Classes "GLmultiPhiGroup" "GLmultiPhiGroupSingle" "GLmultiFGroup" "GLmultiFGroupSingle" "GLCGMatrixGroupSingle"

Description

Objects used to carry information mostly between invisible functions.

Objects from the Class

Objects can be created by calls of the form
new("GLmultiPhiGroup", MatriceCG, group = 'GLgroup', grindex = 'list')
new("GLmultiPhiGroupSingle", MatriceCG, group = 'GLgroup', grindex = 'list')
new("GLmultiFGroup", MatriceCG, group = 'GLgroup', grindex = 'list')
new("GLmultiFGroupSingle", MatriceCG, group = 'GLgroup', grindex = 'list')
new("GLCGMatrixGroupSingle", MatriceCG, group = 'GLgroup', grindex = 'list')

Slots

GLCGMatrixGroupSingle, GLmultiFGroupSingle, GLmultiPhiGroupSingle

.Data:

Object of class "matrix"

group:

Object of class "GLgroup"

grindex:

Object of class "list" containing the indices of the probands of 'group'

GLmultiPhiGroup :

.Data:

Object of class "GLmultiMatrix"

group:

Object of class "GLgroup"

grindex:

Object of class "list" containing the indices of the probands of 'group'

GLmultiFGroup :

.Data:

Object of class "GLmultiVector"

group:

Object of class "GLgroup"

grindex:

Object of class "list" containing the indices of the probands of 'group'

Extends

Class "matrix", from data part. Class "array", by class "matrix", distance 2. Class "structure", by class "matrix", distance 3. Class "vector", by class "matrix", distance 4, with explicit coerce.

Methods

[<-

signature(x = "GLCGMatrixGroupSingle", i = "ANY", j = "ANY", value = "ANY"): ...

[

signature(x = "GLCGMatrixGroupSingle", i = "ANY", j = "ANY", drop = "ANY"): ...

group

signature(x = "GLCGMatrixGroupSingle"): ...

Examples

showClass("GLCGMatrixGroupSingle")
showClass("GLmultiFGroup")
showClass("GLmultiFGroupSingle")
showClass("GLmultiPhiGroupSingle")
showClass("GLmultiPhiGroup")

Classes "GLmultiVector" "GLmultiArray4" "GLmultiMatrix" "GLmultiNumber"

Description

Objects created to carry information mostly between invisible functions.

Objects from the Class

Objects can be created by calls of the form new("GLmultiVector", 'matrix', depth = 'integer')
new("GLmultiArray4", 'Array', depth = 'integer')
new("GLmultiMatrix", 'Array', depth = 'integer')
new("GLmultiNumber", 'numeric', depth = 'integer', .Names = 'character')

Slots

GLmultiVector

.Data:

Object of class "matrix"

depth:

Object of class "integer"

GLmultiArray4, GLmultiMatrix

.Data:

Object of class "array"

depth:

Object of class "integer"

GLmultiNumber

.Data:

Object of class "numeric" ~~

depth:

Object of class "integer" ~~

.Names:

Object of class "character" ~~

Extends

Class "matrix", from data part. Class "array", by class "matrix", distance 2. Class "structure", by class "matrix", distance 3. Class "vector", by class "matrix", distance 4, with explicit coerce.

Methods

[<-

signature(x = "GLmultiVector", i = "ANY", j = "ANY", value = "ANY"): ...

[

signature(x = "GLmultiVector", i = "ANY", j = "ANY", drop = "ANY"): ...

depth

signature(x = "GLmultiVector"): ...

Dim

signature(object = "GLmultiVector"): ...

Examples

showClass("GLmultiVector")
showClass("GLmultiArray4")
showClass("GLmultiMatrix")
showClass("GLmultiNumber")

Class "GLgen"

Description

Object containing a genealogy.

Objects from the Class

Objects can be created by calls of the form gen.genealogy('data.frame').

Extends

Class "GLgroup", directly.

Methods

initialize

signature(.Data="integer",Date="character"): ...

depth

signature(x = "GLgen")

Gives the depth of the genealogy.

length

signature(x = "GLgen")

Gives the number of individuals in the genealogy.

Examples

showClass("GLgen")

Class "GLgroup"

Description

Object representing a set of proband in different groups.
Each element of this list is a group named with the proband number.

Objects from the Class

Objects can be created by calls of the form new("GLgroup", 'list').

Extends

Class "GLgen", directly.

Methods

initialize

signature(.Data=list): ...

[

signature(object = "GLgroup",ANY,ANY,ANY):...

Examples

showClass("GLgroup")

Class "GLmultiList"

Description

Objects created to carry information mostly between invisible functions.

Objects from the Class

Objects can be created by calls of the form new("GLmultiList", 'Array').

Slots

.Data:

Object of class "list" ~~

liste:

Object of class "list" ~~

Extends

Class "list", from data part. Class "vector", by class "list", distance 2.

Methods

No methods defined with class "GLmultiList" in the signature.

Examples

showClass("GLmultiList")

Genealogy subset

Description

Creates an object of class GLgen from an existing GLgen object by selecting specified individuals.

Usage

gen.branching( gen, pro = 0, ancestors = gen.founder(gen), bflag = 0)

Arguments

Value

returns a GLgen object

Note

Note that if there are no links between some probands and ancestors, these ids will not be included.

See Also

gen.genealogy gen.graph gen.lineages

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi)
genJi_part<-gen.branching(genJi,pro=c(2,28)) 

# Plots of original genealogy and of the branched version
layout(matrix(1:2,c(1,2),byrow=TRUE))
gen.graph(genJi) 
mtext("Original", line=2, cex=1.2)
gen.graph(genJi_part) 
mtext("Branched tree\nfor individuals 2 and 28", line=1, cex=1.2)

Get id numbers of children

Description

Returns the id numbers of the children of specified individuals.

Usage

gen.children( gen, individuals, ...)

Arguments

Value

returns a vector of integer

See Also

gen.genealogy gen.pro gen.founder gen.sibship gen.parent

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.children(genJi, individuals=14) 

data(genea140) 
gen140<-gen.genealogy(genea140) 
children51052<-gen.children(gen140,51052)
gen.graph(gen140, pro=c(children51052))

Internal function used by gen.findMRCA

Description

gen.climbPAR is an internal function used by gen.findMRCA.

Usage

gen.climbPAR(gen, individuals, founder)

Arguments

Value

returns a list containing the founder ID and the distance.

