Package 'GeneticsPed' reference manual

Title:	Pedigree and genetic relationship functions
Description:	Classes and methods for handling pedigree data. It also includes functions to calculate genetic relationship measures as relationship and inbreeding coefficients and other utilities. Note that package is not yet stable. Use it with care!
Authors:	Gregor Gorjanc and David A. Henderson <[email protected]>, with code contributions by Brian Kinghorn and Andrew Percy (see file COPYING)
Maintainer:	David Henderson <[email protected]>
License:	LGPL (>= 2.1) \| file LICENSE
Version:	1.69.0
Built:	2024-11-29 05:54:37 UTC
Source:	https://github.com/bioc/GeneticsPed

Check consistency of data in pedigree

Description

check performs a series of checks on pedigree object to ensure consistency of data.

Usage

check(x, ...)
checkId(x)
check(x, ...)
checkId(x)

Arguments

`x`	pedigree, object to be checked
`...`	arguments to other methods, none for now

Details

checkId performs various checks on individuals and their ascendants. These checks are:

idClass: all ids must have the same class
idIsNA: individual can not be NA
idNotUnique: individual must be unique
idEqualAscendant: individual can not be equal to its ascendant
ascendantEqualAscendant: ascendant can not be equal to another ascendant
ascendantInAscendant: ascendant can not appear again as asescendant of other sex i.e. father can not be a mother to someone else
unusedLevels: in case factors are used for id presentation, there might be unused levels for some ids - some functions rely on number of levels and a check is provided for this

checkAttributes is intended primarly for internal use and performs a series of checks on attribute values needed in various functions. It causes stop with error messages for all given attribute checks.

Value

List of more or less self-explanatory errors and "pointers" to these errors for ease of further work i.e. removing errors.

Author(s)

Gregor Gorjanc

Examples

  ## EXAMPLES BELLOW ARE ONLY FOR TESTING PURPOSES AND ARE NOT INTENDED
  ## FOR USERS, BUT IT CAN NOT DO ANY HARM.

  ## --- checkAttributes ---
  tmp <- generatePedigree(5)
  attr(tmp, "sorted") <- FALSE
  attr(tmp, "coded") <- FALSE
  GeneticsPed:::checkAttributes(tmp)
  try(GeneticsPed:::checkAttributes(tmp, sorted=TRUE, coded=TRUE))

  ## --- idClass ---
  tmp <- generatePedigree(5)
  tmp$id <- factor(tmp$id)
  class(tmp$id)
  class(tmp$father)
  try(GeneticsPed:::idClass(tmp))

  ## --- idIsNA ---
  tmp <- generatePedigree(2)
  tmp[1, 1] <- NA
  GeneticsPed:::idIsNA(tmp)

  ## --- idNotUnique ---
  tmp <- generatePedigree(2)
  tmp[2, 1] <- 1
  GeneticsPed:::idNotUnique(tmp)

  ## --- idEqualAscendant ---
  tmp <- generatePedigree(2)
  tmp[3, 2] <- tmp[3, 1]
  GeneticsPed:::idEqualAscendant(tmp)

  ## --- ascendantEqualAscendant ---
  tmp <- generatePedigree(2)
  tmp[3, 2] <- tmp[3, 3]
  GeneticsPed:::ascendantEqualAscendant(tmp)

  ## --- ascendantInAscendant ---
  tmp <- generatePedigree(2)
  tmp[3, 2] <- tmp[5, 3]
  GeneticsPed:::ascendantInAscendant(tmp)
  ## Example with multiple parents
  tmp <- data.frame(id=c("A", "B", "C", "D"),
                    father1=c("E", NA, "F", "H"),
                    father2=c("F", "E", "E", "I"),
                    mother=c("G", NA, "H", "E"))
  tmp <- Pedigree(tmp, ascendant=c("father1", "father2", "mother"),
                  ascendantSex=c(1, 1, 2),
                  ascendantLevel=c(1, 1, 1))
  GeneticsPed:::ascendantInAscendant(tmp)

  ## --- unusedLevels ---
  tmp <- generatePedigree(2, colClass="factor")
  tmp[3:4, 2] <- NA
  GeneticsPed:::unusedLevels(tmp)
## EXAMPLES BELLOW ARE ONLY FOR TESTING PURPOSES AND ARE NOT INTENDED
  ## FOR USERS, BUT IT CAN NOT DO ANY HARM.

  ## --- checkAttributes ---
  tmp <- generatePedigree(5)
  attr(tmp, "sorted") <- FALSE
  attr(tmp, "coded") <- FALSE
  GeneticsPed:::checkAttributes(tmp)
  try(GeneticsPed:::checkAttributes(tmp, sorted=TRUE, coded=TRUE))

  ## --- idClass ---
  tmp <- generatePedigree(5)
  tmp$id <- factor(tmp$id)
  class(tmp$id)
  class(tmp$father)
  try(GeneticsPed:::idClass(tmp))

  ## --- idIsNA ---
  tmp <- generatePedigree(2)
  tmp[1, 1] <- NA
  GeneticsPed:::idIsNA(tmp)

  ## --- idNotUnique ---
  tmp <- generatePedigree(2)
  tmp[2, 1] <- 1
  GeneticsPed:::idNotUnique(tmp)

  ## --- idEqualAscendant ---
  tmp <- generatePedigree(2)
  tmp[3, 2] <- tmp[3, 1]
  GeneticsPed:::idEqualAscendant(tmp)

  ## --- ascendantEqualAscendant ---
  tmp <- generatePedigree(2)
  tmp[3, 2] <- tmp[3, 3]
  GeneticsPed:::ascendantEqualAscendant(tmp)

  ## --- ascendantInAscendant ---
  tmp <- generatePedigree(2)
  tmp[3, 2] <- tmp[5, 3]
  GeneticsPed:::ascendantInAscendant(tmp)
  ## Example with multiple parents
  tmp <- data.frame(id=c("A", "B", "C", "D"),
                    father1=c("E", NA, "F", "H"),
                    father2=c("F", "E", "E", "I"),
                    mother=c("G", NA, "H", "E"))
  tmp <- Pedigree(tmp, ascendant=c("father1", "father2", "mother"),
                  ascendantSex=c(1, 1, 2),
                  ascendantLevel=c(1, 1, 1))
  GeneticsPed:::ascendantInAscendant(tmp)

  ## --- unusedLevels ---
  tmp <- generatePedigree(2, colClass="factor")
  tmp[3:4, 2] <- NA
  GeneticsPed:::unusedLevels(tmp)

Extend pedigree

Description

extend finds ascendants, which do not appear as individuals in pedigree and assigns them as individuals with unknown ascendants in extended pedigree.

Usage

extend(x, ascendant=NULL, col=NULL, top=TRUE)extend(x, ascendant=NULL, col=NULL, top=TRUE)

Arguments

`x`	pedigree object
`ascendant`	character, column names of ascendant(s), see details
`col`	character, column name(s) of attribute(s), see details
`top`	logical, add ascendants as individuals on the top or bottom of the pedigree

Details

Argument ascendant can be used to define, which ascendants will be extended. If ascendant=NULL, which is the default, all ascendant columns in the pedigree are used. The same approach is used with other pedigree attributes such as sex, generation, etc. with argument col. Use col=NA, if none of the pedigree attributes should be extended.

Sex of “new” individuals is infered from attribute ascendantSex as used in Pedigree function. Generation of “new” individuals is infered as minimal (generationOrder="increasing") or maximal (generationOrder="decreasing") generation value in descendants - 1. See Pedigree on this issue. Family values are extended with means of family.

Value

Extended pedigree, where all ascendants also appear as individuals with unknown ascendants and infered other attributes such as sex, generation, etc. if this attributes are in the pedigree.

Author(s)

Gregor Gorjanc

Examples

  # --- Toy example ---
  ped <- generatePedigree(nId=5, nGeneration=4, nFather=1, nMother=2)
  ped <- ped[10:20,]
  ped[5, "father"] <- NA # to test robustnes of extend on NA
  extend(ped)
  extend(ped, top=FALSE)

  ## Extend only ascendant and their generation
  extend(ped, col="generation")
  extend(ped, col=c("generation", "sex"))

  # --- Bigger example ---
  ped <- generatePedigree(nId=1000, nGeneration=10, nFather=100,
                          nMother=500)
  nrow(ped)
  # Now keep some random individuals
  ped <- ped[unique(sort(round(runif(n=nrow(ped)/2, min=1,
                                     max=nrow(ped))))), ]
  nrow(ped)
  nrow(extend(ped))
# --- Toy example ---
  ped <- generatePedigree(nId=5, nGeneration=4, nFather=1, nMother=2)
  ped <- ped[10:20,]
  ped[5, "father"] <- NA # to test robustnes of extend on NA
  extend(ped)
  extend(ped, top=FALSE)

  ## Extend only ascendant and their generation
  extend(ped, col="generation")
  extend(ped, col=c("generation", "sex"))

  # --- Bigger example ---
  ped <- generatePedigree(nId=1000, nGeneration=10, nFather=100,
                          nMother=500)
  nrow(ped)
  # Now keep some random individuals
  ped <- ped[unique(sort(round(runif(n=nrow(ped)/2, min=1,
                                     max=nrow(ped))))), ]
  nrow(ped)
  nrow(extend(ped))

Find families (lines) in the pedigree

Description

family classifies individuals in the pedigree to distinct families or lines. Two individuals are members of one family if they have at least one common ascendant. family<- provides mean to properly add family information into the pedigree.

