The Mega2R package uses as input genetic data that have been reformatted and stored in a 'SQLite' database; this database is initially created by the standalone Mega2 C++ program. Here we give a quick overview of the Mega2 C++ program. For more information, please see the Mega2 documentation, which is available here: https://watson.hgen.pitt.edu/docs/mega2_html/mega2.html
During an association or linkage analysis project, one may need to analyze the data with several different programs. Unfortunately, it can often be quite difficult to get one's data in the proper format desired by each different computer program. Not only must the data be converted to the proper format, but also the loci must be reordered into their proper order. Writing custom reformatting scripts can be error-prone and very time-consuming. To address these problems, we created Mega2.
Mega2 can read input data in several formats: LINKAGE format, PLINK format, IMPUTE format and VCF format. Mega2 allows one to augment these input formats with additional information, if desired. For example, trait locus penetrance information can be specified. The input data are read and validated once, then stored in a 'SQLite' database file.
Mega2 then takes the database file and, via a menu-driven interface, transforms it into various other file formats, thus greatly facilitating a variety of different analyses. In addition, for many of these options, it also sets up a shell script that then can automatically run these analyses (if you are using Mega2 in a Unix or Macintosh environment).
Mega2 is currently structured so that the user proceeds through a series of menus, both to create the database and later to process it, making choices in each menu (or accepting the default values), until the desired output files are created. After the desired output files are created, Mega2 exits. Mega2 can also be run in a hands-free mode, using a control or 'batch' file to specify these choices.
In addition to the ability to reformat data for a variety of analysis programs, other useful features of Mega2 include:
The ability to create publication-quality PDF plots of the results using our nplplot library.
The ability to create custom tracks of results for visualization in the UCSC genome browser.
The ability to run in an automated way using batch files.
The availability of our Genetic Map Interpolator for aiding in constructing genetic maps of markers.
The ability to align allele labels to a reference and to resolve strand issues.
The ability to simulate genotype errors.
Input and output support for Mega2 format files that contain informative header lines and are readable into R.
Input and output support for the widely-used PLINK format files.
Input and output support for Variant Call Format (VCF, BCF, compressed VCF) files, including flexible filtering on input.
Input support for IMPUTE2 GEN format files and binary IMPUTE2 BGEN format files.
The ability to automatically zero out selected genotypes for specific individuals in order to resolve Mendelian inconsistencies.
In most cases, in addition to generating appropriately re-formatted files, Mega2 also generates a shell script that will automatically run the desired program.
Creation of an HTML summary of the most recent run of Mega2, with links to input and output and log files.
Creation of extensive data analysis logs, both during database creation: (files MEGA2.DB.LOG and MEGA2.DB.ERR) and during each analysis: (files MEGA2.LOG and MEGA2.ERR).
The features listed above and the documentation, https://watson.hgen.pitt.edu/docs/mega2_html/mega2.html, describe the Mega2 executable, written in C++. The site https://watson.hgen.pitt.edu/register/ provides Mega2 binaries for a number of different platforms including Windows 7 and 10 as well as the GPL-3 source. (The source for Mega2 as well as Mega2R can be found at https://bitbucket.org/dweeks/mega2 BitBucket site.)
Since Mega2 now produces a 'SQLite' database, it is now easy to load the data that Mega2 has processed into R. This is what Mega2R and this tutorial is all about.
Mega2R loads and manipulates data frames containing genotype,
phenotype, and family information from the input 'SQLite' database. In
addition, we have developed C++ functions to decompress needed
subsets of the genotype data, on the fly, in a memory efficient manner.
We have also created several more functions that
illustrate how to use the data frames as well as perform useful
functions: these permit one to run the 'pedgene' package (https://CRAN.R-project.org/package=pedgene) to carry
out gene-based association tests on family data using selected
marker subsets, to run the 'SKAT' package (https://CRAN.R-project.org/package=SKAT) to carry out gene-based
association tests using selected marker subsets, to output subsets of the Mega2R data as a VCF file (https://github.com/samtools/hts-specs) and
related files (for phenotype and family data), and to convert the
data frames into 'GenABEL' gwaa.data-class objects (https://CRAN.R-project.org/package=GenABEL).
This tutorial shows how to read the 'SQLite'
database and how to access tables in it using this package, Mega2R.
The tutorial shows how to carry out gene-based analyses that select subsets
of the data that corresponds to transcripts or other base pair ranges.
It is important to point out that
like GenABEL (https://CRAN.R-project.org/package=GenABEL),
Mega2R keeps its genotype data in a compressed format that is only expanded
when needed.
We used the SeqSIMLA2 program to generate an example data set to use in this vignette. [SeqSIMLA2: simulating correlated quantitative traits accounting for shared environmental effects in user-specified pedigree structure, Chung RH1, Tsai WY, Hsieh CH, Hung KY, Hsiung CA, Hauser ER., Genet Epidemiol. 2015 Jan;39(1):20-4. doi: 10.1002/gepi.21850. Epub 2014 Sep 22.] We needed to sub-sample the data down to 1,380 people and 1,000 markers to make the size manageable. These data will be used to illustrate Mega2 and Mega2R operations that follow.
Note: The simulator produces markers on only chromosome 1.
The files you will need for this tutorial are provided in this package.
Further, our use of the “mega2” executable expects the Mega2.BATCH.
library(Mega2R)
dump_mega2rtutorial_data()
All the files for this vignette will be created in the temporary directory given by file.path(tempdir(),"Mega2Rtutorial").
In that directory, you will see the following files:
list.files(where_mega2rtutorial_data())
## [1] "MEGA2.BATCH.seqsimr" "MEGA2.BATCH.srdta" "MEGA2.BATCH.vcf"
## [4] "Mega2r.map" "Mega2r.map.gz" "Mega2r.ped"
## [7] "Mega2r.ped.gz" "seqsimr.db" "seqsimr.db.gz"
## [10] "srdta.db" "srdta.db.gz"
*Note: The temporary directory name, given by file.path(tempdir(),"Mega2Rtutorial"), is generated randomly each time this
vignette is run. *
Note: The temporary directory name is also the value of the R expression, where_mega2rtutorial_data().
When you are done with these exercise, the “clean” command will remove these files:
clean_mega2rtutorial_data()
We will assume that you have started an session at which to type the commands in the tutorial.
To run any of these exercises, you should install the package Mega2R.
install.packages("Mega2R")
In Section 6 below, we will carry out gene-based association tests, where 'genes' are defined according to a database containing the boundaries of the gene transcripts. This requires two Bioconductor Annotations databases to be installed. The first line (below) loads the Bioconductor loader and the next two lines install two annotation databases. One annotation database provides the gene transcript locations and the other maps gene names to entrez gene IDs. (Note: As described in Section 5.3.3, you may choose a different transcript database from Bioconductor or construct one of your own.) Please type in R:
source("https://bioconductor.org/biocLite.R")
biocLite("TxDb.Hsapiens.UCSC.hg19.knownGene")
biocLite("org.Hs.eg.db")
The above step is run once.
We have provided files in this package that contain the data from the simulation. These files are in PLINK ped format data:
If you do not wish to install Mega2 right now, you can use the seqsimr.db database that is in the tutorial directory.
You can obtain the Mega2 program from
https://watson.hgen.pitt.edu/register/. Then, you will invoke Mega2
on your data. To make matters simple, we will use a pre-constructed
Mega2 batch file to automate the processing by Mega2. To run Mega2 to
process and create the 'SQLite' database 'seqsimr.db', we issue the following
command at the Unix prompt in the directory containing tutorial data; the name of this directory is given by the R command:
where_mega2rtutorial_data()
mega2 MEGA2.BATCH.seqsimr
Note: This vignette will not invoke mega2, but use the seqsimr.db database that is in this package.
NOTE: To make this tutorial only dependent on R, the above code is not actually run. And its results, shown below, were captured from an environment where we had both R and Mega2 executable available. All the examples of mega2 shown in these exercises have been similarly “fudged”.
The output seen on the screen when we ran Mega2 to create the 'SQLite' database is as follows:
