## Introduction

### Overview

Thanks for checking out rTASSEL! In this document, we will go over the functionalities used to work with the TASSEL software via R.

TASSEL is a software package used to evaluate traits associations, evolutionary patterns, and linkage disequilibrium. Strengths of this software include:

1. The opportunity for a number of new and powerful statistical approaches to association mapping such as a General Linear Model (GLM) and Mixed Linear Model (MLM). MLM is an implementation of the technique which our lab’s published Nature Genetics paper - Unified Mixed-Model Method for Association Mapping - which reduces Type I error in association mapping with complex pedigrees, families, founding effects and population structure.

2. An ability to handle a wide range of indels (insertion & deletions). Most software ignore this type of polymorphism; however, in some species (like maize), this is the most common type of polymorphism.

Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. (2007) TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633-2635.

Detailed documentation and source code can be found on our website:

https://www.maizegenetics.net/tassel

### Motivation

The main goal of developing this package is to construct an R-based front-end to connect to a variety of highly used TASSEL methods and analytical tools. By using R as a front-end, we aim to utilize a unified scripting workflow that exploits the analytical prowess of TASSEL in conjunction with R’s popular data handling and parsing capabilities without ever having the user to switch between these two environments.

### Disclaimer

Due to the experimental nature of this package’s lifecycle, end functionalities are prone to change after end-user input is obtained in the near future.

### Citation

To cite rTASSEL, please use the following citation:

Monier et al., (2022). rTASSEL: An R interface to TASSEL for analyzing genomic diversity. Journal of Open Source Software, 7(76), 4530, https://doi.org/10.21105/joss.04530

## Preliminary steps

### Setting Memory

Since genome-wide association analyses can use up a lot of computational resources, memory allocation to rTASSEL can be modified. To change the amount of memory, use the base options() function and modify the following parameter:

options(java.parameters = c("-Xmx<memory>", "-Xms<memory>"))

Replace <memory> with a specified unit of memory. For example, if I want to allocate a maximum of 6 GB of memory for my operations, I would use the input "-Xmx6g", where g stands for gigabyte (GB). More information about memory allocation can be found here.

NOTE: Setting Java memory options for rTASSEL and any rJava-related packages needs to be set before loading the rTASSEL package!

### The importance of logging your progress

Before we begin analyzing data, optional parameters can be set up to make rTASSEL more efficient. To prevent your R console from being overloaded with TASSEL logging information, it is highly recommended that you start a logging file. This file will house all of TASSEL’s logging output which is beneficial for debugging and tracking the progress of your analytical workflow. To start a logging file, use the following command:

rTASSEL::startLogger(fullPath = NULL, fileName = NULL)

If the rTASSEL::startLogger() parameters are set to NULL, the logging file will be created in your current working directory. If you are unsure of what your working directory is in R, use the base getwd() command.

Additionally, since this is a general walkthrough, certain intricaces of each function may glossed over. If you would like to study a function in full, refer to the R documentation by using ?<function> in the console, where <function> is an rTASSEL-based function.

### Overview

Like TASSEL, rTASSEL will read two main types of data:

• Genotype data
• Phenotype data

This data can be read in several different ways. In the following examples, we will demonstrate various ways genotype and phenotype information can be loaded into rTASSEL objects.

#### From a path

Currently, reading in genotype data to rTASSEL is based off of file locations as paths. Genotype/sequencing data can be stored in a variety of formats. rTASSEL can read and store a wide variety of file types:

• hapmap (HMP)
• HDF5 (hierarchical data format version 5)
• VCF (variant call format)

To load this genotype data, simply store your file location as a string object in R. For this example, we will load two toy data sets - one being a VCF file and the other being a hapmap file. These data sets can be accessed via the rTASSEL package itself:

# Load hapmap data
genoPathHMP <- system.file(
"extdata",
"mdp_genotype.hmp.txt",
package = "rTASSEL"
)
genoPathHMP
## [1] "/home/runner/work/_temp/Library/rTASSEL/extdata/mdp_genotype.hmp.txt"
# Load VCF data
genoPathVCF <- system.file(
"extdata",
"maize_chr9_10thin40000.recode.vcf",
package = "rTASSEL"
)
genoPathVCF
## [1] "/home/runner/work/_temp/Library/rTASSEL/extdata/maize_chr9_10thin40000.recode.vcf"

Now that we have the file paths to this data, we can pass this to TASSEL and create a formal TasselGenotypePhenotype class object in R using the following:

# Load in hapmap file
path = genoPathHMP
)

path = genoPathVCF
)

When we call these objects, a summary of the data will be posted to the R console:

tasGenoHMP
## A TasselGenotypePhenotype Dataset
##   Class.............. TasselGenotypePhenotype
##   Taxa............... 281
##   Positions.......... 3093
##   Taxa x Positions... 869133
## ---
##   Genotype Table..... [x]
##   Phenotype Table.... [ ]

This summary details the number of Taxa (Taxa) and marker positions (Positions) within the data set. Additionally, since we can load both genotype and phenotype information into this object, a helpful check will be displayed to show what is populating the object ([x] or [ ]).

In general, this S4 class data object houses “slot” information relating to TASSEL/Java pointers of the respective data.

class(tasGenoHMP)
## [1] "TasselGenotypePhenotype"
## attr(,"package")
## [1] "rTASSEL"
slotNames(tasGenoHMP)
## [1] "name"            "jTasselObj"      "jTaxaList"       "jPositionList"
## [5] "jGenotypeTable"  "jPhenotypeTable"

Technically, this object does not contain the full information of the data represented in R space, but merely contains addresses to the memory store of the reference TASSEL object ID. For example, if we wanted to extract the GenotypeTable with the S4 @ operator, we would get something that looks like this:

tasGenoHMP@jGenotypeTable
## [1] "Java-Object{net.maizegenetics.dna.snp.CoreGenotypeTable@323b36e0}"

This entity is a rJava internal identifier. It isn’t until we call downstream rTASSEL functions where we will bring the TASSEL data into the R environment.