Usage

family(x)
family(x, col=NULL) <- value
family(x)
family(x, col=NULL) <- value

Arguments

`x`	pedigree object
`col`	character, column name in `x` for family
`value`	family values for individuals in the pedigree

Details

col provides a mean to name or possibly also rename family column with user specified value, say "familia" in Spanish. When col=NULL, which is default, "family" is used.

Value

A vector of family values (integers)

Author(s)

Gregor Gorjanc

Examples

  ## Two families examples
  ped <- data.frame(    id=c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11),
                    father=c(0, 0, 0, 0, 0, 0, 5, 1, 3,  8,  7),
                    mother=c(0, 0, 0, 0, 0, 0, 6, 2, 4,  9, 10),
                generation=c(1, 1, 1, 1, 1, 1, 2, 2, 2,  3,  4))
  ped <- Pedigree(ped, unknown=0, generation="generation")
  family(ped)

  ## After break we get two families
  ped1 <- removeIndividual(ped, individual=11)
  family(ped1)

  ## Subsetting can also be used
  family(ped[1:10,])
  family(ped[7:10,])

  ## Pedigree need not be sorted in advance
  ped2 <- ped[sample(1:10), ]
  family(ped2)

  ## Assign family values to pedigree
  family(ped) <- family(ped)
  ped
  family(ped, col="familia") <- family(ped)
  ped
## Two families examples
  ped <- data.frame(    id=c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11),
                    father=c(0, 0, 0, 0, 0, 0, 5, 1, 3,  8,  7),
                    mother=c(0, 0, 0, 0, 0, 0, 6, 2, 4,  9, 10),
                generation=c(1, 1, 1, 1, 1, 1, 2, 2, 2,  3,  4))
  ped <- Pedigree(ped, unknown=0, generation="generation")
  family(ped)

  ## After break we get two families
  ped1 <- removeIndividual(ped, individual=11)
  family(ped1)

  ## Subsetting can also be used
  family(ped[1:10,])
  family(ped[7:10,])

  ## Pedigree need not be sorted in advance
  ped2 <- ped[sample(1:10), ]
  family(ped2)

  ## Assign family values to pedigree
  family(ped) <- family(ped)
  ped
  family(ped, col="familia") <- family(ped)
  ped

Gene contribution or proportion of genes in pedigree by individual

Description

geneContribution calculates gene contribution as proportion of genes in pedigree by individual with higher number of descendants will have higher values.

Usage

geneContribution(x, relative=TRUE)geneContribution(x, relative=TRUE)

Arguments

`x`	pedigree
`relative`	logical, should results be presented relative to number of individuals in the pedigree

Value

Gene contribution values i.e. higher the values higher the contribution of genes by particular individual in the pedigree. When relative=FALSE, values represent number of individuals (in conceptually additive manner i.e. 0.5 + 0.75 = 1.25 individual) in the pedigree that carry genes of a particular individual. With relative=TRUE, values represent the same result as ratios to all individuals in the pedigree. Value 0 indicates that individual did not pass its genes to next generations.

Author(s)

Gregor Gorjanc

Examples

  ped <- generatePedigree(nId=5, nGeneration=4, nFather=1, nMother=2)
  geneContribution(ped)
  geneContribution(ped, relative=FALSE)
  ## geneContribution(ped[5:15, ]) ## needs [ method

  ## More than one father example
  ped <- data.frame(     id=c(1, 2, 3, 4, 5, 6, 7),
                    father1=c(0, 0, 0, 2, 1, 1, 2),
                    father2=c(0, 0, 0, 0, 0, 2, 0),
                     mother=c(0, 0, 0, 0, 3, 3, 3),
                    generat=c(1, 1, 1, 2, 2, 2, 2))
  ped <- Pedigree(ped, ascendant=c("father1", "father2", "mother"),
                  ascendantSex=c(1, 1, 2), ascendantLevel=c(1, 1, 1),
                  unknown=0, generation="generat")
  geneContribution(ped)
ped <- generatePedigree(nId=5, nGeneration=4, nFather=1, nMother=2)
  geneContribution(ped)
  geneContribution(ped, relative=FALSE)
  ## geneContribution(ped[5:15, ]) ## needs [ method

  ## More than one father example
  ped <- data.frame(     id=c(1, 2, 3, 4, 5, 6, 7),
                    father1=c(0, 0, 0, 2, 1, 1, 2),
                    father2=c(0, 0, 0, 0, 0, 2, 0),
                     mother=c(0, 0, 0, 0, 3, 3, 3),
                    generat=c(1, 1, 1, 2, 2, 2, 2))
  ped <- Pedigree(ped, ascendant=c("father1", "father2", "mother"),
                  ascendantSex=c(1, 1, 2), ascendantLevel=c(1, 1, 1),
                  unknown=0, generation="generat")
  geneContribution(ped)

Gene and gamete flow matrices

Description

geneFlowT and geneFlowTinv creates gene flow matrix (T) and its inverse (Tinv), while gameteFlowM creates gamete flow matrix (M). mendelianSamplingD creates a mendelian sampling covariance matrix (D).

Usage


geneFlowT(x, sort=TRUE, names=TRUE, ...)
geneFlowTinv(x, sort=TRUE, names=TRUE, ...)
gameteFlowM(x, sort=TRUE, names=TRUE, ...)
mendelianSamplingD(x, matrix=TRUE, names=TRUE, ...)

geneFlowT(x, sort=TRUE, names=TRUE, ...)
geneFlowTinv(x, sort=TRUE, names=TRUE, ...)
gameteFlowM(x, sort=TRUE, names=TRUE, ...)
mendelianSamplingD(x, matrix=TRUE, names=TRUE, ...)

Arguments

`x`	Pedigree
`sort`	logical, for the computation the pedigree needs to be sorted, but results are sorted back to original sorting (sort=TRUE) or not (sort=FALSE)
`names`	logical, should returned matrix have row/colnames; this can be used to get leaner matrix
`matrix`	logical, should returned value be a diagonal matrix or a vector
`...`	arguments for other methods

Details

geneFlowT returns a matrix with coefficients that show the flow of genes from one generation to the next one etc. geneFlowTinv is simply the inverse of geneFlowT, but calculated as $I - M$ , where $M$ is gamete flow matrix with coefficients that represent parent gamete contribution to their offspring. mendelianSamplingD is another matrix ( $D$ ) for construction of relationship additive matrix via decomposition i.e. $A=TDT'$ (Henderson, 1976). Mrode (2005) has a very nice introduction to these concepts.

Take care with sort=FALSE, names=FALSE. It is your own responsibility to assure proper handling in this case.

Value

Matrices of $n * n$ dimension, with coeficients as described in the details, where $n$ is number of subjects in x

Author(s)

Gregor Gorjanc

References

Henderson, C. R. (1976) A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics 32(1):69-83

Mrode, R. A. (2005) Linear models for the prediction of animal breeding values. 2nd edition. CAB International. ISBN 0-85199-000-2 http://www.amazon.com/gp/product/0851990002

Examples

if(require(gdata))
  data(Mrode2.1)
  Mrode2.1$dtB <- as.Date(Mrode2.1$dtB)
  x2.1 <- Pedigree(x=Mrode2.1, subject="sub", ascendant=c("fat", "mot"),
                   ascendantSex=c("M", "F"), family="fam", sex="sex",
                   generation="gen", dtBirth="dtB")

  fractions(geneFlowT(x2.1))
  fractions(geneFlowTinv(x2.1))
  fractions(gameteFlowM(x2.1))
  mendelianSamplingD(x2.1)
if(require(gdata))
  data(Mrode2.1)
  Mrode2.1$dtB <- as.Date(Mrode2.1$dtB)
  x2.1 <- Pedigree(x=Mrode2.1, subject="sub", ascendant=c("fat", "mot"),
                   ascendantSex=c("M", "F"), family="fam", sex="sex",
                   generation="gen", dtBirth="dtB")

  fractions(geneFlowT(x2.1))
  fractions(geneFlowTinv(x2.1))
  fractions(gameteFlowM(x2.1))
  mendelianSamplingD(x2.1)

Generate Pedigree example

Description

generatePedigree creates nonoverlapping pedigree example, which can be used for demos and code testing.