See Also

gen.findMRCA gen.getAncestorsPAR

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.findMRCA(genJi, individuals=c(1,29), NbProcess = 1)

Completeness

Description

Returns the completeness of the genealogical data for the specified probands

Usage

gen.completeness( gen, pro="0", genNo=-1, type="MEAN", ...)

Arguments

Value

A data frame with each generation at which the completeness is calculated as rows and one column when type is "MEAN". When type is "IND", the number of columns equals the number of probands specified.

References

Cazes P, Cazes MH. (1996) Comment mesurer la profondeur genealogique d'une ascendance? Population (French Ed) 51:117-140.

See Also

gen.genealogy gen.rec gen.occ gen.implex gen.meangendepth

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.completeness(genJi, type="IND")
# For the 4th generation
gen.completeness(genJi, type="IND", genNo=4)

data(genea140) 
gen140<-gen.genealogy(genea140) 
gen.completeness(gen140)

Variance of completeness index

Description

Returns the variance of the completeness index across probands.

Usage

gen.completenessVar( gen, pro = "0", genNo = -1, ...)

Arguments

Value

A data frame with one column and each generation at which the variance is calculated as rows.

References

Cazes P, Cazes MH. (1996) Comment mesurer la profondeur genealogique d'une ascendance? Population (French Ed) 51:117-140.

See Also

gen.gc

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.completeness(genJi, type="IND")
gen.completenessVar(genJi)
# For the 4th generation
gen.completenessVar(genJi, genNo=4)


data(genea140) 
gen140<-gen.genealogy(genea140) 
gen.completeness(gen140)
gen.completenessVar(gen140)

Genealogy depth

Description

Returns the number of generations in the genealogy from a GLgen object.

Usage

gen.depth(gen)

Arguments

Value

returns a vector of integer

See Also

gen.genealogy gen.min gen.mean gen.min gen.meangendepth

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.depth(genJi)


data(genea140) 
gen140<-gen.genealogy(genea140) 
gen.depth(gen140)

Inbreeding coefficient

Description

Returns the inbreeding coefficients of the specified probands

Usage

gen.f( gen, pro, depthmin= (gen.depth(gen)-1), depthmax= (gen.depth(gen)-1))

Arguments

Value

A vector or GLmultiVector object depending on the number of generations treated. GLmultiVector is an array of vectors, one for each depth. Array of size ('depthMax'-'depthMin'), vectors of size 'length(pro)'. A vector of size 'length(pro)' is returned when ('depthMax'-'depthMin') equals 1.

References

Malecot G. (1948) Les mathematiques de l'heredite. Paris: Masson, p 65.

Thompson EA. (1986) Pedigree Analysis in Human Genetics. Baltimore, MD, USA: Johns Hopkins University Press, p 25.

Karigl G. (1981) A recursive algorithm for the calculation of identity coefficients. Ann Hum Genet 45:299-305.

See Also

gen.genealogy gen.phi

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
f_values<-gen.f(genJi)
f_values
f_allgen<-gen.f(genJi, depthmin=1)  
f_allgen<-unclass(f_allgen)

plot(1:7,f_allgen[1,],type="b",xlab="Generation",ylab="Inbreeding values",ylim=c(0,0.25),pch=0)
points(1:7,f_allgen[3,],  type="b", lty=12, pch=1)
legend("topright", legend=c("Individual 1", "Individual 29"),lty=c(1,12), pch=c(0,1))


data(genea140) 
gen140<-gen.genealogy(genea140)
inbreeding_val<-gen.f(gen140)
boxplot(inbreeding_val, horizontal=TRUE, xlab="Inbreeding values")

Average inbreeding coefficient confidence interval

Description

Returns the confidence interval of the average inbreeding among specified individuals

Usage

gen.fCI(vectF, prob=c(0.025,0.05,0.95,0.975), b="5000", print.it=FALSE)

Arguments

Value

A vector or a GLmultiVector object depending on the type of 'vectF' entered. If 'vectF' is a GLmultiVector, an array of vectors (GLmultiVector), one for each depth, is returned. Array of size ('depthMax'-'depthMin'), vectors of size 'length(probs)'. If 'vectF' is a GLmultiVector, a vector of size 'length(probs)' is returned.

See Also

gen.f

Examples

data(genea140) 
gen140<-gen.genealogy(genea140) 
gen10 <-gen.branching(gen140, pro=gen.pro(gen140)[c(1:10)])
fval<-gen.f(gen10)
gen.fCI(fval)

Finds the minimum distances between pairs of individuals given a set of ancestors.

Description

Returns the shortest distances (number of meioses) between pairs of probands given the matrix of MRCAs output by the gen.findMRCA function.

Usage

gen.find.Min.Distance.MRCA(genMatrix, individuals="ALL", ancestors="ALL")

Arguments

Value

returns a matrix

See Also

gen.genealogy gen.founder gen.findMRCA gen.findDistance gen.findFounders

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
matMRCA<-gen.findMRCA(genJi, individuals=c(1,29), NbProcess = 1)
gen.find.Min.Distance.MRCA(matMRCA)


# Increasing NbProcess will decrease execution time
data(genea140) 
gen140<-gen.genealogy(genea140) 
matMRCA<-gen.findMRCA(gen140, individuals=c(409033,408728,408828), NbProcess = 1)
gen.find.Min.Distance.MRCA(matMRCA, individuals=c(409033,408728))

Minimum genetic distance between two individuals

Description

Returns the minimum distance (number of meioses) between the specified individuals through one specified ancestor.

Usage

gen.findDistance(gen, individuals, ancestor)

Arguments

Value

returns a numeric value

See Also

gen.genealogy gen.founder gen.findMRCA gen.findFounders gen.find.Min.Distance.MRCA

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.findDistance(genJi, individuals=c(1,29), ancestor=17)
gen.findDistance(genJi, individuals=c(1,29), ancestor=26)

data(genea140) 
gen140<-gen.genealogy(genea140) 
gen.findDistance(gen140, individuals=c(409033,408728), ancestor=38714)
gen.findDistance(gen140, individuals=c(408728,408828), ancestor=95080)

Find common founder ancestors

Description

Returns all the ancestors that are founders for specified individuals in the genealogy.

Usage

gen.findFounders(gen, individuals, NbProcess=parallel::detectCores()-1)

Arguments

Value

A vector of integers

Note

Uses slave processes to make the search faster. Those slave processes launch the function gen.getFoundersMPI.