#### From a path

Similar to reading in genotype data, phenotype data can also be read in via paths. If you already have preconstructed phenotype data in a file, this option will most likely work best for you. One caveat to this is how the data file is constructed in terms of columns and trait data for TASSEL analyses. More information about how these files can be found at this link under the Numerical Data section.

Loading this type of data is very similar to how genotype data is loaded. here, we will use the rTASSEL::readPhenotypeFromPath() function:

# Read from phenotype path
phenoPath  <- system.file("extdata", "mdp_traits.txt", package = "rTASSEL")
phenoPath
## [1] "/home/runner/work/_temp/Library/rTASSEL/extdata/mdp_traits.txt"
# Load into rTASSEL TasselGenotypePhenotype object
path = phenoPath
)

# Inspect object
tasPheno
## A TasselGenotypePhenotype Dataset
##   Class.............. TasselGenotypePhenotype
##   Taxa............... 301
##   Positions.......... NA
##   Taxa x Positions... NA
## ---
##   Genotype Table..... [ ]
##   Phenotype Table.... [x]
## ---
##   Traits: Taxa EarHT dpoll EarDia

The object output is very similar to the genotype table output with some minor additions to which traits are displayed in the file.

#### From an R data frame

In some cases you might want to first modify your phenotype data set in R and then load it into the TASSEL environment. If you wish to choose this route, you will need to use the rTASSEL::readPhenotypeFromDataFrame() function along with a couple of parameters. First, we will construct an R data frame and load it with this function:

# Create phenotype data frame
colnames(phenoDF)[1] <- "Taxon"

# Inspect first few rows
head(phenoDF)
##   Taxon EarHT  dpoll     EarDia
## 1   811 59.50 -999.0 -999.00000
## 2 33-16 64.75   64.5 -999.00000
## 3 38-11 92.25   68.5   37.89700
## 4  4226 65.50   59.5   32.21933
## 5  4722 81.13   71.5   32.42100
## 6  A188 27.50   62.0   31.41900
# Load into rTASSEL TasselGenotypePhenotype object
phenotypeDF = phenoDF,
taxaID = "Taxon",
attributeTypes = NULL
)

# Inspect new object
tasPhenoDF
## A TasselGenotypePhenotype Dataset
##   Class.............. TasselGenotypePhenotype
##   Taxa............... 301
##   Positions.......... NA
##   Taxa x Positions... NA
## ---
##   Genotype Table..... [ ]
##   Phenotype Table.... [x]
## ---
##   Traits: Taxa EarHT dpoll EarDia

The phenotypeDF parameter is for the R data frame object. The taxaID parameter is needed to determine which column of your data frame is your TASSEL taxa data. The final parameter (attributeTypes) is optional. If this parameter is set to NULL, all remaining data frame columns will be classified as TASSEL data types. If this is not the case for your data (e.g. if you have covariate or factor data in your experiment), you will need to specify which columns are what TASSEL data type (i.e. data, covariate, or factor). This will have to be passed as an R vector of string elements (e.g. c("data", "factor", "covariate")). Currently, this data type needs to be entered in the same order as they are found in the data frame.

In association studies, we are interested in combining our genotype and phenotype data. To usually run this operation in TASSEL, an intersect combination between the two data sets is needed. To run this in rTASSEL, we can use the rTASSEL::readGenotypePhenotype() function. The parameter input needed for this function is, of course, a genotype and phenotype object. For genotype input, the following can be used:

• a path to a genotype file
• a prior TasselGenotypePhenotype object

For phenotype input, the following can be used:

• a path to a phenotype data set,
• a prior TasselGenotypePhenotype object
• an R data frame

For example, if we wanted to read the prior TasselGenotypePhenotype genotype and phenotype objects from earlier:

tasGenoPheno <- rTASSEL::readGenotypePhenotype(
genoPathOrObj = tasGenoHMP,
phenoPathDFOrObj = tasPheno
)
tasGenoPheno
## A TasselGenotypePhenotype Dataset
##   Class.............. TasselGenotypePhenotype
##   Taxa............... 279
##   Positions.......... 3093
##   Taxa x Positions... 862947
## ---
##   Genotype Table..... [x]
##   Phenotype Table.... [x]
## ---
##   Traits: Taxa EarHT dpoll EarDia

We can also use a combination of the above parameter options (e.g. load a genotype path and a phenotype data frame, etc.). One caveat though, if you load in a phenotype data frame object with this function, the prior parameters from the rTASSEL::readPhenotypeFromDataFrame will be needed (i.e. the taxaID and attributeTypes parameters):

tasGenoPhenoDF <- rTASSEL::readGenotypePhenotype(
genoPathOrObj = genoPathHMP,
phenoPathDFOrObj = phenoDF,
taxaID = "Taxon",
attributeTypes = NULL
)
tasGenoPhenoDF
## A TasselGenotypePhenotype Dataset
##   Class.............. TasselGenotypePhenotype
##   Taxa............... 279
##   Positions.......... 3093
##   Taxa x Positions... 862947
## ---
##   Genotype Table..... [x]
##   Phenotype Table.... [x]
## ---
##   Traits: Taxa EarHT dpoll EarDia

rTASSEL also provides users the ability to read in delimited “flat-file” kinship objects as a TasselDistanceMatrix object using the function, readTasselDistanceMatrix():