Usage

generatePedigree(nId, nGeneration=3, nFather=round(nId/3),
  nMother=nId - nFather, start=1, generationOrder="increasing",
  colClass="integer")generatePedigree(nId, nGeneration=3, nFather=round(nId/3),
  nMother=nId - nFather, start=1, generationOrder="increasing",
  colClass="integer")

Arguments

`nId`	integer, number of individuals per generation, at least 2
`nGeneration`	integer, number of generations
`nFather`	integer, number of fathers per generation
`nMother`	integer, number of mothers per generation
`start`	first generation value
`generationOrder`	character, generation value is "increasing" or "decreasing" through generations
`colClass`	character, class for columns: "integer" or "factor"

Value

An extended, sorted and possibly coded pedigree object with following columns: id, father, mother, generation and sex.

Author(s)

Marcos Rico Gutierrez (author of MATLAB code) and Gregor Gorjanc (R implementation)

References

Rico Gutierrez, M. (1999) Los modelos lineales en la mejora genetica animal. Ediciones Peninsular. ISBN 84-605-9910-8.

Examples

  generatePedigree(5)
  generatePedigree(nId=5, nGeneration=4, nFather=1, nMother=2)
  generatePedigree(nId=5, nGeneration=4, nFather=1, nMother=2,
                   start=0, generationOrder="decreasing")
  generatePedigree(nId=100, nGeneration=20, nFather=10, nMother=50)
generatePedigree(5)
  generatePedigree(nId=5, nGeneration=4, nFather=1, nMother=2)
  generatePedigree(nId=5, nGeneration=4, nFather=1, nMother=2,
                   start=0, generationOrder="decreasing")
  generatePedigree(nId=100, nGeneration=20, nFather=10, nMother=50)

Calculate generation value

Description

generation calculates generation value of individuals in given pedigree. generation<- provides a way to properly add generation information into the pedigree.

Usage

generation(x, start=1, generationOrder=NULL)
generation(x, generationOrder=NULL, col=NULL) <- value
generation(x, start=1, generationOrder=NULL)
generation(x, generationOrder=NULL, col=NULL) <- value

Arguments

`x`	pedigree object
`start`	first generation value
`generationOrder`	character, should be generation values "increasing" or "decreasing" through generations, see details
`col`	character, column name in `x` for generation
`value`	generation values for individuals in the pedigree

Details

Generation value for founders is set to value start, which is by default 1, while other individuals get it according to:

$G_s = max(G_{1a} + G_{2a} + ... G_{na}) + 1$

where G represents generation value for s - individual, a - ascendant e.g. father and mother, where n=2. N might be higher if there are multiple ascendants i.e. this function can also handle pedigrees with higher order ascendants e.g. grandfather.

generationOrder can be used to define "increasing" or "decreasing" order of generation values. If this argument is NULL, which is default, then this information is taken from the pedigree - see Pedigree for more on this issue.

col provides a way to name or possibly also rename generation column with user specified value, say "generazione" in Italian. When col=NULL, which is default, "generation" is used.

Value

A vector of generation values (integers)

Author(s)

Gregor Gorjanc

Examples

  # Nonoverlapping pedigree
  ped <- generatePedigree(nId=5, nGeneration=4, nFather=1, nMother=2)
  ped$generation1 <- generation(ped)
  ped

  # Overlapping Pedigree
  ped <- data.frame(     id=c(1, 2, 3, 4, 5, 6, 7),
                     father=c(0, 0, 2, 2, 2, 4, 4),
                     mother=c(0, 0, 1, 0, 3, 3, 5),
                    dtBirth=c(2, 1, 3, 4, 5, 6, 7))
  ped <- Pedigree(ped, unknown=0, dtBirth="dtBirth")
  generation(ped) <- generation(ped)

  # Overlapping pedigree + one individual (4) comes late in pedigree and
  # has no ascendants
  ped <- data.frame(     id=c(1, 2, 3, 4, 5, 6, 7),
                     father=c(0, 0, 2, 0, 2, 4, 4),
                     mother=c(0, 0, 1, 0, 3, 3, 5),
                    dtBirth=c(2, 1, 3, 2, 5, 6, 7))
  ped <- Pedigree(ped, unknown=0, dtBirth="dtBirth")
  generation(ped)
  generation(ped, generationOrder="decreasing",
             col="generazione") <- generation(ped, generationOrder="decreasing")
# Nonoverlapping pedigree
  ped <- generatePedigree(nId=5, nGeneration=4, nFather=1, nMother=2)
  ped$generation1 <- generation(ped)
  ped

  # Overlapping Pedigree
  ped <- data.frame(     id=c(1, 2, 3, 4, 5, 6, 7),
                     father=c(0, 0, 2, 2, 2, 4, 4),
                     mother=c(0, 0, 1, 0, 3, 3, 5),
                    dtBirth=c(2, 1, 3, 4, 5, 6, 7))
  ped <- Pedigree(ped, unknown=0, dtBirth="dtBirth")
  generation(ped) <- generation(ped)

  # Overlapping pedigree + one individual (4) comes late in pedigree and
  # has no ascendants
  ped <- data.frame(     id=c(1, 2, 3, 4, 5, 6, 7),
                     father=c(0, 0, 2, 0, 2, 4, 4),
                     mother=c(0, 0, 1, 0, 3, 3, 5),
                    dtBirth=c(2, 1, 3, 2, 5, 6, 7))
  ped <- Pedigree(ped, unknown=0, dtBirth="dtBirth")
  generation(ped)
  generation(ped, generationOrder="decreasing",
             col="generazione") <- generation(ped, generationOrder="decreasing")

Genotype probability index

Description

gpi calculates Genotype Probability Index (GPI), which indicates the information content of genotype probabilities derived from segregation analysis.

Usage

  gpi(gp, hwp)
gpi(gp, hwp)

Arguments

`gp`	numeric vector or matrix, individual genotype probabilities
`hwp`	numeric vector or matrix, Hard-Weinberg genotype probabilities

Details

Genotype Probability Index (GPI; Kinghorn, 1997; Percy and Kinghorn, 2005) indicates information that is contained in multi-allele genotype probabilities for diploids derived from segregation analysis, say Thallman et. al (2001a, 2001b). GPI can be used as one of the criteria to help identify which ungenotyped individuals or loci should be genotyped in order to maximise the benefit of genotyping in the population (e.g. Kinghorn, 1999).

gp and hwp arguments accept genotype probabilities for multi-allele loci. If there are two alleles (1 and 2), you should pass vector of probabilities for genotypes (11 and 12) i.e. one value for heterozygotes (12 and 21) and always skipping last homozygote. With three alleles this vector should hold probabilities for genotypes (11, 12, 13, 22, 23) as also shown bellow and in examples. hwp and gpLong2Wide functions can be used to ease the setup for gp and hwp arguments.

     2 alleles: 1 and 2
     11 12
     --> no. dimensions = 2

     3 alleles: 1, 2, and 3
     11 12 13
        22 23
     --> no. dimensions = 5

     ...

     5 alleles: 1, 2, 3, 4, and 5
     11 12 13 14 15
        22 23 24 25
           33 34 35
              44 45
     --> no. dimensions = 14

In general, number of dimensions ( $k$ ) for $n$ alleles is equal to:

$k = (n * (n + 1) / 2) - 1.$

If you have genotype probabilities for more than one individual, you can pass them to gp in a matrix form, where each row represents genotype probabilities of an individual. In case of passing matrix to gp, hwp can still accept a vector of Hardy-Weinberg genotype probabilities, which will be used for all individuals due to recycling. If hwp also gets a matrix, then it must be of the same dimension as that one passed to gp.

