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Main_analysis.Rmd
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Main_analysis.Rmd
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---
title: "prevalent_main_analysis_1015"
author: "Mingzhou_Fu"
date: "10/15/2019"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r Basic personal setups, message=FALSE, warning=FALSE}
rm(list = ls())
library(tidyverse)
library(writexl)
library(ggcorrplot)
library(compareGroups)
library(MendelianRandomization)
library(AER) # For MR instrumental testing
library(sjlabelled)
library(epiflow)
library(pscl) # calculate pseudo r-square
source_path = '/Users/Mingzhou/Desktop/Materials/General R/code/'
work_data_path = '/Users/Mingzhou/Desktop/AD_Grant/Cholesterol/data/'
output_path = '/Users/Mingzhou/Desktop/AD_Grant/Cholesterol/output/prevalent/'
# Original dataset
europ = 'prevalent_europ.rda'
load(paste0(work_data_path, europ)) # -- N = 8,781
load(paste0(work_data_path, 'prevalent_eligible.rda')) # N = 10,882
attach(europ)
```
# Part 0: Univariate analysis/Make tertiles
```{r Distribution check}
# The distributions of DBS HDL/TC are a little bit right-skewed -- but not too much, consider not log-transformed for now
hist(HDL)
hist(TC)
# Below is for sensitivity analysis -- Exclude those in AHEAD and CODA
no_old_europ =
europ %>%
filter(STUDY != 11 & STUDY != 21)
# N = 7,080
```
# Part 1: Univariate analysis: descriptive among ancestry
```{r}
eligible_nolabel = remove_all_labels(final_sample)
table_uni = descrTable(gen_ancestry ~ HDL_clinic_cat + TC_clinic_bin_cat + AD_cat + HDL + TC +
age + sex_cat + education + wave_adj + med_lipid_cat +
stroke_cat + APOE2010_cat + HTN_cat + diabetes_cat + smoking_cat + drinking_cat + proxy_cat +
BMI + PGS_HDL + PGS_TC + PGS_AD_IGAP + PGS_AD_Kunkle + PGS_GENCOG +
im_recall + dl_recall + total_recall + serial7 + backwc + vocab + ms_total + cog_total +
PC1_5A + PC1_5B + PC1_5C + PC1_5D + PC1_5E, eligible_nolabel, hide.no = "No", show.all = T)
compareGroups::export2csv(table_uni, file = paste0(output_path, 'univariate_0708.csv'))
```
# Part 2: Bivariate analysis
```{r Table1: bivariate, message=FALSE, warning=FALSE}
eligible_nolabel_europ = remove_all_labels(europ)
# Build table: 1. Cognitive status
table1_cog = descrTable(AD_cat ~ HDL_clinic_cat + TC_clinic_bin_cat +
age + sex_cat + education + wave_adj + med_lipid_cat +
stroke_cat + APOE2010_cat + HTN_cat + diabetes_cat + smoking_cat + drinking_cat +
BMI + PGS_HDL + PGS_TC + PGS_AD_IGAP + PGS_AD_Kunkle + PGS_GENCOG +
im_recall + dl_recall + total_recall + serial7 + backwc + vocab + ms_total + cog_total,
eligible_nolabel_europ, hide.no = "No", show.all = T)
compareGroups::export2csv(table1_cog, file = paste0(output_path, 'cognitive_europ_0708.csv'))
# Build table: 2. HDL clinical level
table1_hdl = descrTable(HDL_clinic_cat ~ AD_cat + TC_clinic_bin_cat +
age + sex_cat + education + wave_adj + med_lipid_cat +
stroke_cat + APOE2010_cat + HTN_cat + diabetes_cat + smoking_cat + drinking_cat +
BMI + PGS_HDL + PGS_TC + PGS_AD_IGAP + PGS_AD_Kunkle + PGS_GENCOG +
im_recall + dl_recall + total_recall + serial7 + backwc + vocab + ms_total + cog_total,
eligible_nolabel_europ, hide.no = "No", show.all = T)
compareGroups::export2csv(table1_hdl, file = paste0(output_path, 'hdl_europ_0708.csv'))
# Build table: 3. TC clinical level
table1_tc = descrTable(TC_clinic_bin_cat ~ AD_cat + HDL_clinic_cat +
age + sex_cat + education + wave_adj + med_lipid_cat +
stroke_cat + APOE2010_cat + HTN_cat + diabetes_cat + smoking_cat + drinking_cat +
BMI + PGS_HDL + PGS_TC + PGS_AD_IGAP + PGS_AD_Kunkle + PGS_GENCOG +
im_recall + dl_recall + total_recall + serial7 + backwc + vocab + ms_total + cog_total,
eligible_nolabel_europ, hide.no = "No", show.all = T)
compareGroups::export2csv(table1_tc, file = paste0(output_path, 'tc_europ_0708.csv'))
```
# Part 3: Logistic regression
# ================================
## HDL ~ Cognitive status
## 1. Separate the full set into a subset that have complete exposure data (HDL)
```{r Separate datasets, message=FALSE, warning=FALSE}
# HDL sample
hdl_sample =
europ %>%
dplyr::select(HHID, PN, AD_cat, HDL, PGS_HDL) %>%
drop_na() %>%
left_join(europ)
# N = 7,869
```
## 2. Clinical HDL group and cognition
### 1) Prepare datasets
```{r}
cind_normal =
hdl_sample %>%
dplyr::filter(AD_cat == "Normal" | AD_cat == "CIND") %>%
mutate(AD_bin = case_when(
AD_cat == "Normal" ~ 0,
AD_cat == "CIND" ~ 1
))
# N = 7,707
dementia_normal =
hdl_sample %>%
dplyr::filter(AD_cat == "Normal" | AD_cat == "Dementia") %>%
mutate(AD_bin = case_when(
AD_cat == "Normal" ~ 0,
AD_cat == "Dementia" ~ 1
))
# N = 6,926
```
### 2) Name models and adjusted variables
```{r}
lst_model = c('CIND_crude', 'Dem_crude', 'CIND_adj1', 'Dem_adj1', 'full_crude', 'full_adj1')
gene_adj_basic = 'age + sex + education + wave_adj + med_lipid + PC1_5A + PC1_5B + PC1_5C + PC1_5D + PC1_5E'
# Function for improvement Chi-square
G_square = function(L_full, L_reduced) {
output = 2*(L_full - L_reduced)
}
```
### 3) Regression models
#### A. PGS HDL and HDL group
```{r message=FALSE, warning=FALSE}
# Crude
crude = as.formula(paste('HDL_clinic ~ ', 'PGS_HDL'))
CIND_crude = glm(crude, data = cind_normal, family = 'binomial')
Dem_crude = glm(crude, data = dementia_normal, family = 'binomial')
full_crude = glm(crude, data = hdl_sample, family = 'binomial')
