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 [aut, cre], Cedric Gondro [aut]
Maintainer:	Mohammad Ferdosi <[email protected]>
License:	GPL-3
Version:	3.0.0
Built:	2026-05-18 09:25:43 UTC
Source:	https://github.com/cran/hsphase

Phasing, Pedigree Reconstruction, Sire Imputation and Recombination Events 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:	3.0.0
Date:	2026-02-15
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 homozygotes

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

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. J., & Gondro, C. (2014). Detection of recombination events, haplotype reconstruction and imputation of sires using half-sib SNP genotypes. Genetics Selection Evolution, 46(1), 11.

Ferdosi, M. H., Kinghorn, B. P., van der Werf, J. H. J., 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.

Sahoo S., Ferdosi M.H., van der Werf J.H.J., and de las Heras-Saldana S., et al. (2025) Proc. Assoc. Advmt. Anim. Breed. Genet. 26: 323

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, 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, 1:14)

Calculate Genotypic Distances

Description

Calculates a symmetric matrix of distances (canberra) between genotypes, based on a given genotype matrix. Each row in the 'GenotypeMatrix' represents a genotype, and each column represents a marker.

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 (canberra) 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 = 5, numSNP = 1000, recbound = 0:6, type = "genotype")
# Calculate the distance matrix
dist_matrix <- hsphase::.fastdist(genotypes)
print(dist_matrix)

# Simulate genotype data for 40 individuals across 1000 SNPs
genotypes <- .simulateHalfsib(numInd = 5, numSNP = 1000, recbound = 0:6, type = "genotype")
# Calculate the distance matrix
dist_matrix <- hsphase::.fastdist(genotypes)
print(dist_matrix)

Fix strand label rotation across consecutive block-structure columns

Description

Internal helper to enforce a consistent strand-label orientation across adjacent columns of a block-structure matrix.

Usage

.fixRotation(blockStructure)
.fixRotation(blockStructure)

Arguments

blockStructure

A numeric matrix (typically individuals in rows, SNPs in columns) representing a block/strand structure. Values are expected to be small integers (commonly including '0', '1', '2' and possibly other internal codes).

Details

The input typically encodes sire strand-of-origin labels per individual (rows) and marker/SNP (columns), where '0' indicates unknown and non-zero values indicate an assigned strand/state. The native algorithm compares each column to the previous one and, when a "contrast" (swap of strand labels) increases agreement, it relabels the next column to reduce apparent strand-rotation between columns.

This function is a thin R wrapper around the native routine fixRotation implemented in C++ and called via .Call().

At each step, the C++ code computes an agreement score between column i and column i+1 using only positions where both columns are non-zero. It also computes the score after applying a contrast mapping to column i+1 (conceptually swapping strand labels '1' and '2', leaving '0' unchanged). If the contrasted version agrees more with column i, the function relabels column i+1.

The relabeling performed by the native code is:

'1 -> 3'
'2 -> 1'
'3 -> 2'

leaving other values unchanged. (These codes are part of hsphase's internal block/strand encoding.)

Value

A numeric matrix with the same dimensions as blockStructure, where some entries in column i+1 may be relabeled to improve consistency with column i. The transformation is applied iteratively from left to right across columns.

Build haplotype blocks from a BMH result matrix (native routine wrapper)

Description

Internal wrapper around the native C routine hblock. It transforms a BMH result matrix into a block representation, with an optional maximum block size constraint.

Usage

.hblock(bmhResult, MaxBlock = 400)
.hblock(bmhResult, MaxBlock = 400)

Arguments

bmhResult

A numeric/integer matrix containing BMH results (block matching/haplotype-block intermediate output). Must be a matrix.

MaxBlock

Integer scalar. Maximum block size (default: 400).

Details

This function transposes and flattens bmhResult before passing it to compiled code via .C: .C("hblock", ...).

Value

A matrix (same general shape as bmhResult) containing inferred block structure. Row and column names are propagated from bmhResult where available.

