| Title: | Genomics-assisted breeding for polyploids (and diploids) |
|---|---|
| Description: | Genomics-assisted breeding for polyploids (and diploids) |
| Authors: | Jeffrey B. Endelman |
| Maintainer: | Jeffrey Endelman <[email protected]> |
| License: | GPL-3 |
| Version: | 0.46 |
| Built: | 2024-11-28 05:27:00 UTC |
| Source: | https://github.com/jendelman/polyBreedR |
Additive relationship matrix from pedigree
A_mat(ped, ploidy, order.ped = TRUE)A_mat(ped, ploidy, order.ped = TRUE)
ped |
Pedigree in three column format: id, mother, father |
ploidy |
2 or 4 |
order.ped |
TRUE/FALSE does the pedigree need to be ordered so that progeny follow parents |
This is a wrapper that prepares the pedigree in the format required for R package AGHmatrix by Amadeu et al. (2016) (cite them if you use this function). A random bivalents model for tetraploid meiosis is assumed.
Additive relationship matrix (dim: indiv x indiv)
Amadeu et al. (2016) Plant Genome 9, doi:10.3835/plantgenome2016.01.0009
Extract read counts from AD string
ADsplit(AD, ALT, n.core = 1)ADsplit(AD, ALT, n.core = 1)
AD |
array of AD strings |
ALT |
TRUE or FALSE (= REF) |
n.core |
number of cores |
Only valid for a single ALT allele.
integer data with same dimensions as AD
Converts output from Genome Studio (Final Report or Wide) to VCF
array2vcf(array.file, map.file, model.file = NULL, ploidy, vcf.file)array2vcf(array.file, map.file, model.file = NULL, ploidy, vcf.file)
array.file |
name of input file with SNP array allele intensities |
map.file |
vcf file with map positions for the markers |
model.file |
normal mixture model parameters for genotype calls |
ploidy |
sample ploidy, for use with |
vcf.file |
output vcf file |
Auto-detects whether the input file is a Genome Studio Final Report, which is a "long" format with 9-row header, or in "wide" format, where all the data for each marker is one row. XY values are multiplied by 100
Genotype calls will attempt to be imported from the GS Final Report when model.file=NULL. For diploids, columns named "Allele 1 - AB" and "Allele 2 - AB" are expected. For tetraploids, a single column named "Alleles - AB" is expected.
It is assumed that the parameters in model.file lead to genotype calls for the dosage of allele B. For a VCF file, genotype calls need to be based on the dosage of ALT. By default, it is assumed that A is the REF allele. For variants where B is REF, include "REF=B" as INFO in the VCF map.file.
Fraction of simplex or triplex markers
check_ploidy(geno = NULL, map = NULL, vcf.file = NULL, max.missing = 0.1)check_ploidy(geno = NULL, map = NULL, vcf.file = NULL, max.missing = 0.1)
geno |
Genotype matrix (markers x indiv) |
map |
Data frame with marker map (Marker, Chrom, Position) |
vcf.file |
VCF file input |
max.missing |
maximum proportion of missing data allowed per marker |
For every indiv in the genotype matrix, the fraction of markers per chromosome called as simplex or triplex is calculated, which should be low for diploids. A small amount of missing genotype data can be tolerated.
As of v4.2, a VCF file can be used as input instead
List containing
Matrix (indiv x chrom) of results
ggplot2 barplot
Check markers for parent-offspring trio
check_trio(parentage, geno, ploidy)check_trio(parentage, geno, ploidy)
parentage |
Data frame with three columns: id, mother, father |
geno |
Matrix of allele dosages: markers x indiv |
ploidy |
2 or 4 |
Computes the percentage of markers at which the two parents and offspring have incompatible allele dosages (for tetraploids, the random bivalents model is used). For dihaploid offspring of a single tetraploid parent, use ploidy = 4 and "haploid" for the father in parentage, as well as a diploid (0,1,2) genotype for the offspring. A small amount of missing genotype data can be tolerated.