See Also

gen.genealogy gen.founder gen.findMRCA gen.findDistance gen.find.Min.Distance.MRCA

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.founder(genJi)
gen.findFounders(genJi, individuals=c(1,29), NbProcess = 1)

data(genea140) 
gen140<-gen.genealogy(genea140) 
gen2<-gen.branching(gen140,pro=c(409033,408728)) 
# 615 founders in genealogy of #409033 and #408728
all_founders<-gen.founder(gen2) 
length(all_founders)


all_commonFounders<-gen.findFounders(gen140, individuals=c(409033,408728), NbProcess = 1)
length(all_commonFounders) # 127 founders common to #409033 and #408728

Finding most recent common ancestors, MRCAs

Description

Returns MRCAs of pairs of specified individuals and the distance (number of meioses) between individuals through the MRCAs.

Usage

gen.findMRCA(gen, individuals, NbProcess=parallel::detectCores()-1)

Arguments

Value

returns a matrix

Note

This function uses the parallel programming functions of foreach, snow and doSNOW for launching several processes.

See Also

gen.genealogy gen.founder gen.findFounders gen.findDistance gen.find.Min.Distance.MRCA

Examples

data(geneaJi)
genJi<-gen.genealogy(geneaJi)
gen.findMRCA(genJi, individuals=c(1,29), NbProcess = 1)


 # For a more complex example:
 data(genea140) 
 gen140<-gen.genealogy(genea140) 
 all_commonFounders<-gen.findFounders(gen140, individuals=c(409033,408728), NbProcess = 1)
 # 127 founders common to #409033 and #408728
 length(all_commonFounders)
 # 22 most recent common ancestors for #409033 and #408728
 MRCA_2ind<-gen.findMRCA(gen140, individuals=c(409033,408728), NbProcess = 1)

Get founder id numbers

Description

Returns the id numbers of the founders. Founders are defined as the individuals without parents in the genealogy (i.e., mother id=0, father id=0).

Usage

gen.founder( gen, ...)

Arguments

Value

A vector of integer

See Also

gen.genealogy gen.pro gen.half.founder gen.parent

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
# There are 6 founders
gen.founder(genJi)

data(genea140) 
gen140<-gen.genealogy(genea140)
founder140<-gen.founder(gen140)  
# There are 7399 founders
length(founder140)

Genetic contribution of ancestors

Description

Returns the genetic contribution of ancestors to the gene pool of sepcifiec probands

Usage

gen.gc(gen, pro=0, ancestors=0, vctProb=c(0.5,0.5,0.5,0.5), typeCG="IND")

Arguments

Value

A matrix with rows corresponding to probands and columns corrsponding to ancestors when typeCG="IND". For the other typeCG values, rows are ancestors and there is one column containing mean, product, total or cumulative values.

References

Roberts DF. (1968) Genetic effects of population size reduction. Nature, 220, 1084-1088.

O'Brien E, Jorde LB, Ronnlof B, Fellman JO, Eriksson AW. (1988) Founder effect and genetic disease in Sottunga, Finland. American Journal of Physical Anthropology, 77, 335-346.

See Also

gen.genealogy gen.rec gen.occ gen.implex gen.meangendepth gen.completeness

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gc<-gen.gc(genJi, pro=c(1,29), ancestors=c(3,6,10,12,14,16,18,20,26,28))
gc
gc_cum<-gen.gc(genJi, pro=c(1,29), ancestors=c(3,6,10,12,14,16,18,20,26,28), type="CUMUL")
gc_cum

data(genea140) 
gen140<-gen.genealogy(genea140) 
gc<-gen.gc(gen140, pro=c(454422,676521,677273,717537,717634,717709,868572), 
           ancestors=c(18305,18528,31114,18325))
gc
# Mother-daughter transmission only with probability=0.5
gc_MoLi<-gen.gc(gen140, pro=c(217891,302718,409282,802424,409682,443151),
		ancestors=c(18321,218231,296200,39066,18679,442607),vctProb=c(0,0,0,0.5))
gc_MoLi

Create object of class GLgen

Description

Creates an object of class GLgen that contains the ascending genealogies derived from input data in pedigree format

Usage

gen.genealogy(ped, autoComplete=FALSE, ...)

Arguments

Value

An object of class GLgen.

See Also

gen.graph gen.genout

Examples

ind<-c(1,2,3,11:21,101:108,201:202)
father<-c(11,15,15,102,0,102,0,103,103,103,105,105,107,107,0,202,0,202,202,0,202,0,0,0)
mother<-c(12,14,14,101,0,101,0,104,104,104,106,106,108,108,0,201,0,201,201,0,201,0,0,0)
sex<-c(1,2,2,1,2,1,2,1,2,1,2,2,2,1,2,1,1,2,1,2,1,2,2,1)
gen.df<-data.frame(ind, father, mother, sex)  
gen.genealogy(gen.df)

data(geneaJi) 
geneaJi[1:5,]
genJi<-gen.genealogy(geneaJi)
# Print basic information about the genealogy
genJi

Create pedigree data

Description

Creates data frame in pedigree format from an object of class GLgen

Usage

gen.genout( gen, sorted = FALSE)

Arguments

Value

returns a data.frame containing the following: ind father mother sex

See Also

gen.genealogy gen.graph gen.branching gen.lineages

Examples

data(geneaJi) 
# Original data is a data.frame
geneaJi[1:12,]

genJi<-gen.genealogy(geneaJi) 
# As a genealogy object
genJi

# Genealogy as a data.frame
genJi_df<-gen.genout(genJi)
genJi_df[1:12,]

# Maternal lineage
genJi_MaLi<-gen.lineages(geneaJi, maternal = TRUE) 
# Maternal lineage as a data.frame
genJi_MaLi_df<-gen.genout(genJi_MaLi)
genJi_MaLi_df

Internal function used by gen.findMRCA

Description

gen.getAncestorsPAR is an internal function used by gen.findMRCA.

Usage

gen.getAncestorsPAR(gen, pro)

Arguments

Value

Vector of the founders identity.

See Also

gen.findMRCA gen.climbPAR

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.findMRCA(genJi, individuals=c(1,29), NbProcess = 1)

Internal function used by gen.findFounders

Description

gen.getFoundersPAR is an internal function used by gen.findFounders.

Usage

gen.getFoundersPAR(gen, pro)

Arguments

Value

A vector of the founders identity.

See Also

gen.findFounders gen.findDistance

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.founder(genJi, individuals=c(1,29))
gen.findFounders(genJi, individuals=c(1,29), NbProcess = 1)

Pedigree graphical tool

Description

Function that plots pedigrees of GLgen objects.