Calculate minor allele frequency (MAF)

Description

Calculates the minor allele frequency (MAF) for a single SNP coded as: 0 = AA, 1 = AB, 2 = BB, and 9 = missing.

Usage

.maf(snp)
.maf(snp)

Arguments

snp

A numeric vector of genotypes for one SNP. Values must be 0, 1, 2, or 9 (missing).

Value

A single numeric value: the minor allele frequency (MAF).

Examples

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

Convert a one-row haplotype to a two-row haplotype

Description

Converts a haplotype matrix where each individual is represented by one row and alleles are stored in alternating columns (1st allele, 2nd allele, ...) into a two-row-per-individual representation.

Usage

.o2tH(haplotype)
.o2tH(haplotype)

Arguments

haplotype

A haplotype object:

a matrix with individuals in rows and allele columns in pairs (i.e. ncol(haplotype) must be even).

Details

Internally, any allele code of '2' is converted to '0' before conversion.

Value

An integer matrix in a two-row-per-individual format with 2 * nrow(haplotype) rows and ncol(haplotype) / 2 columns. Row names are interleaved using the original individual names.

Convert a two-row haplotype per individual to a one-row representation

Description

Converts a haplotype matrix where each individual is represented by two rows (allele 1 and allele 2) into a single-row-per-individual representation.

Usage

.ptr2por(haplotype)
.ptr2por(haplotype)

Arguments

haplotype

A matrix containing haplotypes with two rows per individual.

Value

A matrix with one row per individual (allele 1 and allele 2 combined).

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)

Add Switches

Description

Add switch points to haplotypes by swapping the two haplotype rows for an individual from each switch point to the end of the chromosome.

Usage

addSwitch(haplotypeMatrix, switchPoints, minLength)
addSwitch(haplotypeMatrix, switchPoints, minLength)

Arguments

haplotypeMatrix

matrix. Haplotypes for a half-sib family (two rows per individual).

switchPoints

list of integer/numeric vectors. Length must equal the number of individuals. Each element contains the switch positions (0-based/1-based depends on how they were produced; see Details). If there are no switches for an individual, use 0.

minLength

integer. Minimum distance between consecutive switch points. Note: in the current implementation this filter may not be enforced (depends on package version).

Details

Important: Each switch point causes a swap of the two haplotype rows for that individual from the switch position to the end.

The switchPoints list must have one element per individual (not per haplotype row). If an element is 0, no switch is applied for that individual.

If you rely on minLength to ignore nearby switches, verify your installed version enforces this rule.

Value

A matrix of the same dimension as haplotypeMatrix with switches applied.

Examples

haplotype <- matrix(c(0, 0, 0, 0,
                      1, 1, 1, 1,
                      0, 0, 1, 1,
                      1, 1, 0, 0,
                      1, 1, 1, 1,
                      0, 0, 0, 0), byrow = TRUE, nrow = 6)

switchPoints <- list(firstInd = c(2), secondInd = c(1, 3), lastInd = 0)
addSwitch(haplotype, switchPoints, 0)
haplotype <- matrix(c(0, 0, 0, 0,
                      1, 1, 1, 1,
                      0, 0, 1, 1,
                      1, 1, 0, 0,
                      1, 1, 1, 1,
                      0, 0, 0, 0), byrow = TRUE, nrow = 6)

switchPoints <- list(firstInd = c(2), secondInd = c(1, 3), lastInd = 0)
addSwitch(haplotype, switchPoints, 0)

All-in-one Phasing

Description

Phasing of a single half-sib family group (single ordered chromosome).