# pR2(full_crude)
# Adding in demographic variables
f1 = as.formula(paste('HDL_clinic ~ PGS_HDL + ', gene_adj_basic))
# Reduced model
f_reduced = as.formula(paste('HDL_clinic ~ ', gene_adj_basic))
CIND_adj1 = glm(f1, data = cind_normal, family = 'binomial')
CIND_reduced = glm(f_reduced, data = cind_normal, family = 'binomial')
cind_G = G_square(logLik(CIND_adj1)[1], logLik(CIND_reduced)[1])
print(paste0("CIND: ", round(cind_G, 2)))
Dem_adj1 = glm(f1, data = dementia_normal, family = 'binomial')
Dem_reduced = glm(f_reduced, data = dementia_normal, family = 'binomial')
dementia_G = G_square(logLik(Dem_adj1)[1], logLik(Dem_reduced)[1])
print(paste0("Dementia: ", round(dementia_G, 2)))
full_adj1 = glm(f1, data = hdl_sample, family = 'binomial')
full_reduced = glm(f_reduced, data = hdl_sample, family = 'binomial')
full_G = G_square(logLik(full_adj1)[1], logLik(full_reduced)[1])
print(paste0("Full: ", round(full_G, 2)))
# pR2(full_adj1)
hdl_pgs = make_OR_table(lst_model, 2, 'hdl_pgs_')
```
#### B. HDL group and Cognitive Status
##### 1) Try ordinal logistic regression
```{r}
library(MASS)
# Relevel categorical variable -- change the order
europ$AD_cat = factor(europ$AD_cat, levels = c("Normal", "CIND", "Dementia"))
# fit ordered logit model and store results 'm'
olm = polr(AD_cat ~ HDL_clinic + age + sex + education + wave_adj + med_lipid_cat + PC1_5A + PC1_5B + PC1_5C + PC1_5D + PC1_5E, data = europ, Hess = TRUE)
# view a summary of the model
summary(olm)
```
```{r}
# Test for proportional odds assumption
library(brant)
brant(olm)
```
##### 2) Back to logistic regression
```{r}
lst_model = c('CIND_crude', 'Dem_crude', 'CIND_adj1', 'Dem_adj1', 'CIND_adj2', 'Dem_adj2', 'CIND_adj3', 'Dem_adj3')
demographic = 'age + sex + education + wave_adj + med_lipid + PC1_5A + PC1_5B + PC1_5C + PC1_5D + PC1_5E'
health_behavior = paste0(demographic, ' + stroke + BMI + HTN + diabetes + smoking_cat + drink + exercise')
gene_add = paste0(health_behavior, ' + APOE2010_bin + PGS_GENCOG')
```
```{r HDL and Cognitive status, message=FALSE, warning=FALSE}
# Crude
crude = as.formula(paste('AD_bin ~ ', 'HDL_clinic'))
CIND_crude = glm(crude, data = cind_normal, family = 'binomial')
Dem_crude = glm(crude, data = dementia_normal, family = 'binomial')
# Adding in demographic variables
f1 = as.formula(paste('AD_bin ~ HDL_clinic + ', demographic))
CIND_adj1 = glm(f1, data = cind_normal, family = 'binomial')
Dem_adj1 = glm(f1, data = dementia_normal, family = 'binomial')
# Adding in health status variables
f2 = as.formula(paste('AD_bin ~ HDL_clinic + ', health_behavior))
CIND_adj2 = glm(f2, data = cind_normal, family = 'binomial')
Dem_adj2 = glm(f2, data = dementia_normal, family = 'binomial')
# Adding in AD genetic variables
f3 = as.formula(paste('AD_bin ~ HDL_clinic + ', gene_add))
CIND_adj3 = glm(f3, data = cind_normal, family = 'binomial')
Dem_adj3 = glm(f3, data = dementia_normal, family = 'binomial')
hdl_ad = make_OR_table(lst_model, 2, 'hdl_ad_')
```
#### C. Cognitive Status ~ HDL PGS + HDL group
##### 1) Total effect
```{r message=FALSE, warning=FALSE}
# Crude
crude = as.formula(paste('AD_bin ~ ', 'PGS_HDL'))
CIND_crude = glm(crude, data = cind_normal, family = 'binomial')
Dem_crude = glm(crude, data = dementia_normal, family = 'binomial')
# Adding in demographic variables
f1 = as.formula(paste('AD_bin ~ PGS_HDL + ', demographic))
CIND_adj1 = glm(f1, data = cind_normal, family = 'binomial')
Dem_adj1 = glm(f1, data = dementia_normal, family = 'binomial')
# Adding in health status variables
f2 = as.formula(paste('AD_bin ~ PGS_HDL + ', health_behavior))
CIND_adj2 = glm(f2, data = cind_normal, family = 'binomial')
Dem_adj2 = glm(f2, data = dementia_normal, family = 'binomial')
# Adding in AD genetic variables
f3 = as.formula(paste('AD_bin ~ PGS_HDL + ', gene_add))
CIND_adj3 = glm(f3, data = cind_normal, family = 'binomial')
Dem_adj3 = glm(f3, data = dementia_normal, family = 'binomial')
hdl_adpgs1_tot = make_OR_table(lst_model, 2, 'hdl_adpgs1_t_')
```
##### 2) Direct effect
```{r HDL PGS and Cognitive status, message=FALSE, warning=FALSE}
# Crude
crude = as.formula(paste('AD_bin ~ ', 'PGS_HDL + HDL_clinic'))
CIND_crude = glm(crude, data = cind_normal, family = 'binomial')
Dem_crude = glm(crude, data = dementia_normal, family = 'binomial')
# Adding in demographic variables
f1 = as.formula(paste('AD_bin ~ PGS_HDL + HDL_clinic + ', demographic))
CIND_adj1 = glm(f1, data = cind_normal, family = 'binomial')
Dem_adj1 = glm(f1, data = dementia_normal, family = 'binomial')
# Adding in health status variables
f2 = as.formula(paste('AD_bin ~ PGS_HDL + HDL_clinic + ', health_behavior))
CIND_adj2 = glm(f2, data = cind_normal, family = 'binomial')
Dem_adj2 = glm(f2, data = dementia_normal, family = 'binomial')
# Adding in AD genetic variables
f3 = as.formula(paste('AD_bin ~ PGS_HDL + HDL_clinic + ', gene_add))
CIND_adj3 = glm(f3, data = cind_normal, family = 'binomial')
Dem_adj3 = glm(f3, data = dementia_normal, family = 'binomial')
hdl_adpgs1 = make_OR_table(lst_model, 2, 'hdl_adpgs1_')
hdl_adpgs2 = make_OR_table(lst_model, 3, 'hdl_adpgs2_')
```
```{r}
main_hdl = rbind(hdl_pgs, hdl_ad, hdl_adpgs1_tot, hdl_adpgs1, hdl_adpgs2)
```
#### D. Mendelian Randomization
```{r}
pc_only = 'PC1_5A + PC1_5B + PC1_5C + PC1_5D + PC1_5E'
demographic = 'age + sex + education + wave_adj + med_lipid + PC1_5A + PC1_5B + PC1_5C + PC1_5D + PC1_5E'
health_behavior = paste0(demographic, ' + stroke + BMI + HTN + diabetes + smoking_cat + drink + exercise')
