LocusZoom Standalone

This page contains information regarding a version of LocusZoom that may be downloaded for personal use. For more information on LocusZoom, see this page.

Quick Start (Requirements)

The following software is required:

• Python 2.6 (do not download the 3.0 branch!)
• R 2.10+
• new_fugue, a program for computing LD, written by Goncalo Abecasis.
• PLINK, written by Shaun Purcell.

For the latest stable LocusZoom package (including database tables), see our download page.

Currently only Unix/Linux is supported, though Mac OS X should be supported in a future release.

Support for Windows may come at a much later date.

Synopsis

First, change directory into examples/. Then, run the following command:

./run_example.py

This script runs the following command for you:

../bin/locuszoom --metal Kathiresan_2009_HDL.txt --refgene FADS1

A PDF plot of the FADS1 locus will be created in the directory. It should look roughly like this:

Voila, your first region plot!

Download

See our download page for links to the latest as well as previous releases.

Installation

Step 1: Install Python

You will need to install Python on your system if it is not already. Head over to www.python.org to download it. Note that you will want to make sure to download the latest from the 2.x branch, and not the 3.0 one.

Step 2: Install R

R is also required for generating the plots. You can download R at www.r-project.org. Version 2.10 or greater is required.

Step 3: Install new_fugue

New_fugue is a program that calculates linkage disequilibrium measures from genotype files. While installing new_fugue is optional, we highly recommend it as it makes the process of generating plots much easier. If you opt to skip installing new_fugue, you will need to provide your own computed LD files for each region that you want to plot.

New_fugue can be downloaded from here.

Once downloaded, extract the tar file using:

 tar zxf /path/to/new_fugue.tar.gz

Change into the generic-new_fugue directory that is created, and run:

 make install 

You may need administrator rights to install this program.

Step 4: Install PLINK

PLINK is now used to calculate LD for all future LD sources / populations that we may add. The program new_fugue (above) is used to calculate LD from older sources (such as hapmap) and older builds (such as hg18) where LD files are sufficiently small.

You can download PLINK and find instructions for installing it here.

Step 5: Install LocusZoom

LocusZoom is provided as a tar archive which contains the following:

• the LocusZoom python application
• the R script used for generating plots
• Human genome build hg18 data, including:
  • genotype files (used for computing LD) from HapMap and 1000G
  • a SQLite database file containing tables describing SNP positions, SNP annotations, gene and exon locations, and recombination rates

Simply unpack the tar to your directory of choice by doing the following:

cd <directory where you want to place locuszoom> 
tar zxf /path/to/locuszoom.tgz 

The tar archive will extract into the following directory structure:

• locuszoom/
  • bin/
    • locuszoom (this is the locuszoom "executable")
    • locuszoom.R (the R script which is used by locuszoom for creating the plots)
  • conf/ (configuration file located here)
  • data/
    • database/ (SQLite file located here)
    • hapmap/ (hapmap genotype files)
    • 1000G/ (1000G genotype files)
  • src/ (source code for locuszoom)

It is important that this directory structure remain intact. To make launching locusoom easier, you could create a link to it from /usr/local/bin, for example:

ln -s bin/locuszoom /usr/local/bin/locuszoom

Sources of information

LocusZoom uses various sources of information for annotation, positions, and calculating LD.

For computing LD:

• HapMap genotypes for populations CEU, YRI, and JPT+CHB.
• 1000 Genomes genotypes

For SNP, gene, and exon positions:

• dbSNP position via the UCSC Genome Browser
• Gene and exon positions via the UCSC Genome Browser. We filtered SNPs that map to more than one location or where no allele matches the reference sequence.

For annotation:

• We used various sources including RefSeq Genes (refFlat), TFBS Conserved (tfbsConsSites), and Conservation (phaseConsElements44wayPlacental), all available from the UCSC Genome Browser.
• Recombination rates from HapMap.

