1 Introduction

The GBScleanR package has been developed to conduct error correction on genotype data obtained via NGS-based genotyping methods such as RAD-seq and GBS (Miller et al. 2007; Elshire et al. 2011). It is designed to estimate true genotypes along chromosomes from given allele read counts in the VCF file generated by SNP callers like GATK and TASSEL-GBS (McKenna et al. 2010; Glaubitz et al. 2014). The current implementation supports genotype data of a mapping population derived from two or more diploid parents followed by selfings, sibling crosses, or random crosses. e.g. F\(_2\) and 8-way RILs. Our method supposes markers to be biallelic and ordered along chromosomes by mapping reads on a reference genome sequence. To support smooth access to large size genotype data, every input VCF file is first converted to a Genomic Data Structure (GDS) file (Zheng et al. 2012). The current implementation does not allow non-biallelic markers, and those markers in an input VCF file will be automatically removed from a resultant GDS file. GBScleanR also provides functions for data visualization, filtering, and loading/writing a VCF file. Furthermore, the data structure of the GDS file created via this package is compatible with those used in the SNPRelate, GWASTools and GENESIS packages those are designed to handle large variant data and conduct downstream analyses including regression analysis (Zheng et al. 2012; Stephanie M. Gogarten et al. 2012; Stephanie M. Gogarten et al. 2019). In this document, we first walk through the utility functions implemented in GBScleanR to introduce a basic usage. Then, the core function of GBScleanR which estimates true genotypes for error correction will be introduced.

2 New Functions in GBScleanR Version 2

The latest release of GBScleanR is version 2. The major update in this version is that GBScleanR supports polyploid populations. GBScleanR tries to estimate the haplotype phases of founder and offspring samples based on given read counts. To process your data as of a polyploid population, set the ploidy argument in the loadGDS() function.

gds <- loadGDS(x = "/path/to/your/gds", ploidy = 4) # For tetraploid
gds <- loadGDS(x = "/path/to/your/gds", ploidy = 6) # For hexaploid

You can use any other functions in the GBScleanR package for your polyploid population in the same way for diploids.

In addition, the setDominantMarkers() function enables you to force GBScleanR to treat some markers as dominant markers. In the iterative parameter optimization of the genotype estimation step, GBScleanR calculates the marker-wise reference-read-bias. We can assume that reads of either reference or alternative allele only can be observed at dominant markers and the marker-wise reference-read-bias must be 1 or 0 for dominant markers that give only reference or alternative allele reads, respectively.

As another important change in estGeno() since version 2, the function now requires the node argument to explicitly specify whether raw or filtered read counts should be used for genotype estimation. If setCallFilter() has not been executed, the user-specified node argument will be ignored, and the “raw” node will be used by default.

The data visualization by GBScleanR was also enhanced by new plot functions: plotReadRatio() and plotDosage(). See the “Plot Read Ratio and Dosage” section for the details.

3 Prerequisites

This package internally uses the following packages.
- ggplot2
- dplyr
- tidyr
- expm
- gdsfmt
- SeqArray

4 Installation

You can install GBScleanR from the Bioconductor repository with the following code.

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

BiocManager::install("GBScleanR")

The code below let you install the package from the github repository. The package released in the github usually get frequent update more than that in Bioconductor due to the release schedule.

if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")
devtools::install_github("tomoyukif/GBScleanR", build_vignettes = TRUE)

5 Error while Handling a GDS File

You may face to the following error message or similar one if you killed the process that was accessing a GDS file.

Stream Read Error, need 12 byte(s) but receive 0

This error message indicates the corruption of the GDS file and you cannot access it anymore. In this case, please remake a GDS file using the gbsrVCF2GDS() function.

6 Loading Data

The main class of the GBScleanR package is GbsrGenotypData which inherits the GenotypeData class in the SeqArray package. The gbsrGenotypeData class object has three slots: sample, marker, and scheme. The data slot holds genotype data as a gds.class object which is defined in the gdsfmt package while snpAnnot and scanAnnot contain objects storing annotation information of SNPs and samples, which are the SnpAnnotationDataFrame and ScanAnnotationDataFrame objects defined in the GWASTools package. See the vignette of GWASTools for more detail. GBScleanR follows the way of GWASTools in which a unique genotyping instance (genotyped sample) is called “scan”.

Load the package.

library("GBScleanR")

GBScleanR only supports a VCF file as input. As an example data, we use sample genotype data for a biparental F2 population derived from inbred parents.

vcf_fn <- system.file("extdata", "sample.vcf", package = "GBScleanR")
gds_fn <- tempfile("sample", fileext = ".gds")

As mentioned above, the GbsrGenotypeData class requires genotype data in the gds.class object which enable us quick access to the genotype data without loading the whole data on RAM. At the beginning of the processing, we need to convert data format of our genotype data from VCF to GDS. This conversion can be achieved using gbsrVCF2GDS() as shown below. A compressed VCF file (.vcf.gz) is also acceptable.

# `force = TRUE` allow the function to over write the GDS file,
# even if a GDS file exists at `out_fn`.
gbsrVCF2GDS(vcf_fn = vcf_fn, out_fn = gds_fn, force = TRUE, verbose = FALSE)

## [1] "/tmp/RtmpQ7MJxU/sample1b14b163350509.gds"

Once we converted the VCF to the GDS, we can create a GbsrGenotypeData instance for our data.

gds <- loadGDS(gds_fn, verbose = FALSE)

The first time to load a newly produced GDS file will take long time due to data reformatting for quick access.

7 Utility Methods

7.1 Getters

Getter functions allow you to retrieve basic information of genotype data, e.g. the number of SNPs and samples, chromosome names, physical position of SNPs and alleles.

# Number of samples
nsam(gds)

## [1] 102

# Number of SNPs
nmar(gds)

## [1] 242

# Indices of chromosome ID of all markers
head(getChromosome(gds))

## [1] "1" "1" "1" "1" "1" "1"

# Chromosome names of all markers
head(getChromosome(gds))

## [1] "1" "1" "1" "1" "1" "1"

# Position (bp) of all markers
head(getPosition(gds))

## [1] 522289 571177 577905 720086 735019 841286

# Reference allele of all markers
head(getAllele(gds))

## [1] "A,G" "A,G" "C,T" "G,C" "C,T" "G,T"

# SNP IDs
head(getMarID(gds))

## [1] 1 2 3 4 5 6

# sample IDs
head(getSamID(gds))

## [1] "F2_1054" "F2_1055" "F2_1059" "F2_1170" "F2_1075" "F2_1070"

The function getGenotype() returns genotype call data in which integer numbers 0, 1, and 2 indicate the number of reference allele.

geno <- getGenotype(gds)

The function getRead() returns read count data as a list with two elements ref and alt containing reference read counts and alternative read counts, respectively.

geno <- getRead(gds)

7.2 Data Summarization

7.2.1 Collect Basic Summary Statistics

countGenotype() and countRead() are class methods of GbsrGenotypeData and they summarize genotype counts and read counts per marker and per sample.

gds <- countGenotype(gds)
gds <- countRead(gds)

7.2.2 Visualize Basic Summary Statistics

These summary statistics can be visualized via plotting functions. With the values obtained via countGenotype(), we can plot histograms of missing rate , heterozygosity, reference allele frequency as shown below.

