Mega2R Tutorial

library(knitr)
# Set so that long lines in R will be wrapped:
opts_chunk$set(tidy.opts=list(width.cutoff=80),tidy=TRUE)

1 Introduction

1.1 What is Mega2

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:

  1. The ability to create publication-quality PDF plots of the results using our nplplot library.

  2. The ability to create custom tracks of results for visualization in the UCSC genome browser.

  3. The ability to run in an automated way using batch files.

  4. The availability of our Genetic Map Interpolator for aiding in constructing genetic maps of markers.

  5. The ability to align allele labels to a reference and to resolve strand issues.

  6. The ability to simulate genotype errors.

  7. Input and output support for Mega2 format files that contain informative header lines and are readable into R.

  8. Input and output support for the widely-used PLINK format files.

  9. Input and output support for Variant Call Format (VCF, BCF, compressed VCF) files, including flexible filtering on input.

  10. Input support for IMPUTE2 GEN format files and binary IMPUTE2 BGEN format files.

  11. The ability to automatically zero out selected genotypes for specific individuals in order to resolve Mendelian inconsistencies.

  12. In most cases, in addition to generating appropriately re-formatted files, Mega2 also generates a shell script that will automatically run the desired program.

  13. Creation of an HTML summary of the most recent run of Mega2, with links to input and output and log files.

  14. 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.)

1.2 What is Mega2R

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.

2 Example Genome-wide Association Data

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.

3 Tutorial Data

The files you will need for this tutorial are provided in this package. Further, our use of the "mega2" executable expects the Mega2.BATCH. files to be in the working directory and the latter files expect their data files to be in the working directory. This is done by running:

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())

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()

4 Installation

4.1 R

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")

4.2 Bioconductor

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.

5 Using Mega2R to access and process genetic data

5.1 Creating a Mega2 database

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()

```{bash, eval=FALSE} 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.

## 5.2 Reading and Examining a Mega2 Database

The Mega2R package facilitates reading genetic data from a Mega2-created 'SQLite' database.

### 5.2.1 Reading a Mega2 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.


```r
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)

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:

5.2.2 Verbose Flag

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.

5.2.3 Use of an Environment

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.

5.2.4 Back to more examples.

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)

A more informative overview of the database can be had with:

showMega2ENV() 

5.2.5 Use standard R operations to examine the created data frames

str(ENV$locus_table)

5.3 Iterating through Gene Transcripts

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.

5.3.1 applyFnToRanges

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$refRanges)
    head(ENV$refRanges)

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$refRanges)
    head(ENV$refRanges)

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$refRanges)
    head(ENV$refRanges)

5.3.2 applyFnToRanges

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)

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, 4000000, 5000000, "range4m",
                             1, 5000000, 6000000, "range5m",
                             1, 6000000, 7000000, "range6m",
                             1, 7000000, 8000000, "range7m",
                             1, 8000000, 9000000, "range8m",
                             1, 9000000,10000000, "range9m"),
                            ncol = 4, nrow = 6, byrow = TRUE),
                    indices_arg = 1:4)

5.3.3 setAnnotations

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$txdb
    ENV$entrezGene

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.

5.3.4 applyFnToGenes

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"))

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"))

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,  5000000, 10000000,
                                     1, 10000000, 15000000),
                                    ncol = 3, nrow = 2, byrow = TRUE))

    # 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])

    # 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 example "show" function that we were using does not compute any statistics; it just shows the data that are available to analyze. The two functions below, "show2" and "show3", compute a fisher exact test for trait vs marker and a chisq test of the same data. Hopefully, the code with comments is easy to understand.

show.stat = function(m, r, e, fn) {
      print(r) 
# collect genotypes for the set of markers "m"
      mm = getgenotypes(m, envir=e) 
# apply xxx.test of trait vs marker (accumulating samples)
      pv = apply(mm, 2, fn)
      names(pv) = m$MarkerName
      print(pv)
}

show2 = function(m, r, e) {
    f = function(x) {
          tryCatch(fisher.test(table(e$fam$trait, x)),
                   error = function(e) {list(p.value = NA)}) $p.value }

    show.stat(m, r, e, f)
}

show3 = function(m, r, e) {
    f = function(x) {
          tryCatch(chisq.test(table(e$fam$trait, x)),
                   error = function(e) {list(p.value = NA)}) $p.value }

    show.stat(m, r, e, f)
}

Try running

    applyFnToGenes(show2, genes_arg = c("CEP104"))

or

    applyFnToGenes(show3, genes_arg = c("CEP104"))

5.4 Iterating through the Genotypes

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.