## ==========================================================
## MEGA2 4.9.2
##
## Copyright 1999-2017, University of Pittsburgh. All Rights Reserved.
##
## Contributors to Mega2: Robert Baron, Justin R. Stickel, Charles P. Kollar,
## Nandita Mukhopadhyay, Lee Almasy, Mark Schroeder, William P. Mulvihill,
## and Daniel E. Weeks.
##
## Last updated: Jun 13 2017, 09:36:42 , valid until June 15, 2018.
## Compiled with gcc version 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.42.1)
##
## Mega2 comes with ABSOLUTELY NO WARRANTY.
## See LICENSE.txt for terms of copying, modifying & redistributing Mega2.
## ==========================================================
## NOTE: For humans, chromosome 23 codes for X, 24 codes for Y and 25 codes for XY.
##
## Run date: 2017-7-24-10-03
##
## Running Mega2 in batch mode from MEGA2.BATCH.seqsimr.
## Input filenames and missing value indicator read in from batch file.
## Dump Analysis option read in from batch file.
## WARNING: Locus selections not specified in batch file.
## WARNING: Going to Reorder menu.
## WARNING: Trait selections not specified in batch file.
## WARNING: Going to Trait selection menu.
## ==========================================================
## Keyword Input_Locus_File not in batch file, Locus file assumed to be unspecified.
## Keyword Input_Map_File not in batch file, Map file assumed to be unspecified.
## Keyword Input_Omit_File not in batch file, Omit file assumed to be unspecified.
## Keyword Input_Frequency_File not in batch file, Frequency file assumed to be unspecified.
## Keyword Input_Penetrance_File not in batch file, Penetrance file assumed to be unspecified.
## Keyword Input_Aux_File not in batch file, Aux file assumed to be unspecified.
## Keyword Input_Phenotype_File not in batch file, Phenotype file assumed to be unspecified.
## Keyword Input_Imputed_Info_File not in batch file, Imputed Info file assumed to be unspecified.
## ===========================================================
## Analysis Class: Dump.
## Quantitative Input Missing Value -9
## Affection Input Missing Value "-9"
## Quantitative Output Missing Value "*"
## Affection Output Missing Value "*"
## Input Format: PLINK PED format (ped)
## Pedigree and map files specified as PLINK format.
## omit, penetrance, and frequency files are always in Mega2 format.
## Input files will be read in as PLINK or Mega2 format files as appropriate.
## Reading PLINK map file for names: Mega2r.map
## Reading map file Mega2r.map ... (4 columns)
## Input Map name: Map, type: average genetic map, units: kosambi centiMorgans
## Input Map name: BP, type: physical map
## Found 2 possible maps in the Mega2r.map file.
## Now checking each record in map file Mega2r.map ...
## Done reading map file: Mega2r.map
##
## ===========================================================
## Total number of loci = 1001
## 1 trait locus
## 1 Affection status locus:
## default
## 1000 Marker loci
## Number of loci found per chromosome (chromosome:number)
## 1:1000
## ===========================================================
## WARNING: No frequency file provided.
## WARNING: Allele frequencies for these will be estimated from data.
## Trait 'default' will be assigned the default penetrance: (0.0500 0.9000 0.9000)
## Reading PLINK .ped file: Mega2r.ped (2006 columns).
## 1000 (of 1000) markers to be included from Mega2r.map
## Reading pedigree information from Mega2r.ped
## 1380 individuals read from Mega2r.ped
## 1380 individuals with nonmissing phenotypes
## 105 cases, 1275 controls, 0 missing
## 620 males, 760 females, 0 of unspecified sex
## 0 founders, 1380 non-founders found
## ===========================================================
## Input pedigree data contains:
## Input pedigree file is in PLINK-fam format.
## Marker Genotypes
## Fully Half
## Pedigrees People Males Females Typed Typed Total
## TOTAL 20 1380 620 760 1380000 0 1380000
## Typed 20 1380 620 760
## Untyped 0 0 0 0
## ===========================================================
## Pedigree exclusion option : Include all pedigrees whether typed or not.
## Count option: all alleles
## Count half-typed individuals' alleles : no
## ===========================================================
## Recoding pedigree genotypes ...
## ===========================================================
## Pedigree data summary after recoding:
## Input pedigree file is in PLINK-fam format.
## Marker Genotypes
## Fully Half
## Pedigrees People Males Females Typed Typed Total
## TOTAL 20 1380 620 760 1380000 0 1380000
## Typed 20 1380 620 760
## Untyped 0 0 0 0
## ===========================================================
## Created linkage ped tree
## Done checking locus integrity.
## Checking pedigree integrity...
## Done checking pedigree integrity.
## ==========================================================
## ===========================================================
## Pedigree statistics after selecting chromosomes and marker loci:
## Input pedigree file is in post-makeped format.
## Marker Genotypes
## Fully Half
## Pedigrees People Males Females Typed Typed Total
## TOTAL 20 1380 620 760 1380000 0 1380000
## Typed 20 1380 620 760
## Untyped 0 0 0 0
## ===========================================================
## Database file "seqsimr.db" will be backed up.
## Moved existing seqsimr.db to seqsimr.db.old
## Dumping SQLite3 DB to file "seqsimr.db"
## ===========================================================
## See run summaries in directory 2017-7-24-10-03
## MEGA2.LOG, MEGA2.RECODE, MEGA2.ERR, MEGA2.KEYS
## The script 'mega2log2html.pl' exited normally.
## To view the HTML-formatted run summaries, open
## /Users/rbaron/mega2/bb/srcdir/R/mega2rtutorial/vignettes/2017-7-24-10-03/MEGA2run.html
## in a web browser.
## ===========================================================
If you do not provide the command-line argument giving the name of the BATCH file, Mega2 will proceed to ask a series of questions to collect the information needed to produce a database. In addition, it will create a Mega2.BATCH file, similar to the one we suggested you use. You can look at the “Quick Start” section of the Mega2 documentation https://watson.hgen.pitt.edu/docs/mega2_html/mega2.html to better understand the interactive process.
The MEGA2.BATCH.seqsimr file begins with a rather long comment indicating the keyword values that may be set and their default value. Toward the end of the file, we see the inputs set to Mega2r.ped and Mega2r.map, indicate the input is PLINK ped format with parameters, and indicate that Mega2 should produce a database called seqsimr.db, etc. (These particular items are in bold face text below.)
Input_Database_Mode=1
Input_Format_Type=4
Input_Pedigree_File=Mega2r.ped
Input_PLINK_Map_File=Mega2r.map
Output_Path=.
Input_Path=.
PLINK_Args= –cM –missing-phenotype -9 –trait default
Input_Untyped_Ped_Option=2
Input_Do_Error_Sim=no
AlleleFreq_SquaredDev=999999999.000000
Value_Marker_Compression=1
Analysis_Option=Dump
Value_Missing_Quant_On_Input=-9.000000
Value_Missing_Affect_On_Input=-9
Count_Genotypes=4
Count_Halftyped=no
Value_Genetic_Distance_Index=0
Value_Genetic_Distance_SexTypeMap=0
Value_Base_Pair_Position_Index=1
Default_Reset_Invalid=no
DBfile_name=seqsimr.db
Default_Outfile_Names=yes
If you wish to use any of the Mega2R functions described here on your own data, you will have to run “mega2” to convert your data into an 'SQLite' database.
The Mega2R package facilitates reading genetic data from a Mega2-created 'SQLite' database.
After you have created the 'SQLite' database, start up the R program. Load the Mega2R package, then
use the function read.Mega2DB to read a Mega2 database.
library(Mega2R)
# Before issuing the next command, make sure you have issued this command
# `dump_mega2rtutorial_data()` first as instructed in the 'Tutorial Data' section
# above.
db = file.path(where_mega2rtutorial_data(), "seqsimr.db")
ENV = read.Mega2DB(db, verbose = TRUE)
The first argument db should be the name of the database (including the path if needed); here we have set db to point to the seqsimr.db example database file provided with this R package. Providing the
optional argument, verbose, causes the read function to summarize the
tables created, their fields and their sizes. Finally, an “R
environment”, that contains the database tables is returned. (If you
are unfamiliar with environments, you can think of them as data
frames. ENV$locus_table will access the locus_table variable
from ENV similar to fetching an “observation” from a data frame. The
difference is when you change a data frame passed to a function, the
change does not affect the original data frame. Only the function's
local value is changed; ALL changes are forgotten when the function
exits. If you change the data in an environment passed to a function,
the change is permanent.)
# Before issuing the next command, make sure you have issued this command
# `dump_mega2rtutorial_data()` first as instructed in the 'Tutorial Data' section
# above.
db = file.path(where_mega2rtutorial_data(), "seqsimr.db")
ENV = read.Mega2DB(db, verbose = TRUE)
## int_table 34 3
## int_table pId key value
##
## pedigree_table 20 8
## pedigree_table pId Num EntryCnt Name PedPre OriginalID origped pedigree_link
##
## person_table 1380 11
## person_table pId UniqueID OrigID FamName PerPre ID Father Mother Sex pedigree_link person_link
##
## pedigree_brkloop_table 20 8
## pedigree_brkloop_table pId Num EntryCnt Name PedPre OriginalID origped pedigree_link
##
## person_brkloop_table 1380 11
## person_brkloop_table pId UniqueID OrigID FamName PerPre ID Father Mother Sex pedigree_link person_link
##
## locus_table 1001 5
## locus_table pId LocusName Type AlleleCnt locus_link
##
## allele_table 2002 5
## allele_table pId AlleleName Frequency indexX locus_link
##
## marker_table 1000 7
## marker_table pId MarkerName pos_avg pos_female pos_male chromosome locus_link
##
## markerscheme_table 1000 4
## markerscheme_table pId key allele1 allele2
##
## map_table 2000 6
## map_table pId marker map position pos_female pos_male
##
## mapnames_table 2 6
## mapnames_table pId map sex_averaged_map male_sex_map female_sex_map name
##
## traitaff_table 1 4
## traitaff_table pId ClassCnt PenCnt locus_link
##
## affectclass_table 1 9
## affectclass_table pId MaleDef FemaleDef AutoDef MalePen FemalePen AutoPen locus_link class_link
##
## phenotype_table 1380 4
## phenotype_table pId person_link bytes data
##
## genotype_table 1380 5
## genotype_table pId person_link chr bytes data
For each table generated, if 'verbose' is TRUE, we emit two lines: one with the number of rows and number of columns of the table and the other with the column names of the table.
We need to make two observations that apply to all Mega2R functions:
When verbose is set in the initial read.Mega2DB, the value will be remembered. It may be used by any subsequent function. If verbose is TRUE, Mega2R functions can print diagnostic information.
All Mega2R functions that do not return an
environment need to have an environment supplied as an argument.
As stated earlier, the environment is used to store the data frames that
contain the 'SQLite' database.
There are two ways to pass the environment. If you assigned the result of
read.Mega2DB to the variable seqsimr, then you could supply the value
seqsimr to any Mega2R function as the named argument envir:
showMega2ENV(envir = seqsimr)
The second choice is a bit of a “hack” but it is very
convenient. Every Mega2R function (that does not return an
environment) has a named envir argument defined to take on the default value ENV:
envir = ENV
as in
showMega2ENV = function(envir = ENV) { ... }
The code above, assigns global variable ENV to the local
variable, envir. Thus if envir is not provided in the function call, R
will look up the value of ENV in the global environment.
This “hack” does not handle the case where ENV is defined in an outer frame which is
not the global environment.
In this situation, we search backwards/upwards from the calling frame to find
the first ENV and use it.
The ls function will show you all the variables in an
environment. (You probably have used it without arguments to show you the
variables in the .GlobalEnv.) Type:
ls(ENV)
## [1] "DBcompress" "LocusCnt"
## [3] "MARKER_SCHEME" "Mega2R"
## [5] "PhenoCnt" "affectclass_table"
## [7] "allele_table" "chr2int"
## [9] "entrezGene" "fam"
## [11] "int_table" "locus_allele_table"
## [13] "locus_table" "map_table"
## [15] "mapnames_table" "marker_table"
## [17] "markers" "markerscheme_table"
## [19] "pedigree_brkloop_table" "pedigree_table"
## [21] "person_brkloop_table" "person_table"
## [23] "phenotype_table" "positionVsName"
## [25] "refIndices" "refRanges"
## [27] "traitaff_table" "txdb"
## [29] "unified_genotype_table" "verbose"
A more informative overview of the database can be had with:
showMega2ENV()
## locus count: 1001; phenotype count: 1; compression: 2 bits
## marker count: 1000; sample count: 1380
##
## genetic and physical maps:
## map name map number
## 1 Map 0
## 2 BP 1
##
## Phenotypes:
## Index Name Type
## 1 1 default affection
##
##
## basic tables:
## rows cols
## affectclass_table 1 9
## allele_table 2002 5
## int_table 34 3
## locus_table 1001 5
## map_table 2000 6
## mapnames_table 2 6
## marker_table 1000 8
## markerscheme_table 1000 4
## pedigree_brkloop_table 20 8
## pedigree_table 20 8
## person_brkloop_table 1380 11
## person_table 1380 11
## phenotype_table 1380 4
## traitaff_table 1 4
##
## derived tables:
## rows cols
## fam 1380 8
## markers 1000 5
## unified_genotype_table 1380 2
str(ENV$locus_table)
## 'data.frame': 1001 obs. of 5 variables:
## $ pId : int 1 2 3 4 5 6 7 8 9 10 ...
## $ LocusName : chr "default" "snp1" "snp2" "snp3" ...
## $ Type : int 2 4 4 4 4 4 4 4 4 4 ...
## $ AlleleCnt : int 2 2 2 2 2 2 2 2 2 2 ...
## $ locus_link: int 0 1 2 3 4 5 6 7 8 9 ...
There are two ways to compute a function on the genotypes (or markers) in all the transcripts. These examples are explained more thoroughly in below.
Mega2R has an internal default list of the chromosome and base pair ranges for many gene transcripts. These transcripts come from the UCSC Genome Browser reference assembly GRCH37. The list was further modified to eliminate multiple records from the same gene with the exact same transcript start and transcript end. The list contains about 29,000 records. We show a bit of the data frame below. Each row contains 5 values: a transcript id, the gene id and three position values: chromosome, start base pair and end base pair.
dim(ENV$refRanges)
## [1] 29062 5
head(ENV$refRanges)
## XX SYMBOL TXCHROM TXSTART TXEND
## 1 NM_005286 NPBWR2 chr20 62737182 62738184
## 2 NR_026775 LINC00240 chr6 26924771 26991753
## 3 NM_007188 ABCB8 chr7 150725509 150744869
## 4 NM_206883 SLC26A5 chr7 102993176 103086624
## 5 NM_206880 OR2V2 chr5 180581942 180582890
## 6 NM_206876 PPP1CB chr2 28974613 29025806
You may load your own range set instead of the default.
