xbreed: Create crossbred population
In xbreed: Genomic Simulation of Purebred and Crossbred Populations
Description
Usage
Arguments
Details
Value
References
See Also
Examples
View source: R/Make_cross_v11_Commented.R
Description
This function can be used for crossing between populations. Different crossbreeding schemes such as two-way, three-way and four-way crossbreeding schemes can be simulated.
Usage
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xbreed(pop1, pop2, founder_pop1, founder_pop2, founder_cross, Selection_pop1,
Selection_pop2, Cross_design, ng, litter_size, train_type, train_pop1,
train_pop2, train_cross, saveAt, output_pop1, output_pop2, output_cross,
Display)
Arguments
pop1
(list) Output of function make_rp, sample_hp or xbreed.
pop2
(list) Output of function make_rp, sample_hp or xbreed.
founder_pop1
(data.frame) Data frame with 2 rows and 3 columns. First row is for the selection of male founders and second row is for the selection of female founders from pop1. The columns are as following:
Colomn 1) "size" is the number of individuals to be selected.
Column 2) "generation" is the generation number from which males/females will be selected.
Column 3) "select" indicates the type of selection with options:
"rnd" Select individuals randomly.
"phen" Select individuals based on their phenotypes.
"tbv" Select individuals based on their true breeding value (tbv).
"gebv" Select individuals based on their genomic estimated breeding value (gebv).
Column 4) "value" Indicates to select high: "h" or low: "l" values. Note: This colomn is ignored if individuals are selected randomly.
founder_pop2
(data.frame) Data frame with 2 rows and 3 columns. First row is for the selection of male founders and second row is for the selection of female founders from pop2. Details are similar to argument founder_pop1.
founder_cross
(data.frame) Data frame with 2 rows and 4 columns that contains information about creating base generation of crossbreds. First row is for the selection of male founders and second row is for the selection of female founders in order to create crossbred population. The columns are as following:
Column 1) "pop" Indication of the population name from which males/females for crossbreeding will be selected. The two possible options are:
"pop1" Select individuals are from pop1.
"pop2" Select individuals are from pop2.
Column 2) "size" is the number of individuals to be selected.
Column 3) "select" indicates the type of selection with options:
"rnd" Select individuals randomly.
"phen" Select individuals based on their phenotypes.
"tbv" Select individuals based on their true breeding value (tbv).
"gebv" Select individuals based on their genomic estimated breeding value (gebv).
Column 4) "value" Indicates to select high: "h" or low: "l" values. This column is ignored if individuals are selected randomly.
Note: After selecting founders for pop1 and pop2, by arguments founder_pop1 and founder_pop2, user can select individuals from these founders as parents of crossbreds for the base generation of crossbreds.
Selection_pop1
(data.frame) Selection design for pop1 (Breed 1). Data frame with 2 rows and 3 columns. First row is for the selection design of males and second row is for the selection design of females. The columns are as following:
Column 1) "Number" is the number of individuals to be selected as sires/dams.
Column 2) "type" indicates the type of selection with options:
"rnd" Select individuals randomly.
"phen" Select individuals based on their phenotypes.
"tbv" Select individuals based on their true breeding value (tbv).
"tbvc" Select individuals based on true breeding value for crossbred performance(tbvc).
"gebv" Select individuals based on their genomic estimated breeding value (gebv).
"gebvc" Select individuals based on genomic estimated breeding value for crossbred performance (gebvc).
Column 3) "value" Indicates to select high: "h" or low: "l" values. Note: This colomn is ignored if individuals are selected randomly.
Selection_pop2
(data.frame) Selection design for pop2 (Breed 2). Details are similar to argument Selection_pop1.
Cross_design
(data.frame) Data frame containing information on how to select individuals from pop1 and pop2 as parents of crossbreds over generations. This argument is a data frame with 2 rows and 4 columns. First row is for the selection of males as sires of crossbreds and second row is for the selection of females as dams of crossbreds. The columns are as following:
Column 1) "pop" Indication of the population name from which males/females for crossing will be selected. The two possible options are:
"pop1" Selected individuals are from pop1.
