Eagle Manual
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Package ‘Eagle’
December 12, 2018
Type Package
Title Multiple Locus Association Mapping on a Genome-Wide Scale
Version 1.2.0
Maintainer Andrew George <andrew.george@csiro.au>
Author Andrew George [aut, cre],
Joshua Bowden [ctb],
Ryan Stephenson [ctb],
Hyun Kang [ctb],
Noah Zaitlen [ctb],
Claire Wade [ctb],
Andrew Kirby [ctb],
David Heckerman [ctb],
Mark Daly [ctb],
Eleazar Eskin [ctb]
Description An implementation of multiple-locus association mapping on a genome-wide scale. 'Ea-
gle' can handle inbred and outbred study populations, populations of arbitrary unknown com-
plexity, and data larger than the memory capacity of the computer. Since 'Eagle' is based on lin-
ear mixed models, it is best suited to the analysis of data on continuous traits. How-
ever, it can tolerate non-normal data. 'Eagle' reports, as its find-
ings, the best set of snp in strongest association with a trait. For users unfamiliar with R, to per-
form an analysis, run 'OpenGUI()'. This opens a web browser to the menu-driven user inter-
face for the input of data, and for performing genome-wide analysis.
License GPL-3
Depends R (>= 3.4), shinyFiles
Imports matrixcalc, shiny, shinythemes, shinyBS, shinyjs, stats,
utils, parallel, data.table
LinkingTo RcppEigen, Rcpp
Roxygen list(wrap=FALSE)
LazyData true
ByteCompile TRUE
NeedsCompilation yes
URL http://eagle.r-forge.r-project.org
Contact eaglehelp@csiro.au
1
2Eagle-package
Rtopics documented:
Eagle-package........................................ 2
AM ............................................. 3
FPR4AM .......................................... 5
OpenGUI .......................................... 8
ReadMap .......................................... 9
ReadMarker......................................... 10
ReadPheno ......................................... 13
ReadZmat.......................................... 15
SummaryAM ........................................ 16
Index 19
Eagle-package Eagle for Genome-wide Association Mapping
Description
An implementation of multiple-locus association mapping on a genome-wide scale. ’Eagle’ can
handle inbred and outbred study populations, populations of arbitrary unknown complexity, and
data larger than the memory capacity of the computer. Since ’Eagle’ is based on linear mixed
models, it is best suited to the analysis of data on continuous traits. However, it can tolerate non-
normal data. ’Eagle’ reports, as its findings, the best set of snp in strongest association with a trait.
For users unfamiliar with R, to perform an analysis, run ’OpenGUI()’. This opens a web browser
to the menu-driven user interface for the input of data, and for performing genome-wide analysis.
Details
Motivation: Data from genome-wide association studies are analyzed, commonly, with single-locus
models. That is, analyzes are performed on a locus-by-locus basis. Multiple-locus approaches that
model the association between a trait and multiple loci simultaneously are more powerful. However,
these methods do not scale well with study size and many of the packages that implement these
methods are not easy to use. Eagle was specifically designed to make genome-wide association
mapping with multiple-locus models simple and practical.
Assumptions
1. Individuals are diploid but they can be inbred or outbred.
2. The marker and phenotype data are in separate files.
3. Marker loci are snps. Dominant and multi-allelic loci will need to be converted into biallelic
(snp-like) loci.
4. The trait is continuous and normally distributed. Eagle can handle non-normally distributed
trait data but there may a loss of power to detect marker-trait associations.
Important Functions:
1. ReadMarker for reading in the snp data.
2. ReadPheno for reading in the phenotypic data (traits and features/covariates)
3. ReadMap for reading in the marker map.
4. AM for performing association mapping on the data.
5. OpenGUI which opens the GUI.
AM 3
Output: The key output from AM is a list of snp. Each snp identifies a separate genomic region of
interest, housing genes that are affecting the trait. Additional summary information such as the size
of the snp effects, their statistical significance, and how much phenotypic variation they explain can
be obtained by running SummaryAM.
Where to get help: A variety of different help options are available.
• At the R prompt, type
library(, "Eagle")
for an overview of the package and its functions.
• For detailed help on a function called "foo" say, type
help("foo")
• Visit the Eagle website at http://eagle.r-forge.r-project.org/ where you can find a
quick start guide, instructions on getting the most out of Eagle, video tutorials, and other
useful information.