gene_add = paste0(health_behavior, ' + APOE2010_bin + PGS_GENCOG')
# List all formula
f0_exposure = as.formula(paste('HDL_clinic ~ ', 'PGS_HDL'))
f0_outcome = as.formula(paste('AD_bin ~ ', 'PGS_HDL'))
f1_exposure = as.formula(paste('HDL_clinic ~ PGS_HDL + ', demographic))
f1_outcome = as.formula(paste('AD_bin ~ PGS_HDL + ', demographic))
f2_outcome = as.formula(paste('AD_bin ~ PGS_HDL + ', health_behavior))
f3_outcome = as.formula(paste('AD_bin ~ PGS_HDL + ', gene_add))
# CIND
CIND_MR_1 = glm(f0_exposure, data = cind_normal, family = 'binomial')
CIND_MR_2 = glm(f0_outcome, data = cind_normal, family = 'binomial')
CIND_crude_mr = get_MR_value(CIND_MR_1, CIND_MR_2, 'CIND_crude')
CIND_MR_3 = glm(f1_exposure, data = cind_normal, family = 'binomial')
CIND_MR_4 = glm(f1_outcome, data = cind_normal, family = 'binomial')
CIND_adjust_mr = get_MR_value(CIND_MR_3, CIND_MR_4, 'CIND_adj')
CIND_MR_5 = glm(f1_exposure, data = cind_normal, family = 'binomial')
CIND_MR_6 = glm(f2_outcome, data = cind_normal, family = 'binomial')
CIND_full_mr = get_MR_value(CIND_MR_5, CIND_MR_6, 'CIND_full')
CIND_MR_7 = glm(f1_exposure, data = cind_normal, family = 'binomial')
CIND_MR_8 = glm(f3_outcome, data = cind_normal, family = 'binomial')
CIND_gene_mr = get_MR_value(CIND_MR_7, CIND_MR_8, 'CIND_gene')
# Dementia
Dem_MR_1 = glm(f0_exposure, data = dementia_normal, family = 'binomial')
Dem_MR_2 = glm(f0_outcome, data = dementia_normal, family = 'binomial')
Dem_crude_mr = get_MR_value(Dem_MR_1, Dem_MR_2, 'Dem_crude')
Dem_MR_3 = glm(f1_exposure, data = dementia_normal, family = 'binomial')
Dem_MR_4 = glm(f1_outcome, data = dementia_normal, family = 'binomial')
Dem_adjust_mr = get_MR_value(Dem_MR_3, Dem_MR_4, 'Dem_adj')
Dem_MR_5 = glm(f1_exposure, data = dementia_normal, family = 'binomial')
Dem_MR_6 = glm(f2_outcome, data = dementia_normal, family = 'binomial')
Dem_full_mr = get_MR_value(Dem_MR_5, Dem_MR_6, 'Dem_full')
Dem_MR_7 = glm(f1_exposure, data = dementia_normal, family = 'binomial')
Dem_MR_8 = glm(f3_outcome, data = dementia_normal, family = 'binomial')
Dem_gene_mr = get_MR_value(Dem_MR_7, Dem_MR_8, 'Dem_gene')
# Bind together
hdl_mr_bind = rbind(CIND_crude_mr, CIND_adjust_mr, CIND_full_mr, CIND_gene_mr,
Dem_crude_mr, Dem_adjust_mr, Dem_full_mr, Dem_gene_mr)
hdl_mr_df = as.data.frame(hdl_mr_bind)
colnames(hdl_mr_df) = c('mark', 'estimate', '95% CI', 'OR', '95% CI_OR', 'p-value')
# Calculate z score (p for heterogen)
convert.z.score <- function(z, one.sided = NULL) {
if(is.null(one.sided)) {
pval = pnorm(-abs(z));
pval = 2 * pval
} else if(one.sided=="-") {
pval = pnorm(z);
} else {
pval = pnorm(-z);
}
return(pval);
}
```
```{r}
sheets_OR = list('main_hdl' = main_hdl,
'hdl_mr_df' = hdl_mr_df)
write_xlsx(sheets_OR, path = paste0(output_path, 'main_HDL_0709.xlsx'))
```
# ================================
## TC ~ Cognitive status
## 1. Separate the full set into a subset that have complete exposure data (HDL)
```{r Separate datasets, message=FALSE, warning=FALSE}
# TC sample
tc_sample =
europ %>%
dplyr::select(HHID, PN, AD_cat, TC) %>%
drop_na() %>%
left_join(europ)
# N = 8,775
```
## 2. Clinical TC group and cognition
### 1) Prepare datasets
```{r}
cind_normal =
tc_sample %>%
filter(AD_cat == "Normal" | AD_cat == "CIND") %>%
mutate(AD_bin = case_when(
AD_cat == "Normal" ~ 0,
AD_cat == "CIND" ~ 1
))
# N = 8,580
dementia_normal =
tc_sample %>%
filter(AD_cat == "Normal" | AD_cat == "Dementia") %>%
mutate(AD_bin = case_when(
AD_cat == "Normal" ~ 0,
AD_cat == "Dementia" ~ 1
))
# N = 7,716
```
### 2) Name models and adjusted variables
```{r}
lst_model = c('CIND_crude', 'Dem_crude', 'CIND_adj1', 'Dem_adj1', 'full_crude', 'full_adj1')
gene_adj_basic = 'age + sex + education + wave_adj + med_lipid + PC1_5A + PC1_5B + PC1_5C + PC1_5D + PC1_5E'
# Function for improvement Chi-square
G_square = function(L_full, L_reduced) {
output = 2*(L_full - L_reduced)
}
```
### 3) Regression models
#### A. PGS TC and TC clinical level
```{r message=FALSE, warning=FALSE}
# Crude
crude = as.formula(paste('TC_clinic_bin ~ ', 'PGS_TC'))
CIND_crude = glm(crude, data = cind_normal, family = 'binomial')
Dem_crude = glm(crude, data = dementia_normal, family = 'binomial')
full_crude = glm(crude, data = tc_sample, family = 'binomial')
# Adding in demographic variables
f1 = as.formula(paste('TC_clinic_bin ~ PGS_TC + ', gene_adj_basic))
# Reduced model
f_reduced = as.formula(paste('TC_clinic_bin ~ ', gene_adj_basic))
CIND_adj1 = glm(f1, data = cind_normal, family = 'binomial')
CIND_reduced = glm(f_reduced, data = cind_normal, family = 'binomial')
cind_G = G_square(logLik(CIND_adj1)[1], logLik(CIND_reduced)[1])
print(paste0("CIND: ", round(cind_G, 2)))
Dem_adj1 = glm(f1, data = dementia_normal, family = 'binomial')
Dem_reduced = glm(f_reduced, data = dementia_normal, family = 'binomial')
dementia_G = G_square(logLik(Dem_adj1)[1], logLik(Dem_reduced)[1])
print(paste0("Dementia: ", round(dementia_G, 2)))
full_adj1 = glm(f1, data = tc_sample, family = 'binomial')
full_reduced = glm(f_reduced, data = tc_sample, family = 'binomial')
full_G = G_square(logLik(full_adj1)[1], logLik(full_reduced)[1])
print(paste0("Full: ", round(full_G, 2)))
tc_pgs = make_OR_table(lst_model, 2, 'tc_pgs_')
```
#### B. TC group and Cognitive Status
##### 1) Try ordinal logistic regression
```{r}