Input

Association results file ("metal" file)

The main input to LocusZoom is a file containing results from an association scan or meta-analysis. The file must have 2 columns: markers (SNPs), and p-values. The file should look something like this:

MarkerName	P-value
rs1	0.423
rs2	1.23e-04
rs3	9.4e-390

This file should be passed to locuszoom using the --metal option.

The file should be tab-delimited, though this can be changed using the --delim option.

If your marker and p-value column names are not "MarkerName" and "P-value", you may set them with --markercol and --pvalcol options.

P-values of any magnitude are supported in scientific notation (we use an arbitrary precision library built-in to python, and transform p-values to the log scale.) If you've already transformed your p-values to the log scale, simply use --no-transform and LocusZoom will not transform them.

Region

You can specify the region to plot in any one of the following ways:

• A reference SNP and flanking region

 --refsnp <your snp> --flank 500kb 

• A reference SNP and chromosome/start/stop specification

 --refsnp <your snp> --chr # --start <base position> --end <base position> 

• A reference SNP and gene:

 --refsnp <your snp> --refgene <your gene> 

This will use the reference gene as the plotting boundaries. You can extend the boundaries by also including --flank.

• A gene and flanking region

 --refgene <your gene> --flank 250kb 

The flank is computed as +/- from the transcription start/end of the gene. From this region, LocusZoom will find the SNP with the most significant p-value, and use this as the reference SNP.

• A gene and chromosome/start/stop specification

 --refgene <your gene> --chr # --start <base position> --end <base position> 

This method is similar to the above, except that an exact region is specified. LD with the SNP with the most significant p-value in this region will be used to color data points.

• A chromosome/start/stop specification

 --chr # --start <base position> --end <base position> 

The SNP with the most significant p-value in this region will be used for estimating LD.

Specifying LD source/population/build

We supply genotype files for computing LD between the reference SNP and all other SNPs within the region you are plotting. The tables below show the supported combinations of LD source, population, and build. Note that you can always provide your own LD files, see User-supplied LD for more information.

1000G

Release	Build	Population	LocusZoom Arguments
Nov 2010	hg19	ASN	--pop ASN --build hg19 --source 1000G_Nov2010
Nov 2010	hg19	AFR	--pop AFR --build hg19 --source 1000G_Nov2010
Nov 2010	hg19	EUR	--pop EUR --build hg19 --source 1000G_Nov2010
June 2010	hg18	CEU	--pop CEU --build hg18 --source 1000G_June2010
June 2010	hg18	YRI	--pop YRI --build hg18 --source 1000G_June2010
June 2010	hg18	JPT+CHB	--pop JPT+CHB --build hg18 --source 1000G_June2010
August 2009	hg18	CEU	--pop CEU --build hg18 --source 1000G_Aug2009
August 2009	hg18	YRI	--pop YRI --build hg18 --source 1000G_Aug2009
August 2009	hg18	JPT+CHB	--pop JPT+CHB --build hg18 --source 1000G_Aug2009

HapMap Phase II

Build	Population	LocusZoom Arguments
hg18	CEU	--pop CEU --build hg18 --source hapmap
hg18	YRI	--pop YRI--build hg18 --source hapmap
hg18	JPT+CHB	--pop JPT+CHB --build hg18 --source hapmap

Batch mode

LocusZoom provides two fast methods for generating plots for a large number of regions:

• --hits , which parses a file for SNP names (rs#) and creates a plot for each one.
• --hitspec , which reads a batch mode specification file.

To use --hits, you need only provide any text file that has SNP names (of the rs### variety) present in the file. LocusZoom will extract SNPs from this file, regardless of formatting. Note that this file shouldn't be very large, or the parsing procedure could take a long time.