You create a data frame that contains at least a chromosome
“observation”, a start position “observation”, an end position “observation”,
and possibly a name “observation”.
And you create an integer vector that contains the column numbers of the chromosome
“observation”, the start position “observation”, the end position “observation”, and optionally
a name. If no name position is provided, a name column indicating the position will be added to
the range.
These two become the arguments to the setRanges function, viz.
ranges = matrix(c(1, 2240000, 2245000, 1, 2245000, 2250000, 1, 3760000, 3761000,
1, 3761000, 3762000, 1, 3762000, 3763000, 1, 3763000, 3764000, 1, 3764000, 3765000,
1, 3765000, 3763760, 1, 3763760, 3767000, 1, 3767000, 3768000, 1, 3768000, 3769000,
1, 3769000, 3770000), ncol = 3, nrow = 12, byrow = TRUE)
setRanges(ranges, 1:3)
dim(ENV$refRanges)
## [1] 12 4
head(ENV$refRanges)
## X1 X2 X3 ChrStartEnd
## 1 1 2240000 2245000 chr1:2240000-2245000
## 2 1 2245000 2250000 chr1:2245000-2250000
## 3 1 3760000 3761000 chr1:3760000-3761000
## 4 1 3761000 3762000 chr1:3761000-3762000
## 5 1 3762000 3763000 chr1:3762000-3763000
## 6 1 3763000 3764000 chr1:3763000-3764000
If you provide an index vector of 4 entries, the last one is assumed to be the column of the name for the range.
ranges = matrix(c(1, 2240000, 2245000, 1, 2245000, 2250000, 1, 3760000, 3761000,
1, 3761000, 3762000, 1, 3762000, 3763000, 1, 3763000, 3764000, 1, 3764000, 3765000,
1, 3765000, 3763760, 1, 3763760, 3767000, 1, 3767000, 3768000, 1, 3768000, 3769000,
1, 3769000, 3770000), ncol = 3, nrow = 12, byrow = TRUE)
ranges = data.frame(ranges)
ranges$name = LETTERS[1:12]
names(ranges) = c("chr", "start", "end", "name")
setRanges(ranges, 1:4)
dim(ENV$refRanges)
## [1] 12 4
head(ENV$refRanges)
## chr start end name
## 1 1 2240000 2245000 A
## 2 1 2245000 2250000 B
## 3 1 3760000 3761000 C
## 4 1 3761000 3762000 D
## 5 1 3762000 3763000 E
## 6 1 3763000 3764000 F
The function:
applyFnToRanges(DOcallback, envir = ENV)
goes through each transcript/range entry in the default list of ranges and finds the markers that fall within the bounds. It then invokes the callback function for the range; the callback function is the first argument of the applyFnToRanges function. For all ranges that contain the same set of markers, the call back function is evaluated only once. (Note: This situation arises either because multiple named transcripts have the same start and end positions or because the granularity of the markers sampled is such that small changes in a range start and end position do not introduce additional markers into the range.)
The callback function is
called with three arguments: the markers in range, the selected transcript/range
entry and the environment. The callback function is expected to build an
appropriate genotype matrix for the samples and each marker in the range (see Section 5.4).
The call back is invoked repeatedly for each transcript range that contains any markers.
If it is necessary to store information between successive invocations the environment (envir)
can be used.
For the examples that follow, we use “show” as the call back function. As you can see, all it does is prints its range argument, markers argument and the head of the generated genotype matrix, in that order. It also prints a banner before each argument. Note: It does not print the environment argument value because it does not change.
show = function(m, r, e) {
print("rrrrrrrrrr")
print(r)
print("mmmmmmmmmm")
print(m)
print("g6g6g6g6g6")
print(head(getgenotypes(m, envir = e)))
}
A simple example is shown below with the ranges value that was last set. We see that the ranges named “A” and “E” have markers in our example data set.
# apply function 'show' to all the ranges ranges
ENV$verbose = FALSE
applyFnToRanges(show)
## [1] "rrrrrrrrrr"
## chr start end name
## 1 1 2240000 2245000 A
## [1] "mmmmmmmmmm"
## locus_link locus_link_fill MarkerName chromosome position
## 57 57 57 snp22730 1 2243896
## 58 58 58 snp22731 1 2243897
## 59 59 59 snp22733 1 2243899
## 60 60 60 snp22735 1 2243901
## 61 61 61 snp23360 1 2244526
## 62 62 62 snp23361 1 2244527
## 63 63 63 snp23362 1 2244528
## 64 64 64 snp23364 1 2244530
## [1] "g6g6g6g6g6"
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] "21" "12" "11" "21" "21" "22" "11" "11"
## [2,] "21" "11" "11" "21" "21" "21" "11" "11"
## [3,] "11" "12" "11" "11" "11" "11" "11" "11"
## [4,] "11" "11" "11" "21" "21" "11" "11" "11"
## [5,] "11" "22" "11" "11" "11" "11" "11" "11"
## [6,] "11" "22" "11" "11" "21" "21" "11" "11"
## [1] "rrrrrrrrrr"
## chr start end name
## 5 1 3762000 3763000 E
## [1] "mmmmmmmmmm"
## locus_link locus_link_fill MarkerName chromosome position
## 65 65 65 snp24037 1 3762181
## 66 66 66 snp24039 1 3762183
## 67 67 67 snp24041 1 3762185
## 68 68 68 snp24048 1 3762192
## 69 69 69 snp24494 1 3762638
## 70 70 70 snp24499 1 3762643
## 71 71 71 snp24506 1 3762650
## 72 72 72 snp24507 1 3762651
## [1] "g6g6g6g6g6"
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] "12" "12" "11" "11" "12" "11" "11" "12"
## [2,] "22" "11" "12" "22" "11" "22" "22" "11"
## [3,] "11" "11" "11" "11" "11" "11" "11" "11"
## [4,] "12" "11" "11" "12" "11" "12" "12" "11"
## [5,] "12" "11" "11" "12" "12" "12" "12" "12"
## [6,] "22" "11" "11" "22" "11" "22" "22" "11"
applyFnToRanges can also be provided explicit ranges as show below. This run is using a different set of ranges and thus finds a different set of ranges with markers: viz. range8m and range9m.
# apply function 'show' to all genotypes on chromosomes 1 ranges
applyFnToRanges(show, ranges_arg = matrix(c(1, 4e+06, 5e+06, "range4m", 1, 5e+06,
6e+06, "range5m", 1, 6e+06, 7e+06, "range6m", 1, 7e+06, 8e+06, "range7m", 1,
8e+06, 9e+06, "range8m", 1, 9e+06, 1e+07, "range9m"), ncol = 4, nrow = 6, byrow = TRUE),
indices_arg = 1:4)
## [1] "rrrrrrrrrr"
## X1 X2 X3 X4
## 5 1 8e+06 9e+06 range8m
## [1] "mmmmmmmmmm"
## locus_link locus_link_fill MarkerName chromosome position
## 73 73 73 snp30480 1 8264348
## 74 74 74 snp30484 1 8264352
## 75 75 75 snp30487 1 8264355
## 76 76 76 snp30491 1 8264359
## [1] "g6g6g6g6g6"
## [,1] [,2] [,3] [,4]
## [1,] "11" "11" "11" "11"
## [2,] "11" "11" "11" "11"
## [3,] "11" "11" "11" "11"
## [4,] "11" "11" "11" "11"
## [5,] "12" "11" "11" "12"
## [6,] "11" "11" "11" "11"
## [1] "rrrrrrrrrr"
## X1 X2 X3 X4
## 6 1 9e+06 1e+07 range9m
## [1] "mmmmmmmmmm"
## locus_link locus_link_fill MarkerName chromosome position
## 77 77 77 snp32565 1 9124463
## 78 78 78 snp32567 1 9124465
## 79 79 79 snp32568 1 9124466
## 80 80 80 snp32570 1 9124468
## [1] "g6g6g6g6g6"
## [,1] [,2] [,3] [,4]
## [1,] "11" "11" "11" "11"
## [2,] "11" "11" "11" "11"
## [3,] "11" "11" "11" "11"
## [4,] "11" "11" "11" "11"
## [5,] "11" "11" "11" "11"
## [6,] "11" "11" "11" "11"
If you are iterating/selecting via genes, the default transcript database is “TxDb.Hsapiens.UCSC.hg19.knownGene” from Bioconductor; it is stored in the environment as shown below:
ENV$txdb
## [1] "TxDb.Hsapiens.UCSC.hg19.knownGene"
ENV$entrezGene
## [1] "org.Hs.eg.db"
Of course, you can change this database. Suppose we want to use build “hg18”, we would run:
setAnnotations("TxDb.Hsapiens.UCSC.hg18.knownGene", "org.Hs.eg.db")
Note: This gene also has transcript uc001akz.2 but its range is included within the uc001aky.1 transcript, so it is not processed.
If you are not using the “hg19” default,
the setAnnotations command must be issued whenever R is started after the Mega2R
library has been loaded. By way of a reminder, Section 4.2
explains how to choose and install “TxDb.Hsapiens.UCSC.hg19.knownGene” or a different
Annotation database.
The function applyFnToGenes is called below to look for the transcripts of genes. We happen to know gene, “CEP104”, is in our data. Remember, this lookup is using “hg18”.
# apply function 'show' to all transcripts on genes ELL2 and CARD15
applyFnToGenes(show, genes_arg = c("CEP104"))
##
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## [1] "rrrrrrrrrr"
## ENTREZID ALIAS SYMBOL TXID TXNAME TXCHROM TXSTRAND TXSTART TXEND
## 1 9731 CEP104 CEP104 4281 uc001aky.2 1 - 3728645 3773797
## [1] "mmmmmmmmmm"
## locus_link locus_link_fill MarkerName chromosome position
## 65 65 65 snp24037 1 3762181
## 66 66 66 snp24039 1 3762183
## 67 67 67 snp24041 1 3762185
## 68 68 68 snp24048 1 3762192
## 69 69 69 snp24494 1 3762638
## 70 70 70 snp24499 1 3762643
## 71 71 71 snp24506 1 3762650
## 72 72 72 snp24507 1 3762651
## [1] "g6g6g6g6g6"
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] "12" "12" "11" "11" "12" "11" "11" "12"
## [2,] "22" "11" "12" "22" "11" "22" "22" "11"
## [3,] "11" "11" "11" "11" "11" "11" "11" "11"
## [4,] "12" "11" "11" "12" "11" "12" "12" "11"
## [5,] "12" "11" "11" "12" "12" "12" "12" "12"
## [6,] "22" "11" "11" "22" "11" "22" "22" "11"
Switching to “hg19”, we see a different transcript id and transcript name as well as different start/end. But the same set of markers from our study fall in each range.
setAnnotations("TxDb.Hsapiens.UCSC.hg19.knownGene", "org.Hs.eg.db")
applyFnToGenes(show, genes_arg = c("CEP104"))
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## [1] "rrrrrrrrrr"
## ENTREZID ALIAS SYMBOL TXID TXNAME TXCHROM TXSTRAND TXSTART TXEND
## 1 9731 CEP104 CEP104 4281 uc001aky.2 1 - 3728645 3773797
## [1] "mmmmmmmmmm"
## locus_link locus_link_fill MarkerName chromosome position
## 65 65 65 snp24037 1 3762181
## 66 66 66 snp24039 1 3762183
## 67 67 67 snp24041 1 3762185
## 68 68 68 snp24048 1 3762192
## 69 69 69 snp24494 1 3762638
## 70 70 70 snp24499 1 3762643
## 71 71 71 snp24506 1 3762650
## 72 72 72 snp24507 1 3762651
## [1] "g6g6g6g6g6"
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] "12" "12" "11" "11" "12" "11" "11" "12"
## [2,] "22" "11" "12" "22" "11" "22" "22" "11"
## [3,] "11" "11" "11" "11" "11" "11" "11" "11"
## [4,] "12" "11" "11" "12" "11" "12" "12" "11"
## [5,] "12" "11" "11" "12" "12" "12" "12" "12"
## [6,] "22" "11" "11" "22" "11" "22" "22" "11"
The applyFnToGenes function has several other optional arguments that can request complete chromosomes, (multiple) ranges of base pairs on chromosomes, or collections of markers, in addition to the genes_arg argument. All these arguments define ranges that are passed to applyFnToRanges for evaluation. Note: If the genes_arg argument is set to the special “gene” string “*”, then all transcripts in the Bioconductor database, will match and be processed.