Value

Vector of $N$ genotype probability indices, where $N$ is number of individuals

Author(s)

Gregor Gorjanc R code, documentation, wrapping into a package; Andrew Percy and Brian P. Kinghorn Fortran code

References

Kinghorn, B. P. (1997) An index of information content for genotype probabilities derived from segregation analysis. Genetics 145(2):479-483 http://www.genetics.org/cgi/content/abstract/145/2/479

Kinghorn, B. P. (1999) Use of segregation analysis to reduce genotyping costs. Journal of Animal Breeding and Genetics 116(3):175-180 http://dx.doi.org/10.1046/j.1439-0388.1999.00192.x

Percy, A. and Kinghorn, B. P. (2005) A genotype probability index for multiple alleles and haplotypes. Journal of Animal Breeding and Genetics 122(6):387-392 http://dx.doi.org/10.1111/j.1439-0388.2005.00553.x

Thallman, R. M. and Bennet, G. L. and Keele, J. W. and Kappes, S. M. (2001a) Efficient computation of genotype probabilities for loci with many alleles: I. Allelic peeling. Journal of Animal Science 79(1):26-33 http://jas.fass.org/cgi/reprint/79/1/34

Thallman, R. M. and Bennet, G. L. and Keele, J. W. and Kappes, S. M. (2001b) Efficient computation of genotype probabilities for loci with many alleles: II. Iterative method for large, complex pedigrees. Journal of Animal Science 79(1):34-44 http://jas.fass.org/cgi/reprint/79/1/34

Examples

  ## --- Example 1 from Percy and Kinghorn (2005) ---
  ## No. alleles: 2
  ## No. individuals: 1
  ## Individual genotype probabilities:
  ##   Pr(11, 12, 22) = (.1, .5, .4)
  ##
  ## Hardy-Weinberg probabilities:
  ##   Pr(1, 2)   = (.75, .25)
  ##   Pr(11, 12,   (.75^2, 2*.75*.25,
  ##           22) =             .25^2)
  ##               = (.5625, .3750,
  ##                         .0625)

  gp <- c(.1, .5)
  hwp <- c(.5625, .3750)
  gpi(gp=gp, hwp=hwp)

  ## --- Example 1 from Percy and Kinghorn (2005) extended ---
  ## No. alleles: 2
  ## No. individuals: 2
  ## Individual genotype probabilities:
  ##   Pr_1(11, 12, 22) = (.1, .5, .4)
  ##   Pr_2(11, 12, 22) = (.2, .5, .3)

  (gp <- matrix(c(.1, .5, .2, .5), nrow=2, ncol=2, byrow=TRUE))
  gpi(gp=gp, hwp=hwp)

  ## --- Example 2 from Percy and Kinghorn (2005) ---
  ## No. alleles: 3
  ## No. individuals: 1
  ## Individual genotype probabilities:
  ##   Pr(11, 12, 13,   (.1, .5, .0,
  ##          22, 23  =      .4, .0,
  ##              33)            .0)
  ##
  ## Hardy-Weinberg probabilities:
  ##   Pr(1, 2, 3)    = (.75, .25, .0)
  ##   Pr(11, 12, 13,   (.75^2, 2*.75*.25, .0,
  ##          22, 23, =            0.25^2, .0,
  ##              33)                      .0)
  ##                  = (.5625, .3750, .0
  ##                            .0625, .0,
  ##                                   .0)

  gp <- c(.1, .5, .0, .4, .0)
  hwp <- c(.5625, .3750, .0, .0625, .0)
  gpi(gp=gp, hwp=hwp)

  ## --- Example 3 from Percy and Kinghorn (2005) ---
  ## No. alleles: 5
  ## No. individuals: 1
  ## Hardy-Weinberg probabilities:
  ##   Pr(1, 2, 3, 4, 5)   = (.2, .2, .2, .2, .2)
  ##   Pr(11, 12, 13, ...) = (Pr(1)^2, 2*Pr(1)+Pr(2), 2*Pr(1)*Pr(3), ...)
  ##
  ## Individual genotype probabilities:
  ##   Pr(11, 12, 13, ...) = gp / 2
  ##   Pr(12) = Pr(12) + .5

  (hwp <- rep(.2, times=5) %*% t(rep(.2, times=5)))
  hwp <- c(hwp[upper.tri(hwp, diag=TRUE)])
  (hwp <- hwp[1:(length(hwp) - 1)])
  gp <- hwp / 2
  gp[2] <- gp[2] + .5
  gp

  gpi(gp=gp, hwp=hwp)

  ## --- Simulate gp for n alleles and i individuals ---
  n <- 3
  i <- 10

  kAll <- (n*(n+1)/2) # without -1 here!
  k <- kAll - 1
  if(require("gtools")) {
    gp <- rdirichlet(n=i, alpha=rep(x=1, times=kAll))[, 1:k]
    hwp <- as.vector(rdirichlet(n=1, alpha=rep(x=1, times=kAll)))[1:k]
    gpi(gp=gp, hwp=hwp)
  }
## --- Example 1 from Percy and Kinghorn (2005) ---
  ## No. alleles: 2
  ## No. individuals: 1
  ## Individual genotype probabilities:
  ##   Pr(11, 12, 22) = (.1, .5, .4)
  ##
  ## Hardy-Weinberg probabilities:
  ##   Pr(1, 2)   = (.75, .25)
  ##   Pr(11, 12,   (.75^2, 2*.75*.25,
  ##           22) =             .25^2)
  ##               = (.5625, .3750,
  ##                         .0625)

  gp <- c(.1, .5)
  hwp <- c(.5625, .3750)
  gpi(gp=gp, hwp=hwp)

  ## --- Example 1 from Percy and Kinghorn (2005) extended ---
  ## No. alleles: 2
  ## No. individuals: 2
  ## Individual genotype probabilities:
  ##   Pr_1(11, 12, 22) = (.1, .5, .4)
  ##   Pr_2(11, 12, 22) = (.2, .5, .3)

  (gp <- matrix(c(.1, .5, .2, .5), nrow=2, ncol=2, byrow=TRUE))
  gpi(gp=gp, hwp=hwp)

  ## --- Example 2 from Percy and Kinghorn (2005) ---
  ## No. alleles: 3
  ## No. individuals: 1
  ## Individual genotype probabilities:
  ##   Pr(11, 12, 13,   (.1, .5, .0,
  ##          22, 23  =      .4, .0,
  ##              33)            .0)
  ##
  ## Hardy-Weinberg probabilities:
  ##   Pr(1, 2, 3)    = (.75, .25, .0)
  ##   Pr(11, 12, 13,   (.75^2, 2*.75*.25, .0,
  ##          22, 23, =            0.25^2, .0,
  ##              33)                      .0)
  ##                  = (.5625, .3750, .0
  ##                            .0625, .0,
  ##                                   .0)

  gp <- c(.1, .5, .0, .4, .0)
  hwp <- c(.5625, .3750, .0, .0625, .0)
  gpi(gp=gp, hwp=hwp)

  ## --- Example 3 from Percy and Kinghorn (2005) ---
  ## No. alleles: 5
  ## No. individuals: 1
  ## Hardy-Weinberg probabilities:
  ##   Pr(1, 2, 3, 4, 5)   = (.2, .2, .2, .2, .2)
  ##   Pr(11, 12, 13, ...) = (Pr(1)^2, 2*Pr(1)+Pr(2), 2*Pr(1)*Pr(3), ...)
  ##
  ## Individual genotype probabilities:
  ##   Pr(11, 12, 13, ...) = gp / 2
  ##   Pr(12) = Pr(12) + .5

  (hwp <- rep(.2, times=5) %*% t(rep(.2, times=5)))
  hwp <- c(hwp[upper.tri(hwp, diag=TRUE)])
  (hwp <- hwp[1:(length(hwp) - 1)])
  gp <- hwp / 2
  gp[2] <- gp[2] + .5
  gp

  gpi(gp=gp, hwp=hwp)

  ## --- Simulate gp for n alleles and i individuals ---
  n <- 3
  i <- 10

  kAll <- (n*(n+1)/2) # without -1 here!
  k <- kAll - 1
  if(require("gtools")) {
    gp <- rdirichlet(n=i, alpha=rep(x=1, times=kAll))[, 1:k]
    hwp <- as.vector(rdirichlet(n=1, alpha=rep(x=1, times=kAll)))[1:k]
    gpi(gp=gp, hwp=hwp)
  }

Utility functions for gpi()

Description

gpLong2Wide changes data.frame with genotype probabilities in long form (one genotype per row) to wide form (one individual per row) for use in gpi.

hwp calculates genotype probabilities according to Hardy-Weinberg law for use in gpi.

Usage

  gpLong2Wide(x, id, genotype, prob, trim=TRUE)
  hwp(x, trim=TRUE)
gpLong2Wide(x, id, genotype, prob, trim=TRUE)
  hwp(x, trim=TRUE)

Arguments

`x`	data.frame for `gpLong2Wide`, `genotype` for `hwp`
`id`	character, column name in `x` holding individual identifications
`genotype`	character, column name in `x` holding genotypes
`prob`	character, column name in `x` holding genotype probabilities
`trim`	logical, remove last column (for `gpLong2Wide`) or value (for `hwp`) of a result

Details

Hardy-Weinberg probabilities for a gene with two alleles A and B, with probabilities $Pr(A)$ and $Pr(B)$ are:

$Pr(AA) = Pr(A)^2$
$Pr(AB) = 2 * Pr(A) * Pr(A)$
$Pr(BB) = Pr(B)^2$

Value

gpLong2Wide returns a matrix with number of rows equal to number of individuals and number of columns equal to number of possible genotypes.

hwp returns a vector with Hardy-Weinberg genotype probabilities.