Data frame with the percentage of incompatible markers for each trio
Convert DArTag to VCF
dart2vcf(counts.file, dosage.file, vcf.file, ploidy, first.data.row = 9)dart2vcf(counts.file, dosage.file, vcf.file, ploidy, first.data.row = 9)
counts.file |
DArTag collapsed counts file |
dosage.file |
DArTag dosage file |
vcf.file |
name of VCF output file |
ploidy |
ploidy |
first.data.row |
default is 9 for DArTag format |
Two input files expected. counts.file is the two-row collapsed counts file, whereas dosage.file has one row per target, with chrom and position in columns 4 and 5. DArT reports dosage of REF, whereas VCF standard is based on dosage of ALT. The dosage is exported as GT field in VCF.
Duplicate samples are renamed by appending the "Target ID".
Genomic relationship matrix
G_mat(geno, ploidy, p.ref = NULL, method = "VR1", sep = "_", n.core = 1)G_mat(geno, ploidy, p.ref = NULL, method = "VR1", sep = "_", n.core = 1)
geno |
genotype matrix or filename |
ploidy |
ploidy |
p.ref |
optional, reference population frequency for method "VR1" |
method |
"VR1" or "AM" |
sep |
character separating id from haplotype for "AM" method |
n.core |
number of cores, only used with "AM" |
For method="VR1", Method 1 of VanRaden (2008) is used, and its polyploid extension (Endelman et al. 2018). Missing data is replaced with the population mean for each marker. If p.ref is NULL, the current population is used as the reference population. For "VR1", geno is an allele dosage matrix for bi-allelic loci (sites x indiv).
For method="AM", the Allele Matching coefficient is calculated, which is the probability that two haplotypes sampled at random (with replacement) are identical (Weir and Goudet 2017). Missing data are not allowed. For "AM", geno is a phased genotype matrix, with alleles coded as positive integers. The name of each column is the id and haplotype concatenated with a separating character, sep. This character needs to be unique (i.e., not present in id or haplotype).
G matrix
VanRaden (2008) J. Dairy Sci 91:4414-4423.
Endelman et al. (2018) Genetics 209:77-87.
Weir and Goudet (2017) Genetics 206:2085-2103.
Genotype calls for genotype-by-sequencing (GBS) data
gbs( in.file, out.file, ploidy, bias = TRUE, n.core = 1, chunk.size = 1000, silent = FALSE, model.fit = TRUE )gbs( in.file, out.file, ploidy, bias = TRUE, n.core = 1, chunk.size = 1000, silent = FALSE, model.fit = TRUE )
in.file |
VCF input file |
out.file |
VCF output file |
ploidy |
ploidy |
bias |
TRUE/FALSE, whether to estimate allelic bias |
n.core |
number of cores |
chunk.size |
number of variants to process at a time |
silent |
TRUE/FALSE |
model.fit |
TRUE/FALSE |
VCF input file must contain AD field. Variants with more than 2 alleles are coerced to zero DP, so better to filter them out first.
Posterior mode and mean genotypes are added as GT and DS fields. GQ is also added based on probability of posterior mode. Binomial calculation uses R/updog package (Gerard et al. 2018) with "norm" prior. Previous INFO is discarded; adds NS, DP.AVG, AF.GT, AB, OD, SE.
When model.fit is FALSE, the software uses AB, OD, and SE parameters from INFO.