# apply function 'show' to all genotypes on chromosomes 1 for two base pair
# ranges
applyFnToGenes(show, ranges_arg = matrix(c(1, 5e+06, 1e+07, 1, 1e+07, 1.5e+07), ncol = 3,
nrow = 2, byrow = TRUE))
## [1] "rrrrrrrrrr"
## ENTREZID ALIAS SYMBOL TXID TXNAME TXCHROM TXSTRAND TXSTART
## 1 - - chr1:5e+06-1e+07 0 - 1 - 5e+06
## TXEND
## 1 1e+07
## [1] "mmmmmmmmmm"
## locus_link locus_link_fill MarkerName chromosome position
## 73 73 73 snp30480 1 8264348
## 74 74 74 snp30484 1 8264352
## 75 75 75 snp30487 1 8264355
## 76 76 76 snp30491 1 8264359
## 77 77 77 snp32565 1 9124463
## 78 78 78 snp32567 1 9124465
## 79 79 79 snp32568 1 9124466
## 80 80 80 snp32570 1 9124468
## [1] "g6g6g6g6g6"
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] "11" "11" "11" "11" "11" "11" "11" "11"
## [2,] "11" "11" "11" "11" "11" "11" "11" "11"
## [3,] "11" "11" "11" "11" "11" "11" "11" "11"
## [4,] "11" "11" "11" "11" "11" "11" "11" "11"
## [5,] "12" "11" "11" "12" "11" "11" "11" "11"
## [6,] "11" "11" "11" "11" "11" "11" "11" "11"
## [1] "rrrrrrrrrr"
## ENTREZID ALIAS SYMBOL TXID TXNAME TXCHROM TXSTRAND TXSTART
## 2 - - chr1:1e+07-1.5e+07 0 - 1 - 1e+07
## TXEND
## 2 1.5e+07
## [1] "mmmmmmmmmm"
## locus_link locus_link_fill MarkerName chromosome position
## 81 81 81 snp34070 1 12812974
## 82 82 82 snp34071 1 12812975
## 83 83 83 snp34074 1 12812978
## 84 84 84 snp34075 1 12812979
## 85 85 85 snp34533 1 12813437
## 86 86 86 snp34534 1 12813438
## 87 87 87 snp34535 1 12813439
## 88 88 88 snp34536 1 12813440
## [1] "g6g6g6g6g6"
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] "11" "11" "11" "21" "22" "22" "22" "11"
## [2,] "11" "11" "11" "11" "21" "21" "21" "11"
## [3,] "11" "11" "11" "11" "22" "22" "22" "11"
## [4,] "11" "11" "11" "11" "21" "21" "21" "11"
## [5,] "11" "11" "11" "11" "22" "22" "22" "11"
## [6,] "11" "12" "11" "11" "21" "21" "21" "11"
# apply function 'show' to all genotypes for first marker in each chromosome (We
# only have data for chromosome 1.) NOTE: Since we are using an arbitrary
# collection of markers, the range is not available.
applyFnToGenes(show, markers_arg = ENV$markers[!duplicated(ENV$markers$chromosome),
3])
## [1] "rrrrrrrrrr"
## NULL
## [1] "mmmmmmmmmm"
## locus_link locus_link_fill MarkerName chromosome position
## 1 1 1 snp1 1 2
## [1] "g6g6g6g6g6"
## [,1]
## [1,] "11"
## [2,] "11"
## [3,] "12"
## [4,] "11"
## [5,] "11"
## [6,] "11"
# apply function 'show' to all genotypes on chromosomes 24 and 26. remember our
# example database is only chr 1
applyFnToGenes(show, chrs_arg = c(24, 26))
The callback functions described above will need the genotype information for the
selected markers. The two functions below will collect that data. The first, getgenotypes,
will return the allele nucleodides as they were coded in the sample data.
(This function was illustrated
by the show function above.) The second, getgenotypesraw, will return the
allele numbers 1 and 2 encoded into an integer. The corresponding nucleotide can be looked
up if needed.
The heart of the callback functions is the calculation of the genotype matrix of samples by markers. The genotype information is most often stored in a compressed 2 bit representation. An Rcpp function does the conversion of the compressed genotype data to nucleotides. The function
getgenotypes(markers, envir = ENV)
returns the matrix for the specified markers. It can take one additional argument that supplies a string to separate the two alleles of each marker. We will build a matrix for the first 10 markers of our data. Remember our database has 1380 samples so we will just show the head of the matrix.
genotype = getgenotypes(ENV$markers[1:10, ])
dim(genotype)
## [1] 1380 10
head(genotype)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] "11" "11" "11" "11" "11" "11" "11" "11" "21" "12"
## [2,] "11" "11" "11" "11" "11" "11" "11" "11" "22" "11"
## [3,] "12" "11" "11" "11" "11" "11" "11" "11" "22" "11"
## [4,] "11" "11" "11" "11" "11" "11" "11" "11" "22" "11"
## [5,] "11" "11" "11" "11" "11" "11" "11" "11" "22" "11"
## [6,] "11" "11" "11" "11" "11" "11" "11" "11" "21" "12"
The function
getgenotypesraw(markers, envir = ENV)
is similar to the getgenotypes function except that the matrix it returns
contains an integer encoding for each genotype. The integer's
high 16 bits are the index for allele1 and the low 16 bits are the index for allele2.
The function getgenotypesraw will be called with the same 10 markers
as above.
# two ints in upper/lower half integer representing allele
raw = getgenotypesraw(ENV$markers[1:10, ])
dim(raw)
## [1] 1380 10
head(raw)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 65537 65537 65537 65537 65537 65537 65537 65537 131073 65538
## [2,] 65537 65537 65537 65537 65537 65537 65537 65537 131074 65537
## [3,] 65538 65537 65537 65537 65537 65537 65537 65537 131074 65537
## [4,] 65537 65537 65537 65537 65537 65537 65537 65537 131074 65537
## [5,] 65537 65537 65537 65537 65537 65537 65537 65537 131074 65537
## [6,] 65537 65537 65537 65537 65537 65537 65537 65537 131073 65538
Note: There are actually two different Rcpp functions for each named function in Section 5.4. One function processes compressed genotype data and the other function processes uncompressed genotype data.
Mega2R provides functions that permit one to run the 'pedgene' package to carry out gene-based association tests on family data looping over selected marker subsets.
The 'pedgene' package implements methods for carrying out gene-based association tests on family data, and is available on CRAN as (https://CRAN.R-project.org/package=pedgene). It was written by Daniel Schaid and Jason Sinnwell: [Schaid DJ, McDonnell SK., Sinnwell JP, Thibodeau SN. (2013) Multiple Genetic Variant Association Testing by Collapsing and Kernel Methods With Pedigree or Population Structured Data, Genet Epidemiol, 37(5):409-18.]
Rather than read the Mega2 'SQLite' database with
the read.Mega2DB function described previously, here we use a specialized init_pedgene function to read the Mega2 database.
This latter function calls a utility function
also used by read.Mega2DB. Then it creates, edits, and rewrites the family data, storing it in a pedgene-compatible data frame, fam . (fam merges data from the pedigree_table, person_table and
phenotype_table.)
init_pedgene purges persons with unknown case/control
status which is necessary for the pedgene calculation.
(When fam is filtered, similar filtering is done to the phenotype_table and the
genotype_table.)
Finally, init_pedgene calculates
some values that will be used repeatedly and stores them in the environment that is returned.
# Before issuing the next command, make sure you have issued this command
# `dump_mega2rtutorial_data()` first as instructed in the 'Tutorial Data' section
# above.
db = file.path(where_mega2rtutorial_data(), "seqsimr.db")
ENV = init_pedgene(db)
Mega2R has an internal default list of the chromosome and base pair
ranges for a number of gene transcripts. These transcripts come from
the UCSC Genome Browser reference assembly GRCH37. The list was
further modified to eliminate multiple records of the same gene with
the exact same transcript start and transcript end. These data contain
about 29,000 records. You may load your own range set instead of the default.
You create a data frame that contains at least a name “observation”, a chromosome
“observation”, a start position “observation” and an end position “observation”.
Then, you create an integer vector that contains the column numbers of the chromosome
“observation”, the start position “observation” and the end position “observation”.
These two objects are then arguments to the setRanges function, viz.
setRanges(Transcripts, Columns)
If you plan to select transcripts by gene name, you must load them from Bioconductor. In Section 4.2, we indicated that you needed to type once to install the package:
source("https://bioconductor.org/biocLite.R")
biocLite("TxDb.Hsapiens.UCSC.hg19.knownGene")
biocLite("org.Hs.eg.db")
And then, to use the desired transcription data base, use this command from the Mega2R package as part of your session:
setAnnotations(txdb, entrezGene)
where txdb is the name of Bioconductor transcription database, and entrezGene is the name of Bioconductor mapping of gene name or gene alias to entrez gene id.
By default, the function Mega2pedgene examines the first 100
default transcripts and prints the results. For this database, the first 100 transcripts
identifies only one transcript with several markers (It is found
because it is on chromosome 1 and it has a range that overlaps with some of the
markers in our study.)
To make this tutorial exercise run
faster, we noticed that the identified transcript appeared at transcript 54; so we will
restrict pedgene to a small range of transcripts around 54, viz. 50 through
60:
Note: verbose needs to be TRUE, for these diagnostics to be printed.