5.4.1 Encoded Genotypes

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)
    head(genotype)

5.4.2 Raw Genotypes

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)
    head(raw)

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.

6 Using Mega2R to carry out automated gene-based association tests using 'pedgene'

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.].

6.1 Loading a Mega2 database

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)

6.2 Gene Ranges reminder

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)

6.3 Gene transcripts reminder

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.

6.4 Running pedgene on transcripts

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)

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.

6.5 Running pedgene on selected genes

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)

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.

7 Using Mega2R to carry out automated gene-based association tests using 'SKAT'

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.].

7.1 Loading a Mega2 database

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)

7.2 Gene Ranges reminder

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)

7.3 Gene transcripts reminder

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)

7.4 Running Mega2SKAT on ranges

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))

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$SKAT_results)
# we will skip this line for the Vignette document production because it takes too long
ENV$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)

7.5 Running Mega2SKAT on genes

Let us run the Mega2SKAT function for a few genes:

ENV$SKAT_results = ENV$SKAT_results[0, ]
Mega2SKAT(ENV$phe[, 3] - 1 ~ 1, "D", kernel = "linear.weighted", weights.beta=c(0.5,0.5), genes = c('DISP1', 'AK5', 'KIF26B', 'ST7L'), envir = ENV)
print(ENV$SKAT_results)

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)

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)

8 Using Mega2R to carry out automated gene-based association tests using 'famSKAT_RC'

Mega2R provides functions to run the 'famSKAT_RC' package that carries out family-based association kernel tests for both rare and common variants while looping over selected marker subsets. With the appropriate parameter settings adjusting the percent of rare variants and the family kinship matrix, 'famSKAT_RC' behaves as SKAT, famSKAT and/or SKAT_RC.

The 'famSKATRC' package is available on CRAN as (https://CRAN.R-project.org/package=famSKATRC). It was written by Mohamad Saad1 and Ellen M. Wijsman1^[Mohamad Saad1 and Ellen M. Wijsman1, Combining family- and population-based imputation data for association analysis of rare and common variants in large pedigrees. Genet Epidemiol. 2014 Nov; 38(7): 579–590., doi: 10.1002/gepi.21844].

8.1 Loading a Mega2 database

The init_famSKATRC function is used to read the Mega2 database and initialize processing. This function calls the utility function, dbmega2_import, to read a database. Then it creates, edits, and rewrites the family data making sure that the members are uniquely labeled; it stores the results in the data frame, fam. (fam contains data merged from the pedigree_table, person_table and phenotype_table.) Next, init_famSKATRC decodes the phenotype_table into a simple data frame, phe which it stores in the environment; missing/zero valued entries are set to 'NA'. You will definitely want to examine ENV$phe to choose which phenotype to use for analysis. Finally, init_famSKATRC calculates some values that will be used repeatedly and stores them in the environment. These data include a kinship matrix calculated from the fam family data and weighting functions provided for both the rare variant and also for the common variant based on the beta distribution.

# 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_famSKATRC(db, verbose = F)

The setfam function is designed to let you prune the family structure and store it. It ensures that all the other original tables that have family member data will be updated. mkphenotype if it is used (which it is in init_famSKATRC), has to be called whenever the fam table is changed (which happens in setfam).

# The special hack below reduces the samples to 20% of the original, so the run will
# finish in reasonable time. There are 20 different pedigrees.
setfam(ENV$fam[(ENV$fam$pedigree_link %in% 0:3),], ENV)

ENV$phe = mkphenotype(ENV)
ENV$phe[ENV$phe == 0] = NA

You can run the init command with verbose equals TRUE to see the details of the database load and to enable later printouts:

# 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_famSKATRC(db, verbose = T)

8.2 Gene Ranges reminder

Mega2R also has an internal default list of the chromosome and base pair ranges for many gene transcripts stored in an internal table refRanges. 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)

8.3 Gene transcripts reminder

Gene transcripts can alternatively be defined according to a Bioconductor database containing the boundaries of the gene transcripts. 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 function is roughly the equivalent of the R language install.packages; it is run once.

By default, Mega2R uses the databases: "TxDb.Hsapiens.UCSC.hg19.knownGene" and "org.Hs.eg.db"; you may choose alternates 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. This command may be issued any time before the mega2famSKATRC command is called. The defaults were set with:

setAnnotations("TxDb.Hsapiens.UCSC.hg19.knownGene", "org.Hs.eg.db")

8.4 Running Mega2famSKATRC on ranges

You should be familiar with the famSKAT_RC function of the famSKATRC package and especially its many arguments which are more completely explained in the documentation.

