Package 'ggroups' reference manual

Title:	Pedigree and Genetic Groups
Description:	Calculates additive and dominance genetic relationship matrices and their inverses, in matrix and tabular-sparse formats. It includes functions for checking and processing pedigree, calculating inbreeding coefficients (Meuwissen & Luo, 1992 <doi:10.1186/1297-9686-24-4-305>), as well as functions to calculate the matrix of genetic group contributions (Q), and adding those contributions to the genetic merit of animals (Quaas (1988) <doi:10.3168/jds.S0022-0302(88)79691-5>). Calculation of Q is computationally extensive. There are computationally optimized functions to calculate Q.
Authors:	Mohammad Ali Nilforooshan [aut, cre]
Maintainer:	Mohammad Ali Nilforooshan <[email protected]>
License:	GPL-3
Version:	2.1.2
Built:	2024-11-18 05:24:06 UTC
Source:	https://github.com/nilforooshan/ggroups

Pedigree and genetic groups

Description

This package contains pedigree processing and analyzing functions, including functions for checking and renumbering the pedigree, making the additive and dominance pedigree relationship matrices and their inverses, in matrix and tabular formats, calculating inbreeding coefficients, as well as functions related to genetic groups.

Details

First, it is recommended to check the pedigree data.frame with the pedcheck function. Pedigree relationship matrix and its inverse are fundamentals in the conventional and modern animal breeding. The concept of genetic groups stems from the fact that not all the unknown parents are of the same genetic level. The genetic group contribution matrix (Q) is required to weight and add genetic group effects (&gcirc;) to the genetic merit of animals (û), which is equal to Q&gcirc; + û (Quaas, 1988). Calculating Q is computationally challenging, and for large pedigree, large RAM and long computational time is required. Therefore, the functions qmatL and its parallel version, qmatXL are introduced. Overlap between sire and dam genetic groups is supported.

Author(s)

Mohammad Ali Nilforooshan [email protected]

References

Meuwissen, T. and Luo, Z. 1992. Computing Inbreeding Coefficients in Large Populations. Genet. Sel. Evol., 24:305. <doi:10.1186/1297-9686-24-4-305>

Mrode, R. A. 2005. Linear Models for the Prediction of Animal Breeding Values, 2nd ed. Cambridge, MA: CABI Publishing.

Quaas, R. L. 1988. Additive Genetic Model with Groups and Relationships. J. Dairy Sci., 71:1338-1345. <doi:10.3168.jds.S0022-0302(88)79691-5>

Relationship matrix A

Description

Builds the pedigree-based additive genetic relationship matrix.

Usage

buildA(ped)
buildA(ped)

Arguments

ped

: data.frame with integer columns corresponding to ID, SIRE, DAM. Missing value is 0.

Value

Relationship matrix A

Examples

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
buildA(ped)

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
buildA(ped)

Relationship matrix D

Description

Builds the pedigree-based dominance relationship matrix.

Usage

buildD(ped, A)
buildD(ped, A)

Arguments

`ped`	: `data.frame` with integer columns corresponding to ID, SIRE, DAM. Missing value is 0.
`A`	: Relationship matrix A created by function `buildA`.

Value

Relationship matrix D

Examples

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
buildD(ped, buildA(ped))

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
buildD(ped, buildA(ped))

Append genetic groups to the pedigree

Description

This function appends parents that are not available in the first column of the pedigree, to the head of the pedigree, and sorts it. Given a pedigree with all missing parents replaced with the corresponding genetic groups, this functions appends genetic groups to the head of the pedigree.

Usage

gghead(ped)
gghead(ped)

Arguments

ped

: data.frame with integer columns corresponding to ID, SIRE, DAM. Missing value is 0.

Details

Consider this simple pedigree:

3 0 0

4 3 0

6 4 5

5 0 0

First, unknown parents are replaced with the corresponding genetic groups.

Please note that unknown parent IDs should be smaller than progeny IDs.

3 1 2

4 3 2

6 4 5

5 1 2

Then, gghead is applied to this pedigree (see the example).

Value

Processed pedigree data.frame

Examples

ped = data.frame(ID=c(3,4,6,5), SIRE=c(1,3,4,1), DAM=c(2,2,5,2))
gghead(ped)

ped = data.frame(ID=c(3,4,6,5), SIRE=c(1,3,4,1), DAM=c(2,2,5,2))
gghead(ped)

Individual's inbreeding coefficient

Description

Calculates inbreeding coefficient for an individual.