ENV$verbose = TRUE
Mega2pedgene(gs = 50:60)
## tryFn() No markers in range: NR_052010, IL11RA, 9, 34653893, 34661898
## tryFn() No markers in range: NR_045785, CHIT1, 1, 203185206, 203198860
## tryFn() No markers in range: NM_017799, TMEM260, 14, 57046510, 57116232
## tryFn() No markers in range: NM_014705, DOCK4, 7, 111366163, 111846462
## tryFn() No markers in range: NM_003759, SLC4A4, 4, 72204769, 72437804
## tryFn() No markers in range: NM_002192, INHBA, 7, 41728600, 41742706
## tryFn() No markers in range: NM_002202, ISL1, 5, 50678957, 50690563
## tryFn() No markers in range: NM_003659, AGPS, 2, 178257470, 178408564
## tryFn() No markers in range: NM_003658, BARX2, 11, 129245880, 129322174
## tryFn() No markers in range: NM_018051, WDR60, 7, 158649268, 158738883
## CEP104 snp24037 520 646 214
## CEP104 snp24039 940 386 54
## CEP104 snp24041 1377 3 0
## CEP104 snp24048 860 460 60
## CEP104 snp24494 789 501 90
## CEP104 snp24499 891 442 47
## CEP104 snp24506 891 442 47
## CEP104 snp24507 789 501 90
## chr gene nvariants start end sKernel_BT pKernel_BT sBurden_BT
## 1 1 CEP104 8 3728644 3773797 31.96998 0.6297541 -0.6496837
## pBurden_BT sKernel_MB pKernel_MB sBurden_MB pBurden_MB sKernel_UW
## 1 0.5158965 1184.995 0.5138866 -1.209563 0.2264468 222.8737
## pKernel_UW sBurden_UW pBurden_UW
## 1 0.4111136 -1.169488 0.242207
You will see many reports of “No markers in range”, because the database only contains markers on a subrange of chromosome 1 whereas the transcripts span the entire genome. Occasionally you will see a listing of a gene name, markers, and count of 0, 1 and 2 genotypes, viz.
CEP104 snp24037 520 646 214
CEP104 snp24039 940 386 54
CEP104 snp24041 1377 3 0
CEP104 snp24048 860 460 60
CEP104 snp24494 789 501 90
CEP104 snp24499 891 442 47
CEP104 snp24506 891 442 47
CEP104 snp24507 789 501 90
The genotype matrix for these markers, along with the markers, the range
used, and the environment are passed
to the call back function DOpedgene. DOpedgene converts the raw genotype
encodings, 0x10001, 0x10002 (or 0x20001), and 0x20002 to the values 0, 1 and 2 (or 2, 1, 0)
if 0x10001 is the
genotype for the allele with the minor allele frequency. Then it runs pedgene.
The results are automatically stored
in a data frame with “observations”: prefix of chromosome, gene,
number of markers and base pair range followed by Pedgene data: kernel
and burden, value and p-values, four values for each of three
weightings of the markers.
These data are saved in the data frame, pedgene_results, in the environment.
They are also printed when verbose is TRUE, viz.
Note: The results are always appended to the data frame. You should truncate it when necessary.
chr gene nvariants start end sKernel_BT pKernel_BT sBurden_BT
1 chr1 CEP104 8 3728644 3773797 31.96998 0.6297541 -0.6496837
pBurden_BT sKernel_MB pKernel_MB sBurden_MB pBurden_MB sKernel_UW pKernel_UW
1 0.5158965 1184.995 0.5138866 -1.209563 0.2264468 222.8737 0.4111136
sBurden_UW pBurden_UW
1 -1.169488 0.242207
You could run Mega2pedgene on all the transcript entries, but it takes a rather long time.
You would type:
# we will skip this line for the Rmd document production because it takes too
# long
applyFnToRanges(DOpedgene, ENV$refRanges, ENV$refIndices, envir = ENV)
If you run the above test, you will see that genes DISP1 and KIF26B have at least one p-value less than 0.01 and AK5 and STL7 at least one less than 0.03.
You may try searching for transcripts of specific genes. Here, the default
transcript database is TxDb.Hsapiens.UCSC.hg19.knownGene from
Bioconductor. Of course you can change it. Type:
setAnnotations("txdb", "genedb")
where “txdb” is a string that is the name of transcript database that was fetched from Bioconductor, and similarly “genedb” is the name of a Bioconductor database that maps a gene id from the input to an entrez gene id. You need to install any new database with biocLite, as shown earlier.
We leave the command below as an exercise. It runs a bit slowly. It needs to find all the transcripts for each gene, to find all the markers between each pair of transcript start/end ranges, to compute the genotype matrix for these markers, and finally to call the callback function with appropriate arguments.
applyFnToGenes(DOpedgene, genes_arg = c("DISP1", "KIF26B", "AK5", "ST7L"), envir = ENV)
But let us run this function for a few genes:
applyFnToGenes(DOpedgene, genes_arg = c("DISP1", "AK5"), envir = ENV)
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## tryFn() No markers in range: 26289, AK5, AK5, 1624, uc001dhm.2, 1, +, 77747662, 77780745
## AK5 snp80127 962 375 43
## AK5 snp80131 1095 272 13
## AK5 snp80142 1303 77 0
## AK5 snp80148 1345 35 0
## chr gene nvariants start end sKernel_BT pKernel_BT sBurden_BT
## 2 1 AK5 4 77747662 78025654 2593.945 0.228311 -1.400928
## pBurden_BT sKernel_MB pKernel_MB sBurden_MB pBurden_MB sKernel_UW
## 2 0.1612355 1899.249 0.06206979 -2.263002 0.02363554 175.4195
## pKernel_UW sBurden_UW pBurden_UW
## 2 0.051013 -2.290307 0.0220035
## DISP1 snp332780 1375 5 0
## DISP1 snp332781 675 559 146
## DISP1 snp332783 1365 15 0
## DISP1 snp332784 898 427 55
## chr gene nvariants start end sKernel_BT pKernel_BT
## 3 1 DISP1 4 222988431 223179337 4559.34 0.004224181
## sBurden_BT pBurden_BT sKernel_MB pKernel_MB sBurden_MB pBurden_MB
## 3 1.44652 0.1480315 5410.868 0.000253584 2.916462 0.003540261
## sKernel_UW pKernel_UW sBurden_UW pBurden_UW
## 3 277.508 0.04407319 2.094983 0.03617254
You could run Mega2pedgene on all the transcript entries, but it takes a rather long time.
You would type:
# we will skip this line for the Rmd document production because it takes too
# long
applyFnToGenes(DOpedgene, genes_arg = "*", envir = ENV)
Note: The default behavior of DOpedgene is to append any new results to the end of the
ENV$pedgene_results data frame.
Mega2R provides functions to run the 'SKAT' package to carry out gene-based association tests using a kernel regression framework while looping over selected marker subsets.
The 'SKAT' package implements methods for carrying out gene-based association tests; it is available on CRAN as (https://CRAN.R-project.org/package=SKAT). It was written by Seunggeun Lee and Michael Wu: [Lee, S., Emond, M.J., Bamshad, M.J., Barnes, K.C., Rieder, M.J., Nickerson, D.A., NHLBI GO Exome Sequencing Project-ESP Lung Project Team, Christiani, D.C., Wurfel, M.M. and Lin, X. (2012) Optimal unified approach for rare variant association testing with application to small sample case-control whole-exome sequencing studies. American Journal of Human Genetics, 91, 224-237.]
The init_SKAT function is used to read the Mega2 database and initialize processing.
This function calls the utility function, dbmega2_import, to read the database.
Then it creates, edits, and rewrites the family data, storing it in the data frame, fam.
(fam contains data merged from the pedigree_table, person_table and
phenotype_table.)
init_SKAT purges persons from the fam data frame with unknown case/control
status. setfam sets fam in the environment and insures that
filtering is done to the phenotype_table and the genotype_table
so they all have the same person_link key.
Next, init_SKAT decodes the phenotype_table into a simple data frame, phe.
You will definitely want to examine ENV$phe to choose which phenotype to use
for the case/control.
In addition, init_SKAT calculates
some values that will be used repeatedly and stores them in the environment that is returned.
Finally, init_SKAT stores the argument allMarkers which tells later processing to ignore
markers that show no variation (if FALSE).
# Before issuing the next command, make sure you have issued this command
# `dump_mega2rtutorial_data()` first as instructed in the 'Tutorial Data' section
# above.
db = file.path(where_mega2rtutorial_data(), "seqsimr.db")
ENV = init_SKAT(db, verbose = F, allMarkers = F)
You can run the command with verbose equals TRUE to see details of the database as it is loaded:
# Before issuing the next command, make sure you have issued this command
# `dump_mega2rtutorial_data()` first as instructed in the 'Tutorial Data' section
# above.
db = file.path(where_mega2rtutorial_data(), "seqsimr.db")
ENV = init_SKAT(db, verbose = T, allMarkers = F)
Mega2R also has an internal default list of the chromosome and base pair
ranges for many gene transcripts. These transcripts come from
the UCSC Genome Browser reference assembly GRCH37. The list was
further modified to eliminate multiple records from the same gene with
the exact same transcript start and transcript end. The list contains
about 29,000 records. You may load your own range set instead of the default.
Create a data frame that contains at least a name “observation”, a chromosome
“observation”, a start position “observation” and an end position “observation”.
And create an integer vector that contains the column numbers of the chromosome
“observation”, the start position “observation” and the end position “observation”.
These two become the arguments to the setRanges function, viz.
setRanges(Ranges, Columns)
Gene transcripts are defined according to a Bioconductor database containing the boundaries of the gene transcripts
or defined by an internal table, refRanges, with the boundaries.
The gene transcripts require two Bioconductor Annotations databases to be installed.
The first line (below) loads the Bioconductor loader and the next
two lines install two annotation databases. One annotation database provides the gene transcript
locations and the other maps gene names to entrez gene IDs. (Note: You
may choose a different transcript database from Bioconductor or
construct one of your own.) Please type in R:
source("https://bioconductor.org/biocLite.R")
biocLite("TxDb.Hsapiens.UCSC.hg19.knownGene")
biocLite("org.Hs.eg.db")
The above step is run once.
By default, Mega2R presumes that the databases, “TxDb.Hsapiens.UCSC.hg19.knownGene” and “org.Hs.eg.db” are selected. Otherwise you must make your choices known to Mega2 via the command:
setAnnotations(txdb, entrezGene)
where txdb is the name of Bioconductor transcription database, and entrezGene is the name of Bioconductor mapping of gene name or gene alias to entrez gene id.
Note that, in this case, if init_SKAT has been run as indicated above, then it loaded both a txdb and a entrezGene into the ENV environment. While these are already the set annotations, we could explicitly set them via this command:
setAnnotations(ENV$txdb, ENV$entrezGene)
You should be familiar with the SKAT functions of the SKAT package before you read this section.
The function, Mega2SKAT, is Mega2R's interface to SKAT, both to SKAT_Null_Model and
SKAT. Its signature is:
Mega2SKAT = function (f, ty, gs = 1:100, skat = SKAT::SKAT, envir = ENV, ...) { }
The gs argument indicates how many default range elements should be processed and the envir
argument specifies the environment that contains all the Mega2R data frames.
Most of the time, before you call SKAT, you need to call SKAT_Null_Model with a formula
and an indicator for the type of the phenotype. Mega2SKAT will take its first two arguments,
a formula and a type (string) and call SKAT_Null_Model with this information, viz.
SKAT_Null_Model(f, out_type = ty)
and store the results (in obj in the environment).
If the formula, f, is NULL, Mega2SKAT will not call SKAT_Null_Model and you must do the
equivalent before calling Mega2SKAT. Store the result object in ENV$obj.
There are several reasons a custom call to the build
the model could be necessary. You might want to use SKAT_Null_Model but provided additional
arguments viz. data, Adjustment, n.Resampling, type.Resampling. Alternatively, you might
need to use a different model viz. SKAT_NULL_emmaX, SKAT_Null_Model_ChrX.
