Package 'hsphase' reference manual

Title:	Phasing, Pedigree Reconstruction, Sire Imputation and Recombination Events Identification of Half-sib Families Using SNP Data
Description:	Identification of recombination events, haplotype reconstruction, sire imputation and pedigree reconstruction using half-sib family SNP data.
Authors:	Mohammad Ferdosi <[email protected]>, Cedric Gondro <[email protected]>
Maintainer:	Mohammad Ferdosi <[email protected]>
License:	GPL-3
Version:	2.0.3
Built:	2024-12-13 06:34:17 UTC
Source:	CRAN

Phasing, Pedigree Reconstruction, Sire Imputation and Recombination Events Identification for Half-sib Families

Description

Identification of recombination events, haplotype reconstruction and sire imputation using half-sib family SNP data.

Details

Package:	hsphase
Type:	Package
Version:	2.0.1
Date:	2014-6-17
License:	GPL 3

Main Functions:
bmh: Block partitioning
ssp: Sire inference
aio: Phasing
imageplot: Image plot of the block structure
rpoh: Reconstruct pedigree based on opposing homozygote

Auxiliary Functions
hss: Half-sib family splitter
cs: Chromosome splitter
para: Parallel data analysis

Note: These functions can be used to analyse large datasets.

Author(s)

Mohammad H. Ferdosi <[email protected]>, Cedric Gondro <[email protected]> Maintainer: Mohammad H. Ferdosi <[email protected]>

References

Ferdosi, M. H., Kinghorn, B. P., van der Werf, J. H., & Gondro, C (2013). Effect of genotype and pedigree error on detection of recombination events, sire imputation and haplotype inference using the hsphase algorithm. In Proc. Assoc. Advmt. Anim. Breed. Genet (Vol. 20, pp. 546-549). AAABG; Napier, New Zealand.

Ferdosi, M. H., Kinghorn, B. P., van der Werf, J. H., & Gondro, C. (2014). Detection of recombination events, haplotype reconstruction and imputation of sires using half-sib SNP genotypes. Genetics, selection, evolution: GSE, 46(1), 11.

Ferdosi, M. H., Kinghorn, B. P., van der Werf, J. H., Lee, S. H., & Gondro, C. (2014). hsphase: an R package for pedigree reconstruction, detection of recombination events, phasing and imputation of half-sib family groups. BMC Bioinformatics, 15(1), 172.

Ferdosi, M. H., & Boerner, V. (2014). A fast method for evaluating opposing homozygosity in large SNP data sets. Livestock Science.

Examples

genotype <- matrix(c(
  0,0,0,0,1,2,2,2,0,0,2,0,0,0,
  2,2,2,2,1,0,0,0,2,2,2,2,2,2,
  2,2,2,2,1,2,2,2,0,0,2,2,2,2,
  2,2,2,2,0,0,0,0,2,2,2,2,2,2,
  0,0,0,0,0,2,2,2,2,2,2,0,0,0), ncol = 14, byrow = TRUE) 
ssp(bmh(genotype), genotype)
aio(genotype)
imageplot(bmh(genotype), title = "ImagePlot example")
rplot(genotype, c(1:14))
genotype <- matrix(c(
  0,0,0,0,1,2,2,2,0,0,2,0,0,0,
  2,2,2,2,1,0,0,0,2,2,2,2,2,2,
  2,2,2,2,1,2,2,2,0,0,2,2,2,2,
  2,2,2,2,0,0,0,0,2,2,2,2,2,2,
  0,0,0,0,0,2,2,2,2,2,2,0,0,0), ncol = 14, byrow = TRUE) 
ssp(bmh(genotype), genotype)
aio(genotype)
imageplot(bmh(genotype), title = "ImagePlot example")
rplot(genotype, c(1:14))

Calculate Genotypic Distances

Description

Calculates a symmetric matrix of distances between genotypes, based on a given genotype matrix. Each row in the 'GenotypeMatrix' represents a genotype, and each column represents a marker. The genotype is coded as 0 for AA, 1 for AB, and 2 for BB. Use 9 to represent missing data.

Usage

.fastdist(GenotypeMatrix)
.fastdist(GenotypeMatrix)

Arguments

GenotypeMatrix

A matrix where each row represents a genotype and each column represents a marker. Genotypes should be coded as 0 for AA, 1 for AB, and 2 for BB, with 9 representing missing data.

Value

Returns a symmetric matrix of distances between the genotypes specified in the 'GenotypeMatrix'. Row and column names of the returned matrix correspond to the row names of the 'GenotypeMatrix'.

Examples

# Simulate genotype data for 40 individuals across 1000 SNPs
# genotypes <- simulateHalfsib(numInd = 40, numSNP = 1000, recbound = 0:6, type = "genotype")
# Calculate the distance matrix
# dist_matrix <- fastdist(genotypes)
# Display the distance matrix
# print(dist_matrix)

# Simulate genotype data for 40 individuals across 1000 SNPs
# genotypes <- simulateHalfsib(numInd = 40, numSNP = 1000, recbound = 0:6, type = "genotype")
# Calculate the distance matrix
# dist_matrix <- fastdist(genotypes)
# Display the distance matrix
# print(dist_matrix)

Calculate Minor Allele Frequency (MAF)

Description

This function calculates the minor allele frequency (MAF) for a given single nucleotide polymorphism (SNP) data. The SNP data should be coded numerically: 0 for homozygous for the first allele (AA), 1 for heterozygous (AB), and 2 for homozygous for the second allele (BB). Missing data should be coded as 9.