Usage

inb(ped, id)
inb(ped, id)

Arguments

`ped`	: `data.frame` with integer columns corresponding to ID, SIRE, DAM. Missing value is 0.
`id`	: Numeric ID of an individual

Value

Inbreeding coefficient of the individual

Examples

ped = data.frame(ID=1:7, SIRE=c(0,0,1,1,3,1,5), DAM=c(0,0,0,2,4,4,6))
inb(ped, 7)

ped = data.frame(ID=1:7, SIRE=c(0,0,1,1,3,1,5), DAM=c(0,0,0,2,4,4,6))
inb(ped, 7)

Inbreeding coefficients

Description

Calculates inbreeding coefficients for all animals in the pedigree.

Usage

inbreed(ped)
inbreed(ped)

Arguments

ped

: data.frame with integer columns corresponding to ID, SIRE, DAM. Missing value is 0.

Value

Vector of inbreeding coefficients

Examples

ped = data.frame(ID=1:7, SIRE=c(0,0,1,1,3,1,5), DAM=c(0,0,0,2,4,4,6))
inbreed(ped)

ped = data.frame(ID=1:7, SIRE=c(0,0,1,1,3,1,5), DAM=c(0,0,0,2,4,4,6))
inbreed(ped)

Matrix to tabular

Description

Converts matrix data to tabular data.

Usage

mat2tab(mat)
mat2tab(mat)

Arguments

mat

: matrix

Value

data.frame with 2 integer (IDs) and 1 numeric (values) columns.

Examples

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
mat2tab(buildA(ped))

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
mat2tab(buildA(ped))

Descendants of an individual per generation

Description

Counts and collects progeny and phenotyped progeny of an individual in successive generations. In pedigrees with generation overlap, animals are reported in the 1st generation that they appear in, rather than in multiple generations.

Usage

offspring(ped, id, pheno)
offspring(ped, id, pheno)

Arguments

`ped`	: `data.frame` with columns corresponding to ID, SIRE, DAM. Missing value is 0.
`id`	: The ID of the individual, for which the descendants to be extracted.
`pheno`	: Vector of phenotyped individuals.

Value

prgn : list of progeny per generation.

prgn.ph : list of phenotyped progeny per generation.

Examples

ped = data.frame(V1 = 1:19,
   V2 = c(0,0,1,1,0,0,0,0,0,4,5,5,7,0,0,9,0,0,12),
   V3 = c(0,0,0,2,0,2,0,3,3,3,0,6,8,8,8,10,11,11,0))
pheno = 10:18
# Find progeny and phenotyped progeny of individual 1.
offspring(ped, 1, pheno)
# Find phenotyped progeny of individual 1, in the 2nd generation.
offspring(ped, 1, 10:18)$prgn.ph[[2]]
# If only interested in finding the progeny of individual 1:
offspring(ped, 1, c())$prgn

ped = data.frame(V1 = 1:19,
   V2 = c(0,0,1,1,0,0,0,0,0,4,5,5,7,0,0,9,0,0,12),
   V3 = c(0,0,0,2,0,2,0,3,3,3,0,6,8,8,8,10,11,11,0))
pheno = 10:18
# Find progeny and phenotyped progeny of individual 1.
offspring(ped, 1, pheno)
# Find phenotyped progeny of individual 1, in the 2nd generation.
offspring(ped, 1, 10:18)$prgn.ph[[2]]
# If only interested in finding the progeny of individual 1:
offspring(ped, 1, c())$prgn

Basic pedigree checks

Description

Performs basic pedigree checks.

Usage

pedcheck(ped)
pedcheck(ped)

Arguments

ped

: data.frame with integer columns corresponding to ID, SIRE, DAM. Missing value is 0.

Examples

set.seed(127)
ped = data.frame(ID=c(1:50,NA,0,1:3),
                 SIRE=c(0, sample(c(0,10:25), 53, replace=TRUE), 51),
                 DAM=c(0, NA, 52, sample(c(0,20:35), 52, replace=TRUE)))
pedcheck(ped)

set.seed(127)
ped = data.frame(ID=c(1:50,NA,0,1:3),
                 SIRE=c(0, sample(c(0,10:25), 53, replace=TRUE), 51),
                 DAM=c(0, NA, 52, sample(c(0,20:35), 52, replace=TRUE)))
pedcheck(ped)

Downward pedigree extraction

Description

Extracts pedigree downward for one or a group of individuals to find their descendants

Usage

peddown(ped, parents, maxgen = c())
peddown(ped, parents, maxgen = c())

Arguments

`ped`	: `data.frame` with integer columns corresponding to ID, SIRE, DAM. Missing value is 0.
`parents`	: Vector of individual ID(s), from which the new pedigree is being extracted.
`maxgen`	: (optional) a positive integer for the maximum number of generations to proceed. If no value is provided, there is no limitation on the maximum number of generations to proceed.