## Get toy kinship data from package ----
kinshipPath <- system.file(
"extdata",
"mdp_kinship.txt",
package = "rTASSEL"
)

rTASSEL::readTasselDistanceMatrix(kinshipPath)
## A TasselDistanceMatrix Object of 277 x 277 elements:
##
##                   33-16      38-11       4226       4722    ...    YU796NS
##        33-16     2.0000     0.1816     0.0187     0.0000    ...     0.1162
##        38-11     0.1816     2.0000     0.0000     0.1120    ...     0.1421
##         4226     0.0187     0.0000     2.0000     0.0000    ...     0.1904
##         4722     0.0000     0.1120     0.0000     2.0000    ...     0.0000
##          ...        ...        ...        ...        ...    ...        ...
##      YU796NS     0.1162     0.1421     0.1904     0.0000    ...     2.0000

## Extracting data from rTASSEL objects

### Get genotype data

If you want to bring in genotype data into the R environment, you can use the rTASSEL::getSumExpFromGenotypeTable() function. All this function needs is a TasselGenotypePhenotype class object containing a genotype table:

tasSumExp <- rTASSEL::getSumExpFromGenotypeTable(
tasObj = tasGenoPheno
)
tasSumExp
## class: RangedSummarizedExperiment
## dim: 3093 279
## assays(1): ''
## rownames: NULL
## rowData names(3): tasselIndex refAllele altAllele
## colnames: NULL
## colData names(2): Sample TasselIndex

As you can see above, the gentoype object is returned as a SummarizedExperiment class R object. More information about these objects can be found here.

From this object, we extract taxa information and their respective TASSEL integer locations:

SummarizedExperiment::colData(tasSumExp)
## DataFrame with 279 rows and 2 columns
##          Sample TasselIndex
##     <character>   <integer>
## 1         33-16           0
## 2         38-11           1
## 3          4226           2
## 4          4722           3
## 5          A188           4
## ...         ...         ...
## 275         W22         274
## 276        W64A         275
## 277          WD         276
## 278         WF9         277
## 279     YU796NS         278

We can also extract the allelic marker data using SummarizedExperiment::rowData():

SummarizedExperiment::rowData(tasSumExp)
## DataFrame with 3093 rows and 3 columns
##      tasselIndex   refAllele   altAllele
##        <integer> <character> <character>
## 1              0           A           C
## 2              1           C           G
## 3              2           G           T
## 4              3           A           T
## 5              4           A           G
## ...          ...         ...         ...
## 3089        3088           A           G
## 3090        3089           C           G
## 3091        3090           C           G
## 3092        3091           A           G
## 3093        3092           C           G

And get marker coordinates using SummarizedExperiment::rowRanges()

SummarizedExperiment::rowRanges(tasSumExp)
## GRanges object with 3093 ranges and 3 metadata columns:
##          seqnames    ranges strand | tasselIndex   refAllele   altAllele
##             <Rle> <IRanges>  <Rle> |   <integer> <character> <character>
##      [1]        1    157104      + |           0           A           C
##      [2]        1   1947984      + |           1           C           G
##      [3]        1   2914066      + |           2           G           T
##      [4]        1   2914171      + |           3           A           T
##      [5]        1   2915078      + |           4           A           G
##      ...      ...       ...    ... .         ...         ...         ...
##   [3089]       10 147819958      + |        3088           A           G
##   [3090]       10 148332885      + |        3089           C           G
##   [3091]       10 148488692      + |        3090           C           G
##   [3092]       10 148816538      + |        3091           A           G
##   [3093]       10 148907116      + |        3092           C           G
##   -------
##   seqinfo: 10 sequences from an unspecified genome; no seqlengths

### Extract phenotype data

If you want to bring in phenotype data into the R environment, you can use the rTASSEL::getPhenotypeDF() function. All this function needs is a TasselGenotypePhenotype class object containing a phenotype table:

tasExportPhenoDF <- rTASSEL::getPhenotypeDF(
tasObj = tasGenoPheno
)
head(tasExportPhenoDF)
##    Taxa EarHT dpoll   EarDia
## 1 33-16 64.75  64.5      NaN
## 2 38-11 92.25  68.5 37.89700
## 3  4226 65.50  59.5 32.21933
## 4  4722 81.13  71.5 32.42100
## 5  A188 27.50  62.0 31.41900
## 6 A214N 65.00  69.0 32.00600

As shown above, an R tibble-based data frame is exported with converted data types translated from TASSEL. See the following table what TASSEL data types are tranlated into within the R environment:

TASSEL Data Converted R Data type
taxa character
data numeric
covariate numeric
factor factor

## Filtering genotype data

NOTE: This is just a “snapshot” of how we can filter genotype information in rTASSEL. For more information, please see the additional vignette, “Filtering Genotype Tables”.

Prior to association analyses, filtration of genotype data may be necessary. In TASSEL, this accomplished through the Filter menu using two primary plugins:

• Filter Site Builder plugin
• Filter Taxa Builder plugin

In rTASSEL, this can also be accomplished using the follwing functions:

• rTASSEL::filterGenotypeTableSites()
• rTASSEL::filterGenotypeTableTaxa()

These objects take a TasselGenotypePhenotype class object. For example, in our genotype data set, if we want to remove monomorphic and low coverage sites, we could use the following parameters in rTASSEL::filterGenotypeTableSites():

tasGenoPhenoFilt <- rTASSEL::filterGenotypeTableSites(
tasObj = tasGenoPheno,
siteMinCount = 150,
siteMinAlleleFreq = 0.05,
siteMaxAlleleFreq = 1.0,
siteRangeFilterType = "none"
)
tasGenoPhenoFilt
## A TasselGenotypePhenotype Dataset
##   Class.............. TasselGenotypePhenotype
##   Taxa............... 279
##   Positions.......... 2555
##   Taxa x Positions... 712845
## ---
##   Genotype Table..... [x]
##   Phenotype Table.... [x]
## ---
##   Traits: Taxa EarHT dpoll EarDia

We can then compare this to our original pre-filtered data set:

tasGenoPheno
## A TasselGenotypePhenotype Dataset
##   Class.............. TasselGenotypePhenotype
##   Taxa............... 279
##   Positions.......... 3093
##   Taxa x Positions... 862947
## ---
##   Genotype Table..... [x]
##   Phenotype Table.... [x]
## ---
##   Traits: Taxa EarHT dpoll EarDia

These functions can work on any TasselGenotypePhenotype class object that contains genotypic data, regardless of single or combined TASSEL objects.

