Package 'mappoly' reference manual

Title:	Genetic Linkage Maps in Autopolyploids
Description:	Construction of genetic maps in autopolyploid full-sib populations. Uses pairwise recombination fraction estimation as the first source of information to sequentially position allelic variants in specific homologous chromosomes. For situations where pairwise analysis has limited power, the algorithm relies on the multilocus likelihood obtained through a hidden Markov model (HMM). For more detail, please see Mollinari and Garcia (2019) <doi:10.1534/g3.119.400378> and Mollinari et al. (2020) <doi:10.1534/g3.119.400620>.
Authors:	Marcelo Mollinari [aut, cre] , Gabriel Gesteira [aut] , Cristiane Taniguti [aut] , Jeekin Lau [aut] , Oscar Riera-Lizarazu [ctb] , Guilhereme Pereira [ctb] , Augusto Garcia [ctb] , Zhao-Bang Zeng [ctb] , Katharine Preedy [ctb, cph] (MDS ordering algorithm), Robert Gentleman [cph] (C code for MLE optimization in src/pairwise_estimation.cpp), Ross Ihaka [cph] (C code for MLE optimization in src/pairwise_estimation.cpp), R Foundation [cph] (C code for MLE optimization in src/pairwise_estimation.cpp), R-core [cph] (C code for MLE optimization in src/pairwise_estimation.cpp)
Maintainer:	Marcelo Mollinari <[email protected]>
License:	GPL-3
Version:	0.4.1
Built:	2024-11-02 05:16:41 UTC
Source:	https://github.com/mmollina/MAPpoly

Add a single marker to a map

Description

Creates a new map by adding a marker in a given position in a pre-built map.

Usage

add_marker(
  input.map,
  mrk,
  pos,
  rf.matrix,
  genoprob = NULL,
  phase.config = "best",
  tol = 0.001,
  extend.tail = NULL,
  r.test = NULL,
  verbose = TRUE
)
add_marker(
  input.map,
  mrk,
  pos,
  rf.matrix,
  genoprob = NULL,
  phase.config = "best",
  tol = 0.001,
  extend.tail = NULL,
  r.test = NULL,
  verbose = TRUE
)

Arguments

`input.map`	an object of class `mappoly.map`
`mrk`	the name of the marker to be inserted
`pos`	the name of the marker after which the new marker should be added. One also can inform the numeric position (between markers) were the new marker should be added. To insert a marker at the beginning of a map, use `pos = 0`
`rf.matrix`	an object of class `mappoly.rf.matrix` containing the recombination fractions and the number of homologues sharing alleles between pairwise markers on `input.map`. It is important that `shared.alleles = TRUE` in function `rf_list_to_matrix` when computing `rf.matrix`.
`genoprob`	an object of class `mappoly.genoprob` containing the genotype probabilities for all marker positions on `input.map`
`phase.config`	which phase configuration should be used. "best" (default) will choose the maximum likelihood configuration
`tol`	the desired accuracy (default = 10e-04)
`extend.tail`	the length of the chain's tail that should be used to calculate the likelihood of the map. If `NULL` (default), the function uses all markers positioned.
`r.test`	for internal use only
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced

Details

add_marker splits the input map into two sub-maps to the left and the right of the given position. Using the genotype probabilities, it computes the log-likelihood of all possible linkage phases under a two-point threshold inherited from function rf_list_to_matrix.

Value

A list of class mappoly.map with two elements:

i) info: a list containing information about the map, regardless of the linkage phase configuration:

`ploidy`	the ploidy level
`n.mrk`	number of markers
`seq.num`	a vector containing the (ordered) indices of markers in the map, according to the input file
`mrk.names`	the names of markers in the map
`seq.dose.p1`	a vector containing the dosage in parent 1 for all markers in the map
`seq.dose.p2`	a vector containing the dosage in parent 2 for all markers in the map
`chrom`	a vector indicating the sequence (usually chromosome) each marker belongs as informed in the input file. If not available, `chrom = NULL`
`genome.pos`	physical position (usually in megabase) of the markers into the sequence
`seq.ref`	reference base used for each marker (i.e. A, T, C, G). If not available, `seq.ref = NULL`
`seq.alt`	alternative base used for each marker (i.e. A, T, C, G). If not available, `seq.ref = NULL`
`chisq.pval`	a vector containing p-values of the chi-squared test of Mendelian segregation for all markers in the map
`data.name`	name of the dataset of class `mappoly.data`
`ph.thres`	the LOD threshold used to define the linkage phase configurations to test

ii) a list of maps with possible linkage phase configuration. Each map in the list is also a list containing

`seq.num`	a vector containing the (ordered) indices of markers in the map, according to the input file
`seq.rf`	a vector of size (`n.mrk - 1`) containing a sequence of recombination fraction between the adjacent markers in the map
`seq.ph`	linkage phase configuration for all markers in both parents
`loglike`	the hmm-based multipoint likelihood

Author(s)

Marcelo Mollinari, [email protected]

Examples


sub.map <- get_submap(maps.hexafake[[1]], 1:20, reestimate.rf = FALSE)
plot(sub.map, mrk.names = TRUE)
s <- make_seq_mappoly(hexafake, sub.map$info$mrk.names)
tpt <- est_pairwise_rf(s)
rf.matrix <- rf_list_to_matrix(input.twopt = tpt,
                               thresh.LOD.ph = 3, 
                               thresh.LOD.rf = 3,
                               shared.alleles = TRUE)
###### Removing marker "M_1" (first) #######
mrk.to.remove <- "M_1"
input.map <- drop_marker(sub.map, mrk.to.remove)
plot(input.map, mrk.names = TRUE)
## Computing conditional probabilities using the resulting map
genoprob <- calc_genoprob(input.map)
res.add.M_1 <- add_marker(input.map = input.map,
                        mrk = "M_1",
                        pos = 0,
                        rf.matrix = rf.matrix,
                        genoprob = genoprob,
                        tol = 10e-4)  
 plot(res.add.M_1, mrk.names = TRUE)                       
 best.phase <- res.add.M_1$maps[[1]]$seq.ph
 names.id <- names(best.phase$P)
 plot_compare_haplotypes(ploidy = 6,
                         hom.allele.p1 = best.phase$P[names.id],
                         hom.allele.q1 = best.phase$Q[names.id],
                         hom.allele.p2 = sub.map$maps[[1]]$seq.ph$P[names.id],
                         hom.allele.q2 = sub.map$maps[[1]]$seq.ph$Q[names.id])
                         
###### Removing marker "M_10" (middle or last) #######
mrk.to.remove <- "M_10"
input.map <- drop_marker(sub.map, mrk.to.remove)
plot(input.map, mrk.names = TRUE)
# Computing conditional probabilities using the resulting map
genoprob <- calc_genoprob(input.map)
res.add.M_10 <- add_marker(input.map = input.map,
                        mrk = "M_10",
                        pos = "M_9",
                        rf.matrix = rf.matrix,
                        genoprob = genoprob,
                        tol = 10e-4)  
 plot(res.add.M_10, mrk.names = TRUE)                       
 best.phase <- res.add.M_10$maps[[1]]$seq.ph
 names.id <- names(best.phase$P)
 plot_compare_haplotypes(ploidy = 6,
                         hom.allele.p1 = best.phase$P[names.id],
                         hom.allele.q1 = best.phase$Q[names.id],
                         hom.allele.p2 = sub.map$maps[[1]]$seq.ph$P[names.id],
                         hom.allele.q2 = sub.map$maps[[1]]$seq.ph$Q[names.id]) 

sub.map <- get_submap(maps.hexafake[[1]], 1:20, reestimate.rf = FALSE)
plot(sub.map, mrk.names = TRUE)
s <- make_seq_mappoly(hexafake, sub.map$info$mrk.names)
tpt <- est_pairwise_rf(s)
rf.matrix <- rf_list_to_matrix(input.twopt = tpt,
                               thresh.LOD.ph = 3, 
                               thresh.LOD.rf = 3,
                               shared.alleles = TRUE)
###### Removing marker "M_1" (first) #######
mrk.to.remove <- "M_1"
input.map <- drop_marker(sub.map, mrk.to.remove)
plot(input.map, mrk.names = TRUE)
## Computing conditional probabilities using the resulting map
genoprob <- calc_genoprob(input.map)
res.add.M_1 <- add_marker(input.map = input.map,
                        mrk = "M_1",
                        pos = 0,
                        rf.matrix = rf.matrix,
                        genoprob = genoprob,
                        tol = 10e-4)  
 plot(res.add.M_1, mrk.names = TRUE)                       
 best.phase <- res.add.M_1$maps[[1]]$seq.ph
 names.id <- names(best.phase$P)
 plot_compare_haplotypes(ploidy = 6,
                         hom.allele.p1 = best.phase$P[names.id],
                         hom.allele.q1 = best.phase$Q[names.id],
                         hom.allele.p2 = sub.map$maps[[1]]$seq.ph$P[names.id],
                         hom.allele.q2 = sub.map$maps[[1]]$seq.ph$Q[names.id])
                         
###### Removing marker "M_10" (middle or last) #######
mrk.to.remove <- "M_10"
input.map <- drop_marker(sub.map, mrk.to.remove)
plot(input.map, mrk.names = TRUE)
# Computing conditional probabilities using the resulting map
genoprob <- calc_genoprob(input.map)
res.add.M_10 <- add_marker(input.map = input.map,
                        mrk = "M_10",
                        pos = "M_9",
                        rf.matrix = rf.matrix,
                        genoprob = genoprob,
                        tol = 10e-4)  
 plot(res.add.M_10, mrk.names = TRUE)                       
 best.phase <- res.add.M_10$maps[[1]]$seq.ph
 names.id <- names(best.phase$P)
 plot_compare_haplotypes(ploidy = 6,
                         hom.allele.p1 = best.phase$P[names.id],
                         hom.allele.q1 = best.phase$Q[names.id],
                         hom.allele.p2 = sub.map$maps[[1]]$seq.ph$P[names.id],
                         hom.allele.q2 = sub.map$maps[[1]]$seq.ph$Q[names.id])

Frequency of genotypes for two-point recombination fraction estimation

Description

Returns the frequency of each genotype for two-point reduction of dimensionality. The frequency is calculated for all pairwise combinations and for all possible linkage phase configurations.

Usage

cache_counts_twopt(
  input.seq,
  cached = FALSE,
  cache.prev = NULL,
  ncpus = 1L,
  verbose = TRUE,
  joint.prob = FALSE
)
cache_counts_twopt(
  input.seq,
  cached = FALSE,
  cache.prev = NULL,
  ncpus = 1L,
  verbose = TRUE,
  joint.prob = FALSE
)

Arguments

`input.seq`	an object of class `mappoly.sequence`
`cached`	If `TRUE`, access the counts for all linkage phase configurations in a internal file (default = FALSE)
`cache.prev`	an object of class `cache.info` containing pre-computed genotype frequencies, obtained with `cache_counts_twopt` (optional, default = NULL)
`ncpus`	Number of parallel processes to spawn (default = 1)
`verbose`	If `TRUE` (default), print the linkage phase configurations. If `cached = TRUE`, nothing is printed, since all linkage phase configurations will be cached.
`joint.prob`	If `FALSE` (default), returns the frequency of genotypes for transition probabilities (conditional probabilities). If `TRUE` returns the frequency for joint probabilities. The latter is especially important to compute the Fisher's Information for a pair of markers.

Value

An object of class cache.info which contains one (conditional probabilities) or two (both conditional and joint probabilities) lists. Each list contains all pairs of dosages between parents for all markers in the sequence. The names in each list are of the form 'A-B-C-D', where: A represents the dosage in parent 1, marker k; B represents the dosage in parent 1, marker k+1; C represents the dosage in parent 2, marker k; and D represents the dosage in parent 2, marker k+1. For each list, the frequencies were computed for all possible linkage phase configurations. The frequencies for each linkage phase configuration are distributed in matrices whose names represents the number of homologous chromosomes that share alleles. The rows on these matrices represents the dosages in markers k and k+1 for an individual in the offspring. See Table 3 of S3 Appendix in Mollinari and Garcia (2019) for an example.

Author(s)

Marcelo Mollinari, [email protected] with updates by Gabriel Gesteira, [email protected]

References

Mollinari, M., and Garcia, A. A. F. (2019) Linkage analysis and haplotype phasing in experimental autopolyploid populations with high ploidy level using hidden Markov models, _G3: Genes, Genomes, Genetics_. doi:10.1534/g3.119.400378

Examples

    all.mrk <- make_seq_mappoly(tetra.solcap, 1:20)
    ## local computation
    counts <- cache_counts_twopt(all.mrk, ncpus = 1)
    ## load from internal file or web-stored counts (especially important for high ploidy levels)
    counts.cached <- cache_counts_twopt(all.mrk, cached = TRUE)

all.mrk <- make_seq_mappoly(tetra.solcap, 1:20)
    ## local computation
    counts <- cache_counts_twopt(all.mrk, ncpus = 1)
    ## load from internal file or web-stored counts (especially important for high ploidy levels)
    counts.cached <- cache_counts_twopt(all.mrk, cached = TRUE)

Compute conditional probabilities of the genotypes

Description

Conditional genotype probabilities are calculated for each marker position and each individual given a map.

Usage

calc_genoprob(input.map, step = 0, phase.config = "best", verbose = TRUE)
calc_genoprob(input.map, step = 0, phase.config = "best", verbose = TRUE)

Arguments

`input.map`	An object of class `mappoly.map`
`step`	Maximum distance (in cM) between positions at which the genotype probabilities are calculated, though for step = 0, probabilities are calculated only at the marker locations.
`phase.config`	which phase configuration should be used. "best" (default) will choose the phase configuration associated with the maximum likelihood
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced

Value

An object of class 'mappoly.genoprob' which has two elements: a tridimensional array containing the probabilities of all possible genotypes for each individual in each marker position; and the marker sequence with it's recombination frequencies

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

 ## tetraploid example
 probs.t <- calc_genoprob(input.map = solcap.dose.map[[1]],
                        verbose = TRUE)
 probs.t
 ## displaying individual 1, 36 genotypic states
 ## (rows) across linkage group 1 (columns)                          
 image(t(probs.t$probs[,,1]))

## tetraploid example
 probs.t <- calc_genoprob(input.map = solcap.dose.map[[1]],
                        verbose = TRUE)
 probs.t
 ## displaying individual 1, 36 genotypic states
 ## (rows) across linkage group 1 (columns)                          
 image(t(probs.t$probs[,,1]))

Compute conditional probabilities of the genotypes using probability distribution of dosages

Description

Conditional genotype probabilities are calculated for each marker position and each individual given a map. In this function, the probabilities are not calculated between markers.

Usage

calc_genoprob_dist(
  input.map,
  dat.prob = NULL,
  phase.config = "best",
  verbose = TRUE
)
calc_genoprob_dist(
  input.map,
  dat.prob = NULL,
  phase.config = "best",
  verbose = TRUE
)

Arguments

`input.map`	An object of class `mappoly.map`
`dat.prob`	an object of class `mappoly.data` containing the probability distribution of the genotypes
`phase.config`	which phase configuration should be used. "best" (default) will choose the phase configuration with the maximum likelihood
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced

Value

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

 ## tetraploid example
 probs.t <- calc_genoprob_dist(input.map = solcap.prior.map[[1]],
                           dat.prob = tetra.solcap.geno.dist,
                           verbose = TRUE)
 probs.t
 ## displaying individual 1, 36 genotypic states 
 ## (rows) across linkage group 1 (columns)                          
 image(t(probs.t$probs[,,1]))

## tetraploid example
 probs.t <- calc_genoprob_dist(input.map = solcap.prior.map[[1]],
                           dat.prob = tetra.solcap.geno.dist,
                           verbose = TRUE)
 probs.t
 ## displaying individual 1, 36 genotypic states 
 ## (rows) across linkage group 1 (columns)                          
 image(t(probs.t$probs[,,1]))

Compute conditional probabilities of the genotypes using global error

Description

Conditional genotype probabilities are calculated for each marker position and each individual given a map.

Usage

calc_genoprob_error(
  input.map,
  step = 0,
  phase.config = "best",
  error = 0.01,
  th.prob = 0.95,
  restricted = TRUE,
  verbose = TRUE
)
calc_genoprob_error(
  input.map,
  step = 0,
  phase.config = "best",
  error = 0.01,
  th.prob = 0.95,
  restricted = TRUE,
  verbose = TRUE
)

Arguments

`input.map`	An object of class `mappoly.map`
`step`	Maximum distance (in cM) between positions at which the genotype probabilities are calculated, though for step = 0, probabilities are calculated only at the marker locations.
`phase.config`	which phase configuration should be used. "best" (default) will choose the maximum likelihood configuration
`error`	the assumed global error rate (default = 0.01)
`th.prob`	the threshold for using global error or genotype probability distribution contained in the dataset (default = 0.95)
`restricted`	if `TRUE` (default), restricts the prior to the possible classes under Mendelian non double-reduced segregation given the parental dosages
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced

Value

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

 
     probs.error <- calc_genoprob_error(input.map = solcap.err.map[[1]],
                                error = 0.05,
                                verbose = TRUE)
 

probs.error <- calc_genoprob_error(input.map = solcap.err.map[[1]],
                                error = 0.05,
                                verbose = TRUE)

Compute conditional probabilities of the genotype (one informative parent)

Description

Conditional genotype probabilities are calculated for each marker position and each individual given a map

Usage

calc_genoprob_single_parent(
  input.map,
  step = 0,
  info.parent = 1,
  uninfo.parent = 2,
  global.err = 0,
  phase.config = "best",
  verbose = TRUE
)
calc_genoprob_single_parent(
  input.map,
  step = 0,
  info.parent = 1,
  uninfo.parent = 2,
  global.err = 0,
  phase.config = "best",
  verbose = TRUE
)

Arguments

`input.map`	An object of class `mappoly.map` (with exceptions)
`step`	Maximum distance (in cM) between positions at which the genotype probabilities are calculated, though for step = 0, probabilities are calculated only at the marker locations.
`info.parent`	index for informative parent
`uninfo.parent`	index for uninformative parent
`global.err`	the assumed global error rate (default = 0.0)
`phase.config`	which phase configuration should be used. "best" (default) will choose the phase configuration associated with the maximum likelihood
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced

Value

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

 ## tetraploid example
 s <- make_seq_mappoly(tetra.solcap, 'seq12', info.parent = "p1")
 tpt <- est_pairwise_rf(s)
 map <- est_rf_hmm_sequential(input.seq = s,
                              twopt = tpt,
                               start.set = 10,
                               thres.twopt = 10, 
                               thres.hmm = 10,
                               extend.tail = 4,
                               info.tail = TRUE, 
                               sub.map.size.diff.limit = 8, 
                               phase.number.limit = 4,
                               reestimate.single.ph.configuration = TRUE,
                               tol = 10e-2,
                               tol.final = 10e-3)
 plot(map)                                     
 probs <- calc_genoprob_single_parent(input.map = map, 
                                   info.parent = 1, 
                                   uninfo.parent = 2, 
                                   step = 1)
 probs
 ## displaying individual 1, 6 genotypic states
 ## (rows) across linkage group 1 (columns)                          
 image(t(probs$probs[,,2]))

## tetraploid example
 s <- make_seq_mappoly(tetra.solcap, 'seq12', info.parent = "p1")
 tpt <- est_pairwise_rf(s)
 map <- est_rf_hmm_sequential(input.seq = s,
                              twopt = tpt,
                               start.set = 10,
                               thres.twopt = 10, 
                               thres.hmm = 10,
                               extend.tail = 4,
                               info.tail = TRUE, 
                               sub.map.size.diff.limit = 8, 
                               phase.number.limit = 4,
                               reestimate.single.ph.configuration = TRUE,
                               tol = 10e-2,
                               tol.final = 10e-3)
 plot(map)                                     
 probs <- calc_genoprob_single_parent(input.map = map, 
                                   info.parent = 1, 
                                   uninfo.parent = 2, 
                                   step = 1)
 probs
 ## displaying individual 1, 6 genotypic states
 ## (rows) across linkage group 1 (columns)                          
 image(t(probs$probs[,,2]))

Homolog probabilities

Description

Compute homolog probabilities for all individuals in the full-sib population given a map and conditional genotype probabilities.