The input file is processed in chunks of size chunk.size.
nothing
Genotype calls based on a normal mixture model
geno_call( data, filename, model.ploidy = 4L, sample.ploidy = 4L, min.posterior = 0, transform = TRUE )geno_call( data, filename, model.ploidy = 4L, sample.ploidy = 4L, min.posterior = 0, transform = TRUE )
data |
matrix (markers x id) of input values for the normal mixture model |
filename |
CSV filename with the model parameters |
model.ploidy |
2 or 4 (default) |
sample.ploidy |
2 or 4 (default) |
min.posterior |
minimum posterior probability (default 0) for genotype call |
transform |
TRUE (default) or FALSE whether to apply arcsin square root transformation |
The first column of the CSV input file should be the SNP ID, followed by columns for the normal distribution means, standard deviations, and mixture probabilities. Genotype calls are based on the maximum a posteriori (MAP) method. If the posterior probability of the MAP genotype is less than min.posterior, then NA is returned for that sample. By default, an arcsin square root transformation is applied to the input values to match the approach used by R package fitPoly. To use a tetraploid mixture model for diploid samples, set sample.ploidy = 2 and model.ploidy = 4.
matrix of allele dosages (0,1,2,..ploidy) with dimensions markers x individuals
Generate pedigree for a set of individuals
get_pedigree(id, pedfile, delim = ",", na.string = "NA", trim = TRUE)get_pedigree(id, pedfile, delim = ",", na.string = "NA", trim = TRUE)
id |
Vector of names of individuals |
pedfile |
Name of pedigree file |
delim |
Delimiter for the pedigree file (default is "," for CSV) |
na.string |
String used for NA in the pedigree file (default is "NA") |
trim |
TRUE/FALSE whether to trim pedigree (see Details) |
Finds ancestors of individuals in a three-column pedigree file (id,mother,father). The id column can be the identifier for an individual or cross. String matches must be exact or based on the naming convention crossID-progenyID. The returned pedigree is ordered using R package pedigree so that offspring follow parents. When trim is TRUE (default), the pedigree is trimmed to remove ancestors with only one offspring (which are not needed to compute the pedigree relationship matrix).
Data frame with columns id, mother, father
Convert GT to ALT allele dosage (DS)
GT2DS(GT, diploidize = FALSE, n.core = 1)GT2DS(GT, diploidize = FALSE, n.core = 1)
GT |
GT string |
diploidize |
TRUE/FALSE |
n.core |
number of cores |
If diploidize is TRUE, data are recoded as a diploid 0,1,2.
integer data with same dimensions as GT
Plot marker-based vs. pedigree-based additive relationship coefficients
GvsA( parentage, G, A, filename = NULL, thresh.G = Inf, thresh.A = 0.5, Gmax = NULL, Amax = NULL )GvsA( parentage, G, A, filename = NULL, thresh.G = Inf, thresh.A = 0.5, Gmax = NULL, Amax = NULL )
parentage |
Data frame of individuals to plot, with 3 columns: id,mother,father |
G |
Genomic relationship matrix |
A |
Pedigree relationship matrix |
filename |
Name of PDF file to save the results (optional for one individual) |
thresh.G |
Threshold above which names are displayed (default Inf) |
thresh.A |
Threshold above which names are displayed (default 0.5) |
Gmax |
Upper limit for y-axis for plotting. If NULL, maximum value in G is used. |
Amax |
Upper limit for x-axis for plotting. If NULL, maximum value in A is used. |
Useful for finding and correcting pedigree errors. If the G or A coefficient for an individual exceeds the threshold, its name is displayed in the figure. If parentage contains one individual, by default a ggplot2 variable will be returned, but the result can also be written to file. If multiple individuals are present, a filename is required.
Impute missing data for bi-allelic markers
impute( in.file, out.file, ploidy, method, geno, min.DP = 1, max.missing, params = NULL, n.core = 1 )impute( in.file, out.file, ploidy, method, geno, min.DP = 1, max.missing, params = NULL, n.core = 1 )
in.file |
VCF input file |
out.file |
VCF output file |
ploidy |
ploidy |
method |
One of the following: "pop","EM","RF" |
geno |
One of the following: "GT","DS" |
min.DP |
genotypes below this depth are set to missing (default=1) |
max.missing |
remove markers above this threshold, as proportion of population |
params |
list of method-specific parameters |
n.core |
multicore processing |
Assumes input file is sorted by position. Markers with no genetic variance are removed.
method="pop" imputes with the population mean for geno="DS" and population mode for geno="GT".
method="EM" uses parameter "tol" (default is 0.02, see rrBLUP A.mat documentation). Imputed values are truncated if needed to fall in the interval [0,ploidy].
method="RF" uses parameters "ntree" (default 100) for number of trees and "nflank" (default 100) for the number of flanking markers (on each side) to use as predictors. Because RF first uses EM to generate a complete dataset, parameter "tol" is also recognized.
