trait_name ~ HLA_DRB1_03_01 + HLA_DRB1_15_01 + sex + PC1 + PC2, data = dat, family = binomial) # a model without an interaction term 576 | model.1 <- glm(trait_name ~ HLA_DRB1_03_01 + HLA_DRB1_15_01 + HLA_DRB1_03_01*HLA_DRB1_15_01 + sex + PC1 + PC2, data = dat, family = binomial) # a model with an interaction term 577 | 578 | anova(model.0,model.1,test="Chisq") 579 | 580 | ################################ 581 | Analysis of Deviance Table 582 | 583 | Model 1: trait_name ~ HLA_DRB1_03_01 + HLA_DRB1_15_01 + sex + PC1 + PC2 584 | Model 2: trait_name ~ HLA_DRB1_03_01 + HLA_DRB1_15_01 + HLA_DRB1_03_01 * 585 | HLA_DRB1_15_01 + sex + PC1 + PC2 586 | Resid. Df Resid. Dev Df Deviance Pr(>Chi) 587 | 1 901 1052.9 588 | 2 900 1052.7 1 0.22015 0.6389 589 | ################################ 590 | ``` 591 | 592 | 593 | 594 | It is also considered to run permutation tests ( by breaking the relationship between phenotype and genotype ) whether the observed interaction effects could happen by random chance. 595 | 596 | 597 | 598 | ### Multi-trait test 599 | 600 | ```r 601 | library(MVLM) 602 | 603 | # Let's use those MAF-QCed HLA alleles in this example. 604 | dose <- read.table("data_assoc/converted.QCed_HLA_tf.MAF.raw", header=T) 605 | dose <- dose[,c(-1,-3:-6)] 606 | allelenames <- as.character(read.table("data_assoc/converted.QCed_HLA_tf.MAF.allele_name.txt")$V1) 607 | colnames(dose) <- c("IID",allelenames) 608 | 609 | # read phenotype of a multiple vector (i.e., a matrix) 610 | pheno <- read.table("data_assoc/phenotype_multi.txt", header=T) 611 | 612 | head(pheno) 613 | 614 | # IID trait.A trait.B trait.C trait.D 615 | #1 HGDP00610 0.47607622 0.2454573 0.219182940 0.05928355 616 | #2 HGDP00982 0.20184835 0.4383009 0.007797759 0.35205303 617 | #3 HGDP00001 0.01328931 0.1882389 0.402212096 0.39625968 618 | #4 HGDP01247 0.33558573 0.2070649 0.268822337 0.18852699 619 | #5 HGDP00309 0.31788202 0.2712361 0.067621683 0.34326023 620 | #6 HGDP00786 0.36187225 0.3786774 0.180634545 0.07881575 621 | 622 | # This is comprised of multiple traits, A, B, C, and D, and... 623 | 624 | all.equal(rowSums(pheno[,2:5]), rep(1,nrow(pheno))) 625 | #[1] TRUE 626 | 627 | # The sum of the traits are (almost) equal to 1 in all samples. 628 | # So we assume this phenotype has 3-degrees of freedom, and thus drop trait.D from the analysis in the subsequent models. 629 | 630 | # read covariates 631 | cov <- read.table("data_assoc/covariates.txt", header=T) 632 | dat <- merge(dose, pheno, by = "IID") 633 | dat <- merge(dat, cov, by = "IID") 634 | 635 | # Let's see the effect of HLA_DRB1*15:01 636 | # We can perform linear regression 637 | model.0 <- lm( cbind(trait.A,trait.B,trait.C) ~ sex + PC1 + PC2, data = dat) 638 | model.1 <- lm( cbind(trait.A,trait.B,trait.C) ~ HLA_DRB1_15_01 + sex + PC1 + PC2, data = dat) 639 | 640 | anova(model.0, model.1) 641 | 642 | ################################ 643 | Analysis of Variance Table 644 | 645 | Model 1: cbind(trait.A, trait.B, trait.C) ~ sex + PC1 + PC2 646 | Model 2: cbind(trait.A, trait.B, trait.C) ~ HLA_DRB1_15_01 + sex + PC1 + 647 | PC2 648 | Res.Df Df Gen.var. Pillai approx F num Df den Df Pr(>F) 649 | 1 903 0.015147 650 | 2 902 -1 0.015150 0.0028114 0.84579 3 900 0.469 651 | ################################ 652 | 653 | 654 | # We can also perform MVLM model 655 | mvlm.res.0 <- mvlm( cbind(trait.A,trait.B,trait.C) ~ sex + PC1 + PC2 , data = dat) 656 | 657 | mvlm.res.1 <- mvlm( cbind(trait.A,trait.B,trait.C) ~ HLA_DRB1_15_01 + sex + PC1 + PC2, data = dat ) 658 | mvlm.res.1$pseudo.rsq["Omnibus Effect",1] 659 | 660 | summary(mvlm.res.1) 661 | 662 | ################################ 663 | Statistic Numer.DF Pseudo.R.Square p.value 664 | Omnibus Effect 0.90858 4 4.013e-03 0.52003 665 | (Intercept) 315.81910 1 < 1e-20 *** 666 | HLA_DRB1_15_01 0.82089 1 9.064e-04 0.45784 667 | sex 0.07375 1 8.144e-05 0.96786 668 | PC1 1.71025 1 1.888e-03 0.16841 669 | PC2 0.97278 1 1.074e-03 0.38646 670 | --- 671 | Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 672 | ################################ 673 | 674 | 675 | # Explained variance by HLA_DRB1_15_01 will be.. 676 | mvlm.res.1$pseudo.rsq["Omnibus Effect",1] - mvlm.res.0$pseudo.rsq["Omnibus Effect",1] 677 | 678 | ``` 679 | 680 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # HLA analyses tutorial 2 | A thorough tutorial on HLA imputation and association, accompanying our manuscript "Tutorial: A statistical genetics guide to identifying HLA alleles driving complex disease" 3 | 4 |
5 | 6 |
7 | 8 | The tutorial consists of two parts: 9 | - #1. [HLA imputation](https://github.com/immunogenomics/HLA_analyses_tutorial/blob/main/tutorial_HLAQCImputation.ipynb) : We introduce protocols to QC genotype, perform haplotype phasing and HLA imputation. We provide useful scripts and example usage, with example genotype and reference datasets. 10 | 11 | - #2. [HLA association and fine-mapping](https://github.com/immunogenomics/HLA_analyses_tutorial/blob/main/tutorial_association.md): We introduce various statistical methods to identify and fine-map disease-associated HLA variations. The HLA imputation results from section #1 will be used. We provide useful scripts with some example phenotype data. 12 | 13 | 14 | 15 | ### Note for Michigan Imputation Server users 16 | 17 | For the amino acid (AA) residue imputation, sometimes the imputed residue is not clear from the post imputation VCF. Please use [this `bim` file](https://github.com/immunogenomics/HLA_analyses_tutorial/blob/main/data/AA_annotated.bim) that summarizes all information about AA residue names (in the 2nd column) that appear on the imputed VCF and the binary allele (residue) difinitions (in the 5th and 6th columns). 18 | 19 | For further naming schemes, please also refer to [this website](https://software.broadinstitute.org/mpg/snp2hla/makereference_manual.html) at the sections of 'Marker Nomenclature'. 20 | 21 | -------------------------------------------------------------------------------- /data/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/immunogenomics/HLA_analyses_tutorial/afddd62b022f503f7c5806298edcfe195aae1343/data/.DS_Store -------------------------------------------------------------------------------- /data/Tutorial_1KGonly.bgl.phased.vcf.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/immunogenomics/HLA_analyses_tutorial/afddd62b022f503f7c5806298edcfe195aae1343/data/Tutorial_1KGonly.bgl.phased.vcf.gz -------------------------------------------------------------------------------- /data/hgdp_chr6.bed: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/immunogenomics/HLA_analyses_tutorial/afddd62b022f503f7c5806298edcfe195aae1343/data/hgdp_chr6.bed -------------------------------------------------------------------------------- /data/hgdp_chr6.fam: -------------------------------------------------------------------------------- 1 | HGDP00001 HGDP00001 0 0 0 -9 2 | HGDP00003 HGDP00003 0 0 0 -9 3 | HGDP00005 HGDP00005 0 0 0 -9 4 | HGDP00007 HGDP00007 0 0 0 -9 5 | HGDP00009 HGDP00009 0 0 0 -9 6 | HGDP00011 HGDP00011 0 0 0 -9 7 | HGDP00013 HGDP00013 0 0 0 -9 8 | HGDP00015 HGDP00015 0 0 0 -9 9 | HGDP00017 HGDP00017 0 0 0 -9 10 | HGDP00019 HGDP00019 0 0 0 -9 11 | HGDP00021 HGDP00021 0 0 0 -9 12 | HGDP00023 HGDP00023 0 0 0 -9 13 | HGDP00025 HGDP00025 0 0 0 -9 14 | HGDP00027 HGDP00027 0 0 0 -9 15 | HGDP00029 HGDP00029 0 0 0 -9 16 | HGDP00031 HGDP00031 0 0 0 -9 17 | HGDP00033 HGDP00033 0 0 0 -9 18 | HGDP00035 HGDP00035 0 0 0 -9 19 | HGDP00037 HGDP00037 0 0 0 -9 20 | HGDP00039 HGDP00039 0 0 0 -9 21 | HGDP00041 HGDP00041 0 0 0 -9 22 | HGDP00043 HGDP00043 0 0 0 -9 23 | HGDP00045 HGDP00045 0 0 0 -9 24 | HGDP00047 HGDP00047 0 0 0 -9 25 | HGDP00049 HGDP00049 0 0 0 -9 26 | HGDP00052 HGDP00052 0 0 0 -9 27 | HGDP00054 HGDP00054 0 0 0 -9 28 | HGDP00056 HGDP00056 0 0 0 -9 29 | HGDP00057 HGDP00057 0 0 0 -9 30 | HGDP00058 HGDP00058 0 0 0 -9 31 | HGDP00060 HGDP00060 0 0 0 -9 32 | HGDP00062 HGDP00062 0 0 0 -9 33 | HGDP00064 HGDP00064 0 0 0 -9 34 | HGDP00066 HGDP00066 0 0 0 -9 35 | HGDP00068 HGDP00068 0 0 0 -9 36 | HGDP00070 HGDP00070 0 0 0 -9 37 | HGDP00072 HGDP00072 0 0 0 -9 38 | HGDP00074 HGDP00074 0 0 0 -9 39 | HGDP00076 HGDP00076 0 0 0 -9 40 | HGDP00078 HGDP00078 0 0 0 -9 41 | HGDP00080 HGDP00080 0 0 0 -9 42 | HGDP00082 HGDP00082 0 0 0 -9 43 | HGDP00086 HGDP00086 0 0 0 -9 44 | HGDP00088 HGDP00088 0 0 0 -9 45 | HGDP00090 HGDP00090 0 0 0 -9 46 | HGDP00092 HGDP00092 0 0 0 -9 47 | HGDP00094 HGDP00094 0 0 0 -9 48 | HGDP00096 HGDP00096 0 0 0 -9 49 | HGDP00098 HGDP00098 0 0 0 -9 50 | HGDP00099 HGDP00099 0 0 0 -9 51 | HGDP00100 HGDP00100 0 0 0 -9 52 | HGDP00102 HGDP00102 0 0 0 -9 53 | HGDP00103 HGDP00103 0 0 0 -9 54 | HGDP00104 HGDP00104 0 0 0 -9 55 | HGDP00105 HGDP00105 0 0 0 -9 56 | HGDP00106 HGDP00106 0 0 0 -9 57 | HGDP00108 HGDP00108 0 0 0 -9 58 | HGDP00109 HGDP00109 0 0 0 -9 59 | HGDP00110 HGDP00110 0 0 0 -9 60 | HGDP00115 HGDP00115 0 0 0 -9 61 | HGDP00118 HGDP00118 0 0 0 -9 62 | HGDP00120 HGDP00120 0 0 0 -9 63 | HGDP00121 HGDP00121 0 0 0 -9 64 | HGDP00122 HGDP00122 0 0 0 -9 65 | HGDP00124 HGDP00124 0 0 0 -9 66 | HGDP00125 HGDP00125 0 0 0 -9 67 | HGDP00127 HGDP00127 0 0 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HGDP01065 HGDP01065 0 0 0 -9 668 | HGDP01066 HGDP01066 0 0 0 -9 669 | HGDP01067 HGDP01067 0 0 0 -9 670 | HGDP01068 HGDP01068 0 0 0 -9 671 | HGDP01069 HGDP01069 0 0 0 -9 672 | HGDP01070 HGDP01070 0 0 0 -9 673 | HGDP01071 HGDP01071 0 0 0 -9 674 | HGDP01072 HGDP01072 0 0 0 -9 675 | HGDP01073 HGDP01073 0 0 0 -9 676 | HGDP01074 HGDP01074 0 0 0 -9 677 | HGDP01075 HGDP01075 0 0 0 -9 678 | HGDP01076 HGDP01076 0 0 0 -9 679 | HGDP01077 HGDP01077 0 0 0 -9 680 | HGDP01078 HGDP01078 0 0 0 -9 681 | HGDP01079 HGDP01079 0 0 0 -9 682 | HGDP01081 HGDP01081 0 0 0 -9 683 | HGDP01086 HGDP01086 0 0 0 -9 684 | HGDP01090 HGDP01090 0 0 0 -9 685 | HGDP01094 HGDP01094 0 0 0 -9 686 | HGDP01095 HGDP01095 0 0 0 -9 687 | HGDP01096 HGDP01096 0 0 0 -9 688 | HGDP01098 HGDP01098 0 0 0 -9 689 | HGDP01099 HGDP01099 0 0 0 -9 690 | HGDP01100 HGDP01100 0 0 0 -9 691 | HGDP01101 HGDP01101 0 0 0 -9 692 | HGDP01102 HGDP01102 0 0 0 -9 693 | HGDP01103 HGDP01103 0 0 0 -9 694 | HGDP01104 HGDP01104 0 0 0 -9 695 | HGDP01149 HGDP01149 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0 0 0 -9 924 | HGDP01414 HGDP01414 0 0 0 -9 925 | HGDP01415 HGDP01415 0 0 0 -9 926 | HGDP01416 HGDP01416 0 0 0 -9 927 | HGDP01417 HGDP01417 0 0 0 -9 928 | HGDP01418 HGDP01418 0 0 0 -9 929 | HGDP01419 HGDP01419 0 0 0 -9 930 | -------------------------------------------------------------------------------- /data_assoc/HLA_DICTIONARY_AA.hg19.imgt3320.AA_tf.in_ref.rds: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/immunogenomics/HLA_analyses_tutorial/afddd62b022f503f7c5806298edcfe195aae1343/data_assoc/HLA_DICTIONARY_AA.hg19.imgt3320.AA_tf.in_ref.rds -------------------------------------------------------------------------------- /data_assoc/covariates.txt: -------------------------------------------------------------------------------- 1 | IID sex PC1 PC2 2 | HGDP00610 1 0.0280253076423443 1.43168951881555 3 | HGDP00982 1 -1.16387919457715 3.17619209827493 4 | HGDP00001 2 0.763274703437039 0.14745773857431 5 | HGDP01247 2 -0.829658733507506 -0.397620652769166 6 | HGDP00309 2 -0.983021100114437 -0.352155418951275 7 | HGDP00786 1 2.38590445001495 0.453655836266455 8 | HGDP00611 1 0.385811679161009 0.374928028455937 9 | HGDP00003 2 -0.469956063666488 -0.152037376282358 10 | HGDP00984 1 2.01283884161895 2.05694218392547 11 | HGDP00787 2 1.45434939297945 0.269877935143667 12 | HGDP01248 1 -0.276762128475043 -1.6400051310367 13 | HGDP00311 2 -1.52536681992517 -1.38358402383936 14 | HGDP00612 1 -0.66952726029171 -0.421944177492037 15 | HGDP00005 1 -0.173379799800961 0.973457627858389 16 | HGDP00985 1 -0.298839210825947 -1.56333424825607 17 | HGDP00788 1 -0.872412361269459 -1.03614823778432 18 | HGDP00313 1 2.75952289139271 0.391659927037654 19 | HGDP01249 1 -0.66642433934396 -0.234914111721196 20 | HGDP00613 1 -0.100325854229605 -0.939915756201599 21 | HGDP00007 1 0.659901414252751 0.0870655188920164 22 | HGDP00986 1 0.655930124362447 0.133242308462825 23 | HGDP00790 2 0.302208617497355 0.857139536193699 24 | HGDP00315 2 -0.340769526834037 -0.437267576963238 25 | HGDP01250 2 0.658999797898378 0.111968812289482 26 | HGDP00614 2 2.03375175806258 -0.53280229862381 27 | HGDP00009 2 1.31601061947374 0.151974488236448 28 | HGDP00987 2 0.344668559002598 0.80580498878675 29 | HGDP00791 1 -0.0472802298061226 -0.417140954493951 30 | HGDP00319 2 -0.264913508827516 0.268881638182434 31 | HGDP01251 2 -0.397262349784505 -1.07593132798567 32 | HGDP00615 1 -1.89490159372197 1.72287177540517 33 | HGDP00011 2 -1.94085069390858 0.727137346676688 34 | HGDP00991 1 1.55031468588178 -0.471105147875521 35 | HGDP00794 1 1.3110110442676 -1.37310231222898 36 | HGDP00323 2 0.32547968425777 -0.412383878370283 37 | HGDP01253 1 -0.213146726927696 -1.34602316569785 38 | HGDP00616 1 -0.817954269224968 0.416980431329832 39 | HGDP00013 2 0.832446245192119 1.07288893054109 40 | HGDP00992 1 -0.183773512590738 -0.0371787996360008 41 | HGDP00795 1 -1.42992366550021 2.62463890863267 42 | HGDP00326 1 -0.611931471976978 1.1425858507713 43 | HGDP01254 2 -1.23341562857128 -1.54133932585825 44 | HGDP00618 2 -0.650979282284673 -0.115032452041772 45 | HGDP00015 2 0.0436882048215001 0.397331544618964 46 | HGDP00993 1 0.960505549864054 0.440586626439923 47 | HGDP00796 1 0.291499926880451 -1.20061997019538 48 | HGDP00328 2 -2.55996227247002 -0.626906969137827 49 | HGDP01255 2 1.19058348550816 -2.78992178688573 50 | HGDP00619 2 -0.196032161098229 1.04254075763742 51 | HGDP00017 1 0.915320281687423 -0.0791357225419555 52 | HGDP00994 2 -0.167077509514096 -1.39673111118193 53 | HGDP00330 2 -0.0475050871108008 0.649137507153369 54 | HGDP00797 1 -0.195593114415228 -0.664367622849232 55 | HGDP00620 1 -0.71337289281514 -0.120817431732975 56 | HGDP01256 1 -0.370976709687866 0.249573569255806 57 | HGDP00019 1 -1.14051472701652 2.01229277968382 58 | HGDP00333 2 1.49293666729331 -1.32990083110323 59 | HGDP00995 1 -1.29413100981771 -0.335566386524463 60 | HGDP00798 1 1.28421075936102 -0.448509334859634 61 | HGDP00621 2 -0.817692430788452 0.425271614092815 62 | HGDP01257 1 2.26340231809989 -0.432807407812599 63 | HGDP00021 1 2.02480347412319 -1.10184946104227 64 | HGDP00338 2 -1.08307438684604 -1.3474827795206 65 | HGDP00999 2 0.802672432469824 -0.411309666027038 66 | HGDP00799 1 0.350269606359993 -0.187853485787742 67 | HGDP00622 2 0.949452607151921 -0.117432267867688 68 | HGDP01258 2 -0.97723083872131 0.938687536484131 69 | HGDP00023 1 -0.175103518353855 -1.2328078172366 70 | HGDP00341 1 0.661236530677419 -0.24247577008779 71 | HGDP01010 2 -1.48034723045168 -2.14974879915291 72 | HGDP00800 1 -0.680436395985525 1.57843997187057 73 | HGDP00623 1 1.30477809061137 1.29635198046096 74 | HGDP00025 1 -1.79185437335583 -0.00955535840176476 75 | HGDP01259 1 -0.300287635815326 -0.148669321150655 76 | HGDP00346 1 0.551506029651083 0.697665101334371 77 | HGDP01013 2 -1.08119543310999 -0.921983279883504 78 | HGDP00624 1 0.92320117231888 -0.315301668893939 79 | HGDP00802 2 0.360588995037732 -0.862191484961975 80 | HGDP00027 1 -1.18578812705491 1.11573864197924 81 | HGDP01260 2 -0.871628424364129 1.5247009100143 82 | HGDP00351 2 2.0867593091945 -1.99846860239629 83 | HGDP01015 2 0.477768167314321 -0.845808047006653 84 | HGDP00625 1 1.62667178398424 -0.00811911088158597 