# fit ordered logit model and store results 'm'
olm = polr(AD_cat ~ TC_clinic_bin + age + sex + education + wave_adj + med_lipid_cat + PC1_5A + PC1_5B + PC1_5C + PC1_5D + PC1_5E, data = europ, Hess = TRUE)
# view a summary of the model
summary(olm)
```
```{r}
# Test for proportional odds assumption
library(brant)
brant(olm)
```
##### 2) Back to logistic regression
```{r}
lst_model = c('CIND_crude', 'Dem_crude', 'CIND_adj1', 'Dem_adj1', 'CIND_adj2', 'Dem_adj2', 'CIND_adj3', 'Dem_adj3')
demographic = 'age + sex + education + wave_adj + med_lipid + PC1_5A + PC1_5B + PC1_5C + PC1_5D + PC1_5E'
health_behavior = paste0(demographic, ' + stroke + BMI + HTN + diabetes + smoking_cat + drink')
gene_add = paste0(health_behavior, ' + APOE2010_bin + PGS_GENCOG')
```
```{r HDL and Cognitive status, message=FALSE, warning=FALSE}
# Crude
crude = as.formula(paste('AD_bin ~ ', 'TC_clinic_bin'))
CIND_crude = glm(crude, data = cind_normal, family = 'binomial')
Dem_crude = glm(crude, data = dementia_normal, family = 'binomial')
# Adding in demographic variables
f1 = as.formula(paste('AD_bin ~ TC_clinic_bin + ', demographic))
CIND_adj1 = glm(f1, data = cind_normal, family = 'binomial')
Dem_adj1 = glm(f1, data = dementia_normal, family = 'binomial')
# Adding in health status variables
f2 = as.formula(paste('AD_bin ~ TC_clinic_bin + ', health_behavior))
CIND_adj2 = glm(f2, data = cind_normal, family = 'binomial')
Dem_adj2 = glm(f2, data = dementia_normal, family = 'binomial')
# Adding in AD genetic variables
f3 = as.formula(paste('AD_bin ~ TC_clinic_bin + ', gene_add))
CIND_adj3 = glm(f3, data = cind_normal, family = 'binomial')
Dem_adj3 = glm(f3, data = dementia_normal, family = 'binomial')
tc_ad = make_OR_table(lst_model, 2, 'tc_ad_')
```
### C. Cognitive Status ~ TC PGS + TC clinical group
##### 1) Total effect
```{r message=FALSE, warning=FALSE}
# Crude
crude = as.formula(paste('AD_bin ~ ', 'PGS_TC'))
CIND_crude = glm(crude, data = cind_normal, family = 'binomial')
Dem_crude = glm(crude, data = dementia_normal, family = 'binomial')
# Adding in demographic variables
f1 = as.formula(paste('AD_bin ~ PGS_TC + ', demographic))
CIND_adj1 = glm(f1, data = cind_normal, family = 'binomial')
Dem_adj1 = glm(f1, data = dementia_normal, family = 'binomial')
# Adding in health status variables
f2 = as.formula(paste('AD_bin ~ PGS_TC + ', health_behavior))
CIND_adj2 = glm(f2, data = cind_normal, family = 'binomial')
Dem_adj2 = glm(f2, data = dementia_normal, family = 'binomial')
# Adding in AD genetic variables
f3 = as.formula(paste('AD_bin ~ PGS_TC + ', gene_add))
CIND_adj3 = glm(f3, data = cind_normal, family = 'binomial')
Dem_adj3 = glm(f3, data = dementia_normal, family = 'binomial')
tc_adpgs1_tot = make_OR_table(lst_model, 2, 'tc_adpgs1_t_')
```
##### 2) Direct effect
```{r HDL PGS and Cognitive status, message=FALSE, warning=FALSE}
# Crude
crude = as.formula(paste('AD_bin ~ ', 'PGS_TC + TC_clinic_bin'))
CIND_crude = glm(crude, data = cind_normal, family = 'binomial')
Dem_crude = glm(crude, data = dementia_normal, family = 'binomial')
# Adding in demographic variables
f1 = as.formula(paste('AD_bin ~ PGS_TC + TC_clinic_bin + ', demographic))
CIND_adj1 = glm(f1, data = cind_normal, family = 'binomial')
Dem_adj1 = glm(f1, data = dementia_normal, family = 'binomial')
# Adding in health status variables
f2 = as.formula(paste('AD_bin ~ PGS_TC + TC_clinic_bin + ', health_behavior))
CIND_adj2 = glm(f2, data = cind_normal, family = 'binomial')
Dem_adj2 = glm(f2, data = dementia_normal, family = 'binomial')
# Adding in AD genetic variables
f3 = as.formula(paste('AD_bin ~ PGS_TC + TC_clinic_bin + ', gene_add))
CIND_adj3 = glm(f3, data = cind_normal, family = 'binomial')
Dem_adj3 = glm(f3, data = dementia_normal, family = 'binomial')
tc_adpgs1 = make_OR_table(lst_model, 2, 'tc_adpgs1_')
tc_adpgs2 = make_OR_table(lst_model, 3, 'tc_adpgs2_')
```
```{r}
main_tc = rbind(tc_pgs, tc_ad, tc_adpgs1_tot, tc_adpgs1, tc_adpgs2)
```
#### D. Mendelian Randomization
```{r}
pc_only = 'PC1_5A + PC1_5B + PC1_5C + PC1_5D + PC1_5E'
demographic = 'age + sex + education + wave_adj + med_lipid + PC1_5A + PC1_5B + PC1_5C + PC1_5D + PC1_5E'
health_behavior = paste0(demographic, ' + stroke + BMI + HTN + diabetes + smoking_cat + drink')
gene_add = paste0(health_behavior, ' + APOE2010_bin + PGS_GENCOG')
# List all formula
f0_exposure = as.formula(paste('TC_clinic_bin ~ ', 'PGS_TC'))
f0_outcome = as.formula(paste('AD_bin ~ ', 'PGS_TC'))
f1_exposure = as.formula(paste('TC_clinic_bin ~ PGS_TC + ', demographic))
f1_outcome = as.formula(paste('AD_bin ~ PGS_TC + ', demographic))
f2_outcome = as.formula(paste('AD_bin ~ PGS_TC + ', health_behavior))
f3_outcome = as.formula(paste('AD_bin ~ PGS_TC + ', gene_add))
# CIND
CIND_MR_1 = glm(f0_exposure, data = cind_normal, family = 'binomial')
CIND_MR_2 = glm(f0_outcome, data = cind_normal, family = 'binomial')
CIND_crude_mr = get_MR_value(CIND_MR_1, CIND_MR_2, 'CIND_crude')
CIND_MR_3 = glm(f1_exposure, data = cind_normal, family = 'binomial')
CIND_MR_4 = glm(f1_outcome, data = cind_normal, family = 'binomial')
CIND_adjust_mr = get_MR_value(CIND_MR_3, CIND_MR_4, 'CIND_adj')
CIND_MR_5 = glm(f1_exposure, data = cind_normal, family = 'binomial')
CIND_MR_6 = glm(f2_outcome, data = cind_normal, family = 'binomial')
CIND_full_mr = get_MR_value(CIND_MR_5, CIND_MR_6, 'CIND_full')
CIND_MR_7 = glm(f1_exposure, data = cind_normal, family = 'binomial')
CIND_MR_8 = glm(f3_outcome, data = cind_normal, family = 'binomial')