Usage

calc_homologprob(input.genoprobs, verbose = TRUE)
calc_homologprob(input.genoprobs, verbose = TRUE)

Arguments

`input.genoprobs`	an object of class `mappoly.genoprob`
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced

Author(s)

Marcelo Mollinari, [email protected]

References

Mollinari M., Olukolu B. A., Pereira G. da S., Khan A., Gemenet D., Yencho G. C., Zeng Z-B. (2020), Unraveling the Hexaploid Sweetpotato Inheritance Using Ultra-Dense Multilocus Mapping, _G3: Genes, Genomes, Genetics_. doi:10.1534/g3.119.400620

Examples

   
     ## tetraploid example
     w1 <- calc_genoprob(solcap.dose.map[[1]])
     h.prob <- calc_homologprob(w1)
     print(h.prob)
     plot(h.prob, ind = 5, use.plotly = FALSE)
     ## using error modeling (removing noise)
     w2 <- calc_genoprob_error(solcap.err.map[[1]])
     h.prob2 <- calc_homologprob(w2)
     print(h.prob2)
     plot(h.prob2, ind = 5, use.plotly = FALSE)
  

## tetraploid example
     w1 <- calc_genoprob(solcap.dose.map[[1]])
     h.prob <- calc_homologprob(w1)
     print(h.prob)
     plot(h.prob, ind = 5, use.plotly = FALSE)
     ## using error modeling (removing noise)
     w2 <- calc_genoprob_error(solcap.err.map[[1]])
     h.prob2 <- calc_homologprob(w2)
     print(h.prob2)
     plot(h.prob2, ind = 5, use.plotly = FALSE)

Preferential pairing profiles

Description

Given the genotype conditional probabilities for a map, this function computes the probability profiles for all possible homolog pairing configurations in both parents.

Usage

calc_prefpair_profiles(input.genoprobs, verbose = TRUE)
calc_prefpair_profiles(input.genoprobs, verbose = TRUE)

Arguments

`input.genoprobs`	an object of class `mappoly.genoprob`
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced

Author(s)

Marcelo Mollinari, [email protected] and Guilherme Pereira, [email protected]

References

Examples


  ## tetraploid example
  w1 <- lapply(solcap.dose.map[1:12], calc_genoprob)
  x1 <- calc_prefpair_profiles(w1)
  print(x1)
  plot(x1)


## tetraploid example
  w1 <- lapply(solcap.dose.map[1:12], calc_genoprob)
  x1 <- calc_prefpair_profiles(w1)
  print(x1)
  plot(x1)

Data sanity check

Description

Checks the consistency of a dataset

Usage

check_data_sanity(x)
check_data_sanity(x)

Arguments

`x`	an object of class `mappoly.data`

Value

if consistent, returns 0. If not consistent, returns a vector with a number of tests, where TRUE indicates a failed test.

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

check_data_sanity(tetra.solcap)
check_data_sanity(tetra.solcap)

Compare a list of maps

Description

Compare lengths, density, maximum gaps and log likelihoods in a list of maps. In order to make the maps comparable, the function uses the intersection of markers among maps.

Usage

compare_maps(...)
compare_maps(...)

Arguments

...

a list of objects of class mappoly.map

Value

A data frame where the lines correspond to the maps in the order provided in input list list

Simulate an autopolyploid full-sib population

Description

Simulate an autopolyploid full-sib population with one or two informative parents under random chromosome segregation.

Usage

cross_simulate(
  parental.phases,
  map.length,
  n.ind,
  draw = FALSE,
  file = "output.pdf",
  prefix = NULL,
  seed = NULL,
  width = 12,
  height = 6,
  prob.P = NULL,
  prob.Q = NULL
)
cross_simulate(
  parental.phases,
  map.length,
  n.ind,
  draw = FALSE,
  file = "output.pdf",
  prefix = NULL,
  seed = NULL,
  width = 12,
  height = 6,
  prob.P = NULL,
  prob.Q = NULL
)

Arguments

`parental.phases`	a list containing the linkage phase information for both parents
`map.length`	the map length
`n.ind`	number of individuals in the offspring
`draw`	if `TRUE`, draws a graphical representation of the parental map, including the linkage phase configuration, in a pdf output (default = FALSE)
`file`	name of the output file. It is ignored if `draw = TRUE`
`prefix`	prefix used in all marker names.
`seed`	random number generator seed (default = NULL)
`width`	the width of the graphics region in inches (default = 12)
`height`	the height of the graphics region in inches (default = 6)
`prob.P`	a vector indicating the proportion of preferential pairing in parent P (currently ignored)
`prob.Q`	a vector indicating the proportion of preferential pairing in parent Q (currently ignored)

Details

parental.phases.p and parental.phases.q are lists of vectors containing linkage phase configurations. Each vector contains the numbers of the homologous chromosomes in which the alleles are located. For instance, a vector containing $(1,3,4)$ means that the marker has three doses located in the chromosomes 1, 3 and 4. For zero doses, use 0. For more sophisticated simulations, we strongly recommend using PedigreeSim V2.0 https://github.com/PBR/pedigreeSim

Value

an object of class mappoly.data. See read_geno for more information

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

    h.temp <- sim_homologous(ploidy = 6, n.mrk = 20)
    fake.poly.dat <- cross_simulate(h.temp, map.length = 100, n.ind = 200)
    plot(fake.poly.dat)
                                   
                                  
h.temp <- sim_homologous(ploidy = 6, n.mrk = 20)
    fake.poly.dat <- cross_simulate(h.temp, map.length = 100, n.ind = 200)
    plot(fake.poly.dat)

Detects which parent is informative

Description

Detects which parent is informative

Usage

detect_info_par(x)
detect_info_par(x)

Arguments

`x`	an object of class `mappoly.sequence` or `mappoly.map`

Remove markers from a map

Description

This function creates a new map by removing markers from an existing one.

Usage

drop_marker(input.map, mrk, verbose = TRUE)
drop_marker(input.map, mrk, verbose = TRUE)

Arguments

`input.map`	an object of class `mappoly.map`
`mrk`	a vector containing markers to be removed from the input map, identified by their names or positions
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced

Value

an object of class mappoly.map

Author(s)

Marcelo Mollinari, [email protected]

Examples

 sub.map <- get_submap(maps.hexafake[[1]], 1:50, reestimate.rf = FALSE)
 plot(sub.map, mrk.names = TRUE)
 mrk.to.remove <- c("M_1", "M_23", "M_34")
 red.map <- drop_marker(sub.map, mrk.to.remove)
 plot(red.map, mrk.names = TRUE)

sub.map <- get_submap(maps.hexafake[[1]], 1:50, reestimate.rf = FALSE)
 plot(sub.map, mrk.names = TRUE)
 mrk.to.remove <- c("M_1", "M_23", "M_34")
 red.map <- drop_marker(sub.map, mrk.to.remove)
 plot(red.map, mrk.names = TRUE)

Edit sequence ordered by reference genome positions comparing to another set order

Description

Edit sequence ordered by reference genome positions comparing to another set order

Usage

edit_order(input.seq, invert = NULL, remove = NULL)
edit_order(input.seq, invert = NULL, remove = NULL)

Arguments

`input.seq`	object of class mappoly.sequence with alternative order (not genomic order)
`invert`	vector of marker names to be inverted
`remove`	vector of marker names to be removed

Value

object of class mappoly.edit.order: a list containing vector of marker names ordered according to editions ('edited_order'); vector of removed markers names ('removed'); vector of inverted markers names ('inverted').

Author(s)

Cristiane Taniguti, [email protected]

Examples

 
  dat <- filter_segregation(tetra.solcap, inter = FALSE)
  seq_dat <- make_seq_mappoly(dat)
  seq_chr <- make_seq_mappoly(seq_dat, arg = seq_dat$seq.mrk.names[which(seq_dat$chrom=="1")])

  tpt <- est_pairwise_rf(seq_chr)
  seq.filt <- rf_snp_filter(tpt, probs = c(0.05, 0.95))
  mat <- rf_list_to_matrix(tpt)
  mat2 <- make_mat_mappoly(mat, seq.filt)

  seq_test_mds <- mds_mappoly(mat2)
  seq_mds <- make_seq_mappoly(seq_test_mds)
  edit_seq <- edit_order(input.seq = seq_mds)
 
 
dat <- filter_segregation(tetra.solcap, inter = FALSE)
  seq_dat <- make_seq_mappoly(dat)
  seq_chr <- make_seq_mappoly(seq_dat, arg = seq_dat$seq.mrk.names[which(seq_dat$chrom=="1")])

  tpt <- est_pairwise_rf(seq_chr)
  seq.filt <- rf_snp_filter(tpt, probs = c(0.05, 0.95))
  mat <- rf_list_to_matrix(tpt)
  mat2 <- make_mat_mappoly(mat, seq.filt)

  seq_test_mds <- mds_mappoly(mat2)
  seq_mds <- make_seq_mappoly(seq_test_mds)
  edit_seq <- edit_order(input.seq = seq_mds)

Eliminate redundant markers

Description

Eliminate markers with identical dosage information for all individuals.

Usage

elim_redundant(input.seq, data = NULL)
elim_redundant(input.seq, data = NULL)

Arguments

`input.seq`	an object of class `mappoly.sequence`
`data`	name of the dataset that contains sequence markers (optional, default = NULL)

Value

An object of class mappoly.unique.seq which is a list containing the following components:

`unique.seq`	an object of class `mappoly.sequence` with the redundant markers removed
`kept`	a vector containing the name of the informative markers
`eliminated`	a vector containing the name of the non-informative (eliminated) markers

Author(s)

Marcelo Mollinari, [email protected], with minor modifications by Gabriel Gesteira, [email protected]

References

Examples

    all.mrk <- make_seq_mappoly(hexafake, 'all')
    red.mrk <- elim_redundant(all.mrk)
    plot(red.mrk)
    unique.mrks <- make_seq_mappoly(red.mrk)
   
all.mrk <- make_seq_mappoly(hexafake, 'all')
    red.mrk <- elim_redundant(all.mrk)
    plot(red.mrk)
    unique.mrks <- make_seq_mappoly(red.mrk)

Re-estimate genetic map given a global genotyping error

Description

This function considers a global error when re-estimating a genetic map using Hidden Markov models. Since this function uses the whole transition space in the HMM, its computation can take a while, especially for hexaploid maps.

Usage

est_full_hmm_with_global_error(
  input.map,
  error = NULL,
  tol = 0.001,
  restricted = TRUE,
  th.prob = 0.95,
  verbose = FALSE
)
est_full_hmm_with_global_error(
  input.map,
  error = NULL,
  tol = 0.001,
  restricted = TRUE,
  th.prob = 0.95,
  verbose = FALSE
)

Arguments

`input.map`	an object of class `mappoly.map`
`error`	the assumed global error rate (default = NULL)
`tol`	the desired accuracy (default = 10e-04)
`restricted`	if `TRUE` (default), restricts the prior to the possible classes under Mendelian, non double-reduced segregation given dosage of the parents
`th.prob`	the threshold for using global error or genotype probability distribution if present in the dataset (default = 0.95)
`verbose`	if `TRUE`, current progress is shown; if `FALSE` (default), no output is produced

Value

A list of class mappoly.map with two elements:

i) info: a list containing information about the map, regardless of the linkage phase configuration:

`ploidy`	the ploidy level
`n.mrk`	number of markers
`seq.num`	a vector containing the (ordered) indices of markers in the map, according to the input file
`mrk.names`	the names of markers in the map
`seq.dose.p1`	a vector containing the dosage in parent 1 for all markers in the map
`seq.dose.p2`	a vector containing the dosage in parent 2 for all markers in the map
`chrom`	a vector indicating the sequence (usually chromosome) each marker belongs as informed in the input file. If not available, `chrom = NULL`
`genome.pos`	physical position (usually in megabase) of the markers into the sequence
`seq.ref`	reference base used for each marker (i.e. A, T, C, G). If not available, `seq.ref = NULL`
`seq.alt`	alternative base used for each marker (i.e. A, T, C, G). If not available, `seq.ref = NULL`
`chisq.pval`	a vector containing p-values of the chi-squared test of Mendelian segregation for all markers in the map
`data.name`	name of the dataset of class `mappoly.data`
`ph.thres`	the LOD threshold used to define the linkage phase configurations to test

ii) a list of maps with possible linkage phase configuration. Each map in the list is also a list containing

`seq.num`	a vector containing the (ordered) indices of markers in the map, according to the input file
`seq.rf`	a vector of size (`n.mrk - 1`) containing a sequence of recombination fraction between the adjacent markers in the map
`seq.ph`	linkage phase configuration for all markers in both parents
`loglike`	the hmm-based multipoint likelihood

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

    submap <- get_submap(solcap.dose.map[[1]], mrk.pos = 1:20, verbose = FALSE)
    err.submap <- est_full_hmm_with_global_error(submap, 
                                                 error = 0.01, 
                                                 tol = 10e-4, 
                                                 verbose = TRUE)
    err.submap
    plot_map_list(list(dose = submap, err = err.submap), 
                  title = "estimation procedure")

submap <- get_submap(solcap.dose.map[[1]], mrk.pos = 1:20, verbose = FALSE)
    err.submap <- est_full_hmm_with_global_error(submap, 
                                                 error = 0.01, 
                                                 tol = 10e-4, 
                                                 verbose = TRUE)
    err.submap
    plot_map_list(list(dose = submap, err = err.submap), 
                  title = "estimation procedure")

Re-estimate genetic map using dosage prior probability distribution

Description

This function considers dosage prior distribution when re-estimating a genetic map using Hidden Markov models

Usage

est_full_hmm_with_prior_prob(
  input.map,
  dat.prob = NULL,
  phase.config = "best",
  tol = 0.001,
  verbose = FALSE
)
est_full_hmm_with_prior_prob(
  input.map,
  dat.prob = NULL,
  phase.config = "best",
  tol = 0.001,
  verbose = FALSE
)

Arguments

`input.map`	an object of class `mappoly.map`
`dat.prob`	an object of class `mappoly.data` containing the probability distribution of the genotypes
`phase.config`	which phase configuration should be used. "best" (default) will choose the maximum likelihood configuration
`tol`	the desired accuracy (default = 10e-04)
`verbose`	if `TRUE`, current progress is shown; if `FALSE` (default), no output is produced

Value

A list of class mappoly.map with two elements:

i) info: a list containing information about the map, regardless of the linkage phase configuration:

`ploidy`	the ploidy level
`n.mrk`	number of markers
`seq.num`	a vector containing the (ordered) indices of markers in the map, according to the input file
`mrk.names`	the names of markers in the map
`seq.dose.p1`	a vector containing the dosage in parent 1 for all markers in the map
`seq.dose.p2`	a vector containing the dosage in parent 2 for all markers in the map
`chrom`	a vector indicating the sequence (usually chromosome) each marker belongs as informed in the input file. If not available, `chrom = NULL`
`genome.pos`	physical position (usually in megabase) of the markers into the sequence
`seq.ref`	reference base used for each marker (i.e. A, T, C, G). If not available, `seq.ref = NULL`
`seq.alt`	alternative base used for each marker (i.e. A, T, C, G). If not available, `seq.ref = NULL`
`chisq.pval`	a vector containing p-values of the chi-squared test of Mendelian segregation for all markers in the map
`data.name`	name of the dataset of class `mappoly.data`
`ph.thres`	the LOD threshold used to define the linkage phase configurations to test

ii) a list of maps with possible linkage phase configuration. Each map in the list is also a list containing

`seq.num`	a vector containing the (ordered) indices of markers in the map, according to the input file
`seq.rf`	a vector of size (`n.mrk - 1`) containing a sequence of recombination fraction between the adjacent markers in the map
`seq.ph`	linkage phase configuration for all markers in both parents
`loglike`	the hmm-based multipoint likelihood

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

    submap <- get_submap(solcap.dose.map[[1]], mrk.pos = 1:20, verbose = FALSE)
    prob.submap <- est_full_hmm_with_prior_prob(submap,
                                                dat.prob = tetra.solcap.geno.dist,
                                                tol = 10e-4, 
                                                verbose = TRUE)
    prob.submap
    plot_map_list(list(dose = submap, prob = prob.submap), 
                  title = "estimation procedure")

submap <- get_submap(solcap.dose.map[[1]], mrk.pos = 1:20, verbose = FALSE)
    prob.submap <- est_full_hmm_with_prior_prob(submap,
                                                dat.prob = tetra.solcap.geno.dist,
                                                tol = 10e-4, 
                                                verbose = TRUE)
    prob.submap
    plot_map_list(list(dose = submap, prob = prob.submap), 
                  title = "estimation procedure")

Pairwise two-point analysis

Description

Performs the two-point pairwise analysis between all markers in a sequence. For each pair, the function estimates the recombination fraction for all possible linkage phase configurations and associated LOD Scores.

Usage

est_pairwise_rf(
  input.seq,
  count.cache = NULL,
  count.matrix = NULL,
  ncpus = 1L,
  mrk.pairs = NULL,
  n.batches = 1L,
  est.type = c("disc", "prob"),
  verbose = TRUE,
  memory.warning = TRUE,
  parallelization.type = c("PSOCK", "FORK"),
  tol = .Machine$double.eps^0.25,
  ll = FALSE
)
est_pairwise_rf(
  input.seq,
  count.cache = NULL,
  count.matrix = NULL,
  ncpus = 1L,
  mrk.pairs = NULL,
  n.batches = 1L,
  est.type = c("disc", "prob"),
  verbose = TRUE,
  memory.warning = TRUE,
  parallelization.type = c("PSOCK", "FORK"),
  tol = .Machine$double.eps^0.25,
  ll = FALSE
)

Arguments

`input.seq`	an object of class `mappoly.sequence`
`count.cache`	an object of class `cache.info` containing pre-computed genotype frequencies, obtained with `cache_counts_twopt`. If `NULL` (default), genotype frequencies are internally loaded.
`count.matrix`	similar to `count.cache`, but in matrix format. Mostly for internal use.
`ncpus`	Number of parallel processes (cores) to spawn (default = 1)
`mrk.pairs`	a matrix of dimensions 2*N, containing N pairs of markers to be analyzed. If `NULL` (default), all pairs are considered
`n.batches`	deprecated. Not available on MAPpoly 0.3.0 or higher
`est.type`	Indicates whether to use the discrete ("disc") or the probabilistic ("prob") dosage scoring when estimating the two-point recombination fractions.
`verbose`	If `TRUE` (default), current progress is shown; if `FALSE`, no output is produced
`memory.warning`	if `TRUE`, prints a memory warning if the number of markers is greater than 10000 for ploidy levels up to 4, and 3000 for ploidy levels > 4.
`parallelization.type`	one of the supported cluster types. This should be either PSOCK (default) or FORK.
`tol`	the desired accuracy. See `optimize()` for details
`ll`	will return log-likelihood instead of LOD scores. (for internal use)

Value

An object of class mappoly.twopt which is a list containing the following components:

data.name: Name of the object of class mappoly.data containing the raw data.
n.mrk: Number of markers in the sequence.
seq.num: A vector containing the (ordered) indices of markers in the sequence, according to the input file.
pairwise: A list of size choose(length(input.seq$seq.num), 2), where each element is a matrix. The rows are named in the format x-y, where x and y indicate how many homologues share the same allelic variant in parents P and Q, respectively (see Mollinari and Garcia, 2019 for notation). The first column indicates the LOD Score for the most likely linkage phase configuration. The second column shows the estimated recombination fraction for each configuration, and the third column indicates the LOD Score for comparing the likelihood under no linkage (r = 0.5) with the estimated recombination fraction (evidence of linkage).
chisq.pval.thres: Threshold used to perform the segregation tests.
chisq.pval: P-values associated with the performed segregation tests.

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

  ## Tetraploid example (first 50 markers) 
  all.mrk <- make_seq_mappoly(tetra.solcap, 1:50)
  red.mrk <- elim_redundant(all.mrk)
  unique.mrks <- make_seq_mappoly(red.mrk)
  all.pairs <- est_pairwise_rf(input.seq = unique.mrks,
                               ncpus = 1, 
                               verbose = TRUE)
   all.pairs
   plot(all.pairs, 20, 21)
   mat <- rf_list_to_matrix(all.pairs)
   plot(mat)
## Tetraploid example (first 50 markers) 
  all.mrk <- make_seq_mappoly(tetra.solcap, 1:50)
  red.mrk <- elim_redundant(all.mrk)
  unique.mrks <- make_seq_mappoly(red.mrk)
  all.pairs <- est_pairwise_rf(input.seq = unique.mrks,
                               ncpus = 1, 
                               verbose = TRUE)
   all.pairs
   plot(all.pairs, 20, 21)
   mat <- rf_list_to_matrix(all.pairs)
   plot(mat)

Pairwise two-point analysis - RcppParallel version

Description

Usage

est_pairwise_rf2(
  input.seq,
  ncpus = 1L,
  mrk.pairs = NULL,
  verbose = TRUE,
  tol = .Machine$double.eps^0.25
)
est_pairwise_rf2(
  input.seq,
  ncpus = 1L,
  mrk.pairs = NULL,
  verbose = TRUE,
  tol = .Machine$double.eps^0.25
)

Arguments

`input.seq`	an object of class `mappoly.sequence`
`ncpus`	Number of parallel processes (cores) to spawn (default = 1)
`mrk.pairs`	a matrix of dimensions 2*N, containing N pairs of markers to be analyzed. If `NULL` (default), all pairs are considered
`verbose`	If `TRUE` (default), current progress is shown; if `FALSE`, no output is produced
`tol`	the desired accuracy. See `optimize()` for details

Details

Differently from est_pairwise_rf this function returns only the values associated to the best linkage phase configuration.

