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# no confounder

run <- function(nsnp, nid, ueff, nsim, prop)
{
	g <- matrix(rbinom(nsnp * nid, 2, 0.5), nid, nsnp)
	vg <- apply(g, 2, var)
	conf <- rnorm(nid)

	res1 <- array(0, nsim)
	res2 <- array(0, nsim)

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---
title: Converting Z scores to betas and log(OR)
author: Gibran Hemani, Philip Haycock
date: 22/11/2017
output: html_document
---

Use this formula to convert any effects and SE to the standardised scale:

```{r, echo=FALSE}

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n <- 1000

nsim <- 100
res1 <- rep(0, nsim)
res2 <- rep(0, nsim)
res3 <- rep(0, nsim)

for(i in 1:nsim)
{

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#!/bin/bash

set -e

# Create shortcut to documents

echo "Creating shortcut to Documents"
cd /mnt/c/Users
un=`ls -dl */ | awk '{ print $9 }' | sed "s@/@@g" | grep -v Default | grep -v Public | grep -v defaultuser0 | head -n 1`

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Using the Linux VM

Your computer's main operating system is Windows, but it has a 'virtual machine' running Linux Ubuntu.

What is a virtual machine? This is basically a simulation of a computer. The software 'VirtualBox' can simulate different operating systems. We're using a virtual machine to provide a Linux environment in which to perform the work required for this unit.

What is Linux Ubuntu? Linux is a basic operating system, but you can add various configurations or collections of software to give it different behaviours etc. These are then released as 'distributions'. There are many different Linux distributions, but Ubuntu is the most popular due to its relative similarity to Windows.

This guide will show you how to log on to the Linux virtual machine.

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Setting up the Linux VM

This process should take 15-20 minutes.

Step 1. Install the virtual machine image

There is a file called Genomics Ubuntu VM.ova in the C:\temp folder. Double click it, it will open up VirtualBox and ask if you want to Import the Virtual Appliance. Click Import.

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load("z_score_matrix.RData")
nom <- colnames(z_score_matrix)
nom <- do.call(rbind, strsplit(nom, split="\\|"))
colnames(z_score_matrix) <- nom[,1]
R <- cor(z_score_matrix, use="pair")^2



findrelsubreddit <- function(cursubs,curops,numret=20, mat=R) {
    curvec = 0

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# Get the GLGC data

system("wget http://csg.sph.umich.edu/abecasis/public/lipids2013/jointGwasMc_HDL.txt.gz")

# Get 1000 genomes frequencies

system("plink1.90 --bfile /panfs/panasas01/shared/alspac/studies/latest/alspac/genetic/variants/arrays/gwas/imputed/1000genomes/released/2015-10-30/data/derived/filtered/bestguess/maf0.01_info0.8/data_chr01 --freq --out chr1")

# Predict effect sizes and standard errors

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# Sample size
nid <- 2000

# Number of SNPs
nsnp <- 1000

# Variance explained by SNPs
varexp <- 0.8

# Effect sizes of SNPs

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# Lipids (GLGC) against CHD
id_exposure <- c(299, 300, 302)
id_outcome <- 7

# Get exposures
exposure_dat <- mv_extract_exposures(id_exposure)

# Chris - if you have external data then is it possible to create the same format as this? 
# - it's in long format
# - each SNP appears as many times as there are exposures