The skat argument specifies the name of the SKAT package function to use; this is
usually SKAT::SKAT, but could be SKAT::SKATBinary, SKAT::SKAT_CommonRare, etc.
Any additional, arguments needed for the “skat” functions are provided to the Mega2SKAT
function and will be passed to the eventual call. All the “skat” functions are called with
a genotype matrix for the markers, the object representing the Null Model and the additional arguments.
The Mega2R loop engine, iterates through each range and determines the set of markers contained. If there are no markers in a range, the genotype matrix is empty and Mega2SKAT will issue a warning. If a marker has no variation, Mega2SKAT will omit it, if the variable allMarkers is FALSE. Mega2SKAT will include it, if allMarkers is TRUE, but SKAT will more than likely issue a warning. The Mega2SKAT function also defines a callback function that converts the raw genotype information 0x10001, 0x10002 (or 0x20001) and 0x20002 to 0, 1, and 2 with the major allele (flipped if necessary to be) 0 and then calls the specified “skat” function with the required arguments. Finally, the callback function stores the results.
By default, the function Mega2SKAT examines the first 100 transcripts and prints the results.
Note: verbose is FALSE to eliminate the (excessive) diagnostics.
A typical invocation of Mega2SKAT could be:
ENV$verbose = FALSE
ENV$SKAT_results = ENV$SKAT_results[0, ]
Mega2SKAT(ENV$phe[, 3] - 1 ~ 1, "D", kernel = "linear.weighted", weights.beta = c(0.5,
0.5))
## Sample size (non-missing y and X) = 1380, which is < 2000. The small sample adjustment is applied!
These data are saved in the data frame, SKAT_results, in the environment.
Note: The results are always appended to the data frame. You should truncate it
when necessary.
print(ENV$SKAT_results)
## chr gene nvariants start end skat
## 1 1 CEP104 8 3728644 3773797 0.4921572
# we will skip this line for the Vignette document production because it takes
# too long
ENV$verbose = FALSE
ENV$SKAT_results = ENV$SKAT_results[0, ]
Mega2SKAT(ENV$phe[, 3] - 1 ~ 1, "D", kernel = "linear.weighted", weights.beta = c(0.5,
0.5), gs = 1:nrow(ENV$refRanges))
print(ENV$SKAT_results)
Let us run the Mega2SKAT function for a few genes:
ENV$SKAT_results = ENV$SKAT_results[0, ]
Mega2SKAT(ENV$phe[, 3] - 1 ~ 1, "D", kernel = "linear.weighted", weights.beta = c(0.5,
0.5), genes = c("DISP1", "AK5", "KIF26B", "ST7L"), envir = ENV)
## Sample size (non-missing y and X) = 1380, which is < 2000. The small sample adjustment is applied!
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
print(ENV$SKAT_results)
## chr gene nvariants start end skat
## 1 1 AK5 4 77747662 78025654 0.0620303121
## 2 1 ST7L 2 113066141 113153625 0.0268389550
## 3 1 KIF26B 8 245318287 245809683 0.0074255053
## 4 1 DISP1 4 222988431 223179337 0.0007740912
Note: we set verbose to FALSE otherwise there will be a large number of print outs indicating no markers are in the range. (Recall the samples come from part of chromosome one, whereas the ranges cover the whole genome.) If you are adventurous you can look at all the transcripts:
ENV$verbose = FALSE
ENV$SKAT_results = ENV$SKAT_results[0, ]
Mega2SKAT(ENV$phe[, 3] - 1 ~ 1, "D", kernel = "linear.weighted", weights.beta = c(0.5,
0.5), genes = "*", envir = ENV)
## Sample size (non-missing y and X) = 1380, which is < 2000. The small sample adjustment is applied!
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
Note: The default behavior of Mega2SKAT is to append any new results to the end of the
ENV$SKAT_results data frame.
print(ENV$SKAT_results)
## chr gene nvariants start end skat
## 1 1 DDX11L1 8 11874 14409 0.0846402961
## 2 1 NBPF20 4 144151519 144830407 0.1648571802
## 3 1 GNG12-AS1 4 68297971 68668670 1.0000000000
## 4 1 MIR548F1 4 186029867 186446655 0.7353797626
## 5 1 TESK2 4 45809555 45956840 0.4674964311
## 6 1 SDCCAG8 8 243419307 243663393 0.7426853536
## 7 1 WDR63 8 85527993 85598821 0.9711157629
## 8 1 CR1 12 207669473 207732829 0.8278369449
## 9 1 GLIS1 4 53971906 54199877 0.4847135739
## 10 1 DAB1 4 57463579 58716211 0.4639555297
## 11 1 DPT 4 168664695 168698442 0.1576203501
## 12 1 AK4 4 65613850 65697828 0.2515412901
## 13 1 EPRS 8 220141942 220220000 0.9899369138
## 14 1 NTNG1 4 107683549 107953207 0.2807769481
## 15 1 ATF6 20 161736034 161933860 0.9339190112
## 16 1 SRGAP2 8 206516200 206628046 0.0905194154
## 17 1 AK5 4 77748287 78025654 0.0620068497
## 18 1 BRINP3 4 190066797 190446759 0.8735658019
## 19 1 LINC01140 4 87458690 87634886 1.0000000000
## 20 1 KCNT2 4 196194913 196577499 0.5044404622
## 21 1 TRABD2B 8 48226200 48462562 0.1929359215
## 22 1 C1orf53 8 197871682 197876497 1.0000000000
## 23 1 NCF2 4 183524697 183559739 0.3826109373
## 24 1 NPR1 4 153651164 153666468 0.9849925340
## 25 1 PLEKHO1 4 150122170 150131825 0.6819138316
## 26 1 PI4KB 8 151264273 151300191 0.4217962428
## 27 1 GDAP2 4 118406107 118472302 0.2977723677
## 28 1 ST7L 2 113084413 113160839 0.0268640918
## 29 1 MARC2 16 220921676 220957596 0.3624510455
## 30 1 KIF26B 8 245318287 245866428 0.0081848424
## 31 1 HHAT 4 210502640 210849638 0.2606710513
## 32 1 GATAD2B 8 153777203 153895451 0.8801710709
## 33 1 LRRC7 4 70225874 70589171 0.5432014880
## 34 1 LRRC7 4 70032868 70340687 1.0000000000
## 35 1 RGS7 4 240938817 241519126 0.5217017756
## 36 1 RYR2 8 237205702 237997288 0.4834652788
## 37 1 SLC2A5 4 9101427 9129887 1.0000000000
## 38 1 WASH7P 12 14362 16765 0.7071342621
## 39 1 WASH7P 8 15603 29370 1.0000000000
## 40 1 EVI5 4 92974253 93257961 0.3026679740
## 41 1 TTC13 4 231044160 231114618 0.8641946732
## 42 1 C1orf21 4 184356150 184598155 0.2630128125
## 43 1 IGSF21 4 18434240 18704977 0.5547831980
## 44 1 DISP1 4 222988431 223179337 0.0007050738
## 45 1 CD2 4 117297086 117311851 0.1018710010
## 46 1 C1orf158 8 12806163 12819698 0.5091904943
## 47 1 NOS1AP 4 162039581 162339813 0.2909785404
## 48 1 CEP104 8 3750235 3773797 0.4914029118
## 49 1 CDK11B 4 1571100 1647917 0.7791731244
The Mega2VCF function can output subsets of the Mega2R database as a VCF file, accompanied by
related files (for phenotype and family data).
The VCF data format (http://www.internationalgenome.org/wiki/Analysis/Variant%20Call%20Format/vcf-variant-call-format-version-40/) was originally defined by the 1000 Genomes Project (http://www.internationalgenome.org/home) for data storage.
The current version of data format can be found at (http://samtools.github.io/hts-specs/).
First, create the directory “vcfr”, so we can keep all the generated data in one place.
# Before issuing the next command, make sure you have issued this command
# `dump_mega2rtutorial_data()` first as instructed in the 'Tutorial Data' section
# above.
vcfdir = file.path(where_mega2rtutorial_data(), "vcfr")
if (!dir.exists(vcfdir)) dir.create(vcfdir)
The Mega2VCF function takes an argument which is the prefix used on
all generated files. We will make it a path that includes the
directory vcfr. Below, we assume that a Mega2 database was stored in
the environment, ENV. If this is not the case, supply to Mega2VCF a
named argument, envir, set to the environment you wish to use. (You
will also have to change the references to the markers data frame,
below, from ENV$markers to
“environment name”$markers.)
Note that Mega2VCF will create the .vcf file as well as a .fam file (in linkage format), a .phe file (phenotypes) and a few others. These files are listed below.
Creating the VCF file and related files is accomplished by typing:
# Before issuing the next command, make sure you have issued this command
# `dump_mega2rtutorial_data()` first as instructed in the 'Tutorial Data' section
# above.
vcffile = file.path(where_mega2rtutorial_data(), "vcfr", "vcf.01")
Mega2VCF(vcffile, ENV$markers[ENV$markers$chromosome == 1, ])
Note: The line above places the data for only chromosome 1 in the files (Recall that our simulated data is only on chromosome 1).
Note: In general, you can filter ENV$markers as needed.
If the second argument is not present, all the markers are written out.
The generated files are listed below:
# Before issuing the next command, make sure you have issued this command
# `dump_mega2rtutorial_data()` first as instructed in the 'Tutorial Data' section
# above.
vcfdir = file.path(where_mega2rtutorial_data(), "vcfr")
list.files(vcfdir)
## [1] "vcf.01.fam" "vcf.01.freq" "vcf.01.map" "vcf.01.pen" "vcf.01.phe"
## [6] "vcf.01.vcf"
You can read the code to see how these files are created from the data frames. It is worth noting that the internal logic does not create a genotype matrix for all the requested markers at once, but rather works on a block of markers at a time, limiting the amount of memory required. The code also illustrates how to extract the phenotype values from the phenotype raw vectors.
Mega2R provides support for converting the data frames into 'GenABEL' format (e.g. as gwaa.data-class objects) using the functions of GenABEL.
The 'GenABEL' package is available on CRAN as either as a toplevel
package (https://CRAN.R-project.org/package=GenABEL) or in the
archive. The Mega2R library does not implicitly include GenABEL.
You must explicitly add the GenABEL library to your workspace.
If Mega2R finds GenABEL in your workspace, the functions
Mega2GenABEL and Mega2ENVGenABEL will be available. Otherwise,
the functions will just return NULL. (In the example below, we use
“require” instead of “library” to illustrate this behavior".)
The reference for GenABEL is [Aulchenko Y.S., Ripke S., Isaacs A., van Duijn C.M. GenABEL: an R package for genome-wide association analysis. Bioinformatics. 2007 23(10):1294-6.]
There are several data formats that Mega2 can read and transform into a database that are not directly accepted by GenABEL. But any Mega2 database that can be read into R, can be transformed to GenABEL following this example using seqsimr.db.
GenABEL (http://www.genabel.org/) can process PLINK .tped/.tfam files with the function
convert.snp.tped to create a GenABEL “raw” file.
Then the “raw” file and a generated phenotype
file can be processed by the function, load.gwaa.data, to yield a gwaa.data-class
object.
Currently, the function, Mega2GenABEL,
uses this mechanism to convert Mega2 data frames to a gwaa.data-class
object.
The .tped/.fam/.phe files are created in a scratch space, file.path(tempdir(), Mega2GenABEL).