Author(s)
Andrew W. George (Data61, CSIRO) with a lot of support from Joshua Bowden (IM&T, CSIRO)
Maintainer: Andrew W. George <andrew.george@csiro.au>
AM multiple-locus Association Mapping
Description
AM performs association mapping within a multiple-locus linear mixed model framework. AM finds
the best set of marker loci in strongest association with a trait while simultaneously accounting for
any fixed effects and the genetic background.
Usage
AM(trait = NULL, fformula = NULL, availmemGb = 8, geno = NULL,
pheno = NULL, map = NULL, Zmat = NULL, ncpu = detectCores(),
ngpu = 0, quiet = TRUE, maxit = 20, fixit = FALSE, gamma = NULL)
Arguments
trait the name of the column in the phenotype data file that contains the trait data.
The name is case sensitive and must match exactly the column name in the
phenotype data file.
fformula the right hand side formula for the fixed effects. See below for details. If not
specified, only an overall mean will be fitted.
availmemGb a numeric value. It specifies the amount of available memory (in Gigabytes).
This should be set to the maximum practical value of available memory for the
analysis. If not specified, 8 GBytes is assumed.
geno the R object obtained from running ReadMarker. This must be specified.
pheno the R object obtained from running ReadPheno. This must be specified.
4AM
map the R object obtained from running ReadMap. If not specified, a generic map
will be assumed.
Zmat the R object obtained from running ReadZmat. If not specified, an identity ma-
trix will be assumed.
ncpu a integer value for the number of CPU that are available for distributed comput-
ing. The default is to determine the number of CPU automatically.
ngpu a integer value for the number of gpu available for computation. The default is
to assume there are no gpu available. This option has not yet been implemented.
quiet a logical value. If set to FALSE, additional runtime output is printed. This is
useful for error checking and monitoring the progress of a large analysis.
maxit an integer value for the maximum number of forward steps to be performed.
This will rarely need adjusting.
fixit a boolean value. If TRUE, then maxit iterations are performed, regardless of the
value of the model fit value extBIC. If FALSE, then the model building process
is stopped when extBIC increases in value.
gamma a value between 0 and 1 for the regularization parameter for the extBIC. Values
close to 0 lead to an anti-conservative test. Values close to 1 lead to a more
conservative test. If this value is left unspecified, a default value of 1 is assumed.
See FPR4AM for an empirical approach for setting the gamma value.
Value
A list with the following components:
trait: column name of the trait being used by ’AM’.
fformula: the fixed effects part of the linear mixed model.
indxNA: a vector containing the row indexes of those individuals, whose trait and fixed effects
data contain missing values and have been removed from the analysis.
Mrk: a vector with the names of the snp in strongest and significant association with the trait.If no
loci are found to be significant, then this component is NA.
Chr: the chromosomes on which the identified snp lie.
Pos: the map positions for the identified snp.
Indx: the column indexes in the marker file of the identified snp.
ncpu: number of cpu used for the calculations.
availmemGb: amount of RAM in gigabytes that has been set by the user.
quiet: boolean value of the parameter.
extBIC: numeric vector with the extended BIC values for the loci found to be in significant asso-
ciation with the trait.
gamma the numeric value of the parameter.
See Also
FPR4AM ,ReadMarker,ReadPheno,ReadZmat, and ReadMap
FPR4AM 5
Examples
## Not run:
# Since the following code takes longer than 5 seconds to run, it has been tagged as dontrun.
# However, the code can be run by the user.
#
#-------------------------
# Example
#------------------------
# read the map
#~~~~~~~~~~~~~~
# File is a plain space separated text file with the first row
# the column headings
complete.name <- system.file('extdata','map.txt',
package='Eagle')
map_obj <- ReadMap(filename=complete.name)
# read marker data
#~~~~~~~~~~~~~~~~~~~~
# Reading in a PLINK ped file
# and setting the available memory on the machine for the reading of the data to 8 gigabytes
complete.name <- system.file('extdata','geno.ped',
package='Eagle')
geno_obj <- ReadMarker(filename=complete.name, type='PLINK', availmemGb=8)
# read phenotype data
#~~~~~~~~~~~~~~~~~~~~~~~
# Read in a plain text file with data on a single trait and two covariates
# The first row of the text file contains the column names y, cov1, and cov2.
complete.name <- system.file('extdata','pheno.txt', package='Eagle')
pheno_obj <- ReadPheno(filename=complete.name)
