UKBiobank Colorectal Cancer Analysis

R Markdown

#For our analysis, select: 1. european; 2. age by category; 3. see the complete dataset (no NA) number; 4. incident cancer cases
dat0=pheno.clean.final[which(pheno.clean.final$race=="White"),]
table(dat0$colorectal_cancer)

## 
##      0      1 
## 377373   4033

dat0$agegroups<-cut(dat0$age, breaks=c( 40, 45,50,55,60,65,70,75), right = FALSE)
dat0=dat0[-which(dat0$colorectal_cancer==1 & dat0$colorectal_cancer_incident!=1),] #exclude non-incident cc

dat0$prs_scaled=scale(dat0$PRS_colorectal,center = T,scale = T)
fit <- glm(colorectal_cancer~prs_scaled+PC1+PC2+PC3+PC4+PC5+PC6+PC7+PC8+PC9+PC10, data=dat0,family = binomial(), model = FALSE, x = FALSE, y = FALSE)
summary(fit)

## 
## Call:
## glm(formula = colorectal_cancer ~ prs_scaled + PC1 + PC2 + PC3 + 
##     PC4 + PC5 + PC6 + PC7 + PC8 + PC9 + PC10, family = binomial(), 
##     data = dat0, model = FALSE, x = FALSE, y = FALSE)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.3164  -0.1242  -0.1061  -0.0922   3.5321  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -5.2860486  0.0819907 -64.471  < 2e-16 ***
## prs_scaled   0.4101889  0.0204574  20.051  < 2e-16 ***
## PC1         -0.0134804  0.0079532  -1.695 0.090082 .  
## PC2         -0.0063994  0.0080499  -0.795 0.426629    
## PC3          0.0118942  0.0099428   1.196 0.231593    
## PC4          0.0030475  0.0045931   0.663 0.507011    
## PC5          0.0116118  0.0027331   4.249 2.15e-05 ***
## PC6          0.0006845  0.0073662   0.093 0.925959    
## PC7          0.0047842  0.0054692   0.875 0.381704    
## PC8         -0.0228026  0.0063326  -3.601 0.000317 ***
## PC9         -0.0039339  0.0043521  -0.904 0.366045    
## PC10        -0.0078056  0.0087750  -0.890 0.373718    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 28788  on 379742  degrees of freedom
## Residual deviance: 28339  on 379731  degrees of freedom
## AIC: 28363
## 
## Number of Fisher Scoring iterations: 8

exp( 0.4101952)

## [1] 1.507112

dat0$alcohol=NA
dat0$alcohol[which(dat0$AlcoholFreq=="Never" | dat0$AlcoholFreq== "Special occasions only")]=0
dat0$alcohol[which(dat0$AlcoholFreq=="Daily or almost daily" | dat0$AlcoholFreq== "Three or four times a week" |
                     dat0$AlcoholFreq== "Once or twice a week" | dat0$AlcoholFreq=="One to three times a month"     )]=1

#let's make NA to be 0 for the sum up
dat0$beef[which(dat0$beef<0)]=0
dat0$pork[which(dat0$pork<0)]=0
dat0$lamb[which(dat0$lamb<0)]=0

dat0$redmeat.numeric=dat0$beef+dat0$pork+dat0$lamb

dat0$cooked_veg[which(dat0$cooked_veg=="-10")]=0
dat0$raw_veg[which(dat0$raw_veg=="-10")]=0
dat0$raw_veg[which(dat0$raw_veg<0)]=NA
dat0$cooked_veg[which(dat0$cooked_veg<0)]=NA

dat0$veg.numeric=dat0$cooked_veg+dat0$raw_veg

dat0$process_meat[which(dat0$process_meat<0)]=NA

dat0$birth.x=as.numeric(as.character(dat0$birth.x))
dat0$birth.y=as.numeric(as.character(dat0$birth.y))


dat0$smoke.status[which(dat0$smoke.status<0)]=NA
#Add social economic and birth location/assessment center location
dat_test=dat0[,c("IID","PRS_colorectal","sex","age","height","waist","smoke.status","activity",
                             "colorectal_cancer","colorectal_cancer_incident","redmeat.numeric","veg.numeric","process_meat",
                             "assessment_center","agegroups", "birth.y","birth.x",
                             "alcohol","PC1","PC2","PC3","PC4","PC5","PC6","PC7","PC8","PC9","PC10")]

dat_test=dat_test[complete.cases(dat_test),]
table(dat_test$colorectal_cancer)

## 
##      0      1 
## 282429   1743

table(dat_test$colorectal_cancer_incident)

## 
##      0      1 
## 282429   1743

dim(dat_test) #[1] 284169     28

## [1] 284172     28

dat_test$prs_scaled=as.vector(scale(dat_test$PRS_colorectal,center = T,scale = T))
dat_test$age.scale=as.vector(scale(dat_test$age,center = T,scale=T))
dat_test$height.scale=as.vector(scale(dat_test$height,center=T,scale = T))
dat_test$waist.scale=as.vector(scale(dat_test$waist,center=T,scale = T))
dat_test$birth.y.scale=as.vector(scale(dat_test$birth.y,center=T,scale = T))
dat_test$birth.x.scale=as.vector(scale(dat_test$birth.x,center=T,scale = T))
dat_test$veg.numeric.scale=as.vector(scale(dat_test$veg.numeric,center=T,scale = T))
dat_test$process_meat.scale=as.vector(scale(dat_test$process_meat,center=T,scale = T))
dat_test$redmeat.numeric.scale=as.vector(scale(dat_test$redmeat.numeric,center=T,scale = T))
table(dat_test$assessment_center) #note that 10003 is too small, we set 11001 as the reference group

## 
## 10003 11001 11002 11003 11004 11005 11006 11007 11008 11009 11010 11011 11012 
##     4  7886  8470 10620 11303 10351 10971 18282 16615 21052 25872 25990  5802 
## 11013 11014 11016 11017 11018 11020 11021 11022 11023 
## 20047 17843 18834 12272 13376 13338 13567  1288   389

#dat_test$assessment_center=factor(dat_test$assessment_center)
#dat_test$assessment_center=relevel(dat_test$assessment_center,ref = "11001")
fit <- glm(colorectal_cancer~prs_scaled+PC1+PC2+PC3+PC4+PC5+PC6+PC7+PC8+PC9+PC10+factor(activity)+agegroups+sex+alcohol+height.scale+relevel(factor(dat_test$assessment_center),ref = "11001")+waist.scale+factor(smoke.status)+redmeat.numeric.scale+process_meat.scale+veg.numeric.scale+birth.x.scale+birth.y.scale+prs_scaled:birth.x.scale+prs_scaled:birth.y.scale+prs_scaled:factor(activity)+prs_scaled:agegroups+prs_scaled:sex+prs_scaled:alcohol+prs_scaled:height.scale+prs_scaled:waist.scale+prs_scaled:veg.numeric.scale+prs_scaled:redmeat.numeric.scale+prs_scaled:factor(smoke.status)+prs_scaled:process_meat.scale, data=dat_test,family = binomial(), model = FALSE, x = FALSE, y = FALSE)
summary(fit)