Value

An object of class mappoly.twopt2

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

  ## Tetraploid example  
  all.mrk <- make_seq_mappoly(tetra.solcap, 100:200)
  all.pairs <- est_pairwise_rf2(input.seq = all.mrk, ncpus = 2)
  m <- rf_list_to_matrix(all.pairs)
  plot(m, fact = 2)
  
## Tetraploid example  
  all.mrk <- make_seq_mappoly(tetra.solcap, 100:200)
  all.pairs <- est_pairwise_rf2(input.seq = all.mrk, ncpus = 2)
  m <- rf_list_to_matrix(all.pairs)
  plot(m, fact = 2)

Multipoint analysis using Hidden Markov Models in autopolyploids

Description

Performs the multipoint analysis proposed by Mollinari and Garcia (2019) in a sequence of markers

Usage

est_rf_hmm(
  input.seq,
  input.ph = NULL,
  thres = 0.5,
  twopt = NULL,
  verbose = FALSE,
  tol = 1e-04,
  est.given.0.rf = FALSE,
  reestimate.single.ph.configuration = TRUE,
  high.prec = TRUE
)

## S3 method for class 'mappoly.map'
print(x, detailed = FALSE, ...)

## S3 method for class 'mappoly.map'
plot(
  x,
  left.lim = 0,
  right.lim = Inf,
  phase = TRUE,
  mrk.names = FALSE,
  cex = 1,
  config = "best",
  P = "Parent 1",
  Q = "Parent 2",
  xlim = NULL,
  ...
)
est_rf_hmm(
  input.seq,
  input.ph = NULL,
  thres = 0.5,
  twopt = NULL,
  verbose = FALSE,
  tol = 1e-04,
  est.given.0.rf = FALSE,
  reestimate.single.ph.configuration = TRUE,
  high.prec = TRUE
)

## S3 method for class 'mappoly.map'
print(x, detailed = FALSE, ...)

## S3 method for class 'mappoly.map'
plot(
  x,
  left.lim = 0,
  right.lim = Inf,
  phase = TRUE,
  mrk.names = FALSE,
  cex = 1,
  config = "best",
  P = "Parent 1",
  Q = "Parent 2",
  xlim = NULL,
  ...
)

Arguments

`input.seq`	an object of class `mappoly.sequence`
`input.ph`	an object of class `two.pts.linkage.phases`. If not available (default = NULL), it will be computed
`thres`	LOD Score threshold used to determine if the linkage phases compared via two-point analysis should be considered. Smaller values will result in smaller number of linkage phase configurations to be evaluated by the multipoint algorithm.
`twopt`	an object of class `mappoly.twopt` containing two-point information
`verbose`	if `TRUE`, current progress is shown; if `FALSE` (default), no output is produced
`tol`	the desired accuracy (default = 1e-04)
`est.given.0.rf`	logical. If TRUE returns a map forcing all recombination fractions equals to 0 (1e-5, for internal use only. Default = FALSE)
`reestimate.single.ph.configuration`	logical. If `TRUE` returns a map without re-estimating the map parameters for cases where there is only one possible linkage phase configuration. This argument is intended to be used in a sequential map construction
`high.prec`	logical. If `TRUE` (default) uses high precision long double numbers in the HMM procedure
`x`	an object of the class `mappoly.map`
`detailed`	logical. if TRUE, prints the linkage phase configuration and the marker position for all maps. If FALSE (default), prints a map summary
`...`	currently ignored
`left.lim`	the left limit of the plot (in cM, default = 0).
`right.lim`	the right limit of the plot (in cM, default = Inf, i.e., will print the entire map)
`phase`	logical. If `TRUE` (default) plots the phase configuration for both parents
`mrk.names`	if TRUE, marker names are displayed (default = FALSE)
`cex`	The magnification to be used for marker names
`config`	should be `'best'` or the position of the configuration to be plotted. If `'best'`, plot the configuration with the highest likelihood
`P`	a string containing the name of parent P
`Q`	a string containing the name of parent Q
`xlim`	range of the x-axis. If `xlim = NULL` (default) it uses the map range.

Details

This function first enumerates a set of linkage phase configurations based on two-point recombination fraction information using a threshold provided by the user (argument thresh). After that, for each configuration, it reconstructs the genetic map using the HMM approach described in Mollinari and Garcia (2019). As result, it returns the multipoint likelihood for each configuration in form of LOD Score comparing each configuration to the most likely one. It is recommended to use a small number of markers (e.g. 50 markers for hexaploids) since the possible linkage phase combinations bounded only by the two-point information can be huge. Also, it can be quite sensible to small changes in 'thresh'. For a large number of markers, please see est_rf_hmm_sequential.

Value

A list of class mappoly.map with two elements:

i) info: a list containing information about the map, regardless of the linkage phase configuration:

`ploidy`	the ploidy level
`n.mrk`	number of markers
`seq.num`	a vector containing the (ordered) indices of markers in the map, according to the input file
`mrk.names`	the names of markers in the map
`seq.dose.p1`	a vector containing the dosage in parent 1 for all markers in the map
`seq.dose.p2`	a vector containing the dosage in parent 2 for all markers in the map
`chrom`	a vector indicating the sequence (usually chromosome) each marker belongs as informed in the input file. If not available, `chrom = NULL`
`genome.pos`	physical position (usually in megabase) of the markers into the sequence
`seq.ref`	reference base used for each marker (i.e. A, T, C, G). If not available, `seq.ref = NULL`
`seq.alt`	alternative base used for each marker (i.e. A, T, C, G). If not available, `seq.ref = NULL`
`chisq.pval`	a vector containing p-values of the chi-squared test of Mendelian segregation for all markers in the map
`data.name`	name of the dataset of class `mappoly.data`
`ph.thres`	the LOD threshold used to define the linkage phase configurations to test

ii) a list of maps with possible linkage phase configuration. Each map in the list is also a list containing

`seq.num`	a vector containing the (ordered) indices of markers in the map, according to the input file
`seq.rf`	a vector of size (`n.mrk - 1`) containing a sequence of recombination fraction between the adjacent markers in the map
`seq.ph`	linkage phase configuration for all markers in both parents
`loglike`	the hmm-based multipoint likelihood

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

    mrk.subset <- make_seq_mappoly(hexafake, 1:10)
    red.mrk <- elim_redundant(mrk.subset)
    unique.mrks <- make_seq_mappoly(red.mrk)
    subset.pairs <- est_pairwise_rf(input.seq = unique.mrks,
                                  ncpus = 1,
                                  verbose = TRUE)

    ## Estimating subset map with a low tolerance for the E.M. procedure
    ## for CRAN testing purposes
    subset.map <- est_rf_hmm(input.seq = unique.mrks,
                             thres = 2,
                             twopt = subset.pairs,
                             verbose = TRUE,
                             tol = 0.1,
                             est.given.0.rf = FALSE)
    subset.map
    ## linkage phase configuration with highest likelihood
    plot(subset.map, mrk.names = TRUE, config = "best")
    ## the second one
    plot(subset.map, mrk.names = TRUE, config = 2)

mrk.subset <- make_seq_mappoly(hexafake, 1:10)
    red.mrk <- elim_redundant(mrk.subset)
    unique.mrks <- make_seq_mappoly(red.mrk)
    subset.pairs <- est_pairwise_rf(input.seq = unique.mrks,
                                  ncpus = 1,
                                  verbose = TRUE)

    ## Estimating subset map with a low tolerance for the E.M. procedure
    ## for CRAN testing purposes
    subset.map <- est_rf_hmm(input.seq = unique.mrks,
                             thres = 2,
                             twopt = subset.pairs,
                             verbose = TRUE,
                             tol = 0.1,
                             est.given.0.rf = FALSE)
    subset.map
    ## linkage phase configuration with highest likelihood
    plot(subset.map, mrk.names = TRUE, config = "best")
    ## the second one
    plot(subset.map, mrk.names = TRUE, config = 2)

Multipoint analysis using Hidden Markov Models: Sequential phase elimination

Description

Performs the multipoint analysis proposed by Mollinari and Garcia (2019) in a sequence of markers removing unlikely phases using sequential multipoint information.

Usage

est_rf_hmm_sequential(
  input.seq,
  twopt,
  start.set = 4,
  thres.twopt = 5,
  thres.hmm = 50,
  extend.tail = NULL,
  phase.number.limit = 20,
  sub.map.size.diff.limit = Inf,
  info.tail = TRUE,
  reestimate.single.ph.configuration = FALSE,
  tol = 0.1,
  tol.final = 0.001,
  verbose = TRUE,
  detailed.verbose = FALSE,
  high.prec = FALSE
)
est_rf_hmm_sequential(
  input.seq,
  twopt,
  start.set = 4,
  thres.twopt = 5,
  thres.hmm = 50,
  extend.tail = NULL,
  phase.number.limit = 20,
  sub.map.size.diff.limit = Inf,
  info.tail = TRUE,
  reestimate.single.ph.configuration = FALSE,
  tol = 0.1,
  tol.final = 0.001,
  verbose = TRUE,
  detailed.verbose = FALSE,
  high.prec = FALSE
)

Arguments

`input.seq`	an object of class `mappoly.sequence`
`twopt`	an object of class `mappoly.twopt` containing the two-point information
`start.set`	number of markers to start the phasing procedure (default = 4)
`thres.twopt`	the LOD threshold used to determine if the linkage phases compared via two-point analysis should be considered for the search space reduction (A.K.A. $\eta$ in Mollinari and Garcia (2019), default = 5)
`thres.hmm`	the LOD threshold used to determine if the linkage phases compared via hmm analysis should be evaluated in the next round of marker inclusion (default = 50)
`extend.tail`	the length of the chain's tail that should be used to calculate the likelihood of the map. If `NULL` (default), the function uses all markers positioned. Even if `info.tail = TRUE`, it uses at least `extend.tail` as the tail length
`phase.number.limit`	the maximum number of linkage phases of the sub-maps defined by arguments `info.tail` and `extend.tail`. Default is 20. If the size exceeds this limit, the marker will not be inserted. If `Inf`, then it will insert all markers.
`sub.map.size.diff.limit`	the maximum accepted length difference between the current and the previous sub-map defined by arguments `info.tail` and `extend.tail`. If the size exceeds this limit, the marker will not be inserted. If `NULL`(default), then it will insert all markers.
`info.tail`	if `TRUE` (default), it uses the complete informative tail of the chain (i.e. number of markers where all homologous ( $ploidy x 2$ ) can be distinguished) to calculate the map likelihood
`reestimate.single.ph.configuration`	logical. If `FALSE` (default) returns a map without re-estimating the map parameters in cases where there are only one possible linkage phase configuration
`tol`	the desired accuracy during the sequential phase (default = 10e-02)
`tol.final`	the desired accuracy for the final map (default = 10e-04)
`verbose`	If `TRUE` (default), current progress is shown; if `FALSE`, no output is produced
`detailed.verbose`	If `TRUE`, the expansion of the current submap is shown;
`high.prec`	logical. If `TRUE` uses high precision (long double) numbers in the HMM procedure implemented in C++, which can take a long time to perform (default = FALSE)

Details

This function sequentially includes markers into a map given an ordered sequence. It uses two-point information to eliminate unlikely linkage phase configurations given thres.twopt. The search is made within a window of size extend.tail. For the remaining configurations, the HMM-based likelihood is computed and the ones that pass the HMM threshold (thres.hmm) are eliminated.

Value

A list of class mappoly.map with two elements:

i) info: a list containing information about the map, regardless of the linkage phase configuration:

`ploidy`	the ploidy level
`n.mrk`	number of markers
`seq.num`	a vector containing the (ordered) indices of markers in the map, according to the input file
`mrk.names`	the names of markers in the map
`seq.dose.p1`	a vector containing the dosage in parent 1 for all markers in the map
`seq.dose.p2`	a vector containing the dosage in parent 2 for all markers in the map
`chrom`	a vector indicating the sequence (usually chromosome) each marker belongs as informed in the input file. If not available, `chrom = NULL`
`genome.pos`	physical position (usually in megabase) of the markers into the sequence
`seq.ref`	reference base used for each marker (i.e. A, T, C, G). If not available, `seq.ref = NULL`
`seq.alt`	alternative base used for each marker (i.e. A, T, C, G). If not available, `seq.ref = NULL`
`chisq.pval`	a vector containing p-values of the chi-squared test of Mendelian segregation for all markers in the map
`data.name`	name of the dataset of class `mappoly.data`
`ph.thres`	the LOD threshold used to define the linkage phase configurations to test

ii) a list of maps with possible linkage phase configuration. Each map in the list is also a list containing

`seq.num`	a vector containing the (ordered) indices of markers in the map, according to the input file
`seq.rf`	a vector of size (`n.mrk - 1`) containing a sequence of recombination fraction between the adjacent markers in the map
`seq.ph`	linkage phase configuration for all markers in both parents
`loglike`	the hmm-based multipoint likelihood

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

 
    mrk.subset <- make_seq_mappoly(hexafake, 1:20)
    red.mrk <- elim_redundant(mrk.subset)
    unique.mrks <- make_seq_mappoly(red.mrk)
    subset.pairs <- est_pairwise_rf(input.seq = unique.mrks,
                                  ncpus = 1,
                                  verbose = TRUE)
    subset.map <- est_rf_hmm_sequential(input.seq = unique.mrks,
                                        thres.twopt = 5,
                                        thres.hmm = 10,
                                        extend.tail = 10,
                                        tol = 0.1,
                                        tol.final = 10e-3,
                                        phase.number.limit = 5,
                                        twopt = subset.pairs,
                                        verbose = TRUE)
     print(subset.map, detailed = TRUE)
     plot(subset.map)
     plot(subset.map, left.lim = 0, right.lim = 1, mrk.names = TRUE)
     plot(subset.map, phase = FALSE)
     
     ## Retrieving simulated linkage phase
     ph.P <- maps.hexafake[[1]]$maps[[1]]$seq.ph$P
     ph.Q <- maps.hexafake[[1]]$maps[[1]]$seq.ph$Q
     ## Estimated linkage phase
     ph.P.est <- subset.map$maps[[1]]$seq.ph$P
     ph.Q.est <- subset.map$maps[[1]]$seq.ph$Q
     compare_haplotypes(ploidy = 6, h1 = ph.P[names(ph.P.est)], h2 = ph.P.est)
     compare_haplotypes(ploidy = 6, h1 = ph.Q[names(ph.Q.est)], h2 = ph.Q.est)
   

mrk.subset <- make_seq_mappoly(hexafake, 1:20)
    red.mrk <- elim_redundant(mrk.subset)
    unique.mrks <- make_seq_mappoly(red.mrk)
    subset.pairs <- est_pairwise_rf(input.seq = unique.mrks,
                                  ncpus = 1,
                                  verbose = TRUE)
    subset.map <- est_rf_hmm_sequential(input.seq = unique.mrks,
                                        thres.twopt = 5,
                                        thres.hmm = 10,
                                        extend.tail = 10,
                                        tol = 0.1,
                                        tol.final = 10e-3,
                                        phase.number.limit = 5,
                                        twopt = subset.pairs,
                                        verbose = TRUE)
     print(subset.map, detailed = TRUE)
     plot(subset.map)
     plot(subset.map, left.lim = 0, right.lim = 1, mrk.names = TRUE)
     plot(subset.map, phase = FALSE)
     
     ## Retrieving simulated linkage phase
     ph.P <- maps.hexafake[[1]]$maps[[1]]$seq.ph$P
     ph.Q <- maps.hexafake[[1]]$maps[[1]]$seq.ph$Q
     ## Estimated linkage phase
     ph.P.est <- subset.map$maps[[1]]$seq.ph$P
     ph.Q.est <- subset.map$maps[[1]]$seq.ph$Q
     compare_haplotypes(ploidy = 6, h1 = ph.P[names(ph.P.est)], h2 = ph.P.est)
     compare_haplotypes(ploidy = 6, h1 = ph.Q[names(ph.Q.est)], h2 = ph.Q.est)

Export data to `polymapR`

Description

See examples at https://rpubs.com/mmollin/tetra_mappoly_vignette.

Usage

export_data_to_polymapR(data.in)
export_data_to_polymapR(data.in)

Arguments

data.in

an object of class mappoly.data

Value

a dosage matrix

Author(s)

Marcelo Mollinari, [email protected]

Export a genetic map to a CSV file

Description

Function to export genetic linkage map(s) generated by MAPpoly. The map(s) should be passed as a single object or a list of objects of class mappoly.map.

Usage

export_map_list(map.list, file = "map_output.csv")
export_map_list(map.list, file = "map_output.csv")

Arguments

`map.list`	A list of objects or a single object of class `mappoly.map`
`file`	either a character string naming a file or a connection open for writing. "" indicates output to the console.

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

 export_map_list(solcap.err.map[[1]], file = "")

export_map_list(solcap.err.map[[1]], file = "")

Export to QTLpoly

Description

Compute homolog probabilities for all individuals in the full-sib population given a map and conditional genotype probabilities, and exports the results to be used for QTL mapping in the QTLpoly package.

Usage

export_qtlpoly(input.genoprobs, verbose = TRUE)
export_qtlpoly(input.genoprobs, verbose = TRUE)

Arguments

`input.genoprobs`	an object of class `mappoly.genoprob`
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

   
     ## tetraploid example
     w1 <- calc_genoprob(solcap.dose.map[[1]])
     h.prob <- export_qtlpoly(w1)
  

## tetraploid example
     w1 <- calc_genoprob(solcap.dose.map[[1]])
     h.prob <- export_qtlpoly(w1)

Extract the maker position from an object of class 'mappoly.map'

Description

Extract the maker position from an object of class 'mappoly.map'

Usage

extract_map(input.map, phase.config = "best")
extract_map(input.map, phase.config = "best")

Arguments

`input.map`	An object of class `mappoly.map`
`phase.config`	which phase configuration should be used. "best" (default) will choose the maximum likelihood configuration

Examples

 x <- maps.hexafake[[1]]$info$genome.pos/1e6
 y <- extract_map(maps.hexafake[[1]])
 plot(y~x, ylab = "Map position (cM)", xlab = "Genome Position (Mbp)")
x <- maps.hexafake[[1]]$info$genome.pos/1e6
 y <- extract_map(maps.hexafake[[1]])
 plot(y~x, ylab = "Map position (cM)", xlab = "Genome Position (Mbp)")

Filter aneuploid chromosomes from progeny individuals

Description

Filter aneuploid chromosomes from progeny individuals

Usage

filter_aneuploid(input.data, aneuploid.info, ploidy, rm_missing = TRUE)
filter_aneuploid(input.data, aneuploid.info, ploidy, rm_missing = TRUE)

Arguments

`input.data`	name of input object (class `mappoly.data`)
`aneuploid.info`	data.frame with ploidy information by chromosome (columns) for each individual in progeny (rows). The chromosome and individuals names must match the ones in the file used as input in mappoly.
`ploidy`	main ploidy
`rm_missing`	remove also genotype information from chromosomes with missing data (NA) in the aneuploid.info file

Value

object of class mappoly.data

Author(s)

Cristiane Taniguti, [email protected]

Examples

     aneuploid.info <- matrix(4, nrow=tetra.solcap$n.ind, ncol = 12)
     set.seed(8080)
     aneuploid.info[sample(1:length(aneuploid.info), round((4*length(aneuploid.info))/100),0)] <- 3
     aneuploid.info[sample(1:length(aneuploid.info), round((4*length(aneuploid.info))/100),0)] <- 5

     colnames(aneuploid.info) <- paste0(1:12)
     aneuploid.info <- cbind(inds = tetra.solcap$ind.names, aneuploid.info)

     filt.dat <- filter_aneuploid(input.data = tetra.solcap, 
     aneuploid.info = aneuploid.info, ploidy = 4)

aneuploid.info <- matrix(4, nrow=tetra.solcap$n.ind, ncol = 12)
     set.seed(8080)
     aneuploid.info[sample(1:length(aneuploid.info), round((4*length(aneuploid.info))/100),0)] <- 3
     aneuploid.info[sample(1:length(aneuploid.info), round((4*length(aneuploid.info))/100),0)] <- 5

     colnames(aneuploid.info) <- paste0(1:12)
     aneuploid.info <- cbind(inds = tetra.solcap$ind.names, aneuploid.info)

     filt.dat <- filter_aneuploid(input.data = tetra.solcap, 
     aneuploid.info = aneuploid.info, ploidy = 4)

Filter out individuals

Description

This function removes individuals from the data set. Individuals can be user-defined or can be accessed via interactive kinship analysis.

Usage

filter_individuals(
  input.data,
  ind.to.remove = NULL,
  inter = TRUE,
  type = c("Gmat", "PCA"),
  verbose = TRUE
)
filter_individuals(
  input.data,
  ind.to.remove = NULL,
  inter = TRUE,
  type = c("Gmat", "PCA"),
  verbose = TRUE
)

Arguments

`input.data`	name of input object (class `mappoly.data`)
`ind.to.remove`	individuals to be removed. If `NULL` it opens an interactive graphic to proceed with the individual selection
`inter`	if `TRUE`, expects user-input to proceed with filtering
`type`	A character string specifying the procedure to be used for detecting outlier offspring. Options include "Gmat", which utilizes the genomic kinship matrix, and "PCA", which employs principal component analysis on the dosage matrix. coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated.
`verbose`	if `TRUE` (default), shows the filtered out individuals

Author(s)

Marcelo Mollinari, [email protected]

Filter missing genotypes

Description

Excludes markers or individuals based on their proportion of missing data.

