panGenomeBreedr

panGenomeBreedr (panGB) is conceptualized to be a unified, crop agnostic platform for pangenome-enabled breeding that follows standardized conventions for natural or casual variant analysis using pangenomes, marker design, and marker QC hypothesis testing (Figure 1). It seeks to simplify and enhance the use of pangenome resources in cultivar development.

Fig. 1. Imagined workflow for the panGenomeBreedr package for pangenome-enabled breeding. To develop trait-predictive, functional markers, the program takes manual inputs of candidate gene(s) and available pangenome resources for any crop. The program utilizes the input information to perform homology searches to identify orthologs/paralogs. The program, then, characterizes mutations within the input candidate gene to identify high-impact or putative causal variants (PCV). Identifying PCVs allows the program to design functional trait-predictive markers. The program implements the validation of designed markers in a hypothesis-driven manner. The program can equally design other types of markers such as precision-introgression markers and background markers.

In its current development version, panGB provides customizable functions for KASP marker design and validation (Steps 2 and 3 in Figure 1).

panGB will host a user-friendly shiny application to enable non-R users to access its functionalities outside R.

LGC Genomics’ current visualization tool is platform-specific — the SNP Viewer program runs only on Windows, thus preventing Mac and other non-Windows platform customers from utilizing it. The SNP Viewer program does not incorporate standardized conventions for visualizing the prediction of positive controls to fully validate a marker. This makes it difficult for users to validate markers conclusively using the existing tool. panGB provides platform-independent functionalities to users to perform hypothesis testing on KASP marker QC and validation.

Submit bug reports and feature suggestions, or track changes on the issues page.

# Install and load required Bioconductor packages
if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager")

  BiocManager::install(c("VariantAnnotation",
                         "Biostrings",
                         "GenomicRanges",
                         "IRanges",
                         "msa"))

Usage

Currently, panGB has functionality for KASP marker design based on causal variants and QC visualizations for marker validation.

Examples

Here, we provide examples on how to use panGB to design a KASP marker based on a causal variant, as well as marker validation for any KASP marker.

KASP Marker Design

The kasp_marker_design() function provides a simplified approach to designing a KASP marker based on identified causal variants.

The user needs two important input data to run the kasp_marker_design(): the whole genome or specific chromosome sequence of the focused crop and a vcf file containing variant calls from putative causal variant analytical pipeline.

The vcf file must contain the Chromosome ID, Position, locus ID, REF and ALT alleles, as well as the genotype data for samples, as shown below in Table 1:

Table 1: An example vcf file for marker design.

CHROM	POS	ID	REF	ALT	IDMM	ISGC	ISGK	ISHC	ISHJ
Chr02	69197088	SNP_Chr02_69197088	G	A	0\\|0	0\\|0	0\\|0	0\\|0	0\\|0
Chr02	69197120	SNP_Chr02_69197120	G	C	0\\|0	0\\|0	0\\|0	0\\|0	0\\|0
Chr02	69197131	SNP_Chr02_69197131	G	T	0\\|0	0\\|0	0\\|0	0\\|0	0\\|0
Chr02	69197209	SNP_Chr02_69197209	G	T	0\\|0	0\\|0	0\\|0	0\\|0	0\\|0
Chr02	69197294	SNP_Chr02_69197294	G	A	0\\|0	0\\|0	0\\|0	0\\|0	0\\|0

# Example to design a KASP marker on a substitution variant
# Set path to alignment output folder 
library(panGenomeBreedr)
path <- tempdir() # (default directory for saving alignment outputs)

# Path to import sorghum genome sequence for Chromosome 2
path1 <- "https://raw.githubusercontent.com/awkena/panGB/main/Chr02.fa.gz"

# Path to import vcf file for variant calls on Chromosome 2
path2 <-  system.file("extdata", "Sobic.002G302700_SNP_snpeff.vcf",
                      package = "panGenomeBreedr",
                     mustWork = TRUE)

# KASP marker design for variant ID: SNP_Chr02_69200443 in vcf file
ma1 <- kasp_marker_design(vcf_file = path2,
                           genome_file = path1,
                           marker_ID = "SNP_Chr02_69200443",
                           chr = "Chr02",
                           plot_draw = TRUE,
                           plot_file = path,
                           vcf_geno_code = c('1|1', '0|1', '0|0', '.|.'),
                           region_name = "ma1",
                           maf = 0.05)
#> using Gonnet