Author(s)

Gregor Gorjanc

Examples


  if(require(genetics)) {
    gen <- genotype(c("A/A", "A/B"))
    hwp(x=gen)
    hwp(x=gen, trim=FALSE)
  }
if(require(genetics)) {
    gen <- genotype(c("A/A", "A/B"))
    hwp(x=gen)
    hwp(x=gen, trim=FALSE)
  }

Inbreeding coefficient

Description

inbreeding calculates inbreeding coefficients of individuals in the pedigree

Usage

inbreeding(x, method="meuwissen", sort=TRUE, names=TRUE, ...)inbreeding(x, method="meuwissen", sort=TRUE, names=TRUE, ...)

Arguments

`x`	pedigree object
`method`	character, method of calculation "tabular", "meuwissen" or "sargolzaei", see details
`sort`	logical, for the computation the pedigree needs to be sorted, but results are sorted back to original sorting (sort=TRUE) or not (sort=FALSE)
`names`	logical, should returned vector have names; this can be used to get leaner returned object
`...`	arguments for other methods

Details

Coefficient of inbreeding ( $F$ ) represents probability that two alleles on a loci are identical by descent (Wright, 1922; Falconer and Mackay, 1996). Wright (1922) showed how $F$ can be calculated but his method of paths is not easy to wrap in a program. Calculation of $F$ can also be performed using tabular method for setting the additive relationship matrix (Henderson, 1976), where $F_i = A_{ii} - 1$ . Meuwissen and Luo (1992) and VanRaden (1992) developed faster algorithms for $F$ calculation. Wiggans et al. (1995) additionally explains method in VanRaden (1992). Sargolzaei et al. (2005) presented yet another fast method.

Take care with sort=FALSE, names=FALSE. It is your own responsibility to assure proper handling in this case.

Value

A vector of length $n$ with inbreeding coefficients, where $n$ is number of subjects in x

Author(s)

Gregor Gorjanc and Dave A. Henderson

References

Falconer, D. S. and Mackay, T. F. C. (1996) Introduction to Quantitative Genetics. 4th edition. Longman, Essex, U.K. http://www.amazon.com/gp/product/0582243025

Henderson, C. R. (1976) A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics 32(1):69-83

Meuwissen, T. H. E. and Luo, Z. (1992) Computing inbreeding coefficients in large populations. Genetics Selection and Evolution 24:305-313

Sargolzaei, M. and Iwaisaki, H. and Colleau, J.-J. (2005) A fast algorithm for computing inbreeding coefficients in large populations. Journal of Animal Breeding and Genetics 122(5):325–331 http://dx.doi.org/10.1111/j.1439-0388.2005.00538.x

VanRaden, P. M. (1992) Accounting for inbreeding and crossbreeding in genetic evaluation for large populations. Journal of Dairy Science 75(11):3136-3144 http://jds.fass.org/cgi/content/abstract/75/11/3136

Wiggans, G. R. and VanRaden, P. M. and Zuurbier, J. (1995) Calculation and use of inbreeding coefficients for genetic evaluation of United States dairy cattle. Journal of Dairy Science 78(7):1584-1590 http://jds.fass.org/cgi/content/abstract/75/11/3136

Wright, S. (1922) Coefficients of inbreeding and relationship. American Naturalist 56:330-338

Examples

  data(Mrode2.1)
  Mrode2.1$dtB <- as.Date(Mrode2.1$dtB)
  x2.1 <- Pedigree(x=Mrode2.1, subject="sub", ascendant=c("fat", "mot"),
                   ascendantSex=c("M", "F"), family="fam", sex="sex",
                   generation="gen", dtBirth="dtB")
  fractions(inbreeding(x=x2.1))

  ## Compare the speed
  ped <- generatePedigree(nId=25)
  system.time(inbreeding(x=ped))
  # system.time(inbreeding(x=ped, method="sargolzaei")) # not yet implemented
  system.time(inbreeding(x=ped, method="tabular"))
data(Mrode2.1)
  Mrode2.1$dtB <- as.Date(Mrode2.1$dtB)
  x2.1 <- Pedigree(x=Mrode2.1, subject="sub", ascendant=c("fat", "mot"),
                   ascendantSex=c("M", "F"), family="fam", sex="sex",
                   generation="gen", dtBirth="dtB")
  fractions(inbreeding(x=x2.1))

  ## Compare the speed
  ped <- generatePedigree(nId=25)
  system.time(inbreeding(x=ped))
  # system.time(inbreeding(x=ped, method="sargolzaei")) # not yet implemented
  system.time(inbreeding(x=ped, method="tabular"))

Founder and non-founder individuals in the pedigree

Description

isFounder classifies individuals in the pedigree as founders (base) or non-founders (non-base individuals).

Usage

  isFounder(x, col=attr(x, ".ascendant"))
isFounder(x, col=attr(x, ".ascendant"))

Arguments

`x`	pedigree object
`col`	character, which columns should be checked, see examples

Details

By definition founders do not have any known ascendants, while the opossite is the case for non-founders i.e. they have at least one known ascendant.

FIXME: any relation with founderGeneSet in GeneticsBase

Value

Boolean vector.

Author(s)

Gregor Gorjanc

Examples

  ped <- generatePedigree(nId=5)
  isFounder(ped)
  ## Based only on fathers
  isFounder(ped, col=c("father"))
  ## Works also only on a part of a pedigree
  isFounder(ped[1:5, ])
ped <- generatePedigree(nId=5)
  isFounder(ped)
  ## Based only on fathers
  isFounder(ped, col=c("father"))
  ## Works also only on a part of a pedigree
  isFounder(ped[1:5, ])

Model matrix for individuals with and without records

Description

model.matrix for pedigree creates design matrix ( $Z$ ) for individuals with and without records. Used mainly for educational purposes.

Usage


## S3 method for class 'Pedigree'
model.matrix(object, y, id, left=TRUE, names=TRUE,
                                ...)
## S3 method for class 'Pedigree'
model.matrix(object, y, id, left=TRUE, names=TRUE,
                                ...)

Arguments

`object`	Pedigree
`names`	logical, should returned matrix have row/colnames; this can be used to get leaner matrix
`y`	numeric, vector of (phenotypic) records
`id`	vector of subjects for `y`
`left`	logical, bind columns of individuals without records to left (`left=TRUE`) or right (`left=FALSE`) side of $Z$
`...`	arguments passed to `model.matrix`

Value

A model matrix of $n * q$ dimension, where $n$ is number of records in y and $q$ is number of subjects in the pedigree

Author(s)

Gregor Gorjanc

Examples

  data(Mrode3.1)
  (x <- Pedigree(x=Mrode3.1, subject="calf", ascendant=c("sire", "dam"),
                 ascendantSex=c("Male", "Female"), sex="sex"))
  model.matrix(object=x, y=x$pwg, id=x$calf)
data(Mrode3.1)
  (x <- Pedigree(x=Mrode3.1, subject="calf", ascendant=c("sire", "dam"),
                 ascendantSex=c("Male", "Female"), sex="sex"))
  model.matrix(object=x, y=x$pwg, id=x$calf)

Pedigree and data examples

Description

Various pedigree and data examples

Usage

data(Falconer5.1)
data(Mrode2.1)
data(Mrode3.1)
data(Falconer5.1)
data(Mrode2.1)
data(Mrode3.1)

Format

Falconer5.1 is a rather complex (inbreed) pedigree example from book by Falconer and Mackay (1996) - page 84 with 18 individuals and following columns:

sub: individual
fat: father
mot: mother

Mrode2.1 is an extended pedigree example from book by Mrode (2005) - page 27 with 6 individuals and following columns:

sub: individual
fat: father
mot: mother
fam: family
sex: sex
gen: generation
dtB: date of birth

Mrode3.1 is a pedigree and data example from book by Mrode (2005) - page 43: it shows a beef breeding scenario with 8 individuals (animals), where 5 of them have phenotypic records (pre-weaning gain) and 3 three of them are without records and link others through the pedigree:

calf: calf
sex: sex of a calf
sire: father of a calf
dam: mother of a calf
pwg: pre-weaning gain of a calf in kg