## Analysis - Relatedness

### Create a kinship matrix object

In TASSEL, for mixed linear model analyses, a kinship matrix calculated from genotype data is necessary. This can be accomplished by calculating a kinship TASSEL object using the function kinshipMatrix(). The main parameter input is a TasselGenotypePhenotype class object that contains a genotype data set:

tasKin <- rTASSEL::kinshipMatrix(tasObj = tasGenoPheno)

This function allows for several types of algorithm to used using the method parameter. More info about these methods can be found here.

### Calculate a distance matrix

Very similar to kinship matrix calculation, a distance matrix can also be calculated using genotype data using the function distanceMatrix():

tasDist <- rTASSEL::distanceMatrix(tasObj = tasGenoPheno)

### TasselDistanceMatrix objects

#### Overview

The prior two functions will generate a pairwise matrix (e.g. $$m \times m$$ dimensions). The return object is an rTASSEL class, TasselDistanceMatrix. When we inspect the prior object we will see something like this:

tasKin
## A TasselDistanceMatrix Object of 279 x 279 elements:
##
##                   33-16      38-11       4226       4722    ...    YU796NS
##        33-16     1.7970     0.0396     0.0606    -0.0087    ...    -0.0010
##        38-11     0.0396     1.9102     0.0196    -0.0063    ...    -0.0193
##         4226     0.0606     0.0196     1.9265    -0.0170    ...     0.1296
##         4722    -0.0087    -0.0063    -0.0170     1.4533    ...     0.0326
##          ...        ...        ...        ...        ...    ...        ...
##      YU796NS    -0.0010    -0.0193     0.1296     0.0326    ...     1.8523

This will showcase the first four rows and columns and the last row and column if the distance matrix exceeds 5 dimensions (which it probably will).

This object, similar to the TasselGenotypePhenotype class, essentially holds pointers to the Java/TASSEL object in memory. Despite this, we can still use some base R methods similar to how we handle matrix objects:

library(magrittr)

tasKin %>% colnames() %>% head()
## [1] "33-16" "38-11" "4226"  "4722"  "A188"  "A214N"
tasKin %>% rownames() %>% head()
## [1] "33-16" "38-11" "4226"  "4722"  "A188"  "A214N"
tasKin %>% dim()
## [1] 279 279
tasKin %>% nrow()
## [1] 279
tasKin %>% ncol()
## [1] 279

#### Coercion

If we want to use additional R methods (e.g. plotting, new models, etc.), we can coerce this object to a general R data object, in this case, a matrix object using the base method as.matrix():

library(magrittr)

## Inspect first 5 rows and columns ----
tasKinR[1:5, 1:5]
##              33-16        38-11        4226         4722        A188
## 33-16  1.796955100  0.039558496  0.06060008 -0.008674403  0.03524417
## 38-11  0.039558496  1.910150800  0.01960260 -0.006317467 -0.02937613
## 4226   0.060600080  0.019602600  1.92645620 -0.017025312  0.02449092
## 4722  -0.008674403 -0.006317467 -0.01702531  1.453315500 -0.02040120
## A188   0.035244170 -0.029376134  0.02449092 -0.020401197  2.00156020

We can also coerce a pairwise matrix object to a TasselDistanceMatrix object using rTASSEL’s function asTasselDistanceMatrix():

library(magrittr)

## Create a dummy pairwise matrix object ----
set.seed(123)
m <- 10
s <- matrix(rnorm(100), m)
s[lower.tri(s)] <- t(s)[lower.tri(s)]
diag(s) <- 2

colnames(s) <- rownames(s) <- paste0("s_", seq_len(m))

testTasselDist <- s %>% asTasselDistanceMatrix()
testTasselDist
## A TasselDistanceMatrix Object of 10 x 10 elements:
##
##                     s_1        s_2        s_3        s_4    ...       s_10
##          s_1     2.0000     1.2241    -1.0678     0.4265    ...     0.9935
##          s_2     1.2241     2.0000    -0.2180    -0.2951    ...     0.5484
##          s_3    -1.0678    -0.2180     2.0000     0.8951    ...     0.2387
##          s_4     0.4265    -0.2951     0.8951     2.0000    ...    -0.6279
##          ...        ...        ...        ...        ...    ...        ...
##         s_10     0.9935     0.5484     0.2387    -0.6279    ...     2.0000

### PCA and MDS

rTASSEL can run principal component analysis (PCA) and multidimensional scaling (MDS) on objects containing a GenotypeTable and TasselDistanceMatrix respectively. To run PCA, simply use the pca() function on a TasselGenotypePhenotype object that contains a TASSEL GenotypeTable

tasGenoHMP
## A TasselGenotypePhenotype Dataset
##   Class.............. TasselGenotypePhenotype
##   Taxa............... 281
##   Positions.......... 3093
##   Taxa x Positions... 869133
## ---
##   Genotype Table..... [x]
##   Phenotype Table.... [ ]
pcaRes <- pca(tasGenoHMP)