# Histgrams of missing rate
histGBSR(gds, stats = "missing")

Missing rate per marker and per sample.

# Histgrams of heterozygosity
histGBSR(gds, stats = "het")

Heterozygosity per marker and per sample.

# Histgrams of reference allele frequency
histGBSR(gds, stats = "raf")

Reference allele frequency per marker and per sample.

With the values obtained via countRead(), we can plot histograms of total read depth , allele read depth , reference read frequency as shown below.

# Histgrams of total read depth
histGBSR(gds, stats = "dp")

Total read depth per marker and per sample.

# Histgrams of allelic read depth
histGBSR(gds, stats = "ad_ref")

Reference read depth per marker and per sample.

# Histgrams of allelic read depth
histGBSR(gds, stats = "ad_alt")

Alternative read depth per marker and per sample.

# Histgrams of reference allele frequency
histGBSR(gds, stats = "rrf")

# Histgrams of mean allelic read depth
histGBSR(gds, stats = "mean_ref")

Mean of reference read depth per marker and per sample.

# Histgrams of standard deviation of read depth
histGBSR(gds, stats = "sd_ref")

SD of reference read depth per marker and per sample.

# Histgrams of standard deviation of read depth
histGBSR(gds, stats = "sd_ref")

SD of alternative read depth per marker and per sample.

plotGBSR() and pairsGBSR() provide other ways to visualize statistics. plotGBSR() draws a line plot of a specified statistics per marker along each chromosome. pairsGBSR() give us a two-dimensional scatter plot to visualize relationship between statistics.

plotGBSR(gds, stats = "missing")

plotGBSR(gds, stats = "geno")

pairsGBSR(gds, stats1 = "missing", stats2 = "dp")

7.2.3 Getter Methods for Summary Statistics

The statistics obtained via countGenotype(), countReat(), and calcReadStats() are sotred in the snpAnnot and scanAnnot slots. They can be retrieved using getter functions as follows.

# Reference genotype count per marker
head(getCountGenoRef(gds, target = "marker"))

## [1] 35 38 33 41 42 47

# Reference genotype count per sample
head(getCountGenoRef(gds, target = "sample"))

## [1] 172  76  76 118  28  89

# Heterozygote count per marker
head(getCountGenoHet(gds, target = "marker"))

## [1] 48 33 45 14 12 25

# Heterozygote count per sample
head(getCountGenoHet(gds, target = "sample"))

## [1]  35  79  26  72 179  97

# Alternative genotype count per marker
head(getCountGenoAlt(gds, target = "marker"))

## [1] 19 28 24 23 29 25

# Alternative genotype count per sample
head(getCountGenoAlt(gds, target = "sample"))

## [1]  25  46 122  46  20  39

# Missing count per marker
head(getCountGenoMissing(gds, target = "marker"))

## [1]  0  3  0 24 19  5

# Missing count per sample
head(getCountGenoMissing(gds, target = "sample"))

## [1] 10 41 18  6 15 17

# Reference allele count per marker
head(getCountAlleleRef(gds, target = "marker"))

## [1] 118 109 111  96  96 119

# Reference allele count per sample
head(getCountAlleleRef(gds, target = "sample"))

## [1] 379 231 178 308 235 275

# Alternative allele count per marker
head(getCountAlleleAlt(gds, target = "marker"))

## [1] 86 89 93 60 70 75

# Alternative allele count per sample
head(getCountAlleleAlt(gds, target = "sample"))

## [1]  85 171 270 164 219 175

# Missing allele count per marker
head(getCountAlleleMissing(gds, target = "marker"))

## [1]  0  6  0 48 38 10

# Missing allele count per sample
head(getCountAlleleMissing(gds, target = "sample"))

## [1] 20 82 36 12 30 34

# Reference read count per marker
head(getCountReadRef(gds, target = "marker"))

## [1]  696 1012  604  182  163  271

# Reference read count per sample
head(getCountReadRef(gds, target = "sample"))

## [1] 1227  489  600 1693 1097  859

# Alternative read count per marker
head(getCountReadAlt(gds, target = "marker"))

## [1]  654 1555  537  156  199  428

# Alternative read count per sample
head(getCountReadAlt(gds, target = "sample"))

## [1]  269  575 1516  613 1103  481

# Sum of reference and alternative read counts per marker
head(getCountRead(gds, target = "marker"))

## [1] 1350 2567 1141  338  362  699

# Sum of reference and alternative read counts per sample
head(getCountRead(gds, target = "sample"))

## [1] 1496 1064 2116 2306 2200 1340

# Mean of reference allele read count per marker
head(getMeanReadRef(gds, target = "marker"))

## [1] 4034.762 2966.011 4032.764 1213.087 1248.329 1702.554

# Mean of reference allele read count per sample
head(getMeanReadRef(gds, target = "sample"))

## [1] 3905.653 3481.716 3993.717 3546.723 2493.182 3237.600

# Mean of Alternative allele read count per marker
head(getMeanReadAlt(gds, target = "marker"))

## [1] 3221.486 2783.018 3501.793 1117.116 1469.465 1836.445

# Mean of Alternative allele read count per sample
head(getMeanReadAlt(gds, target = "sample"))

## [1] 3047.675 3651.443 3958.266 2507.814 2387.446 2804.338

# SD of reference allele read count per marker
head(getSDReadRef(gds, target = "marker"))

## [1] 2244.3271 4346.0563 2620.9744  830.4441  818.4920 1080.3064

# SD of reference allele read count per sample
head(getSDReadRef(gds, target = "sample"))

## [1] 3265.505 2489.965 3467.100 3115.169 2048.550 2443.671

# SD of Alternative allele read count per marker
head(getSDReadAlt(gds, target = "marker"))

## [1] 1814.5382 4932.9596 2109.4078  739.2224  912.1266 1480.2307

# SD of Alternative allele read count per sample
head(getSDReadAlt(gds, target = "sample"))

## [1] 2483.399 3011.280 3373.579 2137.899 1875.358 2070.380

# Minor allele frequency per marker
head(getMAF(gds, target = "marker"))

## [1] 0.4215686 0.4494949 0.4558824 0.3846154 0.4216867 0.3865979

# Minor allele frequency per sample
head(getMAF(gds, target = "sample"))

## [1] 0.1831897 0.4253731 0.3973214 0.3474576 0.4823789 0.3888889

# Minor allele count per marker
head(getMAC(gds, target = "marker"))

## [1] 86 89 93 60 70 75

# Minor allele count per sample
head(getMAC(gds, target = "sample"))

## [1]  85 171 178 164 219 175

You can get the proportion of each genotype call with prop = TRUE.

head(getCountGenoRef(gds, target = "marker", prop = TRUE))

## [1] 0.3431373 0.3838384 0.3235294 0.5256410 0.5060241 0.4845361

head(getCountGenoHet(gds, target = "marker", prop = TRUE))