Usage

gen.graph( gen, pro=gen.pro(gen), ancestors=gen.founder(gen), 
       indVarAffected=gen.genout(gen)$ind, varAffected=gen.genout(gen)$ind, cex="1",
       col="0", symbolsize="1", width="1", packed=FALSE, align=TRUE, ...)

Arguments

Value

Returns the same invisible list as that returned by kinship2::plot.pedigree, which contains the following: plist list containing the information about the pedigree (n, id, pos, fam, spouse) x x-axis position y y-axis position boxw box width boxh box height call the call made to plot.pedigree() (kinship2 package)

See Also

gen.genealogy gen.genout gen.branching

Examples

ind<-c(1,2,3,11:21,101:108,201:202)
father<-c(11,15,15,102,0,102,0,103,103,0,105,105,107,107,0,202,0,202,202,0,202,0,0,0)
mother<-c(12,14,14,101,0,101,0,104,104,0,106,106,108,108,0,201,0,201,201,0,201,0,0,0)
sex<-c(1,2,2,1,2,1,2,1,2,1,2,2,2,1,2,1,1,2,1,2,1,2,2,1)
gen.df<-data.frame(ind, father, mother, sex)  
genEx<-gen.genealogy(gen.df)
gen.graph(genEx)

data(geneaJi) 
geneaJi[1:5,]
genJi<-gen.genealogy(geneaJi)
gen.graph(genJi)

Get half-founder id numbers

Description

Returns the id numbers of the half-founders. Half-founders are defined as the individuals with only one known parent in the genealogy (i.e., either mother id=0 or father id=0).

Usage

gen.half.founder( gen, ...)

Arguments

Value

returns a vector of integer

See Also

gen.genealogy gen.pro gen.founder gen.parent

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
# There are 2 half-founders
gen.half.founder(genJi)

Genealogical implex

Description

Returns the genealogical implex index (a measure of pedigree collapsing) for the specified probands.

Usage

gen.implex( gen, pro = "0", genNo = -1, type = "MEAN", onlyNewAnc = FALSE, ...)

Arguments

Value

A data frame with each generation at which the completeness is calculated as rows and one column when type is "MEAN". When type is "IND", the number of columns equals the number of probands specified.

References

Cazes P, Cazes MH. (1996) Comment mesurer la profondeur genealogique d'une ascendance? Population (French Ed) 51:117-140.

See Also

gen.genealogy gen.occ gen.rec gen.meangendepth gen.completeness gen.gc

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.implex(genJi)
gen.implex(genJi, type="IND")
# For the 5th generation
gen.implex(genJi, type="IND", genNo=5) 


data(genea140) 
gen140<-gen.genealogy(genea140) 
gen.implex(gen140)
gen.implex(gen140, pro=c(454422, 676521, 677273, 717537, 717634, 717709, 868572))
gen.implex(gen140, pro=c(454422, 676521, 677273, 717537, 717634, 717709, 868572), type="IND")

Variance of genealogical implex

Description

Returns the variance of the implex index across probands.

Usage

gen.implexVar( gen, pro = "0", onlyNewAnc = FALSE, genNo = -1, ...)

Arguments

Value

A data frame with one column and each generation at which the variance is calculated as rows

References

Cazes P, Cazes MH. (1996) Comment mesurer la profondeur genealogique d'une ascendance? Population (French Ed) 51:117-140.

See Also

gen.gc

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.implex(genJi, type="IND")
gen.implexVar(genJi)
# For the 5th generation
gen.implexVar(genJi, genNo=5)


data(genea140) 
gen140<-gen.genealogy(genea140) 
gen.implex(gen140)
gen.implex(gen140, pro=c(454422, 676521, 677273, 717537, 717634, 717709, 868572), type="IND")
gen.implexVar(gen140, pro=c(454422, 676521, 677273, 717537, 717634, 717709, 868572), type="IND")

Create object of class GLgen for maternal or paternal lineages

Description

Creates an object of class GLgen that contains maternal or paternal lineages selected from input data in pedigree format

Usage

gen.lineages(ped, pro = "0", maternal = TRUE, ...)

Arguments

Value

returns a GLgen object

See Also

gen.genealogy gen.graph gen.branching gen.genout

Examples

data(geneaJi) 
genJi <- gen.genealogy(geneaJi)
genJi_MaLi<-gen.lineages(geneaJi, maternal = TRUE) 
genJi_FaLi<-gen.lineages(geneaJi, maternal = FALSE) 

# Plots of original genealogy and maternal and paternal lineages
layout(matrix(1:3,c(1,3),byrow=TRUE), widths =c(3,1,1), heights = 1)
gen.graph(genJi) 
mtext("Original", line=2)
gen.graph(genJi_MaLi) 
mtext("Maternal\nlineages", line=1)
gen.graph(genJi_FaLi) 
mtext("Paternal\nlineages", line=1)

Maximum number of generations

Description

Returns the maximum number of generations between all probands and the individuals specified. Probands are defined as the individuals without children in the genealogy.

Usage

gen.max( gen, individuals)

Arguments

Value

returns a vector of integer

See Also

gen.genealogy gen.mean gen.min gen.depth gen.meangendepth

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.min(genJi,c(17,26))
gen.mean(genJi,c(17,26))
gen.max(genJi,c(17,26))


data(genea140) 
gen140<-gen.genealogy(genea140) 
gen.min(gen140,c(18311,18430))
gen.mean(gen140,c(18311,18430))
gen.max(gen140,c(18311,18430))

Mean number of generations

Description

Returns the average number of generations between all probands and the individuals specified. Probands are defined as the individuals without children in the genealogy.

Usage

gen.mean( gen, individuals)

Arguments

Value

returns a numeric value

See Also

gen.genealogy gen.min gen.max gen.depth gen.meangendepth

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.min(genJi,c(17,26))
gen.mean(genJi,c(17,26))
gen.max(genJi,c(17,26))


data(genea140) 
gen140<-gen.genealogy(genea140) 
gen.min(gen140,c(18311,18430))
gen.mean(gen140,c(18311,18430))
gen.max(gen140,c(18311,18430))

Expected Genealogical Depth

Description

Returns the expected genealogical depth.

Usage

gen.meangendepth( gen, pro = "0", type = "MEAN", ...)

Arguments

Value

A data frame with only one numeric value when type is "MEAN". When type is "IND", the number of rows equals the number of probands specified.