Usage

.maf(snp)
.maf(snp)

Arguments

snp

A numeric vector representing the genotype of individuals for a single SNP. The genotype should be coded as 0 for AA, 1 for AB, and 2 for BB. Use 9 to represent missing data.

Value

A numeric value representing the minor allele frequency (MAF) for the SNP data provided.

Examples

snp_data <- c(0, 0, 1, 2, 2, 9)
maf_value <- .maf(snp_data)
print(maf_value)

snp_data <- c(0, 0, 1, 2, 2, 9)
maf_value <- .maf(snp_data)
print(maf_value)

Simulate Half-Sibling Genotypes

Description

This function simulates genotypes for a set of half-siblings based on specified parameters, including the number of individuals, the number of SNPs, recombination boundaries, and the type of data to return. It generates a sire genotype, maternal half-sib genotypes, and combines these to simulate offspring genotypes, optionally returning phased genotypes based on recombination events.

Usage

.simulateHalfsib(
  numInd = 40,
  numSNP = 10000,
  recbound = 0:6,
  type = "genotype"
)
.simulateHalfsib(
  numInd = 40,
  numSNP = 10000,
  recbound = 0:6,
  type = "genotype"
)

Arguments

`numInd`	Integer, the number of half-siblings to simulate.
`numSNP`	Integer, the number of SNPs to simulate for each individual.
`recbound`	Numeric vector, specifying the range of possible recombination events to simulate.
`type`	Character string, specifying the type of data to return: "genotype" for genotypic data or any other string for phased genotypic data.

Value

Depending on the type parameter, this function returns a matrix of simulated genotypic data for half-siblings. If type is "genotype", it returns unphased genotypic data; otherwise, it returns phased genotypic data.

Examples

sim_genotypes <- .simulateHalfsib(numInd = 40, numSNP = 10000, recbound = 0:6, type = "genotype")
dim(sim_genotypes) # Should return 40 rows (individuals) and 100 columns (SNPs)

sim_genotypes <- .simulateHalfsib(numInd = 40, numSNP = 10000, recbound = 0:6, type = "genotype")
dim(sim_genotypes) # Should return 40 rows (individuals) and 100 columns (SNPs)

All-in-one Phasing

Description

Phasing of a half-sib family group.

Usage

aio(genotypeMatrix, bmh_forwardVectorSize = 30, bmh_excludeFP = TRUE,
bmh_nsap = 3, output = "phase")
aio(genotypeMatrix, bmh_forwardVectorSize = 30, bmh_excludeFP = TRUE,
bmh_nsap = 3, output = "phase")

Arguments

`genotypeMatrix`	`matrix` half-sib genotypes (one half-sib per row, with SNP ordered by mapping position in the columns. Data should be numeric. Use 0, 1 and 2 respectively for AA, AB and BB. Use 9 for missing data)
`bmh_forwardVectorSize`	`integer` number of heterozygous sites used to validate recombination events or check for genotyping errors
`bmh_excludeFP`	`logical` excludes SNPs that may cause heterozygous sites in the sire due to genotyping errors or map errors
`bmh_nsap`	`integer` number of SNP per block to validate recombinations
`output`	`character` if equal to the `phase` the 'aio' will only return the phasing results

Details

This function calls the bmh, ssp and phf functions.

Value

Returns a list of matrices. The first element (phasedHalfsibs) is a matrix with two rows (phased haplotypes) per individual (first paternal and second maternal). Data in format 0 (A), 1 (B) and 9 (unphased or missing). The second (sireHaplotype) and third (blockStructure) elements are the same as the output of ssp and bmh.

Note

Only this function needs to be called to phase a half-sib family. The genotype's matrix must contain individuals from only one half-sib family and one ordered chromosome.

Examples

genotype <- matrix(c(       # Define a Half-sib Genotype Matrix
  2,1,0,                    # Individual 1
  2,0,0,                    # Individual 2
  0,0,2                     # Individual 3
  ), byrow = TRUE, ncol = 3)    # There are 3 individulas with three SNPs
  
aio(genotype)               # The genotypes must include only one half-sib family and one chromosome
genotype <- matrix(c(       # Define a Half-sib Genotype Matrix
  2,1,0,                    # Individual 1
  2,0,0,                    # Individual 2
  0,0,2                     # Individual 3
  ), byrow = TRUE, ncol = 3)    # There are 3 individulas with three SNPs
  
aio(genotype)               # The genotypes must include only one half-sib family and one chromosome

Block Partitioning

Description

Identifies the block structure (chromosome segments) in the half-sib family that each individual inherited from its sire.