## [1] 0.4705882 0.3333333 0.4411765 0.1794872 0.1445783 0.2577320

head(getCountGenoAlt(gds, target = "marker", prop = TRUE))

## [1] 0.1862745 0.2828283 0.2352941 0.2948718 0.3493976 0.2577320

head(getCountGenoMissing(gds, target = "marker", prop = TRUE))

## [1] 0.00000000 0.02941176 0.00000000 0.23529412 0.18627451 0.04901961

The proportion of each allele counts.

head(getCountAlleleRef(gds, target = "marker", prop = TRUE))

## [1] 0.5784314 0.5505051 0.5441176 0.6153846 0.5783133 0.6134021

head(getCountAlleleAlt(gds, target = "marker", prop = TRUE))

## [1] 0.4215686 0.4494949 0.4558824 0.3846154 0.4216867 0.3865979

head(getCountAlleleMissing(gds, target = "marker", prop = TRUE))

## [1] 0.00000000 0.02941176 0.00000000 0.23529412 0.18627451 0.04901961

The proportion of each allele read counts.

head(getCountReadRef(gds, target = "marker", prop = TRUE))

## [1] 0.5155556 0.3942345 0.5293602 0.5384615 0.4502762 0.3876967

head(getCountReadAlt(gds, target = "marker", prop = TRUE))

## [1] 0.4844444 0.6057655 0.4706398 0.4615385 0.5497238 0.6123033

8 Filtering and Subsetting

8.1 Filtering Data

Based on the statistics we obtained, we can filter out less reliable markers and samples using setMarFilter() and setSamFilter(), respectively.

gds <- setMarFilter(gds, missing = 0.2, het = c(0.1, 0.9), maf = 0.05)
gds <- setSamFilter(gds, missing = 0.8, het = c(0.25, 0.75))

setCallFilter() is another type of filtering which works on each genotype call that is a data point at a marker in a sample. We can replace some genotype calls with missing based on the specified criteria. If you would like to filter out less reliable genotype calls that are only supported by less than 5 reads, set the arguments as below.

gds <- setCallFilter(gds, dp_count = c(5, Inf))

If needed to remove genotype calls supported by too many reads, which might be the results of mismapping from repetitive sequences, set as follows.

# Filtering genotype calls based on total read counts
gds <- setCallFilter(gds, dp_qtile = c(0, 0.9))

# Filtering genotype calls based on reference read counts 
# and alternative read counts separately.
gds <- setCallFilter(gds, ref_qtile = c(0, 0.9), alt_qtile = c(0, 0.9))

Usually reference reads and alternative reads show different data distributions. Thus, we can set the different thresholds for them via dp_qtile, ref_qtile, and alt_qtile to filter out genotype calls based on quantiles of total, reference, and alternative read counts in each sample.

Here, the following codes filter out calls supported by less than 5 reads and then filter out markers showing a missing rate of more than 20%.

gds <- setCallFilter(gds, dp_count = c(5, Inf))
gds <- setMarFilter(gds, missing = 0.2)

8.1.0.1 Important Instruction for Filtering

Based on our study using simulation data and real data for a rice F2 population derived from a cross between distant relatives (cultivar x wild species), we recommend the setting of ref_qtile = c(0, 0.9), alt_qtile = c(0, 0.9) to filter out markers with over represented reads. If your population contains samples that have only either of reference or alternative reads at the majority of markers, filtering with ref_qtile = c(0, 0.9), alt_qtile = c(0, 0.9) will set missing to a large portion of markers for the samples. In that case, it is better to set dp_qtile = c(0, 0.9). In addition, the error correction by GBScleanR does not require any filtering for markers based on missing rate, heterozygosity, and allele frequency. Therefore, setMarFilter() and setSamFilter() will be used only when you have specific markers and samples that should be removed. In that case, please specify marker IDs and sample IDs to the id argument of setMarFilter() and setSamFilter(), respectively.

gds <- setCallFilter(gds, ref_qtile = c(0, 0.9), alt_qtile = c(0, 0.9))
invalid_mar <- getMarID(gds)[1:5]
gds <- setMarFilter(gds, id = invalid_mar)
invalid_sam <- getSamID(gds)[1:3]
gds <- setSamFilter(gds, id = invalid_sam)

In addition to those statistics based filtering functions, GBScleanR provides a filtering function based on relative marker positions. Markers locating too close each other usually have redundant information, especially if those markers are closer each other than the read length, in which case the markers are supported by completely (or almost) the same set of reads. To select only one marker from those markers, we can sue thinMarker(). This function selects one marker having the least missing rate from each stretch of the specified length. If some markers have the least missing rate, select the first marker in the stretch.

# Here we select only one marker from each 150 bp stretch.
gds <- thinMarker(gds, range = 150)

We can obtain the summary statistics using countGenotype(), countRead(), and calcReadStats() for only the SNPs and samples retained after the filtering. Importantly, we need to set node = "filt if we have apllied setCallFiler(). Otherwise, countGenotype() used the raw genotype calls.

gds <- countGenotype(gds, node = "filt")
gds <- countRead(gds, node = "filt")

We can check which markers and samples are retained after the filtering using validSnp() and validSam().

head(validMar(gds))

## [1] FALSE FALSE FALSE FALSE FALSE FALSE

head(validSam(gds))

## [1] FALSE FALSE FALSE FALSE FALSE FALSE

The class methods of GbsrGenotypeData basically work with only the markers and samples retained after filtering. To use all markers and samples, please specify valid = FALSE to the GbsrGenotypeData class methods.

nmar(gds)

## [1] 151

nmar(gds, valid = FALSE)

## [1] 242

8.2 Reset Filtering

We can reset filtering as following.

# Reset the filter on markers
gds <- resetMarFilter(gds) 

# Reset the filter on samples
gds <- resetSamFilter(gds) 

# Reset the filter on calls
gds <- resetCallFilter(gds) 

# Reset all filters
gds <- resetFilter(gds)

9 Error Correction

The error correction algorithm of GBScleanR bases on the HMM assuming observed allele read counts for each SNP marker along a chromosome as the outputs from a sequence of latent true genotypes. Our model supposes that a population of \(N^o \geq 1\) sampled offspring was originally derived form the crosses between \(N^f \geq 2\) parent individuals. The parents can be inbred lines having homozygotes at all markers and outbred lines in which markers show heterozygous genotype.