References

Cazes P, Cazes MH. (1996) Comment mesurer la profondeur genealogique d'une ascendance? Population (French Ed) 51:117-140.

Kouladjian K. (1986) Une mesure d'entropie genealogique. Chicoutimi, SOREP, Document III-C-43.

De Brakaeleer M, Bellis G. (1994) Genealogies et reconstitutions de familles en genetique humaine. Dossiers et Recherches, no 43, INED, Paris.

See Also

gen.genealogy gen.occ gen.implex gen.rec gen.completeness gen.gc

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.meangendepth(genJi)
gen.meangendepth(genJi, type="IND")


data(genea140) 
gen140<-gen.genealogy(genea140) 
gen.meangendepth(gen140)
gen.meangendepth(gen140, pro=c(454422, 676521, 677273, 717537, 717634, 717709, 868572))
gen.meangendepth(gen140, pro=c(454422, 676521, 677273, 717537, 717634, 717709, 868572), type="IND")

Variance of genealogical depth

Description

Returns the variance of the genealogical depth

Usage

gen.meangendepthVar( gen, pro = "0", type = "MEAN", ...)

Arguments

Value

A data frame with only one numeric value when type is "MEAN". When type is "IND", the number of rows equals the number of probands specified.

References

Cazes P, Cazes MH. (1996) Comment mesurer la profondeur genealogique d'une ascendance? Population (French Ed) 51:117-140.

Kouladjian K. (1986) Une mesure d'entropie genealogique. Chicoutimi, SOREP, Document III-C-43.

De Brakaeleer M, Bellis G. (1994) Genealogies et reconstitutions de familles en genetique humaine. Dossiers et Recherches, no 43, INED, Paris.

See Also

gen.gc

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.meangendepth(genJi, type="IND")
gen.meangendepthVar(genJi, type="IND")
gen.meangendepthVar(genJi, type="MEAN")

data(genea140) 
gen140<-gen.genealogy(genea140) 
gen.meangendepth(gen140)
probands <- c(454422,676521,677273,717537,717634,717709,868572)
gen.meangendepth(gen140, pro=probands)
gen.meangendepthVar(gen140, pro=probands)
gen.meangendepthVar(gen140, pro=probands, type="MEAN")


gen.meangendepth(gen140, pro=probands, type="IND")
gen.meangendepthVar(gen140, pro=probands, type="IND")

Minimum number of generations

Description

Returns the minimum number of generations between all probands and the individuals specified. Probands are defined as the individuals without children in the genealogy.

Usage

gen.min( gen, individuals)

Arguments

Value

returns a vector of integer

See Also

gen.genealogy gen.mean gen.max gen.depth gen.meangendepth

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.min(genJi,c(17,26))
gen.mean(genJi,c(17,26))
gen.max(genJi,c(17,26))


data(genea140) 
gen140<-gen.genealogy(genea140) 
gen.min(gen140,c(18311,18430))
gen.mean(gen140,c(18311,18430))
gen.max(gen140,c(18311,18430))

Number of children

Description

Returns the number of children for specified individuals

Usage

gen.nochildren( gen, individuals)

Arguments

Value

A vector containing the number of children for each individual sepcified.

See Also

gen.genealogy gen.nowomen gen.noind gen.nomen gen.children

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.children(genJi,14)
gen.nochildren(genJi,individuals=c(1,12,14,20))

data(genea140) 
gen140<-gen.genealogy(genea140) 
children51052<-gen.children(gen140,51052)
gen.nochildren(gen140,51052)
gen.graph(gen140, pro=children51052)

Number of individuals

Description

Returns the number of individuals included in the genealogy

Usage

gen.noind( gen)

Arguments

Value

returns a vector of integer

See Also

gen.genealogy gen.nowomen gen.nochildren gen.nomen

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.noind(genJi)

Number of men

Description

Returns the number of men included in the genealogy

Usage

gen.nomen( gen)

Arguments

Value

returns a vector of integer

See Also

gen.genealogy gen.nowomen gen.nochildren gen.noind

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.nomen(genJi)

Number of women

Description

Returns the number of women included in the genealogy

Usage

gen.nowomen( gen)

Arguments

Value

returns a vector of integer

See Also

gen.genealogy gen.nomen gen.nochildren gen.noind

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.nowomen(genJi)

Ancestor occurences

Description

Returns the number of times that the specified ancestors are present in the genalogies of the specified probands.

Usage

gen.occ( gen, pro = "0", ancestors = "0", typeOcc = "IND", ...)

Arguments

Value

A matrix with number of lines equal to the number of ancestors included and the number of columns equal to the number of probands included if typeOcc="BRUT" or only one column if typeOcc="TOTAL"

See Also

gen.genealogy gen.rec gen.implex gen.meangendepth gen.gc

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
# Number of occurences of ancestors in the specified proband's genealogy
gen.occ(genJi, pro=c(1,29), ancestors=c(3,6,10,12,14,16,18,20,26,28))

data(genea140) 
gen140<-gen.genealogy(genea140) 
gen.occ(gen140, pro=c(454422,676521,677273,717537,717634,717709,868572), 
        ancestors=c(18305,18528,31114,18325))

Get id numbers of parents

Description

Returns the id numbers of the parents of specified individuals.

Usage

gen.parent( gen, individuals, output = "FaMo", ...)

Arguments

Value

returns a list containing the following: Fathers Mothers

See Also

gen.genealogy gen.pro gen.founder gen.children gen.sibship

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.parent(genJi, individuals=c(3,21,29)) 

Kinship coefficient

Description

Returns the kinship coefficients between pairs of individuals

Usage

gen.phi(gen, pro, depthmin=(gen.depth(gen)-1), depthmax=(gen.depth(gen)-1), MT=FALSE)

Arguments

Value

A matrix or a GLmultiMatrix object depending on the number of generations treated. GLmultiMatrix is an array of matrices, one for each depth. Array of size ('depthMax'-'depthMin') and matrices of size 'length(pro)' * 'length(pro)'. The matrix object, also of size 'length(pro)' * 'length(pro)', is returned when ('depthMax'-'depthMin') equals 1.

References

Malecot G. (1948) Les mathematiques de l'heredite. Paris: Masson, p 65.

Thompson EA. (1986) Pedigree Analysis in Human Genetics. Baltimore, MD, USA: Johns Hopkins University Press, p 25.

Karigl G. (1981) A recursive algorithm for the calculation of identity coefficients. Ann Hum Genet 45:299-305.