Usage

bmh(GenotypeMatrix, forwardVectorSize = 30, excludeFP = TRUE, nsap = 3)
bmh(GenotypeMatrix, forwardVectorSize = 30, excludeFP = TRUE, nsap = 3)

Arguments

`GenotypeMatrix`	`matrix` half-sib genotypes (one half-sib per row, with SNP ordered by mapping position in the columns. Data should be numeric. Use 0, 1 and 2 respectively for AA, AB and BB. Use 9 for missing data)
`forwardVectorSize`	`integer` number of heterozygous sites used to validate recombination events or check for genotyping errors (50k -> 30, 700k -> 120)
`excludeFP`	`logical` excludes SNPs that may cause heterozygous sites in the sire due to genotyping errors or map errors
`nsap`	`integer` number of SNP per block to validate recombinations (50k -> 3, 700k -> 10)

Value

Returns a matrix of the blocking structure that contains 1s, 2s and 0s. 1s and 2s are the two sire strands. The choice of strand is arbitrary for each chromosome and not consistent across chromosomes. 0s indicate regions of unknown origin.

Note

The genotype's matrix must contain individuals from only one half-sib family and one ordered chromosome.

Examples

genotype <- matrix(c(
0,2,1,1,1,
2,0,1,2,2,
2,2,1,0,2,
2,2,1,1,1,
0,0,2,1,0), ncol = 5, byrow = TRUE) 

(result <- bmh(genotype))

genotype <- matrix(c(
0,2,1,1,1,
2,0,1,2,2,
2,2,1,0,2,
2,2,1,1,1,
0,0,2,1,0), ncol = 5, byrow = TRUE) 

(result <- bmh(genotype))

Crossover Detection

Description

Detect all possible crossover events.

Usage

co(genotypeMatrix)
co(genotypeMatrix)

Arguments

genotypeMatrix

matrix half-sib genotypes (one half-sib per row, with SNP ordered by mapping position in the columns. Data should be numeric. Use 0, 1 and 2 for respectively AA, AB and BB. Use 9 for missing data)

Value

Returns a matrix with the number of crossover events for each site.

Examples

genotype <- matrix(c(           # Define a Half-sib Genotype Matrix
  2,1,0,                        # Individual 1
  2,0,2,                        # Individual 2
  0,0,2                         # Individual 3
  ), byrow = TRUE, ncol = 3)    # There are 3 individuals with three SNPs
  
co(genotype)               

genotype <- matrix(c(           # Define a Half-sib Genotype Matrix
  2,1,0,                        # Individual 1
  2,0,2,                        # Individual 2
  0,0,2                         # Individual 3
  ), byrow = TRUE, ncol = 3)    # There are 3 individuals with three SNPs
  
co(genotype)

Chromosome Splitter

Description

This function splits the genotypes list generated by hss into the different chromosomes based on a map file and orders SNP based on chromosomal position.

Usage

cs(halfsib, mapPath, separator = " ")
cs(halfsib, mapPath, separator = " ")

Arguments

`halfsib`	`list` list with matrices of half-sib genotypes, one family per list item
`mapPath`	`character` path to the map file (column 1 -> SNP names, column 2 -> chromosome name and column 3 -> SNP position in base pairs) or, alternatively, the name of a dataframe with the mapping information (in the same format)
`separator`	`character` separator character used in the the map file

Details

The map file should include only the chromosomes that will be analyzed. For example, the Y and X chromosomes should be excluded (and others optionally). Names of each element in the list can be used for further categorization. The header must be "Name Chr Position".

Value

Returns a list of matrices, the number of elements in this list is the number of half-sib families multiplied by the number of chromosomes.

Examples

# Please run demo(hsphase)
# Please run demo(hsphase)

Example of Genotype Data Set

Description

This data set serves as an example of a genotype matrix intended for use with the hsphase package.

Usage

data(genotypes)
data(genotypes)

Format

The data set is a genotype matrix with specific structure, including:

Columns: Represent Single Nucleotide Polymorphisms (SNPs). Each column corresponds to a specific SNP.
Rows: Represent individual animals. Each row corresponds to the genotypic data for a single animal across various SNPs.

Haplotype Blocks of Phased Data

Description

Creates a blocking structure matrix of the half-sib family based on phased data of the sire and half-sib family.

Usage

hbp(PhasedGenotypeMatrix, PhasedSireGenotype, strand = "auto")
hbp(PhasedGenotypeMatrix, PhasedSireGenotype, strand = "auto")

Arguments

`PhasedGenotypeMatrix`	`matrix` haplotypes for a half-sib family (two rows per individual)
`PhasedSireGenotype`	`matrix` haplotypes of sire
`strand`	`character` method for identification of paternal strand (1 and 2 for strand one and two of the offsprings)

Value

Returns a matrix where 3 or 4 stands for the SNP originating in, respectively, strands 1 and 2. 0 indicates that the source strand for the SNP is unknown.

Note

The input matrices must only contain individuals from one half-sib family and one ordered chromosome. The strand option should be set to "auto" (default value).