Usage

filter_missing(
  input.data,
  type = c("marker", "individual"),
  filter.thres = 0.2,
  inter = TRUE
)
filter_missing(
  input.data,
  type = c("marker", "individual"),
  filter.thres = 0.2,
  inter = TRUE
)

Arguments

`input.data`	an object of class `mappoly.data`.
`type`	one of the following options: `"marker"`: filter out markers based on their percentage of missing data (default). `"individual"`: filter out individuals based on their percentage of missing data. Please notice that removing individuals with certain amount of data can change some marker parameters (such as depth), and can also change the estimated genotypes for other individuals. So, be careful when removing individuals.
`filter.thres`	maximum percentage of missing data (default = 0.2).
`inter`	if `TRUE`, expects user-input to proceed with filtering.

Author(s)

Marcelo Mollinari, [email protected].

Examples

plot(tetra.solcap)
dat.filt.mrk <- filter_missing(input.data = tetra.solcap,
                               type = "marker",
                               filter.thres = 0.1,
                               inter = TRUE)
plot(dat.filt.mrk)

plot(tetra.solcap)
dat.filt.mrk <- filter_missing(input.data = tetra.solcap,
                               type = "marker",
                               filter.thres = 0.1,
                               inter = TRUE)
plot(dat.filt.mrk)

Filter markers based on chi-square test

Description

This function filter markers based on p-values of a chi-square test. The chi-square test assumes that markers follow the expected segregation patterns under Mendelian inheritance, random chromosome bivalent pairing and no double reduction.

Usage

filter_segregation(input.obj, chisq.pval.thres = NULL, inter = TRUE)
filter_segregation(input.obj, chisq.pval.thres = NULL, inter = TRUE)

Arguments

`input.obj`	name of input object (class `mappoly.data`)
`chisq.pval.thres`	p-value threshold used for chi-square tests (default = Bonferroni aproximation with global alpha of 0.05, i.e., 0.05/n.mrk)
`inter`	if TRUE (default), plots distorted vs. non-distorted markers

Value

An object of class mappoly.chitest.seq which contains a list with the following components:

`keep`	markers that follow Mendelian segregation pattern
`exclude`	markers with distorted segregation
`chisq.pval.thres`	threshold p-value used for chi-square tests
`data.name`	input dataset used to perform the chi-square tests

Author(s)

Marcelo Mollinari, [email protected]

Examples

mrks.chi.filt <- filter_segregation(input.obj = tetra.solcap,
                                    chisq.pval.thres = 0.05/tetra.solcap$n.mrk,
                                    inter = TRUE)
seq.init <- make_seq_mappoly(mrks.chi.filt)

mrks.chi.filt <- filter_segregation(input.obj = tetra.solcap,
                                    chisq.pval.thres = 0.05/tetra.solcap$n.mrk,
                                    inter = TRUE)
seq.init <- make_seq_mappoly(mrks.chi.filt)

Allocate markers into linkage blocks

Description

Function to allocate markers into linkage blocks. This is an EXPERIMENTAL FUNCTION and should be used with caution.

Usage

find_blocks(
  input.seq,
  clustering.type = c("rf", "genome"),
  rf.limit = 1e-04,
  genome.block.threshold = 10000,
  rf.mat = NULL,
  ncpus = 1,
  ph.thres = 3,
  phase.number.limit = 10,
  error = 0.05,
  verbose = TRUE,
  tol = 0.01,
  tol.err = 0.001
)
find_blocks(
  input.seq,
  clustering.type = c("rf", "genome"),
  rf.limit = 1e-04,
  genome.block.threshold = 10000,
  rf.mat = NULL,
  ncpus = 1,
  ph.thres = 3,
  phase.number.limit = 10,
  error = 0.05,
  verbose = TRUE,
  tol = 0.01,
  tol.err = 0.001
)

Arguments

`input.seq`	an object of class `mappoly.sequence`.
`clustering.type`	if `'rf'`, it uses UPGMA clusterization based on the recombination fraction matrix to assemble blocks. Linkage blocks are assembled by cutting the clusterization tree at `rf.limit`. If `'genome'`, it splits the marker sequence at neighbor markers morre than `'genome.block.threshold'` apart.
`rf.limit`	the maximum value to consider linked markers in case of `'clustering.type = rf'`
`genome.block.threshold`	the threshold to assume markers are in the same linkage block. to be considered when allocating markers into blocks in case of `'clustering.type = genomee'`
`rf.mat`	an object of class `mappoly.rf.matrix`.
`ncpus`	Number of parallel processes to spawn
`ph.thres`	the threshold used to sequentially phase markers. Used in `thres.twopt` and `thres.hmm`. See `est_rf_hmm_sequential` for details.
`phase.number.limit`	the maximum number of linkage phases of the sub-maps. The default is 10. See `est_rf_hmm_sequential` for details.
`error`	the assumed global genotyping error rate. If `NULL` (default) it does not include an error in the block estimation.
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced.
`tol`	tolerance for the C routine, i.e., the value used to evaluate convergence.
`tol.err`	tolerance for the C routine, i.e., the value used to evaluate convergence, including the global genotyping error in the model.

Value

a list containing 1: a list of blocks in form of mappoly.map objects; 2: a vector containing markers that were not included into blocks.

Author(s)

Marcelo Mollinari, [email protected]

Examples

  ## Not run: 
  ## Selecting 50 markers in chromosome 5
  s5 <- make_seq_mappoly(tetra.solcap, "seq5")
  s5 <- make_seq_mappoly(tetra.solcap, s5$seq.mrk.names[1:50])
  tpt5 <- est_pairwise_rf(s5)
  m5 <- rf_list_to_matrix(tpt5, 3, 3)
  fb.rf <- find_blocks(s5, rf.mat = m5, verbose = FALSE, ncpus = 2)
  bl.rf <- fb.rf$blocks
  plot_map_list(bl.rf)
  
  ## Merging resulting maps
  map.merge <- merge_maps(bl.rf, tpt5)
  plot(map.merge, mrk.names = T)
  
  ## Comparing linkage phases with pre assembled map
  id <- na.omit(match(map.merge$info$mrk.names, solcap.err.map[[5]]$info$mrk.names))
  map.orig <- get_submap(solcap.err.map[[5]], mrk.pos = id)
  p1.m<-map.merge$maps[[1]]$seq.ph$P
  p2.m<-map.merge$maps[[1]]$seq.ph$Q
  names(p1.m) <- names(p2.m) <- map.merge$info$mrk.names
  p1.o<-map.orig$maps[[1]]$seq.ph$P
  p2.o<-map.orig$maps[[1]]$seq.ph$Q
  names(p1.o) <- names(p2.o) <- map.orig$info$mrk.names
  n <- intersect(names(p1.m), names(p1.o))
  plot_compare_haplotypes(4, p1.o[n], p2.o[n], p1.m[n], p2.m[n])
  
  ### Using genome
  fb.geno <- find_blocks(s5, clustering.type = "genome", genome.block.threshold = 10^4)
  plot_map_list(fb.geno$blocks)
  splt <- lapply(fb.geno$blocks, split_mappoly, 1)
  plot_map_list(splt)

## End(Not run)
## Not run: 
  ## Selecting 50 markers in chromosome 5
  s5 <- make_seq_mappoly(tetra.solcap, "seq5")
  s5 <- make_seq_mappoly(tetra.solcap, s5$seq.mrk.names[1:50])
  tpt5 <- est_pairwise_rf(s5)
  m5 <- rf_list_to_matrix(tpt5, 3, 3)
  fb.rf <- find_blocks(s5, rf.mat = m5, verbose = FALSE, ncpus = 2)
  bl.rf <- fb.rf$blocks
  plot_map_list(bl.rf)
  
  ## Merging resulting maps
  map.merge <- merge_maps(bl.rf, tpt5)
  plot(map.merge, mrk.names = T)
  
  ## Comparing linkage phases with pre assembled map
  id <- na.omit(match(map.merge$info$mrk.names, solcap.err.map[[5]]$info$mrk.names))
  map.orig <- get_submap(solcap.err.map[[5]], mrk.pos = id)
  p1.m<-map.merge$maps[[1]]$seq.ph$P
  p2.m<-map.merge$maps[[1]]$seq.ph$Q
  names(p1.m) <- names(p2.m) <- map.merge$info$mrk.names
  p1.o<-map.orig$maps[[1]]$seq.ph$P
  p2.o<-map.orig$maps[[1]]$seq.ph$Q
  names(p1.o) <- names(p2.o) <- map.orig$info$mrk.names
  n <- intersect(names(p1.m), names(p1.o))
  plot_compare_haplotypes(4, p1.o[n], p2.o[n], p1.m[n], p2.m[n])
  
  ### Using genome
  fb.geno <- find_blocks(s5, clustering.type = "genome", genome.block.threshold = 10^4)
  plot_map_list(fb.geno$blocks)
  splt <- lapply(fb.geno$blocks, split_mappoly, 1)
  plot_map_list(splt)

## End(Not run)

Design linkage map framework in two steps: i) estimating the recombination fraction with HMM approach for each parent separately using only markers segregating individually (e.g. map 1 - P1:3 x P2:0, P1: 2x4; map 2 - P1:0 x P2:3, P1:4 x P2:2); ii) merging both maps and re-estimate recombination fractions.

Description

Design linkage map framework in two steps: i) estimating the recombination fraction with HMM approach for each parent separately using only markers segregating individually (e.g. map 1 - P1:3 x P2:0, P1: 2x4; map 2 - P1:0 x P2:3, P1:4 x P2:2); ii) merging both maps and re-estimate recombination fractions.

Usage

framework_map(
  input.seq,
  twopt,
  start.set = 10,
  thres.twopt = 10,
  thres.hmm = 30,
  extend.tail = 30,
  inflation.lim.p1 = 5,
  inflation.lim.p2 = 5,
  phase.number.limit = 10,
  tol = 0.01,
  tol.final = 0.001,
  verbose = TRUE,
  method = "hmm"
)
framework_map(
  input.seq,
  twopt,
  start.set = 10,
  thres.twopt = 10,
  thres.hmm = 30,
  extend.tail = 30,
  inflation.lim.p1 = 5,
  inflation.lim.p2 = 5,
  phase.number.limit = 10,
  tol = 0.01,
  tol.final = 0.001,
  verbose = TRUE,
  method = "hmm"
)

Arguments

`input.seq`	object of class `mappoly.sequence`
`twopt`	object of class `mappoly.twopt`
`start.set`	number of markers to start the phasing procedure (default = 4)
`thres.twopt`	the LOD threshold used to determine if the linkage phases compared via two-point analysis should be considered for the search space reduction (default = 5)
`thres.hmm`	the LOD threshold used to determine if the linkage phases compared via hmm analysis should be evaluated in the next round of marker inclusion (default = 50)
`extend.tail`	the length of the chain's tail that should be used to calculate the likelihood of the map. If NULL (default), the function uses all markers positioned. Even if info.tail = TRUE, it uses at least extend.tail as the tail length
`inflation.lim.p1`	the maximum accepted length difference between the current and the previous parent 1 sub-map defined by arguments info.tail and extend.tail. If the size exceeds this limit, the marker will not be inserted. If NULL(default), then it will insert all markers.
`inflation.lim.p2`	same as 'inflation.lim.p1' but for parent 2 sub-map.
`phase.number.limit`	the maximum number of linkage phases of the sub-maps defined by arguments info.tail and extend.tail. Default is 20. If the size exceeds this limit, the marker will not be inserted. If Inf, then it will insert all markers.
`tol`	the desired accuracy during the sequential phase of each parental map (default = 10e-02)
`tol.final`	the desired accuracy for the final parental map (default = 10e-04)
`verbose`	If TRUE (default), current progress is shown; if FALSE, no output is produced
`method`	indicates whether to use 'hmm' (Hidden Markov Models), 'ols' (Ordinary Least Squares) to re-estimate the recombination fractions while merging the parental maps (default:hmm)

Value

list containing three mappoly.map objects:1) map built with markers with segregation information from parent 1; 2) map built with markers with segregation information from parent 2; 3) maps in 1 and 2 merged

Author(s)

Marcelo Mollinari, [email protected] with documentation and minor modifications by Cristiane Taniguti [email protected]

Genetic Mapping Functions

Description

These functions facilitate the conversion between recombination fractions (r) and genetic distances (d) using various mapping models. The functions starting with 'mf_' convert recombination fractions to genetic distances, while those starting with 'imf_' convert genetic distances back into recombination fractions.

Usage

mf_k(d)

mf_h(d)

mf_m(d)

imf_k(r)

imf_h(r)

imf_m(r)
mf_k(d)

mf_h(d)

mf_m(d)

imf_k(r)

imf_h(r)

imf_m(r)

Arguments

`d`	Numeric or numeric vector, representing genetic distances in centiMorgans (cM) for direct functions (mf_k, mf_h, mf_m).
`r`	Numeric or numeric vector, representing recombination fractions for inverse functions (imf_k, imf_h, imf_m).

Details

The 'mf_' prefixed functions apply different models to convert recombination fractions into genetic distances:

mf_k: Kosambi mapping function.
mf_h: Haldane mapping function.
mf_m: Morgan mapping function.

The 'imf_' prefixed functions convert genetic distances back into recombination fractions:

imf_k: Inverse Kosambi mapping function.
imf_h: Inverse Haldane mapping function.
imf_m: Inverse Morgan mapping function.

References

Kosambi, D.D. (1944). The estimation of map distances from recombination values. Ann Eugen., 12, 172-175. Haldane, J.B.S. (1919). The combination of linkage values, and the calculation of distances between the loci of linked factors. J Genet, 8, 299-309. Morgan, T.H. (1911). Random segregation versus coupling in Mendelian inheritance. Science, 34(873), 384.

Get the genomic position of markers in a sequence

Description

This functions gets the genomic position of markers in a sequence and return an ordered data frame with the name and position of each marker

Usage

get_genomic_order(input.seq, verbose = TRUE)

## S3 method for class 'mappoly.geno.ord'
print(x, ...)

## S3 method for class 'mappoly.geno.ord'
plot(x, ...)
get_genomic_order(input.seq, verbose = TRUE)

## S3 method for class 'mappoly.geno.ord'
print(x, ...)

## S3 method for class 'mappoly.geno.ord'
plot(x, ...)

Arguments

`input.seq`	a sequence object of class `mappoly.sequence`
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced
`x`	an object of the class mappoly.geno.ord
`...`	currently ignored

Author(s)

Marcelo Mollinari, [email protected]

Examples

s1 <- make_seq_mappoly(tetra.solcap, "all")
o1 <- get_genomic_order(s1)
plot(o1)
s.geno.ord <- make_seq_mappoly(o1)
s1 <- make_seq_mappoly(tetra.solcap, "all")
o1 <- get_genomic_order(s1)
plot(o1)
s.geno.ord <- make_seq_mappoly(o1)

Extract sub-map from map

Description

Given a pre-constructed map, it extracts a sub-map for a provided sequence of marker positions. Optionally, it can update the linkage phase configurations and respective recombination fractions.

Usage

get_submap(
  input.map,
  mrk.pos,
  phase.config = "best",
  reestimate.rf = TRUE,
  reestimate.phase = FALSE,
  thres.twopt = 5,
  thres.hmm = 3,
  extend.tail = 50,
  tol = 0.1,
  tol.final = 0.001,
  use.high.precision = FALSE,
  verbose = TRUE
)
get_submap(
  input.map,
  mrk.pos,
  phase.config = "best",
  reestimate.rf = TRUE,
  reestimate.phase = FALSE,
  thres.twopt = 5,
  thres.hmm = 3,
  extend.tail = 50,
  tol = 0.1,
  tol.final = 0.001,
  use.high.precision = FALSE,
  verbose = TRUE
)

Arguments

`input.map`	An object of class `mappoly.map`
`mrk.pos`	positions of the markers that should be considered in the new map. This can be in any order
`phase.config`	which phase configuration should be used. "best" (default) will choose the configuration associated with the maximum likelihood
`reestimate.rf`	logical. If `TRUE` (default) the recombination fractions between markers are re-estimated
`reestimate.phase`	logical. If `TRUE`, the linkage phase configurations are re-estimated (default = FALSE)
`thres.twopt`	the LOD threshold used to determine if the linkage phases compared via two-point analysis should be considered (default = 5)
`thres.hmm`	the threshold used to determine if the linkage phases compared via hmm analysis should be considered (default = 3)
`extend.tail`	the length of the tail of the chain that should be used to calculate the likelihood of the linkage phases. If `info.tail = TRUE`, the function uses at least `extend.tail` as the length of the tail (default = 50)
`tol`	the desired accuracy during the sequential phase (default = 0.1)
`tol.final`	the desired accuracy for the final map (default = 10e-04)
`use.high.precision`	logical. If `TRUE` uses high precision (long double) numbers in the HMM procedure implemented in C++, which can take a long time to perform (default = FALSE)
`verbose`	If `TRUE` (default), current progress is shown; if `FALSE`, no output is produced

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

 
    ## selecting the six first markers in linkage group 1
    ## re-estimating the recombination fractions and linkage phases
    submap1.lg1 <- get_submap(input.map = maps.hexafake[[1]], 
                           mrk.pos = 1:6, verbose = TRUE,
                           reestimate.phase = TRUE,  
                           tol.final = 10e-3)
   ## no recombination fraction re-estimation: first 20 markers
   submap2.lg1 <- get_submap(input.map = maps.hexafake[[1]], 
                           mrk.pos = 1:20, reestimate.rf = FALSE,
                           verbose = TRUE, 
                           tol.final = 10e-3)
  plot(maps.hexafake[[1]])
  plot(submap1.lg1, mrk.names = TRUE, cex = .8)
  plot(submap2.lg1, mrk.names = TRUE, cex = .8)
  

## selecting the six first markers in linkage group 1
    ## re-estimating the recombination fractions and linkage phases
    submap1.lg1 <- get_submap(input.map = maps.hexafake[[1]], 
                           mrk.pos = 1:6, verbose = TRUE,
                           reestimate.phase = TRUE,  
                           tol.final = 10e-3)
   ## no recombination fraction re-estimation: first 20 markers
   submap2.lg1 <- get_submap(input.map = maps.hexafake[[1]], 
                           mrk.pos = 1:20, reestimate.rf = FALSE,
                           verbose = TRUE, 
                           tol.final = 10e-3)
  plot(maps.hexafake[[1]])
  plot(submap1.lg1, mrk.names = TRUE, cex = .8)
  plot(submap2.lg1, mrk.names = TRUE, cex = .8)

Get table of dosage combinations

Description

Internal function

Usage

get_tab_mrks(x)
get_tab_mrks(x)

Arguments

`x`	an object of class `mappoly.map`

Author(s)

Gabriel Gesteira, [email protected]

Assign markers to linkage groups

Description

Identifies linkage groups of markers using the results of two-point (pairwise) analysis.

Usage

group_mappoly(
  input.mat,
  expected.groups = NULL,
  inter = TRUE,
  comp.mat = FALSE,
  LODweight = FALSE,
  verbose = TRUE
)
group_mappoly(
  input.mat,
  expected.groups = NULL,
  inter = TRUE,
  comp.mat = FALSE,
  LODweight = FALSE,
  verbose = TRUE
)

Arguments

`input.mat`	an object of class `mappoly.rf.matrix`
`expected.groups`	when available, inform the number of expected linkage groups (i.e. chromosomes) for the species
`inter`	if `TRUE` (default), plots a dendrogram highlighting the expected groups before continue
`comp.mat`	if `TRUE`, shows a comparison between the reference based and the linkage based grouping, if the chromosome information is available (default = FALSE)
`LODweight`	if `TRUE`, clusterization is weighted by the square of the LOD Score
`verbose`	logical. If `TRUE` (default), current progress is shown; if `FALSE`, no output is produced

Value

Returns an object of class mappoly.group, which is a list containing the following components:

`data.name`	the referred dataset name
`hc.snp`	a list containing information related to the UPGMA grouping method
`expected.groups`	the number of expected linkage groups
`groups.snp`	the groups to which each of the markers belong
`seq.vs.grouped.snp`	comparison between the genomic group information (when available) and the groups provided by `group_mappoly`
`chisq.pval.thres`	the threshold used on the segregation test when reading the dataset
`chisq.pval`	the p-values associated with the segregation test for all markers in the sequence

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

    ## Getting first 20 markers from two linkage groups
    all.mrk <- make_seq_mappoly(hexafake, c(1:20,601:620))
    red.mrk <- elim_redundant(all.mrk)
    unique.mrks <- make_seq_mappoly(red.mrk)
    counts <- cache_counts_twopt(unique.mrks, cached = TRUE)
    all.pairs <- est_pairwise_rf(input.seq = unique.mrks,
                                 count.cache = counts,
                                 ncpus = 1,
                                 verbose = TRUE)

    ## Full recombination fraction matrix
    mat.full <- rf_list_to_matrix(input.twopt = all.pairs)
    plot(mat.full, index = FALSE)

    lgs <- group_mappoly(input.mat = mat.full,
                         expected.groups = 2,
                         inter = TRUE,
                         comp.mat = TRUE, #this data has physical information
                         verbose = TRUE)
    lgs
    plot(lgs)
   
## Getting first 20 markers from two linkage groups
    all.mrk <- make_seq_mappoly(hexafake, c(1:20,601:620))
    red.mrk <- elim_redundant(all.mrk)
    unique.mrks <- make_seq_mappoly(red.mrk)
    counts <- cache_counts_twopt(unique.mrks, cached = TRUE)
    all.pairs <- est_pairwise_rf(input.seq = unique.mrks,
                                 count.cache = counts,
                                 ncpus = 1,
                                 verbose = TRUE)

    ## Full recombination fraction matrix
    mat.full <- rf_list_to_matrix(input.twopt = all.pairs)
    plot(mat.full, index = FALSE)

    lgs <- group_mappoly(input.mat = mat.full,
                         expected.groups = 2,
                         inter = TRUE,
                         comp.mat = TRUE, #this data has physical information
                         verbose = TRUE)
    lgs
    plot(lgs)

Simulated autohexaploid dataset.