The function, Mega2famSKATRC, is Mega2R's interface to famSKAT_RC. Its signature is:

Mega2famSKATRC = function (gs = 1:100, genes = NULL, envir = ENV, ...) { }

The gs argument indicates how many default range elements should be processed, genes if not NULL is a vector of gene names to process (with '*' representing all known genes) and the envir argument specifies the environment that contains all the Mega2R data frames.

Any additional, arguments needed for the "famSKAT_RC" function should be provided to the Mega2famSKATRC function and they will be passed to the eventual call to "famSKAT_RC". The "famSKAT_RC" function is also called with a genotype dosage matrix of the markers.

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 Mega2famSKATRC will issue a warning. If a marker shows no variation, Mega2famSKATRC will omit it. The Mega2famSKATRC function uses an internal function that converts the raw genotype value 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 famSKAT_RC function with the required arguments. Finally, the Mega2famSKATRC callback function stores the results.

By default, the function Mega2famSKATRC processes the first 100 transcripts and prints the results. Note: Set verbose to FALSE to eliminate the (excessive) diagnostics. A typical invocation of Mega2famSKATRC would be:

ENV$verbose = FALSE
Mega2famSKATRC(pheno=3,gs=1:60)

These data are saved in the data frame, famSKATRC_results, in the environment. Note: The results are always appended to the data frame. You should truncate it when necessary. Also, you may want to save these results to a file.

print(ENV$famSKATRC_results)
# we will skip this line for the Vignette document production because it takes too long
ENV$verbose = FALSE
Mega2famSKATRc(pheno=3, gs=1:nrow(ENV$refRanges))
print(ENV$famSKATRC_results)

8.5 Running Mega2famSKATRC on genes

Let us run the Mega2famSKATRC function for a few genes.
Processing time scales with sample size and number of ranges/genes to be processed. It is not dependent on the number of markers. On an old iMac, this statement below takes 3 minutes per gene for the full 1380 samples. But, remember we have reduce the samples to a total of 276 or 20% of the size of the samples in the database.

ENV$verbose = TRUE
Mega2famSKATRC(pheno=3, genes = c('DISP1', 'AK5', 'KIF26B', 'ST7L'), envir = ENV)
print(ENV$famSKATRC_results)

If you are adventurous you can look at all the genes: Note: Below, we set verbose to FALSE; otherwise there would be a large number of printouts indicating no markers are in many of the ranges. (Recall the samples come from part of chromosome one, whereas the ranges cover the whole genome.)

ENV$verbose = FALSE
Mega2famSKATRC(pheno=3, genes = '*', envir = ENV)
print(ENV$famSKATRC_results)

Note: The Mega2famSKATRC function appends any new results to the end of the ENV$famSKATRC_results data frame.

9 Outputting Mega2R data to VCF format

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 (https://www.internationalgenome.org/wiki/Analysis/Variant%20Call%20Format/vcf-variant-call-format-version-40/) was originally defined by the 1000 Genomes Project (https://www.internationalgenome.org/home) for data storage.

The current version of data format can be found at (https://samtools.github.io/hts-specs/).

9.1 Writing a VCF File using Mega2R

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)

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.

10 Converting Mega2R data to GenABEL format

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 (https://CRAN.R-project.org/package=GenABEL) in the archive (GenABEL was archived in late May 2018). 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.].

10.1 Creating a GenABEL gwaa.data-class object using Mega2R

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 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. Since 'GenABEL' is not readily available (and only available from the CRAN Archive), we will show you the commands and then what you would have seen if you had 'GenABEL' installed.

If you typed:

require("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()

You would see:

require("GenABEL")

## Loading required package: GenABEL
## Loading required package: MASS
## 
## Attaching package: 'MASS'
## The following object is masked _by_ '.GlobalEnv':
## 
##     genotype

## Loading required package: GenABEL.data
# Before issuing the next command, make sure you have issued this command
# `dump_mega2rtutorial_data()` first as instructed in the 'sTutorial 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()

## Reading individual ids from file '/var/folders/kn/h793jrls0n39bdym6d7531qr0000gp/T//RtmpNtrIhU/Mega2GenABEL.tfam' ...
## ... done.  Read 1380 individual ids from file '/var/folders/kn/h793jrls0n39bdym6d7531qr0000gp/T//RtmpNtrIhU/Mega2GenABEL.tfam'
## Reading genotypes from file '/var/folders/kn/h793jrls0n39bdym6d7531qr0000gp/T//RtmpNtrIhU/Mega2GenABEL.tped' ...
## ...done.  Read 1000 SNPs from file '/var/folders/kn/h793jrls0n39bdym6d7531qr0000gp/T//RtmpNtrIhU/Mega2GenABEL.tped'
## Writing to file '/var/folders/kn/h793jrls0n39bdym6d7531qr0000gp/T//RtmpNtrIhU/Mega2GenABELtped.raw'
## ...
## ... done.
## ids loaded...
## marker names loaded...
## chromosome data loaded...
## map data loaded...
## allele coding data loaded...
## strand data loaded...
## genotype data loaded...
## snp.data object created...
## assignment of gwaa.data object FORCED; X-errors were not checked!