Usage

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

Arguments

genotypeMatrix

matrix. Half-sib genotypes (one half-sib per row, SNPs ordered by map position in columns). Data must be numeric: 0, 1, 2 for AA, AB, BB. Use 9 for missing.

bmh_forwardVectorSize

integer. Number of heterozygous sites used to validate recombination events or detect genotyping/map errors.

bmh_excludeFP

logical. Exclude SNPs that may induce false heterozygous sites in the sire due to genotyping or map errors.

bmh_nsap

integer. Number of SNP per block to validate recombinations (e.g. 50K $\to$ 3, 700K $\to$ 10).

bmh_fillMissing

logical. If TRUE, recombination points are placed at the mid-point of the ambiguous interval rather than marking surrounding SNPs as missing.

output

character. If "phase", the function returns only the phased haplotypes matrix.

Details

This function calls bmh, ssp, and phf.

Value

If output = "phase", returns a haplotype matrix with two rows per individual (first paternal, second maternal), coded as 0 (allele A), 1 (allele B), and 9 (missing/unphased).

Otherwise returns a list with elements:

phasedHalfsibs
sireHaplotype
blockStructure

Note

Only this function needs to be called to phase a half-sib family. The genotype matrix must contain individuals from a single family and a single 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 a half-sib family that each individual inherited from its sire.

Usage

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

Arguments

GenotypeMatrix

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

forwardVectorSize

integer. Number of heterozygous sites used to validate recombination events or check for genotyping/map errors (50K $\to$ 30, 700K $\to$ 120).

excludeFP

logical. Exclude 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 $\to$ 3, 700K $\to$ 10).

fillMissing

logical. Because the exact point of recombination is unknown, markers around recombination points may be set to missing. If TRUE, the recombination point is assumed to be in the middle of the ambiguous region, reducing missing markers.

Value

A matrix of block structure containing 1, 2, and 0. Values 1 and 2 represent the two sire strands (arbitrary labeling within a chromosome), and 0 indicates unknown origin.

Note

The genotype 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)

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)

bmh(genotype)

Crossover Detection

Description

Detects possible crossover segments by comparing pairs of individuals in a half-sib family.

Usage

co(genotypeMatrix)
co(genotypeMatrix)

Arguments

genotypeMatrix

matrix. Half-sib genotypes (one individual per row; SNPs in columns ordered by map position). Genotypes must be numeric: 0, 1, 2 for AA, AB, BB and 9 for missing.

Value

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

Examples

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

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

co(genotype)

Chromosome Splitter

Description

Splits the genotype list generated by hss into chromosomes based on a map file/data.frame and orders SNPs by chromosomal position.

Usage

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

Arguments

halfsib

list. List of genotype matrices (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. Field separator for 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)

Fixing Switch Errors

Description

Fix switch errors in haplotypes for a half-sib family.

Usage

fixSW(haplotype, ohMax = 0, windowsSize = 100, minLength = 100, cpus = 2)
fixSW(haplotype, ohMax = 0, windowsSize = 100, minLength = 100, cpus = 2)

Arguments

haplotype

matrix. Haplotypes for a half-sib family (two rows per individual).

ohMax

integer. Maximum tolerated opposing homozygotes when grouping each partition (increase if genotyping errors exist).

windowsSize

integer. Partition size (number of SNPs).

minLength

integer. Minimum length between switches.

cpus

integer. Number of CPU threads.

Value

A haplotype matrix with switch errors corrected.

Examples

haplotype <- .simulateHalfsib(7, 2500, type = "haplotype")$phased
switches <- list(500,0,0,1200,c(1000,2000),500,1200)

haplotype2 <- addSwitch(haplotype, switches, 0)

gMat <- groupMatSingle(haplotype2, 100, 2, "haplotype")
imageplot(gMat, title = "Before fixing switches")

haplotype3 <- fixSW(haplotype2, 0, 100, 100)

gMat2 <- groupMatSingle(haplotype3, 100, 2, "haplotype")
imageplot(gMat2, title = "After fixing switches")
haplotype <- .simulateHalfsib(7, 2500, type = "haplotype")$phased
switches <- list(500,0,0,1200,c(1000,2000),500,1200)

haplotype2 <- addSwitch(haplotype, switches, 0)

gMat <- groupMatSingle(haplotype2, 100, 2, "haplotype")
imageplot(gMat, title = "Before fixing switches")

haplotype3 <- fixSW(haplotype2, 0, 100, 100)

gMat2 <- groupMatSingle(haplotype3, 100, 2, "haplotype")
imageplot(gMat2, title = "After fixing switches")

Example Genotype Data Set

Description

An example genotype matrix for the hsphase package.