Examples

sire <- matrix(c(
  0,0,0,0,0,1,                  # Haplotype one of the sire
  0,1,1,1,1,0                   # Haplotype two of the sire
  ), byrow = TRUE, ncol = 6)
  
haplotypeHalfsib <- matrix(c(
  1,0,1,1,1,1,                  # Individual one, haplotype one
  0,1,0,0,0,0,                  # Individual one, haplotype two
  0,1,1,0,1,1,                  # Individual two, haplotype one
  1,0,0,1,0,0                   # Individual two, haplotype two
  ), byrow = TRUE, ncol = 6)    # 0s and 1s are alelle a and b
  
 hbp(haplotypeHalfsib, sire)
sire <- matrix(c(
  0,0,0,0,0,1,                  # Haplotype one of the sire
  0,1,1,1,1,0                   # Haplotype two of the sire
  ), byrow = TRUE, ncol = 6)
  
haplotypeHalfsib <- matrix(c(
  1,0,1,1,1,1,                  # Individual one, haplotype one
  0,1,0,0,0,0,                  # Individual one, haplotype two
  0,1,1,0,1,1,                  # Individual two, haplotype one
  1,0,0,1,0,0                   # Individual two, haplotype two
  ), byrow = TRUE, ncol = 6)    # 0s and 1s are alelle a and b
  
 hbp(haplotypeHalfsib, sire)

Heatmap of Half-sibs

Description

The hh function creates a heatmap of the half-sib families using the matrix of opposing homozygotes.

Usage

	hh(oh, inferredPedigree, realPedigree, pedOnly = TRUE)
hh(oh, inferredPedigree, realPedigree, pedOnly = TRUE)

Arguments

`oh`	`matrix` Opposing homozygotes matrix (output of `ohg`)
`inferredPedigree`	`matrix` inferred pedigree (output of `rpoh`)
`realPedigree`	`matrix` original pedigree
`pedOnly`	`logical` Consider only individuals that are exist in the real pedigree

Value

Returns the heatmap of the matrix of opposing homozygotes with sidebars colour coded by sires from the inferred and original pedigree.

Author(s)

The fuction uses the colour generated by getcol function in the made4 package (Aedin Culhane).

Examples

c1h1 <- .simulateHalfsib(numInd = 62, numSNP = 5000)
c1h2 <- .simulateHalfsib(numInd = 38, numSNP = 5000)
Genotype <- rbind(c1h1, c1h2)
oh <- ohg(Genotype) # creating the Opposing Homozygote matrix
hh(oh)
c1h1 <- .simulateHalfsib(numInd = 62, numSNP = 5000)
c1h2 <- .simulateHalfsib(numInd = 38, numSNP = 5000)
Genotype <- rbind(c1h1, c1h2)
oh <- ohg(Genotype) # creating the Opposing Homozygote matrix
hh(oh)

Half-sib Family Splitter

Description

Splits the dataset into half-sib family groups based on a pedigree.

Usage

hss(pedigree, genotype, check = TRUE)
hss(pedigree, genotype, check = TRUE)

Arguments

`pedigree`	`matrix` the pedigree matrix should contain at least two columns, the first column with the half-sib IDs and the second column with the sires IDs
`genotype`	`matrix` genotype matrix with SNP ordered by mapping position in the columns. Data should be numeric. Use 0, 1 and 2 respectively for AA, AB and BB. Use 9 for missing data
`check`	`logical` check the genotype file for the possible errors

Details

Only half-sib groups that have more than 3 individuals will be returned.

Value

Returns a list of numeric matrices, each matrix is a half-sib family.

Note

Pedigree must have at least two columns with sample ids (Column 1) and sire ids (Column 2).

Examples

# Please run demo(hsphase)
# Please run demo(hsphase)

Image Plot of Blocking Structure

Description

Create an imageplot of the blocking structure.

Usage

imageplot(x, title, rv = FALSE, ...)
imageplot(x, title, rv = FALSE, ...)

Arguments

`x`	`matrix` blocking structure (output of bmh or hbp functions)
`title`	`character` title of imageplot
`rv`	`logical` reverse the colour
`...`	Can be used to set xLabels and yLabels axis.

Details

White indicates regions of unknown origin, red and blue correspond to the two sire strands.

Author(s)

This is a modified version of a function written by Chris Seidel.
http://www.phaget4.org/R/image_matrix.html

Examples

genotype <- matrix(c(
0,2,1,1,1,
2,0,1,2,2,
2,2,1,0,2,
2,2,1,1,1,
0,0,2,1,0), ncol = 5, byrow = TRUE) # each row contains the SNP of individuals
imageplot(bmh(genotype))
genotype <- matrix(c(
0,2,1,1,1,
2,0,1,2,2,
2,2,1,0,2,
2,2,1,1,1,
0,0,2,1,0), ncol = 5, byrow = TRUE) # each row contains the SNP of individuals
imageplot(bmh(genotype))

Impute of Low Density SNP Marker to High Density (Paternal Strand)

Description

Impute the paternal strand from low density to high density utilising high density sire haplotype.

Usage

impute(halfsib_genotype_ld, sire_hd, bmh_forwardVectorSize = 30, 
bmh_excludeFP = TRUE, bmh_nsap = 3)
impute(halfsib_genotype_ld, sire_hd, bmh_forwardVectorSize = 30, 
bmh_excludeFP = TRUE, bmh_nsap = 3)

Arguments

`halfsib_genotype_ld`	`matrix` half-sib genotypes with low density marker (one half-sib per row, with SNP ordered by mapping position in the columns. Data should be numeric. Use 0, 1 and 2 respectively for AA, AB and BB. Use 9 for missing data)
`sire_hd`	`matrix` haplotype of sire (this parameter can be sequence data or any phased sire - the matrix should have rownames which are the sample IDs and colnames which are the SNP names)
`bmh_forwardVectorSize`	`integer` number of heterozygous sites used to validate recombination events or check for genotyping errors
`bmh_excludeFP`	`logical` exclude SNPs that may cause heterozygous sites in the sire due to genotyping errors or map errors
`bmh_nsap`	`integer` number of SNPs per block

Value

Return an imputed half-sib matrix.