To run MDS, simply use the mds() function on a TasselDistanceMatrix object:

tasDist
## A TasselDistanceMatrix Object of 279 x 279 elements:
##
##                   33-16      38-11       4226       4722    ...    YU796NS
##        33-16     0.0000     0.2799     0.2794     0.2698    ...     0.2908
##        38-11     0.2799     0.0000     0.2920     0.2777    ...     0.3001
##         4226     0.2794     0.2920     0.0000     0.2863    ...     0.2795
##         4722     0.2698     0.2777     0.2863     0.0000    ...     0.2759
##          ...        ...        ...        ...        ...    ...        ...
##      YU796NS     0.2908     0.3001     0.2795     0.2759    ...     0.0000
mdsRes <- mds(tasDist)

Both of these will return a DataFrame object that will contain a Taxa ID column and the number of components or axes that were specified in the function call. For example, let’s take a look at the pcaRes object made previously:

str(pcaRes)
## List of 4
##  $PC_Datum :'data.frame': 281 obs. of 6 variables: ## ..$ Taxa: chr [1:281] "33-16" "38-11" "4226" "4722" ...
##   ..$PC1 : num [1:281] 0.916 -0.813 -0.299 1.321 0.41 ... ## ..$ PC2 : num [1:281] 2.48 2.47 3.16 2.93 2.56 ...
##   ..$PC3 : num [1:281] 0.491 -0.301 1.262 2.052 0.335 ... ## ..$ PC4 : num [1:281] -0.164 2.238 1.229 0.688 0.89 ...
##   ..$PC5 : num [1:281] 0.165 -0.41 -1.203 7.643 1.343 ... ##$ Eigenvalues_Datum :'data.frame':  281 obs. of  4 variables:
##   ..$PC : chr [1:281] "0" "1" "2" "3" ... ## ..$ eigenvalue           : num [1:281] 26.05 14.88 9.66 8.09 7.76 ...
##   ..$proportion_of_total : num [1:281] 0.0596 0.034 0.0221 0.0185 0.0178 ... ## ..$ cumulative_proportion: num [1:281] 0.0596 0.0936 0.1157 0.1342 0.152 ...
##  $Eigenvectors_Datum:'data.frame': 3093 obs. of 6 variables: ## ..$ Trait       : chr [1:3093] "PZB00859.1" "PZA01271.1" "PZA03613.2" "PZA03613.1" ...
##   ..$Eigenvector1: num [1:3093] 0.00176 0.02925 0.00274 0.03528 -0.00961 ... ## ..$ Eigenvector2: num [1:3093] -0.01553 0.02205 -0.03279 0.00612 0.04978 ...
##   ..$Eigenvector3: num [1:3093] 0.000913 0.013536 -0.001632 0.000795 -0.023482 ... ## ..$ Eigenvector4: num [1:3093] -0.019939 -0.006206 -0.011926 0.01891 0.000812 ...
##   ..$Eigenvector5: num [1:3093] -0.0401 -0.0387 0.014 -0.0407 0.031 ... ##$ jPcaObj           :Formal class 'jobjRef' [package "rJava"] with 2 slots
##   .. ..@ jobj  :<externalptr>
##   .. ..@ jclass: chr "net/maizegenetics/phenotype/CorePhenotype"

## Analysis - Association

### Overview

One of TASSEL’s most powerful functionalities is its capability of performing a variety of different association modeling techniques. If you have started reading the walkthrough here it is strongly suggested that you read the other components of this walkthrough since the following parameters require what we have previously created!

If you are not familar with these methods, more information about how these operate in base TASSEL can be found at following links:

The rTASSEL::assocModelFitter() function has several primary components:

• tasObj: a TasselGenotypePhenotype class R object
• formula: an R-based linear model formula
• fitMarkers: a boolean parameter to differentiate between BLUE and GLM analyses
• kinship: a TASSEL kinship object
• fastAssociation: a boolean parameter for data sets that have many traits

Probably the most important concept of this function is formula parameter. If you are familar with standard R linear model functions, this concept is fairly similar. In TASSEL, a linear model is composed of the following scheme:

y ~ A

…where y is any TASSEL data type and A is any TASSEL covariate and / or factor types:

<data> ~ <covariate> and/or <factor>

This model can be written out in several ways. With an example phenotype data, we can have the following variables that are represented in TASSEL in the following way:

• Taxon <taxa>
• EarHT <data>
• dpoll <data>
• EarDia <data>
• location <factor>
• Q1 <covariate>
• Q2 <covariate>
• Q3 <covariate>

Using this data, we could write out the following formula in R

list(EarHT, dpoll, EarDia) ~ location + Q1 + Q2 + Q3

In the above example, we use a base list() function to indicate analysis on multiple numeric data types. For covariate and factor information, we use + operator. One problem with this implementation is that it can become cumbersome and prone to error if we want to analyze the entirety of a large data set or all data and/or factor and covariate types.

A work around for this problem is to utilize a special character to indicate all elements within the model (.). By using the . operator we can simplify the above model into the following:

. ~ .

This indicates we want to analyze the whole data set and leave nothing out. If we want to analyze all data types and only a handful of factor and/or covariates, we can use something like this:

. ~ location + Q1 + Q2

Or vice-versa:

list(EarHT, dpoll) ~ .

Take note we can be very specific with what we want to include in our trait model! In the above example we have deliberately left out EarDia from our model.

Additionally, we can also fit marker and kinship data to our model which can change our analytical methods. Since these options in TASSEL are binary, additional parameters are passed for this function. In this case, genotype/marker data is fitted using the fitMarker parameter and kinship is fitted using the kinship parameter.