85 | HGDP00803 1 0.754201358313878 0.157844243269229 86 | HGDP00029 1 -0.437380227928762 -0.568279221648617 87 | HGDP01261 1 -0.273388001988834 -0.00843748034831362 88 | HGDP00356 1 -1.77954788839776 -0.327369348064667 89 | HGDP01021 1 0.472041738634543 -0.614796471705931 90 | HGDP00626 2 -0.682625012858633 -0.584140312878284 91 | HGDP00804 1 0.640984710644621 0.218044464042726 92 | HGDP00031 2 0.405024432217948 -0.320351369246531 93 | HGDP01262 1 -1.48589822607575 -0.553832453307435 94 | HGDP00359 2 3.06304426752499 1.18359140775896 95 | HGDP01023 2 -0.0617278991110892 -1.31167092396435 96 | HGDP00627 2 -0.601062434877994 0.841640287142484 97 | HGDP00805 1 1.17698639160153 -0.607886967927644 98 | HGDP00033 1 -0.398877559834745 0.257856765191873 99 | HGDP01263 2 0.0474367240440786 -1.18557051821439 100 | HGDP00364 2 -1.82738373423494 -1.59921122064091 101 | HGDP01027 1 0.257724976556419 -1.81066689027156 102 | HGDP00628 1 -0.547541129231607 -1.12275350410695 103 | HGDP00806 2 1.47548807475316 -0.910734296185824 104 | HGDP00035 2 -0.916663967361345 0.472512770607675 105 | HGDP01264 1 0.656821358936467 0.526666669903857 106 | HGDP00371 1 0.277025421371361 -1.53984862697493 107 | HGDP01028 2 1.2675171863196 -1.31212951216582 108 | HGDP00629 2 -0.859457543787237 0.743856976973061 109 | HGDP00807 2 1.39911081180625 0.0890567522562573 110 | HGDP00037 1 -1.33367390996991 0.390643705705287 111 | HGDP01265 1 1.14429488384033 -0.236212346965977 112 | HGDP00372 1 1.20334978464259 -1.21998760209354 113 | HGDP01029 2 1.38394805255901 -0.288413320451163 114 | HGDP00630 2 -0.458765540937809 0.0191416084759775 115 | HGDP00808 1 0.601929450394658 -0.706329144990026 116 | HGDP00039 2 0.444777834441528 0.40421238167277 117 | HGDP01266 1 0.335380533718825 1.19527772889729 118 | HGDP00376 1 -0.171907667191729 0.102018763015617 119 | HGDP01030 1 1.47898053544046 0.16656163840374 120 | HGDP00631 2 1.64578717493103 -0.622735881178756 121 | HGDP00041 2 0.667597308334167 -1.79214168528307 122 | HGDP00810 1 -0.206163543158542 -0.188248013601417 123 | HGDP01267 2 -0.252544030091634 2.25513171291401 124 | HGDP00382 1 1.24090805999799 -2.25461861148893 125 | HGDP01031 1 -0.495100826907096 0.618829612161902 126 | HGDP00632 1 -1.71564045412988 -1.00506099295295 127 | HGDP00811 1 -0.752052400086819 -0.0290202919520897 128 | HGDP00043 1 -0.438105297317922 1.21862413644116 129 | HGDP01268 1 -0.44256090190279 0.433923461175316 130 | HGDP00388 1 -0.665400930066813 0.420959369842843 131 | HGDP01032 2 0.103002252735175 1.70062168772827 132 | HGDP00634 2 -0.29384546564911 -0.274586833123327 133 | HGDP00812 2 1.93418314700173 -2.5764292604978 134 | HGDP00045 2 -0.51104850836022 -0.162795340034998 135 | HGDP01269 1 1.43677097204785 0.346688590957371 136 | HGDP00392 2 -0.832268072651696 -1.3973365625873 137 | HGDP01033 2 1.21158953690128 -1.31702135609058 138 | HGDP00635 1 -1.00448531131801 -0.355170829241532 139 | HGDP00813 2 0.873158384838356 -0.378104850804133 140 | HGDP00047 1 1.26902416629621 -1.58698687796773 141 | HGDP01270 1 0.915729936665862 1.80702041777006 142 | HGDP00397 2 0.322024384384559 -1.63816153698649 143 | HGDP01034 1 -0.0275043703717728 -0.748084027653489 144 | HGDP00636 2 -0.370520610904446 -0.660921715404634 145 | HGDP00814 1 0.179050905191376 -0.210519000547359 146 | HGDP00049 2 -1.14471016151313 -0.179071900702896 147 | HGDP00402 1 0.0301140438474852 0.97907927678169 148 | HGDP01271 2 1.28416318647621 -0.766763148982208 149 | HGDP01035 2 -0.512282129328496 0.569326509960892 150 | HGDP00637 2 0.222575896824548 -1.57265856026391 151 | HGDP00815 2 -0.530911593897391 -1.17775397789946 152 | HGDP00052 2 -0.690293591867513 -0.321468933482251 153 | HGDP00407 1 1.84617443388781 1.47520761402088 154 | HGDP01272 1 0.0357576963547988 2.1842197740725 155 | HGDP01036 2 -0.541844174498358 -1.11528183130284 156 | HGDP00638 1 1.34305227853489 -1.04271450286916 157 | HGDP00054 2 0.541070772796146 -0.432284020448313 158 | HGDP00817 1 0.893490537267824 0.0176474457823561 159 | HGDP00412 1 -0.799040847227371 0.0312924968477776 160 | HGDP01274 2 0.774144292506784 1.54859042247932 161 | HGDP01037 2 0.00602780124599756 1.06997046183255 162 | HGDP00639 1 0.633201442248941 -0.220437928833672 163 | HGDP00818 2 0.803525922494659 0.405638999962929 164 | HGDP00056 2 0.282173921748224 -0.467873501575076 165 | HGDP00417 1 -0.204794367660222 1.08808282726215 166 | HGDP01275 1 0.106607834434743 -0.324225331429405 167 | HGDP01041 1 -0.636172872954057 -1.88477345496701 168 | HGDP00640 1 0.56524612360297 0.556122628380942 169 | HGDP00819 1 0.274446864646923 -0.921077502588323 170 | HGDP00057 1 0.31229906256424 0.563287807419133 171 | HGDP00423 2 -1.44222332014959 -0.0174864292718612 172 | HGDP01276 1 -0.00590988491764632 -0.335158281168565 173 | HGDP01044 2 0.838933596454548 -0.660670308953313 174 | HGDP00641 2 -0.809642839179976 2.24599908136561 175 | HGDP00820 1 0.240588607648256 0.167678992379227 176 | HGDP00058 2 2.1592208606803 -2.14441939078324 177 | HGDP00428 2 0.458290220113783 0.16306699031123 178 | HGDP01277 2 -0.577921527164066 -0.127853562348193 179 | HGDP01047 1 0.460723790206759 1.01292465154779 180 | HGDP00821 1 -1.21800076275268 -0.100423513637832 181 | HGDP00642 2 -1.54897262386199 1.2758250626134 182 | HGDP00060 1 0.489830159479463 -0.598435664695175 183 | HGDP00433 1 -0.669843433975237 -0.471800505226307 184 | HGDP01279 2 1.05619145498456 1.08165974646023 185 | HGDP01053 2 -0.0815180188557568 0.104132588765413 186 | HGDP00822 2 0.159600211111372 0.772272512297645 187 | HGDP00643 1 0.605893229092901 -0.506782632940431 188 | HGDP00062 2 0.402332947782486 1.50725761824892 189 | HGDP00438 1 1.66912078025549 1.16303034567263 190 | HGDP01280 1 -0.613416669076803 1.77648047595785 191 | HGDP01055 2 -1.53956913516392 -0.110429484640437 192 | HGDP00644 2 -0.346997727117418 -0.465849619376175 193 | HGDP00828 2 0.916084301734219 0.33270020926694 194 | HGDP00064 1 1.45366472114994 -0.383877362141779 195 | HGDP00444 1 -1.72266313818653 2.07382043572123 196 | HGDP01282 2 0.903096176331677 1.18294445580713 197 | HGDP01056 2 -0.684322573758202 0.0578592489794322 198 | HGDP00645 2 -1.94821688754772 0.0918149721343126 199 | HGDP00832 1 -1.88537716214613 -1.23255188673223 200 | HGDP00066 2 -0.389520338551106 1.06980044252806 201 | HGDP00445 2 0.531584238980834 -0.780656429893473 202 | HGDP01283 2 -0.00562929338322527 0.00424891000888586 203 | HGDP01057 1 -1.3432299647922 1.04613352321535 204 | HGDP00646 2 -0.272843263006323 -2.41225697273122 205 | HGDP00843 1 1.10871311604043 -0.195090672233259 206 | HGDP00068 2 0.827391684826949 0.770738820038597 207 | HGDP00449 2 0.33649845996386 -0.58554482191954 208 | HGDP01285 2 0.389196880261177 1.44671015592115 209 | HGDP01058 1 -0.0617521464693813 -0.322413033705806 210 | HGDP00647 2 -2.16857356790188 0.405590716790052 211 | HGDP00846 2 0.218475858672152 -0.747246196213505 212 | HGDP00070 1 -0.301969740540525 -0.361897166692165 213 | HGDP00450 1 0.941808935978569 1.26247151949461 214 | HGDP01286 2 -1.26293791519047 -0.814391253156896 215 | HGDP01059 2 -0.388160498939909 0.972375658423455 216 | HGDP00648 2 -0.578828509040958 -0.920069595360412 217 | HGDP00849 1 -1.19216456393232 0.417272592393877 218 | HGDP00452 1 1.52641464714056 -0.440826480747027 219 | HGDP00072 1 -0.42691404507169 1.34653289075267 220 | HGDP01287 1 -0.37778868337704 0.604780663590127 221 | HGDP01060 1 -0.207350453942132 0.650757774914374 222 | HGDP00650 2 1.14147527865583 0.686018764941473 223 | HGDP00852 1 -0.291043192243945 -0.983203361682815 224 | HGDP00453 2 0.553069276887795 1.06515737340781 225 | HGDP00074 1 0.252289796717882 -1.12093723987683 226 | HGDP01288 2 0.340842439449744 -0.531734801440545 227 | HGDP01062 2 -0.605871121898494 -0.499838612149235 228 | HGDP00651 2 -2.4951923032692 1.55800195677283 229 | HGDP00854 1 0.109732540818466 1.9168602449148 230 | HGDP00454 2 0.051650472353377 0.834678463783924 231 | HGDP00076 2 0.562896469752711 -0.493452303603529 232 | HGDP01289 2 0.529038961816107 -0.460299348106915 233 | HGDP01063 2 0.325358196861315 1.12632799486245 234 | HGDP00653 1 -0.851632381485907 0.627922899609059 235 | HGDP00455 2 0.451390160403607 0.274618765817231 236 | HGDP00855 1 -1.31188998457374 -1.12114804440428 237 | HGDP00078 2 -1.58992343382994 -0.668141267663694 238 | HGDP01290 2 0.595056539699413 0.684740568993316 239 | HGDP00654 2 0.943747281218484 1.62671282876546 240 | HGDP01064 1 0.830184950553146 0.206838858661544 241 | HGDP00457 1 0.724260305708893 -1.25018137553183 242 | HGDP00856 1 0.302624483039658 -0.777129632460922 243 | HGDP00080 2 -0.440469897367608 -1.52280278516761 244 | HGDP01291 1 1.38855628418662 0.180783983099003 245 | HGDP00655 1 1.13486425993335 0.127240135423031 246 | HGDP00458 2 1.8476313606038 0.342328763065078 247 | HGDP01065 1 -0.491913217730714 0.529144338131914 248 | HGDP00857 1 1.76987865956766 1.09078851255294 249 | HGDP00082 1 0.273376151024318 0.216622473110951 250 | HGDP01292 2 0.80804278574229 -0.944273390622814 251 | HGDP00656 2 -0.936183226585249 -0.117933071172812 252 | HGDP00459 2 -1.02716479031857 1.39914607829357 253 | HGDP01066 2 1.02104326848399 -0.0325356209154599 254 | HGDP00086 2 1.08880459129364 0.56110502504926 255 | HGDP00858 1 -2.41820981058886 -0.794410672337993 256 | HGDP01293 1 -0.18107677354058 0.204048708873704 257 | HGDP00460 1 0.636283754546487 -1.16505017585158 258 | HGDP00661 2 -0.725128432747626 -0.248354162230334 259 | HGDP01067 1 0.312565998610951 -0.386813592598975 260 | HGDP00088 2 -1.71279068978276 1.76076132158541 261 | HGDP00859 2 1.65661424792202 2.30715656008728 262 | HGDP01294 2 0.449173321828807 0.599552933464228 263 | HGDP00461 1 -1.1966657783608 -1.66087746050265 264 | HGDP00662 2 0.435650249986206 -0.795881715050543 265 | HGDP01068 1 -0.424482282525467 0.835946174807375 266 | HGDP00090 2 1.33588411940174 0.561549397873559 267 | HGDP00860 2 0.0717622919419995 -0.501346932110295 268 | HGDP00462 2 1.24459834827546 0.480588355979758 269 | HGDP01295 2 -0.393154120801847 1.26636025662447 270 | HGDP00663 2 0.947552129968948 0.45357706721422 271 | HGDP01069 1 -0.0446457574463257 1.31100790391128 272 | HGDP00092 2 -0.963631755531001 -0.487684456383193 273 | HGDP00861 1 -0.465140870355323 -0.34207524054133 274 | HGDP00463 1 -0.148629357758736 -0.0177279807023327 275 | HGDP01296 1 -0.059989529670667 0.148240632650128 276 | HGDP00664 1 2.8421301825056 -0.93849545573698 277 | HGDP01070 2 -0.895815904121397 0.74134465383418 278 | HGDP00094 1 1.5442056153643 0.498892839492814 279 | HGDP00862 2 -0.771672563080895 -0.909886575734551 280 | HGDP00464 1 0.756727427820458 0.984421127870671 281 | HGDP01297 1 -1.22383186095549 -0.943816855001251 282 | HGDP00666 1 -0.340080014098599 -1.73197212784957 283 | HGDP01071 1 1.0828527995584 0.473764456514691 284 | HGDP00096 2 -0.800943728725872 -0.667895211352111 285 | HGDP00863 2 -0.404404467038976 0.772107922576745 286 | HGDP00466 2 -0.156150227524077 0.361962007509461 287 | HGDP01298 1 -0.0315982920591205 -1.18952487331488 288 | HGDP00667 2 -0.478964014314627 0.134782022627479 289 | HGDP01072 2 0.0205274613344313 1.08608455243335 290 | HGDP00098 1 0.140958422611133 -1.34673219455842 291 | HGDP00864 2 0.150142694918948 -0.328043498568215 292 | HGDP00467 2 1.00933300477386 0.787412520128103 293 | HGDP01299 1 -0.15087389926533 -0.864731307997287 294 | HGDP00668 1 0.574136218407273 0.782825448614355 295 | HGDP01073 2 -0.12168889810302 -0.232200103682519 296 | HGDP00099 1 -1.36420897526109 0.180325863106584 297 | HGDP00469 2 0.197014618228092 -0.660132741386691 298 | HGDP00865 2 -0.0198084115827976 1.76547344800643 299 | HGDP01300 1 -0.697465892118813 -0.818680287621334 300 | HGDP00669 1 -0.726645960091281 -0.371756211533505 301 | HGDP01074 1 1.42363755095127 1.03405754446442 302 | HGDP00868 2 -0.421649120216849 -0.216179117397081 303 | HGDP00100 2 -1.22424095971575 0.639580047245595 304 | HGDP00470 2 1.01388045815611 0.0203201891788521 305 | HGDP00670 2 -0.520448699535588 -0.481882364208751 306 | HGDP01301 2 0.789806401028563 -1.11273616579943 307 | HGDP01075 1 -1.42900161784346 1.26593197312268 308 | HGDP00869 2 1.31347284236759 -0.0663108448049217 309 | HGDP00102 2 -1.3111974968361 -1.11742146991485 310 | HGDP00471 1 -1.2893049015255 -0.531549818668131 311 | HGDP00671 1 0.446564128014565 0.557834826429993 312 | HGDP01302 2 0.248324628577317 1.3347577146858 313 | HGDP01076 2 -1.05185140237024 -0.608114375948785 314 | HGDP00870 2 0.213531142156661 0.149115813147745 315 | HGDP00472 2 0.777985612343511 1.56097065618065 316 | HGDP00103 2 0.766713935920909 0.302619257939871 317 | HGDP00672 1 -0.00516927593639746 0.445679581325786 318 | HGDP01303 1 0.196334982687732 -0.199286037991774 319 | HGDP01077 1 0.00890660159077392 2.09914967199726 320 | HGDP00871 2 -0.538018490951996 0.399251256954332 321 | HGDP00473 2 0.225949125474252 0.502466214357901 322 | HGDP00104 2 -1.20238140364706 -1.35982369550902 323 | HGDP00673 2 -0.644570937133879 0.528814238247816 324 | HGDP01304 2 1.01489647820653 -0.917284860387222 325 | HGDP01078 1 0.0876988105794976 0.559260065411662 326 | HGDP00872 1 0.538345004373748 1.05789715863985 327 | HGDP00474 2 -0.484009862023449 0.99023455369034 328 | HGDP00105 1 -1.53582007512971 1.0296912172037 329 | HGDP00674 2 -1.09545902019485 -0.666792257475643 330 | HGDP01305 2 -1.66025573146786 -0.697040915118611 331 | HGDP01079 1 0.0123169870695713 1.87221281743273 332 | HGDP00475 1 0.123076603132727 0.675922038815166 333 | HGDP00873 2 1.72629278873672 -0.300948053558104 334 | HGDP00106 2 -0.347598031340479 1.03017924708151 335 | HGDP00675 2 -0.514385207058461 0.516782537108288 336 | HGDP01306 1 -0.437255582560218 3.1864144686321 337 | HGDP01081 2 -0.743864245105637 -1.3173273307207 338 | HGDP00476 2 -0.629497601544918 0.822181368611535 339 | HGDP00875 2 -0.468483566351864 0.205237413699294 340 | HGDP00108 2 0.0152948710715959 -1.39350642335383 341 | HGDP00676 1 0.150766001650248 -1.12532180128746 342 | HGDP01307 2 -0.840946807834224 -0.321306254610944 343 | HGDP01086 2 0.477183795082611 -0.309842116908851 344 | HGDP00478 1 -1.22842969673114 0.992689818887604 345 | HGDP00876 1 -1.23193561529495 1.32277781805629 346 | HGDP00109 2 0.732106987384531 0.849280641509797 347 | HGDP00677 1 0.462948111426913 0.295197993295009 348 | HGDP01308 1 0.926711707243407 -0.661549911054653 349 | HGDP00479 1 -1.17772676712567 0.0507662256353227 350 | HGDP01090 2 -0.18721827801648 -0.230139171779637 351 | HGDP00877 1 0.176627386312101 0.682467777512995 352 | HGDP00110 2 -0.362107070119158 -0.612812237096027 353 | HGDP00678 1 -0.31604894079237 0.54170696711729 354 | HGDP01309 1 -1.25064441844698 0.109601572246684 355 | HGDP00491 2 -1.11950889069552 -0.526161686795847 356 | HGDP01094 1 -0.0879871234464083 0.0689389949713384 357 | HGDP00879 2 -1.12787125486303 0.398775080592626 358 | HGDP00115 1 1.3255632118209 -0.0806319858569026 359 | HGDP00679 2 0.269993523183189 0.671331422365642 360 | HGDP01310 2 0.356351997326693 -0.913480482319336 361 | HGDP00511 1 -0.999056161532197 -0.328192838319193 362 | HGDP01095 2 -0.405501702593789 -1.35797875618155 363 | HGDP00880 2 -0.35443296740101 -0.339955194233556 364 | HGDP00118 1 1.50297989577025 -0.101856483341128 365 | HGDP00680 1 0.881288146802202 -1.72367917446453 366 | HGDP00512 2 -1.7343603305357 0.775822116684294 367 | HGDP01311 2 -1.04022935930505 -0.266883336979792 368 | HGDP01096 1 0.968996396912399 0.212520762735019 