# View marker alignment output from temp folder
path3 <- file.path(path, list.files(path = path, "alignment_"))
system(paste0('open "', path3, '"')) # Open PDF file from R

on.exit(unlink(path)) # Clear the temp directory on exit

In the kasp_marker_design() function call above, the user must specify the path to the genome sequence and vcf files using the genome_file and vcf_file arguments, respectively. The user must specify the ID for the variant in the vcf file using the marker_ID argument.

To save memory and enhance the computational speed, the chr argument can be specified to access only the chromosome sequence of the chosen variant from the genome sequence.

The vcf_geno_code argument is used to specify the genotype coding in the vcf file – either phased (1|1) or unphased (1/1) coding.

The plot_draw = TRUE argument indicates the return of the alignment of the 100 bp upstream and downstream sequences to the imported reference genome as PDF file (Figure 2).

The plot_file argument specifies the path to the directory where the alignment should be saved – default is a temporary directory.


Fig. 2. Alignment of the 100 bp upstream and downstream sequences to the reference genome used for KASP marker design.

The required sequence for submission to Intertek for the designed KASp marker is shown in Table 2.

Table 2: Intertek required sequence for a KASP marker.

SNP_Name	SNP	Marker_Name	Chromosome	Chromosome_Position	Sequence	ReferenceAllele	AlternativeAllele
SNP_Chr02_69200443	Substitution	ma1	Chr02	69200443	TAGTTTGATGTTTGCCTTACAATTTGATTTGATGGCAATACCTTTTCCATTTTATCAGCATCTACACCATTTTATATCTTTGGATTAGATTTTTTTTWAA\[A/T\]AAAAAAGTAATATGTTTGTTATGTGCTTTACTCAACAAGATCTACATTTTAAATTAGCTACTTTTTACCATCTTATTTGTTTGTTGTGTGTTTTATTCAA	A	T

KASP Marker Validation

The following example demonstrates how to use the customizable functions in panGB to perform hypothesis testing of allelic discrimination for KASP marker QC and validation.

panGB offers customizable functions for KASP marker validation through hypothesis testing. These functions allow users to easily perform the following tasks:

Import raw or polished KASP genotyping results files (.csv) into R.
Process imported data and assign FAM and HEX fluorescence colors for multiple plates.
Visualize marker QC using FAM and HEX fluorescence scores for each sample.
Validate the effectiveness of trait-predictive or background markers using positive controls.
Visualize plate design and randomization.

Reading Raw KASP Full Results Files (.csv)

The read_kasp_csv() function allows users to import raw or polished KASP genotyping full results file (.csv) into R. The function requires the path of the raw file and the row tags for the different components of data in the raw file as arguments.

For polished files, the user must extract the Data component of the full results file and save it as a csv file before import.

By default, a typical unedited raw KASP data file uses the following row tags for genotyping data: Statistics, DNA, SNPs, Scaling, Data.

The raw file is imported as a list object in R. Thus, all components in the imported data can be extracted using the row tag ID as shown in the code snippet below:

# Import raw KASP genotyping file (.csv) using the read_kasp_csv() function
library(panGenomeBreedr)

# Set path to the directory where your data is located
# path1 <-  "inst/extdata/Genotyping_141.010_01.csv"
path1 <-  system.file("extdata", "Genotyping_141.010_01.csv",
                       package = "panGenomeBreedr",
                      mustWork = TRUE)

# Import raw data file
file1 <- read_kasp_csv(file = path1, 
                       row_tags = c("Statistics", "DNA", "SNPs", "Scaling", "Data"),
                       data_type = 'raw')

# Get KASP genotyping data for plotting
kasp_dat <- file1$Data

Assigning colors and PCH symbols for KASP cluster plotting

The next step after importing data is to assign FAM and HEX fluorescence colors to samples based on their observed genotype calls. This step is accomplished using the kasp_color() function in panGB as shown in the code snippet below:

# Assign KASP fluorescence colors using the kasp_color() function
library(panGenomeBreedr)
# Create a subet variable called plates: masterplate x snpid
  kasp_dat$plates <- paste0(kasp_dat$MasterPlate, '_',
                                 kasp_dat$SNPID)
dat1 <- kasp_color(x = kasp_dat,
                    subset = 'plates',
                    sep = ':',
                    geno_call = 'Call',
                    uncallable = 'Uncallable',
                    unused = '?',
                    blank = 'NTC',
                   assign_cols = c(FAM = "blue", HEX = "gold" , 
                                   het = "forestgreen"))

The kasp_color() function requires the KASP genotype call file as a data frame and can do bulk processing if there are multiple master plates. The default values for the arguments in the kasp_color() function are based on KASP annotations.

The kasp_color() function calls the kasp_pch() function to automatically add PCH plotting symbols that can equally be used to group genotypic clusters on the plot.

When expected genotype calls are available for positive controls in KASP genotyping samples, we recommend the use of the PCH symbols for grouping observed genotypes instead of FAM and HEX colors.

The kasp_color() function expects that genotype calls are for diploid state with alleles separated by a symbol. By default KASP data are separated by : symbols.

The kasp_color() function returns a list object with the processed data for each master plate as the components.

Cluster plot

To test the hypothesis that the designed KASP marker can accurately discriminate between homozygotes and heterozygotes (allelic discrimination), a cluster plot needs to be generated.

The kasp_qc_ggplot() and kasp_qc_ggplot2()functions in panGB can be used to make the cluster plots for each plate and KASP marker as shown below:

# KASP QC plot for Plate 05
library(panGenomeBreedr)
kasp_qc_ggplot2(x = dat1[5],
                    pdf = FALSE,
                    Group_id = NULL,
                    scale = TRUE,
                    expand_axis = 0.6,
                    alpha = 0.9,
                    legend.pos.x = 0.6,
                    legend.pos.y = 0.75)
#> $`SE-24-1088_P01_d1_snpSB00804`

Fig. 3. Cluster plot for Plate 5 using FAM and HEX colors for grouping observed genotypes.

# KASP QC plot for Plate 05
library(panGenomeBreedr)
 kasp_qc_ggplot2(x = dat1[5],
                  pdf = FALSE,
                  Group_id = 'Group',
                  Group_unknown = '?',
                  scale = TRUE,
                  pred_cols = c('Blank' = 'black', 'False' = 'firebrick3',
                              'True' = 'cornflowerblue', 'Unverified' = 'beige'),
                  expand_axis = 0.6,
                  alpha = 0.9,
                  legend.pos.x = 0.6,
                  legend.pos.y = 0.75)
#> $`SE-24-1088_P01_d1_snpSB00804`

Fig. 4. Cluster plot for Plate 5 with an overlay of predictions for positive controls.

Color-blind-friendly color combinations are used to visualize verified genotype predictions (Figure 3).

In Figure 4, the three genotype classes are grouped based on plot PCH symbols using the FAM and HEX scores for observed genotype calls.

To simplify the verified prediction overlay for the expected genotypes for positive controls, all possible outcomes are divided into three categories (TRUE, FALSE, and UNVERIFIED) and color-coded to make it easier to visualize verified predictions.

BLUE (color code for the TRUE category) means genotype prediction matches the observed genotype call for the sample.

RED (color code for the FALSE category) means genotype prediction does not match the observed genotype call for the sample.

BEIGE (color code for the UNVERIFIED category) means three things: an expected genotype call could not be made before KASP genotyping, or an observed genotype call could not be made to verify the prediction.

Users can set the pdf = TRUE argument to save plots as a PDF file in a directory outside R. The kasp_qc_ggplot() and kasp_qc_ggplot2()functions can generate cluster plots for multiple plates simultaneously.

To visualize predictions for positive controls to validate KASP markers, the column name containing expected genotype calls must be provided and passed to the function using the Group_id = 'Group' argument as shown in the code snippets above. If this information is not available, set the argument Group_id = NULL.