Fast Association implements methods described by Shabalin (2012). This method provides an ordinary least squares solution for fixed effect models. For this method to proper work it is necessary that your have:

• No missing data in your phenotype data set
• Phenotypes and genotypes have been merged using an intersect join. Since this is currently the only option of join genotype and phenotype data, you do not have to worry about this for now.

NOTE: since we are working with “toy” data, empirical insight will not be elucidated upon in the following steps. This is simply to show the user how properly use these functions and the outputs that they give.

In the following examples, we will run example data and in return, obtain TASSEL association table reports in the form of an R list object containing tibble-based R data frames.

### Calculate BLUEs

To caclulate best linear unbiased estimates (BLUEs), numeric phenotype data can be used along with covariate and factor data only if it is intended to control for field variation. Since genotype data is not needed for this method, we can leave the fitMarkers, kinship, and fastAssociation to NULL or FALSE:

# Read in phenotype data
phenoPathCov <- system.file("extdata", "mdp_phenotype.txt", package = "rTASSEL")

# Calculate BLUEs
tasBLUE <- rTASSEL::assocModelFitter(
tasObj = tasPhenoCov,
formula = . ~ .,                  # <- All data is used!
fitMarkers = FALSE,
kinship = NULL,
fastAssociation = FALSE
)
## Running all traits...
## Association Analysis : BLUEs
# Return BLUE output
str(tasBLUE)
## List of 2
##  $BLUE :'data.frame': 284 obs. of 4 variables: ## ..$ Taxa  : chr [1:284] "33-16" "38-11" "4226" "4722" ...
##   ..$EarDia: num [1:284] 47.2 46.8 56.7 46.1 43.3 ... ## ..$ EarHT : num [1:284] 82.6 107.2 81.9 92.8 44.5 ...
##   ..$dpoll : num [1:284] 51.7 56.6 53.8 62.5 49.9 ... ##$ BLUE_ANOVA:'data.frame':  3 obs. of  9 variables:
##   ..$Trait : chr [1:3] "EarDia" "EarHT" "dpoll" ## ..$ F      : num [1:3] 6.93 166.54 12.06
##   ..$p : num [1:3] 1.67e-46 2.30e-225 1.01e-76 ## ..$ taxaDF : num [1:3] 281 281 283
##   ..$taxaMS : num [1:3] 28.5 695.8 49.4 ## ..$ errorDF: num [1:3] 241 274 269
##   ..$errorMS: num [1:3] 4.11 4.18 4.09 ## ..$ modelDF: num [1:3] 284 284 286
##   ..$modelMS: num [1:3] 29.1 815.1 68.9 ### Calculate GLM Similar to BLUEs, we can fit a generalized linear model (GLM) by simply fitting marker data to our model. For this, we need a genotype data set combined with our phenotype data in a TasselGenotypePhenotype class object: # Calculate GLM tasGLM <- rTASSEL::assocModelFitter( tasObj = tasGenoPheno, # <- our prior TASSEL object formula = list(EarHT, dpoll) ~ ., # <- only EarHT and dpoll are ran fitMarkers = TRUE, # <- set this to TRUE for GLM kinship = NULL, fastAssociation = FALSE ) ## Running all non <data> traits and/or <taxa>... ## Association Analysis : GLM # Return GLM output str(tasGLM) ## List of 2 ##$ GLM_Stats    :'data.frame':   473 obs. of  18 variables:
##   ..$Trait : chr [1:473] "EarHT" "EarHT" "EarHT" "EarHT" ... ## ..$ Marker    : chr [1:473] "PZA00447.8" "PZB00718.1" "PZD00098.1" "PZA02921.4" ...
##   ..$Chr : chr [1:473] "1" "1" "1" "1" ... ## ..$ Pos       : int [1:473] 9024005 17601375 23267898 25035053 32583282 41505016 47280990 50197162 76054042 90772226 ...
##   ..$marker_F : num [1:473] 13.62 7.78 13.28 13.19 12.81 ... ## ..$ p         : num [1:473] 0.000275 0.000524 0.000323 0.000341 0.000412 ...
##   ..$marker_Rsq: num [1:473] 0.0517 0.0573 0.0488 0.0499 0.0478 ... ## ..$ add_F     : num [1:473] 13.6 15.1 13.3 13.2 12.8 ...
##   ..$add_p : num [1:473] 0.000275 0.000129 0.000323 0.000341 0.000412 ... ## ..$ dom_F     : num [1:473] NaN 0.48 NaN NaN NaN ...
##   ..$dom_p : num [1:473] NaN 0.489 NaN NaN NaN ... ## ..$ marker_df : num [1:473] 1 2 1 1 1 2 2 1 1 2 ...
##   ..$marker_MS : num [1:473] 5363 3030 5217 5170 5093 ... ## ..$ error_df  : num [1:473] 250 256 259 251 255 262 250 270 268 273 ...
##   ..$error_MS : num [1:473] 394 389 393 392 398 ... ## ..$ model_df  : num [1:473] 1 2 1 1 1 2 2 1 1 2 ...
##   ..$model_MS : num [1:473] 5363 3030 5217 5170 5093 ... ## ..$ minorObs  : int [1:473] 124 105 108 101 16 55 63 99 104 127 ...
##  $GLM_Genotypes:'data.frame': 1144 obs. of 7 variables: ## ..$ Trait   : chr [1:1144] "EarHT" "EarHT" "EarHT" "EarHT" ...
##   ..$Marker : chr [1:1144] "PZA00447.8" "PZA00447.8" "PZB00718.1" "PZB00718.1" ... ## ..$ Chr     : chr [1:1144] "1" "1" "1" "1" ...
##   ..$Pos : int [1:1144] 9024005 9024005 17601375 17601375 17601375 23267898 23267898 25035053 25035053 32583282 ... ## ..$ Obs     : int [1:1144] 124 128 105 123 31 153 108 152 101 16 ...
##   ..$Allele : chr [1:1144] "C" "T" "C" "G" ... ## ..$ Estimate: num [1:1144] 9.23 0 -2.45 7.68 0 ...