# Performing multiple-locus genome-wide association mapping with a model
# with fixed effects cov1 and cov2 and an intercept. The intercept
# need not be specified as it is assumed.
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
res <- AM(trait = 'y',
fformula=c('cov1+cov2'),
map = map_obj,
pheno = pheno_obj,
geno = geno_obj, availmemGb=8)
## End(Not run)
FPR4AM Set the false positive rate for AM
6FPR4AM
Description
The gamma parameter in AM controls the false positive rate of the model building process. This
function uses permutation to find the gamma value for a desired false positive rate.
Usage
FPR4AM(falseposrate = 0.05, trait = trait, numreps = 100,
fformula = NULL, availmemGb = 8, numgammas = 20, geno = NULL,
pheno = NULL, map = NULL, Zmat = NULL, ncpu = detectCores(),
ngpu = 0, seed = 101)
Arguments
falseposrate the desired false positive rate.
trait the name of the column in the phenotype data file that contains the trait data.
The name is case sensitive and must match exactly the column name in the
phenotype data file. This parameter must be specified.
numreps the number of replicates upon which to base the calculation of the false positive
rate. We have found 100 replicates to be sufficient.
fformula the right hand side formula for the fixed effects part of the model.
availmemGb a numeric value. It specifies the amount of available memory (in Gigabytes).
This should be set to the maximum practical value of available memory for the
analysis.
numgammas the number of equidistant gamma values from 0 to 1 for which to calculate the
false positive rate of the model building process. This should not need adjusting.
geno the R object obtained from running ReadMarker. This must be specified.
pheno the R object obtained from running ReadPheno. This must be specified.
map the R object obtained from running ReadMap. If not specified, a generic map
will be assumed.
Zmat the R object obtained from running ReadZmat. If not specified, an identity ma-
trix will be assumed.
ncpu a integer value for the number of CPU that are available for distributed comput-
ing. The default is to determine the number of CPU automatically.
ngpu a integer value for the number of gpu available for computation. The default is
to assume there are no gpu available. This option has not yet been implemented.
seed a integer value for the starting seed for the permutations.
Details
The false positive rate for AM is controlled by its gamma parameter. Values close to 1 (0) decreases
(increases) the false positive rate of detecting SNP-trait associations. There is no analytical way of
setting gamma for a specified false positive rate. So we are using permutation to do this empirically.
By setting falseposrate to the desired false positive rate, this function will find the corresponding
gamma value for AM.
A table of other gamma values for a range of false positive rates is also given.
To increase the precision of the gamma estimates, increase numreps.
FPR4AM 7
Value
A list with the following components:
numreps: the number of permutations performed.
gamma: the vector of gamma values.
falsepos: the false positive rates for the gamma values.
setgamma: the gamma value that gives a false positive rate of falseposrate
See Also
AM
Examples
## Not run:
# Since the following code takes longer than 5 seconds to run, it has been tagged as dontrun.
# However, the code can be run by the user.
#
#-------------------------
# Example
#------------------------
# read the map
#~~~~~~~~~~~~~~
# File is a plain space separated text file with the first row
# the column headings
complete.name <- system.file('extdata','map.txt',
package='Eagle')
map_obj <- ReadMap(filename=complete.name)
# read marker data
#~~~~~~~~~~~~~~~~~~~~
# Reading in a PLINK ped file
# and setting the available memory on the machine for the reading of the data to 8 gigabytes
complete.name <- system.file('extdata','geno.ped',
package='Eagle')
geno_obj <- ReadMarker(filename=complete.name, type='PLINK', availmemGb=8)
# read phenotype data
#~~~~~~~~~~~~~~~~~~~~~~~
# Read in a plain text file with data on a single trait and two covariates
# The first row of the text file contains the column names y, cov1, and cov2.
complete.name <- system.file('extdata','pheno.txt', package='Eagle')
pheno_obj <- ReadPheno(filename=complete.name)