Description

A dataset of a hypothetical autohexaploid full-sib population containing three homology groups

Usage

hexafake
hexafake

Format

An object of class mappoly.data which contains a list with the following components:

plody: ploidy level = 6
n.ind: number individuals = 300
n.mrk: total number of markers = 1500
ind.names: the names of the individuals
mrk.names: the names of the markers
dosage.p1: a vector containing the dosage in parent P for all n.mrk markers
dosage.p2: a vector containing the dosage in parent Q for all n.mrk markers
chrom: a vector indicating the chromosome each marker belongs. Zero indicates that the marker was not assigned to any chromosome
genome.pos: Physical position of the markers into the sequence
geno.dose: a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by ploidy_level + 1 = 7
n.phen: There are no phenotypes in this simulation
phen: There are no phenotypes in this simulation
chisq.pval: vector containing p-values for all markers associated to the chi-square test for the expected segregation patterns under Mendelian segregation

Simulated autohexaploid dataset with genotype probabilities.

Description

A dataset of a hypothetical autohexaploid full-sib population containing three homology groups. This dataset contains the probability distribution of the genotypes and 2% of missing data, but is essentially the same dataset found in hexafake

Usage

hexafake.geno.dist
hexafake.geno.dist

Format

An object of class mappoly.data which contains a list with the following components:

ploidy: ploidy level = 6
n.ind: number individuals = 300
n.mrk: total number of markers = 1500
ind.names: the names of the individuals
mrk.names: the names of the markers
dosage.p1: a vector containing the dosage in parent P for all n.mrk markers
dosage.p2: a vector containing the dosage in parent Q for all n.mrk markers
chrom: a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence
genome.pos: Physical position of the markers into the sequence
prob.thres = 0.95: probability threshold to associate a marker call to a dosage. Markers with maximum genotype probability smaller than 'prob.thres' are considered as missing data for the dosage calling purposes
geno: a data.frame containing the probability distribution for each combination of marker and offspring. The first two columns represent the marker and the offspring, respectively. The remaining elements represent the probability associated to each one of the possible dosages
geno.dose: a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by ploidy_level + 1 = 7
n.phen: There are no phenotypes in this simulation
phen: There are no phenotypes in this simulation

Import data from polymapR

Description

Function to import datasets from polymapR.

Usage

import_data_from_polymapR(
  input.data,
  ploidy,
  parent1 = "P1",
  parent2 = "P2",
  input.type = c("discrete", "probabilistic"),
  prob.thres = 0.95,
  pardose = NULL,
  offspring = NULL,
  filter.non.conforming = TRUE,
  verbose = TRUE
)
import_data_from_polymapR(
  input.data,
  ploidy,
  parent1 = "P1",
  parent2 = "P2",
  input.type = c("discrete", "probabilistic"),
  prob.thres = 0.95,
  pardose = NULL,
  offspring = NULL,
  filter.non.conforming = TRUE,
  verbose = TRUE
)

Arguments

`input.data`	a `polymapR` dataset
`ploidy`	the ploidy level
`parent1`	a character string containing the name (or pattern of genotype IDs) of parent 1
`parent2`	a character string containing the name (or pattern of genotype IDs) of parent 2
`input.type`	Indicates whether the input is discrete ("disc") or probabilistic ("prob")
`prob.thres`	threshold probability to assign a dosage to offspring. If the probability is smaller than `thresh.parent.geno`, the data point is converted to 'NA'.
`pardose`	matrix of dimensions (n.mrk x 3) containing the name of the markers in the first column, and the dosage of parents 1 and 2 in columns 2 and 3. (see polymapR vignette)
`offspring`	a character string containing the name (or pattern of genotype IDs) of the offspring individuals. If `NULL` (default) it considers all individuals as offsprings, except `parent1` and `parent2`.
`filter.non.conforming`	if `TRUE` exclude samples with non expected genotypes under no double reduction. Since markers were already filtered in polymapR, the default is `FALSE`.
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced

Details

See examples at https://rpubs.com/mmollin/tetra_mappoly_vignette.

Author(s)

Marcelo Mollinari [email protected]

References

Bourke PM et al: (2019) PolymapR — linkage analysis and genetic map construction from F1 populations of outcrossing polyploids. _Bioinformatics_ 34:3496–3502. doi:10.1093/bioinformatics/bty1002

Import from updog

Description

Read objects with information related to genotype calling in polyploids. Currently this function supports output objects created with the updog (output of multidog function) package. This function creates an object of class mappoly.data

Usage

import_from_updog(
  object,
  prob.thres = 0.95,
  filter.non.conforming = TRUE,
  chrom = NULL,
  genome.pos = NULL,
  verbose = TRUE
)
import_from_updog(
  object,
  prob.thres = 0.95,
  filter.non.conforming = TRUE,
  chrom = NULL,
  genome.pos = NULL,
  verbose = TRUE
)

Arguments

`object`	the name of the object of class `multidog`
`prob.thres`	probability threshold to associate a marker call to a dosage. Markers with maximum genotype probability smaller than 'prob.thres' are considered as missing data for the dosage calling purposes
`filter.non.conforming`	if `TRUE` (default) exclude samples with non expected genotypes under random chromosome pairing and no double reduction
`chrom`	a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence
`genome.pos`	vector with physical position of the markers into the sequence
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced

Value

An object of class mappoly.data which contains a list with the following components:

`ploidy`	ploidy level
`n.ind`	number individuals
`n.mrk`	total number of markers
`ind.names`	the names of the individuals
`mrk.names`	the names of the markers
`dosage.p1`	a vector containing the dosage in parent P for all `n.mrk` markers
`dosage.p2`	a vector containing the dosage in parent Q for all `n.mrk` markers
`chrom`	a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence
`genome.pos`	physical position of the markers into the sequence
`prob.thres`	probability threshold to associate a marker call to a dosage. Markers with maximum genotype probability smaller than 'prob.thres' were considered as missing data in the 'geno.dose' matrix
`geno.dose`	a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by `ploidy_level + 1`
`geno`	a data.frame containing the probability distribution for each combination of marker and offspring. The first two columns represent the marker and the offspring, respectively. The remaining elements represent the probability associated to each one of the possible dosages. Missing data are converted from `NA` to the expected segregation ratio using function `segreg_poly`
`n.phen`	number of phenotypic traits
`phen`	a matrix containing the phenotypic data. The rows correspond to the traits and the columns correspond to the individuals
`chisq.pval`	a vector containing p-values related to the chi-squared test of Mendelian segregation performed for all markers

Author(s)

Gabriel Gesteira, [email protected]

References

Examples


if(requireNamespace("updog", quietly = TRUE)){
library("updog")
data("uitdewilligen")
mout = multidog(refmat = t(uitdewilligen$refmat), 
                sizemat = t(uitdewilligen$sizemat), 
                ploidy = uitdewilligen$ploidy, 
                model = "f1",
                p1_id = colnames(t(uitdewilligen$sizemat))[1],
                p2_id = colnames(t(uitdewilligen$sizemat))[2],
                nc = 2)
mydata = import_from_updog(mout)
mydata
plot(mydata)
}


if(requireNamespace("updog", quietly = TRUE)){
library("updog")
data("uitdewilligen")
mout = multidog(refmat = t(uitdewilligen$refmat), 
                sizemat = t(uitdewilligen$sizemat), 
                ploidy = uitdewilligen$ploidy, 
                model = "f1",
                p1_id = colnames(t(uitdewilligen$sizemat))[1],
                p2_id = colnames(t(uitdewilligen$sizemat))[2],
                nc = 2)
mydata = import_from_updog(mout)
mydata
plot(mydata)
}

Import phased map list from polymapR

Description

Function to import phased map lists from polymapR

Usage

import_phased_maplist_from_polymapR(maplist, mappoly.data, ploidy = NULL)
import_phased_maplist_from_polymapR(maplist, mappoly.data, ploidy = NULL)

Arguments

`maplist`	a list of phased maps obtained using function `create_phased_maplist` from package `polymapR`
`mappoly.data`	a dataset used to obtain `maplist`, converted into class `mappoly.data`
`ploidy`	the ploidy level

Details

See examples at https://rpubs.com/mmollin/tetra_mappoly_vignette.

Author(s)

Marcelo Mollinari [email protected]

References

Bourke PM et al: (2019) PolymapR — linkage analysis and genetic map construction from F1 populations of outcrossing polyploids. _Bioinformatics_ 34:3496–3502. doi:10.1093/bioinformatics/bty1002

Multipoint log-likelihood computation

Description

Update the multipoint log-likelihood of a given map using the method proposed by Mollinari and Garcia (2019).

Usage

loglike_hmm(input.map, input.data = NULL, verbose = FALSE)
loglike_hmm(input.map, input.data = NULL, verbose = FALSE)

Arguments

`input.map`	An object of class `mappoly.map`
`input.data`	An object of class `mappoly.data`, which was used to generate `input.map`
`verbose`	If `TRUE`, map information is shown; if `FALSE`(default), no output is produced

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

 
  hexa.map <- loglike_hmm(maps.hexafake[[1]])
  hexa.map
 
 
hexa.map <- loglike_hmm(maps.hexafake[[1]])
  hexa.map

Subset recombination fraction matrices

Description

Get a subset of an object of class mappoly.rf.matrix, i.e. recombination fraction and LOD score matrices based in a sequence of markers.

Usage

make_mat_mappoly(input.mat, input.seq)
make_mat_mappoly(input.mat, input.seq)

Arguments

`input.mat`	an object of class `mappoly.rf.matrix`
`input.seq`	an object of class `mappoly.sequence`, with a sequence of markers contained in `input.mat`

Value

an object of class mappoly.rf.matrix, which is a subset of 'input.mat'. See rf_list_to_matrix for details

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

    # sequence with 20 markers
    mrk.seq <- make_seq_mappoly(hexafake, 1:20)
    mrk.pairs <- est_pairwise_rf(input.seq = mrk.seq,
                               verbose = TRUE)
    ## Full recombination fraction matrix
    mat <- rf_list_to_matrix(input.twopt = mrk.pairs)
    plot(mat)
    ## Matrix subset
    id <- make_seq_mappoly(hexafake, 1:10)
    mat.sub <- make_mat_mappoly(mat, id)
    plot(mat.sub)
   
# sequence with 20 markers
    mrk.seq <- make_seq_mappoly(hexafake, 1:20)
    mrk.pairs <- est_pairwise_rf(input.seq = mrk.seq,
                               verbose = TRUE)
    ## Full recombination fraction matrix
    mat <- rf_list_to_matrix(input.twopt = mrk.pairs)
    plot(mat)
    ## Matrix subset
    id <- make_seq_mappoly(hexafake, 1:10)
    mat.sub <- make_mat_mappoly(mat, id)
    plot(mat.sub)

Subset pairwise recombination fractions

Description

Get a subset of an object of class mappoly.twopt or mappoly.twopt2 (i.e. recombination fraction) and LOD score statistics for all possible linkage phase combinations based on a sequence of markers.

Usage

make_pairs_mappoly(input.twopt, input.seq)
make_pairs_mappoly(input.twopt, input.seq)

Arguments

`input.twopt`	an object of class `mappoly.twopt`
`input.seq`	an object of class `mappoly.sequence`, with a sequence of markers contained in `input.twopt`

Value

an object of class mappoly.twopt which is a subset of input.twopt. See est_pairwise_rf for details

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

    ## selecting some markers along the genome
    some.mrk <- make_seq_mappoly(hexafake, seq(1, 1500, 30))
    all.pairs <- est_pairwise_rf(input.seq = some.mrk)
    mat.full <- rf_list_to_matrix(input.twopt = all.pairs)
    plot(mat.full)
    
    ## selecting two-point information for chromosome 1
    mrks.1 <- make_seq_mappoly(hexafake, names(which(some.mrk$chrom == 1)))
    p1 <- make_pairs_mappoly(input.seq = mrks.1, input.twopt = all.pairs)
    m1 <- rf_list_to_matrix(input.twopt = p1)
    plot(m1, main.text = "LG1")
   
## selecting some markers along the genome
    some.mrk <- make_seq_mappoly(hexafake, seq(1, 1500, 30))
    all.pairs <- est_pairwise_rf(input.seq = some.mrk)
    mat.full <- rf_list_to_matrix(input.twopt = all.pairs)
    plot(mat.full)
    
    ## selecting two-point information for chromosome 1
    mrks.1 <- make_seq_mappoly(hexafake, names(which(some.mrk$chrom == 1)))
    p1 <- make_pairs_mappoly(input.seq = mrks.1, input.twopt = all.pairs)
    m1 <- rf_list_to_matrix(input.twopt = p1)
    plot(m1, main.text = "LG1")

Create a Sequence of Markers

Description

Constructs a sequence of markers based on an object belonging to various specified classes. This function is versatile, supporting multiple input types and configurations for generating marker sequences.

Usage

make_seq_mappoly(
  input.obj,
  arg = NULL,
  data.name = NULL,
  info.parent = c("all", "p1", "p2"),
  genomic.info = NULL
)

## S3 method for class 'mappoly.sequence'
print(x, ...)

## S3 method for class 'mappoly.sequence'
plot(x, ...)
make_seq_mappoly(
  input.obj,
  arg = NULL,
  data.name = NULL,
  info.parent = c("all", "p1", "p2"),
  genomic.info = NULL
)

## S3 method for class 'mappoly.sequence'
print(x, ...)

## S3 method for class 'mappoly.sequence'
plot(x, ...)

Arguments

`input.obj`	An object belonging to one of the specified classes: `mappoly.data`, `mappoly.map`, `mappoly.sequence`, `mappoly.group`, `mappoly.unique.seq`, `mappoly.pcmap`, `mappoly.pcmap3d`, `mappoly.geno.ord`, or `mappoly.edit.order`.
`arg`	Specifies the markers to include in the sequence, accepting several formats: a string 'all' for all markers; a string or vector of strings 'seqx' where x is the sequence number (0 for unassigned markers); a vector of integers indicating specific markers; or a vector of integers representing linkage group numbers if `input.obj` is of class `mappoly.group`. For certain classes (`mappoly.pcmap`, `mappoly.pcmap3d`, `mappoly.unique.seq`, or `mappoly.geno.ord`), `arg` can be `NULL`.
`data.name`	Name of the `mappoly.data` class object.
`info.parent`	Selection criteria based on parental information: `'all'` for all dosage combinations, `'P1'` for markers informative in parent 1, or `'P2'` for markers informative in parent 2. Default is `'all'`.
`genomic.info`	Optional and applicable only to `mappoly.group` objects. Specifies the use of genomic information in sequence creation. With `NULL` (default), all markers defined by the grouping function are included. Numeric values indicate the use of specific sequences from genomic information, aiming to match the maximum number of markers with the group. Supports single values or vectors for multiple sequence consideration.
`x`	An object of class `mappoly.sequence`.
`...`	Currently ignored.

Value

Returns an object of class 'mappoly.sequence', comprising:

`"seq.num"`	Ordered vector of marker indices according to the input.
`"seq.phases"`	List of linkage phases between markers; -1 for undefined phases.
`"seq.rf"`	Vector of recombination frequencies; -1 for not estimated frequencies.
`"loglike"`	Log-likelihood of the linkage map.
`"data.name"`	Name of the 'mappoly.data' object with raw data.
`"twopt"`	Name of the 'mappoly.twopt' object with 2-point analyses; -1 if not computed.

Author(s)

Marcelo Mollinari [email protected], with modifications by Gabriel Gesteira [email protected]

References

Mollinari, M., and Garcia, A. A. F. (2019). Linkage analysis and haplotype phasing in experimental autopolyploid populations with high ploidy level using hidden Markov models. _G3: Genes|Genomes|Genetics_, doi:10.1534/g3.119.400378.

Examples

all.mrk <- make_seq_mappoly(hexafake, 'all')
seq1.mrk <- make_seq_mappoly(hexafake, 'seq1')
plot(seq1.mrk)
some.mrk.pos <- c(1,4,28,32,45)
some.mrk.1 <- make_seq_mappoly(hexafake, some.mrk.pos)
plot(some.mrk.1)

all.mrk <- make_seq_mappoly(hexafake, 'all')
seq1.mrk <- make_seq_mappoly(hexafake, 'seq1')
plot(seq1.mrk)
some.mrk.pos <- c(1,4,28,32,45)
some.mrk.1 <- make_seq_mappoly(hexafake, some.mrk.pos)
plot(some.mrk.1)

Resulting maps from `hexafake`

Description

A list containing three linkage groups estimated using the procedure available in [MAPpoly's tutorial](https://mmollina.github.io/MAPpoly/#estimating_the_map_for_a_given_order)

Usage

maps.hexafake
maps.hexafake

Format

A list containing three objects of class mappoly.map, each one representing one linkage group in the simulated data.

Estimates loci position using Multidimensional Scaling

Description

Estimates loci position using Multidimensional Scaling proposed by Preedy and Hackett (2016). The code is an adaptation from the package MDSmap, available under GNU GENERAL PUBLIC LICENSE, Version 3, at https://CRAN.R-project.org/package=MDSMap

Usage

mds_mappoly(
  input.mat,
  p = NULL,
  n = NULL,
  ndim = 2,
  weight.exponent = 2,
  verbose = TRUE
)

## S3 method for class 'mappoly.pcmap'
print(x, ...)

## S3 method for class 'mappoly.pcmap3d'
print(x, ...)
mds_mappoly(
  input.mat,
  p = NULL,
  n = NULL,
  ndim = 2,
  weight.exponent = 2,
  verbose = TRUE
)

## S3 method for class 'mappoly.pcmap'
print(x, ...)

## S3 method for class 'mappoly.pcmap3d'
print(x, ...)

Arguments

`input.mat`	an object of class `mappoly.input.matrix`
`p`	integer. The smoothing parameter for the principal curve. If `NULL` (default) this will be done using the leave-one-out cross validation
`n`	vector of integers or strings containing loci to be omitted from the analysis
`ndim`	number of dimensions to be considered in the multidimensional scaling procedure (default = 2)
`weight.exponent`	the exponent that should be used in the LOD score values to weight the MDS procedure (default = 2)
`verbose`	if `TRUE` (default), display information about the analysis
`x`	an object of class `mappoly.mds`
`...`	currently ignored

Value

A list containing:

`M`	the input distance map
`sm`	the unconstrained MDS results
`pc`	the principal curve results
`distmap`	a matrix of pairwise distances between loci where the columns are in the estimated order
`locimap`	a data frame of the loci containing the name and position of each locus in order of increasing distance
`length`	integer giving the total length of the segment
`removed`	a vector of the names of loci removed from the analysis
`scale`	the scaling factor from the MDS
`locikey`	a data frame showing the number associated with each locus name for interpreting the MDS configuration plot
`confplotno`	a data frame showing locus name associated with each number on the MDS configuration plots

Author(s)

Marcelo Mollinari, [email protected] mostly adapted from MDSmap codes, written by Katharine F. Preedy, [email protected]

References

Preedy, K. F., & Hackett, C. A. (2016). A rapid marker ordering approach for high-density genetic linkage maps in experimental autotetraploid populations using multidimensional scaling. _Theoretical and Applied Genetics_, 129(11), 2117-2132. doi:10.1007/s00122-016-2761-8

Examples

    s1 <- make_seq_mappoly(hexafake, 1:20)
    t1 <- est_pairwise_rf(s1, ncpus = 1)
    m1 <- rf_list_to_matrix(t1)
    o1 <- get_genomic_order(s1)
    s.go <- make_seq_mappoly(o1)
    plot(m1, ord = s.go$seq.mrk.names)
    mds.ord <- mds_mappoly(m1)
    plot(mds.ord)
    so <- make_seq_mappoly(mds.ord)
    plot(m1, ord = so$seq.mrk.names)
    plot(so$seq.num ~ I(so$genome.pos/1e6), 
         xlab = "Genome Position",
         ylab = "MDS position")

s1 <- make_seq_mappoly(hexafake, 1:20)
    t1 <- est_pairwise_rf(s1, ncpus = 1)
    m1 <- rf_list_to_matrix(t1)
    o1 <- get_genomic_order(s1)
    s.go <- make_seq_mappoly(o1)
    plot(m1, ord = s.go$seq.mrk.names)
    mds.ord <- mds_mappoly(m1)
    plot(mds.ord)
    so <- make_seq_mappoly(mds.ord)
    plot(m1, ord = so$seq.mrk.names)
    plot(so$seq.num ~ I(so$genome.pos/1e6), 
         xlab = "Genome Position",
         ylab = "MDS position")

Merge datasets

Description

This function merges two datasets of class mappoly.data. This can be useful when individuals of a population were genotyped using two or more techniques and have datasets in different files or formats. Please notice that the datasets should contain the same number of individuals and they must be represented identically in both datasets (e.g. Ind_1 in both datasets, not Ind_1 in one dataset and ind_1 or Ind.1 in the other).