### Calculate MLM

Adding to our complexity, we can fit a mixed linear model (MLM) by adding kinship to our analysis. In addition to the prior parameters, we will also need a TASSEL kinship object (see Create a kinship matrix object in the Analysis - Relatedness section):

# Calculate MLM
tasMLM <- rTASSEL::assocModelFitter(
tasObj = tasGenoPheno,             # <- our prior TASSEL object
formula = EarHT ~ .,               # <- run only EarHT
fitMarkers = TRUE,                 # <- set this to TRUE for GLM
kinship = tasKin,                  # <- our prior kinship object
fastAssociation = FALSE
)
## Running all non <data> traits and/or <taxa>...
## Association Analysis : MLM
# Return GLM output
str(tasMLM)
## List of 3
##  $MLM_Effects :'data.frame': 7211 obs. of 7 variables: ## ..$ Trait : chr [1:7211] "EarHT" "EarHT" "EarHT" "EarHT" ...
##   ..$Marker: chr [1:7211] "PZB00859.1" "PZB00859.1" "PZB00859.1" "PZA01271.1" ... ## ..$ Locus : chr [1:7211] "1" "1" "1" "1" ...
##   ..$Site : chr [1:7211] "157104" "157104" "157104" "1947984" ... ## ..$ Allele: chr [1:7211] "A" "C" "M" "C" ...
##   ..$Effect: num [1:7211] -7.91 -2.72 0 -4.46 0 ... ## ..$ Obs   : int [1:7211] 63 207 3 131 137 78 196 69 207 2 ...
##  $MLM_Stats :'data.frame': 3094 obs. of 18 variables: ## ..$ Trait         : chr [1:3094] "EarHT" "EarHT" "EarHT" "EarHT" ...
##   ..$Marker : chr [1:3094] "None" "PZB00859.1" "PZA01271.1" "PZA03613.2" ... ## ..$ Chr           : chr [1:3094] "" "1" "1" "1" ...
##   ..$Pos : chr [1:3094] "" "157104" "1947984" "2914066" ... ## ..$ df            : int [1:3094] 0 2 1 1 2 1 1 2 2 1 ...
##   ..$F : num [1:3094] NaN 1.6664 2.9836 0.4799 0.0377 ... ## ..$ p             : num [1:3094] NaN 0.1909 0.0853 0.4891 0.963 ...
##   ..$add_effect : num [1:3094] NaN -2.596 NaN NaN 0.126 ... ## ..$ add_F         : num [1:3094] NaN 3.216 NaN NaN 0.00676 ...
##   ..$add_p : num [1:3094] NaN 0.074 NaN NaN 0.935 ... ## ..$ dom_effect    : num [1:3094] NaN 5.32 NaN NaN -2.67 ...
##   ..$dom_F : num [1:3094] NaN 0.242 NaN NaN 0.07 ... ## ..$ dom_p         : num [1:3094] NaN 0.623 NaN NaN 0.792 ...
##   ..$errordf : int [1:3094] 279 273 268 274 278 261 259 257 264 255 ... ## ..$ MarkerR2      : num [1:3094] NaN 0.012231 0.01107 0.001743 0.000271 ...
##   ..$Genetic_Var : num [1:3094] 145 145 145 145 145 ... ## ..$ Residual_Var  : num [1:3094] 105 105 105 105 105 ...
##   ..$-2LnLikelihood: num [1:3094] 2387 2387 2387 2387 2387 ... ##$ MLM_Residuals_EarHT:'data.frame': 279 obs. of  2 variables:
##   ..$Taxa : chr [1:279] "33-16" "38-11" "4226" "4722" ... ## ..$ EarHT: num [1:279] 0.836 7.339 4.049 5.796 -5.334 ...

### Calculate Fast Association

Finally, we can run fast association analysis in our GLM model by setting the fastAssociation parameter to TRUE. NOTE: this is only really effective if you have many phenotype traits:

# Read data - need only non missing data!
phenoPathFast <-system.file(
"extdata",
"mdp_traits_nomissing.txt",
package = "rTASSEL"
)

# Creat rTASSEL object - use prior TASSEL genotype object
genoPathOrObj = tasGenoHMP,
phenoPathDFOrObj = phenoPathFast
)

# Calculate MLM
tasFAST <- rTASSEL::assocModelFitter(
tasObj = tasGenoPhenoFast,         # <- our prior TASSEL object
formula = . ~ .,                   # <- run all of the phenotype data
fitMarkers = TRUE,                 # <- set this to TRUE for GLM
kinship = NULL,
fastAssociation = TRUE             # <- set this to TRUE for fast assoc.
)
## Running all traits...
## Association Analysis : Fast Association
# Return GLM output
str(tasFAST)
## List of 1
##  $FastAssociation:'data.frame': 640 obs. of 7 variables: ## ..$ Trait : chr [1:640] "dpoll" "EarDia" "EarDia" "EarDia" ...
##   ..$Marker: chr [1:640] "PZA00258.3" "PZA02869.8" "PZA02869.4" "PZA02869.2" ... ## ..$ Chr   : chr [1:640] "1" "1" "1" "1" ...
##   ..$Pos : int [1:640] 2973508 4429897 4429927 4430055 5562502 8366411 8367944 9023947 9024005 9024005 ... ## ..$ df    : int [1:640] 1 1 1 1 1 1 1 1 1 1 ...
##   ..$r2 : num [1:640] 0.0435 0.0458 0.0606 0.0504 0.0473 ... ## ..$ p     : num [1:640] 4.66e-04 3.25e-04 3.31e-05 1.60e-04 2.60e-04 ...