Summary of Prediction Verification in Plates

The pred_summary() function produces a summary of predicted genotypes for positive controls in each reaction plate after verification (Table 3), as shown in the code snippet below:

# Get prediction summary for all plates
library(panGenomeBreedr)
my_sum <- pred_summary(x = dat1,
                       snp_id = 'SNPID',
                       Group_id = 'Group',
                       Group_unknown = '?',
                       geno_call = 'Call',
                       rate_out = TRUE)

Table 3: Summary of verified prediction status for samples in plates

plate	snp_id	false	true	unverified
SE-24-1088_P01_d1_snpSB00800	snpSB00800	0.04	0.06	0.90
SE-24-1088_P01_d2_snpSB00800	snpSB00800	0.02	0.06	0.92
SE-24-1088_P01_d1_snpSB00803	snpSB00803	0.00	0.34	0.66
SE-24-1088_P01_d2_snpSB00803	snpSB00803	0.00	0.34	0.66
SE-24-1088_P01_d1_snpSB00804	snpSB00804	0.01	0.33	0.66
SE-24-1088_P01_d2_snpSB00804	snpSB00804	0.01	0.33	0.66
SE-24-1088_P01_d1_snpSB00805	snpSB00805	0.15	0.19	0.66
SE-24-1088_P01_d2_snpSB00805	snpSB00805	0.15	0.19	0.66

The output of the pred_summary() function can be visualized as bar plots using the pred_summary_plot() function as shown in the code snippet below:

# Get prediction summary for snp:snpSB00804
library(panGenomeBreedr)
my_sum <- my_sum$summ
my_sum <- my_sum[my_sum$snp_id == 'snpSB00804',]

 pred_summary_plot(x = my_sum,
                    pdf = FALSE,
                    pred_cols = c('false' = 'firebrick3', 'true' = 'cornflowerblue',
                                  'unverified' = 'beige'),
                    alpha = 1,
                    text_size = 12,
                    width = 6,
                    height = 6,
                    angle = 45)
#> $snpSB00804

Fig. 5. Match/Mismatch rate of predictions for snp: snpSB00804.

Plot Plate Design

Users can visualize the observed genotype calls in a plate design format using the plot_plate() function as depicted in Figure 5, using the code snippet below:

plot_plate(dat1[5], pdf = FALSE)
#> $`SE-24-1088_P01_d1_snpSB00804`

Fig. 6. Observed genotype calls for samples in Plate 5 in a plate design format.

Other Breeder-Centered Functionalities in panGB

panGB provides additional functionalities to test hypotheses on the success of trait introgression pipelines and crosses.

Users can easily generate heatmaps that compare the genetic background of parents to progenies to ascertain if a target locus was successfully introgressed or check for the hybridity of F1s. These plots also allow users to get a visual insight into the amount of parent germplasm recovered in progenies.

To produce these plots, users must have either polymorphic low or mid-density marker data and a map file for the markers. The map file must contain the marker IDs, their chromosome numbers and positions.

panGBcan handle data from KASP, Agriplex and DArTag service providers.

Working with Agriplex Mid-Density Marker Data

Agriplex data is structurally different from KASP or DArTag data in terms of genotype call coding and formatting. Agriplex uses ' / ' as a separator for genotype calls for heterozygotes, and uses single nucleotides to represent homozygous SNP calls.

Creating Heatmaps with panGB

To exemplify the steps for creating heatmap, we will use a mid-density marker data for three groups of near-isogenic lines (NILs) and their parents (Table 4). The NILs and their parents were genotyped using the Agriplex platform. Each NIL group was genotyped using 2421 markers.

The imported data frame has the markers as columns and genotyped samples as rows. It comes with some meta data about the samples. Marker names are informative: chromosome number and position coordinates are embedded in the marker names (Eg. S1_778962: chr = 1, pos = 779862).