Value

Extracted pedigree data.frame

Examples

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
peddown(ped, c(1,4))
peddown(ped, 1, maxgen=1)

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
peddown(ped, c(1,4))
peddown(ped, 1, maxgen=1)

Upward pedigree extraction

Description

Extracts pedigree upward for one or a group of individuals to find their ascendants

Usage

pedup(ped, progeny, maxgen = c())
pedup(ped, progeny, maxgen = c())

Arguments

`ped`	: `data.frame` with integer columns corresponding to ID, SIRE, DAM. Missing value is 0.
`progeny`	: Vector of individual ID(s), from which the new pedigree is being extracted.
`maxgen`	: (optional) a positive integer for the maximum number of generations (continuing from parents of `progeny`) to proceed. If no value is provided, there is no limitation on the maximum number of generations to proceed.

Value

Extracted pedigree data.frame

Examples

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
pedup(ped, c(1,4))
pedup(ped, 6, maxgen=1)

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
pedup(ped, c(1,4))
pedup(ped, 6, maxgen=1)

Pedigree pruning

Description

Pruning pedigree in two different modes (strict, loose)

Usage

pruneped(ped, pheno, mode)
pruneped(ped, pheno, mode)

Arguments

`ped`	: `data.frame` with integer columns corresponding to ID, SIRE, DAM. Missing value is 0.
`pheno`	: Vector of phenotyped individuals
`mode`	: `strict` or `loose`

Details

In strict pruning, individuals without progeny and phenotype are recursively deleted from the pedigree, and then individuals without known parent and without progeny (if any) are deleted. Therefore, all uninfluential individuals are deleted. The downside is that individuals without phenotype or phenotyped progeny cannot receive any genetic merit based on the information from their phenotyped relatives. In loose pruning, the pedigree is upward extracted for phenotyped individuals to thier founders, and then the pedigree is downward extracted from the founders.

Value

Pruned pedigree data.frame

Examples

ped = data.frame(ID=1:7, SIRE=c(0,0,1,3,1,4,0), DAM=c(0,0,2,2,2,5,0))
pheno = c(1,4)
pruneped(ped, pheno, mode="strict")
pruneped(ped, pheno, mode="loose")

ped = data.frame(ID=1:7, SIRE=c(0,0,1,3,1,4,0), DAM=c(0,0,2,2,2,5,0))
pheno = c(1,4)
pruneped(ped, pheno, mode="strict")
pruneped(ped, pheno, mode="loose")

Vector Qg + u

Description

Adds genetic group contributions to the genetic merit of individuals.

Usage

Qgpu(Q, sol)
Qgpu(Q, sol)

Arguments

`Q`	: The output `matrix` from `qmatL` (for more details: `?qmatL`)
`sol`	: `data.frame` with 2 numeric columns corresponding to ID, EBV ([&gcirc;, û]), where &gcirc; and û are the genetic group and genetic merit solutions, respectively. The order of solutions must be the order of columns and the order of rows in matrix Q.

Value

Vector of Q&gcirc; + û

Examples

ped = data.frame(ID=c(3,4,6,5), SIRE=c(1,3,4,1), DAM=c(2,2,5,2))
Q = qmatL(gghead(ped))
ghat = c(0.1, -0.2)
uhat = seq(-1.5, 1.5, 1)
sol = data.frame(ID=1:6, EBV=c(ghat, uhat))
Qgpu(Q, sol)

ped = data.frame(ID=c(3,4,6,5), SIRE=c(1,3,4,1), DAM=c(2,2,5,2))
Q = qmatL(gghead(ped))
ghat = c(0.1, -0.2)
uhat = seq(-1.5, 1.5, 1)
sol = data.frame(ID=1:6, EBV=c(ghat, uhat))
Qgpu(Q, sol)

Matrix Q

Description

Creates the genetic group contribution matrix.