Usage

merge_datasets(dat.1 = NULL, dat.2 = NULL)
merge_datasets(dat.1 = NULL, dat.2 = NULL)

Arguments

`dat.1`	the first dataset of class `mappoly.data` to be merged
`dat.2`	the second dataset of class `mappoly.data` to be merged (default = NULL); if `dat.2 = NULL`, the function returns `dat.1` only

Value

An object of class mappoly.data which contains all markers from both datasets. It will be a list with the following components:

`ploidy`	ploidy level
`n.ind`	number individuals
`n.mrk`	total number of markers
`ind.names`	the names of the individuals
`mrk.names`	the names of the markers
`dosage.p1`	a vector containing the dosage in parent P for all `n.mrk` markers
`dosage.p2`	a vector containing the dosage in parent Q for all `n.mrk` markers
`chrom`	a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence
`genome.pos`	Physical position of the markers into the sequence
`seq.ref`	if one or both datasets originated from read_vcf, it keeps reference alleles from sequencing platform, otherwise is NULL
`seq.alt`	if one or both datasets originated from read_vcf, it keeps alternative alleles from sequencing platform, otherwise is NULL
`all.mrk.depth`	if one or both datasets originated from read_vcf, it keeps marker read depths from sequencing, otherwise is NULL
`prob.thres`	(unused field)
`geno.dose`	a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by `ploidy_level + 1`
`geno`	if both datasets contain genotype distribution information, the final object will contain 'geno'. This is set to NULL otherwise
`nphen`	(0)
`phen`	(NULL)
`chisq.pval`	a vector containing p-values related to the chi-squared test of Mendelian segregation performed for all markers in both datasets
`kept`	if elim.redundant = TRUE when reading any dataset, holds all non-redundant markers
`elim.correspondence`	if elim.redundant = TRUE when reading any dataset, holds all non-redundant markers and its equivalence to the redundant ones

Author(s)

Gabriel Gesteira, [email protected]

References

Examples


## Loading a subset of SNPs from chromosomes 3 and 12 of sweetpotato dataset 
## (SNPs anchored to Ipomoea trifida genome)
dat <- NULL
for(i in c(3, 12)){
  cat("Loading chromosome", i, "...\n")
    tempfl <- tempfile(pattern = paste0("ch", i), fileext = ".vcf.gz")
    x <- "https://github.com/mmollina/MAPpoly_vignettes/raw/master/data/sweet_sample_ch"
    address <- paste0(x, i, ".vcf.gz")
    download.file(url = address, destfile = tempfl)
    dattemp <- read_vcf(file = tempfl, parent.1 = "PARENT1", parent.2 = "PARENT2",
                        ploidy = 6, verbose = FALSE)
    dat <- merge_datasets(dat, dattemp)
  cat("\n")
}
dat
plot(dat)


## Loading a subset of SNPs from chromosomes 3 and 12 of sweetpotato dataset 
## (SNPs anchored to Ipomoea trifida genome)
dat <- NULL
for(i in c(3, 12)){
  cat("Loading chromosome", i, "...\n")
    tempfl <- tempfile(pattern = paste0("ch", i), fileext = ".vcf.gz")
    x <- "https://github.com/mmollina/MAPpoly_vignettes/raw/master/data/sweet_sample_ch"
    address <- paste0(x, i, ".vcf.gz")
    download.file(url = address, destfile = tempfl)
    dattemp <- read_vcf(file = tempfl, parent.1 = "PARENT1", parent.2 = "PARENT2",
                        ploidy = 6, verbose = FALSE)
    dat <- merge_datasets(dat, dattemp)
  cat("\n")
}
dat
plot(dat)

Merge two maps

Description

Estimates the linkage phase and recombination fraction between pre-built maps and creates a new map by merging them.

Usage

merge_maps(
  map.list,
  twopt,
  thres.twopt = 10,
  genoprob.list = NULL,
  thres.hmm = "best",
  tol = 1e-04
)
merge_maps(
  map.list,
  twopt,
  thres.twopt = 10,
  genoprob.list = NULL,
  thres.hmm = "best",
  tol = 1e-04
)

Arguments

`map.list`	a list of objects of class `mappoly.map` to be merged.
`twopt`	an object of class `mappoly.twopt` containing the two-point information for all pairs of markers present in the original maps
`thres.twopt`	the threshold used to determine if the linkage phases compared via two-point analysis should be considered for the search space reduction (default = 3)
`genoprob.list`	a list of objects of class `mappoly.genoprob` containing the genotype probabilities for the maps to be merged. If `NULL` (default), the probabilities are computed.
`thres.hmm`	the threshold used to determine which linkage phase configurations should be returned when merging two maps. If "best" (default), returns only the best linkage phase configuration. NOTE: if merging multiple maps, it always uses the "best" linkage phase configuration at each block insertion.
`tol`	the desired accuracy (default = 10e-04)

Details

merge_maps uses two-point information, under a given LOD threshold, to reduce the linkage phase search space. The remaining linkage phases are tested using the genotype probabilities.

Value

A list of class mappoly.map with two elements:

i) info: a list containing information about the map, regardless of the linkage phase configuration:

`ploidy`	the ploidy level
`n.mrk`	number of markers
`seq.num`	a vector containing the (ordered) indices of markers in the map, according to the input file
`mrk.names`	the names of markers in the map
`seq.dose.p1`	a vector containing the dosage in parent 1 for all markers in the map
`seq.dose.p2`	a vector containing the dosage in parent 2 for all markers in the map
`chrom`	a vector indicating the sequence (usually chromosome) each marker belongs as informed in the input file. If not available, `chrom = NULL`
`genome.pos`	physical position (usually in megabase) of the markers into the sequence
`seq.ref`	reference base used for each marker (i.e. A, T, C, G). If not available, `seq.ref = NULL`
`seq.alt`	alternative base used for each marker (i.e. A, T, C, G). If not available, `seq.ref = NULL`
`chisq.pval`	a vector containing p-values of the chi-squared test of Mendelian segregation for all markers in the map
`data.name`	name of the dataset of class `mappoly.data`
`ph.thres`	the LOD threshold used to define the linkage phase configurations to test

ii) a list of maps with possible linkage phase configuration. Each map in the list is also a list containing

`seq.num`	a vector containing the (ordered) indices of markers in the map, according to the input file
`seq.rf`	a vector of size (`n.mrk - 1`) containing a sequence of recombination fraction between the adjacent markers in the map
`seq.ph`	linkage phase configuration for all markers in both parents
`loglike`	the hmm-based multipoint likelihood

Author(s)

Marcelo Mollinari, [email protected]

Examples


#### Tetraploid example #####
map1 <- get_submap(solcap.dose.map[[1]], 1:5)
map2 <- get_submap(solcap.dose.map[[1]], 6:15)
map3 <- get_submap(solcap.dose.map[[1]], 16:30)
full.map <- get_submap(solcap.dose.map[[1]], 1:30)
s <- make_seq_mappoly(tetra.solcap, full.map$maps[[1]]$seq.num)
twopt <- est_pairwise_rf(input.seq = s)
merged.maps <- merge_maps(map.list = list(map1, map2, map3), 
                        twopt = twopt,
                        thres.twopt = 3)
plot(merged.maps, mrk.names = TRUE)                       
plot(full.map, mrk.names = TRUE)                       
best.phase <- merged.maps$maps[[1]]$seq.ph
names.id <- names(best.phase$P)
compare_haplotypes(ploidy = 4, best.phase$P[names.id], 
                   full.map$maps[[1]]$seq.ph$P[names.id]) 
compare_haplotypes(ploidy = 4, best.phase$Q[names.id], 
                   full.map$maps[[1]]$seq.ph$Q[names.id])

#### Tetraploid example #####
map1 <- get_submap(solcap.dose.map[[1]], 1:5)
map2 <- get_submap(solcap.dose.map[[1]], 6:15)
map3 <- get_submap(solcap.dose.map[[1]], 16:30)
full.map <- get_submap(solcap.dose.map[[1]], 1:30)
s <- make_seq_mappoly(tetra.solcap, full.map$maps[[1]]$seq.num)
twopt <- est_pairwise_rf(input.seq = s)
merged.maps <- merge_maps(map.list = list(map1, map2, map3), 
                        twopt = twopt,
                        thres.twopt = 3)
plot(merged.maps, mrk.names = TRUE)                       
plot(full.map, mrk.names = TRUE)                       
best.phase <- merged.maps$maps[[1]]$seq.ph
names.id <- names(best.phase$P)
compare_haplotypes(ploidy = 4, best.phase$P[names.id], 
                   full.map$maps[[1]]$seq.ph$P[names.id]) 
compare_haplotypes(ploidy = 4, best.phase$Q[names.id], 
                   full.map$maps[[1]]$seq.ph$Q[names.id])

Physical versus genetic distance

Description

This function plots scatterplot(s) of physical distance (in Mbp) versus the genetic distance (in cM). Map(s) should be passed as a single object or a list of objects of class mappoly.map.

Usage

plot_genome_vs_map(
  map.list,
  phase.config = "best",
  same.ch.lg = FALSE,
  alpha = 1/5,
  size = 3
)
plot_genome_vs_map(
  map.list,
  phase.config = "best",
  same.ch.lg = FALSE,
  alpha = 1/5,
  size = 3
)

Arguments

`map.list`	A list or a single object of class `mappoly.map`
`phase.config`	A vector containing which phase configuration should be plotted. If `'best'` (default), plots the configuration with the highest likelihood for all elements in `'map.list'`
`same.ch.lg`	Logical. If `TRUE` displays only the scatterplots between the chromosomes and linkage groups with the same number. Default is `FALSE`.
`alpha`	transparency factor for SNPs points
`size`	size of the SNP points

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

  plot_genome_vs_map(solcap.mds.map, same.ch.lg = TRUE)
  plot_genome_vs_map(solcap.mds.map, same.ch.lg = FALSE, 
                     alpha = 1, size = 1/2)
 
plot_genome_vs_map(solcap.mds.map, same.ch.lg = TRUE)
  plot_genome_vs_map(solcap.mds.map, same.ch.lg = FALSE, 
                     alpha = 1, size = 1/2)

Genotypic information content

Description

This function plots the genotypic information content given an object of class mappoly.homoprob.

Usage

plot_GIC(hprobs, P = "P1", Q = "P2")
plot_GIC(hprobs, P = "P1", Q = "P2")

Arguments

`hprobs`	an object of class `mappoly.homoprob`
`P`	a string containing the name of parent P
`Q`	a string containing the name of parent Q

Examples


     w <- lapply(solcap.err.map[1:3], calc_genoprob)
     h.prob <- calc_homologprob(w)
     plot_GIC(h.prob)


w <- lapply(solcap.err.map[1:3], calc_genoprob)
     h.prob <- calc_homologprob(w)
     plot_GIC(h.prob)

Plot a genetic map

Description

This function plots a genetic linkage map(s) generated by MAPpoly. The map(s) should be passed as a single object or a list of objects of class mappoly.map.

Usage

plot_map_list(
  map.list,
  horiz = TRUE,
  col = "lightgray",
  title = "Linkage group"
)
plot_map_list(
  map.list,
  horiz = TRUE,
  col = "lightgray",
  title = "Linkage group"
)

Arguments

`map.list`	A list of objects or a single object of class `mappoly.map`
`horiz`	logical. If FALSE, the maps are plotted vertically with the first map to the left. If TRUE (default), the maps are plotted horizontally with the first at the bottom
`col`	a vector of colors for each linkage group. (default = 'lightgray') `ggstyle` produces maps using the default `ggplot` color palette.
`title`	a title (string) for the maps (default = 'Linkage group')

Value

A data.frame object containing the name of the markers and their genetic position

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

 ## hexafake map
 plot_map_list(maps.hexafake, horiz = FALSE)
 plot_map_list(maps.hexafake, col = c("#999999", "#E69F00", "#56B4E9"))
 
 ## solcap map
 plot_map_list(solcap.dose.map, col = "ggstyle")
 plot_map_list(solcap.dose.map, col = "mp_pallet3", horiz = FALSE)
 
## hexafake map
 plot_map_list(maps.hexafake, horiz = FALSE)
 plot_map_list(maps.hexafake, col = c("#999999", "#E69F00", "#56B4E9"))
 
 ## solcap map
 plot_map_list(solcap.dose.map, col = "ggstyle")
 plot_map_list(solcap.dose.map, col = "mp_pallet3", horiz = FALSE)

Plot object mappoly.map2

Description

Plot object mappoly.map2

Usage

plot_mappoly.map2(x)
plot_mappoly.map2(x)

Arguments

`x`	object of class `mappoly.map2`

Plot marker information

Description

Plots summary statistics for a given marker

Usage

plot_mrk_info(input.data, mrk)
plot_mrk_info(input.data, mrk)

Arguments

`input.data`	an object of class `mappoly.data`
`mrk`	marker name or position in the dataset

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

 plot_mrk_info(tetra.solcap.geno.dist, 2680)
 plot_mrk_info(tetra.solcap.geno.dist, "solcap_snp_c2_23828")
    
plot_mrk_info(tetra.solcap.geno.dist, 2680)
 plot_mrk_info(tetra.solcap.geno.dist, "solcap_snp_c2_23828")

Display genotypes imputed or changed by the HMM chain given a global genotypic error

Description

Outputs a graphical representation ggplot with the percent of data changed.

Usage

plot_progeny_dosage_change(
  map_list,
  error,
  verbose = TRUE,
  output_corrected = FALSE
)
plot_progeny_dosage_change(
  map_list,
  error,
  verbose = TRUE,
  output_corrected = FALSE
)

Arguments

`map_list`	a list of multiple `mappoly.map.list`
`error`	error rate used in global error in the 'calc_genoprob_error()'
`verbose`	if TRUE (default), current progress is shown; if FALSE, no output is produced
`output_corrected`	logical. if FALSE only the ggplot of the changed dosage is printed, if TRUE then a new corrected dosage matrix is output.

Value

A ggplot of the changed and imputed genotypic dosages

Author(s)

Jeekin Lau, [email protected], with optimization by Cristiane Taniguti, [email protected]

Examples

      x <- get_submap(solcap.err.map[[1]], 1:30, reestimate.rf = FALSE)   
      plot_progeny_dosage_change(list(x), error=0.05, output_corrected=FALSE) 
      corrected_matrix <- plot_progeny_dosage_change(list(x), error=0.05, 
      output_corrected=FALSE) #output corrected

x <- get_submap(solcap.err.map[[1]], 1:30, reestimate.rf = FALSE)   
      plot_progeny_dosage_change(list(x), error=0.05, output_corrected=FALSE) 
      corrected_matrix <- plot_progeny_dosage_change(list(x), error=0.05, 
      output_corrected=FALSE) #output corrected

Plots mappoly.homoprob

Description

Plots mappoly.homoprob

Usage

## S3 method for class 'mappoly.homoprob'
plot(
  x,
  stack = FALSE,
  lg = NULL,
  ind = NULL,
  use.plotly = TRUE,
  verbose = TRUE,
  ...
)
## S3 method for class 'mappoly.homoprob'
plot(
  x,
  stack = FALSE,
  lg = NULL,
  ind = NULL,
  use.plotly = TRUE,
  verbose = TRUE,
  ...
)

Arguments

`x`	an object of class `mappoly.homoprob`
`stack`	logical. If `TRUE`, probability profiles of all homologues are stacked in the plot (default = FALSE)
`lg`	indicates which linkage group should be plotted. If `NULL` (default), it plots the first linkage group. If `"all"`, it plots all linkage groups
`ind`	indicates which individuals should be plotted. It can be the position of the individuals in the dataset or it's name. If `NULL` (default), the function plots the first individual
`use.plotly`	if `TRUE` (default), it uses plotly interactive graphic
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced
`...`	unused arguments

Plots mappoly.prefpair.profiles

Description

Plots mappoly.prefpair.profiles

Usage

## S3 method for class 'mappoly.prefpair.profiles'
plot(
  x,
  type = c("pair.configs", "hom.pairs"),
  min.y.prof = 0,
  max.y.prof = 1,
  thresh = 0.01,
  P1 = "P1",
  P2 = "P2",
  ...
)
## S3 method for class 'mappoly.prefpair.profiles'
plot(
  x,
  type = c("pair.configs", "hom.pairs"),
  min.y.prof = 0,
  max.y.prof = 1,
  thresh = 0.01,
  P1 = "P1",
  P2 = "P2",
  ...
)

Arguments

`x`	an object of class `mappoly.prefpair.profiles`
`type`	a character string indicating which type of graphic is plotted: `"pair.configs"` (default) plots the preferential pairing profile for the pairing configurations or `"hom.pairs"` plots the preferential pairing profile for the homolog pairs
`min.y.prof`	lower bound for y axis on the probability profile graphic (default = 0)
`max.y.prof`	upper bound for y axis on the probability profile graphic (default = 1)
`thresh`	threshold for chi-square test (default = 0.01)
`P1`	a string containing the name of parent P1
`P2`	a string containing the name of parent P2
`...`	unused arguments

Summary of a set of markers

Description

Returns information related to a given set of markers

Usage

print_mrk(input.data, mrks)
print_mrk(input.data, mrks)

Arguments

`input.data`	an object `'mappoly.data'`
`mrks`	marker sequence index (integer vector)

Examples

 print_mrk(tetra.solcap.geno.dist, 1:5)
 print_mrk(hexafake, 256)
print_mrk(tetra.solcap.geno.dist, 1:5)
 print_mrk(hexafake, 256)

Data Input in fitPoly format

Description

Reads an external data file generated as output of saveMarkerModels. This function creates an object of class mappoly.data.