Usage

data(genotypes)
data(genotypes)

Format

A genotype matrix with:

Columns: SNPs
Rows: animals/individuals

Genotype coding follows the package conventions (typically 0, 1, 2 and 9 for missing).

References

Sahoo S., Ferdosi M.H., van der Werf J.H.J., and de las Heras-Saldana S., et al. (2025) Proc. Assoc. Advmt. Anim. Breed. Genet. 26: 323

Grouping a Half-sib Family

Description

Group the genotype or haplotype of a half-sib family into partitions using opposing homozygotes.

Usage

groupMatSingle(haplotype, windowsSize, cpus = 2, input = "haplotype", oh = 0)
groupMatSingle(haplotype, windowsSize, cpus = 2, input = "haplotype", oh = 0)

Arguments

haplotype

matrix. Haplotypes (two rows per individual) or genotypes (one row per individual) depending on input.

windowsSize

integer. Partition size.

cpus

integer. Number of CPU threads.

input

character. Either "haplotype" or "genotype".

oh

integer. Threshold for opposing homozygotes used for grouping (increase if genotyping errors exist).

Value

A grouping matrix.

Examples

haplotype <- .simulateHalfsib(10, 5000, type = "haplotype")$phased
gMat <- groupMatSingle(haplotype, 100, 2, "haplotype")
imageplot(gMat)
haplotype <- .simulateHalfsib(10, 5000, type = "haplotype")$phased
gMat <- groupMatSingle(haplotype, 100, 2, "haplotype")
imageplot(gMat)

Haplotype Blocks of Phased Data

Description

Creates a block-structure matrix for a half-sib family based on phased data of the sire and the 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). Alleles should be coded as 0 and 1; use 9 for missing/unphased if present.

PhasedSireGenotype

matrix. Haplotypes of the sire (two rows; same SNP order as PhasedGenotypeMatrix).

strand

character. Method for identification of paternal strand. Use "auto" (recommended; default) or specify "1" or "2" to force a strand definition.

Value

A matrix in which 3 or 4 indicates the SNP originates from, respectively, sire strand 1 or strand 2. 0 indicates the origin is unknown.

Note

The input matrices must contain individuals from a single half-sib family and a single ordered chromosome. The SNP order must match between inputs.

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

Creates a heatmap of a half-sib dataset using an opposing-homozygotes (OH) matrix, with optional sidebars showing inferred and/or real pedigree groupings.

Usage

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

Arguments

oh

matrix. Opposing-homozygotes matrix (e.g. output of ohg).

inferredPedigree

matrix. Inferred pedigree (e.g. output of rpoh).

realPedigree

matrix. Original pedigree.

pedOnly

logical. If TRUE, consider only individuals that exist in the real pedigree.

Value

Returns a heatmap of the OH matrix with sidebars color-coded by sire groups from the inferred and original pedigrees (where provided).

Author(s)

The function uses colors generated by the 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)
hh(oh)
c1h1 <- .simulateHalfsib(numInd = 62, numSNP = 5000)
c1h2 <- .simulateHalfsib(numInd = 38, numSNP = 5000)
Genotype <- rbind(c1h1, c1h2)

oh <- ohg(Genotype)
hh(oh)

Half-sib Family Splitter

Description

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

Usage

hss(pedigree, genotype, minHS = 4, check = TRUE)
hss(pedigree, genotype, minHS = 4, 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

minHS

integerMinimum number of offspring in a half-sib family

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 image plot of the blocking structure.