Example Map File for Genetic Data

Description

This data set is an example of a map file used within the hsphase package to demonstrate the mapping of SNPs to their respective locations on chromosomes.

Usage

data(map)
data(map)

Format

The data set is formatted as a data frame with the following columns, providing essential information about each SNP:

Name: The unique identifier or name of the SNP.
Chr: The chromosome on which the SNP is located.
Position: The position of the SNP on the chromosome, expressed in base pairs.

Opposing Homozygote Detection

Description

Counts the number of opposing homozygotes for each animal that caused a heterozygus site in the sire.

Usage

ohd(genotypeMatrix, unique_check = FALSE, SNPs = 6000)
ohd(genotypeMatrix, unique_check = FALSE, SNPs = 6000)

Arguments

`genotypeMatrix`	`matrix` half-sib genotypes (one half-sib per row, with SNP ordered by mapping position in the columns. Data should be numeric. Use 0, 1 and 2 respectively for AA, AB and BB. Use 9 for missing data)
`unique_check`	`logical` check if samples uniquely originate an opposing homozygote at a locus
`SNPs`	`integer` number of SNP to use

Value

Returns a vector with the number of heterozygous sites that each sample caused.

Note

This function can be used to identify pedigree errors; i.e., the outliers.

Author(s)

This method is suggested by Bruce Tier <[email protected]> to identify pedigree errors.

Examples

genotype <- matrix(c(        
  2,1,0,                  
  2,0,0,                  
  0,0,2                   
  ), byrow = TRUE, ncol = 3)    
  
ohd(genotype)             
genotype <- matrix(c(        
  2,1,0,                  
  2,0,0,                  
  0,0,2                   
  ), byrow = TRUE, ncol = 3)    
  
ohd(genotype)

Matrix of Opposing Homozygotes

Description

Creates a matrix of opposing homozygotes from the genotype matrix.

Usage

ohg(genotypeMatrix)
ohg(genotypeMatrix)

Arguments

genotypeMatrix

matrix genotype (Data should be numeric. Use 0, 1 and 2 respectively for AA, AB and BB. Use 9 for missing data)

Value

Returns a square matrix (sample X sample) with the pairwise counts of opposing homozygotes.

Note

This function can be slow with a large data set. The fast version of this function will be available after publish of the related manuscript.

Author(s)

Ferdosi, M. H., & Boerner, V. (2014). A fast method for evaluating opposing homozygosity in large SNP data sets. Livestock Science.

Examples

genotype <- matrix(c(        
  2,1,0,                  
  2,0,0,                  
  0,0,2                   
  ), byrow = TRUE, ncol = 3)    
  
ohg(genotype)             
genotype <- matrix(c(        
  2,1,0,                  
  2,0,0,                  
  0,0,2                   
  ), byrow = TRUE, ncol = 3)    
  
ohg(genotype)

Opposing Homozygotes Plot

Description

Plot the sorted vectorized matrix of Opposing Homozygotes.

Usage

ohplot(oh, genotype, pedigree, check = FALSE)
ohplot(oh, genotype, pedigree, check = FALSE)

Arguments

`oh`	`integer` Opposing homozygotes matrix (Output of `ohg`)
`genotype`	`matrix` genotype of one chromosome (data should be numeric. Use 0, 1 and 2 for respectively AA, AB and BB. Use 9 for missing data)
`pedigree`	`matrix` the pedigree matrix should contain at least two columns, the first column with the half-sib IDs and the second column with the sires IDs. This argument is optional.
`check`	`logical` check the genotype file for the possible errors

Details

The cut off line shows the edge of most different groups.

Examples

set.seed(100)
chr <- list()
sire <- list()
set.seed(1)
chr <- list()
for(i in 1:5)
{
	chr[[i]] <- .simulateHalfsib(numInd = 20, numSNP = 5000, recbound = 1:10)
	sire[[i]] <- ssp(bmh(chr[[i]]), chr[[i]])
	sire[[i]] <- sire[[i]][1,] + sire[[i]][2,]
	sire[[i]][sire[[i]] == 18] <- 9
}

Genotype <- do.call(rbind, chr)
rownames(Genotype) <- 6:(nrow(Genotype) + 5)
sire <- do.call(rbind, sire)
rownames(sire) <- 1:5
Genotype <- rbind(sire, Genotype)
oh <- ohg(Genotype)  # creating the Opposing Homozygote matrix
pedigree <- as.matrix(data.frame(c(1:5, 6:(nrow(Genotype))), 
rep = c(rep(0,5), rep(1:5, rep(20,5)))))
ohplot(oh, Genotype, pedigree, check = TRUE)
set.seed(100)
chr <- list()
sire <- list()
set.seed(1)
chr <- list()
for(i in 1:5)
{
	chr[[i]] <- .simulateHalfsib(numInd = 20, numSNP = 5000, recbound = 1:10)
	sire[[i]] <- ssp(bmh(chr[[i]]), chr[[i]])
	sire[[i]] <- sire[[i]][1,] + sire[[i]][2,]
	sire[[i]][sire[[i]] == 18] <- 9
}

Genotype <- do.call(rbind, chr)
rownames(Genotype) <- 6:(nrow(Genotype) + 5)
sire <- do.call(rbind, sire)
rownames(sire) <- 1:5
Genotype <- rbind(sire, Genotype)
oh <- ohg(Genotype)  # creating the Opposing Homozygote matrix
pedigree <- as.matrix(data.frame(c(1:5, 6:(nrow(Genotype))), 
rep = c(rep(0,5), rep(1:5, rep(20,5)))))
ohplot(oh, Genotype, pedigree, check = TRUE)

Parallel Analysis of Data

Description

This function uses the list of matrices (the output of cs) and runs one of the options, on each element of the list, in parallel.