# Suppose we want to perform the AM analysis at a 5% false positive rate.
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
ans <- FPR4AM(falseposrate = 0.05,
trait = 'y',
8OpenGUI
fformula=c('cov1+cov2'),
map = map_obj,
pheno = pheno_obj,
geno = geno_obj)
res <- AM(trait = 'y',
fformula=c('cov1+cov2'),
map = map_obj,
pheno = pheno_obj,
geno = geno_obj,
gamma = ans$setgamma)
## End(Not run)
OpenGUI Browser-based Graphical User Interface
Description
Opens a web browser to act as a user-friendly interface to ’Eagle’
Usage
OpenGUI()
Details
OpenGUI is an easy to use web-based interface for ’Eagle’. By clicking on the navigation tabs at the
top of a page, data can be read and analysed. By using this GUI, a user can avoid having to write R
code.
Note, that even though a web browser is being used as the user interface, everything remains local
to the computer.
Examples
## Not run:
# opens a web browser
OpenGUI()
## End(Not run)
ReadMap 9
ReadMap Read map file
Description
Read in the marker map data.
Usage
ReadMap(filename = NULL, csv = FALSE, header = TRUE)
Arguments
filename contains the name of the map file. The file name needs to be in quotes. If the
file is not in the working directory, then the full path to the file is required.
csv a logical value. When TRUE, a csv file format is assumed. When FALSE, a space
separated format is assumed.
header a logical value. When TRUE, the first row of the file contains the column head-
ings.
Details
Association mapping, unlike classical linkage mapping, does not require a map to find marker-trait
associations. So, reading in a map file is optional. If a map file is supplied, then the marker names
from this file are used when reporting the findings from AM. If a map file is not supplied, then generic
names M1, M2, ..., are assigned to the marker loci where the number refers to the column number
in the marker file.
A space separated text file with column headings is assumed as the default input. The map file can
have three or four columns. If the map file has three columns, then it is assumed that the three
columns are the marker locus names, the chromosome number, and the map position (in any units).
If the map file has four columns as with a ’PLINK map file, then the columns are assumed to be
the marker locus names, the chromosome number, the map position in centimorgans, and the map
position in base pairs.
Missing values are allowed but not in the first column of the file (i.e. the marker labels are not
allowed to be missing).
The order of the marker loci in this file is assumed to be the same order as the loci in the marker
data file.
The first column of the map file is assumed to contain the marker names.
Value
a data frame is returned of the map data.
See Also
ReadMarker and ReadPheno.
10 ReadMarker
Examples
# Read in example map data from ./extdata/
# find the full location of the map data
complete.name <- system.file('extdata','map.txt', package='Eagle')
# read in map data
map_obj <- ReadMap(filename=complete.name)
# look at first few rows of the map file
head(map_obj)
ReadMarker Read marker data.
Description
A function for reading in marker data. Two types of data can be read.
Usage
ReadMarker(filename = NULL, type = "text", missing = NULL, AA = NULL,
AB = NULL, BB = NULL, availmemGb = 16, quiet = TRUE)
Arguments
filename contains the name of the marker file. The file name needs to be in quotes. If the
file is not in the working directory, then the full path to the file is required.
type specify the type of file. Choices are ’text’ (the default) and PLINK.
missing the number or character for a missing genotype in the text file. There is no need
to specify this for a PLINK ped file. Missing allele values in a PLINK file must
be coded as ’0’ or ’-’.
AA the character or number corresponding to the ’AA’ snp genotype in the marker
genotype file. This need only be specified if the file type is ’text’. If a character
then it must be in quotes.
AB the character or number corresponding to the ’AB’ snp genotype in the marker
genotype file. This need only be specified if the file type is ’text’. This can
be left unspecified if there are no heterozygous genotypes (i.e. the individuals
are inbred). Only a single heterozygous genotype is allowed (’Eagle’ does not
distinguish between ’AB’ and ’BA’). If specified and a character, it must be in
quotes.
BB the character or number corresponding to the ’BB’ snp genotype in the marker
genotype file. This need only be specified if the file type is ’text’. If a character,
then it must be in quotes.
availmemGb a numeric value. It specifies the amount of available memory (in Gigabytes).
This should be set to be as large as possible for best performance.
quiet a logical value. If set to TRUE, additional runtime output is printed.
ReadMarker 11
Details
ReadMarker can handle two different types of marker data; namely, genotype data in a plain text
file, and PLINK ped files.