9.1 Set Replicates

The update on Mar 7, 2024 added a function to set sample replicate information to jointly evaluate read counts for replicates in the genotype estimation by the estGeno() function. The estGeno() function sums up the read counts of replicates specified by setReplicates() and estimates genotypes based on the summed-up read counts. The samples specified as replicates in setReplicates() will have the same genotypes at all markers in the estimated genotypes obtained via estGeno(). The setReplicates() function assumes that replicate information would be supplied for all samples in the data including parents via the replicates argument. In addition, the setReplicates() function assumes that the samples that are assigned same numbers or characters via the replicates argument are replicates. Therefore, the ordering of samples in the data and the identifiers in the vector specified to replicates should match. Replicates can be specified as follows. First, it is better to confirm the ordering of samples in the data with the setting “valid = FALSE” to obtain all sample IDs.

sample_id <- getSamID(gds, valid = FALSE)
sample_id

##   [1] "F2_1054"  "F2_1055"  "F2_1059"  "F2_1170"  "F2_1075"  "F2_1070" 
##   [7] "F2_1074"  "F2_1007"  "F2_1009"  "F2_1489"  "F2_1010"  "F2_1014" 
##  [13] "F2_1490"  "F2_1022"  "F2_1267"  "F2_1382"  "F2_1038"  "Founder1"
##  [19] "F2_1216"  "F2_1571"  "F2_1695"  "F2_1575"  "F2_1584"  "F2_1355" 
##  [25] "F2_1236"  "F2_1357"  "F2_1470"  "F2_1887"  "F2_1762"  "F2_1884" 
##  [31] "F2_1763"  "F2_1400"  "F2_1885"  "F2_1643"  "F2_1547"  "F2_1784" 
##  [37] "F2_1785"  "F2_1314"  "F2_1317"  "F2_1600"  "F2_1848"  "F2_1849" 
##  [43] "F2_1840"  "F2_1719"  "F2_1736"  "F2_1850"  "F2_1502"  "F2_1868" 
##  [49] "F2_1740"  "F2_1741"  "F2_1755"  "F2_1756"  "F2_1874"  "Founder2"
##  [55] "F2_1811"  "F2_1812"  "F2_1700"  "F2_1824"  "F2_1827"  "F2_1710" 
##  [61] "F2_1714"  "F2_1166"  "F2_1282"  "F2_1178"  "F2_1192"  "F2_1195" 
##  [67] "F2_1361"  "F2_1122"  "F2_1486"  "F2_1132"  "F2_1378"  "F2_1257" 
##  [73] "F2_1258"  "F2_1262"  "F2_1384"  "F2_1152"  "F2_1274"  "F2_1150" 
##  [79] "F2_1688"  "F2_1681"  "F2_1682"  "F2_1684"  "F2_1686"  "F2_1565" 
##  [85] "F2_1339"  "F2_1333"  "F2_1227"  "F2_1222"  "F2_1592"  "F2_1472" 
##  [91] "F2_1476"  "F2_1404"  "F2_1527"  "F2_1641"  "F2_1666"  "F2_1541" 
##  [97] "F2_1677"  "F2_1671"  "F2_1602"  "F2_1618"  "F2_1635"  "F2_1637"

Here, as an example, we assume that the samples at odd indices in the sample_id vector are the replicates of the next samples at even indices. For example, F2_1054 and F2_1055 are replicates for which DNA samples were extracted from the same F2 individual although they have different samples IDs. In this case, you can set replicate information as shown below.

gds <- setReplicates(gds, replicates = rep(1:51, each = 2))

As another example, if parents in the data are Founder1 and Founder2 and replicates are F2_1054 and F2_1022 for Founder1 and F2_1178 and F2_1637 for Founder2, you should give a vector to the replicates argument like the following.

rep_of_parent1 <- sample_id %in% c("Founder1", "F2_1054", "F2_1022")
rep_of_parent2 <- sample_id %in% c("Founder2", "F2_1178", "F2_1637")
sample_id[rep_of_parent1] <- "Founder1"
sample_id[rep_of_parent2] <- "Founder2"
gds <- setReplicates(gds, replicates = sample_id)

If you set replicates for parents, you should give a sample id of the replicates as an identifier for a parent in setParents() as described in the next section. If you set replicates for parents after setting parents by setParents(), the replicates for parents will be also set as parents with assigning the same member ID for the replicates of each parent.

To reset the assigned replicate information, please use setReplicates() with specifying different values to the replicates argument.

gds <- setReplicates(gds, replicates = seq_len(nsam(gds, valid = FALSE)))

9.2 Set Parents

As the first step for genotype error correction, we have to specify which samples are the parents of the population via setParents(). In the case of genotype data in the biparental population, people usually filter out SNPs which are not monomorphic in each parental sample and not biallelic between parents. setParents() automatically do this filtering, if you set mono = TRUE and bi = TRUE.

p1 <- grep("Founder1", getSamID(gds, valid = FALSE), value = TRUE)
p2 <- grep("Founder2", getSamID(gds, valid = FALSE), value = TRUE)
gds <- setParents(gds, parents = c(p1, p2), mono = TRUE, bi = TRUE)

If you set replicates for parents, you should give a sample id of the replicates as an identifier for a parent in setParents(). In the last example in the previous section, we set three replicates for each parent. To properly set parents, we should specify either of “Founder1”, “F2_1054”, and “F2_1022” for p1 and either of “Founder2”, “F2_1178”, “F2_1637” for p2.

9.3 Data QC and Filtering

The next step is to visualize statistical summaries of the data. Get genotype data summaries as mentioned in the previous section.

gds <- countGenotype(gds)

Then, get histograms.

histGBSR(gds, stats = "missing")

histGBSR(gds, stats = "het")

histGBSR(gds, stats = "raf")

As the histograms showed, the data contains a lot of missing genotype calls with unreasonable heterozygosity in a F2 population. Reference allele frequency shows a bias to reference allele. If you can say your population has no strong segregation distortion in any positions of the genome, you can filter out the markers having too high or too low reference allele frequency.

# filter out markers with reference allele frequency
# less than 5% or more than 95%.
gds <- setMarFilter(gds, maf = 0.05)

However, sometimes filtering based on allele frequency per marker removes all markers from regions truly showing segregation distortion. Although heterozygosity can be a criterion to filter out markers, this will removes too many markers which even contains useful information for genotyping. In addition, as we described in the previous section, the error correction by GBScleanR does not require any filtering for markers based on missing rate, heterozygosity, and allele frequency.

If we found poor quality samples in you dataset based on missing rate, heterozygosity, and reference allele frequency, we can omit those samples with setSamFilter().

# Filter out samples with more than 90% missing genotype calls,
# less than 5% heterozygosity, and less than 5% minor allele frequency.
gds <- setSamFilter(gds, missing = 0.9, het = c(0.05, 1), maf = 0.05)

Before filtering using setMarFilter() and setSamFilter(), we recomend that you conduct filtering on each genotype call based on read depth. The error correction via GBScleanR is robust against low coverage calls, while genotype calls messed up by mismapping might lead less reliable error correction. Therefore, filtering for extremely high coverage calls are necessary rather than that for low coverage ones.

# Filter out genotype calls supported by reads less than 2 reads.
gds <- setCallFilter(gds, dp_count = c(2, Inf))

# Filter out genotype calls supported by reads more than 100.
gds <- setCallFilter(gds, dp_count = c(0, 100))

# Filter out genotype calls based on quantile values 
# of read counts at markers in each sample.
gds <- setCallFilter(gds, ref_qtile = c(0, 0.9), alt_qtile = c(0, 0.9))

Since missing genotype calls left in the data basically give no negative effect on genotype error correction. Therefore, you can leave any missing genotype calls. We can, however, remove markers based on missing genotype calls.