You can use any of the GenABEL provided functions on the results, seqsimgwaa. If you typed:

str(seqsimgwaa)

You would see:

str(seqsimgwaa)

## Formal class 'gwaa.data' [package "GenABEL"] with 2 slots
##   ..@ phdata:'data.frame':   1380 obs. of  3 variables:
##   .. ..$ id     : chr [1:1380] "1SAP039_H05-0107" "1SAP039_H05-0106" "1SAP039_SAP039F14" "1SAP039_SAP039F13" ...
##   .. ..$ sex    : int [1:1380] 0 1 1 1 1 0 0 0 0 0 ...
##   .. ..$ default: int [1:1380] 1 1 1 1 1 1 1 1 1 1 ...
##   ..@ gtdata:Formal class 'snp.data' [package "GenABEL"] with 11 slots
##   .. .. ..@ nbytes    : num 345
##   .. .. ..@ nids      : int 1380
##   .. .. ..@ nsnps     : int 1000
##   .. .. ..@ idnames   : chr [1:1380] "1SAP039_H05-0107" "1SAP039_H05-0106" "1SAP039_SAP039F14" "1SAP039_SAP039F13" ...
##   .. .. ..@ snpnames  : chr [1:1000] "snp1" "snp2" "snp3" "snp4" ...
##   .. .. ..@ chromosome: Factor w/ 1 level "1": 1 1 1 1 1 1 1 1 1 1 ...
##   .. .. .. ..- attr(*, "names")= chr [1:1000] "snp1" "snp2" "snp3" "snp4" ...
##   .. .. ..@ map       : Named num [1:1000] 2 3 4 5 201 ...
##   .. .. .. ..- attr(*, "names")= chr [1:1000] "snp1" "snp2" "snp3" "snp4" ...
##   .. .. ..@ coding    :Formal class 'snp.coding' [package "GenABEL"] with 1 slot
##   .. .. .. .. ..@ .Data: raw [1:1000] 01 01 01 01 ...
##   .. .. ..@ strand    :Formal class 'snp.strand' [package "GenABEL"] with 1 slot
##   .. .. .. .. ..@ .Data: raw [1:1000] 00 00 00 00 ...
##   .. .. ..@ male      : Named int [1:1380] 0 1 1 1 1 0 0 0 0 0 ...
##   .. .. .. ..- attr(*, "names")= chr [1:1380] "1SAP039_H05-0107" "1SAP039_H05-0106" "1SAP039_SAP039F14" "1SAP039_SAP039F13" ...
##   .. .. ..@ gtps      :Formal class 'snp.mx' [package "GenABEL"] with 1 slot
##   .. .. .. .. ..@ .Data: raw [1:345, 1:1000] 59 55 55 55 ...

11 Converting Mega2R data to GDS (CoreArray) format

CoreArray Genetic Data Structure (GDS)^[Zheng, Xiuwen, David Levine, Jess Shen, Stephanie M. Gogarten, Cathy Laurie, and Bruce S. Weir. 2012. “A High-Performance Computing Toolset for Relatedness and Principal Component Analysis of Snp Data.” Bioinformatics (Oxford, England) 28 (24):3326–8. https://doi.org/10.1093/bioinformatics/bts606.] is a C++ package available from SourceForge^[https://sourceforge.net] as http://corearray.sourceforge.net. The Bioconductor^[Gentleman, Robert C., Vincent J. Carey, Douglas M. Bates, Ben Bolstad, Marcel Dettling, Sandrine Dudoit, Byron Ellis, et al. 2004. “Bioconductor: Open Software Development for Computational Biology and Bioinformatics.” Genome Biology 5 (10):1–16. https://doi.org/10.1186/gb-2004-5-10-r80.] package gdsfmt (https://bioconductor.org/packages/release/bioc/html/gdsfmt.html) provides a machine independent R^[R Core Team. 2016. “R: A Language and Environment for Statistical Computing.”] language interface to GDS. gdsfmt provides access to the underlying named vector hierarchy and named matrix hierarchy. The Bioconductor package SeqArray (https://bioconductor.org/packages/release/bioc/html/SeqArray.html) uses the named CoreArray GDS variables to store (and retrieve) a complete VCF file. The SeqArray API can be used to apply analysis functions to the underlying GWAS data. In addition, SeqArray data may be converted to a related format SNPArray (Note the package is called SNPRelate (https://bioconductor.org/packages/release/bioc/html/SNPRelate.html).) which allows linkage analysis to be performed on the GWAS data.