Impute from low to high density markers by Random Forest
impute_L2H( high.file, low.file, out.file, params = list(), exclude = NULL, n.core = 1 )impute_L2H( high.file, low.file, out.file, params = list(), exclude = NULL, n.core = 1 )
high.file |
name of high density file |
low.file |
name of low density file |
out.file |
name of CSV output file for imputed data |
params |
list of parameters (see Details) |
exclude |
optional, vector of high density samples to exclude |
n.core |
multicore processing |
Argument params is a list with three named elements: format, n.tree, n.mark. format can have values "GT" (integer dosage) or "DS" (real numbers between 0 and ploidy). Classification trees are used for GT and regression trees for DS. n.tree is the number of trees (default = 100). n.mark is the number of markers to use as predictors (default = 100), chosen based on minimum distance to the target.
The exclude argument is useful for cross-validation.
Both VCF and CSV are allowable input file formats–they are recognized based on the file extension. For CSV, the first three columns should be marker, chrom, pos. The output file is CSV.
Any missing data are imputed separately for each input file at the outset, using the population mean (DS) or mode (GT) for each marker.
matrix of OOB error with dimensions markers x trees
Impute from low to high density markers by Linkage Analysis
impute_LA(ped.file, high.file, low.file, low.format = "GT", out.file)impute_LA(ped.file, high.file, low.file, low.format = "GT", out.file)
ped.file |
pedigree file for progeny |
high.file |
name of file with phased parental genotypes |
low.file |
name of VCF file with progeny |
low.format |
either "GT" (default) or "AD" |
out.file |
name of CSV output file |
You must have separately installed PolyOrigin and Julia for this function to work.
The high density file contains phased parental genotypes using 0|1 format. The first 3 columns are the genetic map in cM: marker, chrom, position. To output imputed data with physical rather than genetic map positions, including a fourth column named "bp". Subsequent columns are the phased parental genotypes.
VCF is assumed for the low-density file. The pedigree file has four columns: id, pop, mother, father, ploidy.
The output file contains the posterior maximum genotypes.
A temporary directory "tmp" is created to store intermediate files and then deleted.
Multi-Allelic Haplotype Counts for potato DArTag
madc(madc.file, marker)madc(madc.file, marker)
madc.file |
MADC filename |
marker |
Name of marker ("CDF1","OFP20") |
Due to multi-allelism, for some trait markers a correct interpretation is not possible using the collapsed counts file; the MADC (Missing Allele Discovery Count) file is needed.
"CDF1" uses marker CDF1.4_chr05_4488021 to detect the 2C, 2T, and 4 alleles; all other haplotypes are treated as allele 1. Allele 3 is not detected by the assay.
"OFP20" relies on three markers. Marker OFP20_M6_CDS_994 detects OFP20.1 as Alt and most other haplotypes as Ref, but some alleles appear to be NULL. Marker OFP20_M6_CDS_171 detects allele 2 as Alt and alleles 3 and 7 as Ref; other alleles are NULL. Marker OFP20_M6_CDS_24 detects allele 8 as Ref and most other alleles as Alt. Given the high allelic diversity at this locus, this function may not work in all germplasm groups.
matrix of haplotype counts
Merge two genotype matrices and impute missing data by BLUP
merge_impute(geno1, geno2, ploidy)merge_impute(geno1, geno2, ploidy)
geno1 |
Genotype matrix (coded 0...ploidy) with dimensions markers x indiv |
geno2 |
Genotype matrix (coded 0...ploidy) with dimensions markers x indiv |
ploidy |
Either 2 or 4 |
This function is obsolete. Use impute_L2H instead.