# Remove markers having more than 75% of missing genotype calls
gds <- setMarFilter(gds, missing = 0.2) 
nmar(gds)

## [1] 200

To check statistical summaries of the filtered genotype data, we need to set node = "filt. Otherwise, countGenotype() used the raw genotype data.

gds <- countGenotype(gds, node = "filt")

histGBSR(gds, stats = "missing")

histGBSR(gds, stats = "het")

histGBSR(gds, stats = "raf")

We can still see the markers showing distortion in allele frequency, while the expected allele frequency is 0.5 in a F2 population. To investigate that those markers having distorted allele frequency were derived from truly distorted regions or just error prone markers, we must check if there are regions where the markers with distorted allele frequency are clustered.

plotGBSR(gds, stats = "raf")

No region seem to have severe distortion. Based on the histogram of reference allele frequency, we can roughly cut off the markers with frequency more than 0.75 or less than 0.25, in other words, less than 0.25 of minor allele frequency. As we mentioned already in the previous section, the error correction by GBScleanR basically works finely without any filtering for markers based on missing rate, heterozygosity, and allele frequency.

gds <- setMarFilter(gds, maf = 0.25)
nmar(gds)

## [1] 188

Let’s see the statistics again.

gds <- countGenotype(gds)
histGBSR(gds, stats = "missing")

histGBSR(gds, stats = "het")

histGBSR(gds, stats = "raf")

At the end of filtering, check marker density and genotype ratio per marker along chromosomes.

# Marker density
plotGBSR(gds, stats = "marker")

plotGBSR(gds, stats = "geno")

The coord argument controls the number of rows and columns of the facets in the plot.

9.4 Genotype Estimation

9.4.1 Prepare Scheme Information

Before executing the function for true genotype estimation, we need to build a scheme object. The update on May 17, 2024 added a wrapper function for scheme information preparation. If your population has been developed in a relatively simple scheme, you can use the makeScheme() function.

# For selfed F2 population
gds <- makeScheme(gds, generation = 2, crosstype = "self")

##      [,1]
## [1,]    3
## [2,]    3

# For sibling-crossed F2 population
gds <- makeScheme(gds, generation = 2, crosstype = "sib")

##      [,1]
## [1,]    3
## [2,]    3

# For selfed F5 population
gds <- makeScheme(gds, generation = 5, crosstype = "self")

##      [,1]
## [1,]    3
## [2,]    3

##      [,1]
## [1,]    4
## [2,]    4

##      [,1]
## [1,]    5
## [2,]    5

##      [,1]
## [1,]    6
## [2,]    6

# For F1 population
gds <- makeScheme(gds, generation = 1) # the crosstype argument will be ignored.

When your population has \(2^n\) parents specified by setParents(), makeScheme() assumes those parents were crossed in the “funnel” design in which \(2^n\) parents are crossed to obtain \(2^n/2\) F1 hybrids followed by successive intercrossings (pairings) of the hybrids to combine the genomes of all parents in one family of siblings. The makeScheme() function assumes that the parents that were assigned an odd number member ID (N) in setParents() had been crossed with the parent that were assigned an even number (N+1). For example, if you set parents as shown below. The makeScheme() function prepare a scheme information that indicates the intercrossings of “p1 x p2”, “p3 x p4”, “p5 x p6”, and “p7 x p8” followed by crossing of “p1xp2_F1 x p3xp4_F1” and “p5xp6_F1 x p7xp8_F1” and then crossing of the two 4-way crossed liens to produce 8-way crossed hybrid lines. If, for example, generation = 5 indicating an F5 generation was specified to makeScheme(), the function adds 4 successive selfing or sibling crossings in the scheme.

# Do not run.
gds <- setParents(gds, 
                  parents = c("p1", "p2", "p3", "p4", "p5", "p6", "p7", "p8"))
# Member IDs will be 1, 2, 3, 4, 5, 6, 7, and 8 
# for p1, p2, p3, p4, p5, p6, p7, and p8, respectively.

In many cases, makeScheme() is enough to prepare scheme information. However, if your population underwent more complicated crossings, please register scheme information step-by-step as shown below.

Our sample data is of a biparental F2 population derived from inbred parents. Therefore, we should run initScheme() and addScheme() as following.

# As always the first step of breeding scheme would be "pairing" cross(es) of 
# parents, never be "selfing" and a "sibling" cross,
# the argument `crosstype` in initScheme() was deprecated on the update on April 6, 2023.
# gds <- initScheme(gds, crosstype = "pairing", mating = matrix(1:2, 2))
gds <- initScheme(gds, mating = rbind(1, 2))
gds <- addScheme(gds, crosstype = "selfing")

##      [,1]
## [1,]    3
## [2,]    3

The function initScheme() initializes the scheme object with information about parents. You need to specify a matrix indicating combinations of mating, in which each column shows a pair of parental samples. For example, if you have only two parents, the mating matrix should be mating = matrix(1:2, nrow = 2, ncol = 1) or equivalents. The indices used in the matrix should match with the IDs labeled to parental samples by setParents(). To confirm the IDs for parental samples, run the following code.

getParents(gds)

##   sampleID memberID indexes
## 1 Founder1        1      18
## 2 Founder2        2      54

The created GbsrScheme object is set in the scheme slot of the GbsrGenotypeData object. The function addScheme() adds the information about the next breeding step of your population. In the case of our example data, the second step was selfing to produce F2 individuals from the F1 obtained via the first parent crossing.

The codes shown below in the rest of the section “Prepare scheme information” are sample codes assuming some specific situations that are not applicable for the sample data used in this vignette. Therefore, you will get error messages if you run the codes.

If your population was derived from a 4-way or 8-way cross, you need to add more paring steps. In the case of 8-way RILs developed by three pairing crosses followed by five selfing cycles, the scheme object should be built as following. First we need to initialize the scheme object with specifying the first mating scheme. The crosstype argument should be “pairing” and the mating argument should be given as a matrix in which each pairing combination of parents are shown in each column. The following case indicates the pairing of parent 1 and 2 as well as 3 and 4, 5 and 6, and 7 and 8.

# As always the first step of breeding scheme would be "pairing" cross(es) of 
# parents, never be "selfing" and a "sibling" cross,
# the argument `crosstype` in initScheme() was deprecated on the update on April 6, 2023.
# gds <- initScheme(gds, crosstype = "pair", mating = cbind(c(1:2), c(3:4), c(5:6), c(7:8)))

# Do not run.
gds <- initScheme(gds, mating = cbind(c(1:2), c(3:4), c(5:6), c(7:8)))

Now the progenies of the crosses above have member ID 9, 10, 11, and 12 for each combination of mating. You can check IDs with showScheme().

Then, add the step to make 4-way crosses.

# Do not run.
gds <- addScheme(gds, crosstype = "pair", mating = cbind(c(9:10), c(11:12)))

# Check IDs.
showScheme(gds)

Add the last generation of the paring step .