For a more thorough specification of each plot you would like to create, use --hitspec. For this option, you must provide a text file of the following format:

Column	Description
snp	Can be either a SNP, or gene.
chr	Chromosome
start	Start position to display on plot.
end	End position to display on plot.
flank	Flank for region. Can be given instead of chr/start/stop.
run	Should this row be read? Should be "yes" or "no".
m2zargs	List of arguments for customizing plots. You can find a list of them here: Commonly Used LocusZoom Options

The file should be delimited by whitespace (tab, space, multiple spaces), and the header must exist, with column names exactly as specified in the table above. As an example, consider the following file:

snp	chr	start	stop	flank	run	m2zargs
rs7983146	NA	NA	NA	500kb	yes	title="My favorite SNP"
TCF7L2	NA	NA	NA	1.25MB	yes	title="TCF7L2 Region" showRecomb=F
rs7957197	12	119503590	120322280	NA	yes	showAnnot=F

The first row would plot rs7983146 as the reference SNP, and a region of 500kb on either side of it. The plot title would read "My favorite SNP."

The second row would plot 1.25 MB on either side of TCF7L2's transcription start and stop. The SNP with the most significant p-value in your --metal file will be used as the reference SNP. The plot title would read "TCF7L2 Region", and the recombination overlay would be disabled using showRecomb=F.

The third row would plot rs7957197 as the reference SNP, but here we've specifically designated the region to plot, which is chr12:119503590-120322280. We've also disabled showing SNP annotations with showAnnot=F.

User-supplied LD

If new_fugue is installed, LocusZoom will automatically compute LD between the reference SNP and all other SNPs within each region to be plotted. However, you may wish to provide your own file with LD information. This can be done with the --ld option, which requires a file of the following format:

Column	Description
snp1	Any SNP in your plotting region.
snp2	Should always be the reference SNP in the region.
dprime	D' between snp2 (reference SNP) and snp1.
rsquare	r2 between snp2 (reference SNP) and snp1.

The file should be whitespace delimited, and the header (column names shown above) must exist.

Output

LocusZoom will produce a directory for each plot that contains the plot itself, along with a number of temporary files containing information on your particular region. The plot will be a PDF, named with the chr#:start-stop that was plotted.

If you only want the PDF itself, and don't want the other files, you can use the --plotonly option.

Each directory (or PDF, in the case of --plotonly) will have the date included to avoid collisions with previous plots - this behavior can be disabled using --no-date.

You can further customize the directory/PDF names that are created by using the --prefix <name> option. This will append a text string at the beginning of each directory/PDF that is created.

LocusZoom options

LocusZoom has a number of command line options, described in the table below.

Option	Description
Important settings
--metal	This is the data file to provide. Files generated by the meta-analysis program METAL are already formatted appropriately. If your data is not from METAL, it is very simple to format it (see Input.)
--delim	Delimiter for the data file. This defaults to tab, but can be anything. For ease of specification, you can use the following shortcuts: --delim tab, --delim space, --delim comma.
--pvalcol	Name of p-value column in the --metal file.
--markercol	Name of the SNP column in the --metal file.
--refsnp	Reference SNP to be used in the plot.
--refgene	Specify a gene instead of a reference SNP. This will plot a region near a gene, and automatically find the SNP with the most significant p-value to use as the reference SNP.
--flank	Specify the region near a reference SNP or gene as a "flank", instead of having to specify chr/start/stop explicitly. This can be specified in bases, kilobases, or megabases. Examples: 500kb, 1MB, 100141
--chr, --start, --end	Specify chromosome/start/stop as the exact interval to plot. If no --refsnp is specified, the SNP with the most significant p-value in the region will be used as the reference SNP.
Optional settings
--build	Human genome build. This defaults to "hg18", and is the only build we provide data for currently. You can supply your own build-specific data by modifying the conf file, and creating your own SQLite database (see *LINK HERE*).
--ld	Provide a file specifying LD between your reference SNP and all SNPs within the region you wish to plot. You only need to supply this file if you have created LD specifically for your purposes (perhaps a different population or genome build.) Otherwise, LD is computed automatically for you.
--source	Source to use for genotypes when using LD. See Specifying LD source/population/build for more info.
--pop	Population to use when computing LD. See Specifying LD source/population/build for more info.
--snpset	Rug of SNPs to create at the top of the plot. Defaults to the Illumina 1M chip currently.
--plotonly	Create only a PDF of the plot, and remove all temporary files/directories created during plotting.
--no-transform	LocusZoom supports arbitrary precision p-values. However, if your p-values have already been transformed to the log scale, you can use this option to stop LocusZoom from automatically transforming them.
--prefix	Places a text string at the beginning of each plot or directory created. This is mainly used to denote different batches of plots - for example, you could use --prefix using_ceu to denote these plots are computed using CEU LD information.
--db	SQLite database file to use. This is set in the conf file by default, but can be changed on the command line if desired.

