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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("ana.baezaruiz@bristol.ac.uk", "Valerie.Aspin@bristol.ac.uk"))

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

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


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

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# 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)

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CHUNKSIZE=250
NCHUNK=864
CHUNK=range(0,NCHUNK)


rule all:
	input:
		"instrument_specificity_analysis.html"

rule dl:

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---
title: Comparing survey answers before and after intervention
author: Gibran Hemani
---

```{r}
suppressMessages(suppressWarnings(suppressPackageStartupMessages({
	library(knitr)
	library(tidyverse)
})))

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---
title: Analysis of rs13107325 heterogeneity in BMI - osteoarthritis MR analysis
author: Gibran Hemani
date: "`r Sys.time()"
---

```{r}
library(tryx)
library(TwoSampleMR)
library(ieugwasr)

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---
title: Quick MR of fibronectin / cytokines / matrix metalloproteinases on osteoarthritis
date: "`r Sys.time()"
---


This paper presents a mathematical model linking osteoarthritis (OA) to a dynamic inflammation system involving cytokines and fibronectin https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5562782/.

Quick look at whether genetic relationships associate:

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library(gwasglue)
library(ieugwasr)
library(tidyverse)
library(susieR)
library(glue)

# DHA fine mapping
temp <- associations("11:61200000-62100000", "met-d-DHA")
ggplot(temp, aes(x=position, y=-log10(p))) +
geom_point()

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---
title: Comparison of effect allele frequencies in TwoSampleMR
author: Gibran Hemani
date: "`r Sys.time()`"
---


```{r, echo=FALSE}
library(knitr)
opts_chunk$set(warning=FALSE, message=FALSE)

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library(TwoSampleMR)
library(ieugwasr)
library(tidyverse)

# Get harmonised data for exposure and outcome
# e.g. BMI vs CHD
dat <- 	make_dat(exposures="ieu-a-2", outcomes="ieu-a-7")

# infer the AF for controls only for CHD
# first get case control sizes