# Do not run.
gds <- addScheme(gds, crosstype = "pair", mating = cbind(c(13:14)))

#' # Check IDs.
showScheme(gds)

Now we have the scheme information of a 8-way cross. The follwoing steps add the selfing cycles.

# Inbreeding by five times selfing.
# Do not run.
gds <- addScheme(gds, crosstype = "self")
gds <- addScheme(gds, crosstype = "self")
gds <- addScheme(gds, crosstype = "self")
gds <- addScheme(gds, crosstype = "self")
gds <- addScheme(gds, crosstype = "self")

You can set crosstype = "sibling" or crosstype = "random", if your population was developed through sibling crosses or random crosses, respectively.

The update on April 4, 2023 introduced new function to GBScleanR. The genotype estimation algorithm in estGeno() supports populations that consist of samples belonging to different pedigree. For example, if you have a population of F1 samples that derived from three different crosses of four parents: Founder1 x Founder2, Founder1 x Founder3, Founder1 x Founder4. You can build a scheme info as following.

# Do not run.
gds <- setParents(object = gds,
                  parents = c("Founder1", "Founder2", "Founder3", "Founder4"))
gds <- initScheme(object = gds, 
                  mating = cbind(c(1, 2), c(1, 3), c(1,4)))
# The initScheme() function here automatically set 5, 6, and 7 as member ID to
# the progenies of the above maiting (pairing) combinations, respectively.

# Then you have to assign member IDs to your samples to indicate which sample 
# belongs to which pedigree.
gds <- assignScheme(object = gds, 
                    id = c(rep(5, 10), rep(6, 15), rep(7, 20)))

The assignScheme() assign member IDs id to the samples in order. Please confirm the order of the member IDs in id and the order of the sample IDs shown by getSamID(gds).

# Do not run.
# Get sample ID
sample_id <- getSamID(object = gds)

# Initialize the id vector
id <- integer(nsam(gds))

# Assume your samples were named with prefixes that indicate which 
# sample was derived from which combination of parents.
id[grepl("P1xP2", sample_id)] <- 5
id[grepl("P1xP3", sample_id)] <- 6
id[grepl("P1xP4", sample_id)] <- 7
gds <- assignScheme(object = gds, id = id)

9.4.2 Execute Genotype Estimation

The following codes suppose that you built the scheme object for the example data that is a biparental F2 population derived from a cross between inbred parents, not for the 8-way RILs explained above. Now we can execute genotype estimation for error correction. GBScleanR estimates error pattern via iterative optimization of parameters for genotype estimation. We could not guess the best number of iterations, but our simulation tests showed iter = 4 usually saturates the improvement of estimation accuracy.

gds <- estGeno(gds, node = "filt", iter = 4)

As an important change in estGeno() since version 2, the function now requires the node argument to explicitly specify whether raw or filtered read counts should be used for genotype estimation. If setCallFilter() has not been executed, the user-specified node argument will be ignored, and the “raw” node will be used by default.

If your population derived from outbred parents, please set het_parents = TRUE.

# Do nut run
# This is an example to show the case to use "het_parents = TRUE".
gds <- estGeno(gds, het_parent = TRUE, iter = 4)

The larger number of iterations makes running time longer. If you would like to execute no optimization, set optim = FALSE or iter = 1.

# Following codes do the same.

# Do nut run
# These are examples to show the case to set "iter = 1" or "optim = FALSE".
gds <- estGeno(gds, iter = 1)
gds <- estGeno(gds, optim = FALSE)

9.4.3 Get the Results

All of the results of estimation are stored in the gds file linked to the GbsrGenotypeData object. You can obtain the estimated genotype data via the getGenotype() function with node = "cor".

est_geno <- getGenotype(gds, node = "cor")

GBScleanR also estimates phased parent genotypes and you can access it.

parent_geno <- getGenotype(gds, node = "parents")

GBScleanR first internally estimate phased haplotype and then convert them to genotype calls. If you need the estimated haplotype data, run getHaplotype().

est_hap <- getHaplotype(gds)

The function gbsrGDS2VCF() generate a VCF file containing the estimated genotype data and phased haplotype information. The estimated haplotypes are indicated in the FORMAT field with the HAP tag. The parent genotypes correspond to each haplotype are indicated in the INFO field with the PGT tag. HAP shows the pair of haplotype for each marker of each sample, while PGT shows the allele of each haplotype. HAP is indicated by two numbers separated by a pipe symbol “|”. Each of the numbers takes one of the numbers from 1 to 2N, where N is the number of parents. PGT is indicated by 2N numbers separated by a pipe symbol “|”. The first number in PGT represents the allele of haplotype 1 at the marker, and the rest numbers also show the alleles of rest haplotypes 2, 3 and 4, if your population is of biparental diploid samples. As a default, gbsrGDS2VCF() outputs the estimated genotype data as entries of the CGT tag in the FORMAT field. With node = "cor", you can output a VCF file in which GT field was replaced with the estimated genotype data obtained via estGeno().

out_fn <- tempfile("sample_est", fileext = ".vcf.gz")
gbsrGDS2VCF(gds, out_fn)

## [1] "/tmp/RtmpQ7MJxU/sample_est1b14b169bc86f5.vcf.gz"

gbsrGDS2VCF(gds, out_fn, node = "cor")

## [1] "/tmp/RtmpQ7MJxU/sample_est1b14b169bc86f5.vcf.gz"

The output vcf file contains data like the one shown below.