Plotting options

In addition to the options above, there are options that control the plotting engine inside Locuszoom. These are used with a different syntax: arg=value (no spaces allowed).

Option (with default value)	Description
theme=NULL	Select a theme. A theme is a collection of other settings. Options include theme=publication and theme=black.
ymax=10	the display range for log10(p-value) will be at least ymax (extended as necessary to avoid clipping)
axisSize=1	scaling factor for axes
axisTextSize=1	sclaing factor for axis labels
axisTextColor=gray30	color of axis labels
refsnpTextColor=black	color for reference SNP label (use 'transparent' to hide this label)
refsnpTextSize=1	scaling factor for reference SNP text size
refsnpTextAlpha=1	transparency level for reference SNP label (1=opaque,0=transparent)
title = ""	title for plot
titleColor=black	color for title
width=10	width of pdf (inches)
height=7	height of pdf (inches)
leftMarginLines=5	margin (in lines) on left
rightMarginLines=5	margin (in lines) on right
unit=1000000	bp per unit displayed in plot
showAnnot=TRUE	show annotation for each snp?
showGenes=TRUE	show genes?
annotCol='annotation'	column to use for custom annotation, if it exists
annotPch='24,24,25,22,22,8,7,21'	plot symbols for annotation
annotOrder=NULL	ordering of custom annotation classes (comma-separated list annotation strings in order, alphabetical by default)
showRefsnpAnnot=TRUE	show annotation for reference snp too?
ld=NULL	file for LD information
ldCuts="0,.2,.4,.6,.8,1"	cut points for LD coloring
ldColors="gray50,navy, lightskyblue,green, orange,red,purple3"	colors for LD
ldCol='rsquare'	name for LD column
LDTitle=NULL	title for LD legend
smallDot=.4	smallest p-value cex
largeDot=.8	largest p-value cex
refDot=NULL	largest p-value cex
rfrows=4	max number of rows used for displaying genes
showPartialGenes=TRUE	should genes that don't fit completely be displayed?
geneFontSize=.8	size for gene names
geneColor="navy"	color for genes
snpsetFile=NULL	use this file for SNPset rug data
rugColor=gray30	color for snpset rugs
rugAlpha=1	alpha for snpset rugs
metalRug=NULL	if not null, use as label for rug of metal positions
showRecomb=TRUE	show recombination rate?
recombColor=blue	color for recombination rate on plot
recombAxisColor=NULL	color for recombination rate axis labeing (default matches recombColor)
recombAxisAlpha=NULL	color for recombination rate axis labeing
recombOver=FALSE	overlay recombination rate? (else underlay it)
recombFill=FALSE	fill recombination rate? (else line only)
recombFillAlpha=0.2	recomb fill alpha
recombLineAlpha=0.8	recomb line/text alpha
frameColor=gray30	frame color for plots
frameAlpha=1	frame alpha for plots
legendSize=.8	scaling factor of legend
legendAlpha=1	transparency of legend background
legend='auto'	legend? (auto, left, right, or none)
hiStart=0	start of highlighted region
hiEnd=0	end of highlighted region
hiColor=blue	color used for highlighting
hiAlpha=0.1	transparency level for highlighting
prelude=NULL	R code to execute after data is read but before plot is made (allows data modification)
postlude=NULL,	R code to execute after plot is made