"pop2" Selected individuals are from pop2.
As an example if row 1 and column 1 of argument Cross_design is equal to "pop2", this means that sires of crossbred are from pop2.
Column 2) "size" is the number of individuals to be selected as sires/dams.
Column 3) "select" indicates the type of selection with options:
"rnd" Select individuals randomly.
"phen" Select individuals based on their phenotypes.
"tbv" Select individuals based on their true breeding value (tbv).
"tbvc" Select individuals based on true breeding value for crossbred performance(tbvc).
"gebv" Select individuals based on their genomic estimated breeding value (gebv).
"gebvc" Select individuals based on genomic estimated breeding value for crossbred performance (gebvc).
Column 4) "value" Indicates to select high: "h" or low: "l" values. Note: This column is ignored if individuals are selected randomly.
ng
Number of generations. Range: 1 ≤q \code{ng} ≤q 200.
litter_size
Litter size or the number of progeny per dam. Range: 1 ≤q \code{x} ≤q 200.
train_type
Optional (character) Type of training for the estimation of marker effects. The two possible options are:
"purebred" Training will be on purebreds. This means that training for each population is done separately. So, there will be two reference population; one for pop1 (Breed 1) and one for pop2 (Breed 2).
"crossbred" Training will be on crossbreds. So, estimated marker effects will be the same for both pop1 and pop2.
Note: If selection criteria for any population is defined as gebv/gebvc, argument train_type should be defined.
train_pop1
Optional (data.frame) Data frame with 1 row and 8 columns. The columns are as following:
Column 1) "size" is the number of individuals to be selected for training.
Column 2) "sel" Optional (character) Indicates the type of the selection of individuals for training. The possible options are:
"rnd" Select individuals for training randomly.
"min_rel_mrk" Select individuals for training, where genomic relationship among individuals based on marker information is minimum.
"max_rel_mrk" Select individuals for training, where genomic relationship among individuals based on marker information is maximum.
"min_rel_qtl" Select individuals for training, where genomic relationship among individuals based on qtl information is minimum.
"max_rel_qtl" Select individuals for training, where genomic relationship among individuals based on qtl information is maximum.
Default: "rnd"
Column 3) "method" Optional (character) Method used for the estimation of marker effects. The possible options are:
"BRR" Gaussian prior.
"BayesA" scaled-t prior.
"BL" Double-Exponential prior.
"BayesB" two component mixture prior with a point of mass at zero and a scaled-t slab.
"BayesC" two component mixture prior with a point of mass at zero and a Gaussian slab.
Default: "BRR"
Column 4) "nIter" Optional The number of iterations. Default: 1500
Column 5) "burnIn" Optional The number of burn-in. Default: 500
Column 6) "thin" Optional The number of thinning. Default: 5
Column 7) "save" Optional This may include a path and a pre-fix that will be added to the name of the files that are saved as the program runs. Default:"Out_BGLR"
Column 8) "show" Optional (Logical) if TRUE the iteration history is printed. Default: TRUE.
Note: This argument is compulsory if argument train_type is "purbred".
More details about the argument can be found in package BGLR.
train_pop2
Optional (data.frame) Data frame with 1 row and 8 columns similar to argument train_pop1.
Note: This argument is compulsory if argument train_type is "purbred".
train_cross
Optional (data.frame) Data frame with 1 row and 8 columns similar to argument train_pop1.
Note: This argument is compulsory if argument train_type is "crossbred".
saveAt
Optional (character). Name to be used to save output files.
output_pop1
Optional (data.frame). Data frame to specify generation indexs and type of data to be written to output files for "pop1". User can define which type of data and which generations to be written to output files. The possible options are:
"data" Individuals data except their genotypes.
"qtl" QTL genotye of individuals coded as 11,12,21,22.
"marker" Marker genotye of individuals.
"seq" Genotype (both marker (SNP) and QTL) of individuals.
"freq_qtl" QTL allele frequency.