It is not our intention in this document to explain the CoreArray structure or all the structures that are used to represent the VCF GWS data. The reader is encourage to examine the above references. If your data were available in VCF format, or another format that can be processed to produce a Mega2 database, the following section shows how to convert the Mega2 database to either a SNPArray or SeqArray.

Note: Since the packages used here are sourced from Bioconductor, installation is performed by:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("gdsfmt")
BiocManager::install("SeqArray")
BiocManager::install("SNPRelate")

After the installation is performed, you may use the 'library()' function to add the code to your R workspace.

11.1 CoreArray features vs Mega2R database features

There are two important issues that are necessary to understand when converting a Mega2R database to CoreArray format. First, because of the design of CoreArray it is easier (and probably more efficient) to add sucessive new columns to the genotype array. The Mega2R design makes it easy to retrieve complete columns representing samples of arbitrary markers. Thus, the default usage is to create a genotype array that is indexed by sample and then marker. This layout is optimal to fetch columns of samples from the array. However, if there are multiple program that you will use that fetch some markers for selected samples, the genotype array should be organized as markers by samples. In this case, it would be best to create the array with the latter organization; there is an option to make this choice.

Second, SeqArray is designed to store all the information in any VCF file. In particular, it can distinguish both haplotypes and genotypes markers. And it can represent biallelic and multi-allelic markers. Mega2R can not handle either of these options. (Actually, Mega2R does fine with multi-alleleic markers but that is not yet supported by Mega2gdsfmt.) Mega2R will always present the heterozygous genotype value as Ref Allele, then Alt allele. The SNPArray format, like Mega2R, does not support haplotypes or multi-alleleic markers.

11.2 Creating a GDS file using Mega2R

The Mega2R function, Mega2gdsfmt(), creates a '.gds' file from the Mega2R database; it is called as shown below

    Mega2gdsfmt(filename = "test.gds", markers = NULL, snp.order = FALSE, SeqArray = FALSE, envir = ENV)

The arguments are:

We will illustrate the CoreArray files when they are created.

# 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)

Below, we assume that a Mega2 database was stored in the environment, ENV. If this is not the case, supply the named argument, envir, wherever ENV is used.

11.2.1 Creating the GDS file in SeqArray gdsfmt format

Type:

# NOTE: the gds file to be created must be closed with the function below,
# or by using an on.exit(closefn.gds(<name>))
showfile.gds(closeall=T, verbose=F)

# Before issuing the next command, make sure you have issued
# this command `dump_mega2rtutorial_data()` first
# as instructed in the "Tutorial Data' section above.
gdsfile = file.path(where_mega2rtutorial_data(), "foo.gds")
gdsn = Mega2gdsfmt(gdsfile, ENV$markers[ENV$markers$chromosome==1 ,], SeqArray=TRUE)

Note: The line above places the data for only chromosome 1 in the gds file. (Recall that our simulated data is only on chromosome 1). Note: In general, you can filter the second argument, ENV$markers, as needed. If the second argument is not present, all the markers are written out.

The created gds file with its variable heirarchy values is listed below:

print(gdsn)
closefn.gds(gdsn)

11.2.2 Creating the SNPRelate file in SNPArray gdsfmt format

Type:

# NOTE: the gds file to be created must be closed with the function below,
# or by using an on.exit(closefn.gds(<name>))
showfile.gds(closeall=T, verbose=F)
# Before issuing the next command, make sure you have issued
# this command `dump_mega2rtutorial_data()` first
# as instructed in the "Tutorial Data' section above.
gdsfile = file.path(where_mega2rtutorial_data(), "foo.gds")
gdsn = Mega2gdsfmt(gdsfile, ENV$markers[ENV$markers$chromosome==1 ,], SeqArray=FALSE)

Note: The line above places the data for only chromosome 1 in the gds file. (Recall that our simulated data is only on chromosome 1). Note: In general, you can filter the second argument, ENV$markers, as needed. If the second argument is not present, all the markers are written out.

The generated file and contents are listed below:

print(gdsn)
closefn.gds(gdsn)

You can process these files by programs that are designed to read them. For example, the SNPRelate library has functions that perform: LD-based pruning, PCA, and relatedness by IBD. The SeqArray format file has functions that perform: missing rates for variants, missing rates for samples, allele frequencies as well as PCA and inbreeding coefficients.

12 Regression testing

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.