CIND_gene_mr = get_MR_value(CIND_MR_7, CIND_MR_8, 'CIND_gene')
# Dementia
Dem_MR_1 = glm(f0_exposure, data = dementia_normal, family = 'binomial')
Dem_MR_2 = glm(f0_outcome, data = dementia_normal, family = 'binomial')
Dem_crude_mr = get_MR_value(Dem_MR_1, Dem_MR_2, 'Dem_crude')
Dem_MR_3 = glm(f1_exposure, data = dementia_normal, family = 'binomial')
Dem_MR_4 = glm(f1_outcome, data = dementia_normal, family = 'binomial')
Dem_adjust_mr = get_MR_value(Dem_MR_3, Dem_MR_4, 'Dem_adj')
Dem_MR_5 = glm(f1_exposure, data = dementia_normal, family = 'binomial')
Dem_MR_6 = glm(f2_outcome, data = dementia_normal, family = 'binomial')
Dem_full_mr = get_MR_value(Dem_MR_5, Dem_MR_6, 'Dem_full')
Dem_MR_7 = glm(f1_exposure, data = dementia_normal, family = 'binomial')
Dem_MR_8 = glm(f3_outcome, data = dementia_normal, family = 'binomial')
Dem_gene_mr = get_MR_value(Dem_MR_7, Dem_MR_8, 'Dem_gene')
# Bind together
tc_mr_bind = rbind(CIND_crude_mr, CIND_adjust_mr, CIND_full_mr, CIND_gene_mr,
Dem_crude_mr, Dem_adjust_mr, Dem_full_mr, Dem_gene_mr)
tc_mr_df = as.data.frame(tc_mr_bind)
colnames(tc_mr_df) = c('mark', 'estimate', '95% CI', 'OR', '95% CI_OR', 'p-value')
```
```{r}
sheets_OR = list('main_tc' = main_tc,
'tc_mr_df' = tc_mr_df)
write_xlsx(sheets_OR, path = paste0(output_path, 'main_TC_0709.xlsx'))
```
#=================================
# Part 4: Domain analysis
## I. Check for sample size drop
```{r}
sum(!is.na(europ$im_recall)) # N = 0
sum(!is.na(europ$dl_recall)) # N = 0
sum(!is.na(europ$total_recall)) # N = 0
sum(!is.na(europ$serial7)) # N = 0
sum(!is.na(europ$backwc)) # N = 0
sum(!is.na(europ$ms_total)) # N = 2,851
sum(!is.na(europ$vocab)) # N = 5,509
sum(!is.na(europ$cog_total)) # N = 2,851
```
## II. Regression analysis
```{r}
lst_model = c('im_recall_crude', 'dl_recall_crude', 'total_recall_crude', 'serial_crude', 'backwc_crude', 'vocab_crude', 'ms_total_crude', 'cog_total_crude',
'im_recall_adj1', 'dl_recall_adj1', 'total_recall_adj1', 'serial_adj1', 'backwc_adj1', 'vocab_adj1', 'ms_total_adj1', 'cog_total_adj1',
'im_recall_adj2', 'dl_recall_adj2', 'total_recall_adj2', 'serial_adj2', 'backwc_adj2', 'vocab_adj2', 'ms_total_adj2', 'cog_total_adj2',
'im_recall_adj3', 'dl_recall_adj3', 'total_recall_adj3', 'serial_adj3', 'backwc_adj3', 'vocab_adj3', 'ms_total_adj3', 'cog_total_adj3')
demographic = 'age + sex + education + wave_adj + med_lipid + PC1_5A + PC1_5B + PC1_5C + PC1_5D + PC1_5E'
health_behavior = paste0(demographic, ' + stroke + BMI + HTN + diabetes + smoking_cat + drink')
gene_add = paste0(health_behavior, ' + APOE2010_bin + PGS_GENCOG')
```
### 1. HDL and domain cognition score
```{r domain, message=FALSE, warning=FALSE}
## Crude
im_recall_crude = lm(im_recall ~ HDL_clinic, data = europ)
dl_recall_crude = lm(dl_recall ~ HDL_clinic, data = europ)
total_recall_crude = lm(total_recall ~ HDL_clinic, data = europ)
serial_crude = lm(serial7 ~ HDL_clinic, data = europ)
backwc_crude = lm(backwc ~ HDL_clinic, data = europ)
vocab_crude = lm(vocab ~ HDL_clinic, data = europ)
ms_total_crude = lm(ms_total ~ HDL_clinic, data = europ)
cog_total_crude = lm(cog_total ~ HDL_clinic, data = europ)
# Adding in demographic
f1 = as.formula(paste('im_recall ~ HDL_clinic + ', demographic))
im_recall_adj1 = lm(f1, data = europ)
f2 = as.formula(paste('dl_recall ~ HDL_clinic + ', demographic))
dl_recall_adj1 = lm(f2, data = europ)
f3 = as.formula(paste('total_recall ~ HDL_clinic + ', demographic))
total_recall_adj1 = lm(f3, data = europ)
f4 = as.formula(paste('serial7 ~ HDL_clinic + ', demographic))
serial_adj1 = lm(f4, data = europ)
f5 = as.formula(paste('backwc ~ HDL_clinic + ', demographic))
backwc_adj1 = lm(f5, data = europ)
f6 = as.formula(paste('vocab ~ HDL_clinic + ', demographic))
vocab_adj1 = lm(f6, data = europ)
f7 = as.formula(paste('ms_total ~ HDL_clinic + ', demographic))
ms_total_adj1 = lm(f7, data = europ)
f8 = as.formula(paste('cog_total ~ HDL_clinic + ', demographic))
cog_total_adj1 = lm(f8, data = europ)
# Adding in health status
f11 = as.formula(paste('im_recall ~ HDL_clinic + ', health_behavior))
im_recall_adj2 = lm(f11, data = europ)
f21 = as.formula(paste('dl_recall ~ HDL_clinic + ', health_behavior))
dl_recall_adj2 = lm(f21, data = europ)
f31 = as.formula(paste('total_recall ~ HDL_clinic + ', health_behavior))
total_recall_adj2 = lm(f31, data = europ)
f41 = as.formula(paste('serial7 ~ HDL_clinic + ', health_behavior))
serial_adj2 = lm(f41, data = europ)
f51 = as.formula(paste('backwc ~ HDL_clinic + ', health_behavior))
backwc_adj2 = lm(f51, data = europ)
f61 = as.formula(paste('vocab ~ HDL_clinic + ', health_behavior))
vocab_adj2 = lm(f61, data = europ)
f71 = as.formula(paste('ms_total ~ HDL_clinic + ', health_behavior))
ms_total_adj2 = lm(f71, data = europ)
f81 = as.formula(paste('cog_total ~ HDL_clinic + ', health_behavior))
cog_total_adj2 = lm(f81, data = europ)
# Adding in AD genetics
f111 = as.formula(paste('im_recall ~ HDL_clinic + ', gene_add))
im_recall_adj3 = lm(f111, data = europ)
f211 = as.formula(paste('dl_recall ~ HDL_clinic + ', gene_add))
dl_recall_adj3 = lm(f211, data = europ)
f311 = as.formula(paste('total_recall ~ HDL_clinic + ', gene_add))
total_recall_adj3 = lm(f311, data = europ)
f411 = as.formula(paste('serial7 ~ HDL_clinic + ', gene_add))
serial_adj3 = lm(f411, data = europ)
f511 = as.formula(paste('backwc ~ HDL_clinic + ', gene_add))
backwc_adj3 = lm(f511, data = europ)
f611 = as.formula(paste('vocab ~ HDL_clinic + ', gene_add))
