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==Framingham== ===Phenotypes=== <syntaxhighlight lang="rsplus"> options(stringsAsFactors=F) ### Offspring cohort off <- read.table(gzfile("/work/KellerLab/GSCAN/dbGaP/Framingham/PhenoGenotypeFiles/RootStudyConsentSet_phs000007.Framingham.v28.p10.c1.HMB-IRB-MDS/PhenotypeFiles/phs000007.v28.pht000030.v7.p10.c1.ex1_1s.HMB-IRB-MDS.txt.gz"), header=T, sep="\t") ### Generation 3 cohort g3 <- read.table(gzfile("/work/KellerLab/GSCAN/dbGaP/Framingham/PhenoGenotypeFiles/RootStudyConsentSet_phs000007.Framingham.v28.p10.c1.HMB-IRB-MDS/PhenotypeFiles/phs000007.v28.pht000074.v9.p10.c1.ex3_1s.HMB-IRB-MDS.txt.gz"), header=T, skip=10, sep="\t", fill=T) ###---------------------### ### Cigarettes per Day ### ###---------------------### ### Offspring Cohort Exam 1 ### Framingham variable name is "A102". ### "Usual number of cigarettes smoked (now or formerly)" ### Response option is an integer value ### ### table(off$A102) ### 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ### 1208 50 29 44 26 47 31 16 30 3 175 1 20 1 9 94 ### 16 17 18 19 20 22 24 25 26 27 28 30 34 35 40 42 ### 5 6 50 1 556 10 3 39 1 1 2 214 1 15 147 2 ### 45 50 60 80 88 90 ### 4 21 19 1 14 2 ### ### summary(off$A102) ### Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ### 0.00 0.00 7.00 12.11 20.00 90.00 8 cpd.off <- off$A102 cpd.off[cpd.off==0] <- NA ### Set those who smoke "0" cpd to NA cpd.off[cpd.off > 60] <- 60 ### Set those who report smoking >40 cpd to 40 cpd.off[cpd.off <= 5 & cpd.off >= 1] <- 1 cpd.off[cpd.off <= 15 & cpd.off >= 6] <- 2 cpd.off[cpd.off <= 25 & cpd.off >= 16] <- 3 cpd.off[cpd.off <= 35 & cpd.off >= 26] <- 4 cpd.off[cpd.off >= 36 & cpd.off <= 60] <- 5 cpd.off[cpd.off > 60 | is.na(cpd.off)] <- NA ### G3 cohort Exam 1 ### Framingham variable name is "G3A074 ### "IF EVER SMOKED CIGS REGULARLY: ON THE AVERAGE ### OF THE ENTIRE TIME YOU SMOKED, HOW MANY CIGARETTES ### DID YOU SMOKE PER DAY?" ### Response option is an integer value ### ### table(g3$G3A074) ### ### 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ### 3180 57 14 15 20 30 8 13 7 2 122 2 16 3 2 60 ### 16 17 18 19 20 21 22 23 24 25 28 30 35 40 45 50 ### 3 3 10 1 287 2 1 1 1 12 1 53 1 47 1 12 ### 55 60 80 ### 1 6 1 ### ### summary(g3$G3A074) ### Min. 1st Qu. Median Mean 3rd Qu. Max. ### 0.000 0.000 0.000 3.528 0.000 80.000 cpd.g3 <- g3$G3A074 cpd.g3[cpd.g3==0] <- NA ### Set those who smoke "0" cpd to NA cpd.g3[cpd.g3 > 60] <- 60 ### Set those who report smoking >40 cpd to 40 cpd.g3[cpd.g3 <= 5 & cpd.g3 >= 1] <- 1 cpd.g3[cpd.g3 <= 15 & cpd.g3 >= 6] <- 2 cpd.g3[cpd.g3 <= 25 & cpd.g3 >= 16] <- 3 cpd.g3[cpd.g3 <= 35 & cpd.g3 >= 26] <- 4 cpd.g3[cpd.g3 >= 36 & cpd.g3 <= 60] <- 5 cpd.g3[cpd.g3 > 60 | is.na(cpd.g3)] <- NA ###--------------------### ### Smoking initiation ### ###--------------------### ### Offspring Cohort Exam 1 ### Framingham variable is "A99" ### "Smokes cigarettes: Yes(now)/No/Former" ### Response option is fill-in ### 0 is no, 1 is yes (now), 2 is former ### ### table(off$A99) ### ### 0 1 2 ### 1203 1126 566 si.off <- off$A99 si.off[si.off==1] <- 2 ### Set current smoker "1" to smoker "2" si.off[si.off==0] <- 1 ### Set never smoker "0" to nonsmoker "1" ### Former smoker "2" remains as a "2" as smoker ### G3 Cohort Exam 1 ### Framingham variables is "G3A070" ### G3A070 - HAVE YOU EVER SMOKED CIGARETTES REGULARLY? (NO MEANS ### LESS THAN 20 PACKS OF CIGARETTES OR 12 OZ OF TOBACCO IN A ### LIFETIME OR LESS THAN 1 CIGARETTE A DAY FOR A YEAR.)" ### Response options: ### "0" is no, "1" is yes, "0" is unknown ### ### table(g3$G3A070) ### 0 1 20 ### 2137 1629 1 si.g3 <- g3$G3A070 si.g3[si.g3==20] <- NA si.g3[si.g3==1] <- 2 si.g3[si.g3==0] <- 1 ###---------------------### ### Smoking cessation ### ###---------------------### ### Offspring Cohort Exam 1 ### Framingham variable is "A99" ### Smokes cigarettes: Yes(now)/No/Former" ### Response option is fill-in ### 0 is no, 1 is yes (now), 2 is former ### ### table(off$A99) ### ### 0 1 2 ### 1203 1126 566 sc.off <- off$A99 sc.off[sc.off==2] <- 3 ### temporarily set former smoker "2" as "3" sc.off[sc.off==1] <- 2 ### set current smoker "1" as current smoker "2" sc.off[sc.off==3] <- 1 ### set former smoker "3" as former smoker "1" sc.off[sc.off==0] <- NA ### code all non smokers "0" as NA ### G3 Cohort Exam 1 ### Framingham variables are βG3A070β and βG3A072β ### Response option is fill-in ### G3A070 - HAVE YOU EVER SMOKED CIGARETTES REGULARLY? (NO MEANS LESS THAN 20 PACKS OF CIGARETTES OR 12 OZ OF ###TOBACCO IN A LIFETIME OR LESS THAN 1 CIGARETTE A DAY FOR A YEAR.) ###β0β is no, β1β is yes, β20β is unknown ### G3A072 - IF EVER SMOKED CIGARETTES REGULARLY: DO YOU NOW SMOKE CIGARETTES (AS OF 1 MONTH AGO)? ###β0β is no or never smoked, β1β is yes ### ### table(g3$G3A070) ### 0 1 20 ### 2137 1629 1 ### ### table(g3$G3A072) ### 0 1 ### 3179 585 sc.g3 <- g3$G3A072 sc.g3[sc.g3==1] <- 2 ## These are our current smokers sc.g3[sc.g3==0] <- 1 sc.g3 <- ifelse(sc.g3==1 & (g3$G3A070 == 0 | is.na(g3$G3A070)), NA, sc.g3) ###---------------------### ### Age of initiation ### ###---------------------### ### Offspring Cohort Exam 1 ### Framingham variable "A100" ### Age started smoking cigarettes regularly\ ### Response option is an integer value, 88 is doesn't smoke regularly ### table(off$A100) ### ### 0 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 ### 6 2 2 2 8 4 24 37 61 147 266 233 330 166 164 85 42 26 13 25 ### 26 27 28 29 30 31 33 34 35 37 38 39 40 44 45 46 51 88 ### 2 8 4 3 7 1 1 1 3 1 2 1 3 1 1 2 1 105 ### ai.off <- off$A100 ai.off[ai.off < 10] <- NA ### Set AI less than 10 as missing\ ai.off[ai.off > 35] <- NA ### Set AI greater than 35 as missing \ ### G3 Cohort Exam 1 ### Framingham variable "G3A075" ### IF EVER SMOKED CIGARETTES REGULARLY: HOW OLD WERE YOU WHEN YOU FIRST STARTED REGULAR CIGARETTE SMOKING? ### Response option is an integer value, 0 is never smoked ### table(g3$G3A075) ### ### 0 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ### 2138 1 2 2 3 5 14 52 108 159 152 277 193 256 98 107 ### 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 ### 51 31 19 13 31 6 4 6 2 13 2 1 1 4 1 4 ### 37 38 40 42 46 50 ### 2 2 1 1 1 2 ### ai.g3 <- g3$G3A075 ai.g3[ai.g3 < 10] <- NA ### Set AI less than 10 as missing ai.g3[ai.g3 > 35] <- NA ### Set AI greater than 35 as missing ###-------------------### ### Drinks per week ### ###-------------------### ### Offspring Cohort Exam 1 ### Framingham variables "A111", "A112", "A113" ### A111 - Beer (bottles, cans or glasses per week) ### A112 - Wine (glasses per week) ### A113 - Cocktails, highballs, straight drinks (# per week) ### Response options are integer values. ### table(off$A111) ### ### 0 1 2 3 4 5 6 7 8 9 10 12 14 15 16 18 ### 1452 524 171 131 78 49 125 34 33 4 46 68 21 21 5 13 ### 20 21 24 25 26 28 30 35 36 40 42 48 49 50 70 90 ### 18 7 48 5 1 6 6 2 2 4 4 6 1 2 1 1 ### ### table(off$A112) ### ### 0 1 2 3 4 5 6 7 8 9 10 12 14 15 16 18 ### 1329 919 218 105 69 41 22 64 25 3 25 11 30 2 2 2 ### 20 21 24 28 30 32 50 ### 7 4 2 3 1 2 1 ### ### table(off$A113) ### ### 0 1 2 3 4 5 6 7 8 9 10 12 14 15 16 18 ### 861 1094 267 157 96 64 59 65 31 9 49 16 57 13 2 1 ### 19 20 21 24 25 28 30 35 38 42 50 55 60 61 ### 1 12 10 4 2 2 6 2 1 1 1 1 1 1 ### dpw.off <- rowSums(subset(off, select = c("A111", "A112", "A113")), na.rm=T) ### The above assumes all drinks are of equal ETOH content dpw.off[dpw.off == 0] <- NA dpw.off <- log(dpw.off) ### G3 Cohort Exam 1\ ### Framingham variables are "G3A115", "G3A116", "G3A119", "G3A120", ### "G3A123", "G3A124", "G3A127", "G3A128", ### "G3A131", "G3A132" ### G3A115 - BEER: NUMBER OF BEER (12 OZ. BOTTLE, GLASS, CAN) YOU DRINK PER WEEK OVER THE PAST YEAR ### G3A116 - BEER: NUMBER OF BEER (12 OZ. BOTTLE, GLASS, CAN) YOU DRINK PER MONTH OVER THE PAST YEAR ### G3A119 - WHITE WINE: NUMBER OF WHITE WINE (4 OZ GLASS) YOU DRINK PER WEEK OVER THE PAST YEAR ### G3A120 - WHITE WINE: NUMBER OF WHITE WINE (4 OZ GLASS) YOU DRINK PER MONTH OVER THE PAST YEAR ### G3A123 - RED WINE: NUMBER OF RED WINE (4 OZ GLASS) YOU DRINK PER WEEK OVER THE PAST YEAR ### G3A124 - RED WINE: NUMBER OF RED WINE (4 OZ GLASS) YOU DRINK PER MONTH OVER THE PAST YEAR ### G3A127 - LIQUOR/SPIRITS: AVERAGE NUMBER OF LIQUOR/SPIRITS (1 1/4 OZ JIGGER) YOU DRINK PER WEEK OVER THE PAST YEAR ### G3A128 - LIQUOR/SPIRITS: AVERAGE NUMBER OF LIQUOR/SPIRITS (1 1/4 OZ JIGGER) YOU DRINK PER MONTH OVERTHE PAST YEAR ### G3A131 - OTHER BEVERAGE: AVERAGE NUMBER OF OTHER BEVERAGE YOU DRINK PER WEEK OVER THE PAST YEAR ### G3A132 - OTHER BEVERAGE: AVERAGE NUMBER OF OTHER BEVERAGE YOU DRINK PER MONTH OVER THE PAST YEAR ### ### *Participant was allowed to report alcohol consumption in either drinks per week or drinks per ### month. Therefore, to calculate total alcohol consumption, you must use both number of drinks per ### week and number of drinks per month (E.G. DRINKS PER MONTH = SUM(OF (DRINKS PER WEEK)(DRINKS PER ### MONTH/4)). ### ### Response options are integer values. ### all <- subset(g3, select = c("G3A115", "G3A116", "G3A119", "G3A120", "G3A123", "G3A124", "G3A127", "G3A128", "G3A131", "G3A132")) names(all) <- c("beerpw", "beerpm", "wwinepw", "wwinepm", "rwinepw", "rwinepm", "liqpw", "liqpm", "othpw", "othpm") w <- all[,c(1,3,5,7,9)] m <- all[,c(2,4,6,8,10)]/4 dpw.tmp <- data.frame(beer = rowSums(cbind(w$beerpw, m$beerpm), na.rm=T), wwine = rowSums(cbind(w$wwinepw, m$wwinepm), na.rm=T), rwine = rowSums(cbind(w$rwinepw, m$rwinepm), na.rm=T), liq = rowSums(cbind(w$liqpw, m$liqpm), na.rm=T), oth = rowSums(cbind(w$othpw, m$othpm), na.rm=T)) ### WHITE WINE - convert from 4oz to 5oz drink dpw.tmp$wwine <- dpw.tmp$wwine*5/4 ### RED WINE - convert from 4oz to 5oz drink dpw.tmp$rwine <- dpw.tmp$rwine*5/4 ### LIQUOR - convert from 1.25 to 1.50 drink dpw.tmp$liq <- dpw.tmp$liq*1.5/1.25 ### OTHER DRINK is an unknown. leave as-is. dpw.g3 <- rowSums(dpw.tmp, na.rm=T) ### We have a few outliers, draw cuttoff at 70 drinks / week dpw.g3[dpw.g3 > 70] <- NA dpw.g3[dpw.g3 == 0] <- NA dpw.g3 <- log(dpw.g3 + .75) ###---------------------------### ### Drinker vs. non-drinker ### ###---------------------------### ### ### Use the variables already computed for dpw above. Assume ### anyone who is drinking less than 1 drink / month is a non-drinker x <- subset(off, select = c("A111", "A112", "A113")) index <- rep(NA, nrow(x)) for(i in 1:nrow(x)) { if(is.na(x[i,1]) & is.na(x[i,2]) & is.na(x[i,3])) { index[i] <- 1 } else { index[i] <- 0 } } dnd.off <- ifelse(rowSums(subset(off, select = c("A111", "A112", "A113")), na.rm=T) == 0, 1, 2) dnd.off[index==1] <- NA x <- all index <- rep(NA, nrow(x)) for(i in 1:nrow(x)) { if(is.na(x[i,1]) & is.na(x[i,2]) & is.na(x[i,3])) { index[i] <- 1 } else { index[i] <- 0 } } dnd.g3 <- ifelse(rowSums(all, na.rm=T) == 0, 1, 2) dnd.g3[index==1] <- NA ###----------------------------### ### Binge drinking in everyone ### ### (Only available for G3) ### ###----------------------------### ### G3 Cohort Exam 1 ### Framingham variable is "G3A137" ### IF EVER CONSUMED ALCOHOL: WHAT WAS THE MAXIMUM NUMBER OF ### DRINKS YOU HAD IN A 24 HOUR PERIOD DURING THE PAST MONTH? ### Response option is an integer value ### ### summary(g3$G3A137) ### Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ### 0.000 1.000 3.000 3.566 