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===Phenotypes=== <syntaxhighlight lang="rsplus"> phenotypes <- read.table("/work/KellerLab/GSCAN/dbGaP/ARIC/PhenoGenotypeFiles/ChildStudyConsentSet_phs000090.ARIC_RootStudy.v3.p1.c1.HMB-IRB/PhenotypeFiles/phs000090.v3.pht000114.v2.p1.c1.GENEVA_ARIC_Subject_Phenotypes.HMB-IRB.txt.gz",header=T,sep="\t",stringsAsFactors=F) phenotypes <- subset(phenotypes, select=c("geneva_id", "racegrp", "gender","v1age01", "anta01","anta04", "drnkr01", "hom29", 'hom35', "hom32", 'cigt01','evrsmk01', 'dtia90','dtia96', 'dtia97','dtia98', 'cursmk01','forsmk01')) ### rename phenotypes to be readable names(phenotypes)[c(1,2,3,4,5,6)] <- c("geneva_id", "race", "sex", "age", "height", "weight") ### To connect sample ids to geneva ids, take SAMPID (the ID used in the genotype fam file, and SUBJID (aka geneva_id) ) id_map <- read.table(gzfile("/work/KellerLab/GSCAN/dbGaP/ARIC/PhenoGenotypeFiles/ChildStudyConsentSet_phs000090.ARIC_RootStudy.v3.p1.c1.HMB-IRB/GenotypeFiles/phg000035.v1.ARIC_GEI.genotype-qc.MULTI/geno-qc/samp-subj-mapping.csv.gz"),header=T,sep=",",stringsAsFactors=F)[,c(2,1)] names(id_map) <- c("SAMPID", "geneva_id") phenotypes <- merge(phenotypes, id_map, by="geneva_id", all.x=TRUE) ### import genotype data to get family info fam_data <- read.table("/work/KellerLab/GSCAN/dbGaP/ARIC/PhenoGenotypeFiles/ChildStudyConsentSet_phs000090.ARIC_RootStudy.v3.p1.c1.HMB-IRB/GenotypeFiles/phg000035.v1.ARIC_GEI.genotype-calls-matrixfmt.c1.GRU.update1/Genotypes_with_flagged_chromosomal_abnormalities_zeroed_out/ARIC_PLINK_flagged_chromosomal_abnormalities_zeroed_out.fam", col.names = c("fam_id", "SAMPID", "patid", "matid", "sex", "dummy")) ### Replace 0's with "x" for rvTest preferred formatting fam_data$patid <- fam_data$matid <- fam_data$fam_id[fam_data$fam_id == 0] <- "x" ######################################## ###---- Derive GSCAN phenotypes -----### ######################################## ### DRINKER VERSUS NON-DRINKER ### ARIC variable name is "drnkr01". ### Combination of "Do you presently drink alcoholic beverages?" and "Have you ever consumed alcoholic beverages?" ### Response option for both questions are "yes" or "no", which are turned into the options below. ### Response options: ### 1 = Current Drinker ### 2 = Former Drinker ### 3 = Never Drinker ### 4 = Unknown ### ### Descriptives: ### table(phenotypes$drnkr01) ### 1 2 3 4 ### 7257 2309 3153 6 ### ### To obtain GSCAN "DND" collapse across Former and Never Drinkers ### and make "Non-Drinkers". Current Drinkers will be made "Drinkers" dnd <- phenotypes$drnkr01 dnd[dnd == 1] <- "Current Drinker" dnd[dnd == 2 | dnd == 3] <- 1 dnd[dnd == "Current Drinker"] <- 2 dnd[dnd == 4 | is.na(dnd)] <- "x" ### AGE OF INITIATION OF SMOKING ### ### ARIC variable name is "hom29". ### "How old were you when you first started regular cigarette smoking?" ### Response option is an integer value. ### ### Descriptives: ### ### > table(phenotypes$hom29) ### 0 1 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ### 19 1 2 10 11 15 22 32 65 44 154 187 302 659 941 715 ### 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 ### 1219 567 703 447 275 129 88 247 56 45 59 22 100 8 34 8 ### 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 ### 17 51 9 9 10 8 35 3 11 5 5 16 3 4 2 3 ### 50 51 52 55 57 59 60 62 ### 7 1 2 1 1 2 2 1 ### ### > summary(phenotypes$hom29) ### Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ### 0.00 16.00 18.00 18.77 20.00 62.00 5377 ### ai <- phenotypes$hom29 ### remove ages older than 35 and younger than 10 ai[ai > 35 | ai < 10 | is.na(ai)] <- "x" ### CIGARETTES PER DAY ### ARIC variable name is "hom35" ### "On the average of the entire time you smoked, how many cigarettes did you usually smoke per day?" ### Response option is integer, or "0" for <1 cigarette per day ### ### > table(phenotypes$hom35) ### 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ### 46 54 118 187 133 254 146 84 89 17 990 23 106 30 12 520 ### 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 ### 31 30 62 5 2559 1 5 10 5 193 9 3 7 6 771 3 ### 32 33 34 35 36 37 38 40 42 43 45 50 51 54 55 58 ### 1 1 1 44 3 1 1 572 1 3 14 72 1 1 3 1 ### 60 65 70 75 80 86 90 99 ### 100 1 4 2 10 1 1 3 ### > summary(phenotypes$hom35) ### Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ### 0.00 10.00 20.00 19.67 24.00 99.00 5420 ### Responses are binned in accordance with the GSCAN Analysis Plan. cpd <- phenotypes$hom35 cpd[cpd <= 5 & cpd >= 1] <- 1 cpd[cpd <= 15 & cpd >= 6] <- 2 cpd[cpd <= 25 & cpd >= 16] <- 3 cpd[cpd <= 35 & cpd >= 26] <- 4 cpd[cpd >= 36 & cpd <= 60] <- 5 cpd[cpd > 60 | is.na(cpd)] <- "x" ### DRINKS PER WEEK ### ARIC variable names are "dtia96", "dtia97", and "dtia98" ### "dtia96" - "How many glasses of wine do you usualy have per week? (4oz. glasses; round down)." ### "dtia97" - "How many bottles of cans or beer do you usualy have per week? (12oz. bottles or cans; round down)." ### "dtia98" - "How many drinks of hard liquor do you usualy have per week? (4oz. glasses; round down)." ### Response option for all three is integer. ### ### Descriptives: ### ### >table(phenotypes$dtia96) ### 0 1 2 3 4 5 6 7 8 9 10 11 12 14 15 16 ### 5226 844 461 255 147 90 75 50 27 3 34 1 15 28 9 2 ### 17 18 20 21 25 28 30 32 33 35 40 ### 1 3 7 5 1 2 3 1 1 1 1 ### ### >summary(phenotypes$dtia96) ### Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ### 0.000 0.000 0.000 0.868 1.000 40.000 5478 ### ### >table(phenotypes$dtia97) ### 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ### 4356 674 528 312 214 107 297 93 76 10 97 3 186 4 40 28 ### 16 18 19 20 21 22 23 24 25 28 30 32 33 35 36 40 ### 5 28 1 36 19 1 1 89 8 8 12 2 1 10 8 6 ### 42 45 48 49 50 56 60 63 70 72 80 92 ### 13 2 6 1 3 2 4 1 1 2 1 1 ### ### >summary(phenotypes$dtia97) ### Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ### 0.000 0.000 0.000 2.609 2.000 92.000 5474 ### ### >table(phenotypes$dtia98) ### 0 1 2 3 4 5 6 7 8 9 10 11 12 14 15 16 ### 5226 844 461 255 147 90 75 50 27 3 34 1 15 28 9 2 ### 17 18 20 21 25 28 30 32 33 35 40 ### 1 3 7 5 1 2 3 1 1 1 1 ### ### >summary(phenotypes$dtia96) ### Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ### 