vocab_adj3 = lm(f611, data = europ)
f711 = as.formula(paste('ms_total ~ HDL_clinic + ', gene_add))
ms_total_adj3 = lm(f711, data = europ)
f811 = as.formula(paste('cog_total ~ HDL_clinic + ', gene_add))
cog_total_adj3 = lm(f811, data = europ)
hdl_cog = make_coeff_table_lm(lst_model, 2, 'hdl_cog_')
```
### 2. PGS HDL and domain cognition score -- adjusted for HDL level
```{r domain, message=FALSE, warning=FALSE}
## Crude
im_recall_crude = lm(im_recall ~ HDL_clinic + PGS_HDL, data = europ)
dl_recall_crude = lm(dl_recall ~ HDL_clinic + PGS_HDL, data = europ)
total_recall_crude = lm(total_recall ~ HDL_clinic + PGS_HDL, data = europ)
serial_crude = lm(serial7 ~ HDL_clinic + PGS_HDL, data = europ)
backwc_crude = lm(backwc ~ HDL_clinic + PGS_HDL, data = europ)
vocab_crude = lm(vocab ~ HDL_clinic + PGS_HDL, data = europ)
ms_total_crude = lm(ms_total ~ HDL_clinic + PGS_HDL, data = europ)
cog_total_crude = lm(cog_total ~ HDL_clinic + PGS_HDL, data = europ)
# Adding in demographic
f1 = as.formula(paste('im_recall ~ HDL_clinic + PGS_HDL + ', demographic))
im_recall_adj1 = lm(f1, data = europ)
f2 = as.formula(paste('dl_recall ~ HDL_clinic + PGS_HDL + ', demographic))
dl_recall_adj1 = lm(f2, data = europ)
f3 = as.formula(paste('total_recall ~ HDL_clinic + PGS_HDL + ', demographic))
total_recall_adj1 = lm(f3, data = europ)
f4 = as.formula(paste('serial7 ~ HDL_clinic + PGS_HDL + ', demographic))
serial_adj1 = lm(f4, data = europ)
f5 = as.formula(paste('backwc ~ HDL_clinic + PGS_HDL + ', demographic))
backwc_adj1 = lm(f5, data = europ)
f6 = as.formula(paste('vocab ~ HDL_clinic + PGS_HDL + ', demographic))
vocab_adj1 = lm(f6, data = europ)
f7 = as.formula(paste('ms_total ~ HDL_clinic + PGS_HDL + ', demographic))
ms_total_adj1 = lm(f7, data = europ)
f8 = as.formula(paste('cog_total ~ HDL_clinic + PGS_HDL + ', demographic))
cog_total_adj1 = lm(f8, data = europ)
# Adding in health status
f11 = as.formula(paste('im_recall ~ HDL_clinic + PGS_HDL + ', health_behavior))
im_recall_adj2 = lm(f11, data = europ)
f21 = as.formula(paste('dl_recall ~ HDL_clinic + PGS_HDL + ', health_behavior))
dl_recall_adj2 = lm(f21, data = europ)
f31 = as.formula(paste('total_recall ~ HDL_clinic + PGS_HDL + ', health_behavior))
total_recall_adj2 = lm(f31, data = europ)
f41 = as.formula(paste('serial7 ~ HDL_clinic + PGS_HDL + ', health_behavior))
serial_adj2 = lm(f41, data = europ)
f51 = as.formula(paste('backwc ~ HDL_clinic + PGS_HDL + ', health_behavior))
backwc_adj2 = lm(f51, data = europ)
f61 = as.formula(paste('vocab ~ HDL_clinic + PGS_HDL + ', health_behavior))
vocab_adj2 = lm(f61, data = europ)
f71 = as.formula(paste('ms_total ~ HDL_clinic + PGS_HDL + ', health_behavior))
ms_total_adj2 = lm(f71, data = europ)
f81 = as.formula(paste('cog_total ~ HDL_clinic + PGS_HDL + ', health_behavior))
cog_total_adj2 = lm(f81, data = europ)
# Adding in AD genetics
f111 = as.formula(paste('im_recall ~ HDL_clinic + PGS_HDL + ', gene_add))
im_recall_adj3 = lm(f111, data = europ)
f211 = as.formula(paste('dl_recall ~ HDL_clinic + PGS_HDL + ', gene_add))
dl_recall_adj3 = lm(f211, data = europ)
f311 = as.formula(paste('total_recall ~ HDL_clinic + PGS_HDL + ', gene_add))
total_recall_adj3 = lm(f311, data = europ)
f411 = as.formula(paste('serial7 ~ HDL_clinic + PGS_HDL + ', gene_add))
serial_adj3 = lm(f411, data = europ)
f511 = as.formula(paste('backwc ~ HDL_clinic + PGS_HDL + ', gene_add))
backwc_adj3 = lm(f511, data = europ)
f611 = as.formula(paste('vocab ~ HDL_clinic + PGS_HDL + ', gene_add))
vocab_adj3 = lm(f611, data = europ)
f711 = as.formula(paste('ms_total ~ HDL_clinic + PGS_HDL + ', gene_add))
ms_total_adj3 = lm(f711, data = europ)
f811 = as.formula(paste('cog_total ~ HDL_clinic + PGS_HDL + ', gene_add))
cog_total_adj3 = lm(f811, data = europ)
hdl_pgs1 = make_coeff_table_lm(lst_model, 2, 'hdl_pgs1_')
hdl_pgs2 = make_coeff_table_lm(lst_model, 3, 'hdl_pgs2_')
```
### 3. PGS HDL and domain cognition score -- not adjusted for HDL level (total effect)
```{r domain, message=FALSE, warning=FALSE}
## Crude
im_recall_crude = lm(im_recall ~ PGS_HDL, data = europ)
dl_recall_crude = lm(dl_recall ~ PGS_HDL, data = europ)
total_recall_crude = lm(total_recall ~ PGS_HDL, data = europ)
serial_crude = lm(serial7 ~ PGS_HDL, data = europ)
backwc_crude = lm(backwc ~ PGS_HDL, data = europ)
vocab_crude = lm(vocab ~ PGS_HDL, data = europ)
ms_total_crude = lm(ms_total ~ PGS_HDL, data = europ)
cog_total_crude = lm(cog_total ~ PGS_HDL, data = europ)
# Adding in demographic
f1 = as.formula(paste('im_recall ~ PGS_HDL + ', demographic))
im_recall_adj1 = lm(f1, data = europ)
f2 = as.formula(paste('dl_recall ~ PGS_HDL + ', demographic))
dl_recall_adj1 = lm(f2, data = europ)
f3 = as.formula(paste('total_recall ~ PGS_HDL + ', demographic))
total_recall_adj1 = lm(f3, data = europ)
f4 = as.formula(paste('serial7 ~ PGS_HDL + ', demographic))
serial_adj1 = lm(f4, data = europ)
f5 = as.formula(paste('backwc ~ PGS_HDL + ', demographic))
backwc_adj1 = lm(f5, data = europ)
f6 = as.formula(paste('vocab ~ PGS_HDL + ', demographic))
vocab_adj1 = lm(f6, data = europ)
f7 = as.formula(paste('ms_total ~ PGS_HDL + ', demographic))
ms_total_adj1 = lm(f7, data = europ)
f8 = as.formula(paste('cog_total ~ PGS_HDL + ', demographic))
cog_total_adj1 = lm(f8, data = europ)
# Adding in health status
f11 = as.formula(paste('im_recall ~ PGS_HDL + ', health_behavior))
im_recall_adj2 = lm(f11, data = europ)