369 | HGDP00120 2 -1.36131758332345 1.06974943520137 370 | HGDP00881 1 0.561209338308844 0.0732773474774401 371 | HGDP00682 1 -0.547090412747526 -1.15630829086079 372 | HGDP00513 2 0.875945745654088 -0.579218656151579 373 | HGDP01312 2 0.685265087466233 1.2360952419036 374 | HGDP01098 1 0.368916293998345 -0.96022746686625 375 | HGDP00121 2 1.56461194879903 0.853507726121409 376 | HGDP00882 1 1.6703891193486 1.74664025353641 377 | HGDP00683 2 -1.46594816799839 2.88647594802817 378 | HGDP00514 1 0.746080994714525 2.22681516295698 379 | HGDP01313 2 -0.246952585832881 0.180279670185448 380 | HGDP01099 1 -1.26821594434607 -0.9667189091369 381 | HGDP00122 1 0.294288206895886 -0.0925552917355486 382 | HGDP00883 1 -0.141962458825604 1.73119998699994 383 | HGDP00684 1 -1.05752886253207 -0.378497512545905 384 | HGDP00515 1 1.44553463049015 -0.362613376967222 385 | HGDP01314 1 -0.0681449738801745 -0.412525341064963 386 | HGDP01100 2 -0.40700351427234 0.467870318196894 387 | HGDP00124 1 -0.89589621026102 -0.96348091525859 388 | HGDP00685 1 -1.33963228668257 -0.65932829512498 389 | HGDP00884 1 1.101159885911 0.455732679039161 390 | HGDP00516 2 -1.11676558700136 -1.33564683106508 391 | HGDP01317 2 2.38477501033871 1.68926206126622 392 | HGDP00125 1 0.462811759360151 0.569524068171301 393 | HGDP01101 1 0.200217952753312 -0.919331386405993 394 | HGDP00686 2 -0.430031242435317 -2.47166024214017 395 | HGDP00885 2 -0.611311856496058 0.911852847041897 396 | HGDP00517 2 1.66759463038509 0.900695509636198 397 | HGDP01318 1 0.71285382473896 -0.379524525886055 398 | HGDP00127 1 -0.645645971829474 -0.210527684322592 399 | HGDP01102 2 0.18232106522002 -0.828329460173946 400 | HGDP00687 1 -1.72309683405941 -0.558207089446466 401 | HGDP00886 1 1.67756036485253 -1.27859576645036 402 | HGDP00518 2 -1.92049976807242 0.800558428475939 403 | HGDP01319 1 -1.21320552980364 0.673678667976713 404 | HGDP00129 1 -1.2615308268312 0.621861751360999 405 | HGDP01103 2 -0.951132880924486 -1.14534890423371 406 | HGDP00688 2 0.7619521904492 -1.19182404120437 407 | HGDP00887 2 -0.733902289673411 0.0331587250973226 408 | HGDP01320 1 -1.14684630015936 -1.50669008911847 409 | HGDP00519 2 0.141545337162246 -0.415844346714961 410 | HGDP00130 1 1.55959085771672 -0.61674120739923 411 | HGDP01104 1 -0.859932477301544 -0.39455185928936 412 | HGDP00689 1 -1.16158792773218 -0.409439076571967 413 | HGDP00888 2 -0.351739661666156 -0.142042662378077 414 | HGDP00520 1 0.652276427451101 -1.37052424812929 415 | HGDP01321 2 -1.03202872233545 0.837321031536061 416 | HGDP00131 1 0.87643463306788 -0.273391568336366 417 | HGDP01149 1 -0.505849986243009 -0.864224513156887 418 | HGDP00690 1 1.94854638939965 -1.09768128119327 419 | HGDP00889 1 -0.148273248656762 0.599113671870092 420 | HGDP00521 2 -1.57805069302171 0.374607568795651 421 | HGDP01322 2 -0.200181992646757 -0.694170433027751 422 | HGDP00133 2 1.2479695239913 1.34238269662247 423 | HGDP01151 2 0.632485407905028 1.32554565479762 424 | HGDP00691 2 -0.897663952351057 -0.361585025329295 425 | HGDP00890 2 -0.7349504170045 -0.497708582867226 426 | HGDP00522 2 -1.23235042085778 1.54705722584225 427 | HGDP01323 1 -0.586665813402066 1.27798282358316 428 | HGDP00134 1 -0.57132739085223 1.81668112662942 429 | HGDP01152 1 -0.578863325949771 0.985637949162782 430 | HGDP00692 2 0.702015958804214 -0.0778190169745438 431 | HGDP00891 1 -0.987510592929112 1.10267400415478 432 | HGDP00523 2 -0.55566642670018 -0.211699128855019 433 | HGDP01326 1 -0.443897152077351 -0.418272064691887 434 | HGDP00135 1 -0.127027435684875 0.583932537555677 435 | HGDP01153 2 0.458544761305624 -0.972439671466614 436 | HGDP00693 1 -0.527454120508972 -0.588233123790119 437 | HGDP00524 1 -0.26979471078968 1.03139415941639 438 | HGDP00892 2 -0.00458644148507458 -0.651218034356629 439 | HGDP01327 1 1.70536390304641 -1.33032496048428 440 | HGDP00136 1 0.304131553935872 0.32123725161875 441 | HGDP00694 1 1.80267802083295 -0.9433498355887 442 | HGDP01155 1 0.453438517790468 -0.629726629904592 443 | HGDP00525 1 0.374785925137897 0.438761406245391 444 | HGDP00893 1 0.698469778094682 -2.44051050808561 445 | HGDP01328 1 0.750745722425198 0.618931034570214 446 | HGDP00137 1 2.11307823243605 1.42991372296952 447 | HGDP00696 2 0.00525688272143858 -0.496423387004863 448 | HGDP01156 1 0.9822077354321 0.511896416115441 449 | HGDP00526 1 -0.900567929728333 0.603362688096566 450 | HGDP00894 2 -0.958499920909703 -0.751230382244971 451 | HGDP00139 2 0.119854430143838 -0.194135895701091 452 | HGDP01329 1 -0.359652906664183 0.529778110338335 453 | HGDP00697 2 -1.79813526897912 0.796940660453518 454 | HGDP00527 1 -1.85059606221942 1.10479022595244 455 | HGDP01157 2 0.845798032437825 -1.16958563226881 456 | HGDP00895 1 -0.602523624887335 -0.957740296318255 457 | HGDP00140 2 0.314271762602879 0.713462129946685 458 | HGDP01330 1 0.62100640964687 -1.57482315223803 459 | HGDP00698 2 -1.32299407615623 0.226196898827135 460 | HGDP00528 2 1.94942579467396 -0.661390966376999 461 | HGDP00896 1 -1.51278490166578 -0.213978705761156 462 | HGDP01161 1 -1.20736876844719 2.19364901537557 463 | HGDP00141 2 -0.153017419356856 -0.503028138605075 464 | HGDP01331 2 -0.779160494692875 -0.689437158846785 465 | HGDP00699 1 -0.0979813272678787 0.569537248443408 466 | HGDP00529 2 0.730582499292497 1.11038669719569 467 | HGDP00897 2 0.467008219099693 -1.01861080352837 468 | HGDP01162 2 1.04381627385528 0.0693838466192289 469 | HGDP00143 2 0.643339718551586 -0.859416612990281 470 | HGDP01332 1 0.0982879413351071 1.69132564609149 471 | HGDP00700 2 1.0094367051225 0.790356916439171 472 | HGDP00530 1 -0.262648343825966 0.425958260905938 473 | HGDP00898 2 1.8867922662329 -0.253796273509982 474 | HGDP01163 1 -0.822651578886544 -0.64238223139639 475 | HGDP00144 1 2.59628715878239 -0.282320628332909 476 | HGDP01333 1 1.53162910848327 0.0848433308246997 477 | HGDP00701 2 -0.0704778899443241 1.07539572551101 478 | HGDP00531 1 -0.415105877395822 0.0423049222203706 479 | HGDP00899 2 -1.16027957297016 1.05647340486452 480 | HGDP01164 1 0.111110758468947 -0.903129580123977 481 | HGDP00145 2 0.315439703509513 0.703218193957299 482 | HGDP01334 1 -0.404523537524079 -0.744358349046067 483 | HGDP00533 1 0.615653386460359 -0.391417104149271 484 | HGDP00703 1 -1.26992484016054 -0.850885480706998 485 | HGDP00900 2 -1.35699552131406 -0.127865174472646 486 | HGDP01166 2 -0.720869417011125 -0.473567006874326 487 | HGDP01335 2 -0.440820585637511 -0.561349497680916 488 | HGDP00146 2 0.39318719150803 -1.13393947931287 489 | HGDP00534 1 -0.11109585436694 -0.466292263789807 490 | HGDP00704 1 1.37743459605689 -0.230527723362158 491 | HGDP00901 2 -1.4177026255455 0.245348350451779 492 | HGDP01167 1 -0.603914871057882 -0.429202440950095 493 | HGDP00148 2 -0.623243561338751 0.446670038544623 494 | HGDP01336 1 0.769549236670178 1.07850174123657 495 | HGDP00535 1 -0.02562063543591 1.31193049175333 496 | HGDP00706 1 -0.306316454133518 0.188483788675123 497 | HGDP00902 2 0.352946204423492 -0.65455675615934 498 | HGDP01168 1 0.341560074644568 0.752916355125844 499 | HGDP01337 1 1.02898853944699 2.13353124538239 500 | HGDP00149 2 0.27457113501229 0.551402986150657 501 | HGDP00536 2 -0.354434793494798 1.0375697050326 502 | HGDP00710 2 -1.12956943059633 0.257689718772059 503 | HGDP00903 2 0.215372068496709 1.06666310937558 504 | HGDP01169 2 1.13037494879569 1.21525272242616 505 | HGDP01338 1 -1.48621471314954 0.821435180410016 506 | HGDP00150 1 0.263318847021401 -0.955510765975323 507 | HGDP00537 1 -0.431871927534397 0.545425930970175 508 | HGDP00711 2 -1.52108276216847 0.157504006741662 509 | HGDP00904 1 -2.24287271971337 -2.08174295397921 510 | HGDP01171 1 0.122157857988915 -0.740624399692289 511 | HGDP01339 2 -0.147951526114775 -0.228184892235798 512 | HGDP00151 2 -0.649516830593094 -2.3551267580876 513 | HGDP00538 2 0.748345413855676 0.909803724098298 514 | HGDP00712 2 0.939412213234122 -0.790549754831346 515 | HGDP00905 2 1.80192455086031 1.08407544555483 516 | HGDP01340 2 0.792521825332203 0.19751914774367 517 | HGDP01172 2 0.583829399803491 0.506795899761633 518 | HGDP00153 1 1.1298280721347 3.24792091106459 519 | HGDP00539 2 0.169101867188051 1.0590577756078 520 | HGDP00713 1 -1.09533839363934 1.54086729565552 521 | HGDP00906 1 0.375349424491692 0.307808892914531 522 | HGDP01341 2 2.49270597826941 0.12609994283686 523 | HGDP01173 1 -1.57180607112663 1.66272591364831 524 | HGDP00154 2 -0.195303145916462 0.193361619403489 525 | HGDP00540 1 1.17263435423048 -0.914262150348103 526 | HGDP00714 1 0.813803703346774 0.550883379311672 527 | HGDP00907 2 0.335791734314289 0.507237477851945 528 | HGDP01342 2 -0.91160998375583 0.1751621462884 529 | HGDP01174 2 0.843187832497748 0.81872224375882 530 | HGDP00155 2 0.183966558779242 0.916603175284106 531 | HGDP00541 2 0.348620703121479 0.112056645263572 532 | HGDP00715 2 0.773764091801787 -0.763816221860117 533 | HGDP00908 1 -0.803982863411365 -0.131031299526378 534 | HGDP01345 2 -0.619145202404587 0.944938380422273 535 | HGDP00157 2 0.0104732212034452 1.42626429422198 536 | HGDP01177 2 -0.530953461709478 0.316047447520014 537 | HGDP00542 2 -1.24981358565023 -0.939959197815307 538 | HGDP00716 2 -1.54456746209633 -0.152009688967716 539 | HGDP00909 1 -0.478941246373662 0.300276801075327 540 | HGDP01346 2 0.0643702538640958 0.231709673405519 541 | HGDP00158 1 -0.959533094163547 -0.451011135130313 542 | HGDP01179 2 0.435026379709028 0.650423348352312 543 | HGDP00543 2 0.806343390308272 0.856919116634063 544 | HGDP00717 2 0.224288122008924 0.736615685581202 545 | HGDP01347 1 0.269140366712925 -0.132335876843116 546 | HGDP00910 2 -1.45818561905596 0.759159548598431 547 | HGDP00160 1 -0.774437287405925 -1.23512555136504 548 | HGDP00544 1 0.344207875684961 1.00913826515758 549 | HGDP01180 1 -1.73001052367425 0.697115448882445 550 | HGDP00719 2 1.36551632578574 2.08943768682148 551 | HGDP01348 2 -2.39062924254269 -0.0479312717644486 552 | HGDP00161 2 0.0971750576177042 0.680089482445095 553 | HGDP00911 2 -0.214462995651998 -0.939484946869938 554 | HGDP00545 2 0.0851564693353069 0.31714093624246 555 | HGDP01181 1 -0.0416744800514054 1.47821715767042 556 | HGDP00721 2 -0.245485483067515 1.96384663961635 557 | HGDP01349 2 -1.78928647682507 -0.701881746348579 558 | HGDP00912 1 -0.21333720180631 -0.253308617315822 559 | HGDP00163 1 0.888632168125906 -2.02600222952061 560 | HGDP00546 2 -0.249775033290827 -0.363804360852681 561 | HGDP01182 2 1.41001655209669 0.0464280077774555 562 | HGDP00722 1 -1.78640904018393 0.179795725495803 563 | HGDP01350 2 0.826284402677715 0.178905338444681 564 | HGDP00165 1 0.379377955810367 1.45905265210016 565 | HGDP00547 1 0.430714431421847 0.0116146988186159 566 | HGDP00913 2 0.688038778833645 -0.364222810082888 567 | HGDP01183 1 0.519292238549878 -2.35660525387748 568 | HGDP00723 1 0.701829627471912 -0.492608496994863 569 | HGDP01351 2 0.395585899291859 -0.0601070338806346 570 | HGDP00167 1 0.250478571614543 0.105236130055658 571 | HGDP00914 1 -1.19144076189327 0.690971686363715 572 | HGDP00548 2 0.5501806370874 0.259957385614668 573 | HGDP01184 2 -0.57720252784259 0.859815831721359 574 | HGDP00724 2 0.835880252959339 1.54681454265962 575 | HGDP01352 1 -2.01187889946415 -0.772005622872155 576 | HGDP00169 1 0.731141033524535 0.234458076391308 577 | HGDP00549 1 1.72412969067648 0.373176915882183 578 | HGDP00915 1 0.268390241306056 0.590459804157533 579 | HGDP01185 1 -0.181478770202506 0.203117670205852 580 | HGDP00725 1 -0.221369079755586 -0.801327853273133 581 | HGDP01353 1 1.42041367430207 0.554782269846803 582 | HGDP00173 1 0.457287078169175 1.47859876540879 583 | HGDP00550 1 0.0731794859601498 -0.467291668809664 584 | HGDP00917 2 -0.730281799394742 0.476238794103579 585 | HGDP01186 1 -0.241083234210533 -2.38776528852053 586 | HGDP00726 1 -1.06012519266059 -1.30015312532594 587 | HGDP01354 1 -0.504907779442429 -0.281074408481901 588 | HGDP00175 2 0.909729931484196 -0.246941159878858 589 | HGDP00551 1 -0.905657820675867 0.291514276319171 590 | HGDP00918 1 0.430390043063631 -0.0964098804799015 591 | HGDP01187 1 0.858607848117326 -1.56255609388677 592 | HGDP00727 2 -1.99476467776696 -1.78510561380627 593 | HGDP01355 2 -0.111676803965396 -0.638060432662508 594 | HGDP00177 1 -0.00532040116648825 0.769671991042706 595 | HGDP00552 2 0.504008594287424 1.83369719793502 596 | HGDP00920 1 -0.49959734759118 0.796301303021064 597 | HGDP01188 1 -0.390456621137993 -0.0125956663050796 598 | HGDP00729 1 -1.21303648809477 -1.82945075960843 599 | HGDP01356 2 0.0412604852154275 -0.203286962153664 600 | HGDP00179 1 0.476830234155228 0.476698814819727 601 | HGDP00553 1 -0.505515942510525 0.0780761176922851 602 | HGDP00924 2 1.10843327042537 -0.170183713365296 603 | HGDP01189 2 -0.736090040339072 0.757393249176703 604 | HGDP00730 2 -0.736372654679772 -1.38118187653427 605 | HGDP01357 2 -2.19553410256695 0.303071898190312 606 | HGDP00554 1 1.84920724953551 0.665300704628152 607 | HGDP00181 2 -0.321810548343706 1.17707040165822 608 | HGDP00925 2 -1.52857173131693 1.04806720252407 609 | HGDP00731 1 0.651062201968766 -0.147413181958566 610 | HGDP01190 1 -0.0507448170904375 0.867182398277082 611 | HGDP01358 2 2.28716274587103 0.413965558689802 612 | HGDP00555 2 -1.19201502240673 0.377451354590676 613 | HGDP00183 2 1.27212489325947 0.754867695429156 614 | HGDP00926 2 -1.20881234793899 0.140502655271519 615 | HGDP00732 1 -2.45340352867103 -0.405568867433744 616 | HGDP01191 1 0.514111410284321 1.43340782919743 617 | HGDP01359 1 -0.147433390163531 0.591086729184676 618 | HGDP00556 1 0.319816462973018 -1.0505375493334 619 | HGDP00927 1 -0.497341006375058 2.38085984455593 620 | HGDP00185 2 -0.322982110640741 -0.454641801784611 621 | HGDP00733 2 0.43357119882941 0.355689705267268 622 | HGDP01192 2 1.60025845049878 0.861107927178503 623 | HGDP01360 2 0.469179840052223 1.0674516887679 624 | HGDP00557 1 0.652577662025661 -0.317594019965214 625 | HGDP00928 1 0.78865582723519 -0.0662937397972897 626 | HGDP00187 2 0.196453302767436 0.0994106412615003 627 | HGDP00734 2 -0.602251308797549 -0.573872521676188 628 | HGDP01193 2 0.785260712435454 1.40336774792402 629 | HGDP00558 2 0.382213932173637 -1.10189469859912 630 | HGDP01361 1 0.726465995714124 0.468021375148202 631 | HGDP00929 2 0.99819417363197 -0.523601561129403 632 | HGDP00189 1 -1.33749112836546 -1.9155827634725 633 | HGDP00735 2 2.14411890332795 -1.28532936211323 634 | HGDP01194 1 1.68767542443345 1.26279249209356 635 | HGDP00559 1 -0.0181966306268442 -0.562144319244413 636 | HGDP01362 1 0.920052222308015 -1.21938591602102 637 | HGDP00191 1 1.00202536882949 -2.07999766759462 638 | HGDP00930 2 0.883839703967777 -0.49622457177286 639 | HGDP00736 1 0.0506557977879604 0.0730088010657704 640 | HGDP01195 1 1.14061709120381 0.0291731832405443 641 | HGDP00560 1 -0.189690358089728 -1.53977042247816 642 | HGDP01363 2 1.54813440655471 1.06727021863035 643 | HGDP00192 2 -1.94157587037216 0.71274507617732 644 | HGDP00931 1 1.02447802526875 0.42910543048167 645 | HGDP00737 2 -0.422015205896051 -0.659267542416238 646 | HGDP01196 1 0.267520003416697 2.19063706661239 647 | HGDP00561 2 0.783410538220935 -0.15301345980717 648 | HGDP01364 2 0.169412516003061 1.01415400797406 649 | HGDP00195 2 0.233711577312907 -1.36345134666384 650 | HGDP00932 1 0.712318001836866 1.53590668862605 651 | HGDP00738 2 0.309385073410883 -1.36235244042667 652 | HGDP01197 1 1.26502390272279 0.361847229080447 653 | HGDP00562 2 0.331642572386406 -0.118708826111351 654 | HGDP01365 2 0.706844978297925 -0.861328148774043 655 | HGDP00197 1 -1.59540958791005 -1.27426382860306 656 | HGDP00933 1 0.079003666426928 1.15237760336719 657 | HGDP00739 1 -0.799524931857961 1.51256757756155 658 | HGDP01198 2 0.293795431402484 0.10338829399919 659 | HGDP00563 2 -0.134631414504803 -1.39550711591616 660 | HGDP01366 1 -1.43740429497142 -0.299624458619206 661 | HGDP00199 1 -0.71657186092086 1.06571364814383 662 | HGDP00934 1 -0.755308623009203 -1.13375791730432 663 | HGDP00740 2 0.610961668018271 0.481662087672956 664 | HGDP01199 2 -0.0749819301067247 0.135795631461931 665 | HGDP00564 2 -0.906580518053703 -0.15192601902348 666 | HGDP01367 2 0.484647050033304 1.43527285555473 667 | HGDP00201 1 -1.27387875043545 -0.73566702503803 668 | HGDP00935 2 1.09580025930015 -0.923568343400898 669 | HGDP00741 1 0.706679099547821 0.870156647772226 670 | HGDP01200 1 0.431521846174759 0.0973516878288297 671 | HGDP00565 1 -0.390669443037755 1.62089378980111 672 | HGDP01368 2 -0.944728743778485 0.17034270874784 673 | HGDP00205 2 -0.980981030754433 0.293659375099346 674 | HGDP00936 2 -0.3279306528901 -0.508788069026379 675 | HGDP00744 2 -0.295765803335805 -0.471486795759704 676 | HGDP01201 1 -1.85424160662939 -0.252366736593831 677 | HGDP00566 1 -1.25823038879898 0.910499197956298 678 | HGDP00206 2 -0.603078699889266 0.136240764720287 679 | HGDP01369 2 -1.02058392589015 2.00084447956428 680 | HGDP00937 2 -1.82202753172907 1.17021318720287 681 | HGDP00745 2 -0.862412179766505 0.136377940862711 682 | HGDP01202 2 1.97452001693554 -0.0285720750520613 683 | HGDP00567 1 1.83032232319878 -0.0562479658512194 684 | HGDP00208 2 -0.649053359783151 -0.245311977657024 685 | HGDP01370 1 -2.59660539899264 -1.10920960561333 686 | HGDP00938 1 1.39209557174284 -1.58315102468318 687 | HGDP00746 2 0.302129669505088 -0.0771625518607221 688 | HGDP01203 1 0.0101839184605604 1.51253904352865 689 | HGDP00210 1 3.00776673196129 -0.808047135238216 690 | HGDP01372 1 -0.857665949034531 0.134002315651764 691 | HGDP00568 1 -0.437223749742489 -0.0217701494855922 692 | HGDP00939 2 -0.402174354967189 -0.349832097842176 693 | HGDP00747 2 0.887241146837165 0.350899834158401 694 | HGDP01204 1 1.02930283089341 -0.418583357450398 695 | HGDP00213 2 -0.657265798769955 -0.22500631216464 696 | HGDP00569 2 2.04420641045807 0.897287566480274 697 | HGDP01373 2 -0.575914747818531 -0.199017998277205 698 | HGDP00940 1 0.831853002724065 -0.0333970858565892 699 | HGDP00748 1 -1.09657967070438 -0.53074742540973 700 | HGDP01205 1 -0.378866826058421 0.867028854024675 701 | HGDP00214 1 0.785388138958203 -1.3801338285494 702 | HGDP00571 2 0.0539995157569513 0.783481005904721 703 | HGDP01374 1 -0.144133122853283 0.214179760200105 704 | HGDP00941 1 -1.07073115092256 -0.169911082736007 705 | HGDP00749 2 -0.122782644834058 2.42464197289279 706 | HGDP01206 2 1.46021201170559 0.097649719699854 707 | HGDP00216 2 1.09619538012963 -1.88764033698222 708 | HGDP00572 1 1.01806575565539 -0.24111307051119 709 | HGDP01375 1 -1.93542183998054 -0.664813935498651 710 | HGDP00942 2 0.0060554313112697 -0.213603111943862 711 | HGDP00750 2 -0.647465465118369 -0.573355629831948 712 | HGDP01207 2 -0.3538885300846 -1.95651023402366 713 | HGDP00218 1 -0.0926688743787167 0.481528499342438 714 | HGDP00573 2 0.0228914169640972 0.587005809894146 715 | HGDP01376 1 -0.0746588460180577 0.733557570093106 716 | HGDP00943 2 1.6865842595084 0.572068027346411 717 | HGDP00751 1 -1.82601714283097 1.32914998710085 718 | HGDP01208 2 0.87473238333795 2.67646381445704 719 | HGDP00574 1 -0.163855594125615 0.478290483591861 720 | HGDP00222 1 -1.75676466203227 1.54517113298397 721 | HGDP00944 1 0.33252948384499 -0.19423492145524 722 | HGDP01377 1 -3.32160922346031 -0.0694791335307279 723 | HGDP00752 2 -0.250015666838241 1.8438110469258 724 | HGDP01209 2 -1.3442578885356 -0.470454218937133 725 | HGDP00575 2 0.927136083721879 -0.856489883119686 726 | HGDP00224 1 0.571146243145375 0.622977834039102 727 | HGDP00945 2 1.11630227848232 -0.303117149164322 728 | HGDP00753 2 1.71758982712545 1.63471363757142 729 | HGDP01378 1 -0.25920523643976 -0.99489068916135 730 | HGDP01211 2 -0.376025694145489 1.92443328391409 731 | HGDP00576 2 1.52112715361738 1.46979267828464 732 | HGDP00226 1 -1.384103812721 1.39132219555654 733 | HGDP00946 2 -0.403272247564015 0.640433677652196 734 | HGDP00754 1 0.0561928154274793 -0.208076125012973 735 | HGDP01379 1 0.317516792830841 -0.699908300360537 736 | HGDP01212 2 -0.87056774602859 -0.517918521661654 737 | HGDP00577 1 -0.605004529292685 0.706312296637042 738 | HGDP00228 1 -0.589237442996245 0.137427991695162 739 | HGDP00947 2 -0.28957865329216 -0.658647973207844 740 | HGDP00755 2 0.954528843930314 0.858560057206 741 | HGDP01380 1 -0.289701118973607 0.201943965567965 742 | HGDP01213 2 -0.274847231817648 0.661537127227061 743 | HGDP00578 2 -0.735203554700449 0.154378630776304 744 | HGDP00230 1 -0.0103067376182535 -0.707701346381654 745 | HGDP00948 2 -0.468152486306021 -0.162082039254051 746 | HGDP00756 1 -0.210053994948542 1.45757455950665 747 | HGDP01382 1 0.569081128167459 -0.0919430828174044 748 | HGDP01214 1 1.29670117690726 -1.69500985803623 749 | HGDP00579 2 -0.767416632373079 -0.714668610395385 750 | HGDP00232 1 -0.289273281931369 0.25622562341952 751 | HGDP00949 1 0.14874223319545 0.842799729973093 752 | HGDP00757 2 -1.82456986165045 0.758523352530198 753 | HGDP01383 1 -1.02703551456644 -1.24031141620204 754 | HGDP01215 2 1.5593315077901 0.534139101012443 755 | HGDP00580 2 0.120530316948822 -0.981018513365244 756 | HGDP00234 2 -0.421726870587954 -0.91198836216856 757 | HGDP00950 1 -0.873821593543518 -2.17095507571932 758 | HGDP00758 2 0.628214822255778 -0.519819354065303 759 | HGDP01384 1 1.25635187668052 0.129804056969195 760 | HGDP01216 1 0.113595870431391 0.0906188149264491 761 | HGDP00581 1 0.783919483365332 -1.12610490722934 762 | HGDP00237 2 -1.97975759078556 0.618532513857287 763 | HGDP00951 1 1.05464640360191 1.79255256268554 764 | HGDP00759 1 -0.441322731109672 0.50663375246144 765 | HGDP01385 2 2.16213269527795 -0.58032538202588 766 | HGDP01217 2 -2.9911288923289 -0.890241832632456 767 | HGDP00582 1 -1.9828879081866 0.198005178628307 768 | HGDP00239 1 -0.37952060541686 0.996531640298379 769 | HGDP00952 1 1.24174245240481 -0.701304445623166 770 | HGDP00760 1 -1.73560329904133 1.67905193799254 771 | HGDP01386 1 0.120230868773094 -0.37762197392781 772 | HGDP01218 1 0.112199749283979 -0.712787276199767 773 | HGDP00583 1 -0.138630026842948 1.43108751632084 774 | HGDP00241 2 1.04414305012241 -0.538232913723489 775 | HGDP00953 1 -0.121270691284627 -0.222804823230253 776 | HGDP00761 2 -0.527122066325698 1.22038317813869 777 | HGDP01387 1 -0.672571756399321 -0.0357214689854654 778 | HGDP01220 1 -1.00840019346397 -0.973146779091102 779 | HGDP00584 2 -0.508851997604026 -2.15271701356534 780 | HGDP00243 1 -0.0470135382645295 0.481837647650928 781 | HGDP00954 2 -0.105457430751965 -0.503106691622746 782 | HGDP00762 1 0.989720489952972 1.34782077582662 783 | HGDP01388 1 0.81995634873917 0.439307225272753 784 | HGDP01221 1 0.581073745459083 1.27521719850649 785 | HGDP00244 2 1.46507856387399 -0.716572807473232 786 | HGDP00586 1 -0.230688576136916 0.192500396771389 787 | HGDP00955 1 0.79587387308155 -2.06583154799165 788 | HGDP00764 2 0.403745522492329 -0.933054477567334 789 | HGDP01396 2 0.824678310329986 0.360297814364883 790 | HGDP01222 1 1.15367423385034 -0.59495340376504 791 | HGDP00247 2 -0.608089133563411 -0.464763616467842 792 | HGDP00587 1 0.170415643139023 0.960024245561304 793 | HGDP00956 1 1.37618356050853 -0.543157668326597 794 | HGDP00765 2 1.54500378965069 0.667640934251557 795 | HGDP01397 2 0.368111735578541 0.279127033948228 796 | HGDP01223 1 -0.737266990722435 1.09881422067777 797 | HGDP00248 1 -1.78958048731654 0.170727142625734 798 | HGDP00588 1 0.630738544787708 -0.164697489890308 799 | HGDP00957 2 0.842583133225183 -0.312848063815913 800 | HGDP00766 2 0.0724043986991091 2.06293119223321 801 | HGDP01398 2 -1.45020178746166 1.03766266686136 802 | HGDP01224 1 0.222000895617249 -1.40294690034191 803 | HGDP00251 2 0.519823224757196 1.11302325475913 804 | HGDP00590 2 1.88186396118178 -1.93614465512278 805 | HGDP00958 2 -0.324739721824171 -1.13784963397713 806 | HGDP01399 1 -0.615395711254043 0.178341987133094 807 | HGDP00767 1 -0.305499383365401 1.40373301349702 808 | HGDP01225 1 0.285758357947551 -0.144440401323829 809 | HGDP00254 2 -1.92023436865786 -0.702135428534021 810 | HGDP00591 2 0.527806937468664 -1.78449813635148 811 | HGDP00959 1 -0.283999586474282 1.99817357077064 812 | HGDP01400 2 -0.393138152083973 -0.930940501495848 813 | HGDP00769 1 0.556498730043882 -0.0320075035806219 814 | HGDP01227 2 -0.722615710576174 -1.1983794086255 815 | HGDP00258 1 -1.18726361324687 -1.62400934395378 816 | HGDP00594 2 -0.554785476406941 1.42803191136726 817 | HGDP00960 2 1.07424792904717 -1.15854728779701 818 | HGDP01401 1 1.7717009606692 0.456607310024401 819 | HGDP00771 1 0.941623642363817 -0.539265921372859 820 | HGDP01228 1 -0.459909606403737 -0.30080979764925 821 | HGDP00259 2 -0.729316678843739 -0.541674970590516 822 | HGDP00595 2 0.707880803226026 1.59170688647038 823 | HGDP00961 1 -0.561417479086712 2.05855692375497 824 | HGDP01402 2 -0.68968595245719 0.890430546722849 825 | HGDP00772 2 -1.73660362601439 -0.320037809116473 826 | HGDP01229 1 0.935024808447206 1.05593167897811 827 | HGDP00262 1 3.09313228455997 1.56049430490477 828 | HGDP00597 1 0.84055063642359 -0.463864275573152 829 | HGDP00962 2 -0.215140071122916 0.287679143994837 830 | HGDP01403 1 0.475455785899649 -0.497481846981495 831 | HGDP00773 2 1.21856421790748 -0.0756188826643511 832 | HGDP01230 2 -1.66124976417259 -1.33525015373979 833 | HGDP00264 1 -0.835577655475489 -0.0752426399780639 834 | HGDP00598 1 -0.553491519385089 -1.06288923533151 835 | HGDP00963 2 -0.409520713922487 0.857236239474321 836 | HGDP01404 2 0.501592915240537 -0.299209381794673 837 | HGDP00774 2 -0.569693142352863 -0.810630279432887 838 | HGDP01231 2 -0.391598049056211 0.357972169808958 839 | HGDP00274 2 -0.193359427491361 2.09997231884923 840 | HGDP00599 2 -0.153953691685306 0.137018743748803 841 | HGDP00964 1 0.18481759695015 0.183300272956839 842 | HGDP00775 1 0.493030553129206 0.0419589632326065 843 | HGDP01405 2 0.159865122707327 0.242264140463535 844 | HGDP01232 1 0.841970171777274 0.964448274177911 845 | HGDP00277 1 2.00540054342796 -0.464198248602147 846 | HGDP00600 2 -0.629297688897252 -0.0567352686627682 847 | HGDP00965 1 -1.85978084989518 -0.571837942859908 848 | HGDP00776 2 0.59867369384503 -0.565262511636441 849 | HGDP01406 2 0.619698280350884 -0.791646133936933 850 | HGDP01233 1 1.53522352222283 0.804941668305782 851 | HGDP00279 2 -0.99208961746568 1.63432752554245 852 | HGDP00601 1 0.282980330118332 1.2964569079926 853 | HGDP00966 1 -2.01294994062408 -0.0205323035114125 854 | HGDP01408 2 0.361400899588381 -0.198966961382656 855 | HGDP00777 1 -0.184864048975307 -0.663719380372259 856 | HGDP01234 2 -0.889277531738189 0.700582658649551 857 | HGDP00281 1 -1.26365650480697 -0.662881439567875 858 | HGDP00602 1 -1.66099193557519 0.305168453038247 859 | HGDP00967 2 -1.16780666823919 -1.87345855893631 860 | HGDP01411 2 -0.678996487266586 0.807137825922025 861 | HGDP00778 2 -0.164130999814755 0.253476865056066 862 | HGDP01236 2 -0.595377335159298 3.09908913965031 863 | HGDP00285 2 -0.54730786984478 0.0861273092133435 864 | HGDP00604 1 -1.08776017662596 -0.955252484441078 865 | HGDP00968 1 -0.242242395599078 3.1751904111502 866 | HGDP01412 2 -0.571965010983676 -1.49343617967487 867 | HGDP00779 2 -0.88436908830375 -1.45562154622227 868 | HGDP01237 2 -0.535480808765089 0.543598202997538 869 | HGDP00286 1 0.197963779913821 0.00131327288759289 870 | HGDP00606 2 0.431323895399406 0.247092534039071 871 | HGDP00969 2 0.197685274042714 -0.410868371473638 872 | HGDP01414 1 0.726413438474965 -0.131519273869692 873 | HGDP00780 1 0.488627377883011 -0.0644114485489679 874 | HGDP01238 1 -0.397523780010573 0.610361348668073 875 | HGDP00288 2 -0.684345997258538 0.676506007565883 876 | HGDP00607 2 -0.93573810608461 -0.911895639628711 877 | HGDP00970 1 -0.535501713013399 0.237642798428137 878 | HGDP01415 1 1.0177099187885 -1.27142377411241 879 | HGDP00781 2 -0.774458959966244 0.753811747191286 880 | HGDP01239 1 -0.284592466828842 -0.728423464772794 881 | HGDP00290 2 0.517978716264623 -1.15398113567432 882 | HGDP00608 2 -0.0202060843277245 -1.08944220478559 883 | HGDP00971 1 -0.342598115699356 2.11784508336644 884 | HGDP01416 2 -0.96003679211999 -2.12635386295969 885 | HGDP00782 2 -1.28569305900156 0.740694997017381 886 | HGDP01240 1 -0.0806750003695131 -1.09591127072146 887 | HGDP00298 1 0.309836831967528 2.09042540842 888 | HGDP00609 2 -0.0117851169712006 0.235222334430157 889 | HGDP00973 1 0.59576234864581 1.24818402633462 890 | HGDP01417 2 -0.57522775099408 -1.27370410994118 891 | HGDP00783 2 -0.356919642693947 -0.262514649861029 892 | HGDP01241 1 0.639775032212341 -0.152592486588214 893 | HGDP00302 1 0.695416015257341 1.30673402211457 894 | HGDP00974 1 -1.34875852251758 1.27553822544878 895 | HGDP01418 1 -0.202244409926265 -1.57388448913773 896 | HGDP00784 2 -2.02688035224609 -0.0905203322635049 897 | HGDP01242 2 1.17098458457012 -0.712282057260015 898 | HGDP00304 1 -0.544449998568804 1.73365769265558 899 | HGDP00975 2 0.694718305296155 -1.69209529210987 900 | HGDP00785 2 -1.21478683357312 -2.40686675695087 901 | HGDP01419 2 1.28371189000875 -1.98168169924285 902 | HGDP00307 2 0.281031877766293 1.25439614702897 903 | HGDP01243 2 -0.714275440940483 -0.601131607286282 904 | HGDP00976 2 0.35582590350739 0.634522454172437 905 | HGDP01244 1 0.975443193466353 0.821819292059155 906 | HGDP00977 2 1.32393821217214 -0.407844821034771 907 | HGDP01245 2 -0.201248254688505 0.949350187841812 908 | HGDP01246 1 -0.406774220119386 -1.20556269087565 909 | -------------------------------------------------------------------------------- /data_assoc/phenotype.txt: -------------------------------------------------------------------------------- 1 | IID trait_name 2 | HGDP00610 1 3 | HGDP00982 1 4 | HGDP00001 1 5 | HGDP01247 2 6 | HGDP00309 1 7 | HGDP00786 2 8 | HGDP00611 2 9 | HGDP00003 2 10 | HGDP00984 1 11 | HGDP00787 1 12 | HGDP01248 1 13 | HGDP00311 2 14 | HGDP00612 1 15 | HGDP00005 1 16 | HGDP00985 1 17 | HGDP00788 1 18 | HGDP00313 2 19 | HGDP01249 2 20 | HGDP00613 2 21 | HGDP00007 2 22 | HGDP00986 1 23 | HGDP00790 1 24 | HGDP00315 1 25 | HGDP01250 1 26 | HGDP00614 1 27 | HGDP00009 1 28 | HGDP00987 2 29 | HGDP00791 2 30 | HGDP00319 1 31 | HGDP01251 1 32 | HGDP00615 2 33 | HGDP00011 2 34 | HGDP00991 1 35 | HGDP00794 1 36 | HGDP00323 2 37 | HGDP01253 2 38 | HGDP00616 2 39 | HGDP00013 1 40 | HGDP00992 2 41 | HGDP00795 2 42 | HGDP00326 1 43 | HGDP01254 2 44 | HGDP00618 1 45 | HGDP00015 1 46 | HGDP00993 1 47 | HGDP00796 2 48 | HGDP00328 1 49 | HGDP01255 1 50 | HGDP00619 2 51 | HGDP00017 2 52 | HGDP00994 1 53 | HGDP00330 2 54 | HGDP00797 1 55 | HGDP00620 1 56 | HGDP01256 1 57 | HGDP00019 1 58 | HGDP00333 1 59 | HGDP00995 1 60 | HGDP00798 2 61 | HGDP00621 1 62 | HGDP01257 1 63 | HGDP00021 1 64 | HGDP00338 1 65 | HGDP00999 1 66 | HGDP00799 1 67 | HGDP00622 2 68 | HGDP01258 1 69 | HGDP00023 2 70 | HGDP00341 2 71 | HGDP01010 2 72 | HGDP00800 1 73 | HGDP00623 1 74 | HGDP00025 1 75 | HGDP01259 2 76 | HGDP00346 1 77 | HGDP01013 1 78 | HGDP00624 1 79 | HGDP00802 1 80 | HGDP00027 2 81 | HGDP01260 1 82 | HGDP00351 1 83 | HGDP01015 1 84 | HGDP00625 1 85 | HGDP00803 2 86 | HGDP00029 1 87 | HGDP01261 1 88 | HGDP00356 1 89 | HGDP01021 1 90 | HGDP00626 2 91 | HGDP00804 2 92 | HGDP00031 1 93 | HGDP01262 1 94 | HGDP00359 1 95 | HGDP01023 2 96 | HGDP00627 1 97 | HGDP00805 1 98 | HGDP00033 1 99 | HGDP01263 2 100 | HGDP00364 2 101 | HGDP01027 2 102 | HGDP00628 1 103 | HGDP00806 1 104 | HGDP00035 1 105 | HGDP01264 1 106 | HGDP00371 2 107 | HGDP01028 1 108 | HGDP00629 1 109 | HGDP00807 2 110 | HGDP00037 1 111 | HGDP01265 1 112 | HGDP00372 2 113 | HGDP01029 1 114 | HGDP00630 2 115 | HGDP00808 2 116 | HGDP00039 1 117 | HGDP01266 2 118 | HGDP00376 1 119 | HGDP01030 2 120 | HGDP00631 1 121 | HGDP00041 1 122 | HGDP00810 1 123 | HGDP01267 1 124 | HGDP00382 2 125 | HGDP01031 1 126 | HGDP00632 2 127 | HGDP00811 2 128 | HGDP00043 1 129 | HGDP01268 1 130 | HGDP00388 1 131 | HGDP01032 1 132 | HGDP00634 1 133 | HGDP00812 2 134 | HGDP00045 1 135 | HGDP01269 2 136 | HGDP00392 1 137 | HGDP01033 2 138 | HGDP00635 1 139 | HGDP00813 1 140 | HGDP00047 1 141 | HGDP01270 1 142 | HGDP00397 1 143 | HGDP01034 2 144 | HGDP00636 1 145 | HGDP00814 1 146 | HGDP00049 2 147 | HGDP00402 1 148 | 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= open(OUTINFOMATRIX, "w") 9 | out = "\t".join(["SNP","CHRPOS","REF","ALT","R2","AF"]) 10 | print(out, file = OUT) 11 | 12 | variant_dic = {} 13 | with open(CONVERTER, "r") as f: 14 | for line in f: 15 | line = line.rstrip().split() 16 | POS = line[0] 17 | SNP = line[1] 18 | A1 = line[2] 19 | A2 = line[3] 20 | variant_dic.setdefault(":".join(["6",POS]), []).append([SNP,A1,A2]) 21 | 22 | with gzip.open(INPUTVCF, "rt", "utf_8") as f: 23 | for line in f: 24 | line = line.rstrip() 25 | if line.find("#") > -1: 26 | print(line) 27 | else: 28 | line = line.split("\t") 29 | variant =line[2] 30 | info = line[7].split(";") 31 | ref = line[3] 32 | alt = line[4] 33 | converted = "NA" 34 | for cand in variant_dic[variant]: 35 | if (ref == cand[1] and alt == cand[2]) or (ref == cand[2] and alt == cand[1]): 36 | converted = cand[0] 37 | out = "\t".join([line[0],line[1],converted]) + "\t" + "\t".join(line[3:]) 38 | print(out) 39 | info = line[7].split(";") 40 | for i in range(len(info)): 41 | if info[i].find("R2") == 0: 42 | R2 = info[i].split("=")[1] 43 | elif info[i].find("AF") == 0: 44 | AF = info[i].split("=")[1] 45 | out = "\t".join([converted,variant,ref,alt,R2,AF]) 46 | print(out, file = OUT) 47 | OUT.close() 48 | 49 | -------------------------------------------------------------------------------- /script_assoc/run_omnibus_AAtest.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import sys 3 | import argparse 4 | import os 5 | 6 | # parse arguments 7 | parser = argparse.ArgumentParser() 8 | parser.add_argument('--aaraw', '-a', default=None, type=str, 9 | help='Name of the amino acid dosage *.raw file', 10 | required=True) 11 | parser.add_argument('--out', '-o', default=None, type=str, 12 | help='Prefix of the output file', 13 | required=True) 14 | parser.add_argument('--allele', '-l', default=None, type=str, 15 | help='Name of the amino acid allele name file', 16 | required=True) 17 | parser.add_argument('--pheno', '-p', default=None, type=str, 18 | help='Name of the phenotype file', 19 | required=True) 20 | parser.add_argument('--cov', '-c', default=None, type=str, 21 | help='Name of the covariate file', 22 | required=True) 23 | parser.add_argument('--phenoname', '-n', default=None, type=str, 24 | help='Name of the phenotype (2nd column of the phenotype file)', 25 | required=True) 26 | 27 | parser.add_argument('--covname', '-m', default=None, nargs='+', 28 | help='Name of the covariates', 29 | required=True) 30 | 31 | args = parser.parse_args() 32 | 33 | cmd = 'echo "library(data.table)" > ' + args.out + "_runcode.R" 34 | os.system(cmd) 35 | cmd = 'echo \'dose<-fread("' + args.aaraw + '", header=T)\' >> ' + args.out + "_runcode.R" 36 | os.system(cmd) 37 | cmd = 'echo \'dose<-dose[,c(-1,-3:-6)]\' >> ' + args.out + "_runcode.R" 38 | os.system(cmd) 39 | cmd = 'echo \'allelenames<-as.character(read.table("' + args.allele + '")$V1)\' >> ' + args.out + "_runcode.R" 40 | os.system(cmd) 41 | cmd = 'echo \'colnames(dose)<-c("IID",allelenames)\' >> ' + args.out + "_runcode.R" 42 | os.system(cmd) 43 | cmd = 'echo \'pheno <- read.table("' + args.pheno + '", header=T)\' >> ' + args.out + "_runcode.R" 44 | os.system(cmd) 45 | cmd = 'echo \'if(max(pheno[,2])==2){pheno[,2]<-pheno[,2] - 1}\' >> ' + args.out + "_runcode.R" 46 | os.system(cmd) 47 | cmd = 'echo \'cov <- read.table("' + args.cov + '", header=T)\' >> ' + args.out + "_runcode.R" 48 | os.system(cmd) 49 | cmd = 'echo \'d <- merge(dose, pheno, by = "IID")\' >> ' + args.out + "_runcode.R" 50 | os.system(cmd) 51 | cmd = 'echo \'d <- merge(d, cov, by = "IID")\' >> ' + args.out + "_runcode.R" 52 | os.system(cmd) 53 | 54 | 55 | OUT = open(args.out + "_runcode.R", "a") 56 | OUT2 = open(args.out + "_alleles.txt", "w") 57 | 58 | print('pval_list<-NULL', file = OUT) 59 | print('deviance_list<-NULL', file = OUT) 60 | 61 | AA_allele_dic = {} 62 | 63 | with open(args.allele, "r") as f: 64 | for line in f: 65 | line = line.rstrip() 66 | if line.count("_") == 5: 67 | AA = "_".join(line.split("_")[:5]) 68 | this_allele = line 69 | AA_allele_dic.setdefault(AA,[]).append(this_allele) 70 | else: 71 | AA = line 72 | this_allele = line 73 | AA_allele_dic.setdefault(AA,[]).append(this_allele) 74 | 75 | 76 | for AA in AA_allele_dic: 77 | num_allele = len(AA_allele_dic[AA]) 78 | print('obj1<-glm(' + args.phenoname + '~' + '+'.join(args.covname) + ', data=d,family=binomial)' ,file = OUT) 79 | alleles = '+'.join(AA_allele_dic[AA]) 80 | out = 'obj2<-glm(' + args.phenoname + '~' + alleles + '+' + '+'.join(args.covname) + ',data=d,family=binomial(link="logit"))' 81 | print(out, file = OUT) 82 | print('Chisqtest <- anova(obj1, obj2, test="Chisq")', file = OUT) 83 | print('pval <- Chisqtest$`Pr(>Chi)`[2]', file = OUT) 84 | print('deviance <- Chisqtest$Deviance[2]', file = OUT) 85 | print('pval_list <- c(pval_list,pval)', file = OUT) 86 | print('deviance_list <- c(deviance_list,deviance)', file = OUT) 87 | print(AA, file = OUT2) 88 | 89 | OUT.close() 90 | OUT2.close() 91 | 92 | cmd = 'echo \'alleles <- as.character(read.table("' + args.out + '_alleles.txt")$V1)\' >> ' + args.out + "_runcode.R" 93 | os.system(cmd) 94 | cmd = 'echo \'summary <- data.frame(ALLELE_NAME = alleles, OMNIBUS_DEVIANCE = deviance_list, OMNIBUS_PVALUE = pval_list)\' >> ' + args.out + "_runcode.R" 95 | os.system(cmd) 96 | cmd = 'echo \'outfile <- "' + args.out + '_omnibus_result.txt"\' >> ' + args.out + "_runcode.R" 97 | os.system(cmd) 98 | cmd = 'echo \'write.table(summary, outfile, sep="\t", quote=F, row.names=F)\' >> ' + args.out + "_runcode.R" 99 | os.system(cmd) 100 | 101 | cmd = 'Rscript ' + args.out + "_runcode.R > " + args.out + ".log" 102 | os.system(cmd) 103 | 104 | 105 | -------------------------------------------------------------------------------- /scripts/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/immunogenomics/HLA_analyses_tutorial/afddd62b022f503f7c5806298edcfe195aae1343/scripts/.DS_Store -------------------------------------------------------------------------------- /scripts/SNP2HLA.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import os, sys, re 4 | import argparse, textwrap 5 | from platform import platform 6 | 7 | 8 | 9 | ########## < Core Global Variables > ########## 10 | 11 | std_MAIN_PROCESS_NAME = "\n[%s]: " % (os.path.basename(__file__)) 12 | std_ERROR_MAIN_PROCESS_NAME = "\n[%s::ERROR]: " % (os.path.basename(__file__)) 13 | std_WARNING_MAIN_PROCESS_NAME = "\n[%s::WARNING]: " % (os.path.basename(__file__)) 14 | 15 | 16 | 17 | def SNP2HLA(_input, _reference_panel, _out, 18 | _mem = "2000m", _marker_window_size=1000, _tolerated_diff=.15, 19 | _beagle_NTHREADS=1, _beagle_ITER=5, _beagle_MAP=None, 20 | _dependency="./"): 21 | 22 | 23 | 24 | ### Optional Arguments check. 25 | 26 | if not os.path.exists(_dependency): 27 | print(std_ERROR_MAIN_PROCESS_NAME + "The path(folder) of depedency('{}') doesn't exist. Please check it again.".format(_dependency)) 28 | sys.exit() 29 | 30 | p_Mb = re.compile(r'\d+m') 31 | p_Gb = re.compile(r'\d+[gG]') 32 | 33 | if p_Mb.match(_mem): 34 | pass # No problem. 35 | elif p_Gb.match(_mem): 36 | _mem = re.sub(r'[gG]', '000m', _mem) 37 | else: 38 | print(std_ERROR_MAIN_PROCESS_NAME + "Given Java memory value('{}') has bizzare representation. Please check it again.".format(_mem)) 39 | sys.exit() 40 | 41 | 42 | 43 | ### Check dependencies. 44 | 45 | _plink = os.path.join(_dependency, "plink_mac" if not bool(re.search(pattern="Linux", string=platform())) else "plink") #plink v1.9 46 | _beagle = os.path.join(_dependency, "beagle.jar") # Beagle(v4.1) 47 | #_linkage2beagle = os.path.join(_dependency, "linkage2beagle.jar") 48 | #_beagle2linkage = os.path.join(_dependency, "beagle2linkage.jar") 49 | _vcf2gprobs = os.path.join(_dependency, "vcf2gprobs.jar") 50 | _merge_table = os.path.join("src/merge_tables.pl") 51 | _parse_dosage = os.path.join("src/ParseDosage.csh") 52 | 53 | 54 | if not os.path.exists(_plink): 55 | print(std_ERROR_MAIN_PROCESS_NAME + "Please prepare PLINK(v1.90) in 'dependency/' folder.") 56 | sys.exit() 57 | if not os.path.exists(_beagle): 58 | print(std_ERROR_MAIN_PROCESS_NAME + "Please prepare Beagle(v4.1) in 'dependency/' folder.") 59 | sys.exit() 60 | #if not os.path.exists(_linkage2beagle): 61 | # print(std_ERROR_MAIN_PROCESS_NAME + "Please prepare 'linkage2beagle.jar' in 'dependency/' folder.") 62 | # sys.exit() 63 | #if not os.path.exists(_beagle2linkage): 64 | # print(std_ERROR_MAIN_PROCESS_NAME + "Please prepare 'beagle2linkage.jar' in 'dependency/' folder.") 65 | # sys.exit() 66 | if not os.path.exists(_merge_table): 67 | print(std_ERROR_MAIN_PROCESS_NAME + "Please prepare 'merge_tables.pl' in 'src/' folder.") 68 | sys.exit() 69 | if not os.path.exists(_parse_dosage): 70 | print(std_ERROR_MAIN_PROCESS_NAME + "Please prepare 'ParseDosage.csh' in 'src/' folder.") 71 | sys.exit() 72 | 73 | 74 | 75 | ### Intermediate path. 76 | 77 | OUTPUT = _out if not _out.endswith('/') else _out.rstrip('/') 78 | if bool(os.path.dirname(OUTPUT)): 79 | INTERMEDIATE_PATH = os.path.dirname(OUTPUT) 80 | os.makedirs(INTERMEDIATE_PATH, exist_ok=True) 81 | else: 82 | # If `os.path.dirname(OUTPUT)` doesn't exist, then it means the output of MakeReference should be genrated in current directory. 83 | INTERMEDIATE_PATH = "./" 84 | 85 | 86 | JAVATMP = _out+".javatmpdir" 87 | os.system("mkdir -p " + JAVATMP) 88 | 89 | 90 | 91 | ### Setting commands 92 | 93 | PLINK = ' '.join([_plink, "--silent", "--allow-no-sex"]) # "--noweb" won't be included because it is Plink1.9 94 | BEAGLE = ' '.join(["java", "-Djava.io.tmpdir="+JAVATMP, "-Xmx"+_mem, "-jar", _beagle]) 95 | #LINKAGE2BEAGLE = ' '.join(["java", "-Djava.io.tmpdir="+JAVATMP, "-Xss5M -Xmx"+_mem, "-jar", _linkage2beagle]) 96 | #BEAGLE2LINKAGE = ' '.join(["java", "-Djava.io.tmpdir="+JAVATMP, "-Xmx"+_mem, "-jar", _beagle2linkage]) 97 | 98 | # MERGE = ' '.join(["perl", _merge_table]) 99 | MERGE = _merge_table 100 | PARSEDOSAGE = _parse_dosage 101 | 102 | 103 | 104 | ### Control Flags 105 | EXTRACT_MHC = 1 106 | FLIP = 1 107 | IMPUTE = 1 108 | 109 | 110 | print("SNP2HLA: Performing HLA imputation for dataset {}".format(_input)) 111 | print("- Java memory = {}(Mb)".format(_mem)) 112 | print("- Beagle(v4.1) window size = \"{}\" markers".format(_marker_window_size)) 113 | 114 | 115 | index= 1 116 | __MHC__ = _out+".MHC" 117 | 118 | 119 | if EXTRACT_MHC: 120 | 121 | print("[{}] Extracting SNPs from the MHC.".format(index)); index += 1 122 | #MAF >1% as imputation threshold 123 | command = ' '.join([PLINK, "--bfile", _input, "--chr 6", "--from-mb 28 --to-mb 34", "--maf 0.01", "--make-bed", "--out", __MHC__]) 124 | print(command) 125 | os.system(command) 126 | 127 | 128 | if FLIP: 129 | 130 | print("[{}] Performing SNP quality control.".format(index)); index += 1 131 | 132 | ### Identifying non-A/T non-C/G SNPs to flip 133 | command = ' '.join(["echo", "SNP POS A1 A2", ">", OUTPUT+".tmp1"]) 134 | print(command) 135 | os.system(command) 136 | command = ' '.join(["cut", "-f2,4-", __MHC__+".bim", ">>", OUTPUT+".tmp1"]) 137 | print(command) 138 | os.system(command) 139 | 140 | command = ' '.join(["echo", "SNP POSR A1R A2R", ">", OUTPUT+".tmp2"]) 141 | print(command) 142 | os.system(command) 143 | command = ' '.join(["cut", "-f2,4-", _reference_panel+".bim", ">>", OUTPUT+".tmp2"]) 144 | print(command) 145 | os.system(command) 146 | 147 | command = ' '.join([MERGE, OUTPUT+".tmp2", OUTPUT+".tmp1", "SNP", "|", "grep -v -w NA", ">", OUTPUT+".SNPS.alleles"]) 148 | print(command) 149 | os.system(command) 150 | 151 | 152 | 153 | ### < Major flip 1 > ### 154 | 155 | command = ' '.join(["awk", "'{if ($3 != $6 && $3 != $7){print $1}}'", OUTPUT+".SNPS.alleles", ">", OUTPUT+".SNPS.toflip1"]) 156 | print(command) 157 | os.system(command) 158 | 159 | command = ' '.join([PLINK, "--bfile", __MHC__, "--flip", OUTPUT+".SNPS.toflip1", "--make-bed", "--out", __MHC__+".FLP"]) 160 | print(command) 161 | os.system(command) 162 | 163 | ## Calculating allele freqeuncy 164 | command = ' '.join([PLINK, "--bfile", __MHC__+".FLP", "--freq", "--out", __MHC__+".FLP.FRQ"]) 165 | print(command) 166 | os.system(command) 167 | 168 | 169 | command = ' '.join(["sed 's/A1/A1I/g'", __MHC__+".FLP.FRQ.frq", "|", "sed 's/A2/A2I/g'", "|", "sed 's/MAF/MAF_I/g'", ">", OUTPUT+".tmp"]) 170 | print(command) 171 | os.system(command) 172 | 173 | 174 | 175 | command = ' '.join(["mv", OUTPUT+".tmp", __MHC__+".FLP.FRQ"]) 176 | print(command) 177 | os.system(command) 178 | 179 | command = ' '.join([MERGE, _reference_panel+".FRQ.frq", __MHC__+".FLP.FRQ.frq", "SNP", "|", "grep -v -w NA", ">", OUTPUT+".SNPS.frq"]) 180 | print(command) 181 | os.system(command) 182 | 183 | 184 | 185 | 186 | ### < Major flip 2 > ### (*.parsed file) 187 | command = ' '.join(["sed 's/ /\t/g'", OUTPUT+".SNPS.frq", "|", 188 | 'awk \'{if ($3 != $8){print $2 "\t" $3 "\t" $4 "\t" $5 "\t" $9 "\t" $8 "\t" 1-$10 "\t*"}else{print $2 "\t" $3 "\t" $4 "\t" $5 "\t" $8 "\t" $9 "\t" $10 "\t."}}\'', 189 | ">", OUTPUT+".SNPS.frq.parsed"]) 190 | print(command) 191 | os.system(command) 192 | 193 | 194 | 195 | ### < Major flip 3 > ### 196 | # Finding A/T and C/G SNPs 197 | command = ' '.join(['awk \'{if (($2 == "A" && $3 == "T") || ($2 == "T" && $3 == "A") || ($2 == "C" && $3 == "G") || ($2 == "G" && $3 == "C")){if ($4 > $7){diff=$4 - $7; if ($4 > 1-$7){corrected=$4-(1-$7)}else{corrected=(1-$7)-$4}}else{diff=$7-$4;if($7 > (1-$4)){corrected=$7-(1-$4)}else{corrected=(1-$4)-$7}};print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" diff "\t" corrected}}\'', 198 | OUTPUT+".SNPS.frq.parsed", ">", OUTPUT+".SNPS.ATCG.frq"]) 199 | print(command) 200 | os.system(command) 201 | 202 | 203 | 204 | ### < Major flip 4 > ### 205 | 206 | # Identifying A/T and C/G SNPs to flip or remove 207 | command = ' '.join(["awk '{if ($10 < $9 && $10 < ", str(_tolerated_diff) ,"){print $1}}'", OUTPUT+".SNPS.ATCG.frq", ">", OUTPUT+".SNPS.toflip2"]) # modified by Saori 208 | print(command) 209 | os.system(command) 210 | 211 | command = ' '.join(["awk '{if ($4 > 0.5){print $1}}'", OUTPUT+".SNPS.ATCG.frq", ">", OUTPUT+".SNPS.toremove"]) # increased from 0.4 by Saori 212 | print(command) 213 | os.system(command) 214 | 215 | 216 | ## Identifying non A/T and non C/G SNPs to remove 217 | command = ' '.join(['awk \'{if (!