##fileformat=VCFv4.2
##fileDate=20240909
##source=SeqArray_Format_v1.0
##INFO=<ID=PGT,Number=1,Type=String,Description="Estimated allele of parental haplotype by GBScleanR">
##FILTER=<ID=PASS,Description="All filters passed">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=AD,Number=.,Type=Integer,Description="Allelic depths for the reference and alternate alleles in the order listed">
##FORMAT=<ID=FAD,Number=2,Type=Integer,Description="Call-filtered read counts generated by GBScleanR">
##FORMAT=<ID=FGT,Number=2,Type=Float,Description="Estimated mismapping rate by GBScleanR">
##FORMAT=<ID=HAP,Number=1,Type=String,Description="Estimated haplotype data by GBScleanR">
#CHROM  POS  ID  REF  ALT  QUAL  FILTER  INFO  FORMAT  F2_1054  F2_1055  F2_1059  F2_1170  F2_1075  F2_1070  F2_1074  F2_1007  F2_1009  F2_1489  F2_1010  F2_1014  F2_1490  F2_1022  F2_1267  F2_1382  F2_1038  Founder1  F2_1216  F2_1571  F2_1695  F2_1575  F2_1584  F2_1355  F2_1236  F2_1357  F2_1470  F2_1762  F2_1884  F2_1763  F2_1400  F2_1885  F2_1643  F2_1547  F2_1784  F2_1785  F2_1314  F2_1317  F2_1600  F2_1848  F2_1849  F2_1719  F2_1850  F2_1502  F2_1868  F2_1740  F2_1741  F2_1755  F2_1756  F2_1874  Founder2  F2_1811  F2_1812  F2_1700  F2_1824  F2_1827  F2_1710  F2_1714  F2_1166  F2_1282  F2_1178  F2_1192  F2_1195  F2_1361  F2_1122  F2_1486  F2_1132  F2_1378  F2_1257  F2_1258  F2_1262  F2_1384  F2_1152  F2_1274  F2_1150  F2_1688  F2_1681  F2_1682  F2_1684  F2_1686  F2_1565  F2_1339  F2_1333  F2_1222  F2_1592  F2_1472  F2_1476  F2_1404  F2_1527  F2_1641  F2_1666  F2_1541  F2_1677  F2_1671  F2_1602  F2_1618  F2_1635  F2_1637
1  522289  S01_522289  A  G  100  PASS  PGT=0|0|1|1;ADB=0.520833;MR=0,0  GT:AD:FAD:FGT:HAP:EDS  0/0:10,0:10,0:0/0:1|1:0  1/0:6,6:6,6:0/1:2|1:1  0/0:6,0:6,0:0/0:1|1:0  1/1:0,12:0,0:./.:2|2:2  1/0:12,9:0,0:./.:2|1:1  1/0:3,1:3,1:0/1:2|1:1  0/1:5,5:5,5:0/1:1|2:1  1/0:5,2:5,2:0/1:2|1:1  0/1:4,1:4,1:0/1:1|2:1  1/0:1,4:1,4:0/1:2|1:1  1/1:0,14:0,0:./.:2|2:2  1/0:3,6:3,6:0/1:2|1:1  1/0:6,5:6,5:0/1:2|1:1  1/1:9,6:0,0:./.:2|2:2  1/1:0,14:0,14:1/1:2|2:2  0/0:12,0:12,0:0/0:1|1:0  0/0:1,0:1,0:0/0:1|1:0  0/0:146,0:146,0:0/0:1|1:0  0/0:8,0:8,0:0/0:1|1:0  1/0:4,6:4,6:0/1:2|1:1  0/1:11,3:11,3:0/1:1|2:1  0/1:2,2:2,2:0/1:1|2:1  0/1:11,8:11,8:0/1:1|2:1  0/1:6,6:6,6:0/1:1|2:1  0/1:3,2:3,2:0/1:1|2:1  0/0:16,0:16,0:0/0:1|1:0  0/0:24,0:0,0:./.:1|1:0  1/0:6,10:6,10:0/1:2|1:1  0/0:19,0:19,0:0/0:1|1:0  0/0:7,0:7,0:0/0:1|1:0  0/0:8,0:8,0:0/0:1|1:0  1/1:0,9:0,9:1/1:2|2:2  ./.:3,6:0,0:./.:.|.:0  0/0:19,0:19,0:0/0:1|1:0  0/0:8,0:8,0:0/0:1|1:0  0/0:6,0:6,0:0/0:1|1:0  1/0:7,4:7,4:0/1:2|1:1  0/1:10,7:10,7:0/1:1|2:1  0/1:4,8:4,8:0/1:1|2:1  0/1:6,4:6,4:0/1:1|2:1  1/0:7,1:7,1:0/0:2|1:1  0/1:4,8:4,8:0/1:1|2:1  0/1:7,3:7,3:0/1:1|2:1  0/0:5,0:0,0:./.:1|1:0  0/1:4,18:4,18:0/1:1|2:1  0/0:8,0:8,0:0/0:1|1:0  0/1:4,6:0,0:./.:1|2:1  0/1:5,4:5,4:0/1:1|2:1  0/0:8,0:8,0:0/0:1|1:0  1/1:0,8:0,0:./.:2|2:2  1/1:0,285:0,285:1/1:2|2:2  0/1:3,2:3,2:0/1:1|2:1  1/0:6,3:6,3:0/1:2|1:1  1/0:4,5:4,5:0/1:2|1:1  1/0:10,4:0,0:./.:2|1:1  0/0:12,0:12,0:0/0:1|1:0  0/0:10,0:10,0:0/0:1|1:0  0/0:12,0:0,0:./.:1|1:0  0/0:13,0:0,0:./.:1|1:0  0/1:5,3:5,3:0/1:1|2:1  0/0:8,0:8,0:0/0:1|1:0  0/1:5,1:5,1:0/1:1|2:1  ./.:0,6:0,6:1/1:.|.:0  1/0:2,7:2,7:0/1:2|1:1  1/1:0,7:0,7:1/1:2|2:2  1/0:3,1:3,1:0/1:2|1:1  0/0:8,0:0,0:./.:1|1:0  1/0:4,2:4,2:0/1:2|1:1  0/1:2,1:2,1:0/1:1|2:1  1/1:0,10:0,10:1/1:2|2:2  1/0:5,2:5,2:0/1:2|1:1  0/0:3,0:3,0:0/0:1|1:0  0/0:10,0:10,0:0/0:1|1:0  1/1:0,4:0,4:1/1:2|2:2  0/0:4,0:4,0:0/0:1|1:0  0/0:9,0:9,0:0/0:1|1:0  1/1:0,11:0,11:1/1:2|2:2  0/1:3,2:3,2:0/1:1|2:1  0/1:5,2:5,2:0/1:1|2:1  0/0:4,0:4,0:0/0:1|1:0  1/0:3,3:3,3:0/1:2|1:1  1/1:0,8:0,0:./.:2|2:2  0/0:9,0:9,0:0/0:1|1:0  0/1:4,5:4,5:0/1:1|2:1  0/0:10,0:10,0:0/0:1|1:0  0/0:7,0:7,0:0/0:1|1:0  0/1:1,3:1,3:0/1:1|2:1  0/0:7,0:7,0:0/0:1|1:0  1/1:0,11:0,11:1/1:2|2:2  1/0:0,3:0,3:1/1:2|1:1  1/1:0,11:0,0:./.:2|2:2  0/1:6,5:6,5:0/1:1|2:1  1/0:1,4:1,4:0/1:2|1:1  1/0:4,2:4,2:0/1:2|1:1  1/1:0,4:0,4:1/1:2|2:2  0/0:9,0:9,0:0/0:1|1:0  1/0:4,4:4,4:0/1:2|1:1  1/0:6,6:0,0:./.:2|1:1

In the output vcf, “PGT=0|0|1|1” in the INFO field indicates the parental samples alleles for haplotype 1, 2, 3, and 4 separated by |. The numbers 0 and 1 represent reference and alternative alleles that are A and G at the marker shown above, resectively. FAD and FGT in the FORMAT field show the allele read counts and genotype call after filtering by setCallFiler(). HAP and EDS indicate descendent haplotypes and haplotype dosage at the given marker. EDS will be provided only if you specified two parents in setParents() and specified het_parent = FALSE for estGeno().