Usage

read_fitpoly(
  file.in,
  ploidy,
  parent1,
  parent2,
  offspring = NULL,
  filter.non.conforming = TRUE,
  elim.redundant = TRUE,
  parent.geno = c("joint", "max"),
  thresh.parent.geno = 0.95,
  prob.thres = 0.95,
  file.type = c("table", "csv"),
  verbose = TRUE
)
read_fitpoly(
  file.in,
  ploidy,
  parent1,
  parent2,
  offspring = NULL,
  filter.non.conforming = TRUE,
  elim.redundant = TRUE,
  parent.geno = c("joint", "max"),
  thresh.parent.geno = 0.95,
  prob.thres = 0.95,
  file.type = c("table", "csv"),
  verbose = TRUE
)

Arguments

`file.in`	a character string with the name of (or full path to) the input file
`ploidy`	the ploidy level
`parent1`	a character string containing the name (or pattern of genotype IDs) of parent 1
`parent2`	a character string containing the name (or pattern of genotype IDs) of parent 2
`offspring`	a character string containing the name (or pattern of genotype IDs) of the offspring individuals. If `NULL` (default) it considers all individuals as offsprings, except `parent1` and `parent2`.
`filter.non.conforming`	if `TRUE` (default) converts data points with unexpected genotypes (i.e. no double reduction) to 'NA'. See function `segreg_poly` for information on expected classes and their respective frequencies.
`elim.redundant`	logical. If `TRUE` (default), removes redundant markers during map construction, keeping them annotated to in order to include them in the final map.
`parent.geno`	indicates whether to use the joint probability `'joint'` (default) or the maximum probability of multiple replicates (if available) to assign dosage to parents. If there is one observation per parent, both options will yield the same results.
`thresh.parent.geno`	threshold probability to assign a dosage to parents. If the probability is smaller than `thresh.parent.geno`, the marker is discarded.
`prob.thres`	threshold probability to assign a dosage to offspring. If the probability is smaller than `prob.thres`, the data point is converted to 'NA'.
`file.type`	indicates whether the characters in the input file are separated by 'white spaces' ("table") or by commas ("csv").
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced

Value

An object of class mappoly.data which contains a list with the following components:

`ploidy`	ploidy level
`n.ind`	number individuals
`n.mrk`	total number of markers
`ind.names`	the names of the individuals
`mrk.names`	the names of the markers
`dosage.p1`	a vector containing the dosage in parent P for all `n.mrk` markers
`dosage.p2`	a vector containing the dosage in parent Q for all `n.mrk` markers
`chrom`	a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence
`genome.pos`	Physical position of the markers into the sequence
`seq.ref`	NULL (unused in this type of data)
`seq.alt`	NULL (unused in this type of data)
`all.mrk.depth`	NULL (unused in this type of data)
`geno.dose`	a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by `ploidy_level + 1`
`n.phen`	number of phenotypic traits
`phen`	a matrix containing the phenotypic data. The rows correspond to the traits and the columns correspond to the individuals
`kept`	if elim.redundant = TRUE, holds all non-redundant markers
`elim.correspondence`	if elim.redundant = TRUE, holds all non-redundant markers and its equivalence to the redundant ones

Author(s)

Marcelo Mollinari, [email protected]

References

Voorrips, R.E., Gort, G. & Vosman, B. (2011) Genotype calling in tetraploid species from bi-allelic marker data using mixture models. _BMC Bioinformatics_. doi:10.1186/1471-2105-12-172

Examples


#### Tetraploid Example
ft <- "https://raw.githubusercontent.com/mmollina/MAPpoly_vignettes/master/data/fitpoly.dat"
tempfl <- tempfile()
download.file(ft, destfile = tempfl)
fitpoly.dat <- read_fitpoly(file.in = tempfl, ploidy = 4, 
                            parent1 = "P1", parent2 = "P2", 
                            verbose = TRUE)
print(fitpoly.dat, detailed = TRUE)
plot(fitpoly.dat)
plot_mrk_info(fitpoly.dat, 37)


#### Tetraploid Example
ft <- "https://raw.githubusercontent.com/mmollina/MAPpoly_vignettes/master/data/fitpoly.dat"
tempfl <- tempfile()
download.file(ft, destfile = tempfl)
fitpoly.dat <- read_fitpoly(file.in = tempfl, ploidy = 4, 
                            parent1 = "P1", parent2 = "P2", 
                            verbose = TRUE)
print(fitpoly.dat, detailed = TRUE)
plot(fitpoly.dat)
plot_mrk_info(fitpoly.dat, 37)

Data Input

Description

Reads an external data file. The format of the file is described in the Details section. This function creates an object of class mappoly.data

Usage

read_geno(
  file.in,
  filter.non.conforming = TRUE,
  elim.redundant = TRUE,
  verbose = TRUE
)

## S3 method for class 'mappoly.data'
print(x, detailed = FALSE, ...)

## S3 method for class 'mappoly.data'
plot(x, thresh.line = 1e-05, ...)
read_geno(
  file.in,
  filter.non.conforming = TRUE,
  elim.redundant = TRUE,
  verbose = TRUE
)

## S3 method for class 'mappoly.data'
print(x, detailed = FALSE, ...)

## S3 method for class 'mappoly.data'
plot(x, thresh.line = 1e-05, ...)

Arguments

`file.in`	a character string with the name of (or full path to) the input file which contains the data to be read
`filter.non.conforming`	if `TRUE` (default) converts data points with unexpected genotypes (i.e. no double reduction) to 'NA'. See function `segreg_poly` for information on expected classes and their respective frequencies.
`elim.redundant`	logical. If `TRUE` (default), removes redundant markers during map construction, keeping them annotated to export to the final map.
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced
`x`	an object of class `mappoly.data`
`detailed`	if available, print the number of markers per sequence (default = FALSE)
`...`	currently ignored
`thresh.line`	position of a threshold line for p values of the segregation test (default = 10e-06)

Details

The first line of the input file contains the string ploidy followed by the ploidy level of the parents. The second and third lines contain the strings n.ind and n.mrk followed by the number of individuals in the dataset and the total number of markers, respectively. Lines number 4 and 5 contain the strings mrk.names and ind.names followed by a sequence of the names of the markers and the name of the individuals, respectively. Lines 6 and 7 contain the strings dosageP and dosageQ followed by a sequence of numbers containing the dosage of all markers in parent P and Q. Line 8, contains the string seq followed by a sequence of integer numbers indicating the chromosome each marker belongs. It can be any 'a priori' information regarding the physical distance between markers. For example, these numbers could refer to chromosomes, scaffolds or even contigs, in which the markers are positioned. If this information is not available for a particular marker, NA should be used. If this information is not available for any of the markers, the string seq should be followed by a single NA. Line number 9 contains the string seqpos followed by the physical position of the markers into the sequence. The physical position can be given in any unity of physical genomic distance (base pairs, for instance). However, the user should be able to make decisions based on these values, such as the occurrence of crossing overs, etc. Line number 10 should contain the string nphen followed by the number of phenotypic traits. Line number 11 is skipped (Usually used as a spacer). The next elements are strings containing the name of the phenotypic trait with no space characters followed by the phenotypic values. The number of lines should be the same number of phenotypic traits. NA represents missing values. The line number 12 + nphen is skipped. Finally, the last element is a table containing the dosage for each marker (rows) for each individual (columns). NA represents missing values.

Value

An object of class mappoly.data which contains a list with the following components:

`ploidy`	ploidy level
`n.ind`	number individuals
`n.mrk`	total number of markers
`ind.names`	the names of the individuals
`mrk.names`	the names of the markers
`dosage.p1`	a vector containing the dosage in parent P for all `n.mrk` markers
`dosage.p2`	a vector containing the dosage in parent Q for all `n.mrk` markers
`chrom`	a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence
`genome.pos`	Physical position of the markers into the sequence
`seq.ref`	NULL (unused in this type of data)
`seq.alt`	NULL (unused in this type of data)
`all.mrk.depth`	NULL (unused in this type of data)
`geno.dose`	a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by `ploidy_level + 1`
`n.phen`	number of phenotypic traits
`phen`	a matrix containing the phenotypic data. The rows correspond to the traits and the columns correspond to the individuals
`kept`	if elim.redundant = TRUE, holds all non-redundant markers
`elim.correspondence`	if elim.redundant = TRUE, holds all non-redundant markers and its equivalence to the redundant ones

Author(s)

Marcelo Mollinari, [email protected]

References

Examples


#### Tetraploid Example
fl1 = "https://raw.githubusercontent.com/mmollina/MAPpoly_vignettes/master/data/SolCAP_dosage"
tempfl <- tempfile()
download.file(fl1, destfile = tempfl)
SolCAP.dose <- read_geno(file.in  = tempfl)
print(SolCAP.dose, detailed = TRUE)
plot(SolCAP.dose)

#### Tetraploid Example
fl1 = "https://raw.githubusercontent.com/mmollina/MAPpoly_vignettes/master/data/SolCAP_dosage"
tempfl <- tempfile()
download.file(fl1, destfile = tempfl)
SolCAP.dose <- read_geno(file.in  = tempfl)
print(SolCAP.dose, detailed = TRUE)
plot(SolCAP.dose)

Data Input in CSV format

Description

Reads an external comma-separated values (CSV) data file. The format of the file is described in the Details section. This function creates an object of class mappoly.data.

Usage

read_geno_csv(
  file.in,
  ploidy,
  filter.non.conforming = TRUE,
  elim.redundant = TRUE,
  verbose = TRUE
)
read_geno_csv(
  file.in,
  ploidy,
  filter.non.conforming = TRUE,
  elim.redundant = TRUE,
  verbose = TRUE
)

Arguments

`file.in`	a character string with the name of (or full path to) the input file containing the data to be read
`ploidy`	the ploidy level
`filter.non.conforming`	if `TRUE` (default) converts data points with unexpected genotypes (i.e. no double reduction) to 'NA'. See function `segreg_poly` for information on expected classes and their respective frequencies.
`elim.redundant`	logical. If `TRUE` (default), removes redundant markers during map construction, keeping them annotated to export to the final map.
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced

Details

This is an alternative and a somewhat more straightforward version of the function read_geno. The input is a standard CSV file where the rows represent the markers, except for the first row which is used as a header. The first five columns contain the marker names, the dosage in parents 1 and 2, the chromosome information (i.e. chromosome, scaffold, contig, etc) and the position of the marker within the sequence. The remaining columns contain the dosage of the full-sib population. A tetraploid example of such file can be found in the Examples section.

Value

An object of class mappoly.data which contains a list with the following components:

`ploidy`	ploidy level
`n.ind`	number individuals
`n.mrk`	total number of markers
`ind.names`	the names of the individuals
`mrk.names`	the names of the markers
`dosage.p1`	a vector containing the dosage in parent P for all `n.mrk` markers
`dosage.p2`	a vector containing the dosage in parent Q for all `n.mrk` markers
`chrom`	a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence
`genome.pos`	Physical position of the markers into the sequence
`seq.ref`	NULL (unused in this type of data)
`seq.alt`	NULL (unused in this type of data)
`all.mrk.depth`	NULL (unused in this type of data)
`geno.dose`	a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by `ploidy_level + 1`
`n.phen`	number of phenotypic traits
`phen`	a matrix containing the phenotypic data. The rows correspond to the traits and the columns correspond to the individuals
`kept`	if elim.redundant = TRUE, holds all non-redundant markers
`elim.correspondence`	if elim.redundant = TRUE, holds all non-redundant markers and its equivalence to the redundant ones

Author(s)

Marcelo Mollinari, [email protected], with minor changes by Gabriel Gesteira, [email protected]

References

Examples


#### Tetraploid Example
ft = "https://raw.githubusercontent.com/mmollina/MAPpoly_vignettes/master/data/tetra_solcap.csv"
tempfl <- tempfile()
download.file(ft, destfile = tempfl)
SolCAP.dose <- read_geno_csv(file.in  = tempfl, ploidy = 4)
print(SolCAP.dose, detailed = TRUE)
plot(SolCAP.dose)

#### Tetraploid Example
ft = "https://raw.githubusercontent.com/mmollina/MAPpoly_vignettes/master/data/tetra_solcap.csv"
tempfl <- tempfile()
download.file(ft, destfile = tempfl)
SolCAP.dose <- read_geno_csv(file.in  = tempfl, ploidy = 4)
print(SolCAP.dose, detailed = TRUE)
plot(SolCAP.dose)

Data Input

Description

Reads an external data file. The format of the file is described in the Details section. This function creates an object of class mappoly.data

Usage

read_geno_prob(
  file.in,
  prob.thres = 0.95,
  filter.non.conforming = TRUE,
  elim.redundant = TRUE,
  verbose = TRUE
)
read_geno_prob(
  file.in,
  prob.thres = 0.95,
  filter.non.conforming = TRUE,
  elim.redundant = TRUE,
  verbose = TRUE
)

Arguments

`file.in`	a character string with the name of (or full path to) the input file which contains the data to be read
`prob.thres`	probability threshold to associate a marker call to a dosage. Markers with maximum genotype probability smaller than `prob.thres` are considered as missing data for the dosage calling purposes (default = 0.95)
`filter.non.conforming`	if `TRUE` (default) converts data points with unexpected genotypes (i.e. no double reduction) to 'NA'. See function `segreg_poly` for information on expected classes and their respective frequencies.
`elim.redundant`	logical. If `TRUE` (default), removes redundant markers during map construction, keeping them annotated to export to the final map.
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced

Details

The first line of the input file contains the string ploidy followed by the ploidy level of the parents. The second and third lines contains the strings n.ind and n.mrk followed by the number of individuals in the dataset and the total number of markers, respectively. Lines number 4 and 5 contain the string mrk.names and ind.names followed by a sequence of the names of the markers and the name of the individuals, respectively. Lines 6 and 7 contain the strings dosageP and dosageQ followed by a sequence of numbers containing the dosage of all markers in parent P and Q. Line 8, contains the string seq followed by a sequence of integer numbers indicating the chromosome each marker belongs. It can be any 'a priori' information regarding the physical distance between markers. For example, these numbers could refer to chromosomes, scaffolds or even contigs, in which the markers are positioned. If this information is not available for a particular marker, NA should be used. If this information is not available for any of the markers, the string seq should be followed by a single NA. Line number 9 contains the string seqpos followed by the physical position of the markers into the sequence. The physical position can be given in any unity of physical genomic distance (base pairs, for instance). However, the user should be able to make decisions based on these values, such as the occurrence of crossing overs, etc. Line number 10 should contain the string nphen followed by the number of phenotypic traits. Line number 11 is skipped (Usually used as a spacer). The next elements are strings containing the name of the phenotypic trait with no space characters followed by the phenotypic values. The number of lines should be the same number of phenotypic traits. NA represents missing values. The line number 12 + nphen is skipped. Finally, the last element is a table containing the probability distribution for each combination of marker and offspring. The first two columns represent the marker and the offspring, respectively. The remaining elements represent the probability associated with each one of the possible dosages. NA represents missing data.

Value

an object of class mappoly.data which contains a list with the following components:

`ploidy`	ploidy level
`n.ind`	number individuals
`n.mrk`	total number of markers
`ind.names`	the names of the individuals
`mrk.names`	the names of the markers
`dosage.p1`	a vector containing the dosage in parent P for all `n.mrk` markers
`dosage.p2`	a vector containing the dosage in parent Q for all `n.mrk` markers
`chrom`	a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence
`genome.pos`	physical position of the markers into the sequence
`seq.ref`	NULL (unused in this type of data)
`seq.alt`	NULL (unused in this type of data)
`all.mrk.depth`	NULL (unused in this type of data)
`prob.thres`	probability threshold to associate a marker call to a dosage. Markers with maximum genotype probability smaller than 'prob.thres' were considered as missing data in the 'geno.dose' matrix
`geno.dose`	a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by `ploidy_level + 1`
`geno`	a data.frame containing the probability distribution for each combination of marker and offspring. The first two columns represent the marker and the offspring, respectively. The remaining elements represent the probability associated to each one of the possible dosages. Missing data are converted from NA to the expected segregation ratio using function `segreg_poly`
`n.phen`	number of phenotypic traits
`phen`	a matrix containing the phenotypic data. The rows correspond to the traits and the columns correspond to the individuals
`chisq.pval`	a vector containing p-values related to the chi-squared test of Mendelian segregation performed for all markers
`kept`	if elim.redundant = TRUE, holds all non-redundant markers
`elim.correspondence`	if elim.redundant = TRUE, holds all non-redundant markers and its equivalence to the redundant ones

Author(s)

Marcelo Mollinari, [email protected]

References

Examples


#### Tetraploid Example
ft = "https://raw.githubusercontent.com/mmollina/MAPpoly_vignettes/master/data/hexa_sample"
tempfl <- tempfile()
download.file(ft, destfile = tempfl)
SolCAP.dose.prob <- read_geno_prob(file.in  = tempfl)
print(SolCAP.dose.prob, detailed = TRUE)
plot(SolCAP.dose.prob)


#### Tetraploid Example
ft = "https://raw.githubusercontent.com/mmollina/MAPpoly_vignettes/master/data/hexa_sample"
tempfl <- tempfile()
download.file(ft, destfile = tempfl)
SolCAP.dose.prob <- read_geno_prob(file.in  = tempfl)
print(SolCAP.dose.prob, detailed = TRUE)
plot(SolCAP.dose.prob)

Data Input VCF

Description

Reads an external VCF file and creates an object of class mappoly.data

Usage

read_vcf(
  file.in,
  parent.1,
  parent.2,
  ploidy = NA,
  filter.non.conforming = TRUE,
  thresh.line = 0.05,
  min.gt.depth = 0,
  min.av.depth = 0,
  max.missing = 1,
  elim.redundant = TRUE,
  verbose = TRUE,
  read.geno.prob = FALSE,
  prob.thres = 0.95
)
read_vcf(
  file.in,
  parent.1,
  parent.2,
  ploidy = NA,
  filter.non.conforming = TRUE,
  thresh.line = 0.05,
  min.gt.depth = 0,
  min.av.depth = 0,
  max.missing = 1,
  elim.redundant = TRUE,
  verbose = TRUE,
  read.geno.prob = FALSE,
  prob.thres = 0.95
)

Arguments

`file.in`	a character string with the name of (or full path to) the input file which contains the data (VCF format)
`parent.1`	a character string containing the name of parent 1
`parent.2`	a character string containing the name of parent 2
`ploidy`	the species ploidy (optional, it will be automatically detected)
`filter.non.conforming`	if `TRUE` (default) converts data points with unexpected genotypes (i.e. no double reduction) to 'NA'. See function `segreg_poly` for information on expected classes and their respective frequencies.
`thresh.line`	threshold used for p-values on segregation test (default = 0.05)
`min.gt.depth`	minimum genotype depth to keep information. If the genotype depth is below `min.gt.depth`, it will be replaced with NA (default = 0)
`min.av.depth`	minimum average depth to keep markers (default = 0)
`max.missing`	maximum proportion of missing data to keep markers (range = 0-1; default = 1)
`elim.redundant`	logical. If `TRUE` (default), removes redundant markers during map construction, keeping them annotated to export to the final map.
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced
`read.geno.prob`	If genotypic probabilities are available (PL field), generates a probability-based dataframe (default = `FALSE`).
`prob.thres`	probability threshold to associate a marker call to a dosage. Markers with maximum genotype probability smaller than `prob.thres` are considered as missing data for the dosage calling purposes (default = 0.95)

Details

This function can handle .vcf files versions 4.0 or higher. The ploidy can be automatically detected, but it is highly recommended that you inform it to check for mismatches. All individual and marker names will be kept as they are in the .vcf file.

Value

An object of class mappoly.data which contains a list with the following components:

`ploidy`	ploidy level
`n.ind`	number individuals
`n.mrk`	total number of markers
`ind.names`	the names of the individuals
`mrk.names`	the names of the markers
`dosage.p1`	a vector containing the dosage in parent P for all `n.mrk` markers
`dosage.p2`	a vector containing the dosage in parent Q for all `n.mrk` markers
`chrom`	a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence
`genome.pos`	Physical position of the markers into the sequence
`seq.ref`	Reference base used for each marker (i.e. A, T, C, G)
`seq.alt`	Alternative base used for each marker (i.e. A, T, C, G)
`prob.thres`	(unused field)
`geno.dose`	a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by `ploidy_level + 1`
`geno`	a dataframe containing all genotypic probabilities columns for each marker and individual combination (rows). Missing data are represented by `ploidy_level + 1`
`nphen`	(unused field)
`phen`	(unused field)
`all.mrk.depth`	DP information for all markers on VCF file
`chisq.pval`	a vector containing p-values related to the chi-squared test of Mendelian segregation performed for all markers
`kept`	if elim.redundant = TRUE, holds all non-redundant markers
`elim.correspondence`	if elim.redundant = TRUE, holds all non-redundant markers and its equivalence to the redundant ones

Author(s)

Gabriel Gesteira, [email protected]

References

Examples


## Hexaploid sweetpotato: Subset of chromosome 3
fl = "https://github.com/mmollina/MAPpoly_vignettes/raw/master/data/sweet_sample_ch3.vcf.gz"
tempfl <- tempfile(pattern = 'chr3_', fileext = '.vcf.gz')
download.file(fl, destfile = tempfl)
dat.dose.vcf = read_vcf(file = tempfl, parent.1 = "PARENT1", parent.2 = "PARENT2")
print(dat.dose.vcf)
plot(dat.dose.vcf)


## Hexaploid sweetpotato: Subset of chromosome 3
fl = "https://github.com/mmollina/MAPpoly_vignettes/raw/master/data/sweet_sample_ch3.vcf.gz"
tempfl <- tempfile(pattern = 'chr3_', fileext = '.vcf.gz')
download.file(fl, destfile = tempfl)
dat.dose.vcf = read_vcf(file = tempfl, parent.1 = "PARENT1", parent.2 = "PARENT2")
print(dat.dose.vcf)
plot(dat.dose.vcf)

Re-estimate the recombination fractions in a genetic map

Description

This function re-estimates the recombination fractions between all markers in a given map.

Usage

reest_rf(
  input.map,
  input.mat = NULL,
  tol = 0.01,
  phase.config = "all",
  method = c("hmm", "ols", "wMDS_to_1D_pc"),
  weight = TRUE,
  verbose = TRUE,
  high.prec = FALSE,
  max.rf.to.break.EM = 0.5,
  input.mds = NULL
)
reest_rf(
  input.map,
  input.mat = NULL,
  tol = 0.01,
  phase.config = "all",
  method = c("hmm", "ols", "wMDS_to_1D_pc"),
  weight = TRUE,
  verbose = TRUE,
  high.prec = FALSE,
  max.rf.to.break.EM = 0.5,
  input.mds = NULL
)

Arguments

`input.map`	An object of class `mappoly.map`
`input.mat`	An object of class `mappoly.rf.matrix`
`tol`	tolerance for determining convergence (default = 10e-03)
`phase.config`	which phase configuration should be used. "best" (default) will choose the maximum likelihood configuration
`method`	indicates whether to use `'hmm'` (Hidden Markov Models), `'ols'` (Ordinary Least Squares) or `'wMDS_to_1D_pc'` (weighted MDS followed by fitting a one dimensional principal curve) to re-estimate the recombination fractions.
`weight`	if `TRUE` (default), it uses the LOD scores to perform a weighted regression when the Ordinary Least Squares is chosen
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced
`high.prec`	logical. If `TRUE` uses high precision (long double) numbers in the HMM procedure implemented in C++, which can take a long time to perform (default = FALSE)
`max.rf.to.break.EM`	for internal use only.
`input.mds`	An object of class `mappoly.map`

Value

An updated object of class mappoly.pcmap whose order was used in the input.map

References

Stam P (1993) Construction of integrated genetic-linkage maps by means of a new computer package: Joinmap. _Plant J_ 3:739–744 doi:10.1111/j.1365-313X.1993.00739.x

Reverse map

Description

Provides the reverse of a given map.