They are deleted when the Mega2GenABEL function exits. The
GenABEL functions convert.snp.tped and load.gwaa.data are explained in
https://cran.r-project.org/package=GenABEL.
If you haven't already, first install the 'GenABEL' package from CRAN.
Then type:
require("GenABEL")
## Loading required package: GenABEL
## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'GenABEL'
# Before issuing the next command, make sure you have issued this command
# `dump_mega2rtutorial_data()` first as instructed in the 'Tutorial Data' section
# above.
db = file.path(where_mega2rtutorial_data(), "seqsimr.db")
ENV = read.Mega2DB(db)
# This line converts the database to a gwaa.data-class object. The intermediate
# files are in tempdir() and begin with 'Mega2GenABEL'
seqsimgwaa = Mega2GenABEL()
## Warning in Mega2GenABEL(): genABEL has been archived and is not available
You can use any of the GenABEL provided functions on the results, seqsimgwaa.
str(seqsimgwaa)
## NULL
To verify that the functions described above have worked correctly, we can compare their output files to those created by Mega2, itself, to verify that they are identical, as expected. This type of testing is known as regression testing.
If you have a executable copy of Mega2, you can run the code below using the MEGA2.BATCH.vcf file and provided example database.
Cut and paste the shell code and you should see similar results to what is presented here.
Otherwise, the text below is an illustration of what should happen. These lines use the C++ Mega2 program to populate the vcf directory with the same set of files that the were created in the vcfr directory.
At the Unix command prompt in the temporary directory with the name given by where_mega2rtutorial_data(), type:
mkdir vcf
mega2 MEGA2.BATCH.vcf
Note: To make this tutorial only dependent on R, the above code is not actually run. And the results, shown below, were captured from an environment where we had both R and the Mega2 executable available.
The output should be similar to that below (except for time stamps):
## ==========================================================
## MEGA2 4.9.2
##
## Copyright 1999-2017, University of Pittsburgh. All Rights Reserved.
##
## Contributors to Mega2: Robert Baron, Justin R. Stickel, Charles P. Kollar,
## Nandita Mukhopadhyay, Lee Almasy, Mark Schroeder, William P. Mulvihill,
## and Daniel E. Weeks.
##
## Last updated: Jun 13 2017, 09:36:42 , valid until June 15, 2018.
## Compiled with gcc version 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.42.1)
##
## Mega2 comes with ABSOLUTELY NO WARRANTY.
## See LICENSE.txt for terms of copying, modifying & redistributing Mega2.
## ==========================================================
## NOTE: For humans, chromosome 23 codes for X, 24 codes for Y and 25 codes for XY.
##
## Run date: 2017-7-24-10-05
##
## Running Mega2 in batch mode from MEGA2.BATCH.vcf.
## Analysis option read in from batch file.
## Chromosome(s) and markers read in from batch file.
## Trait selection(s) read in from batch file.
## ==========================================================
## Analysis Class: VCF.
## Quantitative Output Missing Value "-9"
## Affection Output Missing Value "-9"
## Reading SQLite3 DB from file "seqsimr.db"
## ===========================================================
## The path to this SQLite3 database is seqsimr.db.
## This database was created using Mega2 version 4.9.2.
## This database was created using SQLite3 3.9.2 on 2017-7-24-10-03.
## This database was processed using SQLite3 3.9.2 on 2017-7-24-10-05.
## This database was created from PLINK PED format data using the following files:
## Pedigree file Mega2r.ped
## PLINK Map file Mega2r.map
## This database contains:
## 1380 persons (20 pedigrees)
## 1000 markers
## 1 trait
## genetic distance(map name/type) "Map"/kosambi, Sex map type AVERAGED_MAP
## base pair distance(map name) "BP"
##
## ==========================================================
## 1 trait locus
## 1 Affection status locus:
## default
## ===========================================================
## Selected map Map.
## Selected chromosome 1
## Output will combine markers and the following selected traits:
## default [MARKERS]
## After selecting traits and covariates
## 1 trait locus
## 1 Affection status locus:
## default
## ===========================================================
## Pedigree statistics after selecting chromosomes and marker loci:
## Input pedigree file is in post-makeped format.
## Marker Genotypes
## Fully Half
## Pedigrees People Males Females Typed Typed Total
## TOTAL 20 1380 620 760 1380000 0 1380000
## Typed 20 1380 620 760
## Untyped 0 0 0 0
## ===========================================================
## Mega2 created the following file(s) for VCF Format:
## VCF file created using allele ordering setting: Original_Order
## VCF format file: vcf/vcf.01.vcf
## VCF pedigree file: vcf/vcf.01.fam
## VCF phenotype file: vcf/vcf.01.phe
## VCF map file: vcf/vcf.01.map
## VCF freq file: vcf/vcf.01.freq
## VCF pen file: vcf/vcf.01.pen
##
## SQLite3 database "seqsimr.db" was processed to generate this output.
## Output is in vcf
## ===========================================================
## See run summaries in directory 2017-7-24-10-05
## MEGA2.LOG, MEGA2.ERR, MEGA2.KEYS
## The script 'mega2log2html.pl' exited normally.
## To view the HTML-formatted run summaries, open
## /Users/rbaron/mega2/bb/srcdir/R/mega2rtutorial/vignettes/2017-7-24-10-05/MEGA2run.html
## in a web browser.
## ===========================================================
## If you use Mega2 as part of a published work, please reference
## Baron RV, Kollar C, Mukhopadhyay N, Weeks DE
## Mega2: validated data-reformatting for linkage and association analyses
## Source Code for Biology and Medicine.2014, 9:26
## DOI: 10.1186/s13029-014-0026-y
## as well as the version used, which is currently Version 4.9.2
## ===========================================================
The abbreviated MEGA2.BATCH.vcf file is below. (The initial comment section is not shown and important lines are shown in a bold typeface.) Notice that there are no INPUT FILES, just a database file.
Input_Database_Mode=2
Align_Strand_Input=no
Output_Path=vcf
Input_Untyped_Ped_Option=2
Input_Do_Error_Sim=no
AlleleFreq_SquaredDev=999999999.000000
Analysis_Option=VCF
Value_Missing_Quant_On_Output=-9
Value_Missing_Affect_On_Output=-9
DBfile_name=seqsimr.db
Chromosome_Single=1
Traits_Combine=1 2 e
file_name_stem=vcf
human_genome_build=B37
VCF_output_file_type=1
VCF_Allele_Order=Original_Order
Default_Outfile_Names=yes
You should compare the two directories: vcf and vcfr. Please add the
-w flag to the diff command. This causes the comparison to ignore
differences in white space. Lines that
contain dates may be different between the two directories.
At the Unix command prompt in the temporary directory, with the name given by where_mega2rtutorial_data(), type:
diff -wrs vcf vcfr
Note: To make this tutorial only dependent on R, the above code is not actually run. And the results, shown below, were captured from an environment where we had both R and the Mega2 executable available.
The diff should show:
## Files vcf/vcf.01.fam and vcfr/vcf.01.fam are identical
## Files vcf/vcf.01.freq and vcfr/vcf.01.freq are identical
## Files vcf/vcf.01.map and vcfr/vcf.01.map are identical
## Files vcf/vcf.01.pen and vcfr/vcf.01.pen are identical
## Files vcf/vcf.01.phe and vcfr/vcf.01.phe are identical
## Files vcf/vcf.01.vcf and vcfr/vcf.01.vcf are identical
This test is a bit more complicated.
We intend to verify that the object generated
by Mega2GenABEL is the same as a GenABEL object we started with.
First, we load GenABEL and access its data:
NOTE: The code in this section can not really be executed, if you do not have GenABEL available on your machine.
GotGenABEL = require("GenABEL", quietly = FALSE)
## Loading required package: GenABEL
## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'GenABEL'
if (GotGenABEL) data(srdta)
Then we use the GenABEL export.plink() function to dump the GenABEL data as PLINK .ped/.map/.phe files. The latter will be processed by Mega2 to generate a database.
GotGenABEL = require("GenABEL", quietly = FALSE)
## Loading required package: GenABEL
# Before issuing the next command, make sure you have issued this command
# `dump_mega2rtutorial_data()` first as instructed in the 'Tutorial Data' section
# above.
srdtafile = file.path(where_mega2rtutorial_data(), "srdta")
if (GotGenABEL) export.plink(srdta, transpose = FALSE, filebasename = srdtafile,
phenotypes = names(srdta@phdata)[-(1:2)])
To produce the Mega2 database srdta.db, we switch to the Unix command line, cd to
the temporary directory, with the name given by the results of this R command where_mega2rtutorial_data(), and run:
mega2 MEGA2.BATCH.srdta
NOTE: To make this tutorial only dependent on R, the above code is not actually run. And its results, shown below, were captured from an environment where we had both R and the Mega2 executable available.