Alternatively, you can also output the genotype data into a CSV file using gbsrGDS2CSV()

out_fn <- tempfile("sample_est", fileext = ".csv")
gbsrGDS2CSV(gds, out_fn)

## [1] "/tmp/RtmpQ7MJxU/sample_est1b14b150631f34.csv"

gbsrGDS2CSV(gds, out_fn, node = "cor")

## [1] "/tmp/RtmpQ7MJxU/sample_est1b14b150631f34.csv"

You can format the output for the qtl package.

out_fn <- tempfile("sample_est", fileext = ".csv")
gbsrGDS2CSV(gds, out_fn, format = "qtl")

## [1] "/tmp/RtmpQ7MJxU/sample_est1b14b168906a13.csv"

gbsrGDS2CSV(gds, out_fn, node = "cor", format = "qtl")

## [1] "/tmp/RtmpQ7MJxU/sample_est1b14b168906a13.csv"

When you set format = "qtl", the marker positions will be automatically converted from physical positions (bp) to genetic distances (cM). The conversion is performed by multiplying the physical positions by the value set to bp2cm. The default is bp2cm = 4e-06 if format = "qtl".

Please use reopenGDS() to open the connection again if you need.

gds <- reopenGDS(gds)

10 Plot Read Ratio and Dosage

The ratio of reference and alternative allele reads can be visualized in dot plots using the plotReadRatio() function. Every single call of plotReadRatio() generates dot plots for all chromosomes but only for a single sample.

plotReadRatio(x = gds, coord = c(3, 4), ind = 3, node = "raw", size = 2)

Since the sample data contains only one chromosome, coord = c(3, 4) does not change any coordinate of the plot. If, for example, you sample has 12 chromosomes, the dot plots for the chromosomes will be shown in 3 rows and 4 columns.

If you need to draw plots for all samples in your dataset, please loop the function over your samples.

for(i in seq_len(nsam(gds))){
plotReadRatio(x = gds, coord = c(3, 4), ind = i, node = "raw")
}

If you set node = "filt", you can visualize the read ratio obtained by applying the setCallFilter() function.

plotReadRatio(x = gds, coord = c(3, 4), ind = 3, node = "filt", size = 2)

The plotDosage() function enables you to visualize the estimated dosage overlaying on the read ratio plot. Since this function visualize dosages estimated by the estGeno() function, you will get an error message if you call this function before running estGeno().

plotDosage(x = gds, coord = c(3, 4), ind = 3, node = "raw", size = 2)

The magenta line indicates the estimated alternative allele dosage par marker while colored dots shows alternative allele read ratio par marker. The plotDosage() function also draw plots for a single sample in a single call. Please loop the function over samples if needed.

You can also use the filtered read count data for the read ratio visualization in the dosage plot.

plotDosage(x = gds, coord = c(3, 4), ind = 3, node = "filt", size = 2)

11 Closing the Connection

To safely close the connection to the GDS file, use closeGDS().

closeGDS(gds)

Session Info

sessionInfo()

## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] GBScleanR_2.1.1  SeqArray_1.47.0  gdsfmt_1.43.0    BiocStyle_2.35.0
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.6            xfun_0.48               bslib_0.8.0            
##  [4] ggplot2_3.5.1           lattice_0.22-6          vctrs_0.6.5            
##  [7] tools_4.5.0             bitops_1.0-9            generics_0.1.3         
## [10] stats4_4.5.0            parallel_4.5.0          tibble_3.2.1           
## [13] fansi_1.0.6             highr_0.11              pkgconfig_2.0.3        
## [16] Matrix_1.7-1            S4Vectors_0.45.0        RcppParallel_5.1.9     
## [19] lifecycle_1.0.4         GenomeInfoDbData_1.2.13 compiler_4.5.0         
## [22] farver_2.1.2            Rsamtools_2.23.0        Biostrings_2.75.0      
## [25] munsell_0.5.1           tinytex_0.53            codetools_0.2-20       
## [28] GenomeInfoDb_1.43.0     htmltools_0.5.8.1       sass_0.4.9             
## [31] yaml_2.3.10             pillar_1.9.0            crayon_1.5.3           
## [34] jquerylib_0.1.4         tidyr_1.3.1             BiocParallel_1.41.0    
## [37] cachem_1.1.0            magick_2.8.5            tidyselect_1.2.1       
## [40] digest_0.6.37           dplyr_1.1.4             purrr_1.0.2            
## [43] bookdown_0.41           labeling_0.4.3          fastmap_1.2.0          
## [46] grid_4.5.0              colorspace_2.1-1        expm_1.0-0             
## [49] cli_3.6.3               magrittr_2.0.3          utf8_1.2.4             
## [52] withr_3.0.2             scales_1.3.0            UCSC.utils_1.3.0       
## [55] rmarkdown_2.28          XVector_0.47.0          httr_1.4.7             
## [58] evaluate_1.0.1          knitr_1.48              GenomicRanges_1.59.0   
## [61] IRanges_2.41.0          rlang_1.1.4             Rcpp_1.0.13            
## [64] glue_1.8.0              BiocManager_1.30.25     BiocGenerics_0.53.0    
## [67] jsonlite_1.8.9          R6_2.5.1                zlibbioc_1.53.0

Elshire, Robert J., Jeffrey C. Glaubitz, Qi Sun, Jesse A. Poland, Ken Kawamoto, Edward S. Buckler, and Sharon E. Mitchell. 2011. “A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species.” PLoS ONE 6 (5): e19379. https://doi.org/10.1371/journal.pone.0019379.

Glaubitz, Jeffrey C., Terry M. Casstevens, Fei Lu, James Harriman, Robert J. Elshire, Qi Sun, and Edward S. Buckler. 2014. “TASSEL-GBS: A High Capacity Genotyping by Sequencing Analysis Pipeline.” PLoS ONE 9 (2): e90346. https://doi.org/10.1371/journal.pone.0090346.

Gogarten, Stephanie M., Tushar Bhangale, Matthew P. Conomos, Cecelia A. Laurie, Caitlin P. McHugh, Ian Painter, Xiuwen Zheng, et al. 2012. “GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies.” Bioinformatics 28 (24): 3329–31. https://doi.org/10.1093/BIOINFORMATICS/BTS610.

Gogarten, Stephanie M, Tamar Sofer, Han Chen, Chaoyu Yu, Jennifer A Brody, Timothy A Thornton, Kenneth M Rice, and Matthew P Conomos. 2019. “Genetic association testing using the GENESIS R/Bioconductor package.” Bioinformatics 35 (24): 5346–48. https://doi.org/10.1093/BIOINFORMATICS/BTZ567.

McKenna, Aaron, Matthew Hanna, Eric Banks, Andrey Sivachenko, Kristian Cibulskis, Andrew Kernytsky, Kiran Garimella, et al. 2010. “The genome analysis toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data.” Genome Research 20 (9): 1297–1303. https://doi.org/10.1101/gr.107524.110.

Miller, Michael R., Joseph P. Dunham, Angel Amores, William A. Cresko, and Eric A. Johnson. 2007. “Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers.” Genome Research 17 (2): 240–48. https://doi.org/10.1101/gr.5681207.

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 28 (24): 3326–28. https://doi.org/10.1093/BIOINFORMATICS/BTS606.

Basic usage of GBScleanR

Aug 27, 2024

Package