Reading in a plain text file containing the marker genotypes: To load a text file that contains
snp genotypes, run ReadMarker with filename set to the name of the file, and AA,AB,BB set to
the corresponding genotype values. The genotype values in the text file can be numeric, character,
or a mix of both.
We make the following assumptions
• The text file does not contain row or column headings
• The file is allowed to contain missing genotypes that have been coded according to missing
• Individuals are diploid
• The rows of the text file are the individuals and the columns are the marker loci
• The file is space separated
• The mapping of the observed genotypes in the marker file to AA,AB, and BB, remains the same
for all loci
• Individuals are outbred when AA,AB, and BB are specified and inbred when only AA, and BB
are specified
• For a text file, the same alphanumeric value is used for all missing marker genotypes. For a
PLINK ped file, the missing allele is allowed to be ’0’ or ’-’.
For example, suppose we have a space separated text file with marker genotype data collected
from five snp loci on three individuals where the snp genotype AA has been coded 0, the snp
genotype AB has been coded 1, the snp genotype BB has been coded 2, and missing genotypes
are coded as 99
0 1 2 0 2
1102 0
221199
The file is called geno.txt and is located in the directory /my/dir/.
To load these data, we would use the command
geno_obj <- ReadMarker(filename='/my/dir/geno.txt', AA=0, AB=1, BB=2, type='text', missing=99)
where the results from running the function are placed in geno_obj.
As another example, suppose we have a space separated text file with marker genotype data col-
lected from five snp loci on three individuals where the snp genotype AA has been coded a/a, the
snp genotype AB has been coded a/b, and the snp genotype BB has been coded b/b
a/a a/b b/b a/a b/b
a/b a/b a/a b/b a/a
b/b b/b a/b a/b NA
The file is called geno.txt and is located in the same directory from which R is being run (i.e. the
working directory).
To load these data, we would use the command
geno_obj <- ReadMarker(filename='geno.txt', AA='a/a', AB='a/b', BB='b/b',
type='text', missing = 'NA')
where the results from running the function are placed in geno_obj.
12 ReadMarker
Reading in a PLINK ped file: PLINK is a well known toolkit for the analysis of genome-wide
association data. See https://www.cog-genomics.org/plink2 for details.
Full details of PLINK ped files can be found https://www.cog-genomics.org/plink/1.9/
formats#ped. Briefly, the PED file is a space delimited file (tabs are not allowed): the first six
columns are mandatory:
Family ID
Individual ID
Paternal ID
Maternal ID
Sex (1=male; 2=female; other=unknown)
Phenotype
Here, these columns can be any values since ReadMarker ignores these columns.
Genotypes (column 7 onwards) can be any character (e.g. 1,2,3,4 or A,C,G,T or anything else)
except 0 which is, by default, the missing genotype character. All markers should be biallelic. All
snps must have two alleles specified. Missing alleles (i.e 0 or -) are allowed. No column headings
should be given.
As an example, suppose we have data on three individuals genotyped for four snp loci
FAM001 101 0 0 1 0 A G C C C G A A
FAM001 201 0 0 2 0 A A C T G G T A
FAM001 300 101 201 2 0 G A T T C G A T
Then to load these data, we would use the command
geno_obj <- ReadMarker(filename='PLINK.ped', type='PLINK')
where geno_obj is used by AM, and the file PLINK.ped is located in the working directory (i.e.
the directory from which R is being run).
Reading in other formats: Having first installed the stand-alone PLINK software, it is possi-
ble to convert other file formats into PLINK ped files. See https://www.cog-genomics.org/
plink/1.9/formats for details.
For example, to convert vcf file into a PLINK ped file, at the unix prompt, use the PLINK com-
mand
PLINK --vcf filename.vcf --recode --out newfilename
and to convert a binary ped file (bed) into a ped file, use the PLINK command
PLINK --bfile filename --recode --tab --out newfilename
Value
To allow AM to handle data larger than the memory capacity of a machine, ReadMarker doesn’t
load the marker data into memory. Instead, it creates a reformatted file of the marker data and
its transpose. The object returned by ReadMarker is a list object with the elements asciifileM ,
asciifileMt, and dim_of_ascii_M which is the full file name (name and path) of the reformatted
file for the marker data, the full file name of the reformatted file for the transpose of the marker data,
and a 2 element vector with the first element the number of individuals and the second element the
number of marker loci.