Usage

para(halfsibs, cpus = 1, option = "bmh", type = "SOCK", bmh_forwardVectorSize = 30,
bmh_excludeFP = TRUE, bmh_nsap = 3, pmMethod = "constant")
para(halfsibs, cpus = 1, option = "bmh", type = "SOCK", bmh_forwardVectorSize = 30,
bmh_excludeFP = TRUE, bmh_nsap = 3, pmMethod = "constant")

Arguments

`halfsibs`	`list` list of matrices of half-sibs (can be generated with `hss` and `cs` functions)
`cpus`	`numeric` number of CPUs (thread)
`option`	`character` type of analysis
`type`	`character` type of cluster for parallel analysis
`bmh_forwardVectorSize`	`integer` number of heterozygous sites used to validate recombination events or check for genotyping errors
`bmh_excludeFP`	`logical` exclude SNPs that may cause heterozygous sites in the sire due to genotyping errors or map errors
`bmh_nsap`	`integer` number of SNPs per block
`pmMethod`	`character` method for creating the recombination matrix

Details

Type of analysis can be bmh, ssp, aio, pm, or rec (refer to pm, rplot and vignette for more information about rec).

Value

Returns a list of matrices with the results (formats specific to the option selected).

Examples

# Please run demo(hsphase)

# Please run demo(hsphase)

Example Pedigree Data Set

Description

This dataset provides an example of a pedigree, specifically designed for use with the hsphase package.

Usage

data(pedigree)
data(pedigree)

Format

The dataset is structured as a data frame with detailed familial relationships, including:

First Column: Identifiers for half-sibs.
Second Column: Identifiers for sires.

Fix Pedigree Errors

Description

Tries to link the inferred pedigree from rpoh with the sire IDs in the original pedigree and fix pedigree errors.

Usage

pedigreeNaming(inferredPedigree, realPedigree)
pedigreeNaming(inferredPedigree, realPedigree)

Arguments

`inferredPedigree`	`matrix` inferred pedigree (output of `rpoh` )
`realPedigree`	`matrix` original pedigree

Details

This function calls the bmh and recombinations functions to count the number of recombinations in each half-sib group.

Value

Returns the inferred pedigree with the best fit to the sire names used in the original pedigree file.

Examples

# Please run demo(hsphase)
# Please run demo(hsphase)

Half-Sib Family Phasing

Description

Phases the half-sib family by using the blocking structure and imputed sire matrices.

Usage

phf(GenotypeMatrix, blockMatrix, sirePhasedMatrix)
phf(GenotypeMatrix, blockMatrix, sirePhasedMatrix)

Arguments

`GenotypeMatrix`	`matrix` half-sib genotypes (one half-sib per row, with SNP ordered by mapping position in the columns. Data should be numeric. Use 0, 1 and 2 respectively for AA, AB and BB. Use 9 for missing data)
`blockMatrix`	`matrix` blocking structure (output of `bmh`)
`sirePhasedMatrix`	`matrix` imputed sire (output of `ssp`)

Value

Returns a matrix that contains the phased parental haplotypes of the half-sibs. It uses 1, 0 and 9 for A, B and missing.

Note

The genotype matrix must only contain individuals from one half-sib family and one ordered chromosome. This function is used by the aio function for complete phasing of a half-sib group.

Examples

genotype <- matrix(c(
  2,1,0,
  2,0,0,
  0,0,2), byrow = TRUE, ncol = 3)
block <- bmh(genotype)
phf(genotype, block, ssp(block, genotype))
genotype <- matrix(c(
  2,1,0,
  2,0,0,
  0,0,2), byrow = TRUE, ncol = 3)
block <- bmh(genotype)
phf(genotype, block, ssp(block, genotype))

Probability Matrix

Description

Creates a recombination matrix based on the blocking structure.

Usage

pm(blockMatrix, method = "constant") 
pm(blockMatrix, method = "constant")

Arguments

`blockMatrix`	`matrix` blocking structure (Output of `bmh`)
`method`	`character` method for creating the recombination matrix

Details

This function finds the recombination between two consecutive sites, and marks the recombination site with a 1; if there are unknown sites between two blocks it will also mark these sites with a 1 (constant method) or 1 divided by number of unknown site (relative method).