Usage

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

Arguments

x

matrix. Blocking structure (output of bmh or hbp).

title

character (or NULL). Title of the image plot.

rv

logical. If TRUE, reverse the colour scheme.

...

Optional graphical parameters.

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, available at 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)

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)

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

Description

An example map dataset for the hsphase package.

Usage

data(map)
data(map)

Format

A data.frame with the following columns:

Name: SNP identifier
Chr: Chromosome
Position: SNP position in base pairs

References

Sahoo S., Ferdosi M.H., van der Werf J.H.J., and de las Heras-Saldana S., et al. (2025) Proc. Assoc. Advmt. Anim. Breed. Genet. 26: 323

Opposing Homozygote Detection

Description

Counts, for each animal, the number of loci where it contributes opposing homozygotes in sites that imply heterozygosity 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, SNPs ordered by mapping position in the columns). Data should be numeric: 0, 1, 2 for AA, AB, BB and 9 for missing.

unique_check

logical. If TRUE, counts opposing homozygotes using a uniqueness rule (see Details).

SNPs

integer. Number of SNPs to use. (Only applicable if supported by the installed version.)

Value

A numeric vector with the number of heterozygous sites that each sample caused.

Note

This function can be used to identify pedigree errors; i.e., outliers with unusually high values.

Author(s)

This method was 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 pairwise opposing-homozygote (OH) counts from a genotype matrix.

Usage

ohg(genotypeMatrix)
ohg(genotypeMatrix)

Arguments

genotypeMatrix

matrix. Genotypes (numeric): 0, 1, 2 for AA, AB, BB and 9 for missing.

Value

Returns a square matrix (sample $\times$ sample) of pairwise counts of opposing homozygotes. (Some versions may return this matrix inside a named list element.)

Note

This function can be slow for large datasets.

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

bmh_fillMissing

logical Because the exact point of the recombinations is unknown, the markers around the recombination points are considered missing. By setting this argument to true, the recombination point is assumed to be in the middle unknown point, so no missing SNPs at the recombination point would be considered.

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

Description

An example pedigree dataset for the hsphase package.

Usage

data(pedigree)
data(pedigree)

Format

A data.frame with the following columns:

First column: Half-sib (offspring) IDs
Second column: Sire IDs

References

Sahoo S., Ferdosi M.H., van der Werf J.H.J., and de las Heras-Saldana S., et al. (2025) Proc. Assoc. Advmt. Anim. Breed. Genet. 26: 323

Fix Pedigree Errors

Description

Tries to link the inferred pedigree from rpoh with 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 bmh and recombinations to count the number of recombinations in each half-sib group.

Value

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

Examples

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

Half-sib Family Phasing

Description

Phases a half-sib family using the block structure and an imputed sire haplotype matrix.

Usage

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

Arguments

GenotypeMatrix

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

blockMatrix

matrix. Blocking structure (output of bmh).

sirePhasedMatrix

matrix. Imputed sire haplotypes (output of ssp).

Value

Returns a matrix containing the phased parental haplotypes of the half-sibs (two rows per individual). Alleles are coded as 0 (A), 1 (B), and 9 (missing/unphased).

Note

The genotype matrix must contain individuals from one half-sib family and one ordered chromosome. This function is used by aio 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 (probability) 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. Typically "constant" or "relative".

Details

This function identifies recombination between two consecutive sites and marks recombination sites with 1. If there are unknown sites between two blocks, it marks these sites with:

1 for the "constant" method, or
$1 / m$ for the "relative" method, where $m$ is the number of unknown sites.

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)

block <- bmh(genotype)
pm(block)
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)

block <- bmh(genotype)
pm(block)

Parent-Offspring Group Constructor

Description

Assign offspring to parents based on an opposing-homozygotes (OH) matrix.

Usage

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

Arguments

oh

matrix. Opposing homozygotes matrix (output of ohg).

genotypeError

integer. Number of genotyping errors allowed when interpreting the oh matrix.