ReadPheno 13
Examples
#--------------------------------
# Example 1
#-------------------------------
#
# Read in the genotype data contained in the text file geno.txt
#
# The function system.file() gives the full file name (name + full path).
complete.name <- system.file('extdata','geno.txt', package='Eagle')
#
# The full path and name of the file is
print(complete.name)
# Here, 0 values are being treated as genotype AA,
# 1 values are being treated as genotype AB,
# and 2 values are being treated as genotype BB.
# 4 gigabytes of memory has been specified.
# The file is space separated with the rows the individuals
# and the columns the snp loci.
geno_obj <- ReadMarker(filename=complete.name, type='text', AA=0, AB=1, BB=2, availmemGb=4)
# view list contents of geno_obj
print(geno_obj)
#--------------------------------
# Example 2
#-------------------------------
#
# Read in the allelic data contained in the PLINK ped file geno.ped
#
# The function system.file() gives the full file name (name + full path).
complete.name <- system.file('extdata','geno.ped', package='Eagle')
#
# The full path and name of the file is
print(complete.name)
# Here, the first 6 columns are being ignored and the allelic
# information in columns 7 - 10002 is being converted into a reformatted file.
# 4 gigabytes of memory has been specified.
# The file is space separated with the rows the individuals
# and the columns the snp loci.
geno_obj <- ReadMarker(filename=complete.name, type='PLINK', availmemGb=4)
# view list contents of geno_obj
print(geno_obj)
ReadPheno Read phenotype file
Description
Read in the phenotype data.
14 ReadPheno
Usage
ReadPheno(filename = NULL, header = TRUE, csv = FALSE, missing = NULL)
Arguments
filename contains the name of the phenotype file. The file name needs to be in quotes. If
the file is not in the working directory, then the full path to the file is required.
header a logical value. When TRUE, the first row of the file contains the names of the
columns. Default is TRUE.
csv a logical value. When TRUE, a csv file format is assumed. When FALSE, a space
separated format is assumed. Default is FALSE.
missing the number or character for a missing phenotype value.
Details
ReadPheno reads in the phenotype data which are data measured on traits and any fixed effects (or
predictors/features/explanatory variables). A space separated plain text file is assumed. Each row
in this file corresponds to an individual. The number of rows in the phenotype file must be the same
as the number of rows in the marker data file. Also, the ordering of the individuals must be the same
in the two files. A space separated file with column headings is the default but can be changed with
the header and csv options.
The phenotype file may contain multiple traits and fixed effects variables.
Missing values are allowed. Eagle is told which value should be treated as missing by setting the
missing parameter to the value.
For example, suppose we have three individuals for which we have collected data on two quantita-
tive traits (y1 and y2), and four explanatory variables (age, weight, height, and sex). The data looks
like
y1 y2 age weight height sex
112.02 -3.123 26 75 168.5 M
156.44 1.2 45 102 NA NA
10.3 NA 28 98 189.4 F
where the first row has the column headings and the next three rows contain the observed data on
three individuals.
To load these data, we would use the command
pheno_obj <- ReadPheno(filename='pheno.dat', missing='NA')
where pheno.dat is the name of the phenotype file, and pheno_obj is the R object that contains the
results from reading in the phenotype data. The file is located in the working directory so there is
no need to specify the full path, just the file name is suffice.
Dealing with missing trait data:
AM deals automatically with individuals with missing trait data. These individuals are removed
from the analysis and a warning message is generated.
Dealing with missing fixed effects values:
AM deals automatically with individuals with missing fixed effects values. These individuals are
removed from the analysis and a warning message is generated
ReadZmat 15
Value
a data frame is returned of the phenotype data. If header is true, the names of the columns will be
as specified by the first row of the phenotype file. If header is FALSE, generic names are supplied
by R in the form of V1, V2, etc. If no column headings are given, these generic names will need to
be used in the trait and fformula parameters in AM. You can print out the column names of the
data frame by using
names(pheno_obj)
The column names are also printed along with other summary information when ReadPheno is run.
See Also
ReadMarker for reading in marker data, AM for performing association mapping.