Examples

genotype <- matrix(c(
0,2,0,1,0,
2,0,1,2,2,
2,2,1,0,2,
2,2,1,1,1,
0,0,2,1,0), ncol = 5, byrow = TRUE) # each row contains the SNP of individuals

(result <- bmh(genotype))
pm(result)
genotype <- matrix(c(
0,2,0,1,0,
2,0,1,2,2,
2,2,1,0,2,
2,2,1,1,1,
0,0,2,1,0), ncol = 5, byrow = TRUE) # each row contains the SNP of individuals

(result <- bmh(genotype))
pm(result)

Parent Offspring Group Constructor

Description

Assign offsprings to the parents.

Usage

pogc(oh, genotypeError)
pogc(oh, genotypeError)

Arguments

`oh`	`integer` opposing homozygotes matrix (Output of `ohg`)
`genotypeError`	`integer` number of genotypeing error allowed in the `oh` matrix

Value

Return a data frame with two columns. The first column is the animal ID and the second column is the parent ID.

Examples

set.seed(100)
chr <- list()
sire <- list()
set.seed(1)
chr <- list()
for(i in 1:5)
{
	chr[[i]] <- .simulateHalfsib(numInd = 20, numSNP = 5000, recbound = 1:10)
	sire[[i]] <- ssp(bmh(chr[[i]]), chr[[i]])
	sire[[i]] <- sire[[i]][1,] + sire[[i]][2,]
	sire[[i]][sire[[i]] == 18] <- 9
}

Genotype <- do.call(rbind, chr)
rownames(Genotype) <- 6:(nrow(Genotype) + 5)
sire <- do.call(rbind, sire)
rownames(sire) <- 1:5
Genotype <- rbind(sire, Genotype)
oh <- ohg(Genotype)  # creating the Opposing Homozygote matrix
pogc(oh, 5)
set.seed(100)
chr <- list()
sire <- list()
set.seed(1)
chr <- list()
for(i in 1:5)
{
	chr[[i]] <- .simulateHalfsib(numInd = 20, numSNP = 5000, recbound = 1:10)
	sire[[i]] <- ssp(bmh(chr[[i]]), chr[[i]])
	sire[[i]] <- sire[[i]][1,] + sire[[i]][2,]
	sire[[i]][sire[[i]] == 18] <- 9
}

Genotype <- do.call(rbind, chr)
rownames(Genotype) <- 6:(nrow(Genotype) + 5)
sire <- do.call(rbind, sire)
rownames(sire) <- 1:5
Genotype <- rbind(sire, Genotype)
oh <- ohg(Genotype)  # creating the Opposing Homozygote matrix
pogc(oh, 5)

Read and Check the Genotype File

Description

This function reads and checks genotype files.

Usage

readGenotype(genotypePath, separatorGenotype = " ", check = TRUE)
readGenotype(genotypePath, separatorGenotype = " ", check = TRUE)

Arguments

`genotypePath`	`character` genotype path (animals (rows) and SNP (columns), SNP should be coded as 0, 1 and 2 for respectively AA, AB and BB. Use 9 for missing data. please refer to vignette for more information)
`separatorGenotype`	`character` separator character for genotype
`check`	`logical` check the genotype file for possible errors

Value

Returns the genotype matrix.

Note

Please refer to vignette for more information.

Examples

# A comprehensive demo and example dataset is available from
# http://www-personal.une.edu.au/~cgondro2/hsphase.html
# A comprehensive demo and example dataset is available from
# http://www-personal.une.edu.au/~cgondro2/hsphase.html

Recombination Number

Description

Counts the number of recombinations for each individual.

Usage

recombinations(blockMatrix)
recombinations(blockMatrix)

Arguments

blockMatrix

matrix block structure (Output of bmh)

Value

Returns a vector of recombinations. The number of elements in this vector is equal to the number of individuals, i.e. each element holds the number of recombinations identified for each sample.

Examples

genotype <- matrix(c(          
  2,1,0,0,                    
  2,0,2,2,                    
  0,0,2,2,
  0,2,0,0                     
  ), byrow = TRUE, ncol = 4)      
  
recombinations(bmh(genotype))             
genotype <- matrix(c(          
  2,1,0,0,                    
  2,0,2,2,                    
  0,0,2,2,
  0,2,0,0                     
  ), byrow = TRUE, ncol = 4)      
  
recombinations(bmh(genotype))

Recombination Plot

Description

This function creates a plot which shows the sum of all recombination events across a half-sib family.

Usage

rplot(x, distance, start = 1, end = ncol(x), maximum = 100,
overwrite = FALSE, method = "constant")
rplot(x, distance, start = 1, end = ncol(x), maximum = 100,
overwrite = FALSE, method = "constant")

Arguments

`x`	`matrix` of half-sib genotypes (one half-sib per row, with SNP ordered by mapping position in the columns. Data should be numeric. Use 0, 1 and 2 for respectively AA, AB and BB. Use 9 for missing data).
`distance`	`integer` of physical distances between markers
`start`	`integer` first marker selected for the plot
`end`	`integer` last marker selected for the plot
`maximum`	`integer` maximum number of recombinations to show (higher recombination rates will be omitted from the plot)
`overwrite`	`logical` draw a diagram over the current diagram (default `FALSE`)
`method`	`character` please refer to the `pm` document

Examples

genotype <- matrix(c(
0,2,0,1,0,
2,0,1,2,2,
2,2,1,0,2,
2,2,1,1,1,
0,0,2,1,0), ncol = 5, byrow = TRUE) # each row contains the SNP of individuals

rplot(genotype, c(1,2,3,4,8)) 
genotype <- matrix(c(
0,2,0,1,0,
2,0,1,2,2,
2,2,1,0,2,
2,2,1,1,1,
0,0,2,1,0), ncol = 5, byrow = TRUE) # each row contains the SNP of individuals

rplot(genotype, c(1,2,3,4,8))

Reconstruct Pedigree Based on Matrix of Opposing Homozygotes

Description

Reconstructs a half-sib pedigree based on a matrix of opposing homozygotes.