Usage

qmat(ped2)
qmat(ped2)

Arguments

ped2

: The output data.frame from gghead (for more details: ?gghead)

Value

Q matrix

Examples

ped = data.frame(ID=c(3,4,6,5), SIRE=c(1,3,4,1), DAM=c(2,2,5,2))
ped2 = gghead(ped)
qmat(ped2)

ped = data.frame(ID=c(3,4,6,5), SIRE=c(1,3,4,1), DAM=c(2,2,5,2))
ped2 = gghead(ped)
qmat(ped2)

Matrix Q for large pedigrees

Description

Creates the genetic group contribution matrix for large pedigrees.

Usage

qmatL(ped2)
qmatL(ped2)

Arguments

ped2

: The output data.frame from gghead (for more details: ?gghead)

Value

Q matrix

Examples

ped = data.frame(ID=c(3,4,6,5), SIRE=c(1,3,4,1), DAM=c(2,2,5,2))
ped2 = gghead(ped)
qmatL(ped2)

ped = data.frame(ID=c(3,4,6,5), SIRE=c(1,3,4,1), DAM=c(2,2,5,2))
ped2 = gghead(ped)
qmatL(ped2)

Matrix Q for large pedigrees (parallel processing)

Description

Creates the genetic group contribution matrix for large pedigrees, with parallel processing.

Usage

qmatXL(ped2, ncl)
qmatXL(ped2, ncl)

Arguments

`ped2`	: The output `data.frame` from `gghead` (for more details: `?gghead`)
`ncl`	: User defined number of nodes; if the number of user defined nodes is greater than the number of genetic groups, the number genetic groups is considered as the number of nodes.

Details

This function is the parallel version of qmatL. It requires foreach and doParallel packages.

Value

Q matrix

Examples

ped = data.frame(ID=c(3,4,6,5), SIRE=c(1,3,4,1), DAM=c(2,2,5,2))
ped2 = gghead(ped)
qmatXL(ped2, 2)

ped = data.frame(ID=c(3,4,6,5), SIRE=c(1,3,4,1), DAM=c(2,2,5,2))
ped2 = gghead(ped)
qmatXL(ped2, 2)

Pedigree renumbering

Description

Renumbering pedigree to numerical IDs, so that progeny's ID is smaller than parents' IDs.

Usage

renum(ped)
renum(ped)

Arguments

ped

: data.frame with columns corresponding to ID, SIRE, DAM. Missing value is 0.

Value

newped : Pedigree data.frame with renumberred IDs.

xrf : Cross-reference data.frame with 2 columns for original and renumberred IDs.

Examples

ped = data.frame(ID=letters[1:6], SIRE=c(0,0,letters[c(1,3,1,4)]), DAM=c(0,0,letters[c(2,2,2,5)]))
renum(ped)$newped
renum(ped)$xrf

ped = data.frame(ID=letters[1:6], SIRE=c(0,0,letters[c(1,3,1,4)]), DAM=c(0,0,letters[c(2,2,2,5)]))
renum(ped)$newped
renum(ped)$xrf

Genetic relationship coefficient

Description

Calculates genetic relationship coefficient between two individuals.

Usage

rg(ped, id1, id2)
rg(ped, id1, id2)

Arguments

`ped`	: `data.frame` with integer columns corresponding to ID, SIRE, DAM. Missing value is 0.
`id1`	: Numeric ID of an individual
`id2`	: Numeric ID of an individual

Value

Genetic relationship coefficient between the two individuals

Examples

ped = data.frame(ID=1:7, SIRE=c(0,0,1,1,3,1,5), DAM=c(0,0,0,2,4,4,6))
rg(ped, 5, 6)

ped = data.frame(ID=1:7, SIRE=c(0,0,1,1,3,1,5), DAM=c(0,0,0,2,4,4,6))
rg(ped, 5, 6)

Sire-maternal grandsire (S-MGS) pedigree

Description

Extract sire-maternal grandsire (S-MGS) pedigree from a sire-dam pedigree. Sire and MGS information is extracted for sires of phenotyped individuals.

Usage

smgsped(ped, pheno)
smgsped(ped, pheno)

Arguments

`ped`	: `data.frame` with integer columns corresponding to ID, SIRE, DAM. Missing value is 0.
`pheno`	: Vector of phenotyped individuals

Value

S-MGS pedigree data.frame

Examples

ped = data.frame(ID=1:10, SIRE=c(0,0,1,2,0,5,4,4,0,8), DAM=c(0,0,0,3,3,0,6,6,6,0))
smgsped(ped, 7:10)

ped = data.frame(ID=1:10, SIRE=c(0,0,1,2,0,5,4,4,0,8), DAM=c(0,0,0,3,3,0,6,6,6,0))
smgsped(ped, 7:10)

Tabular to matrix

Description

Converts tabular data to matrix data.