See Also

gen.genealogy gen.f

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
kinship<-gen.phi(genJi)
kinship

kinship_allgenerations<-gen.phi(genJi, depthmin =1)
kinship_allgenerations
# 7th generations back in time is equivalent to considering all generations
kinship_allgenerations <- unclass(kinship_allgenerations)
kinship_allgenerations[,,7]==kinship

kinship_allgenerations[1,2,]

# Plot of kinship varying according to number of generations considered
plot(1:7,kinship_allgenerations[1,2,], type="b", xlab="Generation", ylab="Kinship value", 
     ylim=c(0,0.6), pch=0)
points(1:7,kinship_allgenerations[1,3,], type="b", lty=12, pch=1)
legend("topright", legend=c("Individuals 1 and 2", "Individuals 2 and 29"),lty=c(1,12), pch=c(0,1))

Average kinship confidence interval

Description

Returns the confidence interval of the average kinship among pairs of specified individuals

Usage

gen.phiCI(phiMatrix, prob=c(0.025,0.05,0.95,0.975), b=5000, print.it=FALSE)

Arguments

Value

A vector or a GLmultiVector object depending on the type of 'phiMatrix' entered. If 'phiMatrix' is a GLmultiMatrix, an array of vectors (GLmultiVector), one for each depth, is returned. Array of size ('depthMax'-'depthMin'), vectors of size 'length(probs)'. If 'phiMatrix' is a matrix, a vector of size 'length(probs)' is returned.

See Also

gen.genealogy gen.phi gen.phiOver gen.phiMean

Examples

data(genea140)
gen140<-gen.genealogy(genea140)
ge07<-gen.branching(gen140, pro=gen.pro(gen140)[c(1:7)])
phiMat<-gen.phi(ge07)
gen.phiCI(phiMat)

Average kinship

Description

Returns the average kinship among pairs of specified individuals

Usage

gen.phiMean( phiMatrix)

Arguments

Value

returns a numeric value

See Also

gen.genealogy gen.phi gen.phiOver gen.phiCI

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
kinship<-gen.phi(genJi)
gen.phiMean(kinship)


data(genea140) 
gen140<-gen.genealogy(genea140) 
phi6subjects<-gen.phi(gen140, pro=c(454422, 676521, 677273, 717537, 717634, 717709, 868572))
gen.phiMean(phi6subjects)

Kinship above threshold

Description

Returns the pairs of individuals with kinship coefficient values greater than specified threshold.

Usage

gen.phiOver( phiMatrix, threshold)

Arguments

Value

A data frame containing the probands and their kinship.

See Also

gen.genealogy gen.phi gen.phiMean gen.phiCI

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
kinship<-gen.phi(genJi)
gen.phiOver(kinship, 0.1)


data(genea140) 
gen140<-gen.genealogy(genea140) 
phi9subj<-gen.phi(gen140,pro=c(408758,408950,409082,409111,676521,717537,717634,717709,868572))
gen.phiOver(phi9subj,0.025)

Get proband id numbers

Description

Returns the id numbers of the probands. Probands are defined as the individuals without children in the genealogy.

Usage

gen.pro( gen, ...)

Arguments

Value

returns a vector of integer

See Also

gen.genealogy gen.founder gen.half.founder gen.parent gen.children

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
# There are 3 probands
gen.pro(genJi)


data(genea140) 
gen140<-gen.genealogy(genea140)
gen.pro(gen140)
# There are 140 probands

Ancestors coverage

Description

Returns the number of specified probands genealogically related to specified ancestors (i.e., ancestor occurs in the proband's genealogy).

Usage

gen.rec( gen, pro = "0", ancestors = "0", ...)

Arguments

Value

A matrix with number of lines equal to the number of ancestors specified and one column.

Note

If an ancestor is also a proband, he/she will be counted in his/her coverage.

See Also

gen.genealogy gen.occ gen.implex gen.meangendepth gen.gc

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
# Number of probands which are descendants of an ancestor
gen.rec(genJi)


data(genea140) 
gen140<-gen.genealogy(genea140) 
gen.rec(gen140, ancestors=c(18305, 18528, 31114, 18325))

Get id numbers of siblings

Description

Returns the id numbers of the siblings of specified individuals.

Usage

gen.sibship( gen, individuals, halfSibling = TRUE, ...)

Arguments

Value

returns a vector of integer

See Also

gen.genealogy gen.pro gen.founder gen.children gen.parent

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.sibship(genJi, individuals=21,halfSibling=TRUE) 


data(genea140) 
gen140<-gen.genealogy(genea140) 
sibs<-gen.sibship(gen140,individuals=10174, halfSibling=FALSE)
gen.graph(gen140, pro=c(10174,sibs))

Gene dropping simulations - haplotypes

Description

Gene dropping from founders to specified probands according to specified model of meioisis. Output .txt files will have simulated haplotypes, labelled w.r.t. the ancestor of origin

Usage

gen.simuHaplo(gen, pro = NULL, ancestors = NULL, simulNo = 1, model
                        = 1, model_params, cM_len, BP_len, physical_map_Mo =
                        NULL, physical_map_Fa = NULL, seed = 0, all_nodes = 0,
                        outDir = getwd())

Arguments

Value

Will generate output files "Proband_Haplotypes.txt" and "All_nodes_haplotypes.txt" in the specified output directory.

See Also

gen.genealogy gen.simuHaplo_traceback gen.simuHaplo_IBD_compare gen.simuSampleFreq

Examples

gen_table <- data.frame(ind=c(1,2,3,4,5,6,7),
                        mother=c(0,0,2,0,0,5,3),
                        father=c(0,0,1,0,0,4,6),
                        sex=c(1,2,2,1,2,1,1)
)

#convert the genealogy table into a GENLIB object
sample_gen<-gen.genealogy(gen_table) 

gen.graph(sample_gen)
# Simulate the gene dropping from founders to probamds

gen.simuHaplo(sample_gen, simulNo=5, model = 1, model_params = c(1,1), cM_len=c(100,100),
                BP_len = 100000000, outDir=tempdir())

f<-file(paste(tempdir(),"Proband_Haplotypes.txt",sep="/"),"r")
print(readLines(f,n=10))

Compare proband haplotypes for IBD sharing

Description

Analyze the simulated haplotypes produced by gen.simuHaplo and identify segments of IBD sharing between the diploid haplotypes of a pair of probands

Usage

gen.simuHaplo_IBD_compare(proID_1, proID_2, BP_len, proband_haplotypes_path)

Arguments

Value

returns a dataframe with the following columns: "simulNo", "n_seg", "pIBD", "mean_seg_len". Each row is a simulation wherein the pair of probands had some IBD sharing. The "simulNo" columns gives the simulation number, "n_seg" is the number of non-contiguous segments shared between the pair (divide this value by 2 for an average value for the pair), "pIBD" is the percent of the simulated chromosome shared IBD between the pair, and "mean_seg_len" is the mean length of IBD segments. Additionally, the function will print the exact location of each IBD segment to the R console. This can be toggled by disabling messages.