## 
## Call:
## glm(formula = colorectal_cancer ~ prs_scaled + PC1 + PC2 + PC3 + 
##     PC4 + PC5 + PC6 + PC7 + PC8 + PC9 + PC10 + factor(activity) + 
##     agegroups + sex + alcohol + height.scale + relevel(factor(dat_test$assessment_center), 
##     ref = "11001") + waist.scale + factor(smoke.status) + redmeat.numeric.scale + 
##     process_meat.scale + veg.numeric.scale + birth.x.scale + 
##     birth.y.scale + prs_scaled:birth.x.scale + prs_scaled:birth.y.scale + 
##     prs_scaled:factor(activity) + prs_scaled:agegroups + prs_scaled:sex + 
##     prs_scaled:alcohol + prs_scaled:height.scale + prs_scaled:waist.scale + 
##     prs_scaled:veg.numeric.scale + prs_scaled:redmeat.numeric.scale + 
##     prs_scaled:factor(smoke.status) + prs_scaled:process_meat.scale, 
##     family = binomial(), data = dat_test, model = FALSE, x = FALSE, 
##     y = FALSE)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.4718  -0.1291  -0.0979  -0.0696   3.9891  
## 
## Coefficients:
##                                                                  Estimate
## (Intercept)                                                     -7.101399
## prs_scaled                                                       0.655239
## PC1                                                             -0.024151
## PC2                                                              0.013532
## PC3                                                              0.007292
## PC4                                                             -0.003510
## PC5                                                              0.009853
## PC6                                                              0.002921
## PC7                                                              0.003657
## PC8                                                             -0.026568
## PC9                                                             -0.006859
## PC10                                                            -0.013153
## factor(activity)1                                                0.023742
## factor(activity)2                                                0.017799
## agegroups[45,50)                                                 0.709890
## agegroups[50,55)                                                 1.383273
## agegroups[55,60)                                                 1.650811
## agegroups[60,65)                                                 1.895994
## agegroups[65,70)                                                 2.148956
## agegroups[70,75)                                                 2.398049
## sexMale                                                          0.227228
## alcohol                                                         -0.015469
## height.scale                                                     0.056854
## relevel(factor(dat_test$assessment_center), ref = "11001")10003 -6.431823
## relevel(factor(dat_test$assessment_center), ref = "11001")11002 -0.038508
## relevel(factor(dat_test$assessment_center), ref = "11001")11003 -0.260607
## relevel(factor(dat_test$assessment_center), ref = "11001")11004 -0.295590
## relevel(factor(dat_test$assessment_center), ref = "11001")11005 -0.171025
## relevel(factor(dat_test$assessment_center), ref = "11001")11006 -0.259218
## relevel(factor(dat_test$assessment_center), ref = "11001")11007 -0.168623
## relevel(factor(dat_test$assessment_center), ref = "11001")11008 -0.233069
## relevel(factor(dat_test$assessment_center), ref = "11001")11009 -0.273151
## relevel(factor(dat_test$assessment_center), ref = "11001")11010 -0.292404
## relevel(factor(dat_test$assessment_center), ref = "11001")11011 -0.240929
## relevel(factor(dat_test$assessment_center), ref = "11001")11012 -0.208486
## relevel(factor(dat_test$assessment_center), ref = "11001")11013 -0.347291
## relevel(factor(dat_test$assessment_center), ref = "11001")11014 -0.349974
## relevel(factor(dat_test$assessment_center), ref = "11001")11016 -0.318156
## relevel(factor(dat_test$assessment_center), ref = "11001")11017 -0.443371
## relevel(factor(dat_test$assessment_center), ref = "11001")11018 -0.359613
## relevel(factor(dat_test$assessment_center), ref = "11001")11020 -0.449314
## relevel(factor(dat_test$assessment_center), ref = "11001")11021 -0.687207
## relevel(factor(dat_test$assessment_center), ref = "11001")11022 -0.953463
## relevel(factor(dat_test$assessment_center), ref = "11001")11023 -1.338564
## waist.scale                                                      0.129664
## factor(smoke.status)1                                            0.173482
## factor(smoke.status)2                                           -0.084341
## redmeat.numeric.scale                                            0.073132
## process_meat.scale                                               0.066610
## veg.numeric.scale                                                0.005826
## birth.x.scale                                                   -0.056858
## birth.y.scale                                                    0.004340
## prs_scaled:birth.x.scale                                         0.005865
## prs_scaled:birth.y.scale                                         0.024590
## prs_scaled:factor(activity)1                                    -0.117096
## prs_scaled:factor(activity)2                                    -0.073661
## prs_scaled:agegroups[45,50)                                     -0.095915
## prs_scaled:agegroups[50,55)                                     -0.108503
## prs_scaled:agegroups[55,60)                                     -0.035762
## prs_scaled:agegroups[60,65)                                     -0.133928
## prs_scaled:agegroups[65,70)                                     -0.159267
## prs_scaled:agegroups[70,75)                                     -0.800574
## prs_scaled:sexMale                                               0.018454
## prs_scaled:alcohol                                              -0.056909
## prs_scaled:height.scale                                          0.017142
## prs_scaled:waist.scale                                          -0.019931
## prs_scaled:veg.numeric.scale                                    -0.011235
## prs_scaled:redmeat.numeric.scale                                -0.008710
## prs_scaled:factor(smoke.status)1                                 0.021224
## prs_scaled:factor(smoke.status)2                                 0.063039
## prs_scaled:process_meat.scale                                   -0.030052
##                                                                 Std. Error
## (Intercept)                                                       0.282510
## prs_scaled                                                        0.185517
## PC1                                                               0.011810
## PC2                                                               0.013633
## PC3                                                               0.014603
## PC4                                                               0.007020
## PC5                                                               0.004138
## PC6                                                               0.013928
## PC7                                                               0.009036
## PC8                                                               0.010570
## PC9                                                               0.005874
## PC10                                                              0.011748
## factor(activity)1                                                 0.073977
## factor(activity)2                                                 0.075635
## agegroups[45,50)                                                  0.227717
## agegroups[50,55)                                                  0.209969
## agegroups[55,60)                                                  0.205157
## agegroups[60,65)                                                  0.201298
## agegroups[65,70)                                                  0.202005
## agegroups[70,75)                                                  0.330289
## sexMale                                                           0.082133
## alcohol                                                           0.074986
## height.scale                                                      0.038566
## relevel(factor(dat_test$assessment_center), ref = "11001")10003  98.388597
## relevel(factor(dat_test$assessment_center), ref = "11001")11002   0.184021
## relevel(factor(dat_test$assessment_center), ref = "11001")11003   0.184971
## relevel(factor(dat_test$assessment_center), ref = "11001")11004   0.185246
## relevel(factor(dat_test$assessment_center), ref = "11001")11005   0.182502
## relevel(factor(dat_test$assessment_center), ref = "11001")11006   0.177249
## relevel(factor(dat_test$assessment_center), ref = "11001")11007   0.162411
## relevel(factor(dat_test$assessment_center), ref = "11001")11008   0.161748
## relevel(factor(dat_test$assessment_center), ref = "11001")11009   0.160575
## relevel(factor(dat_test$assessment_center), ref = "11001")11010   0.154377
## relevel(factor(dat_test$assessment_center), ref = "11001")11011   0.155995
## relevel(factor(dat_test$assessment_center), ref = "11001")11012   0.221011
## relevel(factor(dat_test$assessment_center), ref = "11001")11013   0.162058
## relevel(factor(dat_test$assessment_center), ref = "11001")11014   0.164782
## relevel(factor(dat_test$assessment_center), ref = "11001")11016   0.158828
## relevel(factor(dat_test$assessment_center), ref = "11001")11017   0.183390
## relevel(factor(dat_test$assessment_center), ref = "11001")11018   0.178403
## relevel(factor(dat_test$assessment_center), ref = "11001")11020   0.182685
## relevel(factor(dat_test$assessment_center), ref = "11001")11021   0.187201
## relevel(factor(dat_test$assessment_center), ref = "11001")11022   0.472268
## relevel(factor(dat_test$assessment_center), ref = "11001")11023   1.011719
## waist.scale                                                       0.030524
## factor(smoke.status)1                                             0.055744
## factor(smoke.status)2                                             0.100561
## redmeat.numeric.scale                                             0.028190
## process_meat.scale                                                0.029215
## veg.numeric.scale                                                 0.026081
## birth.x.scale                                                     0.028572
## birth.y.scale                                                     0.034810
## prs_scaled:birth.x.scale                                          0.022996
## prs_scaled:birth.y.scale                                          0.023222
## prs_scaled:factor(activity)1                                      0.065899
## prs_scaled:factor(activity)2                                      0.066910
## prs_scaled:agegroups[45,50)                                       0.198006
## prs_scaled:agegroups[50,55)                                       0.181515
## prs_scaled:agegroups[55,60)                                       0.176573
## prs_scaled:agegroups[60,65)                                       0.173597
## prs_scaled:agegroups[65,70)                                       0.174258
## prs_scaled:agegroups[70,75)                                       0.294442
## prs_scaled:sexMale                                                0.074247
## prs_scaled:alcohol                                                0.066819
## prs_scaled:height.scale                                           0.034799
## prs_scaled:waist.scale                                            0.027616