5.000 50.000 3 bde <- g3$G3A137 sex <- g3$G3A440 ### bde is defined differently for males & females ### sex == 1 is male, sex == 2 is female tmp <- rep(NA, length(bde)) ### temporary vector to hold results and avoid conflicts tmp[sex == 1 & bde < 5] <- 1 tmp[sex == 1 & bde >=5] <- 2 tmp[sex == 2 & bde < 4] <- 1 tmp[sex == 2 & bde >=4] <- 2 bde.g3 <- tmp ###-----------------------------------------### ### Binge-drinking (in lifetime drinkers) ### ### (Only available for G3) ### ###-----------------------------------------### ### G3 Cohort Exam 1 ### Framingham variable is "G3A112" ### HAVE YOU EVER CONSUMED ALCOHOLIC BEVERAGES (BEER, WINE, LIQUOR/SPIRITS)? \ ### 0 is no, 1 is yes lifednd <- g3$G3A112 ## No missings, so next command has no problem bdl.g3 <- bde.g3 bdl.g3 <- ifelse(lifednd == 0, NA, bde.g3) ################## ################## ### ### ### Covariates ### ### ### ################## ################## ### HEIGHT ### Offspring Cohort variable is βA51β ### Generation 3 Cohort variable is βG3A446β height.off <- off$A51 height.g3 <- g3$G3A446 ### WEIGHT ### Offspring Cohort variable is βA50β ### Generation 3 Cohort variable is βG3A444β weight.off <- off$A50 weight.g3 <- g3$G3A444 ###-------------### ### Age at exam ### ###-------------### ### Get BIRTHDATE birth <- read.table(gzfile("/work/KellerLab/GSCAN/dbGaP/Framingham/PhenoGenotypeFiles/RootStudyConsentSet_phs000007.Framingham.v28.p10.c1.HMB-IRB-MDS/PhenotypeFiles/phs000007.v28.pht000740.v7.p10.c1.birthyr_alls.HMB-IRB-MDS.txt.gz"), header=T, sep="\t") off.IDs <- off$dbGaP_Subject_ID g3.IDs <- g3$dbGaP_Subject_ID birth.off <- subset(birth, dbGaP_Subject_ID %in% off.IDs) birth.g3 <- subset(birth, dbGaP_Subject_ID %in% g3.IDs) ### True exam dates spanned 1971 - 1975 for offspring ### True exam dates spanned 2002 - 2005 for generation 3 ### A good-enough approximation is to take the middle ### year and subtract birth year to get age at exam ### Offspring Cohort off.age <- data.frame(dbGaP_Subject_ID = birth.off$dbGaP_Subject_ID, Age = 1973 - birth.off$birthyr) ### Framingham Cohort g3.age <- data.frame(dbGaP_Subject_ID = birth.g3$dbGaP_Subject_ID, Age = 2004 - birth.g3$birthyr) age <- rbind(off.age, g3.age) #################################### #################################### ### ### ### Create Phenotype Data Frames ### ### ### #################################### #################################### offspring <- data.frame(dbGaP_Subject_ID = off$dbGaP_Subject_ID, shareid = off$shareid, cpd = cpd.off, si = si.off, sc = sc.off, ai = ai.off, dpw = dpw.off, dnd = dnd.off, bde = rep(NA, nrow(off)), bdl = rep(NA, nrow(off)), height = height.off, weight = weight.off, cohort = rep(1, nrow(off))) generation3 <- data.frame(dbGaP_Subject_ID = g3$dbGaP_Subject_ID, shareid = g3$shareid, cpd = cpd.g3, si = si.g3, sc = sc.g3, ai = ai.g3, dpw = dpw.g3, dnd = dnd.g3, bde = bde.g3, bdl = bdl.g3, height = height.g3, weight = weight.g3, cohort = rep(2, nrow(g3))) tmp <- rbind(offspring, generation3) phenotypes <- merge(tmp, age, by="dbGaP_Subject_ID") phenotypes$age2 <- phenotypes$Age^2 ################## ### ID MAPPING ### ################## ### ID mapping file from dbGaP_Subject_ID to SAMPID ID.map <- read.table(gzfile("/work/KellerLab/GSCAN/dbGaP/Framingham/PhenoGenotypeFiles/RootStudyConsentSet_phs000007.Framingham.v28.p10.c1.HMB-IRB-MDS/PhenotypeFiles/phs000007.v28.pht001415.v16.p10.Framingham_Sample.MULTI.txt.gz"), header=T, sep="\t", stringsAsFactors=F) ### Genotype files genotype.IDs <- read.table("/work/KellerLab/GSCAN/dbGaP/Framingham/PhenoGenotypeFiles/ChildStudyConsentSet_phs000342.Framingham.v16.p10.c1.HMB-IRB-MDS/GenotypeFiles/phg000006.v9.FHS_SHARe_Affy500K.genotype-calls-matrixfmt.c1/subject_level_PLINK_sets/FHS_SHARe_Affy500K_subjects_c1.fam", header=F) names(genotype.IDs) <- c("famid", "SAMPID", "patid", "matid", "sex", "phenotype") length(which(genotype.IDs$SAMPID %in% ID.map$SAMPID)) ## 6954 x <- merge(genotype.IDs, ID.map, by="SAMPID", all.x=T) x <- x[,c(1:5,7)] ### There are many duplicates, which I can remove because I only want ### a single entry for every SAMPID-SUBJID x <- x[which(!duplicated(x$dbGaP_Subject_ID)),] almost.final <- merge(phenotypes, x, by="dbGaP_Subject_ID", all.x=T) #################################################### ### Bring in genetic PCs and ancestry categories ### #################################################### PCs <- read.table("/work/KellerLab/Zhen/FRAMINGHAM/PCA/FRAMINGHAM_pcs_and_ancestries.txt", header=T) names(PCs)[2] <- c("SAMPID") final <- merge(almost.final, PCs, by="SAMPID", all.x=T) phenotypes.ped <- subset(final, ancestry=="EUR", select=c("famid", "SAMPID", "patid", "matid", "sex", "cpd", "ai", "si", "sc", "dpw", "dnd", "bde", "bdl")) phenotypes.ped[is.na(phenotypes.ped)] <- "x" covariates.ped <- subset(final, ancestry=="EUR", select=c("famid", "SAMPID", "patid", "matid", "sex", "Age", "age2", "sc", "height", "weight", "cohort", "PC1", "PC2", "PC3", "PC4", "PC5", "PC6", "PC7", "PC8", "PC9", "PC10")) covariates.ped[is.na(covariates.ped)] <- "x" write.table(phenotypes.ped, file="Framingham.EUR.phenotypes.ped", quote=F, col.names=T, row.names=F) write.table(covariates.ped, file="Framingham.EUR.covariates.ped", quote=F, col.names=T, row.names=F) </syntaxhighlight> ===Genotypes=== We used the Affy 500K genotypes found here: /work/KellerLab/GSCAN/dbGaP/Framingham/PhenoGenotypeFiles/ChildStudyConsentSet_phs000342.Framingham.v16.p10.c1.HMB-IRB-MDS/GenotypeFiles/phg000006.v9.FHS_SHARe_Affy500K.genotype-calls-matrixfmt.c1/subject_level_PLINK_sets/FHS_SHARe_Affy500K_subjects_c1.[bed|bim|fam]
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