See Also

gen.genealogy gen.simuHaplo gen.simuHaplo_traceback

Examples

gen_table <- data.frame(ind=c(1,2,3,4,5,6,7,8),
                        mother=c(0,0,2,0,0,5,3,3),
                        father=c(0,0,1,0,0,4,6,6),
                        sex=c(1,2,2,1,2,1,1,2)
)

#convert the genealogy table into a GENLIB object
sample_gen<-gen.genealogy(gen_table) 

gen.graph(sample_gen)
# Simulate the gene dropping from founders to probamds

gen.simuHaplo(sample_gen, simulNo=5, model = 1, model_params = c(1,1), cM_len=c(100,100),
                BP_len = 100000000, outDir=tempdir())

IBD_compare <- gen.simuHaplo_IBD_compare(7, 8, 100000000, 
                paste(tempdir(), "Proband_Haplotypes.txt", sep="/"))

Convert proband simulation results into sequence data given founder haplotypes

Description

Convert the "Proband_Haplotypes.txt" output file produced by gen.simulHaplo into sequence data according to input sequence data for founders.

Usage

gen.simuHaplo_convert(dir)

Arguments

Details

See https://github.com/R-GENLIB/simuhaplo_functions for examples on how to format the founder sequence input files.

Value

creates a new file in the specified directory "Proband_Genotypes.txt"

See Also

gen.genealogy gen.simuHaplo

Examples

#gen_table <- data.frame(ind=c(1,2,3,4,5,6,7,8),
#                        mother=c(0,0,2,0,0,5,3,3),
#                        father=c(0,0,1,0,0,4,6,6),
#                        sex=c(1,2,2,1,2,1,1,2))

#convert the genealogy table into a GENLIB object
#sample_gen<-gen.genealogy(gen_table) 


# Simulate the gene dropping from founders to probamds
#gen.simuHaplo(sample_gen, simulNo=5, model = 1, model_params = c(1,1), cM_len=c(100,100),
#                BP_len = 100000000, outDir=tempdir())

#after creating a "founders.map" and a "founders.hap" file in the same directory then can ran
#gen.simulHaplo_convert(dir=tempdir())

Trace inheritance path for results from gene dropping simulation

Description

Given the output of a gene dropping simulation using gen.simuHaplo, this function can trace the inheritance path of a proband segment back to the founder it is inherited from

Usage

gen.simuHaplo_traceback(gen, proID, ancestorID, all_nodes_path, proband_haplotypes_path)

Arguments

Value

returns a dataframe with the following columns: "simulNo", "seg_length", "path_no". For every simulation in the output files where the specified proband inherits a segment from the specified founder. "seg_length" is the length of the inherited segment in BP. "path_no" is an integer that specifies the path of inheritance of the segment Unique paths of inheritance are simply identified by the order they appear in the outputs file, and the full paths (ID of every individual along the path) are printed to the R console. If this is not appearing check that messages are not disabled.

See Also

gen.genealogy gen.simuHaplo gen.simuHaplo_IBD_compare

Examples

gen_table <- data.frame(ind=c(1,2,3,4,5,6,7),
                        mother=c(0,0,2,0,0,5,3),
                        father=c(0,0,1,0,0,4,6),
                        sex=c(1,2,2,1,2,1,1)
)

#convert the genealogy table into a GENLIB object
sample_gen<-gen.genealogy(gen_table) 

gen.graph(sample_gen)
# Simulate the gene dropping from founders to probamds

gen.simuHaplo(sample_gen, simulNo=5, model = 1, model_params = c(1,1), cM_len=c(100,100),
                BP_len = 100000000, all_nodes = 1, outDir=tempdir())

traceback <-gen.simuHaplo_traceback(sample_gen, 7, 1,
                        paste(tempdir(), "All_nodes_haplotypes.txt", sep = "/"),
                        paste(tempdir(), "Proband_Haplotypes.txt", sep="/"))

Gene dropping simulations - Probabilities

Description

Returns the probabilities that specified probands inherit disease alleles from ancestors.

Usage

gen.simuProb(gen, pro, statePro, ancestors, stateAncestors, simulNo=5000, 
                    probRecomb=c(0,0), probSurvival=1.0)

Arguments

Value

A list containing the following:

the joint probability of specified statePro for all probands,

the probability of specified statePro for each proband,

the probability that, 0, 1, ..., and all probands inherit the specified number of disease alleles.

See Also

gen.genealogy gen.simuSample gen.simuSet gen.simuSampleFreq

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
# Probability that subjects 1 and 29 get 1 and 2 alleles from ancestors 20 and 25,
# that have themselves 2 and 1.
gen.simuProb(genJi, pro=c(1,29), statePro=c(1,2), ancestors=c(20,25), stateAncestors=c(2,1),
             simulNo=10000)
# Probability that subjects 1 and 29 get 1 segment from ancestors 25,
# knowing the segment has a male
# recombination rate of 0.02 and a female recombination rate of 0.04.
gen.simuProb(genJi, pro=c(1,29), statePro=c(1,1), ancestors=c(25), stateAncestors=c(1),
             simulNo=10000, probRecomb = c(0.02, 0.04))
# Probability that subjects 1 and 29 get 1 and 2 alleles from ancestors 20 and 25,
# that have themselves 2 and 1 and knowing that homozygous people have a survival rate
# of 0.50.
gen.simuProb(genJi, pro=c(1,29), statePro=c(1,2), ancestors=c(20,25), stateAncestors=c(2,1),
             simulNo=10000, probSurvival=0.5)

Gene dropping simulations - Sample

Description

Returns the number of alleles transmitted to specified probands from ancestors

Usage

gen.simuSample(gen, pro, ancestors, stateAncestors, simulNo = 5000)

Arguments

Value

A matrix with number of columns equal to the number of simulations and number of rows equal to the number of probands.

