Title:	Pedigree and Genetic Groups
Version:	2.1.2
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.
License:	GPL-3
URL:	https://github.com/nilforooshan/ggroups
BugReports:	https://github.com/nilforooshan/ggroups/issues
Suggests:	doParallel (≥ 1.0.14), foreach (≥ 1.4.4)
RoxygenNote:	7.1.2
Encoding:	UTF-8
Repository:	CRAN
NeedsCompilation:	no
Packaged:	2022-03-27 21:07:19 UTC; monil0
Author:	Mohammad Ali Nilforooshan [aut, cre]
Maintainer:	Mohammad Ali Nilforooshan <m.a.nilforooshan@gmail.com>
Date/Publication:	2022-03-27 21:20:02 UTC

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 (ĝ) to the genetic merit of animals (û), which is equal to Qĝ + 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 m.a.nilforooshan@gmail.com

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>

Vector Qg + u

Description

Adds genetic group contributions to the genetic merit of individuals.

Usage

Qgpu(Q, sol)

Arguments

Value

Vector of Qĝ + 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)

Relationship matrix A

Description

Builds the pedigree-based additive genetic relationship matrix.

Usage

buildA(ped)

Arguments

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)

Relationship matrix D

Description

Builds the pedigree-based dominance relationship matrix.

Usage

buildD(ped, A)

Arguments

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))

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)

Arguments

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)

Individual's inbreeding coefficient

Description

Calculates inbreeding coefficient for an individual.

Usage

inb(ped, id)

Arguments

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)

Inbreeding coefficients

Description

Calculates inbreeding coefficients for all animals in the pedigree.

Usage

inbreed(ped)

Arguments

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)

Matrix to tabular

Description

Converts matrix data to tabular data.

Usage

mat2tab(mat)

Arguments

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))

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)

Arguments

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

Basic pedigree checks

Description

Performs basic pedigree checks.

Usage

pedcheck(ped)

Arguments

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)

Downward pedigree extraction

Description

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

Usage

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

Arguments

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)

Upward pedigree extraction

Description

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

Usage

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

Arguments

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)

Pedigree pruning

Description

Pruning pedigree in two different modes (strict, loose)

Usage

pruneped(ped, pheno, mode)

Arguments

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")

Matrix Q

Description

Creates the genetic group contribution matrix.

Usage

qmat(ped2)

Arguments

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)

Matrix Q for large pedigrees

Description

Creates the genetic group contribution matrix for large pedigrees.

Usage

qmatL(ped2)

Arguments

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)

Matrix Q for large pedigrees (parallel processing)

Description

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

Usage

qmatXL(ped2, ncl)

Arguments

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)

Pedigree renumbering

Description

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

Usage

renum(ped)

Arguments

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

Genetic relationship coefficient

Description

Calculates genetic relationship coefficient between two individuals.

Usage

rg(ped, id1, id2)

Arguments

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)

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)

Arguments

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)

Tabular to matrix

Description

Converts tabular data to matrix data.

Usage

tab2mat(tab)

Arguments

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))

Relationship matrix A in a tabular format

Description

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

Usage

tabA(ped)

Arguments

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)

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)

Arguments

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)

Dominance relationship matrix D in a tabular-sparse format

Description

Creates the pedigree-based dominance relationship data.frame.

Usage

tabD(ped, A)

Arguments

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))

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)

Arguments

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))