References

Falconer, D. S. and Mackay, T. F. C. (1996) Introduction to Quantitative Genetics. 4th edition. Longman, Essex, U.K. http://www.amazon.com/gp/product/0582243025

Mrode, R. A. (2005) Linear models for the prediction of animal breeding values. 2nd edition. CAB International. ISBN 0-85199-000-2 http://www.amazon.com/gp/product/0851990002

Examples

  data(Falconer5.1)
  Pedigree(x=Falconer5.1, subject="sub", ascendant=c("fat", "mot"))

  data(Mrode2.1)
  Mrode2.1$dtB <- as.Date(Mrode2.1$dtB)
  Pedigree(x=Mrode2.1, subject="sub", ascendant=c("fat", "mot"),
           ascendantSex=c("M", "F"), family="fam", sex="sex",
           generation="gen", dtBirth="dtB")

  data(Mrode3.1)
  Pedigree(x=Mrode3.1, subject="calf", ascendant=c("sire", "dam"),
           ascendantSex=c("Male", "Female"), sex="sex")

data(Falconer5.1)
  Pedigree(x=Falconer5.1, subject="sub", ascendant=c("fat", "mot"))

  data(Mrode2.1)
  Mrode2.1$dtB <- as.Date(Mrode2.1$dtB)
  Pedigree(x=Mrode2.1, subject="sub", ascendant=c("fat", "mot"),
           ascendantSex=c("M", "F"), family="fam", sex="sex",
           generation="gen", dtBirth="dtB")

  data(Mrode3.1)
  Pedigree(x=Mrode3.1, subject="calf", ascendant=c("sire", "dam"),
           ascendantSex=c("Male", "Female"), sex="sex")

Number of individuals in a pedigree

Description

nIndividual returns number of individuals (individuals and/or ascendants) in a pedigree object.

Usage

nIndividual(x, col=NULL, extend=TRUE, drop=TRUE)nIndividual(x, col=NULL, extend=TRUE, drop=TRUE)

Arguments

`x`	pedigree
`col`	character, which id column should be the source: "id" (default) or particular ascendant i.e. "father" and "mother"
`extend`	logical, extend pedigree
`drop`	logical, drop unused levels in case factors are used

Details

FIXME - this will change a lot!!!! There is always one additional level in levels in case factors are used to represent individuals in a pedigree as described in Pedigree. However, nlevels.Pedigree prints out the number of levels actually used to represent individuals i.e. level unknown is not included into the result.

Author(s)

Gregor Gorjanc

Examples

  # Deafult example
  ped <- generatePedigree(5)
  nIndividual(ped)

  # Other id columns
  nIndividual(ped, col="father")
  nIndividual(ped, col="mother")

  # Remove individuals with unknown fathers - FIXME
  # ped <- ped[!is.na(ped, col="father"), ]
  # nIndividual(ped)
  # nIndividual(ped, extend=FALSE)
# Deafult example
  ped <- generatePedigree(5)
  nIndividual(ped)

  # Other id columns
  nIndividual(ped, col="father")
  nIndividual(ped, col="mother")

  # Remove individuals with unknown fathers - FIXME
  # ped <- ped[!is.na(ped, col="father"), ]
  # nIndividual(ped)
  # nIndividual(ped, extend=FALSE)

Pedigree

Description

Pedigree function creates a pedigree object

Usage


Pedigree(x, subject="id", ascendant=c("father", "mother"),
  ascendantSex=c(1, 2), ascendantLevel=c(1, 1), unknown=NA, sex=NA,
  dtBirth=NA, generation=NA, family=NA, generationOrder="increasing",
  check=TRUE, sort=FALSE, extend=FALSE, drop=TRUE, codes=FALSE)

Pedigree(x, subject="id", ascendant=c("father", "mother"),
  ascendantSex=c(1, 2), ascendantLevel=c(1, 1), unknown=NA, sex=NA,
  dtBirth=NA, generation=NA, family=NA, generationOrder="increasing",
  check=TRUE, sort=FALSE, extend=FALSE, drop=TRUE, codes=FALSE)

Arguments

`x`	data.frame or matrix
`subject`	character, column name in `x` for subjects
`ascendant`	character, column name(s) in `x` for ascendants
`family`	character, column name in `x` for family
`ascendantSex`	integer orcharacter, sex of ascendant(s); see details
`ascendantLevel`	integer, generation level of ascendant(s); see details
`unknown`	vector or list, uknown representation of identification and other data in the pedigree; see details
`sex`	character, column name in `x` for sex
`dtBirth`	character, column name in `x` for date of birth
`generation`	character, column name in `x` for generation
`generationOrder`	character, generation value is "increasing" or "decreasing" through generations; see details
`check`	logical, check for common errors
`sort`	logical, sort pedigree
`extend`	logical, extend pedigree
`drop`	logical, drop unused levels if factors are used
`codes`	logical, code individuals into integers

Details

FIXME: study geneSet class

Pedigree can be one source of information on genetic relationship between relatives. Take for example the following pedigree:

  paternal    paternal     maternal    maternal
  grandfather grandmother  grandfather grandmother
      |           |            |           |
      -------------            -------------
            |                        |
          father                   mother
            |                        |
            -------------------------
                        |
                     subject

This information can be stored in a data.frame as

mother	maternal grandfather	maternal grandmother
father	paternal grandfather	paternal grandmother
subject	father	mother

There is considerable variability in terminology between as well as within various fields of genetics. We use the following terms throughout the help and code: individual (any individual in a pedigree), subject (individual whose pedigree is given i.e. individuals in the first column in upper data.frame), ascendant and descendant. Additionally, family, sex, dtBirth and generation are used for additional data in the pedigree. Their meaning should be clear. For these, argument col is usually used in function calls.

family TODO

ascendantSex can be used to define sex of ascendant(s); for example c("Male", "Female") or c("M", "F") or even c(1, 2) for father and mother or c(2, 1, 1) for mother and two possible fathers or c(1, 1) for father and maternal father etc. This data is needed only for the structure of the class and defaults should be ok for the majority. But you need to make sure that data defined here must be in accordance with values in sex column.

ascendantLevel can be used to define generation level of ascendant(s) in relation to a subject; for example c(1, 1) for father and mother or c(1, 1, 1) for mother and two possible fathers or c(1, 2) for father and maternal father etc. This data is needed only for the structure of the class and defaults should be ok for the majority.

There is no need for as.integer TODO in arguments ascendantLevel as this is done internally.

unknown TODO

Sex TODO

Date of birth TODO

generationOrder defines in which order are generation values: "increasing" if values increase from ascendants to descendants and "decreasing" if values decrease from ascendants to descendants.

check, sort, extend, and codes are actions on the pedigree and have their own help pages.

Individuals can be stored as either integer, numeric or factor TODO. In any case all id columns must have the same class and this is automatically checked. Argument drop can be used to drop unused levels, if factors are used.

as.Pedigree.* FIXME as.*.Pedigree FIXME

Object of Pedigree class is a data.frame with columns that can be divided into core columns (subject, ascendant(s), sex, dtBirth and generationTODO) and possibly other columns such as data on phenotype and genotype and other subject attributes, for example factors and covariates TODO.

Additionally, the following attributes are set on pedigree:

.subjectcharacter, column name of subject identification in pedigree
.ascendantcharacter, column name(s) of ascendant(s) identification in pedigree
.familycharacter, column name of family identification in pedigree
.ascendantSexinteger, sex of ascendant(s)
.ascendantLevelinteger, generation level of ascendant(s)
.sexcharacter, column name of subject's sex
.dtBirthcharacter, column name of subject's date of birth
.generationcharacter, column name of subject's generation
.generationOrdercharacter, generation value is "increasing" or "decreasing" through generations
.colClasscharacter, storage class for id columns: "integer", "numeric" or "factor"
.checkedlogical, is pedigree checked for common errors
.sortedlogical, is pedigree sorted; by TODO
.extendedlogical, is pedigree extended
.codedlogical, is pedigree coded
.unknownlist, uknown representation for individual identification and other data in the pedigree; names of the list are c(".id", ".family", ".sex", ".dtBirth", ".generation")

Value

Pedigree object as described in the details

Author(s)

Gregor Gorjanc

Examples


data(Mrode2.1)
Mrode2.1$dtB <- as.Date(Mrode2.1$dtB)
x2.1 <- Pedigree(x=Mrode2.1, subject="sub", ascendant=c("fat", "mot"),
                 ascendantSex=c("M", "F"), family="fam", sex="sex",
                 generation="gen", dtBirth="dtB")


if (FALSE) {
  ## How to handle different pedigree types
  ## * multiple parents
  ped2 <- ped
  ped2$father1 <- ped$father
  ped2$father2 <- ped$father
  ped2$father <- NULL
  ped2 <- as.data.frame(ped2)
  str(Pedigree(ped2, ascendant=c("father1", "father2", "mother"),
               ascendantSex=c(1, 1, 2), ascendantLevel=c(1, 1, 1)))