f21 = as.formula(paste('dl_recall ~ PGS_HDL + ', health_behavior))
dl_recall_adj2 = lm(f21, data = europ)
f31 = as.formula(paste('total_recall ~ PGS_HDL + ', health_behavior))
total_recall_adj2 = lm(f31, data = europ)
f41 = as.formula(paste('serial7 ~ PGS_HDL + ', health_behavior))
serial_adj2 = lm(f41, data = europ)
f51 = as.formula(paste('backwc ~ PGS_HDL + ', health_behavior))
backwc_adj2 = lm(f51, data = europ)
f61 = as.formula(paste('vocab ~ PGS_HDL + ', health_behavior))
vocab_adj2 = lm(f61, data = europ)
f71 = as.formula(paste('ms_total ~ PGS_HDL + ', health_behavior))
ms_total_adj2 = lm(f71, data = europ)
f81 = as.formula(paste('cog_total ~ PGS_HDL + ', health_behavior))
cog_total_adj2 = lm(f81, data = europ)
# Adding in AD genetics
f111 = as.formula(paste('im_recall ~ PGS_HDL + ', gene_add))
im_recall_adj3 = lm(f111, data = europ)
f211 = as.formula(paste('dl_recall ~ PGS_HDL + ', gene_add))
dl_recall_adj3 = lm(f211, data = europ)
f311 = as.formula(paste('total_recall ~ PGS_HDL + ', gene_add))
total_recall_adj3 = lm(f311, data = europ)
f411 = as.formula(paste('serial7 ~ PGS_HDL + ', gene_add))
serial_adj3 = lm(f411, data = europ)
f511 = as.formula(paste('backwc ~ PGS_HDL + ', gene_add))
backwc_adj3 = lm(f511, data = europ)
f611 = as.formula(paste('vocab ~ PGS_HDL + ', gene_add))
vocab_adj3 = lm(f611, data = europ)
f711 = as.formula(paste('ms_total ~ PGS_HDL + ', gene_add))
ms_total_adj3 = lm(f711, data = europ)
f811 = as.formula(paste('cog_total ~ PGS_HDL + ', gene_add))
cog_total_adj3 = lm(f811, data = europ)
hdl_pgs_total = make_coeff_table_lm(lst_model, 2, 'hdl_pgs_total_')
```
```{r}
domain_hdl = rbind(hdl_cog, hdl_pgs1, hdl_pgs2, hdl_pgs_total)
```
### 4. TC and domain cognition score
```{r domain, message=FALSE, warning=FALSE}
## Crude
im_recall_crude = lm(im_recall ~ TC_clinic_bin, data = europ)
dl_recall_crude = lm(dl_recall ~ TC_clinic_bin, data = europ)
total_recall_crude = lm(total_recall ~ TC_clinic_bin, data = europ)
serial_crude = lm(serial7 ~ TC_clinic_bin, data = europ)
backwc_crude = lm(backwc ~ TC_clinic_bin, data = europ)
vocab_crude = lm(vocab ~ TC_clinic_bin, data = europ)
ms_total_crude = lm(ms_total ~ TC_clinic_bin, data = europ)
cog_total_crude = lm(cog_total ~ TC_clinic_bin, data = europ)
# Adding in demographic
f1 = as.formula(paste('im_recall ~ TC_clinic_bin + ', demographic))
im_recall_adj1 = lm(f1, data = europ)
f2 = as.formula(paste('dl_recall ~ TC_clinic_bin + ', demographic))
dl_recall_adj1 = lm(f2, data = europ)
f3 = as.formula(paste('total_recall ~ TC_clinic_bin + ', demographic))
total_recall_adj1 = lm(f3, data = europ)
f4 = as.formula(paste('serial7 ~ TC_clinic_bin + ', demographic))
serial_adj1 = lm(f4, data = europ)
f5 = as.formula(paste('backwc ~ TC_clinic_bin + ', demographic))
backwc_adj1 = lm(f5, data = europ)
f6 = as.formula(paste('vocab ~ TC_clinic_bin + ', demographic))
vocab_adj1 = lm(f6, data = europ)
f7 = as.formula(paste('ms_total ~ TC_clinic_bin + ', demographic))
ms_total_adj1 = lm(f7, data = europ)
f8 = as.formula(paste('cog_total ~ TC_clinic_bin + ', demographic))
cog_total_adj1 = lm(f8, data = europ)
# Adding in health status
f11 = as.formula(paste('im_recall ~ TC_clinic_bin + ', health_behavior))
im_recall_adj2 = lm(f11, data = europ)
f21 = as.formula(paste('dl_recall ~ TC_clinic_bin + ', health_behavior))
dl_recall_adj2 = lm(f21, data = europ)
f31 = as.formula(paste('total_recall ~ TC_clinic_bin + ', health_behavior))
total_recall_adj2 = lm(f31, data = europ)
f41 = as.formula(paste('serial7 ~ TC_clinic_bin + ', health_behavior))
serial_adj2 = lm(f41, data = europ)
f51 = as.formula(paste('backwc ~ TC_clinic_bin + ', health_behavior))
backwc_adj2 = lm(f51, data = europ)
f61 = as.formula(paste('vocab ~ TC_clinic_bin + ', health_behavior))
vocab_adj2 = lm(f61, data = europ)
f71 = as.formula(paste('ms_total ~ TC_clinic_bin + ', health_behavior))
ms_total_adj2 = lm(f71, data = europ)
f81 = as.formula(paste('cog_total ~ TC_clinic_bin + ', health_behavior))
cog_total_adj2 = lm(f81, data = europ)
# Adding in AD genetics
f111 = as.formula(paste('im_recall ~ TC_clinic_bin + ', gene_add))
im_recall_adj3 = lm(f111, data = europ)
f211 = as.formula(paste('dl_recall ~ TC_clinic_bin + ', gene_add))
dl_recall_adj3 = lm(f211, data = europ)
f311 = as.formula(paste('total_recall ~ TC_clinic_bin + ', gene_add))
total_recall_adj3 = lm(f311, data = europ)
f411 = as.formula(paste('serial7 ~ TC_clinic_bin + ', gene_add))
serial_adj3 = lm(f411, data = europ)
f511 = as.formula(paste('backwc ~ TC_clinic_bin + ', gene_add))
backwc_adj3 = lm(f511, data = europ)
f611 = as.formula(paste('vocab ~ TC_clinic_bin + ', gene_add))
vocab_adj3 = lm(f611, data = europ)
f711 = as.formula(paste('ms_total ~ TC_clinic_bin + ', gene_add))
ms_total_adj3 = lm(f711, data = europ)
f811 = as.formula(paste('cog_total ~ TC_clinic_bin + ', gene_add))
cog_total_adj3 = lm(f811, data = europ)
tc_cog = make_coeff_table_lm(lst_model, 2, 'tc_cog_')
```
### 5. PGS TC and domain cognition score -- adjusted for TC level
```{r domain, message=FALSE, warning=FALSE}
## Crude
im_recall_crude = lm(im_recall ~ TC_clinic_bin + PGS_TC, data = europ)
dl_recall_crude = lm(dl_recall ~ TC_clinic_bin + PGS_TC, data = europ)
total_recall_crude = lm(total_recall ~ TC_clinic_bin + PGS_TC, data = europ)
serial_crude = lm(serial7 ~ TC_clinic_bin + PGS_TC, data = europ)
backwc_crude = lm(backwc ~ TC_clinic_bin + PGS_TC, data = europ)