12.1 VCF regression

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:

```{bash, eval=FALSE} 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:

```{bash, diffvcf,eval=FALSE}
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

12.2 GenABEL regression

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. But we have an old version of GenABEL and will show you in the example below what you should see.

GotGenABEL = require("GenABEL", quietly=FALSE)
if (GotGenABEL) data(srdta) else srdta=NULL
The previous code does not display anything.

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)

# 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)])

The previous code does not display anything.

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:

```{bash, eval=FALSE} 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 MEGA2.BATCH.srdata file

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().  If you type:

```r
GotGenABEL = require("GenABEL", quietly=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.
sdb = file.path(where_mega2rtutorial_data(), "srdta.db")
ENV = read.Mega2DB(sdb)

mega = Mega2GenABEL()

You will see:

GotGenABEL = require("GenABEL", quietly = 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.

sdb = file.path(where_mega2rtutorial_data(), "srdta.db")
ENV = read.Mega2DB(sdb)

mega = Mega2GenABEL()

## Reading individual ids from file '/var/folders/kn/h793jrls0n39bdym6d7531qr0000gp/T//RtmpNtrIhU/Mega2GenABEL.tfam' ...
## ... done.  Read 2500 individual ids from file '/var/folders/kn/h793jrls0n39bdym6d7531qr0000gp/T//RtmpNtrIhU/Mega2GenABEL.tfam'
## Reading genotypes from file '/var/folders/kn/h793jrls0n39bdym6d7531qr0000gp/T//RtmpNtrIhU/Mega2GenABEL.tped' ...
## ...done.  Read 833 SNPs from file '/var/folders/kn/h793jrls0n39bdym6d7531qr0000gp/T//RtmpNtrIhU/Mega2GenABEL.tped'
## Writing to file '/var/folders/kn/h793jrls0n39bdym6d7531qr0000gp/T//RtmpNtrIhU/Mega2GenABELtped.raw' ...
## ... done.
## ids loaded...
## marker names loaded...
## chromosome data loaded...
## map data loaded...
## allele coding data loaded...
## strand data loaded...
## genotype data loaded...
## snp.data object created...
## assignment of gwaa.data object FORCED; X-errors were not checked!

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. But, we will show you the expected results.

mega should be the same as the srdta gwaa.data-class object. You can compare them however you prefer. For example, in R, if you type:

GotGenABEL = require("GenABEL", quietly=FALSE)
str(mega)
if (GotGenABEL) str(srdta)

You will see:

GotGenABEL = require("GenABEL", quietly=FALSE)
str(mega)

## Formal class 'gwaa.data' [package "GenABEL"] with 2 slots
##   ..@ phdata:'data.frame':   2500 obs. of  8 variables:
##   .. ..$ id     : chr [1:2500] "1_p1" "2_p2" "3_p3" "4_p4" ...
##   .. ..$ sex    : int [1:2500] 1 1 0 1 1 0 0 1 0 0 ...
##   .. ..$ age    : num [1:2500] 43.4 48.2 37.9 53.8 47.5 45 52 42.5 29.7 45.8 ...
##   .. ..$ qt1    : num [1:2500] -0.58 0.8 -0.52 -1.55 0.25 0.15 -0.56 -9 -2.26 -1.32 ...
##   .. ..$ qt2    : num [1:2500] 4.46 6.32 3.26 888 5.7 4.65 4.64 5.77 0.71 3.26 ...
##   .. ..$ qt3    : num [1:2500] 1.43 3.9 5.05 3.76 2.89 1.87 2.49 2.68 1.45 0.85 ...
##   .. ..$ bt     : int [1:2500] 0 1 1 1 1 0 0 1 0 0 ...
##   .. ..$ default: int [1:2500] 0 0 0 0 0 0 0 0 0 0 ...
##   ..@ gtdata:Formal class 'snp.data' [package "GenABEL"] with 11 slots
##   .. .. ..@ nbytes    : num 625
##   .. .. ..@ nids      : int 2500
##   .. .. ..@ nsnps     : int 833
##   .. .. ..@ idnames   : chr [1:2500] "1_p1" "2_p2" "3_p3" "4_p4" ...
##   .. .. ..@ snpnames  : chr [1:833] "rs10" "rs18" "rs29" "rs65" ...
##   .. .. ..@ chromosome: Factor w/ 1 level "1": 1 1 1 1 1 1 1 1 1 1 ...
##   .. .. .. ..- attr(*, "names")= chr [1:833] "rs10" "rs18" "rs29" "rs65" ...
##   .. .. ..@ map       : Named num [1:833] 2500 3500 5750 13500 14250 ...
##   .. .. .. ..- attr(*, "names")= chr [1:833] "rs10" "rs18" "rs29" "rs65" ...
##   .. .. ..@ coding    :Formal class 'snp.coding' [package "GenABEL"] with 1 slot
##   .. .. .. .. ..@ .Data: raw [1:833] 08 0b 0c 07 ...
##   .. .. ..@ strand    :Formal class 'snp.strand' [package "GenABEL"] with 1 slot
##   .. .. .. .. ..@ .Data: raw [1:833] 00 00 00 00 ...
##   .. .. ..@ male      : Named int [1:2500] 1 1 0 1 1 0 0 1 0 0 ...
##   .. .. .. ..- attr(*, "names")= chr [1:2500] "1_p1" "2_p2" "3_p3" "4_p4" ...
##   .. .. ..@ gtps      :Formal class 'snp.mx' [package "GenABEL"] with 1 slot
##   .. .. .. .. ..@ .Data: raw [1:625, 1:833] 55 59 55 a5 ...