Examples
# Read in phenotype data from ./extdata/
# find the full location of the phenotype data
complete.name <- system.file('extdata','pheno.txt', package='Eagle')
pheno_obj <- ReadPheno(filename=complete.name)
## print a couple of lines of the data file
head(pheno_obj)
ReadZmat Read Z matrix
Description
Read in the Z matrix that assigns groups/strains/lines to their trait measurements.
Usage
ReadZmat(filename = NULL)
Arguments
filename contains the name of the Z matrix file. The file name needs to be in quotes. If
the file is not in the working directory, then the full path to the file is required.
Details
The underlying linear mixed model is of the form
Y=Xβ +Zug+e
where Z is a (n x ng) matrix that contains ones and zeros, n is the number of trait measurements,
and ngis the number of groups/strains/lines. If n and ngare the same, then there is no need to
16 SummaryAM
specify Z. However, if a group/strain/line has multiple trait measurements (i.e. n > ng) then the Z
matrix is needed to tell Eagle which trait measurements belong to which groups/strains/lines.
A space separated text file is assumed. Each row of the matrix contains multiple zeroes but only a
single one. The file cannot contain column or row headings. The file also cannot contain a row of
only zeroes. Here, n must be larger than ngotherwise an error will be issued.
Value
a data matrix is returned of the Z matrix.
See Also
ReadMarker and ReadPheno.
Examples
# Read in example Z matrix from ./extdata/
# find the full location of the Z matrix data
complete.name <- system.file('extdata','Z.txt', package='Eagle')
# read in Z matrix data
Z_obj <- ReadZmat(filename=complete.name)
# look at first few rows of the Z matrix file
head(Z_obj)
SummaryAM Summary of multiple locus association mapping results
Description
A summary function that provides additional information on the significant marker-trait associations
found by AM
Usage
SummaryAM(AMobj = NULL, pheno = NULL, geno = NULL, map = NULL)
Arguments
AMobj the (list) object obtained from running AM. Must be specified.
pheno the (data frame) object obtained from running ReadPheno. Must be specified.
geno the (list) object obtained from running ReadMarker. Must be specified.
map the (data frame) object obtained from running ReadMap. The default is to assume
a map object has not been supplied. Optional.
SummaryAM 17
Details
SummaryAM produces two tables of results. First, a table of results is produced with the additive
effect size and p-value for each fixed effect in the final model. Second, a table of results is produced
with the proportion of phenotypes variance explained by the different multiple-locus models. Each
row in this table is the proportion of phenotypic variance explained (Sun et al. 2010) after the
marker locus has been added to the multiple locus model.
References
Sun G., Zhu C., Kramer MH., Yang S-S., et al. 2010. Variation explained in mixed model associa-
tion mapping. Heredity 105, 330-340.
See Also
AM
Examples
## Not run:
# Since the following code takes longer than 5 seconds to run, it has been tagged as dontrun.
# However, the code can be run by the user.
#
#---------------
# read the map
#---------------
#
# File is a plain space separated text file with the first row
# the column headings
complete.name <- system.file('extdata','map.txt',
package='Eagle')
map_obj <- ReadMap(filename=complete.name)
# to look at the first few rows of the map file
head(map_obj)
#------------------
# read marker data
#------------------
# Reading in a PLINK ped file
# and setting the available memory on the machine for the reading of the data to 8 gigabytes
complete.name <- system.file('extdata','geno.ped',
package='Eagle')
geno_obj <- ReadMarker(filename=complete.name, type='PLINK', availmemGb=8)
#----------------------
# read phenotype data
#-----------------------
# Read in a plain text file with data on a single trait and two fixed effects
# The first row of the text file contains the column names y, cov1, and cov2.
complete.name <- system.file('extdata','pheno.txt', package='Eagle')
pheno_obj <- ReadPheno(filename=complete.name)
#-------------------------------------------------------
18 SummaryAM
# Perform multiple-locus genome-wide association mapping
#-------------------------------------------------------
res <- AM(trait = 'y',
fformula=c("cov1 + cov2"),
map = map_obj,
pheno = pheno_obj,
geno = geno_obj, availmemGb=8)
#-----------------------------------------
# Produce additional summary information
#------------------------------------------
SummaryAM(AMobj=res, pheno=pheno_obj, geno=geno_obj, map=map_obj)
## End(Not run)