  ## * different level of parents
  ped3 <- as.data.frame(ped)
  ped3$m.grandfather <- ped3$mother
  ped3$mother <- NULL
  str(Pedigree(ped3, ascendant=c("father", "m.grandfather"),
               ascendantSex=c(1, 1), ascendantLevel=c(1, 2)))
}

data(Mrode2.1)
Mrode2.1$dtB <- as.Date(Mrode2.1$dtB)
x2.1 <- Pedigree(x=Mrode2.1, subject="sub", ascendant=c("fat", "mot"),
                 ascendantSex=c("M", "F"), family="fam", sex="sex",
                 generation="gen", dtBirth="dtB")


if (FALSE) {
  ## How to handle different pedigree types
  ## * multiple parents
  ped2 <- ped
  ped2$father1 <- ped$father
  ped2$father2 <- ped$father
  ped2$father <- NULL
  ped2 <- as.data.frame(ped2)
  str(Pedigree(ped2, ascendant=c("father1", "father2", "mother"),
               ascendantSex=c(1, 1, 2), ascendantLevel=c(1, 1, 1)))

  ## * different level of parents
  ped3 <- as.data.frame(ped)
  ped3$m.grandfather <- ped3$mother
  ped3$mother <- NULL
  str(Pedigree(ped3, ascendant=c("father", "m.grandfather"),
               ascendantSex=c(1, 1), ascendantLevel=c(1, 2)))
}

Prune pedigree

Description

prune removes noninformative individuals from a pedigree. This process is usually called trimming or pruning. Individuals are removed if they do not provide any ancestral ties between other individuals. It is possible to add some additional criteria. See details.

Usage


prune(x, id, father, mother, unknown=NA, testAdd=NULL, verbose=FALSE)

prune(x, id, father, mother, unknown=NA, testAdd=NULL, verbose=FALSE)

Arguments

`x`	data.frame, pedigree data
`id`	character, individuals's identification column name
`father`	character, father's identification column name
`mother`	character, mother's identification column name
`unknown`	value(s) used for representing unknown parent in `x`
`testAdd`	logical, additional criteria; see details
`verbose`	logical, print some more info

Details

NOTE: this function does not yet work with Pedigree class.

There are always some individuals in the pedigree that jut out. Usually this are older individuals without known ancestors, founders. If such individuals have only one (first) descendant and no phenotype/genotype data, then they do not give us any additional information and can be safely removed from the pedigree. This process resembles cutting/pruning the branches of a tree.

By default prune iteratively removes individuals from the pedigree (from top to bottom) if:

they are founders, have both ancestors i.e. father and mother unknown and
have only one or no (first) descendants i.e. children

If there is a need to take into account availability of say phenotype/genotype data or any other information, argument testAdd can be used. Value of this argument must be logical and with length equal to number of rows in the pedigree. The easiest way to achieve this is to merge any data to the pedigree and then to perform a test, which will return logical values. Note that value of TRUE in testAdd means to remove an individual - this function is removing individuals! To keep an individual without known parents and one or no children, value of testAdd must be FALSE for that particular individual. Take a look at the examples.

There are various conventions on representing unknown/missing ancestors, say 0. R's default is to use NA. If other values than NA are present, argument unknown can be used to convert unknown/missing values to NA.

It is assumed that pedigree is in extended form i.e. that each father and mother has each own record as an individual. Otherwise error is returned with information on which parents do not appear as individuals.

prune does not only remove lines for pruned individuals but also removes them from father and mother columns.

Pruning is done from top to bottom of the pedigree i.e. from oldest individuals towards younger ones. Take for example the following part of the pedigree in example section:

Individual 7 is not removed since it has two (first) descendants i.e. 8 and 5 (not shown here). Consecutively, individuals 8 and 9 are also not removed from the pedigree. Individual 10 is removed, since it has only one descendant. Why should individuals 8 and 9 and therefore also 7 stay in the pedigree? Current behaviour is reasonable if pedigree is built in such a way that first individuals with some phenotype or genotype data are gathered and then their pedigree is being built. Say, individual 9 has pehnotype/genotype data and its pedigree is build and there is therefore no need to remove such an individual. However, if pedigree is not built in such a way, then prunPedigree function can not prune all noninformative individuals. Argument testAdd can not help with this issue, since basic tests (founder and one or no first descendants) and testAdd are combined with &.

Value

prune returns a data.frame with possibly fewer individuals. Read also the details.

Author(s)

Gregor Gorjanc

Examples


  ## Pedigree example
  x <- data.frame(oseba=c(1,  9, 11, 2, 3, 10, 8, 12, 13,  4, 5, 6, 7, 14, 15, 16, 17),
                    oce=c(2, 10, 12, 5, 5,  0, 7,  0,  0,  0, 7, 0, 0,  0,  0,  0,  0),
                   mama=c(3,  8, 13, 0, 4,  0, 0,  0,  0, 14, 6, 0, 0, 15, 16, 17,  0),
                   spol=c(2,  2,  2, 1, 2,  1, 2,  1,  2,  2, 1, 2, 1,  1,  1,  1,  1),
             generacija=c(1,  1,  1, 2, 2,  2, 2,  2,  2,  3, 3, 4, 4,  5,  6,  7,  8),
                   last=c(2, NA,  8, 4, 1,  6,NA, NA, NA, NA,NA,NA,NA, NA, NA, NA, NA))

  ## Default case
  prune(x=x, id="oseba", father="oce", mother="mama", unknown=0)

  ## Use of additional test i.e. do not remove individual if it has
  ## known value for "last"
  prune(x=x, id="oseba", father="oce", mother="mama", unknown=0,
                testAdd=is.na(x$last))

  ## Use of other data
  y <- data.frame(oseba=c( 11,  15, 16),
                  last2=c(8.5, 7.5, NA))

  x <- merge(x=x, y=y, all.x=TRUE)
  prune(x=x, id="oseba", father="oce", mother="mama", unknown=0,
                testAdd=is.na(x$last2))

## Pedigree example
  x <- data.frame(oseba=c(1,  9, 11, 2, 3, 10, 8, 12, 13,  4, 5, 6, 7, 14, 15, 16, 17),
                    oce=c(2, 10, 12, 5, 5,  0, 7,  0,  0,  0, 7, 0, 0,  0,  0,  0,  0),
                   mama=c(3,  8, 13, 0, 4,  0, 0,  0,  0, 14, 6, 0, 0, 15, 16, 17,  0),
                   spol=c(2,  2,  2, 1, 2,  1, 2,  1,  2,  2, 1, 2, 1,  1,  1,  1,  1),
             generacija=c(1,  1,  1, 2, 2,  2, 2,  2,  2,  3, 3, 4, 4,  5,  6,  7,  8),
                   last=c(2, NA,  8, 4, 1,  6,NA, NA, NA, NA,NA,NA,NA, NA, NA, NA, NA))

  ## Default case
  prune(x=x, id="oseba", father="oce", mother="mama", unknown=0)

  ## Use of additional test i.e. do not remove individual if it has
  ## known value for "last"
  prune(x=x, id="oseba", father="oce", mother="mama", unknown=0,
                testAdd=is.na(x$last))

  ## Use of other data
  y <- data.frame(oseba=c( 11,  15, 16),
                  last2=c(8.5, 7.5, NA))

  x <- merge(x=x, y=y, all.x=TRUE)
  prune(x=x, id="oseba", father="oce", mother="mama", unknown=0,
                testAdd=is.na(x$last2))

Additive relationship matrix and its inverse

Description

relationshipAdditive creates additive relationship matrix, while inverseAdditive creates its inverse directly from a pedigree. kinship is another definition of relationship and is equal to half of additive relationship.

Usage


relationshipAdditive(x, sort=TRUE, names=TRUE, ...)
inverseAdditive(x, sort=TRUE, names=TRUE, ...)
kinship(x, sort=TRUE, names=TRUE, ...)

relationshipAdditive(x, sort=TRUE, names=TRUE, ...)
inverseAdditive(x, sort=TRUE, names=TRUE, ...)
kinship(x, sort=TRUE, names=TRUE, ...)