## prs_scaled:veg.numeric.scale                                      0.024105
## prs_scaled:redmeat.numeric.scale                                  0.025640
## prs_scaled:factor(smoke.status)1                                  0.050630
## prs_scaled:factor(smoke.status)2                                  0.089542
## prs_scaled:process_meat.scale                                     0.026427
##                                                                 z value
## (Intercept)                                                     -25.137
## prs_scaled                                                        3.532
## PC1                                                              -2.045
## PC2                                                               0.993
## PC3                                                               0.499
## PC4                                                              -0.500
## PC5                                                               2.381
## PC6                                                               0.210
## PC7                                                               0.405
## PC8                                                              -2.514
## PC9                                                              -1.168
## PC10                                                             -1.120
## factor(activity)1                                                 0.321
## factor(activity)2                                                 0.235
## agegroups[45,50)                                                  3.117
## agegroups[50,55)                                                  6.588
## agegroups[55,60)                                                  8.047
## agegroups[60,65)                                                  9.419
## agegroups[65,70)                                                 10.638
## agegroups[70,75)                                                  7.260
## sexMale                                                           2.767
## alcohol                                                          -0.206
## height.scale                                                      1.474
## relevel(factor(dat_test$assessment_center), ref = "11001")10003  -0.065
## relevel(factor(dat_test$assessment_center), ref = "11001")11002  -0.209
## relevel(factor(dat_test$assessment_center), ref = "11001")11003  -1.409
## relevel(factor(dat_test$assessment_center), ref = "11001")11004  -1.596
## relevel(factor(dat_test$assessment_center), ref = "11001")11005  -0.937
## relevel(factor(dat_test$assessment_center), ref = "11001")11006  -1.462
## relevel(factor(dat_test$assessment_center), ref = "11001")11007  -1.038
## relevel(factor(dat_test$assessment_center), ref = "11001")11008  -1.441
## relevel(factor(dat_test$assessment_center), ref = "11001")11009  -1.701
## relevel(factor(dat_test$assessment_center), ref = "11001")11010  -1.894
## relevel(factor(dat_test$assessment_center), ref = "11001")11011  -1.544
## relevel(factor(dat_test$assessment_center), ref = "11001")11012  -0.943
## relevel(factor(dat_test$assessment_center), ref = "11001")11013  -2.143
## relevel(factor(dat_test$assessment_center), ref = "11001")11014  -2.124
## relevel(factor(dat_test$assessment_center), ref = "11001")11016  -2.003
## relevel(factor(dat_test$assessment_center), ref = "11001")11017  -2.418
## relevel(factor(dat_test$assessment_center), ref = "11001")11018  -2.016
## relevel(factor(dat_test$assessment_center), ref = "11001")11020  -2.459
## relevel(factor(dat_test$assessment_center), ref = "11001")11021  -3.671
## relevel(factor(dat_test$assessment_center), ref = "11001")11022  -2.019
## relevel(factor(dat_test$assessment_center), ref = "11001")11023  -1.323
## waist.scale                                                       4.248
## factor(smoke.status)1                                             3.112
## factor(smoke.status)2                                            -0.839
## redmeat.numeric.scale                                             2.594
## process_meat.scale                                                2.280
## veg.numeric.scale                                                 0.223
## birth.x.scale                                                    -1.990
## birth.y.scale                                                     0.125
## prs_scaled:birth.x.scale                                          0.255
## prs_scaled:birth.y.scale                                          1.059
## prs_scaled:factor(activity)1                                     -1.777
## prs_scaled:factor(activity)2                                     -1.101
## prs_scaled:agegroups[45,50)                                      -0.484
## prs_scaled:agegroups[50,55)                                      -0.598
## prs_scaled:agegroups[55,60)                                      -0.203
## prs_scaled:agegroups[60,65)                                      -0.771
## prs_scaled:agegroups[65,70)                                      -0.914
## prs_scaled:agegroups[70,75)                                      -2.719
## prs_scaled:sexMale                                                0.249
## prs_scaled:alcohol                                               -0.852
## prs_scaled:height.scale                                           0.493
## prs_scaled:waist.scale                                           -0.722
## prs_scaled:veg.numeric.scale                                     -0.466
## prs_scaled:redmeat.numeric.scale                                 -0.340
## prs_scaled:factor(smoke.status)1                                  0.419
## prs_scaled:factor(smoke.status)2                                  0.704
## prs_scaled:process_meat.scale                                    -1.137
##                                                                 Pr(>|z|)    
## (Intercept)                                                      < 2e-16 ***
## prs_scaled                                                      0.000412 ***
## PC1                                                             0.040847 *  
## PC2                                                             0.320899    
## PC3                                                             0.617517    
## PC4                                                             0.617127    
## PC5                                                             0.017269 *  
## PC6                                                             0.833909    
## PC7                                                             0.685715    
## PC8                                                             0.011953 *  
## PC9                                                             0.242883    
## PC10                                                            0.262860    
## factor(activity)1                                               0.748254    
## factor(activity)2                                               0.813953    
## agegroups[45,50)                                                0.001824 ** 
## agegroups[50,55)                                                4.46e-11 ***
## agegroups[55,60)                                                8.51e-16 ***
## agegroups[60,65)                                                 < 2e-16 ***
## agegroups[65,70)                                                 < 2e-16 ***
## agegroups[70,75)                                                3.86e-13 ***
## sexMale                                                         0.005664 ** 
## alcohol                                                         0.836560    
## height.scale                                                    0.140431    
## relevel(factor(dat_test$assessment_center), ref = "11001")10003 0.947878    
## relevel(factor(dat_test$assessment_center), ref = "11001")11002 0.834248    
## relevel(factor(dat_test$assessment_center), ref = "11001")11003 0.158863    
## relevel(factor(dat_test$assessment_center), ref = "11001")11004 0.110563    
## relevel(factor(dat_test$assessment_center), ref = "11001")11005 0.348700    
## relevel(factor(dat_test$assessment_center), ref = "11001")11006 0.143617    
## relevel(factor(dat_test$assessment_center), ref = "11001")11007 0.299155    
## relevel(factor(dat_test$assessment_center), ref = "11001")11008 0.149603    
## relevel(factor(dat_test$assessment_center), ref = "11001")11009 0.088929 .  
## relevel(factor(dat_test$assessment_center), ref = "11001")11010 0.058213 .  
## relevel(factor(dat_test$assessment_center), ref = "11001")11011 0.122474    
## relevel(factor(dat_test$assessment_center), ref = "11001")11012 0.345514    
## relevel(factor(dat_test$assessment_center), ref = "11001")11013 0.032112 *  
## relevel(factor(dat_test$assessment_center), ref = "11001")11014 0.033682 *  
## relevel(factor(dat_test$assessment_center), ref = "11001")11016 0.045162 *  
## relevel(factor(dat_test$assessment_center), ref = "11001")11017 0.015622 *  
## relevel(factor(dat_test$assessment_center), ref = "11001")11018 0.043828 *  
## relevel(factor(dat_test$assessment_center), ref = "11001")11020 0.013913 *  
## relevel(factor(dat_test$assessment_center), ref = "11001")11021 0.000242 ***
## relevel(factor(dat_test$assessment_center), ref = "11001")11022 0.043497 *  
## relevel(factor(dat_test$assessment_center), ref = "11001")11023 0.185815    
## waist.scale                                                     2.16e-05 ***
## factor(smoke.status)1                                           0.001858 ** 
## factor(smoke.status)2                                           0.401633    
## redmeat.numeric.scale                                           0.009480 ** 
## process_meat.scale                                              0.022606 *  
## veg.numeric.scale                                               0.823244    
## birth.x.scale                                                   0.046597 *  
## birth.y.scale                                                   0.900771    
## prs_scaled:birth.x.scale                                        0.798686    
## prs_scaled:birth.y.scale                                        0.289651    
## prs_scaled:factor(activity)1                                    0.075587 .  
## prs_scaled:factor(activity)2                                    0.270941    
## prs_scaled:agegroups[45,50)                                     0.628097    
## prs_scaled:agegroups[50,55)                                     0.549995    
## prs_scaled:agegroups[55,60)                                     0.839499    
## prs_scaled:agegroups[60,65)                                     0.440416    
## prs_scaled:agegroups[65,70)                                     0.360731    
## prs_scaled:agegroups[70,75)                                     0.006549 ** 
## prs_scaled:sexMale                                              0.803711    
## prs_scaled:alcohol                                              0.394380    
## prs_scaled:height.scale                                         0.622294    
## prs_scaled:waist.scale                                          0.470468    
## prs_scaled:veg.numeric.scale                                    0.641150    
## prs_scaled:redmeat.numeric.scale                                0.734070    
## prs_scaled:factor(smoke.status)1                                0.675066    
## prs_scaled:factor(smoke.status)2                                0.481425    
## prs_scaled:process_meat.scale                                   0.255469    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 21233  on 284171  degrees of freedom
## Residual deviance: 20188  on 284101  degrees of freedom
## AIC: 20330
## 
## Number of Fisher Scoring iterations: 10