"freq_mrk" Marker allele frequency.
Note: Both arguments output_pop1 and saveAt should present in the function in order to write the output files for "pop1".
output_pop2
Optional (data.frame). Data frame to specify generations indexs and type of data to be written to output files for pop2. Details are similar to argument output_pop1.
Note: Both arguments output_pop2 and saveAt should present in the function in order to write the output files for "pop2".
output_cross
Optional (data.frame). Data frame to specify generations indexs and type of data to be written to output files for crossbred population. Details are similar to argument output_pop1.
Note: Both arguments output_cross and saveAt should present in the function in order to write the output files for crossbred population.
Display
Optional (Logical) Display summary of the simulated generations if is not FALSE. Default: TRUE.
Details
Function xbreed is used for crossing between populations. These populations can be the ones created by functions sample_hp and make_rp. Also, if user would like to have multi-way crossbreeding schemes such as three-way crossbreeding, then function xbreed can be used to get the output of itself as input data in order to create the multi-way crossbreed populations. Simulations of two-way and multi-way crossbreeding schemes are presented in the package vignette.
Value
list with all data of simulated populations.
- $pop1
(list) Two-level list ($pop1[[]][[]]) containing information about simulated generations. First index (x) indicates generation number. It should be noted that as data for base generation (0) is also stored by the function, to retrive data for a specific generation, index should be equal to generation number plus one. As an example to observe data for generation 2 index should be 3 i.e, $pop1[[3]]$data. Second index (y) that ranges from 1 to 6 contain the information as following:
$pop1[[x]]$data Individuals data except their genotypes. Here x is the generation index.
$pop1[[x]]$qtl QTL genotye of individuals.
$pop1[[x]]$mrk Marker genotye of individuals.
$pop1[[x]]$sequ Genotype (both marker (SNP) and QTL) of individuals.
$pop1[[x]]$freqQTL QTL allele frequency.
$pop1[[x]]$freqMRK Marker allele frequency.
- $pop2
Similar to $pop1
- $cross
(list) Two-level list ($cross$output[[x]][[y]]) containing information about simulated crossbred generations. Details are similar to $pop1 such as:
$cross$output[[x]]$data Crossbred individuals data except their genotypes. Here x is the generation index.
$cross$output[[x]]$qtl QTL genotye of crossbred individuals.
$cross$output[[x]]$mrk Marker genotye of individuals.
$cross$output[[x]]$sequ Genotype (both marker (SNP) and QTL) of individuals.
$cross$output[[x]]$freqQTL QTL allele frequency.
$cross$output[[x]]$freqMRK Marker allele frequency.
- $cross$summary_data_cross
Data frame with summary of simulated crossbreds
.
- $summary_data_pop1
Data frame with summary of simulated generations for pop1 (Breed 1)
.
- $summary_data_pop2
Data frame with summary of simulated generations for pop2 (Breed 2)
.
- $linkage_map_qtl
Linkage map for qtl
.
- $linkage_map_mrk
Linkage map for marker
.
- $linkage_map_qtl_mrk
Integrated linkage map for both marker and qtl
.
- $allele_effcts
QTL allele effects
.
- $trait
Trait specifications
.
- $genome
Genome specifications
.
References
Esfandyari H., A.C Sorensen and P. Bijma. 2015. A crossbred reference population can improve the response to genomic selection for crossbred performance. Genetics Selection Evolution 47: 76
Esfandyari H., A.C Sorensen and P. Bijma. 2015. Maximizing crossbred performance through purebred genomic selection. Genetics Selection Evolution 47: 16
See Also
Examples
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## Not run:
# # Simulation of a two-way crossbreeding program.
# The crossbreeding scheme in this example involves three steps:
#Step 1: Historical population is created.
#Step 2: Two recent populations named as Breed A and B
# are created by sampling individuals from historical population.
#Step 3: Breed A and B are crossed.