Value

A data.frame with two columns:

animal ID
assigned parent ID

Examples

set.seed(1)
chr <- list()
sire <- 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)
pogc(oh, 5)
set.seed(1)
chr <- list()
sire <- 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)
pogc(oh, 5)

Read and Check the Genotype File

Description

Reads a genotype file and optionally checks it for common formatting/data issues.

Usage

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

Arguments

genotypePath

character. Path to the genotype file (animals in rows and SNPs in columns). SNPs should be coded as 0, 1, 2 for AA, AB, BB. Use 9 for missing data. Please refer to the vignette for more information.

separatorGenotype

character. Field separator used in the genotype file.

check

logical. If TRUE, check the genotype file for possible errors.

Value

A genotype matrix.

Note

Please refer to the vignette for more information.

Recombination Number

Description

Counts the number of recombinations for each individual based on the block structure.

Usage

recombinations(blockMatrix)
recombinations(blockMatrix)

Arguments

blockMatrix

matrix. Block structure (output of bmh).

Value

A numeric vector of recombination counts with length equal to the number of individuals (rows) in blockMatrix.

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

Creates a plot showing the sum of 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. Half-sib genotypes (one half-sib per row; SNPs ordered by mapping position in columns). Numeric coding: 0, 1, 2 for AA, AB, BB. Use 9 for missing data.

distance

numeric (or integer). Physical distances between markers (length must match ncol(x) or the plotted range).

start

integer. First marker index for the plot.

end

integer. Last marker index for the plot.

maximum

integer. Maximum number of recombinations to show (higher recombination rates will be omitted).

overwrite

logical. Draw over the current plot (default FALSE).

method

character. Method passed to pm (e.g., "constant" or "relative").

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)

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)

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

Infers (imputes) and phases the sire haplotypes based on the block structure matrix 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-sib genotypes (one individual per row). Genotypes coded as 0, 1, 2 for AA, AB, BB. Use 9 for missing data.

Value

A matrix with two rows (one per sire haplotype) and columns corresponding to SNPs in genotype order. 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)

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)

ssp(bmh(genotype), genotype)

Switch Detector

Description

Detect switch errors in the haplotypes of a half-sib family.

Usage

switchDetector(groupMatrix)
switchDetector(groupMatrix)

Arguments

groupMatrix

matrix. Group matrix generated by groupMatSingle.

Value

A list of integer vectors. The list length equals the number of individuals. Each vector contains the locations of detected switch errors for that individual.

Examples

haplotype <- .simulateHalfsib(8, 3000, type = "haplotype")$phased
switches <- list(2500,0,0,1200,c(1000,2000),500,2000,0)

haplotype2 <- addSwitch(haplotype, switches, 0)
gMat <- groupMatSingle(haplotype2, 100, 2, "haplotype")

switchDetector(gMat)
haplotype <- .simulateHalfsib(8, 3000, type = "haplotype")$phased
switches <- list(2500,0,0,1200,c(1000,2000),500,2000,0)

haplotype2 <- addSwitch(haplotype, switches, 0)
gMat <- groupMatSingle(haplotype2, 100, 2, "haplotype")

switchDetector(gMat)

Package 'hsphase'

Help Index

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

Description

Details

Author(s)

References

Examples

Calculate Genotypic Distances

Description

Usage

Arguments

Value

Examples

Fix strand label rotation across consecutive block-structure columns

Description

Usage

Arguments

Details

Value

See Also

Build haplotype blocks from a BMH result matrix (native routine wrapper)

Description

Usage

Arguments

Details

Value

Calculate minor allele frequency (MAF)

Description

Usage

Arguments

Value

Examples

Convert a one-row haplotype to a two-row haplotype

Description

Usage

Arguments

Details

Value

Convert a two-row haplotype per individual to a one-row representation

Description

Usage

Arguments

Value

Simulate Half-Sibling Genotypes

Description

Usage

Arguments

Value

Examples

Add Switches

Description

Usage

Arguments

Details

Value

See Also

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