0.000 0.000 0.000 0.868 1.000 40.000 5478 ### ### >table(phenotypes$dtia97) ### 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ### 4356 674 528 312 214 107 297 93 76 10 97 3 186 4 40 28 ### 16 18 19 20 21 22 23 24 25 28 30 32 33 35 36 40 ### 5 28 1 36 19 1 1 89 8 8 12 2 1 10 8 6 ### 42 45 48 49 50 56 60 63 70 72 80 92 ### 13 2 6 1 3 2 4 1 1 2 1 1 ### ### >summary(phenotypes$dtia97) ### Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ### 0.000 0.000 0.000 2.609 2.000 92.000 5474 ### ### >table(phenotypes$dtia98) ### 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ### 4387 735 551 295 239 190 148 138 70 11 151 14 57 3 103 33 ### 16 17 18 20 21 24 25 26 27 28 30 32 33 34 35 36 ### 9 9 7 36 30 1 10 1 1 9 6 2 1 2 4 1 ### 39 40 44 45 47 48 50 51 52 54 55 56 63 64 75 77 ### 1 7 1 1 1 2 5 1 1 1 1 3 1 2 1 2 ### 90 99 ### 1 2 ### ### >summary(phenotypes$dtia98) ### Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ### 0.000 0.000 0.000 2.227 2.000 99.000 5483 wine <- phenotypes$dtia96 # 1 drink = 4oz beer <- phenotypes$dtia97 # 1 drink = 12oz spirits <- phenotypes$dtia98 # 1 drink = 1.5oz ### muliply wine by 4 and divide wine by 5 to normalize to standard drink of 5 oz for wine ### Combine all alcohol types, left-anchor at 1, and log dpw <- log((wine*4/5 + beer + spirits) + 1) dpw[is.na(dpw)] <- "x" ### SMOKING INITIATION ### ARIC variable name is "evrsmk01" ### The variable checks answers to "Have you ever smoked cigarettes?" and "Do you now smoke cigarettes?". ### Response options are "yes" or "no". ### ### Descriptives: ### ### >table(phenotypes$evrsmk01) ### 0 1 ### 5328 7434 ### ### >summary(phenotypes$evrsmk01) ### Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ### 0.0000 0.0000 1.0000 0.5825 1.0000 1.0000 9 si <- phenotypes$evrsmk01 si[si == 1] <- 2 si[si == 0] <- 1 si[is.na(si)] <- "x" ### SMOKING CESSATION ### ARIC variable names are "cursmk01" and "forsmk01" ### Both varaiables take into account the questions: "Have you ever smoked cigarettes?" and "Do you now smoke cigarettes?" ### Response options are "yes" or "no". ### Smoking inititation (si) is coded as "2" for "Smoker" if "yes" to "Have you ever smoked cigarettes?" ### If a subsequent "yes" to "Do you now smoke cigarettes?", smoking cessation (sc) is coded as "2" for "Current Smoker". ### If a subsequent "no" to "Do you now smoke cigarettes?", smoking cessation (sc) is coded as "1" for "Former Smoker". ### Smoking inititation (si) is coded as "1" for "Non Smoker" if "no" to "Have you ever smoked cigarettes?" current.smoker <- subset(phenotypes, select=c("cursmk01")) former.smoker <- subset(phenotypes, select=c("forsmk01")) N <- nrow(phenotypes) sc <- rep(NA, N) for(i in 1:N){ if(is.na(current.smoker[i,1]) | is.na(former.smoker[i,1])){ sc[i] <- NA } else if (current.smoker[i,1] == 0 & former.smoker[i,1] == 0){ sc[i] <- NA } else if (current.smoker[i,1] == 0 & former.smoker[i,1] == 1){ sc[i] <- 1 ### former smokers are coded as 1 } else if (current.smoker[i,1] == 1 & former.smoker[i,1] == 0){ sc[i] <- 2 ### current smokers are coded as 2 } } sc[is.na(sc)] <- "x" ### Create dataframe with our new GSCAN variables N <- nrow(phenotypes) NAs <- rep("x", N) gscan.phenotypes <- data.frame(SAMPID = phenotypes$SAMPID, famid = NAs,geneva_id = phenotypes$geneva_id,patid = NAs,matid = NAs,sex = ifelse(phenotypes$sex == "M", 1, 2),cpd = cpd,ai = ai,si = si,sc = sc,dnd = dnd,dpw = dpw,age = phenotypes$age,age2 = phenotypes$age^2,height = phenotypes$height,weight = phenotypes$weight,currentformersmoker = sc) ### Merge in the SAMPID, which is used in the genotype files gscan.phenotypes <- merge(gscan.phenotypes, id_map, by="geneva_id", all.x=TRUE) ### Reorder phenotype file to make pedigree file consistent with genotype IDs gscan.phenotypes <- gscan.phenotypes[c(17,2,16,3:15)] colnames(gscan.phenotypes) [2] <- "SAMPID" ### Read in PCs and add to pedigree file, then write out to a phenotype and covariate file ### [ here read in PCs and merge into phenotype file (probably by the SAMPID) ] pcs <- read.table("/rc_scratch/hayo0753/aric/aric_ancestry_and_pcs", head=TRUE, stringsAsFactors=F) colnames(pcs) [1] <- "SAMPID" pcs <- merge(pcs, gscan.phenotypes, by="SAMPID", all.x=TRUE) ############# PRELIMINARY ##################### ### Write to file [NOTE TO HANNAH: will have to be changed once we ### have PCs and ancestry groups identified. PCs will have to be read ### in like with read.table() and we'll have to subset the dataset ### into European and African ancestry, and then write out one ### phenotype and covariate file per ancestry group. ### EUROPEANS phenotypes.EUR.ped <- subset(pcs, ancestry == "EUR",select=c("famid","SAMPID","patid","matid", "sex","cpd", "ai","si", "sc", "dnd","dpw")) write.table(phenotypes.EUR.ped, file="ARIC.EUR.phenotypes1.ped", quote=F, col.names=T, row.names=F, sep="\t") covariates.EUR.ped <- subset(pcs, ancestry == "EUR", select=c("famid","SAMPID","patid","matid", "sex","age", "age2", "height", "weight", "currentformersmoker","PC1", "PC2", "PC3", "PC4", "PC5", "PC6", "PC7", "PC8","PC9", "PC10")) write.table(covariates.EUR.ped, file="ARIC.EUR.covariates1.ped", quote=F, col.names=T, row.names=F, sep="\t") phenotypes.AFR.ped <- subset(pcs, ancestry == "AFR",select=c("famid","SAMPID","patid","matid", "sex","cpd", "ai","si", "sc", "dnd","dpw")) write.table(phenotypes.AFR.ped, file="ARIC.AFR.phenotypes1.ped", quote=F, col.names=T, row.names=F, sep="\t") covariates.AFR.ped <- subset(pcs, ancestry == "AFR", select=c("famid","SAMPID","patid","matid", "sex","age", "age2", "height", "weight", "currentformersmoker", "PC1", "PC2", "PC3", "PC4", "PC5", "PC6", "PC7", "PC8","PC9", "PC10")) write.table(covariates.AFR.ped, file="ARIC.AFR.covariates1.ped", quote=F, col.names=T, row.names=F, sep="\t") ### Must remove duplicates from all files, use UNIX ### sort ARIC.EUR.phenotypes1.ped | uniq > ARIC.EUR.phenotypes.ped ### sort ARIC.EUR.covariates.ped | uniq > ARIC.EUR.covariates.ped ### sort ARIC.AFR.phenotypes1.ped | uniq > ARIC.AFR.phenotypes.ped ### sort ARIC.AFR.covariates.ped | uniq > ARIC.AFR.covariates.ped </syntaxhighlight>
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