Usage

rpoh(genotypeMatrix, oh, forwardVectorSize = 30, excludeFP = TRUE, nsap = 3,
maxRec = 15, intercept = 26.3415, coefficient = 77.3171, snpnooh, method, maxsnpnooh)
rpoh(genotypeMatrix, oh, forwardVectorSize = 30, excludeFP = TRUE, nsap = 3,
maxRec = 15, intercept = 26.3415, coefficient = 77.3171, snpnooh, method, maxsnpnooh)

Arguments

`genotypeMatrix`	`matrix` genotype of one chromosome (data should be numeric. Use 0, 1 and 2 for respectively AA, AB and BB. Use 9 for missing data)
`oh`	`integer` Opposing homozygotes matrix (Output of `ohg`)
`forwardVectorSize`	`integer` number of heterozygous sites used to validate recombination events or check for genotyping errors
`excludeFP`	`logical` excludes SNPs that may cause heterozygous sites in the sire due to genotyping errors or map errors
`nsap`	`integer` number of SNP per block to validate recombinations
`maxRec`	`integer` maximum number of expected recombinations per individual
`intercept`	`integer` intercept of fitted model
`coefficient`	`integer` coefficient of fitted model
`snpnooh`	`integer` number of SNPs used to create oh matrix (this number must be divided by 1000)
`method`	`character` pedigree reconstruction method
`maxsnpnooh`	`numeric` the maximum number of allowing opposing homozygote in a half-sib family

Details

Four methods simple, recombinations, calus and manual can be utilized to reconstruct the pedigree.

The following examples show the arguments require for each method.

pedigree1 <- rpoh(oh = oh, snpnooh = 732, method = "simple")
pedigree2 <- rpoh(genotypeMatrix = genotypeChr1, oh = ohg(genotype), maxRec = 10 , method = "recombinations")
pedigree3 <- rpoh(genotypeMatrix = genotype, oh = oh, method = "calus")
pedigree4 <- rpoh(oh = oh, maxsnpnooh = 31662, method = "manual")

Value

Returns a data frame with two columns, the first column is animals' ID and the second column is sire identifiers (randomly generated).

Note

Method can be recombinations, simple, calus or manual. Please refer to vignette for more information.

The sire genotype should be removed before using this function utilizing pogc function.

Examples

# Please run demo(hsphase)
# Please run demo(hsphase)

Sire Imputation and Phasing

Description

Infer (impute) and phase sire's genotype based on the block structure matrix (recombination blocks) and homozygous sites of the half-sib genotype matrix.

Usage

ssp(blockMatrix, genotypeMatrix)
ssp(blockMatrix, genotypeMatrix)

Arguments

`blockMatrix`	`matrix` block structure (Output of `bmh`)
`genotypeMatrix`	`matrix` half-sibs genotype (each row includes the SNP of individuals, 0, 1 and 2 for respectively AA, AB and BB. Use 9 for missing data)

Value

Returns a matrix (Imputed Sire) with two rows one for each haplotype of the sire (columns are SNP in the order of the genotype matrix). Alleles are coded as 0 (A) and 1 (B). Alleles that could not be imputed are coded as 9.

Examples

genotype <- matrix(c(
0,2,1,1,1,
2,0,1,2,2,
2,2,1,0,2,
2,2,1,1,1,
0,0,2,1,0), ncol = 5, byrow = TRUE) # each row contains the SNP of individuals

(result <- ssp(bmh(genotype), genotype)) 
genotype <- matrix(c(
0,2,1,1,1,
2,0,1,2,2,
2,2,1,0,2,
2,2,1,1,1,
0,0,2,1,0), ncol = 5, byrow = TRUE) # each row contains the SNP of individuals

(result <- ssp(bmh(genotype), genotype))

Package 'hsphase'

Help Index

Phasing, Pedigree Reconstruction, Sire Imputation and Recombination Events Identification for Half-sib Families

Description

Details

Author(s)

References

Examples

Calculate Genotypic Distances

Description

Usage

Arguments

Value

Examples

Calculate Minor Allele Frequency (MAF)

Description

Usage

Arguments

Value

Examples

Simulate Half-Sibling Genotypes

Description

Usage

Arguments

Value

Examples

All-in-one Phasing

Description

Usage

Arguments

Details

Value

Note

See Also

Examples

Block Partitioning

Description

Usage

Arguments

Value

Note

See Also

Examples

Crossover Detection

Description

Usage

Arguments

Value

Examples

Chromosome Splitter

Description

Usage

Arguments

Details

Value

Examples

Example of Genotype Data Set

Description

Usage

Format

Haplotype Blocks of Phased Data

Description

Usage

Arguments

Value

Note

See Also

Examples

Heatmap of Half-sibs

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Half-sib Family Splitter

Description

Usage

Arguments