#--------------------------------------
# # STEP 1: CREATE HISTORICAL POPULATION
# Genome consisted of 3 chromosomes
genome<-data.frame(matrix(NA, nrow=3, ncol=6))
names(genome)<-c("chr","len","nmrk","mpos","nqtl","qpos")
genome$chr<-c(1:3)
genome$len<-rep(100,3)
genome$nmrk<-rep(100,3)
genome$mpos<-rep('rnd',3)
genome$nqtl<-rep(25,3)
genome$qpos<-rep('rnd',3)
genome
historical<-make_hp(hpsize=300
,ng=10,h2=0.25,d2=0.10,phen_var=1
,genome=genome,mutr=5*10**-4,laf=0.5)
# # STEP 2: MAKE BREED A AND B
# BREED A
Breed_A_Male_fndrs<-data.frame(number=50,select='rnd')
Breed_A_Female_fndrs<-data.frame(number=50,select='rnd')
# Selection and matings in Breed A
# Selection of 50 sires and 50 dam
# Selection criteria is "rnd" for both sires and dams
Selection<-data.frame(matrix(NA, nrow=2, ncol=2))
names(Selection)<-c("Number","type")
Selection$Number[1:2]<-c(50,50)
Selection$type[1:2]<-c("rnd","rnd")
Selection
Breed_A<-sample_hp(hp_out=historical,Male_founders=
Breed_A_Male_fndrs,Female_founders=Breed_A_Female_fndrs,
ng=5,Selection=Selection,
litter_size=3,Display=TRUE)
# BREED B
Breed_B_Male_fndrs<-data.frame(number=50,select="rnd")
Breed_B_Female_fndrs<-data.frame(number=50,select="rnd")
# Selection and matings in Breed B
# Selection of 50 sires and 50 dam
# Selection criteria is "phen" for both sires and dams
Selection<-data.frame(matrix(NA, nrow=2, ncol=3))
names(Selection)<-c("Number","type","Value")
Selection$Number[1:2]<-c(50,50)
Selection$type[1:2]<-c("phen","phen")
Selection$Value[1:2]<-c("h","h")
Selection
Breed_B<-sample_hp(hp_out=historical,Male_founders=
Breed_B_Male_fndrs,Female_founders=Breed_B_Female_fndrs,
ng=5,Selection=Selection,
litter_size=3,Display=TRUE)
# # STEP 3: CROSSING BETWEEN BREED A AND B
# Selection of founders in crossbreeding for Breed A
# Selection of 50 sires and 50 dams
# from last generation of pop in step 2.
# Selection criteria is "rnd" for both sires and dams
founder_pop1<-data.frame(matrix(NA, nrow=2, ncol=3))
names(founder_pop1)<-c("size","generation","select")
founder_pop1[1,]<-c(50,5,"rnd")
founder_pop1[2,]<-c(50,5,"rnd")
founder_pop1
# Selection of founders in crossbreeding for Breed B
# Selection of 40 sires and 40 dams
# Selection criteria is "phen" for sires
# Selection criteria is "rnd" for dams
founder_pop2<-data.frame(matrix(NA, nrow=2, ncol=4))
names(founder_pop2)<-c("size","generation","select","value")
founder_pop2[1,]<-c(40,5,"phen","h")
founder_pop2[2,]<-c(40,5,"rnd","h") # "h" will be ignored as SC is "rnd"
founder_pop2
# Selection of animals from founder_pop1 and founder_pop2 to be crossed
founder_cross<-data.frame(matrix(NA, nrow=2, ncol=4))
names(founder_cross)<-c("pop","size","select","value")
founder_cross[1,]<-c("pop1",35,"tbv","h") # Select males from Breed A
founder_cross[2,]<-c("pop2",40,"phen","h") # Select females from Breed B
founder_cross
# Selection scheme in Breed A to produce purebred replacement animals
Selection_pop1<-data.frame(matrix(NA, nrow=2, ncol=3))
names(Selection_pop1)<-c("Number","type","Value")
Selection_pop1$Number[1:2]<-c(70,70)
Selection_pop1$type[1:2]<-c("tbv","tbv")
Selection_pop1$Value[1:2]<-c("h","h")
Selection_pop1
# Selection scheme in Breed B to produce purebred replacement animals
Selection_pop2<-data.frame(matrix(NA, nrow=2, ncol=3))