(($2 == "A" && $3 == "T") || ($2 == "T" && $3 == "A") || ($2 == "C" && $3 == "G") || ($2 == "G" && $3 == "C"))){if ($4 > $7){diff=$4 - $7;}else{diff=$7-$4}; if (diff > \'%f\'){print $1}}}\''%(_tolerated_diff), 218 | OUTPUT+".SNPS.frq.parsed", ">>", OUTPUT+".SNPS.toremove"]) 219 | print(command) 220 | os.system(command) 221 | 222 | 223 | command = ' '.join(['awk \'{if (($2 != "A" && $2 != "C" && $2 != "G" && $2 != "T") || ($3 != "A" && $3 != "C" && $3 != "G" && $3 != "T")){print $1}}\'', 224 | OUTPUT+".SNPS.frq.parsed", ">>", OUTPUT+".SNPS.toremove"]) 225 | print(command) 226 | os.system(command) 227 | 228 | command = ' '.join(['awk \'{if (($2 == $5 && $3 != $6) || ($3 == $6 && $2 != $5)){print $1}}\'', 229 | OUTPUT+".SNPS.frq.parsed", ">>", OUTPUT+".SNPS.toremove"]) 230 | print(command) 231 | os.system(command) 232 | 233 | command = ' '.join(["sort", OUTPUT+".SNPS.toremove", "|","uniq > temp" ]) 234 | print(command) 235 | os.system(command) 236 | 237 | command = ' '.join(['mv temp',OUTPUT+".SNPS.toremove" ] ) 238 | print(command) 239 | os.system(command) 240 | 241 | ## Making QCd SNP file 242 | command = ' '.join([PLINK, "--bfile", __MHC__+".FLP", "--geno 0.2", "--exclude", OUTPUT+".SNPS.toremove", "--flip", OUTPUT+".SNPS.toflip2", "--make-bed", "--out", __MHC__+".QC"]) 243 | print(command) 244 | os.system(command) 245 | 246 | command = ' '.join([PLINK, "--bfile", __MHC__+".QC", "--freq", "--out", __MHC__+".QC.FRQ"]) 247 | print(command) 248 | os.system(command) 249 | 250 | command = ' '.join(["sed 's/A1/A1I/g'", __MHC__+".QC.FRQ.frq", "|", "sed 's/A2/A2I/g'", "|", "sed 's/MAF/MAF_I/g'", ">", OUTPUT+".tmp"]) 251 | print(command) 252 | os.system(command) 253 | 254 | command = ' '.join(["mv", OUTPUT+".tmp", __MHC__+".QC.FRQ.frq"]) 255 | print(command) 256 | os.system(command) 257 | 258 | command = ' '.join([MERGE, _reference_panel+".FRQ.frq", __MHC__+".QC.FRQ.frq", "SNP", "|", "grep -v -w NA", ">", OUTPUT+".SNPS.QC.frq"]) 259 | print(command) 260 | os.system(command) 261 | 262 | 263 | command = ' '.join(["cut -f2", OUTPUT+".SNPS.QC.frq", "|", "awk '{if (NR > 1){print $1}}'", ">", OUTPUT+".SNPS.toinclude"]) 264 | print(command) 265 | os.system(command) 266 | 267 | command = ' '.join(['echo "SNP POS A1 A2"', ">", OUTPUT+".tmp1"]) 268 | print(command) 269 | os.system(command) 270 | 271 | command = ' '.join(["cut -f2,4-", __MHC__+".QC.bim", ">>", OUTPUT+".tmp1"]) 272 | print(command) 273 | os.system(command) 274 | 275 | command = ' '.join([MERGE, OUTPUT+".tmp2", OUTPUT+".tmp1", "SNP", "|", 'awk \'{if (NR > 1){if ($5 != "NA"){pos=$5}else{pos=$2}; print "6\t" $1 "\t0\t" pos "\t" $3 "\t" $4}}\'', 276 | ">", __MHC__+".QC.bim"]) 277 | print(command) 278 | os.system(command) 279 | 280 | 281 | 282 | ### < Making *.vcf file for imputation (Beagle v4.x.x.) > ### 283 | 284 | """ 285 | # Extracting SNPs and recoding QC'd file as vcf 286 | plink --bfile $MHC.QC --extract $OUTPUT.SNPS.toinclude --make-bed --out $MHC.QC.reorder 287 | plink --bfile $MHC.QC.reorder --recode vcf-iid --a1-allele $REFERENCE.markers 4 1 --out $MHC.QC 288 | 289 | """ 290 | 291 | # Extracting SNPs and recoding QC'd file as vcf 292 | command = ' '.join([PLINK, "--bfile", __MHC__+".QC", "--extract", OUTPUT+".SNPS.toinclude", "--make-bed --out", __MHC__+".QC.reorder" ]) 293 | print(command) 294 | os.system(command) 295 | 296 | command = ' '.join([PLINK, "--bfile", __MHC__+".QC.reorder", "--recode vcf-iid --a1-allele", _reference_panel+".markers 4 1", "--out", __MHC__+".QC" ]) 297 | print(command) 298 | os.system(command) 299 | 300 | 301 | # Just in case of storage problem. 302 | command = ' '.join(["gzip -f", __MHC__+".QC.vcf"]) 303 | print(command) 304 | os.system(command) 305 | 306 | 307 | 308 | ## Remove temporary files. 309 | os.system(' '.join(["rm ", OUTPUT+".tmp{1,2}"])) 310 | os.system(' '.join(["rm ", __MHC__+".FLP.*"])) 311 | os.system(' '.join(["rm ", __MHC__+".QC.{ped,map}"])) 312 | os.system(' '.join(["rm ", OUTPUT+".SNPS.*"])) 313 | 314 | # Beagle v4.x.x. 315 | os.system(' '.join(["rm ", __MHC__+".{bed,bim,fam,log}"])) 316 | os.system(' '.join(["rm ", __MHC__+".QC.reorder.*"])) 317 | os.system(' '.join(["rm ", __MHC__+".QC.{bed,bim,fam,log}"])) 318 | os.system(' '.join(["rm ", __MHC__+".QC.FRQ.{frq,log}"])) 319 | 320 | 321 | 322 | 323 | if IMPUTE: 324 | 325 | """ 326 | if ($#argv >= 8) then 327 | beagle ref=$REFERENCE.bgl.vcf.gz gt=$MHC.QC.vcf impute=true gprobs=true nthreads=$THREAD chrom=6 niterations=$ITER lowmem=true out=$OUTPUT.bgl map=$MAP 328 | else 329 | beagle ref=$REFERENCE.bgl.vcf.gz gt=$MHC.QC.vcf impute=true gprobs=true nthreads=$THREAD chrom=6 niterations=$ITER lowmem=true out=$OUTPUT.bgl 330 | """ 331 | 332 | print("[{}] Performing HLA imputation.".format(index)); index += 1 333 | 334 | ## new beagle (>v4), assuming 4 threads and 10 interations 335 | command=' '.join([BEAGLE, 336 | "gt=" + __MHC__+".QC.vcf.gz", 337 | 'ref='+_reference_panel+".bgl.phased.vcf.gz", 338 | 'impute=true', 339 | 'gprobs=true', 340 | 'nthreads={}'.format(_beagle_NTHREADS), 341 | 'chrom=6', 342 | 'niterations={}'.format(_beagle_ITER), 343 | 'lowmem=true', 344 | ('map={}'.format(_beagle_MAP) if bool(_beagle_MAP) else ''), 345 | 'out='+ OUTPUT+".bgl.phased"]) 346 | 347 | print(command) 348 | os.system(command) 349 | 350 | __IMPUTED__ = OUTPUT+".bgl.phased.vcf.gz" 351 | 352 | 353 | 354 | """ 355 | (1) Imputation result in *.vcf.gz file 356 | (2) Imputation result in *.{bed,bim,fam} files (*.vcf.gz => *.{bed,bim,fam}) 357 | (2) Dosage file (*.gprobs => *.dosage) 358 | """ 359 | 360 | 361 | # (2) Imputation result in *.{bed,bim,fam} files (*.vcf.gz => *.{bed,bim,fam}) 362 | command = ' '.join([PLINK, "--make-bed", "--vcf", __IMPUTED__, "--a1-allele {} 4 1".format(_reference_panel+".markers"), "--out", OUTPUT]) 363 | #print(command) 364 | #os.system(command) 365 | 366 | 367 | # (3) Dosage file 368 | command = ' '.join(["gunzip -c", __IMPUTED__, "|", "cat", "|", "java -jar {} > {}".format(_vcf2gprobs, OUTPUT+".bgl.gprobs")]) 369 | #print(command) 370 | #os.system(command) 371 | 372 | __gprobs__ = OUTPUT+".bgl.gprobs" 373 | 374 | 375 | command = ' '.join(["tail -n +2 {}".format(__gprobs__), "|", 376 | PARSEDOSAGE, "- > {}".format(OUTPUT+".dosage")]) 377 | #print(command) 378 | #os.system(command) 379 | 380 | 381 | 382 | os.system(' '.join(["rm ", __MHC__+".QC.vcf.gz"])) 383 | os.system(' '.join(["rm -rf", JAVATMP])) 384 | 385 | 386 | 387 | 388 | print("Done\n") 389 | 390 | return 0 391 | 392 | 393 | 394 | if __name__ == "__main__" : 395 | 396 | parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter, 397 | description=textwrap.dedent('''\ 398 | ################################################################################################# 399 | 400 | < SNP2HLA.py > 401 | 402 | SNP2HLA: Imputation of HLA amino acids and classical alleles from SNP genotypes 403 | 404 | Author: Sherman Jia (xiaomingjia@gmail.com) 405 | + Small modifications by Buhm Han (buhmhan@broadinstitute.org): 8/7/12 406 | + Extensive modifications by Phil Stuart (pstuart@umich.edu) on 4/19/16 to allow use of Beagle 4.1: 407 | verified to work with 22Feb16, 15Apr16, and 03May16 versions of Beagle. 408 | + Small modifications by Yang Luo (yangluo@broadinstitute.org): 09/30/16: verfiied working with Bealge 4.1 27Jun16. 409 | + Recoded to Python and updated by Wanson Choi(wschoi.bhlab@gmail.com) : 2019/02/06 410 | 411 | 412 | DESCRIPTION: This script runs imputation of HLA amino acids and classical alleles using SNP data. 413 | 414 | INPUTS: 415 | 1. Plink dataset (*.bed/bim/fam) 416 | 2. Reference dataset (*.bgl.phased.vcf.gz(Beagle 4.1), *.markers(Beagle 3.0.4); *.fam/.bim/.FRQ.frq in PLINK format) 417 | 418 | DEPENDENCIES: (download and place in the same folder as this script) 419 | 1. PLINK (1.9) (Will not work with older Plink 1.07) 420 | 2. Beagle (4.1) (Need to rename java executable as beagle.jar) 421 | 3. vcf2gprobs.jar (Beagle utility for generating a Beagle v3 genotypes probability file from a Beagle 4.1 vcf file with GT field data) 422 | 4. [Optional] If genetic_map_file argument is specified, PLINK format genetic map on cM scale 423 | (plink.chr6.GRCh36.map, downloaded from http://bochet.gcc.biostat.washington.edu/beagle/genetic_maps/) 424 | 425 | USAGE: 426 | python3 SNP2HLA.py 427 | --input `DATA (.bed/.bim/.fam)` 428 | --reference `REFERENCE (.bgl.phased.vcf.gz/.markers/.fam/.bim/.bed/.FRQ.frq)` 429 | --out `OUTPUT` 430 | 431 | (ex1) 432 | python3 SNP2HLA.py 433 | --input data/1958BC 434 | --reference data/Reference_Panel_bglv4/HM_CEU_REF.hg18.imgt3320.bglv4 435 | --out TEST_ex1_SNP2HLA 436 | 437 | (ex2) 438 | python3 SNP2HLA.py 439 | --input data/1958BC 440 | --reference data/Reference_Panel_bglv4/HM_CEU_REF.hg18.imgt3320.bglv4 441 | --out TEST_ex2_SNP2HLA 442 | --dependency dependency/ 443 | --java-mem 10G 444 | --nthreads 4 445 | --iter 10 446 | 447 | ################################################################################################# 448 | '''), 449 | add_help=False) 450 | 451 | 452 | parser._optionals.title = "OPTIONS" 453 | 454 | parser.add_argument("-h", "--help", help="\nShow this help message and exit\n\n", action='help') 455 | 456 | parser.add_argument("--input", "-i", help="\nInput Plink data file prefix(.bed/.bim/.fam)\n\n", required=True) 457 | parser.add_argument("--out", "-o", help="\nOutput file prefix\n\n", required=True) 458 | parser.add_argument("--reference", "-rf", help="\nThe file prefix of reference panel for imputation.\n\n", required=True) 459 | 460 | parser.add_argument("--tolerated-diff", help="\nTolerated diff (default : 0.15).\n\n", default=0.15) 461 | parser.add_argument("--dependency", help="\nPath(folder) to dependecy software.\n\n", default="./") # Default : the folder where SNP2HLA.py is implemented. 462 | 463 | # Beagle(v4). 464 | parser.add_argument("--java-mem", "-mem", help="\nJava memory allocation(ex. '2000m', '2g', or '2G').\n\n", default='2000m') 465 | parser.add_argument("--marker-window", help="\n(Beagle4.1) Marker window size for imputation (default: 1000).\n\n", default=1000) 466 | parser.add_argument("--nthreads", help="\n(Beagle4.1) The number of threads to be used in imputation. (default: 1)\n\n", default=1) 467 | parser.add_argument("--iter", help="\n(Beagle4.1) The number of iteration in imputation (default: 5).\n\n", default=5) 468 | parser.add_argument("--plink-genetic-map", help="\n(Beagle4.1) Plink genetic map file to be utilized in imputation (default: None).\n\n", default=None) 469 | 470 | 471 | ##### ##### 472 | 473 | 474 | ##### ##### 475 | 476 | args = parser.parse_args() 477 | print(args) 478 | 479 | 480 | 481 | SNP2HLA(args.input, args.reference, args.out, 482 | _mem=args.java_mem, _marker_window_size=args.marker_window, _tolerated_diff=float(args.tolerated_diff), 483 | _beagle_NTHREADS=args.nthreads, _beagle_ITER=args.iter, _beagle_MAP=args.plink_genetic_map, 484 | _dependency=args.dependency) 485 | -------------------------------------------------------------------------------- /scripts/beagle.jar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/immunogenomics/HLA_analyses_tutorial/afddd62b022f503f7c5806298edcfe195aae1343/scripts/beagle.jar -------------------------------------------------------------------------------- /scripts/beagle2linkage.jar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/immunogenomics/HLA_analyses_tutorial/afddd62b022f503f7c5806298edcfe195aae1343/scripts/beagle2linkage.jar -------------------------------------------------------------------------------- /scripts/beagle2vcf.jar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/immunogenomics/HLA_analyses_tutorial/afddd62b022f503f7c5806298edcfe195aae1343/scripts/beagle2vcf.jar -------------------------------------------------------------------------------- /scripts/get_duprem_var.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import sys 3 | INPUT = sys.argv[1] 4 | 5 | RS_missing = {} 6 | 7 | with open(INPUT + ".lmiss") as f: 8 | for line in f: 9 | if line.find("CHR") == -1: 10 | line = line.rstrip().split() 11 | RS_missing[line[1]] = float(line[4]) 12 | 13 | 14 | OUT = open(INPUT + ".remdup.snp","w") 15 | 16 | pos_snp_dic = {} 17 | 18 | with open(INPUT + ".bim") as f: 19 | for line in f: 20 | line = line.rstrip().split() 21 | pos_snp_dic.setdefault((line[0],line[3],line[4],line[5]),[]).append(line[1]) 22 | 23 | for pos in pos_snp_dic: 24 | if len(pos_snp_dic[pos]) > 1: 25 | snp_list = pos_snp_dic[pos] 26 | missings = {} 27 | for snp in snp_list: 28 | missings[snp] = RS_missing[snp] 29 | name, min_mis = min(missings.items(), key = lambda x: x[1]) 30 | for snp in snp_list: 31 | if snp != name: 32 | print(snp, file = OUT) 33 | 34 | OUT.close() 35 | -------------------------------------------------------------------------------- /scripts/get_remID.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | import sys 3 | missfile = sys.argv[1] 4 | ibdfile = sys.argv[2] 5 | outfile = sys.argv[3] 6 | 7 | missing={} 8 | with open(missfile) as f: 9 | for line in f: 10 | line=line.rstrip().split() 11 | if line[0] != "FID": 12 | missing[(line[0],line[1])]=float(line[5]) 13 | 14 | REMOVE=open(outfile,"w") 15 | 16 | rem_list=[] 17 | with open(ibdfile) as f: 18 | for line in f: 19 | line=line.rstrip().split() 20 | if line[0] != "FID1": 21 | if missing[(line[0],line[1])] >= missing[(line[2],line[3])]: 22 | print(line[0]+"\t"+line[1],file=REMOVE) 23 | rem_list.append(line[0]) 24 | elif missing[(line[0],line[1])] < missing[(line[2],line[3])]: 25 | print(line[2]+"\t"+line[3],file=REMOVE) 26 | rem_list.append(line[1]) 27 | 28 | REMOVE.close() -------------------------------------------------------------------------------- /scripts/rename_bim.py: -------------------------------------------------------------------------------- 1 | #!/use/bin/env python 2 | import sys 3 | REFBIMFILE = sys.argv[1] 4 | OUTBIMFILE = sys.argv[2] 5 | 6 | REF = {} 7 | with open(REFBIMFILE) as f: 8 | for line in f: 9 | line = line.rstrip().split() 10 | CHRPOS = ":".join([line[0],line[3]]) 11 | VARNAME = line[1] 12 | A1 = line[4] 13 | A2 = line[5] 14 | REF.setdefault(CHRPOS,[]).append([VARNAME,A1,A2]) 15 | OUT = open(OUTBIMFILE, "w") 16 | with open(OUTBIMFILE + ".old") as f: 17 | for line in f: 18 | line = line.rstrip().split() 19 | CHRPOS = ":".join([line[0],line[3]]) 20 | VARNAME = line[1] 21 | A1 = line[4] 22 | A2 = line[5] 23 | if CHRPOS in REF: 24 | for candidate in REF[CHRPOS]: 25 | if (A1 == candidate[1] and A2 == candidate[2]) or (A1 == candidate[2] and A2 == candidate[1]): 26 | VARNAME = candidate[0] 27 | out = " ".join([line[0], VARNAME, line[2], line[3], line[4], line[5]]) 28 | print(out, file = OUT) 29 | OUT.close() 30 | -------------------------------------------------------------------------------- /scripts/src/ParseDosage.csh: -------------------------------------------------------------------------------- 1 | #!/bin/csh -f 2 | 3 | ############################################################### 4 | # 5 | # Convert .gprobs to .dos file 6 | # Usage: ./ParseDosage.csh file.gprobs > file.dos 7 | # 8 | ############################################################### 9 | 10 | set INPUT=$1 11 | awk '{printf("%s\t%s\t%s",$1,$2,$3); for (f = 4; f <= NF; f+=3){printf("\t%.3f",$f*2+$(f+1));} printf("\n");}' $INPUT 12 | -------------------------------------------------------------------------------- /scripts/src/merge_tables.pl: -------------------------------------------------------------------------------- 1 | #!