Package 'mappoly'

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
)

Arguments

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:

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

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])

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
)

Arguments

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)

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)

Arguments

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

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

## 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]))

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
)

Arguments

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

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

## 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]))

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
)

Arguments

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

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

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

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
)

Arguments

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

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

## 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]))

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)

Arguments

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)

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)

Arguments

Author(s)

Marcelo Mollinari, [email protected] and Guilherme Pereira, [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 <- lapply(solcap.dose.map[1:12], calc_genoprob)
  x1 <- calc_prefpair_profiles(w1)
  print(x1)
  plot(x1)

Description

Checks the consistency of a dataset

Usage

check_data_sanity(x)

Arguments

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

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

check_data_sanity(tetra.solcap)

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(...)

Arguments

Value

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

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
)

Arguments

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

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

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)

Description

Detects which parent is informative

Usage

detect_info_par(x)

Arguments

Description

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

Usage

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

Arguments

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)

Description

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

Usage

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

Arguments

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)

Description

Eliminate markers with identical dosage information for all individuals.

Usage

elim_redundant(input.seq, data = NULL)

Arguments

Value

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

Author(s)

Marcelo Mollinari, [email protected], with minor 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')
    red.mrk <- elim_redundant(all.mrk)
    plot(red.mrk)
    unique.mrks <- make_seq_mappoly(red.mrk)

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
)

Arguments

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:

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

Author(s)

Marcelo Mollinari, [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

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")

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
)

Arguments

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:

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

Author(s)

Marcelo Mollinari, [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

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")

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
)

Arguments

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

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

## 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)

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_rf2(
  input.seq,
  ncpus = 1L,
  mrk.pairs = NULL,
  verbose = TRUE,
  tol = .Machine$double.eps^0.25
)

Arguments

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

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

## 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)

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,
  ...
)

Arguments

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:

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

Author(s)

Marcelo Mollinari, [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_. https://doi.org/10.1534/g3.119.400378

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)

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
)

Arguments

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:

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

Author(s)

Marcelo Mollinari, [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

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)

Description

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

Usage

export_data_to_polymapR(data.in)

Arguments

Value

a dosage matrix

Author(s)

Marcelo Mollinari, [email protected]

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")

Arguments

Author(s)

Marcelo Mollinari, [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

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

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)

Arguments

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 <- export_qtlpoly(w1)

Description

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

Usage

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

Arguments

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)")

Description

Filter aneuploid chromosomes from progeny individuals

Usage

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

Arguments

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)

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
)

Arguments

Author(s)

Marcelo Mollinari, [email protected]

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
)

Arguments

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)

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)

Arguments

Value

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

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)

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
)

Arguments

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)

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"
)

Arguments

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]

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)

Arguments

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.

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, ...)

Arguments

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)

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
)

Arguments

Author(s)

Marcelo Mollinari, [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

## 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)

Description

Internal function

Usage

get_tab_mrks(x)

Arguments

Author(s)

Gabriel Gesteira, [email protected]

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
)

Arguments

Value

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

Author(s)

Marcelo Mollinari, [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

## 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)

Description

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

Usage

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

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

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

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
)

Arguments

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

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

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
)

Arguments

Value

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

Author(s)

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

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)
}

Description

Function to import phased map lists from polymapR

Usage

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

Arguments

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

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

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)

Arguments

Author(s)

Marcelo Mollinari, [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

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

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)

Arguments

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

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

# 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)

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)

Arguments

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

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

## 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")

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, ...)

Arguments

Value

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

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)

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

Format

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

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, ...)

Arguments

Value

A list containing:

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")

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)

Arguments

Value

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

Author(s)

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

## 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)

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
)

Arguments

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:

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

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])

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
)

Arguments

Author(s)

Marcelo Mollinari, [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

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)

Description

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

Usage

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

Arguments

Examples

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

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"
)

Arguments

Value

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

Author(s)

Marcelo Mollinari, [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

## 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)

Description

Plot object mappoly.map2

Usage

plot_mappoly.map2(x)

Arguments

Description

Plots summary statistics for a given marker

Usage

plot_mrk_info(input.data, mrk)

Arguments

Author(s)

Marcelo Mollinari, [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

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

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
)

Arguments

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

Description

Plots mappoly.homoprob

Usage

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

Arguments

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",
  ...
)

Arguments

Description

Returns information related to a given set of markers

Usage

print_mrk(input.data, mrks)

Arguments

Examples

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

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
)

Arguments

Value

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

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)

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, ...)

Arguments

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:

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

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

#### 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)

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
)

Arguments

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:

Author(s)

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

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

#### 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)

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
)

Arguments