#Now let's select the control samples for the case/control study
table(dat_test$colorectal_cancer)

## 
##      0      1 
## 282429   1743

control=dat_test[which(dat_test$colorectal_cancer==0),]
set.seed(10252022)
#set.seed(11022022)
control=control[sample(1:dim(control)[1],size=1743,replace = F),]
dat_test_casecontrol=rbind(control,dat_test[which(dat_test$colorectal_cancer==1),])
table(dat_test_casecontrol$colorectal_cancer)

## 
##    0    1 
## 1743 1743

dat_test_casecontrol$prs_scaled=as.vector(scale(dat_test_casecontrol$PRS_colorectal,center = T,scale = T))
dat_test_casecontrol$age.scale=as.vector(scale(dat_test_casecontrol$age,center = T,scale=T))
dat_test_casecontrol$height.scale=as.vector(scale(dat_test_casecontrol$height,center=T,scale = T))
dat_test_casecontrol$waist.scale=as.vector(scale(dat_test_casecontrol$waist,center=T,scale = T))
dat_test_casecontrol$birth.y.scale=as.vector(scale(dat_test_casecontrol$birth.y,center=T,scale = T))
dat_test_casecontrol$birth.x.scale=as.vector(scale(dat_test_casecontrol$birth.x,center=T,scale = T))
dat_test_casecontrol$veg.numeric.scale=as.vector(scale(dat_test_casecontrol$veg.numeric,center=T,scale = T))
dat_test_casecontrol$process_meat.scale=as.vector(scale(dat_test_casecontrol$process_meat,center=T,scale = T))
dat_test_casecontrol$redmeat.numeric.scale=as.vector(scale(dat_test_casecontrol$redmeat.numeric,center=T,scale = T))

fit_normal<-prs_e_function_gr(data=dat_test_casecontrol,
                                         formula = colorectal_cancer~prs_scaled+PC1+PC2+PC3+PC4+PC5+PC6+PC7+PC8+PC9+PC10+factor(activity)+agegroups+sex+alcohol+height.scale+assessment_center+waist.scale+factor(smoke.status)+redmeat.numeric.scale+process_meat.scale+veg.numeric.scale+birth.x.scale+birth.y.scale+prs_scaled:birth.x.scale+prs_scaled:birth.y.scale+prs_scaled:factor(activity)+prs_scaled:agegroups+prs_scaled:sex+prs_scaled:alcohol+prs_scaled:height.scale+prs_scaled:waist.scale+prs_scaled:veg.numeric.scale+prs_scaled:redmeat.numeric.scale+prs_scaled:factor(smoke.status)+prs_scaled:process_meat.scale,
                                         formula_prs = prs_scaled ~ PC1+PC2+PC3+PC4+PC5+PC6+PC7+PC8+PC9+PC10+birth.x.scale+birth.y.scale,
                                         numDeriv = F,
                                         facVar="assessment_center")