names(Selection_pop2)<-c("Number","type","Value")
Selection_pop2$Number[1:2]<-c(40,40)
Selection_pop2$type[1:2]<-c("phen","phen")
Selection_pop2$Value[1:2]<-c("h","h")
Selection_pop2
# Selection scheme for crossing between A and B
Cross_design<-data.frame(matrix(NA, nrow=2, ncol=4))
names(Cross_design)<-c("pop","size","select","value")
Cross_design[1,]<-c("pop1",50,"phen","h")
Cross_design[2,]<-c("pop2",100,"phen","h")
Cross_design
# Save data for crossbred AB
output_cross<-data.frame(matrix(NA, nrow=1, ncol=1))
output_cross[,1]<-c(1)
output_cross
cross_AB<-xbreed(pop1=Breed_A,pop2=Breed_B,founder_pop1=
founder_pop1,founder_pop2=founder_pop2,
founder_cross=founder_cross,
Selection_pop1=Selection_pop1,Selection_pop2=Selection_pop2,
Cross_design=Cross_design,ng=2,litter_size=4,
saveAt="cross_pop",output_cross=output_cross,Display=TRUE)
## End(Not run)
xbreed documentation built on Oct. 8, 2019, 9:05 a.m.
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Description Usage Arguments Details Value References See Also Examples
View source: R/Make_cross_v11_Commented.R
Description
This function can be used for crossing between populations. Different crossbreeding schemes such as two-way, three-way and four-way crossbreeding schemes can be simulated.
Usage
1 2 3 4 | xbreed(pop1, pop2, founder_pop1, founder_pop2, founder_cross, Selection_pop1,
Selection_pop2, Cross_design, ng, litter_size, train_type, train_pop1,
train_pop2, train_cross, saveAt, output_pop1, output_pop2, output_cross,
Display)
|
Arguments
pop1 |
( |
pop2 |
( |
founder_pop1 |
(
Column 4) "value" Indicates to select high: "h" or low: "l" values. Note: This colomn is ignored if individuals are selected randomly. |
founder_pop2 |
( |
founder_cross |
(
Column 2) "size" is the number of individuals to be selected.
Column 4) "value" Indicates to select high: "h" or low: "l" values. This column is ignored if individuals are selected randomly. |
Selection_pop1 |
(
Column 3) "value" Indicates to select high: "h" or low: "l" values. Note: This colomn is ignored if individuals are selected randomly. |
Selection_pop2 |
( |
Cross_design |
(
As an example if row 1 and column 1 of argument
Column 4) "value" Indicates to select high: "h" or low: "l" values. Note: This column is ignored if individuals are selected randomly. |
ng |
Number of generations. Range: 1 ≤q \code{ng} ≤q 200. |
litter_size |
Litter size or the number of progeny per dam. Range: 1 ≤q \code{x} ≤q 200. |
train_type |
Optional (
Note: If selection criteria for any population is defined as gebv/gebvc, argument |
train_pop1 |
Optional (
Default: "rnd"
Default: "BRR" |
train_pop2 |
Optional ( |
train_cross |
Optional ( |
saveAt |
Optional ( |
output_pop1 |
Optional ( |
output_pop2 |
Optional ( |
output_cross |
Optional ( |
Display |
Optional ( |
Details
Function xbreed is used for crossing between populations. These populations can be the ones created by functions sample_hp and make_rp. Also, if user would like to have multi-way crossbreeding schemes such as three-way crossbreeding, then function xbreed can be used to get the output of itself as input data in order to create the multi-way crossbreed populations. Simulations of two-way and multi-way crossbreeding schemes are presented in the package vignette.
Value
list with all data of simulated populations.