## Analysis - Phylogeny

rTASSEL allows for interfacing with TASSEL’s tree generation methods from genotype information. This can be performed using the createTree() method with a TasselGenotypePhenotype object containing genotype table information:

phyloTree <- createTree(
tasObj = tasGenoHMP,
clustMethod = "Neighbor_Joining"
)

The above function allows for two clustering methods:

• Neighbor_Joining - Neighbor Joining method. More info can be found here
• UPGMA - Unweighted Pair Group Method with Arithmetic Mean. More info can be found here.

Upon creation, the phyloTree object is returned as a phylo object generated by the ape package:

phyloTree
##
## Phylogenetic tree with 281 tips and 279 internal nodes.
##
## Tip labels:
##   33-16, CI7, L317, MOG, 38-11, PA91, ...
##
## Unrooted; includes branch lengths.

This object can then be used by common base-R methods (e.g. plot()) or other visualization libraries such as ggtree.

## Visualizations

### Manhattan plots

rTASSEL supports automated visualizations for Manhattan plots and linkage disequilibrium (LD) analyses. In this example, we will generate a Manhattan plot for our prior MLM object:

# Generate Manhattan plot for ear height trait
manhattanEH <- manhattanPlot(
assocStats = tasMLM\$MLM_Stats,
trait      = "EarHT",
threshold  = 5
)

manhattanEH

### LD plots

Similarly, we can also visualize LD using automated methods. Like most LD plots, it is wise to filter your genotype information to a specific region of interest:

# Filter genotype table by position
tasGenoPhenoFilt <- filterGenotypeTableSites(
tasObj              = tasGenoPheno,
siteRangeFilterType = "position",
startPos            = 228e6,
endPos              = 300e6,
startChr            = 2,
endChr              = 2
)

# Generate and visualize LD
myLD <- ldPlot(
tasObj  = tasGenoPhenoFilt,
ldType  = "All",
plotVal = "r2",
verbose = FALSE
)

myLD

## Interactive Visualizations

### Overview

Since rTASSEL can essentially interact with all of TASSEL’s API, interactive “legacy” Java-based visualizers can be accessed. Currently, rTASSEL has capabilities to use the LD viewer and Archaeopteryx.

### LD Viewer

TASSEL’s linkage disequilibrium (LD) viewer can be used via the ldJavaApp() function. This method will take a TasselGenotypePhenotype object containing genotype information. A common parameter to set is the window size (windowSize) since creating a full genotype matrix is rather impractical at this point in time for most modern machines and experimental design. This will create comparisons only within a given range of indexes:

library(magrittr)

tasGenoHMP %>% ldJavaApp(windowSize = 100)

### Tree Viewer - Archaeopteryx

Since TASSEL allows for phylogenetic tree creation, one common Java-based visualizer to use is the Archaeopteryx tree viewer which is implemented in the source code. To view this, we can use the treeJavaApp() function. In the following example, we will first filter 6 taxa and then pass the filtered genotype object to the Java visualizer:

library(magrittr)

tasGenoHMP %>%
filterGenotypeTableTaxa(
taxa = c("33-16", "38-11", "4226", "4722", "A188", "A214N")
) %>%
treeJavaApp()

## Genomic Prediction

### Overview

rTASSEL also allows for phenotypic prediction through genotype information via genomic best linear unbiased predictors (gBLUPs). It proceeds by fitting a mixed model that uses kinship to capture covariance between taxa. The mixed model can calculate BLUPs for taxa that do not have phenotypes based on the phenotypes of lines with relationship information.

A phenotype dataset and a kinship matrix must be supplied as input to the method by selecting both then choosing Analysis/Genomic Selection. In addition to trait values, the phenotype dataset may also contain factors or covariates which will be used as fixed effects in the model. All taxa in the phenotype dataset can only appear once. No repeated values are allowed for a single taxon. When the analysis is run, the user is presented with the choice to run k-fold cross-validation. If cross- validation is selected, then the number of folds and the number of iterations can be entered. For each iteration and each fold within an iteration, the correlation between the observed and predicted values will be reported. If cross-validation is not selected, then the original observations, predicted values and PEVs (prediction error variance) will be reported for all taxa in the dataset.

When k-fold cross-validation is performed, only taxa with phenotypes and rows in the kinship matrix are used. That set of taxa are divided into k subsets of equal size. Each subset in turn is used as the validation set. Phenotypes of the individuals in the validation are set to 0 then predicted using the remaining individuals as the training set. The correlation (r) of the observed values and predicted values is calculated for the validation set and reported. The mean and standard deviation of the mean of the r’s are calculated for each trait and reported in the comments section of the “Accuracy” data set that is output by the analysis. In general, the results are not very sensitive to the choice of k. The number of iterations affects the standard error of the mean for the accuracy estimates. The defaults of k = 5 and iterations = 20 will be adequate for most users.

tasCV <- genomicPrediction(
tasPhenoObj = tasGenoPheno,
doCV        = TRUE,
kFolds      = 5,
nIter       = 1
)
head(tasCV)
## DataFrame with 6 rows and 4 columns
##         Trait Iteration      Fold  Accuracy
##   <character> <numeric> <numeric> <numeric>
## 1       EarHT         0         0  0.501444
## 2       EarHT         0         1  0.376098
## 3       EarHT         0         2  0.506010
## 4       EarHT         0         3  0.594796
## 5       EarHT         0         4  0.528185
## 6       dpoll         0         0  0.785085