if (GotGenABEL) str(srdta)


## Formal class 'gwaa.data' [package "GenABEL"] with 2 slots
##   ..@ phdata:'data.frame':   2500 obs. of  7 variables:
##   .. ..$ id : chr [1:2500] "p1" "p2" "p3" "p4" ...
##   .. ..$ sex: int [1:2500] 1 1 0 1 1 0 0 1 0 0 ...
##   .. ..$ age: num [1:2500] 43.4 48.2 37.9 53.8 47.5 45 52 42.5 29.7 45.8 ...
##   .. ..$ qt1: num [1:2500] -0.58 0.8 -0.52 -1.55 0.25 0.15 -0.56 NA -2.26 -1.32 ...
##   .. ..$ qt2: num [1:2500] 4.46 6.32 3.26 888 5.7 4.65 4.64 5.77 0.71 3.26 ...
##   .. ..$ qt3: num [1:2500] 1.43 3.9 5.05 3.76 2.89 1.87 2.49 2.68 1.45 0.85 ...
##   .. ..$ bt : int [1:2500] 0 1 1 1 1 0 0 1 0 0 ...
##   ..@ gtdata:Formal class 'snp.data' [package "GenABEL"] with 11 slots
##   .. .. ..@ nbytes    : num 625
##   .. .. ..@ nids      : int 2500
##   .. .. ..@ nsnps     : int 833
##   .. .. ..@ idnames   : chr [1:2500] "p1" "p2" "p3" "p4" ...
##   .. .. ..@ snpnames  : chr [1:833] "rs10" "rs18" "rs29" "rs65" ...
##   .. .. ..@ chromosome: Factor w/ 1 level "1": 1 1 1 1 1 1 1 1 1 1 ...
##   .. .. .. ..- attr(*, "names")= chr [1:833] "rs10" "rs18" "rs29" "rs65" ...
##   .. .. ..@ map       : Named num [1:833] 2500 3500 5750 13500 14250 ...
##   .. .. .. ..- attr(*, "names")= chr [1:833] "rs10" "rs18" "rs29" "rs65" ...
##   .. .. ..@ coding    :Formal class 'snp.coding' [package "GenABEL"] with 1 slot
##   .. .. .. .. ..@ .Data: raw [1:833] 08 0b 0c 03 ...
##   .. .. ..@ strand    :Formal class 'snp.strand' [package "GenABEL"] with 1 slot
##   .. .. .. .. ..@ .Data: raw [1:833] 01 01 02 01 ...
##   .. .. ..@ male      : Named int [1:2500] 1 1 0 1 1 0 0 1 0 0 ...
##   .. .. .. ..- attr(*, "names")= chr [1:2500] "p1" "p2" "p3" "p4" ...
##   .. .. ..@ gtps      :Formal class 'snp.mx' [package "GenABEL"] with 1 slot
##   .. .. .. .. ..@ .Data: raw [1:625, 1:833] 55 59 55 a5 ...

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. If you type:

GotGenABEL = require("GenABEL", quietly=FALSE)
options(max.print = 30)
Mega2GenABELtst(mega_ = mega, gwaa_ = srdta)

You will see:

GotGenABEL = require("GenABEL", quietly = FALSE)
options(max.print = 30)
Mega2GenABELtst(mega_ = mega, gwaa_ = srdta)