Usage

tab2mat(tab)
tab2mat(tab)

Arguments

tab

: data.frame with 2 integer (IDs) and 1 numeric (values) columns.

Value

Converted data.frame to matrix

Examples

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
tab2mat(tabA(ped))

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
tab2mat(tabA(ped))

Relationship matrix A in a tabular format

Description

Creates the pedigree-based additive genetic relationship data.frame.

Usage

tabA(ped)
tabA(ped)

Arguments

ped

: data.frame with integer columns corresponding to ID, SIRE, DAM. Missing value is 0.

Value

Genetic relationship data.frame

Examples

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
tabA(ped)

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
tabA(ped)

Inverse of the relationship matrix A in a tabular format

Description

Creates the data.frame of the inverse of the pedigree-based genetic relationship matrix.

Usage

tabAinv(ped, inbr)
tabAinv(ped, inbr)

Arguments

`ped`	: `data.frame` with integer columns corresponding to ID, SIRE, DAM. Missing value is 0.
`inbr`	: Vector of inbreeding coefficients in the order of individuals in the relationship matrix.

Value

data.frame of the inverse of the genetic relationship matrix

Examples

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
inbr = c(0, 0, 0, 0.25, 0, 0.25)
# or
(inbr = diag(buildA(ped)) - 1)
# or
inbr = tabA(ped); (inbr = inbr[inbr[,1]==inbr[,2],]$a - 1)
# or
# For individual inbreeding values, use function inb.
tabAinv(ped, inbr)

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
inbr = c(0, 0, 0, 0.25, 0, 0.25)
# or
(inbr = diag(buildA(ped)) - 1)
# or
inbr = tabA(ped); (inbr = inbr[inbr[,1]==inbr[,2],]$a - 1)
# or
# For individual inbreeding values, use function inb.
tabAinv(ped, inbr)

Dominance relationship matrix D in a tabular-sparse format

Description

Creates the pedigree-based dominance relationship data.frame.

Usage

tabD(ped, A)
tabD(ped, A)

Arguments

`ped`	: `data.frame` with integer columns corresponding to ID, SIRE, DAM. Missing value is 0.
`A`	: Relationship matrix A in a tabular format created by function `tabA`.

Value

Dominance relationship data.frame

Examples

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
tabD(ped, tabA(ped))

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
tabD(ped, tabA(ped))

Inverse of the dominance relationship matrix D in a tabular format

Description

Creates the data.frame of the inverse of the pedigree-based dominance relationship matrix.

Usage

tabDinv(ped, A)
tabDinv(ped, A)

Arguments

`ped`	: `data.frame` with integer columns corresponding to ID, SIRE, DAM. Missing value is 0.
`A`	: Relationship matrix A in a tabular format created by function `tabA`.

Value

data.frame of the inverse of the dominance relationship matrix

Examples

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
tabDinv(ped, tabA(ped))

ped = data.frame(ID=1:6, SIRE=c(0,0,1,3,1,4), DAM=c(0,0,2,2,2,5))
tabDinv(ped, tabA(ped))

Package 'ggroups'

Help Index

Pedigree and genetic groups

Description

Details

Author(s)

References

Relationship matrix A

Description

Usage

Arguments

Value

Examples

Relationship matrix D

Description

Usage

Arguments

Value

Examples

Append genetic groups to the pedigree

Description

Usage

Arguments

Details

Value

Examples

Individual's inbreeding coefficient

Description

Usage

Arguments

Value

Examples

Inbreeding coefficients

Description

Usage

Arguments

Value

Examples

Matrix to tabular

Description

Usage

Arguments

Value

Examples

Descendants of an individual per generation

Description

Usage

Arguments

Value

Examples

Basic pedigree checks

Description

Usage

Arguments

Examples

Downward pedigree extraction

Description

Usage

Arguments

Value

Examples

Upward pedigree extraction

Description

Usage

Arguments

Value

Examples

Pedigree pruning

Description

Usage

Arguments

Details

Value

Examples

Vector Qg + u

Description

Usage

Arguments

Value

Examples