See Also

gen.genealogy gen.simuProb gen.simuSet gen.simuSampleFreq

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
simu_1000<-gen.simuSample(genJi, pro=c(1,29), ancestors=c(20,25), stateAncestors=c(2,1),
                          simulNo=1000)
# Number of alleles received by probants
table(simu_1000)
# Number of alleles received by simulation
table(colSums(simu_1000))
# Number of alleles received by each probant
table(simu_1000[1,],simu_1000[2,])

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
simu_5000<-gen.simuSample(genJi, pro=c(1,29), ancestors=c(20,25), stateAncestors=c(2,1),
                          simulNo=5000)
# Number of alleles received by probants
table(simu_5000)
# Number of alleles received by simulation
table(colSums(simu_5000))
# Number of alleles received by each probant
table(simu_5000[1,],simu_5000[2,])

Gene dropping simulations - Frequencies

Description

Returns the number of alleles transmitted to specified probands from ancestors

Usage

gen.simuSampleFreq( gen, pro, ancestors, stateAncestors, simulNo = "5000")

Arguments

Value

A data frame containing for each row (one row per proband): Alleles.transmitted.0 Alleles.transmitted.1 Alleles.transmitted.2

See Also

gen.genealogy gen.simuProb gen.simuSet gen.simuSample

Examples

data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
gen.simuSampleFreq(genJi, pro=c(1,29), ancestors=c(20,25),stateAncestors=c(2,1), simulNo = 1000)

Gene dropping simulations with specified transmission probabilities

Description

Returns the number of alleles transmitted to specified probands from ancestors considering sepcified transmission probabilities.

Usage

gen.simuSet(gen, pro, ancestors, stateAncestors, 
                   probMatrix=matrix(c(
                                    c(1,0.5,0,0.5,0.25,0,0,0,0,1,1,1,1,0.75,0.5,1,0.5,0),
                                    c(1,0.5,0,0.5,0.25,0,0,0,0,1,1,1,1,0.75,0.5,1,0.5,0)),
                                     nrow=3, ncol=12),
                   simulNo = 5000)

Arguments

Value

A matrix with number of columns equal to the number of simulations and number of rows equal to the number of probands.

See Also

gen.genealogy gen.simuProb gen.simuSample

Examples

# A case where only male subjects can receive alleles
data(geneaJi) 
genJi<-gen.genealogy(geneaJi) 
onlyThroughMale<-matrix(c(c(1,0.5,0,0.5,0.25,0,0,0,0,1,1,1,1,0.75,0.5,1,0.5,0), rep(1,18)), 
                        nrow=3, ncol=12) 
gen.graph(genJi, indVarAffected=c(28,27,25,17,12,8,4,1), varAffected=c(28,27,25,17,12,8,4,1))
simu_1000a<-gen.simuSet(genJi, pro=c(1,28), ancestors=c(17,25), stateAncestors=c(1,1), 
                        simulNo = 1000,probMatrix=onlyThroughMale)
# Number of alleles received by probants
table(simu_1000a)
# Number of alleles received by all probants at each simulation
table(colSums(simu_1000a))
# Number of alleles received by each probant
table(simu_1000a[1,],simu_1000a[2,])

# A case where subjects are limited to one copy compared to what is normally expected
max1Allreceived<-matrix(rep(c(1,0.5,0,0.5,0.25,0,0,0,0,1,1,1,1,1,1,1,1,1), 2) , nrow=3, ncol=12)  
gen.graph(genJi)
simu_1000b<-gen.simuSet(genJi, pro=c(1,29), ancestors=25, stateAncestors=1, simulNo=10000,
                        probMatrix=max1Allreceived)
# Normal case matrix not changed
simu_1000original<-gen.simuSet(genJi, pro=c(1,29), ancestors=25, stateAncestors=1, simulNo=10000)
# Number of alleles received by each probant
table(simu_1000b) 
table(simu_1000original) 
# Number of alleles received by all probants at each simulation
table(colSums(simu_1000b)) 
table(colSums(simu_1000original)) 
# Number of alleles received by each probant
table(simu_1000b[1,],simu_1000b[2,])
table(simu_1000original[1,],simu_1000original[2,]) 

Genealogical information for 140 individuals from the Quebec Reference Sample

Description

A genealogical corpus made of 41523 individuals from the province of Quebec, Canada. A total of 140 individuals have been sampled in seven sub-populations, listed in pop140, and their genealogies were reconstructed as far back as possible using the BALSAC population register and the Early Quebec Population Register.

Usage

data(genea140)

Format

A data frame with 41523 observations on 11 variables.

[,1] ind: An individual's ID number

[,2] father: This indidividual's father ID number

[,3] mother: This indidividual's mother ID number

[,4] sex: Individual's sex coded 1/2 for male/female

Source

Balsac

References

http://www.quebecgenpop.ca

Roy-Gagnon, M.-H., Moreau, C., Bherer, C., St-Onge, P., Sinnett, D., Laprise, C., Vezina, H., Labuda, D. (2011). Genomic and genealogical investigation of the French Canadian founder population structure. Human Genetics, 129(5), 521-31.

Highly inbred pedigree

Description

A modified version of a pedigree of two Jicaque Indians studied by Chapman & Jacquard (1971).

Usage

data(geneaJi)

Format

A data frame with 29 observations on 4 variables.

[, 1] ind An individual's ID number

[, 2] father This indidividual's father ID number

[, 3] mother This indidividual's mother ID number

[, 4] sex Individual's sex coded 1/2 for male/female

Source

CHAPMAN & JACQUART (1971)

References

Chapman, A.M., & Jacquard, A. (1971). Un isolat d'Amerique centrale: Les indiens Jicaques du Honduras. Paris: Presses universitaires de France.

Population of origin of the 140 Quebec samples

Description

The 140 individuals from the genealogical corpus from Quebec were sampled from 7 different populations from 5 regions; Quebec City, Montreal, Saguenay, North Shore, Gaspesia. In Gaspesia we find 3 different populations: French-Canadians, Acadians and Loyalists.

Usage

data(pop140)

Format

[, 1] ind: An individual's ID number

[, 2] pop: This indidividual's population

Source

Balsac

References

http://www.quebecgenpop.ca/home.html

Roy-Gagnon, M.-H., Moreau, C., Bherer, C., St-Onge, P., Sinnett, D., Laprise, C., Vezina, H., Labuda, D. (2011). Genomic and genealogical investigation of the French Canadian founder population structure. Human Genetics, 129(5), 521-31.