## all(mega_@phdata$sex == gwaa_@phdata$sex) [1] TRUE
## all(mega_@phdata$age == gwaa_@phdata$age) [1] TRUE
## all(mega_@phdata$qt1 == gwaa_@phdata$qt1) [1] TRUE
## all(mega_@phdata$qt2 == gwaa_@phdata$qt2) [1] TRUE
## all(mega_@phdata$qt3 == gwaa_@phdata$qt3) [1] TRUE
## all(mega_@phdata$bt == gwaa_@phdata$bt) [1] TRUE
## all(mega_@gtdata@nids == gwaa_@gtdata@nids)[1] TRUE
## all(mega_@gtdata@nsnps == gwaa_@gtdata@nsnps)[1] TRUE
## all(mega_@gtdata@nbytes == gwaa_@gtdata@nbytes)[1] TRUE
## all(mega_@gtdata@idnames == gwaa_@gtdata@idnames)[1] FALSE
## all(mega_@gtdata@snpnames == gwaa_@gtdata@snpnames)[1] TRUE
## all(mega_@gtdata@chromosome == gwaa_@gtdata@chromosome)[1] TRUE
## all(mega_@gtdata@map == gwaa_@gtdata@map)[1] TRUE
## all(mega_@gtdata@male == gwaa_@gtdata@male)[1] TRUE
## all(mega_@gtdata@coding == gwaa_@gtdata@coding)[1] FALSE
## all(mega_@gtdata@strand == gwaa_@gtdata@strand)[1] FALSE
## all(mega_@gtdata@gtps == gwaa_@gtdata@gtps)[1] FALSE
## all(mega_@gtdata == gwaa_@gtdata)[1] TRUE
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1] "markers that differ"
## [1] "markers that differ"
##     locus_link locus_link_fill MarkerName chromosome position
## 4            9               9       rs65          1    13500
## 8           13              13      rs130          1    27250
## 10          15              15      rs150          1    33250
## 20          25              25      rs324          1    82500
## 23          28              28      rs348          1    88000
## 24          29              29      rs361          1    90000
##  [ reached getOption("max.print") -- omitted 209 rows ]
## [1] "allele values for markers that differ"
## [1] "allele values for markers that differ"
##     locus_link pId.x AlleleName.x Frequency.x indexX.x pId.y AlleleName.y
## 4            9    19            A  0.28027754        1    20            T
## 8           13    27            A  0.30622110        1    28            G
## 10          15    31            A  0.65998312        1    32            C
##     Frequency.y indexX.y
## 4    0.71972246        2
## 8    0.69377890        2
## 10   0.34001688        2
##  [ reached getOption("max.print") -- omitted 212 rows ]

**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.

12.3 SeqArray regression

Rather than spell out all the details, this time we will only sketch how to match the SeqArray/SNPArray files calculated by R library APIs and converted from a Mega2R database.

First, you will need to find a suitable VCF file. (As stated earlier, it would be preferable if the genotype data were without haplotypes and if the markers were bi-alleleic.) Section 11.1 explains how to convert a VCF file to a Mega2 database. Earlier, in Section 10.2/10.2.1 and 10.2/10.2.2, we showed how to convert the Mega2 database to SeqArray format and SnpArray format, respectively.

Next, the SeqArray library contains a function, seqVCF2SEQ(), that converts a VCF file to a SeqArray. In addition, SeqArray contains another function, seqSEQ2SNP(), that converts a CoreArray file in SeqArray format to SNPArray format.

Our goal is to compare the underlying data calculated in the above two paragraphs. Let us illustrate with the SNPArray data. The Mega2R calculation gives you an open gdsfmt file descriptor. If you print the descriptor, it will show all the CoreArray variables; one of which is 'sample.id'. The native calculation uses seqVCF2SEQ() and then seqSEQ2SNP() to produce SNPArray data in a gdsfmt file. Open this gdsfmt file using openfn.gds(<file name>), and then proceed to print the gds object and look at its variables. To compare both versions of 'sample.id's, use the low level gdsfmt primitives, 'read.gsdn', and 'index.gdsn' as in:

        all(read.gdsn(index.gdsn(<file name open descriptor>, 'sample.id')) ==
            read.gdsn(index.gdsn(Mega2Rgdsfmt descriptor>, 'sample.id')) )

The above test should be applied to all the variables in the gds object hierarchy. Further, a similar test can be applied to the gds variables of a SeqArray, using the SeqArray variable hierarchy rather than those of the SNPArray.

13 Next steps

If you are left with questions as to how you can use Mega2R for your own research, you can:

  1. 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.

  2. Read the source of the various functions if you understand R; the code is not particularly subtle.

  3. Write to the Mega2 discussion group: https://groups.google.com/forum/#!forum/mega2-users

clean_mega2rtutorial_data()

14 Acknowlegements

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).

15 References



Try the Mega2R package in your browser

Any scripts or data that you put into this service are public.

Mega2R documentation built on Oct. 23, 2020, 8:19 p.m.