- $pop1
(
list) Two-level list ($pop1[[]][[]]) containing information about simulated generations. First index (x) indicates generation number. It should be noted that as data for base generation (0) is also stored by the function, to retrive data for a specific generation, index should be equal to generation number plus one. As an example to observe data for generation 2 index should be 3 i.e,$pop1[[3]]$data. Second index (y) that ranges from 1 to 6 contain the information as following:$pop1[[x]]$dataIndividuals data except their genotypes. Here x is the generation index.$pop1[[x]]$qtlQTL genotye of individuals.$pop1[[x]]$mrkMarker genotye of individuals.$pop1[[x]]$sequGenotype (both marker (SNP) and QTL) of individuals.$pop1[[x]]$freqQTLQTL allele frequency.$pop1[[x]]$freqMRKMarker allele frequency.
- $pop2
Similar to $pop1
- $cross
(
list) Two-level list ($cross$output[[x]][[y]]) containing information about simulated crossbred generations. Details are similar to$pop1such as:$cross$output[[x]]$dataCrossbred individuals data except their genotypes. Here x is the generation index.$cross$output[[x]]$qtlQTL genotye of crossbred individuals.$cross$output[[x]]$mrkMarker genotye of individuals.$cross$output[[x]]$sequGenotype (both marker (SNP) and QTL) of individuals.$cross$output[[x]]$freqQTLQTL allele frequency.$cross$output[[x]]$freqMRKMarker allele frequency.
- $cross$summary_data_cross
Data frame with summary of simulated crossbreds
.
- $summary_data_pop1
Data frame with summary of simulated generations for pop1 (Breed 1)
.
- $summary_data_pop2
Data frame with summary of simulated generations for pop2 (Breed 2)
.
- $linkage_map_qtl
Linkage map for qtl
.
- $linkage_map_mrk
Linkage map for marker
.
- $linkage_map_qtl_mrk
Integrated linkage map for both marker and qtl
.
- $allele_effcts
QTL allele effects
.
- $trait
Trait specifications
.
- $genome
Genome specifications
.
References
Esfandyari H., A.C Sorensen and P. Bijma. 2015. A crossbred reference population can improve the response to genomic selection for crossbred performance. Genetics Selection Evolution 47: 76
Esfandyari H., A.C Sorensen and P. Bijma. 2015. Maximizing crossbred performance through purebred genomic selection. Genetics Selection Evolution 47: 16
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | ## Not run:
# # Simulation of a two-way crossbreeding program.
# The crossbreeding scheme in this example involves three steps:
#Step 1: Historical population is created.
#Step 2: Two recent populations named as Breed A and B
# are created by sampling individuals from historical population.
#Step 3: Breed A and B are crossed.
#--------------------------------------
# # STEP 1: CREATE HISTORICAL POPULATION
# Genome consisted of 3 chromosomes
genome<-data.frame(matrix(NA, nrow=3, ncol=6))
names(genome)<-c("chr","len","nmrk","mpos","nqtl","qpos")
genome$chr<-c(1:3)
genome$len<-rep(100,3)
genome$nmrk<-rep(100,3)
genome$mpos<-rep('rnd',3)
genome$nqtl<-rep(25,3)
genome$qpos<-rep('rnd',3)
genome
historical<-make_hp(hpsize=300
,ng=10,h2=0.25,d2=0.10,phen_var=1
,genome=genome,mutr=5*10**-4,laf=0.5)
# # STEP 2: MAKE BREED A AND B
# BREED A
Breed_A_Male_fndrs<-data.frame(number=50,select='rnd')
Breed_A_Female_fndrs<-data.frame(number=50,select='rnd')
# Selection and matings in Breed A
# Selection of 50 sires and 50 dam
# Selection criteria is "rnd" for both sires and dams
Selection<-data.frame(matrix(NA, nrow=2, ncol=2))
names(Selection)<-c("Number","type")
Selection$Number[1:2]<-c(50,50)
Selection$type[1:2]<-c("rnd","rnd")
Selection
Breed_A<-sample_hp(hp_out=historical,Male_founders=
Breed_A_Male_fndrs,Female_founders=Breed_A_Female_fndrs,
ng=5,Selection=Selection,
litter_size=3,Display=TRUE)
# BREED B
Breed_B_Male_fndrs<-data.frame(number=50,select="rnd")
Breed_B_Female_fndrs<-data.frame(number=50,select="rnd")
# Selection and matings in Breed B
# Selection of 50 sires and 50 dam
# Selection criteria is "phen" for both sires and dams
Selection<-data.frame(matrix(NA, nrow=2, ncol=3))
names(Selection)<-c("Number","type","Value")
Selection$Number[1:2]<-c(50,50)
Selection$type[1:2]<-c("phen","phen")
Selection$Value[1:2]<-c("h","h")
Selection
Breed_B<-sample_hp(hp_out=historical,Male_founders=
Breed_B_Male_fndrs,Female_founders=Breed_B_Female_fndrs,
ng=5,Selection=Selection,
litter_size=3,Display=TRUE)