## ==========================================================
## MEGA2 4.9.2
## Copyright 1999-2017, University of Pittsburgh. All Rights Reserved.
## Contributors to Mega2: Robert Baron, Justin R. Stickel, Charles P. Kollar,
## Nandita Mukhopadhyay, Lee Almasy, Mark Schroeder, William P. Mulvihill,
## and Daniel E. Weeks.
##
## Last updated: Oct 13 2017, 09:55:04 , valid until June 15, 2018.
## Compiled with gcc version 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.42.1)
##
## Mega2 comes with ABSOLUTELY NO WARRANTY.
## See LICENSE.txt for terms of copying, modifying & redistributing Mega2.
## ==========================================================
## NOTE: For humans, chromosome 23 codes for X, 24 codes for Y and 25 codes for XY.
##
## Run date: 2017-10-13-10-06
##
## Running Mega2 in batch mode from MEGA2.BATCH.srdta.
## Input filenames and missing value indicator read in from batch file.
## Dump Analysis option read in from batch file.
## WARNING: Locus selections not specified in batch file.
## WARNING: Going to Reorder menu.
## WARNING: Trait selections not specified in batch file.
## WARNING: Going to Trait selection menu.
## ==========================================================
## Keyword Input_Locus_File not in batch file, Locus file assumed to be unspecified.
## Keyword Input_Map_File not in batch file, Map file assumed to be unspecified.
## Keyword Input_Omit_File not in batch file, Omit file assumed to be unspecified.
## Keyword Input_Frequency_File not in batch file, Frequency file assumed to be unspecified.
## Keyword Input_Penetrance_File not in batch file, Penetrance file assumed to be unspecified.
## Keyword Input_Aux_File not in batch file, Aux file assumed to be unspecified.
## Keyword Input_Imputed_Info_File not in batch file, Imputed Info file assumed to be unspecified.
## ===========================================================
## Analysis Class: Dump.
## Quantitative Input Missing Value -9
## Affection Input Missing Value "-9"
## Quantitative Output Missing Value "*"
## Affection Output Missing Value "*"
## Input Format: PLINK PED format (ped)
## Pedigree and map files specified as PLINK format.
## omit, penetrance, and frequency files are always in Mega2 format.
## Input files will be read in as PLINK or Mega2 format files as appropriate.
## reading phenotype file srdta.phe ... (5 columns)
## Reading PLINK map file for names: srdta.map
## Reading map file srdta.map ... (4 columns)
## Input Map name: Map, type: average genetic map, units: kosambi Morgans
## Input Map name: BP, type: physical map
## Found 2 possible maps in the srdta.map file.
## Now checking each record in map file srdta.map ...
## Done reading map file: srdta.map
##
## ===========================================================
## Total number of loci = 839
## 6 trait loci
## 2 Affection status loci:
## bt default
## 4 Quantitative loci:
## age qt1 qt2 qt3
## 833 Marker loci
## Number of loci found per chromosome (chromosome:number)
## 1:833
## ===========================================================
## WARNING: No frequency file provided.
## WARNING: Allele frequencies for these will be estimated from data.
## Trait 'bt' will be assigned the default penetrance: (0.0500 0.9000 0.9000)
## Trait 'default' will be assigned the default penetrance: (0.0500 0.9000 0.9000)
## Reading PLINK .ped file: srdta.ped (1672 columns).
## 833 (of 833) markers to be included from srdta.map
## Reading pedigree information from srdta.ped
## 2500 individuals read from srdta.ped
## 2500 individuals with nonmissing phenotypes
## 0 cases, 0 controls, 2500 missing
## 1275 males, 1225 females, 0 of unspecified sex
## 0 founders, 2500 non-founders found
## ===========================================================
## Input pedigree data contains:
## Input pedigree file is in PLINK-fam format.
## Marker Genotypes
## Fully Half
## Pedigrees People Males Females Typed Typed Total
## TOTAL 2500 2500 1275 1225 1978958 0 2082500
## Typed 2500 2500 1275 1225
## Untyped 0 0 0 0
## ===========================================================
## Pedigree exclusion option : Include all pedigrees whether typed or not.
## Count option: all alleles
## Count half-typed individuals' alleles : no
## ===========================================================
## Recoding pedigree genotypes ...
## ===========================================================
## Pedigree data summary after recoding:
## Input pedigree file is in PLINK-fam format.
## Marker Genotypes
## Fully Half
## Pedigrees People Males Females Typed Typed Total
## TOTAL 2500 2500 1275 1225 1978958 0 2082500
## Typed 2500 2500 1275 1225
## Untyped 0 0 0 0
## ===========================================================
## Created linkage ped tree
##
## ===== Messages of type "quant_stat":
## ------------------------------------------------------------
## Per-pedigree quantitative phenotype summary:
## Pedigree Mean Std Dev Minimum Maximum #Phenotypes
## ------------------------------------------------------------
## age
## ------------------------------------------------------------
## ------------------------------------------------------------
## 1 43.40000 0.00000 43.40000 43.40000 1
## 2 48.20000 0.00000 48.20000 48.20000 1
## 3 37.90000 0.00000 37.90000 37.90000 1
## 4 53.80000 0.00000 53.80000 53.80000 1
## 5 47.50000 0.00000 47.50000 47.50000 1
## 6 45.00000 0.00000 45.00000 45.00000 1
## 7 52.00000 0.00000 52.00000 52.00000 1
## 8 42.50000 0.00000 42.50000 42.50000 1
## 9 29.70000 0.00000 29.70000 29.70000 1
## ===== Too many "quant_stat" records, display is temporarily suspended ..
## qt1
## ------------------------------------------------------------
## ------------------------------------------------------------
## 1 -0.58000 0.00000 -0.58000 -0.58000 1
## 2 0.80000 0.00000 0.80000 0.80000 1
## 3 -0.52000 0.00000 -0.52000 -0.52000 1
## 4 -1.55000 0.00000 -1.55000 -1.55000 1
## 5 0.25000 0.00000 0.25000 0.25000 1
## 6 0.15000 0.00000 0.15000 0.15000 1
## 7 -0.56000 0.00000 -0.56000 -0.56000 1
## 8 0.00000 0.00000 0.00000 0.00000 0
## 9 -2.26000 0.00000 -2.26000 -2.26000 1
## ===== Too many "quant_stat" records, display is temporarily suspended ..
## qt2
## ------------------------------------------------------------
## ------------------------------------------------------------
## 1 4.46000 0.00000 4.46000 4.46000 1
## 2 6.32000 0.00000 6.32000 6.32000 1
## 3 3.26000 0.00000 3.26000 3.26000 1
## 4 888.00000 0.00000 888.00000 888.00000 1
## 5 5.70000 0.00000 5.70000 5.70000 1
## 6 4.65000 0.00000 4.65000 4.65000 1
## 7 4.64000 0.00000 4.64000 4.64000 1
## 8 5.77000 0.00000 5.77000 5.77000 1
## 9 0.71000 0.00000 0.71000 0.71000 1
## ===== Too many "quant_stat" records, display is temporarily suspended ..
## qt3
## ------------------------------------------------------------
## ------------------------------------------------------------
## 1 1.43000 0.00000 1.43000 1.43000 1
## 2 3.90000 0.00000 3.90000 3.90000 1
## 3 5.05000 0.00000 5.05000 5.05000 1
## 4 3.76000 0.00000 3.76000 3.76000 1
## 5 2.89000 0.00000 2.89000 2.89000 1
## 6 1.87000 0.00000 1.87000 1.87000 1
## 7 2.49000 0.00000 2.49000 2.49000 1
## 8 2.68000 0.00000 2.68000 2.68000 1
## 9 1.45000 0.00000 1.45000 1.45000 1
## ===== Too many "quant_stat" records, display is temporarily suspended ..
## ===== 2501 total records of type "quant_stat" are in MEGA2.LOG
##
##
## ===== Messages of type "quant_stat_sum":
## Quantitative trait phenotype statistics
## -------------------------------------------------------------------------------
## QTL Missing Minimum Maximum Total Pedigrees Total
## pedigrees phenotyped phenotypes
## -------------------------------------------------------------------------------
## age 0 24.100 71.600 2500 2500 2500
## qt1 3 -4.600 3.200 2500 2497 2497
## qt2 0 0.000 888.000 2500 2500 2500
## qt3 11 -1.970 6.340 2500 2489 2489
## NOTE: The Missing QTL value on input has been assigned as '-9.00'.
## -------------------------------------------------------------------------
## QTL Mean Std Dev Skewness Kurtosis
## -------------------------------------------------------------------------
## age 50.038 7.060 0.003 -0.063
## qt1 -0.298 1.001 -0.075 0.126
## qt2 6.122 30.601 28.734 825.077
## qt3 2.609 1.101 0.033 -0.090
## ===========================================================
## ===== 4 total records of type "quant_stat_sum" are in MEGA2.LOG
##
## Done checking locus integrity.
## Checking pedigree integrity...
## Done checking pedigree integrity.
## ==========================================================
## ===========================================================
## Pedigree statistics after selecting chromosomes and marker loci:
## Input pedigree file is in post-makeped format.
## Marker Genotypes
## Fully Half
## Pedigrees People Males Females Typed Typed Total
## TOTAL 2500 2500 1275 1225 1978958 0 2082500
## Typed 2500 2500 1275 1225
## Untyped 0 0 0 0
## ===========================================================
## Database file "srdta.db" will be backed up.
## Moved existing srdta.db to srdta.db.old
## Dumping SQLite3 DB to file "srdta.db"
## ===========================================================
## See run summaries in directory 2017-10-13-10-06
## MEGA2.LOG, MEGA2.RECODE, MEGA2.ERR, MEGA2.KEYS
## The script 'mega2log2html.pl' exited normally.
## To view the HTML-formatted run summaries, open
## /Users/rbaron/mega2/bb/srcdir/R/2017-10-13-10-06/MEGA2run.html
## in a web browser.
## ===========================================================
The abbreviated MEGA2.BATCH.srdta file is below. (The initial comment section is not shown and important lines are shown in a bold typeface.)
Input_Database_Mode=1
Input_Format_Type=4
Input_Pedigree_File=srdta.ped
Input_PLINK_Map_File=srdta.map
Input_Phenotype_File=srdta.phe
Output_Path=.
Input_Path=.
PLINK_Args= –missing-phenotype -9 –trait default
Input_Untyped_Ped_Option=2
Input_Do_Error_Sim=no
AlleleFreq_SquaredDev=999999999.000000
Value_Marker_Compression=1
Analysis_Option=Dump
Value_Missing_Quant_On_Input=-9.000000
Value_Missing_Affect_On_Input=-9
Count_Genotypes=4
Count_Halftyped=no
Value_Genetic_Distance_Index=0
Value_Genetic_Distance_SexTypeMap=0
Value_Base_Pair_Position_Index=1
Default_Reset_Invalid=no
DBfile_name=srdta.db
Default_Outfile_Names=yes
Note: We have provided a copy of the srdta.db database in the Mega2R package. So you can proceed with the steps that follow, even if you did not create your own database.
Now, we go back into R and run Mega2GenABEL(). Type:
GotGenABEL = require("GenABEL", quietly = FALSE)
## Loading required package: GenABEL
## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'GenABEL'
# Before issuing the next command, make sure you have issued this command
# `dump_mega2rtutorial_data()` first as instructed in the 'Tutorial Data' section
# above.
sdb = file.path(where_mega2rtutorial_data(), "srdta.db")
ENV = read.Mega2DB(sdb)
mega = Mega2GenABEL()
## Warning in Mega2GenABEL(): genABEL has been archived and is not available
NOTE: The code below will check if you have GenABEL available on your machine, if it is not available the code in this section can not really be executed.
mega should be the same as the srdta gwaa.data-class object. You can
compare them however you prefer. For example, in R, type:
GotGenABEL = require("GenABEL", quietly = FALSE)
## Loading required package: GenABEL
## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'GenABEL'
str(mega)
## NULL
if (GotGenABEL) str(srdta)
You can compare these printouts by eye. Alternatively, the function Mega2GenABELtst() can be used to compare the phenotype data, genotype data, and object metadata, item by item.
GotGenABEL = require("GenABEL", quietly = FALSE)
## Loading required package: GenABEL
## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'GenABEL'
options(max.print = 30)
Mega2GenABELtst(mega_ = mega, gwaa_ = srdta)
## Warning in Mega2GenABELtst(mega_ = mega, gwaa_ = srdta): One or both GenABEL arguments are NULL. Aborting.
## NULL
**NOTE These results are somewhat disappointing. But …
First, “mega2” prefers to use the pedigree id concatenated to the person id as the sample name, even if the person id itself is unique.
The second difference in gtdata\@strand occurs because Mega2 does not keep track of strand orientation and always returns 0.
The final difference is more subtle. The gtdata comparison line
decodes the genotype to characters then normalizes the two
heterozygous cases to one value. These results are the same for the
two different gwaa.class-objects; hence the data are really the same. But
Mega2GenABEL converts the data frames using the convert.snp.tped
function while the srdta object, generated by GenABEL, uses the
convert.snp.text. Though both functions encode the genotype to a
number, the convert.snp.tped function rearranges the genotype so
that the major allele appears first, the convert.snp.text does no
such rearrangement. Because gtdata\@coding and gtdata\@gtps
compare the encoded allele pairs and corresponding encoded genotypes,
they sometimes differ.
If you are left with questions as to how you can use Mega2R for your own research, you can:
Read our paper “The Mega2R R package: tools for accessing and processing common genetic data formats in R” (in preparation), which extends this document by providing implementation details.
Read the source of the various functions if you understand R; the code is not particularly subtle.
Write to the Mega2 discussion group: https://groups.google.com/forum/#!forum/mega2-users
The Mega2 C++ program and this Mega2R R package are both open source and are freely available, along with extensive documentation, from our https://watson.hgen.pitt.edu/register web site. This work was supported by NIH grant R01 GM076667 (PI: Weeks).