## After removing missing values, the number of observations is 3486 
## initial  value 7079.971407 
## iter  50 value 7058.783575
## final  value 7057.612449 
## converged

fit_cc_glm <- glm(colorectal_cancer~prs_scaled+PC1+PC2+PC3+PC4+PC5+PC6+PC7+PC8+PC9+PC10+factor(activity)+agegroups+sex+alcohol+height.scale+assessment_center+waist.scale+factor(smoke.status)+redmeat.numeric.scale+process_meat.scale+veg.numeric.scale+birth.x.scale+birth.y.scale+prs_scaled:birth.x.scale+prs_scaled:birth.y.scale+prs_scaled:factor(activity)+prs_scaled:agegroups+prs_scaled:sex+prs_scaled:alcohol+prs_scaled:height.scale+prs_scaled:waist.scale+prs_scaled:veg.numeric.scale+prs_scaled:redmeat.numeric.scale+prs_scaled:factor(smoke.status)+prs_scaled:process_meat.scale, data=dat_test_casecontrol,family = binomial(), model = FALSE, x = FALSE, y = FALSE)

#print results
fit_normal$res_normal

##                                       Estimate   Std.Error      Z.value
## (Intercept)                      -1.8624592303 0.372146401 -5.004641256
## prs_scaled                        0.7494519119 0.196056025  3.822641575
## PC1                              -0.0328349339 0.017714973 -1.853513054
## PC2                              -0.0025914285 0.020716835 -0.125088053
## PC3                               0.0023456304 0.022389950  0.104762642
## PC4                              -0.0081625534 0.010991280 -0.742639031
## PC5                               0.0121852278 0.006360451  1.915780523
## PC6                               0.0044850555 0.021470378  0.208895042
## PC7                              -0.0054891427 0.012909426 -0.425204230
## PC8                              -0.0358245037 0.015510612 -2.309677020
## PC9                              -0.0180150297 0.009044649 -1.991788670
## PC10                             -0.0129227671 0.017843378 -0.724233213
## factor(activity)1                -0.0764839442 0.103639558 -0.737980225
## factor(activity)2                -0.0667222143 0.105673711 -0.631398422
## agegroups[45,50)                  0.6465500883 0.237398203  2.723483500
## agegroups[50,55)                  1.2740540143 0.220569487  5.776202467
## agegroups[55,60)                  1.7144391654 0.216458437  7.920408127
## agegroups[60,65)                  1.9273764498 0.211578467  9.109511372
## agegroups[65,70)                  2.2121974290 0.214557067 10.310531662
## agegroups[70,75)                  2.2321323853 0.508886132  4.386310113
## sexMale                           0.2004358175 0.115802577  1.730840730
## alcohol                           0.0167083502 0.102961531  0.162277600
## height.scale                      0.0384969521 0.054420669  0.707395789
## assessment_center11002           -0.1686919280 0.291866288 -0.577976747
## assessment_center11003           -0.3582593073 0.287849082 -1.244608128
## assessment_center11004           -0.1219663054 0.292953815 -0.416332880
## assessment_center11005           -0.0004021919 0.290539906 -0.001384291
## assessment_center11006           -0.2419307211 0.280712875 -0.861844049
## assessment_center11007           -0.1746538535 0.260269362 -0.671050377
## assessment_center11008           -0.3179623068 0.256398561 -1.240109559
## assessment_center11009           -0.3171081063 0.252479815 -1.255974092
## assessment_center11010           -0.3835740938 0.244467200 -1.569020687
## assessment_center11011           -0.2148826657 0.250534289 -0.857697630
## assessment_center11012           -0.3182985756 0.333113613 -0.955525573
## assessment_center11013           -0.3609548944 0.256604149 -1.406660396
## assessment_center11014           -0.2974060282 0.260774925 -1.140470191
## assessment_center11016           -0.5095579379 0.250514758 -2.034043587
## assessment_center11017           -0.2572761827 0.287347733 -0.895347878
## assessment_center11018           -0.3339763493 0.278990198 -1.197089904
## assessment_center11020           -0.5180131591 0.281790322 -1.838292941
## assessment_center11021           -0.7375506705 0.280730200 -2.627258020
## assessment_center11022           -1.0696518474 0.659346845 -1.622290083
## assessment_center11023           -1.4376261721 1.463113595 -0.982580011
## waist.scale                       0.1170583807 0.044041991  2.657881231
## factor(smoke.status)1             0.1680032918 0.078872353  2.130065678
## factor(smoke.status)2            -0.0870663714 0.133616490 -0.651613969
## redmeat.numeric.scale             0.0572790745 0.039495405  1.450271870
## process_meat.scale                0.1019869442 0.040878919  2.494854221
## veg.numeric.scale                -0.0115146116 0.037069724 -0.310620375
## birth.x.scale                    -0.0385289512 0.042340161 -0.909985939
## birth.y.scale                    -0.0047980723 0.052045005 -0.092190832
## prs_scaled:birth.x.scale         -0.0039143295 0.035829409 -0.109249068
## prs_scaled:birth.y.scale          0.0305684431 0.036986733  0.826470482
## prs_scaled:factor(activity)1     -0.1321675541 0.068734664 -1.922866073
## prs_scaled:factor(activity)2     -0.0840250729 0.070035955 -1.199741952
## prs_scaled:agegroups[45,50)      -0.1268838784 0.205333087 -0.617941708
## prs_scaled:agegroups[50,55)      -0.1234983412 0.188409370 -0.655478766
## prs_scaled:agegroups[55,60)      -0.0519407917 0.183019229 -0.283799642
## prs_scaled:agegroups[60,65)      -0.1724790531 0.179985960 -0.958291711
## prs_scaled:agegroups[65,70)      -0.1918974806 0.180517864 -1.063038728
## prs_scaled:agegroups[70,75)      -0.9689031226 0.314308519 -3.082649891
## prs_scaled:sexMale                0.0086383467 0.078271013  0.110364571
## prs_scaled:alcohol               -0.0710648216 0.070469673 -1.008445463
## prs_scaled:height.scale           0.0176433534 0.036039787  0.489552104
## prs_scaled:waist.scale           -0.0119408331 0.029409240 -0.406023180
## prs_scaled:veg.numeric.scale     -0.0143603947 0.025599873 -0.560955696
## prs_scaled:redmeat.numeric.scale -0.0140805779 0.026564866 -0.530045122
## prs_scaled:factor(smoke.status)1  0.0189597650 0.052979183  0.357871981
## prs_scaled:factor(smoke.status)2  0.0651317037 0.093276271  0.698266592
## prs_scaled:process_meat.scale    -0.0328308507 0.027466171 -1.195319525
## eta_X.Intercept.                 -0.1201589913 0.085503044 -1.405318290
## eta_PC1                           0.0065416887 0.007198291  0.908783590
## eta_PC2                          -0.0060058771 0.008740841 -0.687105217
## eta_PC3                           0.0016542777 0.009759041  0.169512324
## eta_PC4                           0.0001886295 0.004844563  0.038936323
## eta_PC5                          -0.0019827462 0.002727663 -0.726902998
## eta_PC6                          -0.0002292867 0.009622912 -0.023827161
## eta_PC7                           0.0095141354 0.005493888  1.731767346
## eta_PC8                          -0.0004760320 0.006774566 -0.070267518
## eta_PC9                          -0.0011852478 0.003846085 -0.308169956
## eta_PC10                          0.0049047785 0.007766306  0.631545848
## eta_birth.x.scale                -0.0056059954 0.024847847 -0.225612922
## eta_birth.y.scale                 0.0195512376 0.026080744  0.749642626
## sigma_stratadata[, facVar]11001   0.9824778839 0.060636419 16.202768771
## sigma_stratadata[, facVar]11002   0.9443578708 0.055867199 16.903619506
## sigma_stratadata[, facVar]11003   1.0057258040 0.054926018 18.310553746
## sigma_stratadata[, facVar]11004   0.9794200032 0.052675825 18.593349247
## sigma_stratadata[, facVar]11005   0.9537129785 0.052615789 18.125984408
## sigma_stratadata[, facVar]11006   0.9070368536 0.051031199 17.774163062
## sigma_stratadata[, facVar]11007   0.9843861926 0.042022081 23.425451091
## sigma_stratadata[, facVar]11008   0.9580022583 0.041689405 22.979513757
## sigma_stratadata[, facVar]11009   0.9637053159 0.038063748 25.318192648
## sigma_stratadata[, facVar]11010   0.9873322180 0.035731109 27.632285649
## sigma_stratadata[, facVar]11011   0.9643073100 0.035793949 26.940511671
## sigma_stratadata[, facVar]11012   0.9965794024 0.075173284 13.257095432
## sigma_stratadata[, facVar]11013   1.0300137708 0.043467484 23.696190016
## sigma_stratadata[, facVar]11014   0.8927168012 0.040410188 22.091379415
## sigma_stratadata[, facVar]11016   0.9789042156 0.039860426 24.558297956
## sigma_stratadata[, facVar]11017   0.8727957140 0.050802546 17.180156804
## sigma_stratadata[, facVar]11018   0.9639861527 0.051032064 18.889813277
## sigma_stratadata[, facVar]11020   1.0242921321 0.055896763 18.324712725
## sigma_stratadata[, facVar]11021   1.0251153058 0.056234973 18.229142010
## sigma_stratadata[, facVar]11022   0.8472679240 0.170236628  4.977001334
## sigma_stratadata[, facVar]11023   1.3340849771 0.565234336  2.360233434
##                                         Pvalue
## (Intercept)                       5.596616e-07
## prs_scaled                        1.320296e-04
## PC1                               6.380886e-02
## PC2                               9.004538e-01
## PC3                               9.165642e-01
## PC4                               4.577003e-01
## PC5                               5.539304e-02
## PC6                               8.345302e-01
## PC7                               6.706878e-01
## PC8                               2.090604e-02
## PC9                               4.639425e-02
## PC10                              4.689226e-01
## factor(activity)1                 4.605265e-01
## factor(activity)2                 5.277800e-01
## agegroups[45,50)                  6.459744e-03
## agegroups[50,55)                  7.640543e-09
## agegroups[55,60)                  2.367322e-15
## agegroups[60,65)                  8.275383e-20
## agegroups[65,70)                  6.315099e-25
## agegroups[70,75)                  1.152897e-05
## sexMale                           8.348018e-02
## alcohol                           8.710873e-01
## height.scale                      4.793206e-01
## assessment_center11002            5.632798e-01
## assessment_center11003            2.132758e-01
## assessment_center11004            6.771664e-01
## assessment_center11005            9.988955e-01
## assessment_center11006            3.887733e-01
## assessment_center11007            5.021884e-01
## assessment_center11008            2.149349e-01
## assessment_center11009            2.091254e-01
## assessment_center11010            1.166431e-01
## assessment_center11011            3.910594e-01
## assessment_center11012            3.393120e-01
## assessment_center11013            1.595281e-01
## assessment_center11014            2.540905e-01
## assessment_center11016            4.194720e-02
## assessment_center11017            3.706012e-01
## assessment_center11018            2.312715e-01
## assessment_center11020            6.601925e-02
## assessment_center11021            8.607604e-03
## assessment_center11022            1.047413e-01
## assessment_center11023            3.258142e-01
## waist.scale                       7.863360e-03
## factor(smoke.status)1             3.316619e-02
## factor(smoke.status)2             5.146502e-01
## redmeat.numeric.scale             1.469827e-01
## process_meat.scale                1.260089e-02
## veg.numeric.scale                 7.560892e-01
## birth.x.scale                     3.628299e-01
## birth.y.scale                     9.265464e-01
## prs_scaled:birth.x.scale          9.130049e-01
## prs_scaled:birth.y.scale          4.085373e-01
## prs_scaled:factor(activity)1      5.449687e-02
## prs_scaled:factor(activity)2      2.302396e-01
## prs_scaled:agegroups[45,50)       5.366138e-01
## prs_scaled:agegroups[50,55)       5.121596e-01
## prs_scaled:agegroups[55,60)       7.765639e-01
## prs_scaled:agegroups[60,65)       3.379157e-01
## prs_scaled:agegroups[65,70)       2.877644e-01
## prs_scaled:agegroups[70,75)       2.051664e-03
## prs_scaled:sexMale                9.121202e-01
## prs_scaled:alcohol                3.132407e-01
## prs_scaled:height.scale           6.244509e-01
## prs_scaled:waist.scale            6.847256e-01
## prs_scaled:veg.numeric.scale      5.748277e-01
## prs_scaled:redmeat.numeric.scale  5.960806e-01
## prs_scaled:factor(smoke.status)1  7.204391e-01
## prs_scaled:factor(smoke.status)2  4.850105e-01
## prs_scaled:process_meat.scale     2.319622e-01
## eta_X.Intercept.                  1.599267e-01
## eta_PC1                           3.634644e-01
## eta_PC2                           4.920164e-01
## eta_PC3                           8.653937e-01
## eta_PC4                           9.689412e-01
## eta_PC5                           4.672854e-01
## eta_PC6                           9.809905e-01
## eta_PC7                           8.331499e-02
## eta_PC8                           9.439807e-01
## eta_PC9                           7.579530e-01
## eta_PC10                          5.276837e-01
## eta_birth.x.scale                 8.215025e-01
## eta_birth.y.scale                 4.534700e-01
## sigma_stratadata[, facVar]11001   4.820683e-59
## sigma_stratadata[, facVar]11002   4.231180e-64
## sigma_stratadata[, facVar]11003   6.817297e-75
## sigma_stratadata[, facVar]11004   3.637392e-77
## sigma_stratadata[, facVar]11005   1.987540e-73
## sigma_stratadata[, facVar]11006   1.120575e-70
## sigma_stratadata[, facVar]11007  2.352523e-121
## sigma_stratadata[, facVar]11008  7.471907e-117
## sigma_stratadata[, facVar]11009  2.014135e-141
## sigma_stratadata[, facVar]11010  4.556747e-168
## sigma_stratadata[, facVar]11011  7.368652e-160
## sigma_stratadata[, facVar]11012   4.104889e-40
## sigma_stratadata[, facVar]11013  3.947025e-124
## sigma_stratadata[, facVar]11014  3.825351e-108
## sigma_stratadata[, facVar]11016  3.526570e-133
## sigma_stratadata[, facVar]11017   3.738992e-66
## sigma_stratadata[, facVar]11018   1.383342e-79
## sigma_stratadata[, facVar]11020   5.255818e-75
## sigma_stratadata[, facVar]11021   3.030379e-74
## sigma_stratadata[, facVar]11022   6.457688e-07
## sigma_stratadata[, facVar]11023   1.826344e-02

summary(fit_cc_glm)