Examples

A quick peek at a particular SNP

--metal your_data --refsnp rs1002227

A quick peek at a particular gene

--metal your_data --refgene CETP

Plot 500kb on either side of a SNP

--metal your_data --refsnp rs7983146 --flank 500kb

Create a plot for each SNP in a file

--metal your_data --hits file_with_snps

Use 1000 genomes, CEU for LD instead of the default (HapMap r22 CEU)

--metal your_data --refsnp rs11899863 --source 1000G

Use HapMap YRI for LD

--metal your_data --refsnp rs11899863 --pop YRI

Specify a specific region and reference SNP to plot

--metal your_data --refsnp rs1552224 --chr 11 --start 71810746 --end 72710746

An example using plotting options

Note in this example the plotting options are placed at the end of the command-line, and are of the format arg=value. The value should be double-quoted if spaces are included in the value (see title= below.)

--metal your_data --refsnp rs7903146 title="My region" geneFontSize=1.1 recombColor="gray"

Advanced configuration

Creating a SQLite database

As a starting point, we provide a SQLite database based on UCSC human genome build hg18, which includes the following tables:

• snp_pos: SNP positions
• refFlat: gene information (exons, transcription start/stops, etc.)
• recomb_rate: recombination rates from hapmap phase 2
• snp_set: maps each SNP to a "set" - for example, all SNPs on the Illumina 1M chip
• refsnp_trans: a table that maps SNPs from previous builds to the current build

To create your own database, we provide a script bin/dbmeister.py that can insert these tables for you. We recommend creating your own database file, rather than inserting tables into the default LocusZoom database. This script is capable of using python's built-in sqlite support, but for faster insertion of tables (about 2x faster), we recommend installing sqlite3 from www.sqlite.org.

Inserting snp_pos

First, create a file that looks like the following:

snp	chr	pos
rs38343	1	93919141
rs918141	7	763263
chr4:9181	4	9181

The file should be: tab-delimited, must have a header, and the columns should be exactly in that order.

Now, you can create your own database, and insert this file by using:

 dbmeister.py --db my_database.db --snp_pos my_snp_pos_file 

This command creates a database called "my_database.db" and inserts the SNP position table into it. If "my_database.db" had existed already, it would drop the snp_pos table in it, and insert yours in its place.

One special note about adding SNP position tables: a refsnp_trans table will automatically be created for you, where each SNP maps to itself. If you have a list of SNPs from previous builds that you would like to map to a SNP in the current build, you can then insert your own refsnp_trans table (see below for more information on this table.)

Inserting refsnp_trans

The refsnp_trans table looks like the following:

rs_orig	rs_current
rs840	rs715
rs1086	rs940
rs1234	rs1067

The first column contains SNP names from older genome builds, and the rs_current column contains SNP names from the current genome build (i.e., the build your database file is anchored to.)

Inserting this table into your database is simply then:

 dbmeister.py --db my_database.db --trans my_snp_translations_file 

You will want to execute this command AFTER inserting the snp_pos table, since that command drops the existing translation table.

Inserting refFlat

The refFlat table mirrors what is currently supplied by the refFlat table in the UCSC database. The file should look like:

geneName	name	chrom	strand	txStart	txEnd	cdsStart	cdsEnd	exonCount	exonStarts	exonEnds
EFCAB1	NM_024593	chr8	-	49798505	49810423	49799853	49810263	6	49798505,	49799913,
HECTD3	NM_024602	chr1	-	45240806	45249614	45241750	45249516	21	45240806,45241927,	45241835,45241999,
PTPN20B	NM_001042361	chr10	-	48357047	48447587	48358657	48447532	8	48357047,48359393,48391320,	48358692,48359456,48391411,