Designed to impute from low to high density markers. The BLUP method is equivalent to Eq. 4 of Poland et al. (2012), but this function is not iterative. Additional shrinkage toward the mean is applied if needed to keep the imputed values within the range [0,ploidy]. Missing data in the input matrices are imputed with the population mean for each marker. If an individual appears in both input matrices, it is renamed with suffixes ".1" and ".2" and treated as two different individuals. Monomorphic markers are removed.
Imputed genotype matrix (markers x indiv)
Poland et al. (2012) Plant Genome 5:103-113.
Read SNP array intensity data
readXY(filename, skip = 9, output = "ratio")readXY(filename, skip = 9, output = "ratio")
filename |
filename |
skip |
number of lines to skip before the header line with the column names |
output |
One of three options: "ratio","theta","AD" |
The first two columns of the tab-delimited input file should be the SNP and Sample ID. Columns labeled "X" and "Y" contain the signal intensities for the two alleles. Use output to specify whether to return the ratio = Y/(X+Y) or theta = atan(Y/X)*2/pi. Option "AD" exports the XY data in the allele depth format for a VCF file ("X,Y"), with the X and Y values multiplied by 100 and rounded to the nearest integer.
matrix with dimensions markers x individuals
Update names based on data frame with alias and preferred name
update_alias(x, alias, remove.space = TRUE, filename = NULL)update_alias(x, alias, remove.space = TRUE, filename = NULL)
x |
Vector of names to update |
alias |
Data frame with two columns: first is the preferred name and second is the alias |
remove.space |
TRUE/FALSE |
filename |
update names in CSV file |
Parameter remove.space indicates whether blank spaces should be removed before string matching.
Vector with updated names
Convert VCF to CSV
vcf2csv(vcf.file, csv.file, format)vcf2csv(vcf.file, csv.file, format)
vcf.file |
Input file |
csv.file |
Output file |
format |
Name of FORMAT key to export, either "GT" or "DS" |
none
Create VCFv4.3 file
write_vcf(filename, fixed, geno, other.meta = NULL)write_vcf(filename, fixed, geno, other.meta = NULL)
filename |
VCF file name |
fixed |
character matrix with 8 columns: CHROM, POS, ID, REF, ALT, QUAL, FILTER, INFO |
geno |
named list of genotype matrices, see Details |
other.meta |
optional, other metadata (without ##) besides INFO and FORMAT keys |
Several standard INFO key are recognized:
##INFO=<ID=REF,Number=A,Type=Character,Description=\"Array allele (A/B) in reference genome\">
##INFO=<ID=NS,Number=1,Type=Integer,Description="Number of samples with data">
##INFO=<ID=DP.AVG,Number=1,Type=Float,Description="Average Sample Depth">
##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">
##INFO=<ID=AB,Number=1,Type=Float,Description="Allelic Bias">
##INFO=<ID=SE,Number=1,Type=Integer,Description="Sequencing Error (PHRED)">
##INFO=<ID=OD,Number=1,Type=Integer,Description="OverDispersion (PHRED)">
##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">
##INFO=<ID=AF.GT,Number=A,Type=Float,Description="Allele Frequency based on GT">
##INFO=<ID=AC,Number=A,Type=Integer,Description="Allele count in genotypes">"
##INFO=<ID=AN,Number=1,Type=Integer,Description="Total number of alleles">"
Every element of geno is m x n matrix (m variants, n samples), e.g., AD, GT. The FORMAT field is created from the order and names of geno. Sample names taken from colnames of geno. Metadata for geno is generated from the names of the list:
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=AD,Number=R,Type=Integer,Description="Allele Depth">
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Sample Depth">
##FORMAT=<ID=DS,Number=1,Type=Float,Description="Posterior Mean Dosage">
##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">
Any additional metadata should be included without the ## prefix.