Arguments

`x`	Pedigree
`sort`	logical, for the computation the pedigree needs to be sorted, but results are sorted back to original sorting (sort=TRUE) or not (sort=FALSE)
`names`	logical, should returned matrix have row/colnames; this can be used to get leaner matrix
`...`	arguments for other methods

Details

Additive or numerator relationship matrix is symetric and contains $1 + F_i$ on diagonal, where $F_i$ is an inbreeding coefficients (see inbreeding) for subject $i$ . Off-diagonal elements represent numerator or relationship coefficient bewteen subjects $i$ and $j$ as defined by Wright (1922). Henderson (1976) showed a way to setup inverse of relationship matrix directly. Mrode (2005) has a very nice introduction to these concepts.

Take care with sort=FALSE, names=FALSE. It is your own responsibility to assure proper handling in this case.

Value

A matrix of $n * n$ dimension, where $n$ is number of subjects in x

Author(s)

Gregor Gorjanc and Dave A. Henderson

References

Henderson, C. R. (1976) A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics 32(1):69-83

Mrode, R. A. (2005) Linear models for the prediction of animal breeding values. 2nd edition. CAB International. ISBN 0-85199-000-2 http://www.amazon.com/gp/product/0851990002

Wright, S. (1922) Coefficients of inbreeding and relationship. American Naturalist 56:330-338

Examples

  data(Mrode2.1)
  Mrode2.1$dtB <- as.Date(Mrode2.1$dtB)
  x2.1 <- Pedigree(x=Mrode2.1, subject="sub", ascendant=c("fat", "mot"),
                   ascendantSex=c("M", "F"), family="fam", sex="sex",
                   generation="gen", dtBirth="dtB")

  (A <- relationshipAdditive(x2.1))
  fractions(A)
  solve(A)
  inverseAdditive(x2.1)
  relationshipAdditive(x2.1[3:6, ])
  
  ## Compare the speed
  ped <- generatePedigree(nId=10, nGeneration=3, nFather=1, nMother=2)
  system.time(solve(relationshipAdditive(ped)))
  system.time(inverseAdditive(ped))
data(Mrode2.1)
  Mrode2.1$dtB <- as.Date(Mrode2.1$dtB)
  x2.1 <- Pedigree(x=Mrode2.1, subject="sub", ascendant=c("fat", "mot"),
                   ascendantSex=c("M", "F"), family="fam", sex="sex",
                   generation="gen", dtBirth="dtB")

  (A <- relationshipAdditive(x2.1))
  fractions(A)
  solve(A)
  inverseAdditive(x2.1)
  relationshipAdditive(x2.1[3:6, ])
  
  ## Compare the speed
  ped <- generatePedigree(nId=10, nGeneration=3, nFather=1, nMother=2)
  system.time(solve(relationshipAdditive(ped)))
  system.time(inverseAdditive(ped))

Remove individual from pedigree

Description

removeIndividual provides utility for removing individuals from a pedigree.

Usage

removeIndividual(x, individual, remove="all")removeIndividual(x, individual, remove="all")

Arguments

`x`	pedigree
`individual`	vector of individuals
`remove`	character, column names of id columns and/or "all", see details

Details

Individuals passed to argument individual will be removed from the pedigree. If there is a pedigree with individual "id" and two ascendants, say "father" and "mother", then one can pass any combination of these three id columns or "all" for all of them in short to argument remove. In case only "id" is passed to remove, individuals will be removed from the pedigree, but not from ascendant id columns, which might be a matter of interest only if specified individuals show up as ascendants for some other individuals. In case you want to remove an individual completely from the pedigree "all" must be used.

Individuals in id column are removed via removal of the whole record from the pedigree. Individuals in ascendant id columns are only replaced by attr(x, "unknown").

If founder is removed, attribute extended status is changed to FALSE.

Author(s)

Gregor Gorjanc

Examples

  ped <- generatePedigree(3)
  summary(ped)
  removeIndividual(ped, individual=c(1, 3, 4), remove="father")
  removeIndividual(ped, individual=c(1, 3, 4), remove=c("mother", "father"))
  (ped <- removeIndividual(ped, individual=c(1, 3, 4), remove=c("all")))
  summary(ped)
ped <- generatePedigree(3)
  summary(ped)
  removeIndividual(ped, individual=c(1, 3, 4), remove="father")
  removeIndividual(ped, individual=c(1, 3, 4), remove=c("mother", "father"))
  (ped <- removeIndividual(ped, individual=c(1, 3, 4), remove=c("all")))
  summary(ped)

Sort pedigree

Description

Pedigree sort

Usage

## S3 method for class 'Pedigree'
sort(x, decreasing=FALSE, na.last=TRUE, ..., by="default")
## S3 method for class 'Pedigree'
sort(x, decreasing=FALSE, na.last=TRUE, ..., by="default")

Arguments

`x`	pedigree, object to be sorted
`decreasing`	logical, sort order
`na.last`	logical, control treatment of `NA`s; check `order`
`...`	arguments passed to `order`, see details
`by`	character, sort by "default", "pedigree", "generation", or "dtBirth" information, see details

Details

Sorting of the pedigree can be performed in different ways. Since pedigree can contain date of birth, sorting by this would be the most obvious way and it would be the most detailed sort. However, there might be the case that date of birth is not available for some or all individuals. Therefore, this function by default (when by="default") tries to figure out what would be the best way to perform the sort. If date of birth is available for all individuals then date of birth is used for sorting. If not, generation information is used, but only if it is known for all individuals (it should be more or less easy to figure out the generation for all individuals in the pedigree CHECK). Again if not, sorting is done via information in pedigree i.e. ascendants will precede descendants or vice versa. User can always define it's own preference by argument by. When by="dtBirth" or by="generation" sorting is performed via order and its arguments na.last and decreasing can be used. With by="pedigree" argument decreasing has an effect.

Generation values can have different meaning i.e. values might either increase or decrease from ascendants to descendant with the same meaning. This information is stored in attribute generationOrder (at the time of creating the pedigree object via Pedigree) and used for determining the order of sorting if sorting is by generation. The output of the result might therefore be opposite of what user might expect. If that is the case, use argument decreasing as defined in order. Look also into examples bellow.

Value

Sorted pedigree

Author(s)

Gregor Gorjanc

Examples

  ped <- generatePedigree(nId=5)
  ped <- ped[sample(1:nrow(ped)), ]
  sort(ped)
  ## sort(ped, by="dtBirth")       ## TODO
  sort(ped, by="generation")
  ## try(sort(ped, by="pedigree")) ## TODO

  ## Sorting with decreasing generation values from ascendants to descendants
  ped1 <- generatePedigree(nId=5, generationOrder="decreasing")
  sort(ped1, by="generation")
  sort(ped1, decreasing=TRUE, by="generation")
  sort(ped1, decreasing=FALSE, by="generation")

  ## Sorting with unknown values
  ped[1, "generation"] <- NA
  sort(ped, na.last=TRUE, by="generation")
  sort(ped, na.last=FALSE, by="generation")
  sort(ped, na.last=NA, by="generation")
ped <- generatePedigree(nId=5)
  ped <- ped[sample(1:nrow(ped)), ]
  sort(ped)
  ## sort(ped, by="dtBirth")       ## TODO
  sort(ped, by="generation")
  ## try(sort(ped, by="pedigree")) ## TODO

  ## Sorting with decreasing generation values from ascendants to descendants
  ped1 <- generatePedigree(nId=5, generationOrder="decreasing")
  sort(ped1, by="generation")
  sort(ped1, decreasing=TRUE, by="generation")
  sort(ped1, decreasing=FALSE, by="generation")

  ## Sorting with unknown values
  ped[1, "generation"] <- NA
  sort(ped, na.last=TRUE, by="generation")
  sort(ped, na.last=FALSE, by="generation")
  sort(ped, na.last=NA, by="generation")

Pedigree summary

Description

summary.Pedigree reports TODO.

Usage

## S3 method for class 'Pedigree'
summary(object, ...)## S3 method for class 'Pedigree'
summary(object, ...)

Arguments

`object`	pedigree object
`...`	additional arguments for other methods (not used)

Details

TODO.

Value

TODO.

Author(s)

Gregor Gorjanc

Examples

  ped <- generatePedigree(nId=5)
  summary(ped)
ped <- generatePedigree(nId=5)
  summary(ped)

Undocumented functions

Description

These functions are undocumented. Some are internal and not intended for direct use. Some are not yet ready for end users. Others simply haven't been documented yet.

Package 'GeneticsPed'

Help Index

Check consistency of data in pedigree

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Extend pedigree

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Find families (lines) in the pedigree

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Gene contribution or proportion of genes in pedigree by individual

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Gene and gamete flow matrices

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Generate Pedigree example

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Calculate generation value

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Genotype probability index

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Utility functions for gpi()

Description

Usage

Arguments

Details