# # STEP 3: CROSSING BETWEEN BREED A AND B
# Selection of founders in crossbreeding for Breed A
# Selection of 50 sires and 50 dams
# from last generation of pop in step 2.
# Selection criteria is "rnd" for both sires and dams
founder_pop1<-data.frame(matrix(NA, nrow=2, ncol=3))
names(founder_pop1)<-c("size","generation","select")
founder_pop1[1,]<-c(50,5,"rnd")
founder_pop1[2,]<-c(50,5,"rnd")
founder_pop1
# Selection of founders in crossbreeding for Breed B
# Selection of 40 sires and 40 dams
# Selection criteria is "phen" for sires
# Selection criteria is "rnd" for dams
founder_pop2<-data.frame(matrix(NA, nrow=2, ncol=4))
names(founder_pop2)<-c("size","generation","select","value")
founder_pop2[1,]<-c(40,5,"phen","h")
founder_pop2[2,]<-c(40,5,"rnd","h") # "h" will be ignored as SC is "rnd"
founder_pop2
# Selection of animals from founder_pop1 and founder_pop2 to be crossed
founder_cross<-data.frame(matrix(NA, nrow=2, ncol=4))
names(founder_cross)<-c("pop","size","select","value")
founder_cross[1,]<-c("pop1",35,"tbv","h") # Select males from Breed A
founder_cross[2,]<-c("pop2",40,"phen","h") # Select females from Breed B
founder_cross
# Selection scheme in Breed A to produce purebred replacement animals
Selection_pop1<-data.frame(matrix(NA, nrow=2, ncol=3))
names(Selection_pop1)<-c("Number","type","Value")
Selection_pop1$Number[1:2]<-c(70,70)
Selection_pop1$type[1:2]<-c("tbv","tbv")
Selection_pop1$Value[1:2]<-c("h","h")
Selection_pop1
# Selection scheme in Breed B to produce purebred replacement animals
Selection_pop2<-data.frame(matrix(NA, nrow=2, ncol=3))
names(Selection_pop2)<-c("Number","type","Value")
Selection_pop2$Number[1:2]<-c(40,40)
Selection_pop2$type[1:2]<-c("phen","phen")
Selection_pop2$Value[1:2]<-c("h","h")
Selection_pop2
# Selection scheme for crossing between A and B
Cross_design<-data.frame(matrix(NA, nrow=2, ncol=4))
names(Cross_design)<-c("pop","size","select","value")
Cross_design[1,]<-c("pop1",50,"phen","h")
Cross_design[2,]<-c("pop2",100,"phen","h")
Cross_design
# Save data for crossbred AB
output_cross<-data.frame(matrix(NA, nrow=1, ncol=1))
output_cross[,1]<-c(1)
output_cross
cross_AB<-xbreed(pop1=Breed_A,pop2=Breed_B,founder_pop1=
founder_pop1,founder_pop2=founder_pop2,
founder_cross=founder_cross,
Selection_pop1=Selection_pop1,Selection_pop2=Selection_pop2,
Cross_design=Cross_design,ng=2,litter_size=4,
saveAt="cross_pop",output_cross=output_cross,Display=TRUE)
## End(Not run)
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