## 
## Call:
## glm(formula = colorectal_cancer ~ prs_scaled + PC1 + PC2 + PC3 + 
##     PC4 + PC5 + PC6 + PC7 + PC8 + PC9 + PC10 + factor(activity) + 
##     agegroups + sex + alcohol + height.scale + assessment_center + 
##     waist.scale + factor(smoke.status) + redmeat.numeric.scale + 
##     process_meat.scale + veg.numeric.scale + birth.x.scale + 
##     birth.y.scale + prs_scaled:birth.x.scale + prs_scaled:birth.y.scale + 
##     prs_scaled:factor(activity) + prs_scaled:agegroups + prs_scaled:sex + 
##     prs_scaled:alcohol + prs_scaled:height.scale + prs_scaled:waist.scale + 
##     prs_scaled:veg.numeric.scale + prs_scaled:redmeat.numeric.scale + 
##     prs_scaled:factor(smoke.status) + prs_scaled:process_meat.scale, 
##     family = binomial(), data = dat_test_casecontrol, model = FALSE, 
##     x = FALSE, y = FALSE)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -2.318  -1.052   0.511   1.014   2.429  
## 
## Coefficients:
##                                   Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                      -1.863871   0.373154  -4.995 5.89e-07 ***
## prs_scaled                        0.499497   0.238532   2.094  0.03626 *  
## PC1                              -0.031221   0.017375  -1.797  0.07235 .  
## PC2                              -0.001645   0.020718  -0.079  0.93672    
## PC3                               0.001455   0.022458   0.065  0.94835    
## PC4                              -0.007664   0.011006  -0.696  0.48620    
## PC5                               0.012662   0.006421   1.972  0.04861 *  
## PC6                               0.003961   0.021718   0.182  0.85527    
## PC7                              -0.007049   0.013208  -0.534  0.59355    
## PC8                              -0.036849   0.015732  -2.342  0.01917 *  
## PC9                              -0.017327   0.009147  -1.894  0.05821 .  
## PC10                             -0.010266   0.018004  -0.570  0.56853    
## factor(activity)1                -0.069999   0.105282  -0.665  0.50613    
## factor(activity)2                -0.044384   0.107349  -0.413  0.67927    
## agegroups[45,50)                  0.677336   0.238974   2.834  0.00459 ** 
## agegroups[50,55)                  1.262668   0.221802   5.693 1.25e-08 ***
## agegroups[55,60)                  1.721197   0.217963   7.897 2.86e-15 ***
## agegroups[60,65)                  1.946960   0.213114   9.136  < 2e-16 ***
## agegroups[65,70)                  2.228565   0.216040  10.315  < 2e-16 ***
## agegroups[70,75)                  2.227709   0.526871   4.228 2.36e-05 ***
## sexMale                           0.218233   0.118283   1.845  0.06504 .  
## alcohol                           0.022692   0.104569   0.217  0.82820    
## height.scale                      0.034745   0.055813   0.623  0.53360    
## assessment_center11002           -0.189062   0.298327  -0.634  0.52625    
## assessment_center11003           -0.329364   0.293504  -1.122  0.26179    
## assessment_center11004           -0.155371   0.301725  -0.515  0.60659    
## assessment_center11005            0.004084   0.298811   0.014  0.98910    
## assessment_center11006           -0.234589   0.287361  -0.816  0.41430    
## assessment_center11007           -0.162683   0.265678  -0.612  0.54032    
## assessment_center11008           -0.360973   0.261192  -1.382  0.16697    
## assessment_center11009           -0.327795   0.258923  -1.266  0.20552    
## assessment_center11010           -0.411078   0.249548  -1.647  0.09950 .  
## assessment_center11011           -0.227754   0.255604  -0.891  0.37291    
## assessment_center11012           -0.312083   0.340233  -0.917  0.35901    
## assessment_center11013           -0.411595   0.262348  -1.569  0.11667    
## assessment_center11014           -0.335384   0.266277  -1.260  0.20784    
## assessment_center11016           -0.583197   0.255896  -2.279  0.02266 *  
## assessment_center11017           -0.257912   0.293121  -0.880  0.37892    
## assessment_center11018           -0.376571   0.285428  -1.319  0.18706    
## assessment_center11020           -0.600235   0.288220  -2.083  0.03729 *  
## assessment_center11021           -0.713118   0.287651  -2.479  0.01317 *  
## assessment_center11022           -0.932136   0.674587  -1.382  0.16704    
## assessment_center11023           -1.889872   1.476241  -1.280  0.20048    
## waist.scale                       0.122917   0.044748   2.747  0.00602 ** 
## factor(smoke.status)1             0.179899   0.080391   2.238  0.02523 *  
## factor(smoke.status)2            -0.098684   0.135728  -0.727  0.46718    
## redmeat.numeric.scale             0.065471   0.040199   1.629  0.10338    
## process_meat.scale                0.099055   0.041569   2.383  0.01718 *  
## veg.numeric.scale                -0.014606   0.037740  -0.387  0.69875    
## birth.x.scale                    -0.036320   0.042846  -0.848  0.39662    
## birth.y.scale                    -0.005420   0.052999  -0.102  0.91855    
## prs_scaled:birth.x.scale          0.019313   0.038553   0.501  0.61641    
## prs_scaled:birth.y.scale          0.036272   0.039235   0.924  0.35524    
## prs_scaled:factor(activity)1     -0.091153   0.108538  -0.840  0.40101    
## prs_scaled:factor(activity)2     -0.011432   0.111961  -0.102  0.91867    
## prs_scaled:agegroups[45,50)      -0.016787   0.241636  -0.069  0.94461    
## prs_scaled:agegroups[50,55)      -0.175490   0.223036  -0.787  0.43139    
## prs_scaled:agegroups[55,60)       0.062828   0.220620   0.285  0.77581    
## prs_scaled:agegroups[60,65)      -0.129553   0.212461  -0.610  0.54201    
## prs_scaled:agegroups[65,70)      -0.113264   0.216266  -0.524  0.60047    
## prs_scaled:agegroups[70,75)      -0.874803   0.506897  -1.726  0.08438 .  
## prs_scaled:sexMale                0.128997   0.125349   1.029  0.30343    
## prs_scaled:alcohol               -0.001814   0.106803  -0.017  0.98645    
## prs_scaled:height.scale          -0.040993   0.059002  -0.695  0.48720    
## prs_scaled:waist.scale           -0.015674   0.046448  -0.337  0.73578    
## prs_scaled:veg.numeric.scale     -0.019426   0.037445  -0.519  0.60390    
## prs_scaled:redmeat.numeric.scale  0.024491   0.042442   0.577  0.56391    
## prs_scaled:factor(smoke.status)1  0.124121   0.085627   1.450  0.14718    
## prs_scaled:factor(smoke.status)2  0.121209   0.137469   0.882  0.37793    
## prs_scaled:process_meat.scale    -0.044091   0.043437  -1.015  0.31008    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 4832.6  on 3485  degrees of freedom
## Residual deviance: 4253.3  on 3416  degrees of freedom
## AIC: 4393.3
## 
## Number of Fisher Scoring iterations: 4

