explodecomputer

<?php 
/* Feeds a database with the content of a bibtex file parsed with bibtexbrowser
 * See: http://www.monperrus.net/martin/feeding-mysql-database-with-bibtexbrowser
 * Author: Martin Monperrus
 * Date: Feb 2012
 */
 

/** MySQL database username */
@define('DB_USER', 'root');

explodecomputer

library(SNPlocs.Hsapiens.dbSNP.20120608)
library(plyr)

# Read in bim file
bimname <- "mnd_b37_flipped.bim"
bim <- read.table(bimname, colClasses=c("numeric", "character", "numeric", "numeric", "character", "character"))
bim$index <- 1:nrow(bim)


# For each chromosome download the SNP locations and match to bim file

explodecomputer

# Usage:

# R --no-save --args snplist.txt /ibscratch/wrayvisscher/imputation/arichu/data/imputed/chr\*/arichu_1kg_p1v3_\* outputfile < extractSnps.R

# - The first argument is the SNP list (just a list of rs SNP ids and nothing else)
# - The second argument specifies the root name for the genotype data (in binary plink format). Replace the chromosome number with "\*"
# - The third argument is the output file root name that you want to save everything to.


library(snpStats)

explodecomputer

library(ggbio)
library(grid)
library(gridExtra)
library(plyr)
library(GenomicRanges)
library(qgraph)

multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL)
{
  require(grid)

explodecomputer

n <- 1000
m <- 200
X <- matrix(rbinom(m*n, 2, 0.5), n)
p <- apply(X, 2, mean)/2
pv <- apply(X, 2, sd)
Xp <- t((t(X) - 2*p) / pv)
A <- Xp %*% t(Xp) / m
A[1:10,1:10]

explodecomputer

readGRM <- function(rootname)
{
	bin.file.name <- paste(rootname, ".grm.bin", sep="")
	n.file.name <- paste(rootname, ".grm.N.bin", sep="")
	id.file.name <- paste(rootname, ".grm.id", sep="")

	cat("Reading IDs\n")
	id <- read.table(id.file.name, colClasses="character")
	n <- dim(id)[1]
	cat("Reading GRM\n")

explodecomputer

library(plyr)
library(ggbio)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(rtracklayer)


#=================================================#
#=================================================#

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# Initial parameters
sample_size <- 120000
bmi_mean <- 25
bmi_sd <- 4
allele_freq <- 0.19
effect_size <- -0.23
proportion_smokers <- 0.5


# The transformation of BMI to z^2 allows detection of variance heterogeneity, e.g.:

explodecomputer

#!/bin/bash

# set -e

snplistfile=${1}
plinkrt=${2}
outfile=${3}

touch ${outfile}_mergelist.txt
rm ${outfile}_mergelist.txt

explodecomputer

manhattanPlot <- function(p,
		 chromosome,
		 ylim = NULL,
		 trunc.lines = TRUE,
         signif = 5e-8,
		 ...)
{
    stopifnot(length(p) == length(chromosome))