Usage

rev_map(input.map)
rev_map(input.map)

Arguments

input.map

an object of class mappoly.map

Author(s)

Marcelo Mollinari, [email protected]

Examples

plot_genome_vs_map(solcap.mds.map[[1]])
plot_genome_vs_map(rev_map(solcap.mds.map[[1]]))
plot_genome_vs_map(solcap.mds.map[[1]])
plot_genome_vs_map(rev_map(solcap.mds.map[[1]]))

Recombination fraction list to matrix

Description

Transforms the recombination fraction list contained in an object of class mappoly.twopt or mappoly.twopt2 into a recombination fraction matrix

Usage

rf_list_to_matrix(
  input.twopt,
  thresh.LOD.ph = 0,
  thresh.LOD.rf = 0,
  thresh.rf = 0.5,
  ncpus = 1L,
  shared.alleles = FALSE,
  verbose = TRUE
)

## S3 method for class 'mappoly.rf.matrix'
print(x, ...)

## S3 method for class 'mappoly.rf.matrix'
plot(
  x,
  type = c("rf", "lod"),
  ord = NULL,
  rem = NULL,
  main.text = NULL,
  index = FALSE,
  fact = 1,
  ...
)
rf_list_to_matrix(
  input.twopt,
  thresh.LOD.ph = 0,
  thresh.LOD.rf = 0,
  thresh.rf = 0.5,
  ncpus = 1L,
  shared.alleles = FALSE,
  verbose = TRUE
)

## S3 method for class 'mappoly.rf.matrix'
print(x, ...)

## S3 method for class 'mappoly.rf.matrix'
plot(
  x,
  type = c("rf", "lod"),
  ord = NULL,
  rem = NULL,
  main.text = NULL,
  index = FALSE,
  fact = 1,
  ...
)

Arguments

`input.twopt`	an object of class `mappoly.twopt` or `mappoly.twopt2`
`thresh.LOD.ph`	LOD score threshold for linkage phase configurations (default = 0)
`thresh.LOD.rf`	LOD score threshold for recombination fractions (default = 0)
`thresh.rf`	the threshold used for recombination fraction filtering (default = 0.5)
`ncpus`	number of parallel processes (i.e. cores) to spawn (default = 1)
`shared.alleles`	if `TRUE`, computes two matrices (for both parents) indicating the number of homologues that share alleles (default = FALSE)
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced
`x`	an object of class `mappoly.rf.matrix`
`...`	currently ignored
`type`	type of matrix that should be printed. Can be one of the following: `"rf"`, for recombination fraction or `"lod"` for LOD Score
`ord`	the order in which the markers should be plotted (default = NULL)
`rem`	which markers should be removed from the heatmap (default = NULL)
`main.text`	a character string as the title of the heatmap (default = NULL)
`index`	`logical` should the name of the markers be printed in the diagonal of the heatmap? (default = FALSE)
`fact`	positive integer. factor expressed as number of cells to be aggregated (default = 1, no aggregation)

Details

thresh_LOD_ph should be set in order to only select recombination fractions that have LOD scores associated to the linkage phase configuration higher than thresh_LOD_ph when compared to the second most likely linkage phase configuration.

Value

A list containing two matrices. The first one contains the filtered recombination fraction and the second one contains the information matrix

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

    all.mrk <- make_seq_mappoly(hexafake, 1:20)
    red.mrk <- elim_redundant(all.mrk)
    unique.mrks <- make_seq_mappoly(red.mrk)
    all.pairs <- est_pairwise_rf(input.seq = unique.mrks,
                               ncpus = 1,
                               verbose = TRUE)

    ## Full recombination fraction matrix
    mat.full <- rf_list_to_matrix(input.twopt = all.pairs)
    plot(mat.full)
    plot(mat.full, type = "lod")
 
all.mrk <- make_seq_mappoly(hexafake, 1:20)
    red.mrk <- elim_redundant(all.mrk)
    unique.mrks <- make_seq_mappoly(red.mrk)
    all.pairs <- est_pairwise_rf(input.seq = unique.mrks,
                               ncpus = 1,
                               verbose = TRUE)

    ## Full recombination fraction matrix
    mat.full <- rf_list_to_matrix(input.twopt = all.pairs)
    plot(mat.full)
    plot(mat.full, type = "lod")

Remove markers that do not meet a LOD criteria

Description

Remove markers that do not meet a LOD and recombination fraction criteria for at least a percentage of the pairwise marker combinations. It also removes markers with strong evidence of linkage across the whole linkage group (false positive).

Usage

rf_snp_filter(
  input.twopt,
  thresh.LOD.ph = 5,
  thresh.LOD.rf = 5,
  thresh.rf = 0.15,
  probs = c(0.05, 1),
  diag.markers = NULL,
  mrk.order = NULL,
  ncpus = 1L,
  diagnostic.plot = TRUE,
  breaks = 100
)
rf_snp_filter(
  input.twopt,
  thresh.LOD.ph = 5,
  thresh.LOD.rf = 5,
  thresh.rf = 0.15,
  probs = c(0.05, 1),
  diag.markers = NULL,
  mrk.order = NULL,
  ncpus = 1L,
  diagnostic.plot = TRUE,
  breaks = 100
)

Arguments

`input.twopt`	an object of class `mappoly.twopt`
`thresh.LOD.ph`	LOD score threshold for linkage phase configuration (default = 5)
`thresh.LOD.rf`	LOD score threshold for recombination fraction (default = 5)
`thresh.rf`	threshold for recombination fractions (default = 0.15)
`probs`	indicates the probability corresponding to the filtering quantiles. (default = c(0.05, 1))
`diag.markers`	A window where marker pairs should be considered. If NULL (default), all markers are considered.
`mrk.order`	marker order. Only has effect if 'diag.markers' is not NULL
`ncpus`	number of parallel processes (i.e. cores) to spawn (default = 1)
`diagnostic.plot`	if `TRUE` produces a diagnostic plot
`breaks`	number of cells for the histogram

Details

thresh.LOD.ph should be set in order to only select recombination fractions that have LOD scores associated to the linkage phase configuration higher than thresh_LOD_ph when compared to the second most likely linkage phase configuration. That action usually eliminates markers that are unlinked to the set of analyzed markers.

Value

A filtered object of class mappoly.sequence. See make_seq_mappoly for details

Author(s)

Marcelo Mollinari, [email protected] with updates by Gabriel Gesteira, [email protected]

References

Examples

    all.mrk <- make_seq_mappoly(hexafake, 1:20)
    red.mrk <- elim_redundant(all.mrk)
    unique.mrks <- make_seq_mappoly(red.mrk)
    all.pairs <- est_pairwise_rf(input.seq = unique.mrks,
                               ncpus = 1,
                               verbose = TRUE)

    ## Full recombination fraction matrix
    mat.full <- rf_list_to_matrix(input.twopt = all.pairs)
    plot(mat.full)

    ## Removing disruptive SNPs
    tpt.filt <- rf_snp_filter(all.pairs, 2, 2, 0.07, probs = c(0.15, 1))
    p1.filt <- make_pairs_mappoly(input.seq = tpt.filt, input.twopt = all.pairs)
    m1.filt <- rf_list_to_matrix(input.twopt = p1.filt)
    plot(mat.full, main.text = "LG1")
    plot(m1.filt, main.text = "LG1.filt")

all.mrk <- make_seq_mappoly(hexafake, 1:20)
    red.mrk <- elim_redundant(all.mrk)
    unique.mrks <- make_seq_mappoly(red.mrk)
    all.pairs <- est_pairwise_rf(input.seq = unique.mrks,
                               ncpus = 1,
                               verbose = TRUE)

    ## Full recombination fraction matrix
    mat.full <- rf_list_to_matrix(input.twopt = all.pairs)
    plot(mat.full)

    ## Removing disruptive SNPs
    tpt.filt <- rf_snp_filter(all.pairs, 2, 2, 0.07, probs = c(0.15, 1))
    p1.filt <- make_pairs_mappoly(input.seq = tpt.filt, input.twopt = all.pairs)
    m1.filt <- rf_list_to_matrix(input.twopt = p1.filt)
    plot(mat.full, main.text = "LG1")
    plot(m1.filt, main.text = "LG1.filt")

Polysomic segregation frequency

Description

Computes the polysomic segregation frequency given a ploidy level and the dosage of the locus in both parents. It does not consider double reduction.

Usage

segreg_poly(ploidy, dP, dQ)
segreg_poly(ploidy, dP, dQ)

Arguments

`ploidy`	the ploidy level
`dP`	the dosage in parent P
`dQ`	the dosage in parent Q

Value

a vector containing the expected segregation frequency for all possible genotypic classes.

Author(s)

Marcelo Mollinari, [email protected]

References

Serang O, Mollinari M, Garcia AAF (2012) Efficient Exact Maximum a Posteriori Computation for Bayesian SNP Genotyping in Polyploids. _PLoS ONE_ 7(2): e30906.

Examples

# autohexaploid with two and three doses in parents P and Q,
# respectively
seg <- segreg_poly(ploidy = 6, dP = 2, dQ = 3)
barplot(seg, las = 2)

# autohexaploid with two and three doses in parents P and Q,
# respectively
seg <- segreg_poly(ploidy = 6, dP = 2, dQ = 3)
barplot(seg, las = 2)

Simulate homology groups

Description

Simulate two homology groups (one for each parent) and their linkage phase configuration.

Usage

sim_homologous(ploidy, n.mrk, prob.dose = NULL, seed = NULL)
sim_homologous(ploidy, n.mrk, prob.dose = NULL, seed = NULL)

Arguments

`ploidy`	ploidy level. Must be an even number
`n.mrk`	number of markers
`prob.dose`	a vector indicating the proportion of markers for different dosage to be simulated (default = NULL)
`seed`	random number generator seed

Details

This function prevents the simulation of linkage phase configurations which are impossible to estimate via two point methods

Value

a list containing the following components:

`hom.allele.p`	a list of vectors containing linkage phase configurations. Each vector contains the numbers of the homologous chromosomes in which the alleles are located. For instance, a vector containing $(1,3,4)$ means that the marker has three doses located in the chromosomes 1, 3 and 4. For zero doses, use 0
`p`	contains the indices of the starting positions of the dosages, considering that the vectors contained in `p` are concatenated. Markers with no doses (zero doses are also considered)
`hom.allele.q`	Analogously to `hom.allele.p`
`q`	Analogously to `p`
`ploidy`	ploidy level

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

    h.temp <- sim_homologous(ploidy = 6, n.mrk = 20)

h.temp <- sim_homologous(ploidy = 6, n.mrk = 20)

Resulting maps from `tetra.solcap`

Description

A list containing 12 linkage groups estimated using genomic order and dosage call

Usage

solcap.dose.map
solcap.dose.map

Format

A list containing 12 objects of class mappoly.map, each one representing one linkage group in the tetra.solcap dataset.

Resulting maps from `tetra.solcap`

Description

A list containing 12 linkage groups estimated using genomic order, dosage call and global calling error

Usage

solcap.err.map
solcap.err.map

Format

A list containing 12 objects of class mappoly.map, each one representing one linkage group in the tetra.solcap dataset.

Resulting maps from `tetra.solcap`

Description

A list containing 12 linkage groups estimated using mds_mappoly order and dosage call

Usage

solcap.mds.map
solcap.mds.map

Format

A list containing 12 objects of class mappoly.map, each one representing one linkage group in the tetra.solcap dataset.

Resulting maps from `tetra.solcap.geno.dist`

Description

A list containing 12 linkage groups estimated using genomic order and prior probability distribution

Usage

solcap.prior.map
solcap.prior.map

Format

A list containing 12 objects of class mappoly.map, each one representing one linkage group in the tetra.solcap.geno.dist dataset.

Divides map in sub-maps and re-phase them

Description

The function splits the input map in sub-maps given a distance threshold of neighboring markers and evaluates alternative phases between the sub-maps.

Usage

split_and_rephase(
  input.map,
  twopt,
  gap.threshold = 5,
  size.rem.cluster = 1,
  phase.config = "best",
  thres.twopt = 3,
  thres.hmm = "best",
  tol.merge = 0.001,
  tol.final = 0.001,
  verbose = TRUE
)
split_and_rephase(
  input.map,
  twopt,
  gap.threshold = 5,
  size.rem.cluster = 1,
  phase.config = "best",
  thres.twopt = 3,
  thres.hmm = "best",
  tol.merge = 0.001,
  tol.final = 0.001,
  verbose = TRUE
)

Arguments

`input.map`	an object of class `mappoly.map`
`twopt`	an object of class `mappoly.twopt` containing the two-point information for the markers contained in `input.map`
`gap.threshold`	distance threshold of neighboring markers where the map should be spitted. The default value is 5 cM
`size.rem.cluster`	the size of the marker cluster (in number of markers) from which the cluster should be removed. The default value is 1
`phase.config`	which phase configuration should be used. "best" (default) will choose the maximum likelihood phase configuration
`thres.twopt`	the threshold used to determine if the linkage phases compared via two-point analysis should be considered for the search space reduction (default = 3)
`thres.hmm`	the threshold used to determine which linkage phase configurations should be returned when merging two maps. If "best" (default), returns only the best linkage phase configuration. NOTE: if merging multiple maps, it always uses the "best" linkage phase configuration at each block insertion.
`tol.merge`	the desired accuracy for merging maps (default = 10e-04)
`tol.final`	the desired accuracy for the final map (default = 10e-04)
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced

Value

An object of class mappoly.map

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

 map <- get_submap(solcap.dose.map[[1]], 1:20, verbose = FALSE)
 tpt <- est_pairwise_rf(make_seq_mappoly(map))
 new.map <- split_and_rephase(map, tpt, 1, 1)
 map
 new.map
 plot_map_list(list(old.map = map, new.map = new.map), col = "ggstyle")

map <- get_submap(solcap.dose.map[[1]], 1:20, verbose = FALSE)
 tpt <- est_pairwise_rf(make_seq_mappoly(map))
 new.map <- split_and_rephase(map, tpt, 1, 1)
 map
 new.map
 plot_map_list(list(old.map = map, new.map = new.map), col = "ggstyle")

Summary maps

Description

This function generates a brief summary table of a list of mappoly.map objects

Usage

summary_maps(map.list, verbose = TRUE)
summary_maps(map.list, verbose = TRUE)

Arguments

`map.list`	a list of objects of class `mappoly.map`
`verbose`	if `TRUE` (default), the current progress is shown; if `FALSE`, no output is produced

Value

a data frame containing a brief summary of all maps contained in map.list

Author(s)

Gabriel Gesteira, [email protected]

Examples

tetra.sum <- summary_maps(solcap.err.map)
tetra.sum
tetra.sum <- summary_maps(solcap.err.map)
tetra.sum

Autotetraploid potato dataset.

Description

A dataset of the B2721 population which derived from a cross between two tetraploid potato varieties: Atlantic × B1829-5. The population comprises 160 offsprings genotyped with the SolCAP Infinium 8303 potato array. The original data set can be found in [The Solanaceae Coordinated Agricultural Project (SolCAP) webpage](http://solcap.msu.edu/potato_infinium.shtml) The dataset also contains the genomic order of the SNPs from the Solanum tuberosum genome version 4.03. The genotype calling was performed using the fitPoly R package.

Usage

tetra.solcap
tetra.solcap

Format

An object of class mappoly.data which contains a list with the following components:

ploidy: ploidy level = 4
n.ind: number individuals = 160
n.mrk: total number of markers = 4017
ind.names: the names of the individuals
mrk.names: the names of the markers
dosage.p1: a vector containing the dosage in parent P for all n.mrk markers
dosage.p2: a vector containing the dosage in parent Q for all n.mrk markers
chrom: a vector indicating the chromosome each marker belongs. Zero indicates that the marker was not assigned to any sequence
genome.pos: Physical position of the markers into the sequence
geno.dose: a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by ploidy_level + 1 = 5
n.phen: There are no phenotypes in this simulation
phen: There are no phenotypes in this simulation
chisq.pval: vector containing p-values for all markers associated to the chi-square test for the expected segregation patterns under Mendelian segregation

Autotetraploid potato dataset with genotype probabilities.

Description

Usage

tetra.solcap.geno.dist
tetra.solcap.geno.dist

Format

An object of class mappoly.data which contains a list with the following components:

ploidy: ploidy level = 4
n.ind: number individuals = 160
n.mrk: total number of markers = 4017
ind.names: the names of the individuals
mrk.names: the names of the markers
dosage.p1: a vector containing the dosage in parent P for all n.mrk markers
dosage.p2: a vector containing the dosage in parent Q for all n.mrk markers
chrom: a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence
genome.pos: Physical position of the markers into the sequence
prob.thres = 0.95: probability threshold to associate a marker call to a dosage. Markers with maximum genotype probability smaller than 'prob.thres' are considered as missing data for the dosage calling purposes
geno: a data.frame containing the probability distribution for each combination of marker and offspring. The first two columns represent the marker and the offspring, respectively. The remaining elements represent the probability associated to each one of the possible dosages
geno.dose: a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by ploidy_level + 1 = 5
n.phen: There are no phenotypes in this simulation
phen: There are no phenotypes in this simulation

Add markers that are informative in both parents using HMM approach and evaluating difference in LOD and gap size

Description

Add markers that are informative in both parents using HMM approach and evaluating difference in LOD and gap size

Usage

update_framework_map(
  input.map.list,
  input.seq,
  twopt,
  thres.twopt = 10,
  init.LOD = 30,
  verbose = TRUE,
  method = "hmm",
  input.mds = NULL,
  max.rounds = 50,
  size.rem.cluster = 2,
  gap.threshold = 4
)
update_framework_map(
  input.map.list,
  input.seq,
  twopt,
  thres.twopt = 10,
  init.LOD = 30,
  verbose = TRUE,
  method = "hmm",
  input.mds = NULL,
  max.rounds = 50,
  size.rem.cluster = 2,
  gap.threshold = 4
)

Arguments

`input.map.list`	list containing three `mappoly.map` objects:1) map built with markers with segregation information from parent 1; 2) map built with markers with segregation information from parent 2; 3) maps in 1 and 2 merged
`input.seq`	object of class `mappoly.sequence` containing all markers for specific group
`twopt`	object of class `mappoly.twopt`
`thres.twopt`	the LOD threshold used to determine if the linkage phases compared via two-point analysis should be considered for the search space reduction (default = 5)
`init.LOD`	the LOD threshold used to determine if the marker will be included or not after hmm analysis (default = 30)
`verbose`	If TRUE (default), current progress is shown; if FALSE, no output is produced
`method`	indicates whether to use 'hmm' (Hidden Markov Models), 'ols' (Ordinary Least Squares) or 'wMDS_to_1D_pc' (weighted MDS followed by fitting a one dimensional principal curve) to re-estimate the recombination fractions after adding markers
`input.mds`	An object of class `mappoly.map`
`max.rounds`	integer defining number of times to try to fit the remaining markers in the sequence
`size.rem.cluster`	threshold for number of markers that must contain in a segment after a gap is removed to keep this segment in the sequence
`gap.threshold`	threshold for gap size

Value

object of class mappoly.map2

Author(s)

Marcelo Mollinari, [email protected] with documentation and minor modifications by Cristiane Taniguti [email protected]

Update map

Description

This function takes an object of class mappoly.map and checks for removed redundant markers in the original dataset. Once redundant markers are found, they are re-added to the map in their respective equivalent positions and another HMM round is performed.

Usage

update_map(input.maps, verbose = TRUE)
update_map(input.maps, verbose = TRUE)

Arguments

`input.maps`	a single map or a list of maps of class `mappoly.map`
`verbose`	if TRUE (default), shows information about each update process

Value

an updated map (or list of maps) of class mappoly.map, containing the original map(s) plus redundant markers

Author(s)

Gabriel Gesteira, [email protected]

Examples

orig.map <- solcap.err.map
up.map <- lapply(solcap.err.map, update_map)
summary_maps(orig.map)
summary_maps(up.map)
orig.map <- solcap.err.map
up.map <- lapply(solcap.err.map, update_map)
summary_maps(orig.map)
summary_maps(up.map)

Package 'mappoly'

Help Index

Add a single marker to a map

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Frequency of genotypes for two-point recombination fraction estimation

Description

Usage

Arguments

Value

Author(s)

References

Examples

Compute conditional probabilities of the genotypes

Description

Usage

Arguments

Value

Author(s)

References

Examples

Compute conditional probabilities of the genotypes using probability distribution of dosages

Description

Usage

Arguments

Value

Author(s)

References

Examples

Compute conditional probabilities of the genotypes using global error

Description

Usage

Arguments

Value

Author(s)

References

Examples

Compute conditional probabilities of the genotype (one informative parent)

Description

Usage

Arguments

Value

Author(s)

References

Examples

Homolog probabilities

Description

Usage

Arguments

Author(s)

References

Examples

Preferential pairing profiles

Description

Usage

Arguments

Author(s)

References

Examples

Data sanity check

Description

Usage

Arguments

Value

Author(s)

References

Examples

Compare a list of maps

Description

Usage

Arguments

Value

Simulate an autopolyploid full-sib population

Description

Usage