# Set path to the directory where your data is located
path1 <-  system.file("extdata", "agriplex_dat.csv",
                       package = "panGenomeBreedr",
                      mustWork = TRUE)

# Import raw Agriplex data file
geno <- read.csv(file = path1, header = TRUE, colClasses = c("character")) # genotype calls

library(knitr)
knitr::kable(geno[1:6, 1:10], caption = 'Table 4: Agriplex data format', format = 'html', booktabs = TRUE)

Table 4: Agriplex data format

Plate.name	Well	Sample_ID	Batch	Genotype	Status	S1_778962	S1_1019896	S1_1613105	S1_1954298
RHODES_PLATE1	D04	NIL_1	1	RTx430a	Recurrent parent	A	G	G	A
RHODES_PLATE1	F04	NIL_2	1	RTx430b	Recurrent parent	A	G	G	A
RHODES_PLATE1	G04	NIL_3	1	IRAT204a	Donor parent	G	C	G	A
RHODES_PLATE1	A05	NIL_4	1	IRAT204b	Donor Parent	G	C	G	A
RHODES_PLATE1	D07	NIL_5	1	RMES1+\\|+\_1	NIL+	A	G	G	A
RHODES_PLATE1	F08	NIL_6	1	RMES1+\\|+\_2	NIL+	A	G	G	A

To create a heatmap that compares the genetic background of parents and NILs across all markers, we need to first process the raw Agriplex data into a numeric format. The panGB package has customizable data wrangling functions for KASP, Agriplex, and DArTag data.

The rm_mono() function can be used to filter out all monomorphic loci from the data.

Since our imported Agriplex data has informative SNP IDs, we can use the parse_marker_ns() function to generate a map file (Table 5) for the markers.
The generated map file is then passed to the proc_kasp() function to order the SNP markers according to their chromosome numbers and positions.

The kasp_numeric() function converts the output of the proc_kasp() function into a numeric format (Table 6). The re-coding to numeric format is done as follows:

Homozygous for Parent 1 allele = 1.
Homozygous for Parent 2 allele = 0.
Heterozygous = 0.5.
Monomorphic loci = -1.
Loci with a suspected genotype error = -2.
Loci with at least one missing parental or any other genotype = -5.

# Parse snp ids to generate a map file
library(panGenomeBreedr)

# Data for stg5 NILs
stg5 <- geno[geno$Batch == 3, -c(1:6)] 
rownames(stg5) <- geno$Genotype[17:25]

# Remove monomorphic loci from data
stg5 <- rm_mono(stg5)

# Parse snp ids to generate a map file
snps <- colnames(stg5) # Get snp ids
map_file <- parse_marker_ns(x = snps, sep = '_', prefix = 'S')

Table 5: Map file for the imported Agriplex data.

snpid	chr	pos
S1_778962	1	778962
S1_1613105	1	1613105
S1_1954298	1	1954298
S1_1985365	1	1985365
S1_3751888	1	3751888
S1_13156348	1	13156348
S1_15905614	1	15905614
S1_18104582	1	18104582

# Process genotype data to re-order SNPs based on chromosome and positions
stg5 <- proc_kasp(x = stg5,
                  kasp_map = map_file,
                  map_snp_id = "snpid",
                  sample_id = "Genotype",
                  marker_start = 1,
                  chr = 'chr',
                  chr_pos = 'pos')

map_file_ord <- stg5$ordered_map # Ordered map
stg5_ord <- stg5$ordered_geno # ordered geno

# Convert to numeric format for plotting
num_geno <- kasp_numeric(x = stg5_ord,
                         rp_row = 1,
                         dp_row = 3,
                         sep = ' / ',
                         data_type = 'agriplex')

Table 6: Agriplex data converted to a numeric format.

	S1_402592	S1_778962	S1_825853	S1_1218846	S1_1613105	S1_1727150	S1_1954298	S1_1985365
BTx623a	1	1	1	1	1	1	1	1
BTx623b	1	1	1	1	1	1	1	1
BTx642a	0	0	0	0	0	0	0	0
BTx642b	0	0	0	0	0	0	0	0
Stg5+\\|+\_1	1	0	0	0	0	0	0	0
Stg5+\\|+\_2	0	0	0	0	0	1	1	1
Stg5-\\|-\_1	1	1	1	1	1	1	1	1
Stg5-\\|-\_2	1	1	1	1	1	1	1	1
Stg5-\\|-\_3	1	1	1	1	1	1	1	1

All is now set to generate the heatmap (Figure 6) using the cross_qc_ggplot() function, as shown in the code snippet below:

# Get prediction summary for snp:snpSB00804
library(panGenomeBreedr)
# Create a heatmap that compares the parents to progenies
cross_qc_ggplot(x = num_geno,
                map_file = map_file_ord,
                snp_ids = 'snpid',
                chr = 'chr',
                chr_pos = 'pos',
                parents = c("BTx623", "BTx642"),
                group_sz = 5L,
                pdf = FALSE,
                filename = 'background_heatmap',
                legend_title = 'stg5_NILs',
                alpha = 0.9,
                text_size = 15)
#> $Batch1

Fig. 6. A heatmap that compares the genetic background of parents and stg5 NIL progenies across all markers.