vocab_crude = lm(vocab ~ TC_clinic_bin + PGS_TC, data = europ)
ms_total_crude = lm(ms_total ~ TC_clinic_bin + PGS_TC, data = europ)
cog_total_crude = lm(cog_total ~ TC_clinic_bin + PGS_TC, data = europ)
# Adding in demographic
f1 = as.formula(paste('im_recall ~ TC_clinic_bin + PGS_TC + ', demographic))
im_recall_adj1 = lm(f1, data = europ)
f2 = as.formula(paste('dl_recall ~ TC_clinic_bin + PGS_TC + ', demographic))
dl_recall_adj1 = lm(f2, data = europ)
f3 = as.formula(paste('total_recall ~ TC_clinic_bin + PGS_TC + ', demographic))
total_recall_adj1 = lm(f3, data = europ)
f4 = as.formula(paste('serial7 ~ TC_clinic_bin + PGS_TC + ', demographic))
serial_adj1 = lm(f4, data = europ)
f5 = as.formula(paste('backwc ~ TC_clinic_bin + PGS_TC + ', demographic))
backwc_adj1 = lm(f5, data = europ)
f6 = as.formula(paste('vocab ~ TC_clinic_bin + PGS_TC + ', demographic))
vocab_adj1 = lm(f6, data = europ)
f7 = as.formula(paste('ms_total ~ TC_clinic_bin + PGS_TC + ', demographic))
ms_total_adj1 = lm(f7, data = europ)
f8 = as.formula(paste('cog_total ~ TC_clinic_bin + PGS_TC + ', demographic))
cog_total_adj1 = lm(f8, data = europ)
# Adding in health status
f11 = as.formula(paste('im_recall ~ TC_clinic_bin + PGS_TC + ', health_behavior))
im_recall_adj2 = lm(f11, data = europ)
f21 = as.formula(paste('dl_recall ~ TC_clinic_bin + PGS_TC + ', health_behavior))
dl_recall_adj2 = lm(f21, data = europ)
f31 = as.formula(paste('total_recall ~ TC_clinic_bin + PGS_TC + ', health_behavior))
total_recall_adj2 = lm(f31, data = europ)
f41 = as.formula(paste('serial7 ~ TC_clinic_bin + PGS_TC + ', health_behavior))
serial_adj2 = lm(f41, data = europ)
f51 = as.formula(paste('backwc ~ TC_clinic_bin + PGS_TC + ', health_behavior))
backwc_adj2 = lm(f51, data = europ)
f61 = as.formula(paste('vocab ~ TC_clinic_bin + PGS_TC + ', health_behavior))
vocab_adj2 = lm(f61, data = europ)
f71 = as.formula(paste('ms_total ~ TC_clinic_bin + PGS_TC + ', health_behavior))
ms_total_adj2 = lm(f71, data = europ)
f81 = as.formula(paste('cog_total ~ TC_clinic_bin + PGS_TC + ', health_behavior))
cog_total_adj2 = lm(f81, data = europ)
# Adding in AD genetics
f111 = as.formula(paste('im_recall ~ TC_clinic_bin + PGS_TC + ', gene_add))
im_recall_adj3 = lm(f111, data = europ)
f211 = as.formula(paste('dl_recall ~ TC_clinic_bin + PGS_TC + ', gene_add))
dl_recall_adj3 = lm(f211, data = europ)
f311 = as.formula(paste('total_recall ~ TC_clinic_bin + PGS_TC + ', gene_add))
total_recall_adj3 = lm(f311, data = europ)
f411 = as.formula(paste('serial7 ~ TC_clinic_bin + PGS_TC + ', gene_add))
serial_adj3 = lm(f411, data = europ)
f511 = as.formula(paste('backwc ~ TC_clinic_bin + PGS_TC + ', gene_add))
backwc_adj3 = lm(f511, data = europ)
f611 = as.formula(paste('vocab ~ TC_clinic_bin + PGS_TC + ', gene_add))
vocab_adj3 = lm(f611, data = europ)
f711 = as.formula(paste('ms_total ~ TC_clinic_bin + PGS_TC + ', gene_add))
ms_total_adj3 = lm(f711, data = europ)
f811 = as.formula(paste('cog_total ~ TC_clinic_bin + PGS_TC + ', gene_add))
cog_total_adj3 = lm(f811, data = europ)
tc_pgs1 = make_coeff_table_lm(lst_model, 2, 'tc_pgs1_')
tc_pgs2 = make_coeff_table_lm(lst_model, 3, 'tc_pgs2_')
```
### 6. PGS TC and domain cognition score -- not adjusted for TC level (total effect)
```{r domain, message=FALSE, warning=FALSE}
## Crude
im_recall_crude = lm(im_recall ~ PGS_TC, data = europ)
dl_recall_crude = lm(dl_recall ~ PGS_TC, data = europ)
total_recall_crude = lm(total_recall ~ PGS_TC, data = europ)
serial_crude = lm(serial7 ~ PGS_TC, data = europ)
backwc_crude = lm(backwc ~ PGS_TC, data = europ)
vocab_crude = lm(vocab ~ PGS_TC, data = europ)
ms_total_crude = lm(ms_total ~ PGS_TC, data = europ)
cog_total_crude = lm(cog_total ~ PGS_TC, data = europ)
# Adding in demographic
f1 = as.formula(paste('im_recall ~ PGS_TC + ', demographic))
im_recall_adj1 = lm(f1, data = europ)
f2 = as.formula(paste('dl_recall ~ PGS_TC + ', demographic))
dl_recall_adj1 = lm(f2, data = europ)
f3 = as.formula(paste('total_recall ~ PGS_TC + ', demographic))
total_recall_adj1 = lm(f3, data = europ)
f4 = as.formula(paste('serial7 ~ PGS_TC + ', demographic))
serial_adj1 = lm(f4, data = europ)
f5 = as.formula(paste('backwc ~ PGS_TC + ', demographic))
backwc_adj1 = lm(f5, data = europ)
f6 = as.formula(paste('vocab ~ PGS_TC + ', demographic))
vocab_adj1 = lm(f6, data = europ)
f7 = as.formula(paste('ms_total ~ PGS_TC + ', demographic))
ms_total_adj1 = lm(f7, data = europ)
f8 = as.formula(paste('cog_total ~ PGS_TC + ', demographic))
cog_total_adj1 = lm(f8, data = europ)
# Adding in health status
f11 = as.formula(paste('im_recall ~ PGS_TC + ', health_behavior))
im_recall_adj2 = lm(f11, data = europ)
f21 = as.formula(paste('dl_recall ~ PGS_TC + ', health_behavior))
dl_recall_adj2 = lm(f21, data = europ)
f31 = as.formula(paste('total_recall ~ PGS_TC + ', health_behavior))
total_recall_adj2 = lm(f31, data = europ)
f41 = as.formula(paste('serial7 ~ PGS_TC + ', health_behavior))
serial_adj2 = lm(f41, data = europ)
f51 = as.formula(paste('backwc ~ PGS_TC + ', health_behavior))
backwc_adj2 = lm(f51, data = europ)
f61 = as.formula(paste('vocab ~ PGS_TC + ', health_behavior))
vocab_adj2 = lm(f61, data = europ)
f71 = as.formula(paste('ms_total ~ PGS_TC + ', health_behavior))
ms_total_adj2 = lm(f71, data = europ)
f81 = as.formula(paste('cog_total ~ PGS_TC + ', health_behavior))
cog_total_adj2 = lm(f81, data = europ)
# Adding in AD genetics