/usr/bin/perl 2 | # 3 | 4 | use strict; 5 | 6 | my $Table1 = $ARGV[0]; 7 | my $Table2 = $ARGV[1]; 8 | my $IndexStr = $ARGV[2]; 9 | 10 | if (scalar(@ARGV) != 3) { 11 | print "usage: %>merge_tables.pl datafile_1 datafile_2 index_string\n"; 12 | print "prints all rows of datafile_2, followed by corresponding rows (if available) from datafile_1\n"; 13 | exit(); 14 | } 15 | 16 | 17 | my @headers1 = (); 18 | my %data1 = (); 19 | open(T1,$Table1); 20 | 21 | my $linecount = 0; 22 | my $IndexCol = -1; 23 | 24 | while(my $c = ) { 25 | $c=~s/\s+$//; 26 | $c=~s/^\s+//; 27 | my @line = split /\s+/, $c; 28 | 29 | if ( $linecount == 0 ) { 30 | for (my $i=0; $i<=$#line; $i++) { 31 | if ( $line[$i] eq $IndexStr ) { 32 | if ( $IndexCol >= 0 ) { 33 | die "Duplicate column label $line[$i] in $Table1 - exiting.\n"; 34 | } 35 | $IndexCol = $i; 36 | } 37 | $headers1[$i] = $line[$i]; 38 | } 39 | } 40 | else { 41 | if ( $IndexCol == -1 ) { 42 | die "Did not find label $IndexStr in $Table1 - exiting.\n"; 43 | } 44 | 45 | for (my $i=0; $i<=$#line; $i++) { 46 | if ( $line[$IndexCol] ne "NA" ) { 47 | $data1{$line[$IndexCol]}{$headers1[$i]} = $line[$i]; 48 | } 49 | } 50 | } 51 | 52 | $linecount++; 53 | } 54 | close(T1); 55 | 56 | #print STDERR "read $linecount lines from $Table1\n"; 57 | 58 | open(T2,$Table2); 59 | 60 | $linecount = 0; 61 | $IndexCol = -1; 62 | 63 | while(my $c = ) { 64 | $c=~s/\s+$//; 65 | $c=~s/^\s+//; 66 | my @line = split /\s+/, $c; 67 | 68 | if ( $linecount == 0 ) { 69 | for (my $i=0; $i<=$#line; $i++) { 70 | if ( $line[$i] eq $IndexStr ) { 71 | if ( $IndexCol >= 0 ) { 72 | die "Duplicate column label $line[$i] in $Table2 - exiting.\n"; 73 | } 74 | $IndexCol = $i; 75 | } 76 | print "$line[$i]\t"; 77 | } 78 | foreach my $header (@headers1) { 79 | if ( $header ne $IndexStr ) { 80 | print "$header\t"; 81 | } 82 | } 83 | print "\n"; 84 | } 85 | else { 86 | if ( $IndexCol == -1 ) { 87 | die "Did not find label $IndexStr in $Table2 - exiting.\n"; 88 | } 89 | 90 | for (my $i=0; $i<=$#line; $i++) { 91 | print "$line[$i]\t"; 92 | } 93 | foreach my $header (@headers1) { 94 | if ( $header ne $IndexStr ) { 95 | if ( exists($data1{$line[$IndexCol]}{$header}) ) { 96 | print "$data1{$line[$IndexCol]}{$header}\t"; 97 | } else { 98 | print "NA "; 99 | } 100 | } 101 | } 102 | print "\n"; 103 | } 104 | 105 | $linecount++; 106 | } 107 | close(T2); 108 | 109 | #print STDERR "read $linecount lines from $Table2\n"; 110 | 111 | 112 | -------------------------------------------------------------------------------- /tutorial_association.md: -------------------------------------------------------------------------------- 1 | # Tutorial for statistical test for HLA association with complex traits 2 | 3 | 4 | 5 | Author: Saori Sakaue (ssakaue@broadinstitute.org) 6 | 7 | Lastly updated: 11/08/2022 8 | 9 | 10 | 11 | ## HLA association and fine-mapping 12 | 13 | This tutorial corresponds to the section "*HLA association and fine-mapping*" in the manuscript. 14 | 15 | We use outputs from the HLA imputation tutorial. Other data are in `data_assoc` directory. Most scripts are in `script_assoc` directory. 16 | 17 | 18 | 19 | **Note!** 20 | 21 | If you use Minimac3 imputation in the above section, you should first convert the allele name (CHR:POS) to the original name (HLA_A*XX:XX) in the output VCF file. 22 | 23 | 24 | 25 | If you use `SNP2HLA.csh`, `SNP2HLA.py` or MIS, you do not have to do this procedure. 26 | 27 | 28 | 29 | ```bash 30 | refVCF="data/Tutorial_1KGonly.bgl.phased" 31 | output="hgdp_chr6.final.EAGLE.phased.imputed" 32 | 33 | # first, we create a correspondence file between position and the allele name in the reference VCF. 34 | zcat ${refVCF}.vcf.gz | grep -v "#" | awk '{print $2,$3,$4,$5}' > ${refVCF}.converter 35 | 36 | head ${refVCF}.converter 37 | #27970031 rs149946 G T 38 | #27976200 rs9380032 G T 39 | #27979188 rs4141691 A G 40 | #27979625 rs10484402 A G 41 | #27981673 rs9368540 G A 42 | 43 | python script_assoc/convert_vcf_allele.py ${output}.dose.vcf.gz ${refVCF}.converter data_assoc/converted.info | bgzip -c > data_assoc/converted.vcf.gz 44 | ``` 45 | 46 | 47 | 48 | `data_assoc/converted.info` provides R2 and AF information embedded in the VCF file. 49 | 50 | ```bash 51 | head data_assoc/converted.info 52 | 53 | #SNP CHRPOS REF ALT R2 AF 54 | #rs149946 6:27970031 G T 0.85845 0.20976 55 | #rs9380032 6:27976200 G T 0.83970 0.07228 56 | #rs4141691 6:27979188 A G 0.92693 0.12770 57 | #rs10484402 6:27979625 A G 0.76467 0.10351 58 | 59 | ``` 60 | 61 | `data_assoc/converted.vcf.gz` is the output imputed VCF file with corrected variant names. 62 | 63 | ```bash 64 | zcat data_assoc/converted.vcf.gz | less -S 65 | 66 | ##fileformat=VCFv4.1 67 | ##filedate=2022.7.18 68 | ##source=Minimac3 69 | ##contig= 70 | ##FORMAT= 71 | ##FORMAT= 72 | ##INFO= 73 | ##INFO= 74 | ##INFO= 75 | ##INFO= 76 | #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT HGDP00309 77 | 6 27970031 rs149946 G T . PASS AF=0.20976;MAF=0.20976;R2=0.85845 78 | 6 27976200 rs9380032 G T . PASS AF=0.07228;MAF=0.07228;R2=0.83970 79 | 6 27979188 rs4141691 A G . PASS AF=0.12770;MAF=0.12770;R2=0.92693 80 | 6 27979625 rs10484402 A G . PASS AF=0.10351;MAF=0.10351;R2=0.76467 81 | 6 27981673 rs9368540 G A . PASS AF=0.08438;MAF=0.08438;R2=0.82351 82 | 6 27984726 rs74505854 A C . PASS AF=0.03692;MAF=0.03692;R2=0.91953 83 | ``` 84 | 85 | 86 | 87 | 88 | 89 | ### Single-marker test 90 | 91 | We first convert imputed genotype to dosage txt file by `PLINK2`. 92 | 93 | ```bash 94 | imputed="data_assoc/converted" 95 | 96 | plink2 \ 97 | --vcf ${imputed}.vcf.gz dosage=DS \ 98 | --make-pgen --out ${imputed} 99 | 100 | # this will create ${imputed}.{pgen,psam,pvar} 101 | 102 | plink2 --pfile ${imputed} \ 103 | --pheno data_assoc/phenotype.txt \ 104 | --covar data_assoc/covariates.txt \ 105 | --pheno-name trait_name \ 106 | --glm omit-ref hide-covar cols=chrom,pos,ref,alt,test,nobs,beta,se,ci,tz,p,a1freqcc,a1freq \ 107 | --ci 0.95 \ 108 | --out single_marker_assoc \ 109 | --covar-variance-standardize 110 | 111 | ``` 112 | 113 | 114 | 115 | `single_marker_assoc.trait_name.glm.logistic.hybrid` is the output association statistics, and you can extract results for HLA alleles by `grep "HLA_"`, HLA amino acids by `grep "AA_"`, and HLA intragenic SNPs by `grep "SNPS_"`. 116 | 117 | 118 | 119 | You can also do this by using custom R script. 120 | 121 | ```bash 122 | # when you only test for HLA alleles and amino acids (modify as necessry) 123 | cat ${imputed}.pvar | grep -v "#" | grep -E 'HLA_|AA_' | cut -f3 > test_markers.txt 124 | cat ${imputed}.pvar | grep -v "#" | grep -E 'HLA_|AA_' | awk '{print $3,"T"}' > test_markers_alleles.txt # this is to make sure to output the dosages of the "presence" of the allele coded as T, but not the "absence" coded as A. 125 | 126 | plink2 --vcf ${imputed}.vcf.gz dosage=DS --export A --extract test_markers.txt --export-allele test_markers_alleles.txt --out ${imputed} 127 | ``` 128 | 129 | `${imputed}.raw` is the table of dosages for HLA alleles and amino acids. 130 | 131 | 132 | 133 | (In `R`) 134 | 135 | ```r 136 | d <- read.table("data_assoc/converted.raw", header=T) 137 | pheno <- read.table("data_assoc/phenotype.txt", header=T) 138 | cov <- read.table("data_assoc/covariates.txt", header=T) 139 | 140 | d <- merge(d, pheno, by = "IID") 141 | d <- merge(d, cov, by = "IID") 142 | d$trait_name <- d$trait_name - 1 # cases should be coded as 1 and controls should be coded as 0 143 | 144 | # if you want to test for HLA_A*01:01 145 | summary(glm(trait_name ~ HLA_A.01.01_T + sex + PC1 + PC2, data = d, family = binomial)) 146 | 147 | ``` 148 | 149 | 150 | 151 | Then, you can see the same statistics found in PLINK output. 152 | 153 | ```r 154 | Call: 155 | glm(formula = trait_name ~ HLA_A.01.01_T + sex + PC1 + PC2, family = binomial, data = d) 156 | 157 | Deviance Residuals: 158 | Min 1Q Median 3Q Max 159 | -1.0457 -0.8305 -0.7443 1.4218 1.9433 160 | 161 | Coefficients: 162 | Estimate Std. Error z value Pr(>|z|) 163 | (Intercept) -0.39449 0.23453 -1.682 0.0926 . 164 | HLA_A.01.01_T -0.03740 0.21231 -0.176 0.8602 165 | sex -0.38172 0.15030 -2.540 0.0111 * 166 | PC1 0.05466 0.07488 0.730 0.4654 167 | PC2 -0.17217 0.07520 -2.289 0.0221 * 168 | --- 169 | Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 170 | 171 | (Dispersion parameter for binomial family taken to be 1) 172 | 173 | Null deviance: 1068.1 on 906 degrees of freedom 174 | Residual deviance: 1056.0 on 902 degrees of freedom 175 | AIC: 1066 176 | 177 | Number of Fisher Scoring iterations: 4 178 | ``` 179 | 180 | 181 | 182 | ### Omnibus test 183 | 184 | First, we extract amino acid polymorphisms from the dosage output and creat `*.raw` file by using `PLINK2`. 185 | 186 | In this example, we also apply QC to extract any amino acid polymorphisms with *Rsq* > 0.7. 187 | 188 | ```bash 189 | sed 1d data_assoc/converted.info | grep "^AA_" | awk '{if($5>0.7)print $1}' > QCed_AA_variants.txt 190 | sed 1d data_assoc/converted.info | grep "^AA_" | awk '{if($5>0.7)print $1,"T"}' > QCed_AA_variants_alleles.txt 191 | 192 | # an extracted variant list for AAs 193 | head QCed_AA_variants.txt 194 | #AA_A_-22_29910338_exon1_I 195 | #AA_A_-22_29910338_exon1_V 196 | #AA_A_-15_29910359_exon1_L 197 | #AA_A_-15_29910359_exon1_V 198 | #AA_A_-11_29910371_exon1_L 199 | 200 | # We want to create a table of dosage for "presence" = "T" allele of these AAs 201 | head QCed_AA_variants_alleles.txt 202 | #AA_A_-22_29910338_exon1_I T 203 | #AA_A_-22_29910338_exon1_V T 204 | #AA_A_-15_29910359_exon1_L T 205 | #AA_A_-15_29910359_exon1_V T 206 | #AA_A_-11_29910371_exon1_L T 207 | 208 | plink2 --pfile ${imputed} \ 209 | --extract QCed_AA_variants.txt \ 210 | --export-allele QCed_AA_variants_alleles.txt \ 211 | --export A \ 212 | --out ${imputed}.QCed_AA 213 | 214 | # output amino acid names without "_T" and by converting "-" with "minus" as a workaround in R 215 | head -n1 ${imputed}.QCed_AA.raw | cut -f7- | tr "\t" "\n" | sed -e "s/_T$//" | sed 's/-/minus/' > ${imputed}.QCed_AA.allele_name.txt 216 | 217 | 218 | # run omnibus test by using those input files! 219 | python3 script_assoc/run_omnibus_AAtest.py \ 220 | --aaraw ${imputed}.QCed_AA.raw \ 221 | --out test_output \ 222 | --allele ${imputed}.QCed_AA.allele_name.txt \ 223 | --pheno data_assoc/phenotype.txt \ 224 | --cov data_assoc/covariates.txt \ 225 | --phenoname trait_name \ 226 | --covname sex PC1 PC2 227 | 228 | ``` 229 | 230 | 231 | 232 | In this example, `test_output_omnibus_result.txt` is the output statistics with the tested amino acid positions, deviance explained by including the tested amino acid position into the model, and p value from the ANOVA. 233 | 234 | ```bash 235 | $ head test_output_omnibus_result.txt 236 | 237 | ALLELE_NAME OMNIBUS_DEVIANCE OMNIBUS_PVALUE 238 | AA_A_minus22_29910338_exon1 1.25834923435173 0.533031574597542 239 | AA_A_minus15_29910359_exon1 3.52122377743922 0.171939623712512 240 | AA_A_minus11_29910371_exon1 2.15789903921791 0.339952451524512 241 | AA_A_minus2_29910398_exon1 4.53914553021627 0.103356328081297 242 | AA_A_9_29910558_exon2 5.54515225056707 0.593743388681586 243 | AA_A_12_29910567_exon2 0.0109563791947949 0.916635505963766 244 | AA_A_17_29910582_exon2 0.0188695939268655 0.890740998058756 245 | AA_A_19_29910588_exon2 0.000823349387019334 0.977108589575259 246 | AA_A_43_29910660_exon2 0.523234132611833 0.769805751833586 247 | ``` 248 | 249 | 250 | 251 | 252 | 253 | ### Conditional haplotype test 254 | 255 | First, we extract two-field allele dosages from the imputed genotype after QC. 256 | 257 | We convert the imputed dosages into a table (`.raw` file, rows are samples and columns are alleles ) for these alleles by `plink2`. Please note that we use some tricks to avoid having special characters in the alleles such as "*" and ":" by replacing them with "_" (underscore) with `sed` command as shown below. 258 | 259 | e.g., `HLA_DRB1*04:01` will be converted to `HLA_DRB1_04_01` 260 | 261 | 262 | 263 | ```bash 264 | sed 1d data_assoc/converted.info | grep "^HLA_" | cut -f1,5 | grep ":" | awk '{if($2>0.7)print $1}' > QCed_HLA_tf.txt 265 | sed 1d data_assoc/converted.info | grep "^HLA_" | cut -f1,5 | grep ":" | awk '{if($2>0.7)print $1,"T"}' > QCed_HLA_tf_alleles.txt 266 | 267 | plink2 --pfile ${imputed} \ 268 | --extract QCed_HLA_tf.txt \ 269 | --export-allele QCed_HLA_tf_alleles.txt \ 270 | --export A \ 271 | --out ${imputed}.QCed_HLA_tf 272 | 273 | # output HLA names without "_T", "*", and ":" as a workaround in R 274 | head -n1 ${imputed}.QCed_HLA_tf.raw | cut -f7- | tr "\t" "\n" | sed -e "s/_T$//" | sed 's/*/_/' | sed 's/:/_/' > ${imputed}.QCed_HLA_tf.allele_name.txt 275 | 276 | 277 | ``` 278 | 279 | 280 | 281 | We first perform the same omnibus test for single AA position but using the two-fiedl allele information. 282 | 283 | Let's do this for HLA-DRB1 as an example. 284 | 285 | ```r 286 | library(dplyr) 287 | library(data.table) 288 | 289 | # info file summarizes all information on the correspondence between two-field alleles and amino acid residues 290 | info <- readRDS("data_assoc/HLA_DICTIONARY_AA.hg19.imgt3320.AA_tf.in_ref.rds") 291 | 292 | HLA="DRB1" 293 | info <- info[info$gene==HLA,] 294 | 295 | info$tag <- paste0(info$pos,":",info$AA) 296 | 297 | allpos <- sort( unique(info$pos) ) 298 | 299 | res <- data.frame() 300 | for( pos in allpos ){ 301 | y <- info[ info$pos %in% c(pos), ] 302 | for( k in 1:length( unique( y$tag )) ){ 303 | ytag <- unique( y$tag )[ k ] 304 | hap <- ytag 305 | y_4d <- subset(y, tag == ytag )$hla 306 | hap_4d <- y_4d 307 | if( length(hap_4d) > 0 ){ 308 | out <- data.frame(hap, hla = hap_4d, pos ) 309 | res <- rbind(res, out) 310 | }}} 311 | 312 | # res will be used to group two-field alleles based on amino acid residues at each position. 313 | 314 | 315 | # read imputed two field alleles 316 | dose <- read.table("data_assoc/converted.QCed_HLA_tf.raw", header=T) 317 | dose <- dose[,c(-1,-3:-6)] 318 | allelenames <- as.character(read.table("data_assoc/converted.QCed_HLA_tf.allele_name.txt")$V1) 319 | colnames(dose) <- c("IID",allelenames) 320 | 321 | # read phenotype and covariates 322 | pheno <- read.table("data_assoc/phenotype.txt", header=T) 323 | if(max(pheno[,2])==2){pheno[,2]<-pheno[,2] - 1} 324 | cov <- read.table("data_assoc/covariates.txt", header=T) 325 | dat <- merge(dose, pheno, by = "IID") 326 | dat <- merge(dat, cov, by = "IID") 327 | 328 | pval_list<-NULL 329 | deviance_list<-NULL 330 | 331 | for(thispos in allpos){ 332 | thishaps<-as.character(unique(subset(res,pos==thispos)$hap)) 333 | adopted<-NULL 334 | for(thishap in thishaps){ 335 | hlas<-as.character(subset(res,hap==thishap)$hla) # extracting all two-field alleles explained by this position-AA residue pairs 336 | hlas<-hlas[hlas %in% allelenames] # restrict two-field alleles to those in our QCed data 337 | if(length(hlas)>0){ 338 | dat$thishap <- rowSums(dat[hlas]) 339 | colnames(dat)[ncol(dat)] <- thishap 340 | adopted<-c(adopted,thishap) 341 | } 342 | } 343 | obj1<-glm(trait_name ~ sex+PC1+PC2,data=dat,family=binomial(link="logit")) # model with covariates 344 | obj2<-glm(trait_name~as.matrix(dat[adopted])+sex+PC1+PC2,data=dat,family=binomial(link="logit")) # model with this AA position 345 | Chisqtest <- anova(obj1,obj2,test="Chisq") 346 | pval<-Chisqtest$`Pr(>Chi)`[2] 347 | deviance<-Chisqtest$Deviance[2] 348 | pval_list<-c(pval_list,pval) 349 | deviance_list<-c(deviance_list,deviance) 350 | } 351 | 352 | summary<-data.frame(POSITION=allpos,OMNIBUS_DEVIANCE=deviance_list,OMNIBUS_PVALUE = pval_list) 353 | 354 | # this summary is a summary for the first round of conditional haplotype test. 355 | head(summary) 356 | 357 | # POSITION OMNIBUS_DEVIANCE OMNIBUS_PVALUE 358 | #1 -25 0.1598153 0.9232016 359 | #2 -24 0.1573953 0.9243193 360 | #3 -17 2.5568379 0.2784772 361 | #4 -16 0.1598153 0.9232016 362 | #5 -1 0.1670549 0.9198658 363 | #6 4 4.4417129 0.1085161 364 | ``` 365 | 366 | 367 | 368 | If the strongest association among all the positions was at position 11, we next run similar analyses conditioned on the position 11. 