Case-only method

summary.caseonly=function (parms, sd, sided = 2) 
{
  if (sided != 1) 
    sided <- 2
  cols <- c("Estimate", "Std.Error", "Z.value", "Pvalue")
  n <- length(parms)
  ret <- matrix(data = NA, nrow = n, ncol = 4)
  pnames <- c("prs",paste0("prs:",names(parms)[-1]))
  rownames(ret) <- pnames
  colnames(ret) <- cols
  ret[, 1] <- parms
  if (is.null(pnames)) 
    pnames <- 1:n
  cov <- sd
  ret[, 2] <- cov
  ret[, 3] <- parms/cov
  ret[, 4] <- sided * pnorm(abs(ret[, 3]), lower.tail = FALSE)
  ret
}
dat_caseonly=dat_test_casecontrol[which(dat_test_casecontrol$colorectal_cancer==1),]
fit_caseonly <- lm(prs_scaled~PC1+PC2+PC3+PC4+PC5+PC6+PC7+PC8+PC9+PC10+factor(activity)+agegroups+sex+alcohol+height.scale+assessment_center+waist.scale+factor(smoke.status)+redmeat.numeric.scale+process_meat.scale+veg.numeric.scale+birth.x.scale+birth.y.scale,data=dat_caseonly)
beta_int=fit_caseonly$coefficients[-1]/(sd(fit_caseonly$residuals))^2
sd_int=summary(fit_caseonly)$coef[-1,2]/(sd(fit_caseonly$residuals))^2
mean_prs=mean(dat_test$prs_scaled)
beta_prs=(fit_caseonly$coefficients[1]-mean_prs)/(sd(fit_caseonly$residuals))^2
sd_prs= sqrt(( (summary(fit_caseonly)$coef[1,2])^2 +(sd(fit_caseonly$residuals))^2/dim(dat_test)[1]) / (sd(fit_caseonly$residuals))^4)
res_caseonly=summary.caseonly(parms = c(beta_prs,beta_int),sd=c(sd_prs,sd_int))
print(res_caseonly)

##                                 Estimate   Std.Error     Z.value      Pvalue
## prs                         5.827184e-01 0.284327199  2.04946427 0.040416738
## prs:PC1                    -4.138740e-04 0.013928493 -0.02971420 0.976294986
## prs:PC2                    -1.084352e-02 0.015645398 -0.69308061 0.488258965
## prs:PC3                     9.942647e-03 0.015962695  0.62286768 0.533371481
## prs:PC4                    -1.728694e-03 0.007570297 -0.22835216 0.819372480
## prs:PC5                     1.655533e-03 0.004485775  0.36906280 0.712080913
## prs:PC6                     1.338895e-03 0.015327354  0.08735331 0.930390677
## prs:PC7                     9.665850e-03 0.009585812  1.00834967 0.313286626
## prs:PC8                     7.401666e-05 0.011339744  0.00652719 0.994792093
## prs:PC9                    -5.193578e-03 0.005982193 -0.86817300 0.385299633
## prs:PC10                    5.718053e-03 0.012443497  0.45952142 0.645859774
## prs:factor(activity)1      -1.361187e-01 0.070945011 -1.91865041 0.055028591
## prs:factor(activity)2      -8.637115e-02 0.073004923 -1.18308670 0.236774788
## prs:agegroups[45,50)       -1.104074e-01 0.212560037 -0.51941757 0.603469579
## prs:agegroups[50,55)       -1.104802e-01 0.195033043 -0.56646892 0.571075054
## prs:agegroups[55,60)       -5.270008e-02 0.190052069 -0.27729283 0.781555265
## prs:agegroups[60,65)       -1.796503e-01 0.186657671 -0.96245867 0.335819256
## prs:agegroups[65,70)       -1.871272e-01 0.187352497 -0.99879728 0.317892903
## prs:agegroups[70,75)       -1.028864e+00 0.322094902 -3.19428764 0.001401764
## prs:sexMale                 2.047746e-03 0.082894540  0.02470303 0.980291841
## prs:alcohol                -9.183786e-02 0.073045747 -1.25726497 0.208657702
## prs:height.scale            2.249915e-02 0.038453041  0.58510725 0.558475606
## prs:assessment_center11002 -9.751612e-03 0.196582064 -0.04960581 0.960436520
## prs:assessment_center11003 -2.862691e-01 0.195658613 -1.46310515 0.143438609
## prs:assessment_center11004  5.769232e-02 0.191975672  0.30051891 0.763781375
## prs:assessment_center11005 -5.696873e-03 0.191467975 -0.02975366 0.976263518
## prs:assessment_center11006 -1.887606e-01 0.187322530 -1.00767698 0.313609558
## prs:assessment_center11007 -6.237638e-02 0.173380904 -0.35976500 0.719022876
## prs:assessment_center11008  7.616300e-02 0.170262194  0.44732773 0.654638452
## prs:assessment_center11009  4.951012e-02 0.168042389  0.29462875 0.768277494
## prs:assessment_center11010  5.378413e-02 0.163033002  0.32989718 0.741477654
## prs:assessment_center11011 -8.087335e-02 0.166989485 -0.48430204 0.628171527
## prs:assessment_center11012 -4.101707e-02 0.232775657 -0.17620857 0.860130084
## prs:assessment_center11013  1.556109e-01 0.171923282  0.90511846 0.365402635
## prs:assessment_center11014 -1.880283e-02 0.174286809 -0.10788443 0.914087370
## prs:assessment_center11016  8.111268e-02 0.167391108  0.48456980 0.627981542
## prs:assessment_center11017 -1.421893e-01 0.193431363 -0.73508899 0.462285298
## prs:assessment_center11018  8.689313e-02 0.189931785  0.45749653 0.647314199
## prs:assessment_center11020  1.562422e-01 0.194013487  0.80531607 0.420637311
## prs:assessment_center11021 -1.243123e-01 0.197577143 -0.62918355 0.529228894
## prs:assessment_center11022 -5.704380e-01 0.494862660 -1.15271979 0.249025416
## prs:assessment_center11023  1.518699e+00 1.068121091  1.42184170 0.155072210
## prs:waist.scale            -1.423286e-02 0.030962494 -0.45968063 0.645745475
## prs:factor(smoke.status)1   2.310899e-02 0.055229472  0.41841767 0.675641768
## prs:factor(smoke.status)2   9.970760e-02 0.096695707  1.03114812 0.302471364
## prs:redmeat.numeric.scale  -1.423033e-02 0.027508188 -0.51731267 0.604937908
## prs:process_meat.scale     -3.017425e-02 0.028076845 -1.07470220 0.282508080
## prs:veg.numeric.scale      -1.356384e-02 0.027089596 -0.50070298 0.616580177
## prs:birth.x.scale          -2.236925e-02 0.029285715 -0.76382799 0.444969756
## prs:birth.y.scale           2.313971e-02 0.035927116  0.64407370 0.519527644