The cross_qc_ggplot() function is a wrapper for functions in the ggplot2 package.

Users must specify the IDs for the two parents using the parents argument. In the code snippet above, the recurrent parent is BTx623 and the donor parent for the stg5 locus is BTx642.

The group_sz argument must be specified to plot the heatmap in batches of progenies to avoid cluttering the plot with many observations.

Users can set the pdf = TRUE argument to save plots as a PDF file in a directory outside R.

Trait Introgression Hypothesis Testing

To test the hypothesis that the stg5 NIL development was effective, we can use the cross_qc_annotate() function to generate a heatmap (Figure 7) with an annotation of the position of the stg5 locus on Chr 1, as shown below:

###########################################################################
# Subset data for the first 30 markers on Chr 1
stg5_ch1 <- num_geno[, map_file_ord$chr == 1][,1:30] 

# Get the map file for subset data
stg5_ch1_map <- map_file_ord[map_file_ord$chr == 1,][1:30,]

# Annotate a heatmap to show the stg5 locus on Chr 1
# The locus is between positions 0.98 - 1.8 Mbp on Chr 1
cross_qc_annotate(x = stg5_ch1,
                  map_file = stg5_ch1_map,
                  snp_ids = 'snpid',
                  chr = 'chr',
                  chr_pos = 'pos',
                  parents = c("BTx623", "BTx642"),
                  trait_pos = list(stg5 = c(start = .98e6, end = 1.8e6)),
                  text_scale_fct = 0.3,
                  group_sz = 5L,
                  pdf = FALSE,
                  legend_title = 'Stg5_NILs',
                  alpha = 0.9,
                  text_size = 15)
#> $Batch1

Fig. 7. Heatmap annotation of the stg5 locus on Chr 1.

In the code snippet above, the numeric matrix of genotype calls and its associated map file are required.

The recurrent and donor parents must be specified using the parents argument.

The snp_ids, chr, and chr_pos arguments can be used to specify the column names for marker IDs, chromosome number and positions in the attached map file.
The trait_pos argument was used to specify the position of the target locus (stg5) on chromosome one. Users can specify the positions of multiple target loci as components of a list object for annotation.

In Figure 7, the color intensity correlates positively with the marker density or coverage. Thus, areas with no color (white vertical gaps) depicts gaps in the marker coverage in the data.

Decision Support for Marker-Assisted Backcrossing in panGB

Users can use the calc_rpp_bc() function in panGB to calculate the proportion of recurrent parent background (RPP) fully recovered in backcross progenies.

In the computation, partially regions are ignored, hence, heterozygous scores are not used.

The output for he calc_rpp_bc() function can be passed to the rpp_barplot() function to visualize the computed RPP values for progenies as a bar plot. Users can specify an RPP threshold to easily identify lines that have RPP values above or equal to the defined RPP threshold on the bar plot.

We can compute and visualize the observed RPP values for the stg5 NILs across all polymorphic loci as shown in the code snippet below:

# Calculate weighted RPP
rpp <- calc_rpp_bc(x = num_geno,
                   map_file = map_file_ord,
                   map_chr = 'chr',
                   map_pos = 'pos',
                   map_snp_ids = 'snpid',
                   rp = 1,
                   rp_num_code = 1,
                   na_code = -5,
                   weighted = TRUE)

# Generate bar plot for RPP values
rpp_barplot(rpp_df = rpp,
            rpp_threshold = 0.93,
            text_size = 18,
            text_scale_fct = 0.1,
            alpha = 0.9,
            bar_width = 0.5,
            aspect_ratio = 0.5,
            pdf = FALSE)

Fig. 8. Computed RPP values for the stg5 NILs.

