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#!/bin/bash

#!/bin/bash

for chr in {1..22}
do
  # Get the list of variants that pass the threshold
  bcftools filter -e 'INFO/R2<0.8' /mnt/project/Bulk/Imputation/Imputation\ from\ genotype\ \(TOPmed\)/helper_files/ukb21007_c${chr}_b0_v1.sites.vcf.gz | \
  bcftools filter -e 'INFO/AF<0.01' | \
  bcftools filter -e 'INFO/AF>0.99' | \

explodecomputer

library(readxl)
library(dplyr)

download.file("https://www.biorxiv.org/content/biorxiv/early/2022/06/18/2022.06.17.496443/DC2/embed/media-2.xlsx?download=true", here("data", "media-2.xlsx"))
a <- read_xlsx(here("data", "media-2.xlsx"), sheet="ST6", skip=2) %>% 
  dplyr::select(
    exposure="Assay Target",
    id.exposure="Assay Target",
    SNP="rsID", 
    beta.exposure="BETA (discovery, wrt. A1)", 

explodecomputer

library(dplyr)

standardise <- function(d, ea_col="ea", oa_col="oa", beta_col="beta", eaf_col="eaf", chr_col="chr", pos_col="pos", vid_col="vid")
{
    toflip <- d[[ea_col]] > d[[oa_col]]
    d[[eaf_col]][toflip] <- 1 - d[[eaf_col]][toflip]
    d[[beta_col]][toflip] <- d[[beta_col]][toflip] * -1
    temp <- d[[oa_col]][toflip]
    d[[oa_col]][toflip] <- d[[ea_col]][toflip]
    d[[ea_col]][toflip] <- temp

explodecomputer

library(ieugwasr)
library(dplyr)

ukb <- tophits("ukb-d-I9_IHD")
bbj <- tophits("bbj-a-159", pop="EAS", r2=0.0011)
table(duplicated(c(ukb$rsid, bbj$rsid)))

cl <- bind_rows(ukb, bbj) %>% select(rsid, p) %>% ld_clump()

a <- associations(cl$rsid, c("bbj-a-159", "ukb-d-I9_IHD"))

explodecomputer

library(dplyr)

#' Genarte genotypes for nuclear families
#' 
#' @param af Vector of allele frequencies for SNPs (length = number of SNPs to simulate)
#' @param nfam Number of families to generate
#' 
#' @return List of genotype matrices for each family member type (mum, dad, sib1, sib2)
make_families <- function(af, nfam)
{

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library(lubridate)
library(dplyr)
library(knitr)

#' Create agenda
#'
#' Alternating weeks of Talks and Discussions.
#' Presenter for a talk leads discussion in the following week. 
#'
#' @param start Start date in format "yyyy/mm/dd"

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# MR-Base (i.e. using the cloud)

library(TwoSampleMR)
metab_ids <- c("met-e-1", "met-e-2")

# Extract data - from API
exp_dat <- extract_instruments("alz_id")
out_dat <- extract_outcome_data(exp_dat$SNP, metab_ids)

# Harmonise and run

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library(tidyverse)
library(readxl)

a <- read_xlsx("~/Downloads/Letter to the Vice Chancellor 2021-11-10 (Responses)(1).xlsx")

to_remove <- tolower(c("[email protected]", "[email protected]"))

dat <- a %>%
	filter(! tolower(`Email Address`) %in% to_remove) %>%
	arrange(`School or department`, `Surname`) %>%

explodecomputer

---
title: "Reverse MR Sims"
output: html_notebook
---


```{r}
library(simulateGP)
library(TwoSampleMR)
```

explodecomputer

# Model of control-only Huntington's disease analysis
# If you have some CAG expansion cases who are non-symptomatic then what distinguishes them from non expansion individuals?
# This script shows the analysis is akin to an interaction analysis

library(dplyr)
n <- 1000000
cag <- rpois(n, 2)
mod <- rbinom(n, 2, 0.4) - 1

d <- rep(0, n)