369 | 370 | ```r 371 | # This script is continued from the above R workspace. 372 | 373 | # 374 | res.prev <- res # transfer res information from the previous round into res.prev 375 | 376 | condpos = "11" # this is a position we want to condition on 377 | thishaps<-as.character(unique(subset(res.prev, pos==condpos)$hap)) 378 | 379 | thishaps 380 | 381 | adopted.prev<-NULL 382 | for(thishap in thishaps){ 383 | hlas<-as.character(subset(res,hap==thishap)$hla) 384 | hlas<-hlas[hlas %in% allelenames] 385 | if(length(hlas)>0){ 386 | dat$thishap<-rowSums(dat[hlas]) 387 | colnames(dat)[ncol(dat)]<-thishap 388 | adopted.prev<-c(adopted.prev,thishap) 389 | } 390 | } 391 | 392 | adopted.prev 393 | # [1] "11:L" "11:S" "11:V" "11:G" "11:D" "11:P" 394 | # These are the amino acid residues obverved in the data in the previous round at position 11. 395 | 396 | # Let's move on to the next round 397 | allpos <- setdiff(allpos, condpos) # all the other positions to analyse 398 | 399 | res <- data.frame() 400 | for( pos in allpos ){ 401 | x <- info[ info$pos %in% c(condpos), ] # haplotype information at the position I want to condition on 402 | y <- info[ info$pos %in% c(pos), ] # haplotype information at the position I want to analyze 403 | for( i in 1:length( unique( x$tag )) ){ 404 | for( k in 1:length( unique( y$tag )) ){ 405 | xtag <- unique( x$tag )[ i ] 406 | ytag <- unique( y$tag )[ k ] 407 | hap <- paste0(xtag,"_",ytag) 408 | x_4d <- subset(x, tag == xtag )$hla 409 | y_4d <- subset(y, tag == ytag )$hla 410 | hap_4d <- intersect( x_4d, y_4d ) 411 | if( length(hap_4d) > 0 ){ 412 | out <- data.frame(hap, hla = hap_4d, pos ) 413 | res <- rbind(res, out) 414 | }}}} 415 | 416 | head(res) 417 | 418 | # hap hla pos 419 | #1 11:L_-25:K HLA_DRB1_01_01 -25 420 | #2 11:L_-25:K HLA_DRB1_01_02 -25 421 | #3 11:L_-25:K HLA_DRB1_01_03 -25 422 | #4 11:S_-25:R HLA_DRB1_03_01 -25 423 | #5 11:S_-25:R HLA_DRB1_03_02 -25 424 | #6 11:S_-25:R HLA_DRB1_08_01 -25 425 | 426 | # Haplotypes defined by two amino acid positions and their correspondence to the two-field alleles 427 | 428 | pval_list<-NULL 429 | deviance_list<-NULL 430 | 431 | for(thispos in allpos){ 432 | thishaps<-as.character(unique(subset(res,pos==thispos)$hap)) 433 | adopted<-NULL 434 | for(thishap in thishaps){ 435 | hlas<-as.character(subset(res,hap==thishap)$hla) 436 | hlas<-hlas[hlas %in% allelenames] 437 | if(length(hlas)>0){ 438 | dat$thishap<-rowSums(dat[hlas]) 439 | colnames(dat)[ncol(dat)]<-thishap 440 | adopted<-c(adopted,thishap) 441 | } 442 | } 443 | obj1<-glm(trait_name ~ as.matrix(dat[adopted.prev])+sex+PC1+PC2,data=dat,family=binomial(link="logit")) # a model including groups defined by the previous round (single position) 444 | obj2<-glm(trait_name ~ as.matrix(dat[adopted])+sex+PC1+PC2,data=dat,family=binomial(link="logit")) # a model including groups defined by this round (two positions) 445 | Chisqtest <- anova(obj1,obj2,test="Chisq") 446 | pval<-Chisqtest$`Pr(>Chi)`[2] 447 | deviance<-Chisqtest$Deviance[2] 448 | pval_list<-c(pval_list,pval) 449 | deviance_list<-c(deviance_list,deviance) 450 | } 451 | 452 | summary<-data.frame(POSITION=allpos,OMNIBUS_DEVIANCE=deviance_list,OMNIBUS_PVALUE = pval_list) 453 | 454 | 455 | # this "summary" is a summary for the second round of conditional haplotype test. 456 | head(summary) 457 | 458 | # POSITION OMNIBUS_DEVIANCE OMNIBUS_PVALUE 459 | #1 -25 2.6065100 0.1064257 460 | #2 -24 2.6065100 0.1064257 461 | #3 -17 0.0000000 NA 462 | #4 -16 2.6065100 0.1064257 463 | #5 -1 0.8824553 0.3475301 464 | #6 4 0.0000000 NA 465 | 466 | ``` 467 | 468 | 469 | 470 | We continue these procedures until we do not get any significant results (`OMNIBUS_PVALUE`). 471 | 472 | 473 | 474 | - Non-additive association test 475 | 476 | We use the same data to test non-additive effect. 477 | 478 | In this vignette, we generate and use best-guess genotype from the imputation result. 479 | 480 | ```bash 481 | plink2 --vcf ${imputed}.vcf.gz \ 482 | --extract QCed_HLA_tf.txt \ 483 | --export-allele QCed_HLA_tf_alleles.txt \ 484 | --export A \ 485 | --out ${imputed}.QCed_HLA_tf.best_guess 486 | 487 | # Here we do not specify dosage and rather use best-guess genotype to export the data to a matrix 488 | 489 | # output HLA names without "_T", "*", and ":" as a workaround in R 490 | head -n1 ${imputed}.QCed_HLA_tf.best_guess.raw | cut -f7- | tr "\t" "\n" | sed -e "s/_T$//" | sed 's/*/_/' | sed 's/:/_/' > ${imputed}.QCed_HLA_tf.best_guess.allele_name.txt 491 | 492 | ``` 493 | 494 | 495 | 496 | Then, in `R`, 497 | 498 | ```r 499 | dose <- read.table("data_assoc/converted.QCed_HLA_tf.best_guess.raw", header=T) 500 | dose <- dose[,c(-1,-3:-6)] 501 | allelenames <- as.character(read.table("data_assoc/converted.QCed_HLA_tf.best_guess.allele_name.txt")$V1) 502 | colnames(dose) <- c("IID",allelenames) 503 | 504 | # read phenotype and covariates 505 | pheno <- read.table("data_assoc/phenotype.txt", header=T) 506 | if(max(pheno[,2])==2){pheno[,2]<-pheno[,2] - 1} # cases to be 1 and controls to be 0 507 | 508 | cov <- read.table("data_assoc/covariates.txt", header=T) 509 | dat <- merge(dose, pheno, by = "IID") 510 | dat <- merge(dat, cov, by = "IID") 511 | 512 | # if you want to test for HLA_A*01:01's non-additive effect 513 | 514 | tested_allele = "HLA_A_01_01" 515 | dat$non_add <- 0 516 | dat[dat[,tested_allele]==1,]$non_add <- 1 # define non-additive term to have 1 if and only if the genotype is heterozygous. 517 | 518 | model.0 <- glm(trait_name ~ as.matrix(dat[,tested_allele]) + sex + PC1 + PC2, data = dat, family = binomial) 519 | model.1 <- glm(trait_name ~ as.matrix(dat[,tested_allele]) + non_add + sex + PC1 + PC2, data = dat, family = binomial) 520 | 521 | Chisqtest <- anova(model.0,model.1,test="Chisq") # assess the improvement of the model by including non-additive term 522 | model.pval<-Chisqtest$`Pr(>Chi)`[2] 523 | model.deviance<-Chisqtest$Deviance[2] 524 | SUMC<-summary(model.1)$coefficients 525 | main.beta<-SUMC[2,1] # we get coefficients of the additive term 526 | main.se<-SUMC[2,2] 527 | main.p<-SUMC[2,4] 528 | non_add.beta<-SUMC[3,1] # we get coefficients of the non-additive term 529 | non_add.se<-SUMC[3,2] 530 | non_add.p<-SUMC[3,4] 531 | out<-data.frame(allele=tested_allele, anova.deviance=model.deviance, anova.p=model.pval, main.beta=main.beta, main.se=main.se, main.p=main.p, non_add.beta=non_add.beta, non_add.se=non_add.se, non_add.p=non_add.p) 532 | 533 | # this "out" summarizes the statistics at this allele (HLA_A*01:01), both additive and non-additive 534 | out 535 | 536 | # allele anova.deviance anova.p main.beta main.se main.p 537 | #1 HLA_A_01_01 0.1587487 0.6903112 -0.2378009 0.5629893 0.6727405 538 | # non_add.beta non_add.se non_add.p 539 | #1 0.2325363 0.6026695 0.6996124 540 | ``` 541 | 542 | 543 | 544 | 545 | 546 | ### Interaction test 547 | 548 | Let's test interaction between two HLA alleles of the same gene. 549 | 550 | As we explained in the manuscript, it is sometimes important to restrict the analyses to common alleles. The rare x rare allele combination could yield inflated statistics due to the noisy estimate of the effect sizes for both alleles. 551 | 552 | If we decide to QC based on MAF > 0.05 for this interaction analysis, 553 | 554 | ```bash 555 | plink2 --pfile ${imputed} \ 556 | --extract QCed_HLA_tf.txt \ 557 | --export-allele QCed_HLA_tf_alleles.txt \ 558 | --export A \ 559 | --maf 0.05 \ 560 | --out ${imputed}.QCed_HLA_tf.MAF 561 | 562 | # output HLA names without "_T", "*", and ":" as a workaround in R 563 | head -n1 ${imputed}.QCed_HLA_tf.MAF.raw | cut -f7- | tr "\t" "\n" | sed -e "s/_T$//" | sed 's/*/_/' | sed 's/:/_/' > ${imputed}.QCed_HLA_tf.MAF.allele_name.txt 564 | 565 | ``` 566 | 567 | 568 | 569 | And in `R` 570 | 571 | ```r 572 | dose <- read.table("data_assoc/converted.QCed_HLA_tf.MAF.raw", header=T) 573 | dose <- dose[,c(-1,-3:-6)] 574 | allelenames <- as.character(read.table("data_assoc/converted.QCed_HLA_tf.MAF.allele_name.txt")$V1) 575 | colnames(dose) <- c("IID",allelenames) 576 | 577 | # read phenotype and covariates 578 | pheno <- read.table("data_assoc/phenotype.txt", header=T) 579 | if(max(pheno[,2])==2){pheno[,2]<-pheno[,2] - 1} # cases to be 1 and controls to be 0 580 | 581 | cov <- read.table("data_assoc/covariates.txt", header=T) 582 | dat <- merge(dose, pheno, by = "IID") 583 | dat <- merge(dat, cov, by = "IID") 584 | 585 | 586 | # For example, if you want to test for HLA_DRB1*03:01 and HLA_DRB1*15:01 587 | 588 | first_allele = "HLA_DRB1_03_01" 589 | seconde_allele = "HLA_DRB1_15_01" 590 | 591 | # It is always nice to check the distribution of samples based on these two alleles. 592 | 593 | table(round(dat[,c(first_allele, seconde_allele)])) 594 | 595 | # HLA_DRB1_15_01 596 | #HLA_DRB1_03_01 0 1 2 597 | # 0 624 89 4 598 | # 1 156 14 0 599 | # 2 20 0 0 600 | 601 | # We confirmed that the number of samples based on allelic combination of HLA-DRB1. 602 | 603 | summary(glm(trait_name ~ HLA_DRB1_03_01 + HLA_DRB1_15_01 + HLA_DRB1_03_01*HLA_DRB1_15_01 + sex + PC1 + PC2, data = dat, family = binomial)) 604 | 605 | 606 | ################################ 607 | Call: 608 | glm(formula = trait_name ~ HLA_DRB1_03_01 + HLA_DRB1_15_01 + 609 | HLA_DRB1_03_01 * HLA_DRB1_15_01 + sex + PC1 + PC2, family = binomial, 610 | data = dat) 611 | 612 | Deviance Residuals: 613 | Min 1Q Median 3Q Max 614 | -1.0787 -0.8305 -0.7422 1.3944 1.9409 615 | 616 | Coefficients: 617 | Estimate Std. Error z value Pr(>|z|) 618 | (Intercept) -0.39243 0.23787 -1.650 0.0990 . 619 | HLA_DRB1_03_01 0.11719 0.16267 0.720 0.4713 620 | HLA_DRB1_15_01 -0.40575 0.27059 -1.500 0.1337 621 | sex -0.37822 0.15060 -2.511 0.0120 * 622 | PC1 0.05676 0.07515 0.755 0.4501 623 | PC2 -0.17298 0.07519 -2.300 0.0214 * 624 | HLA_DRB1_03_01:HLA_DRB1_15_01 0.33678 0.70759 0.476 0.6341 625 | --- 626 | Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 627 | 628 | (Dispersion parameter for binomial family taken to be 1) 629 | 630 | Null deviance: 1068.1 on 906 degrees of freedom 631 | Residual deviance: 1052.7 on 900 degrees of freedom 632 | AIC: 1066.7 633 | 634 | Number of Fisher Scoring iterations: 4 635 | ################################ 636 | 637 | # "HLA_DRB1_03_01:HLA_DRB1_15_01" is showing the effect of interaction between two alleles. 638 | 639 | model.0 <- glm(trait_name ~ HLA_DRB1_03_01 + HLA_DRB1_15_01 + sex + PC1 + PC2, data = dat, family = binomial) # a model without an interaction term 640 | model.1 <- glm(trait_name ~ HLA_DRB1_03_01 + HLA_DRB1_15_01 + HLA_DRB1_03_01*HLA_DRB1_15_01 + sex + PC1 + PC2, data = dat, family = binomial) # a model with an interaction term 641 | 642 | anova(model.0,model.1,test="Chisq") 643 | 644 | ################################ 645 | Analysis of Deviance Table 646 | 647 | Model 1: trait_name ~ HLA_DRB1_03_01 + HLA_DRB1_15_01 + sex + PC1 + PC2 648 | Model 2: trait_name ~ HLA_DRB1_03_01 + HLA_DRB1_15_01 + HLA_DRB1_03_01 * 649 | HLA_DRB1_15_01 + sex + PC1 + PC2 650 | Resid. Df Resid. Dev Df Deviance Pr(>Chi) 651 | 1 901 1052.9 652 | 2 900 1052.7 1 0.22015 0.6389 653 | ################################ 654 | ``` 655 | 656 | 657 | 658 | It is also considered to run permutation tests ( by breaking the relationship between phenotype and genotype ) whether the observed interaction effects could happen by random chance. 659 | 660 | 661 | 662 | ### Multi-trait test 663 | 664 | ```r 665 | library(MVLM) 666 | 667 | # Let's use those MAF-QCed HLA alleles in this example. 668 | dose <- read.table("data_assoc/converted.QCed_HLA_tf.MAF.raw", header=T) 669 | dose <- dose[,c(-1,-3:-6)] 670 | allelenames <- as.character(read.table("data_assoc/converted.QCed_HLA_tf.MAF.allele_name.txt")$V1) 671 | colnames(dose) <- c("IID",allelenames) 672 | 673 | # read phenotype of a multiple vector (i.e., a matrix) 674 | pheno <- read.table("data_assoc/phenotype_multi.txt", header=T) 675 | 676 | head(pheno) 677 | 678 | # IID trait.A trait.B trait.C trait.D 679 | #1 HGDP00610 0.47607622 0.2454573 0.219182940 0.05928355 680 | #2 HGDP00982 0.20184835 0.4383009 0.007797759 0.35205303 681 | #3 HGDP00001 0.01328931 0.1882389 0.402212096 0.39625968 682 | #4 HGDP01247 0.33558573 0.2070649 0.268822337 0.18852699 683 | #5 HGDP00309 0.31788202 0.2712361 0.067621683 0.34326023 684 | #6 HGDP00786 0.36187225 0.3786774 0.180634545 0.07881575 685 | 686 | # This is comprised of multiple traits, A, B, C, and D, and... 687 | 688 | all.equal(rowSums(pheno[,2:5]), rep(1,nrow(pheno))) 689 | #[1] TRUE 690 | 691 | # The sum of the traits are (almost) equal to 1 in all samples. 692 | # So we assume this phenotype has 3-degrees of freedom, and thus drop trait.D from the analysis in the subsequent models. 693 | 694 | # read covariates 695 | cov <- read.table("data_assoc/covariates.txt", header=T) 696 | dat <- merge(dose, pheno, by = "IID") 697 | dat <- merge(dat, cov, by = "IID") 698 | 699 | # Let's see the effect of HLA_DRB1*15:01 700 | # We can perform linear regression 701 | model.0 <- lm( cbind(trait.A,trait.B,trait.C) ~ sex + PC1 + PC2, data = dat) 702 | model.1 <- lm( cbind(trait.A,trait.B,trait.C) ~ HLA_DRB1_15_01 + sex + PC1 + PC2, data = dat) 703 | 704 | anova(model.0, model.1) 705 | 706 | ################################ 707 | Analysis of Variance Table 708 | 709 | Model 1: cbind(trait.A, trait.B, trait.C) ~ sex + PC1 + PC2 710 | Model 2: cbind(trait.A, trait.B, trait.C) ~ HLA_DRB1_15_01 + sex + PC1 + 711 | PC2 712 | Res.Df Df Gen.var. Pillai approx F num Df den Df Pr(>F) 713 | 1 903 0.015147 714 | 2 902 -1 0.015150 0.0028114 0.84579 3 900 0.469 715 | ################################ 716 | 717 | 718 | # We can also perform MVLM model 719 | mvlm.res.0 <- mvlm( cbind(trait.A,trait.B,trait.C) ~ sex + PC1 + PC2 , data = dat) 720 | 721 | mvlm.res.1 <- mvlm( cbind(trait.A,trait.B,trait.C) ~ HLA_DRB1_15_01 + sex + PC1 + PC2, data = dat ) 722 | mvlm.res.1$pseudo.rsq["Omnibus Effect",1] 723 | 724 | summary(mvlm.res.1) 725 | 726 | ################################ 727 | Statistic Numer.DF Pseudo.R.Square p.value 728 | Omnibus Effect 0.90858 4 4.013e-03 0.52003 729 | (Intercept) 315.81910 1 < 1e-20 *** 730 | HLA_DRB1_15_01 0.82089 1 9.064e-04 0.45784 731 | sex 0.07375 1 8.144e-05 0.96786 732 | PC1 1.71025 1 1.888e-03 0.16841 733 | PC2 0.97278 1 1.074e-03 0.38646 734 | --- 735 | Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 736 | ################################ 737 | 738 | 739 | # Explained variance by HLA_DRB1_15_01 will be.. 740 | mvlm.res.1$pseudo.rsq["Omnibus Effect",1] - mvlm.res.0$pseudo.rsq["Omnibus Effect",1] 741 | 742 | ``` 743 | 744 | --------------------------------------------------------------------------------