The calc_rpp_bc() function in panGB provides two algorithms for computing the observed RPP values: weighted and unweighted RPP values. We recommend the use of the weighted algorithm to account for differences in the marker coverage across the genome.

The algorithm for the weighted RPP values is explained below.

Weighted RPP Computation in panGB

Let $w_i$ represent the weight for marker $i$, based on the relative distances to its adjacent markers.

For a set of markers with positions $p_1, p_2, \ldots, p_n$, where $d_i = p_{i+1} - p_i$ represents the distance between adjacent markers, the weights can be calculated as follows:

For the first marker $i = 1$:

$$w_1 = \frac{d_1}{2 \sum_{j=1}^{n-1} d_j}$$
For a middle marker $1 < i < n$:

$$w_i = \frac{d_{i-1} + d_i}{2 \sum_{j=1}^{n-1} d_j}$$
For the last marker $i = n$:

$$w_n = \frac{d_{n-1}}{2 \sum_{j=1}^{n-1} d_j}$$

where:

$d_i$ is the distance between marker $i$ and marker $i+1$,
$sum_{j=1}^{n-1} d_j$ is the total distance across all segments, used for normalization.

Let $RPP$ represent the Recurrent Parent Proportion based on relative distance weighting. If $w_i$ is the weight for each marker $i$, and $m_i$ represents whether marker $i$ matches the recurrent parent $m_i = 1$ if it matches, $m_i = 0$ otherwise), then the weighted RPP is calculated as:

$$RPP_{weighted} = \sum_{i=1}^n w_i\cdot m_i$$

The unweighted RPP is calculated without the use of the weights as follows:

$$RPP_{unweighted} = \sum_{i=1}^n m_i$$

where:

$w_i$ is the weight of marker $i$, calculated based on the relative distance it covers,
$m_i$ is the match indicator for marker $i$ (1 if matching the recurrent parent, 0 otherwise),
$n$ is the total number of markers.

This formula provides the sum of the weighted contributions from each marker, representing the proportion of the recurrent parent genome in the individual.

Troubleshooting

If the package does not run as expected, check the following:

Was the package properly installed?
Do you have the required dependencies installed?
Were any warnings or error messages returned during package installation?
Are all packages up to date before installing panGB?

Authors and contributors

License

GNU GPLv3

Support and Feedback

For support and submission of feedback, email the maintainer Alexander Kena, PhD at alex.kena24@gmail.com

Name		Name	Last commit message	Last commit date
Latest commit History 98 Commits
R		R
data		data
inst/extdata		inst/extdata
man		man
tests		tests
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
NAMESPACE		NAMESPACE
README.Rmd		README.Rmd
README.md		README.md
panGenomeBreedr.Rproj		panGenomeBreedr.Rproj

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panGenomeBreedr

Table of contents

Requirements

Recommended packages

Installation

Installing Bioconductor dependency packages

Usage

Examples

KASP Marker Design

KASP Marker Validation

Reading Raw KASP Full Results Files (.csv)

Assigning colors and PCH symbols for KASP cluster plotting

Cluster plot

Summary of Prediction Verification in Plates

Plot Plate Design

Other Breeder-Centered Functionalities in panGB

Working with Agriplex Mid-Density Marker Data

Creating Heatmaps with panGB

Trait Introgression Hypothesis Testing

Decision Support for Marker-Assisted Backcrossing in panGB

Weighted RPP Computation in panGB

Troubleshooting

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awkena/panGenomeBreedr

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panGenomeBreedr

Table of contents

Requirements

Recommended packages

Installation

Installing Bioconductor dependency packages

Usage

Examples

KASP Marker Design

KASP Marker Validation

Reading Raw KASP Full Results Files (.csv)

Assigning colors and PCH symbols for KASP cluster plotting

Cluster plot

Summary of Prediction Verification in Plates

Plot Plate Design

Other Breeder-Centered Functionalities in panGB

Working with Agriplex Mid-Density Marker Data

Creating Heatmaps with panGB

Trait Introgression Hypothesis Testing

Decision Support for Marker-Assisted Backcrossing in panGB

Weighted RPP Computation in panGB

Troubleshooting

Authors and contributors

License

Support and Feedback

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