You can insert this table into the database using:

 dbmeister.py --db my_database.db --refflat my_refflat_file 

Inserting recomb_rate

The recomb_rate table mirrors what is available from HapMap. The format is:

chr	pos	recomb	cm_pos
1	72434	0.0015	0
1	78032	0.0015	8.397e-06
1	554461	0.0015	0.00072304
1	554484	0.0015	0.000723075
1	555296	0.0015	0.000724293
1	558185	0.0015	0.000728627

The table can be inserted using:

 dbmeister.py --db my_database.db --recomb_rate my_recomb_rate_file 

Inserting snp_set

The snp_set table simply carries a mapping of SNPs to a particular set they may belong to. The table looks like:

snp	snp_set
rs1000	Illu1M
rs1000	Illu1M
rs1000	Illu1M
rs1000000	Illu1M
rs10000000	Illu1M
rs10000009	Illu1M

The first column is a SNP, and the second is the name of the set it belongs to. If a SNP belongs to multiple sets, you can duplicate that SNP multiple times, one for each set.

Inserting the table into your database can be done using:

 dbmeister.py --db my_database.db --snp_set my_snpset_file 

Making LocusZoom aware of your new database

Now that you've created your own database file, you need to make LocusZoom aware that it exists. There are two ways to do this:

1. Edit conf/m2zfast.conf, and change the SQLITE_DB variable
2. Supply the --db command line option when invoking bin/locuszoom

Editing the m2zfast.conf file entails changing the following block of code:

SQLITE_DB = {
  'hg18' : "data/database/locuszoom_hg18.db"
};

Here, we've mapped build "hg18" to the default database file that comes with LocusZoom. You can mimic this format and insert your own, for example:

SQLITE_DB = {
  'hg18' : "data/database/locuszoom_hg18.db",
  'hg19' : "data/database/my_database.db"
};

The location of your database should be either an absolute path to your file, or a path relative to the locuszoom/ root (like those seen above.)

If you wish for your database to become the default, change the LATEST_BUILD variable in the m2zfast.conf file to whatever you have chosen above (in our example, our new database became mapped to 'hg19'.)

Changing m2zfast.conf settings

The m2zfast.conf configuration file contains a number of settings that are typically static, but could require user configuration. The table below lists each variable, and its purpose.

Variable	Description
LATEST_BUILD	Latest build (i.e. the default build.) This is used to index SQLITE_DB, as well as LD_DB.
NEWFUGUE_PATH	Path to the new_fugue binary. Defaults to "new_fugue", which simply means it is searched for on your path. If new_fugue is not on your path, you will need to specify the full path here.
OFFLINE	For the standalone version of LocusZoom, this should always be True.
SQLITE_DB	See Making LocusZoom aware of your database.
LD_DB	Contains a "tree" which maps a tuple of (genotype source, genotype population, genome build) to genotype files.
DEFAULT_SNP_FLANK	If only a --refsnp is given with no --flank, this variable sets the default flank that is used.
DEFAULT_GENE_FLANK	If only a --refgene is given with no --flank, this variable sets the default flank that is used.

LD caching

LocusZoom attempts to remember LD calculations that were made on previous runs of the program to avoid having to re-calculate the same regional LD for subsequent runs. The process works as follows:

• For a given reference SNP and chr/start/stop:
  • If LD has not been previously computed, use new_fugue to compute LD with the reference SNP and all other SNPs in the region, and store this result to the LD cache
  • Else, retrieve the previously stored LD results

The cache intelligently stores LD from separate sources (hapmap, 1000G), populations, builds, and even different versions of genotype files separately.

Upon running LocusZoom, a file called "ld_cache.db" will automatically be created in the current directory, and LD computations will be stored there. If you wish to change the location of the LD cache, use --cache <file>. If you wish to disable LD caching, you can use --cache None.

Python note: The ld_cache.db is actually a shelve, and you can explore its contents using:

import shelve
d = shelve.open("ld_cache.db")

License

Copyright 2010 Ryan Welch, Randall Pruim

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.