Type:	Package
Title:	Genetic Linkage Maps in Autopolyploids
Version:	0.4.1
Maintainer:	Marcelo Mollinari <mmollin@ncsu.edu>
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>.
License:	GPL-3
LazyData:	TRUE
LazyDataCompression:	xz
Depends:	R (≥ 4.0.0)
Imports:	Rcpp (≥ 0.12.6), RcppParallel, RCurl, fields, ggpubr, ggsci, rstudioapi, plot3D, dplyr, crayon, cli, magrittr, reshape2, ggplot2, smacof, princurve, dendextend, vcfR, zoo, plotly
LinkingTo:	Rcpp, RcppParallel
RoxygenNote:	7.3.1
SystemRequirements:	GNU make
Encoding:	UTF-8
Suggests:	testthat, updog, fitPoly, polymapR, AGHmatrix, gatepoints, knitr, rmarkdown, stringr
URL:	https://github.com/mmollina/MAPpoly
BugReports:	https://github.com/mmollina/MAPpoly/issues
VignetteBuilder:	knitr
NeedsCompilation:	yes
Packaged:	2024-03-06 03:10:56 UTC; mmollin
Author:	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)
Repository:	CRAN
Date/Publication:	2024-03-06 17:20:02 UTC

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
)

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, mmollin@ncsu.edu

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]) 

Add markers to a pre-existing sequence using HMM analysis and evaluating difference in LOD

Description

Add markers to a pre-existing sequence using HMM analysis and evaluating difference in LOD

Usage

add_md_markers(
  input.map,
  mrk.to.include,
  input.seq,
  input.matrix,
  input.genoprob,
  input.data,
  input.mds = NULL,
  thresh = 500,
  extend.tail = 50,
  method = c("hmm", "wMDS_to_1D_pc"),
  verbose = TRUE
)

Arguments

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu with documentation and minor modifications by Cristiane Taniguti chtaniguti@tamu.edu

add a single marker at the tail of a linkage phase list

Description

add a single marker at the tail of a linkage phase list

Usage

add_mrk_at_tail_ph_list(ph.list.1, ph.list.2, cor.index)

Aggregate matrix cells (lower the resolution by a factor)

Description

Aggregate matrix cells (lower the resolution by a factor)

Usage

aggregate_matrix(M, fact)

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
)

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, mmollin@ncsu.edu with updates by Gabriel Gesteira, gdesiqu@ncsu.edu

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)

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)

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, mmollin@ncsu.edu

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]))

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
)

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, mmollin@ncsu.edu

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]))

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
)

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, mmollin@ncsu.edu

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)
 

Compute conditional probabilities of the genotypes given a sequence of block markers

Description

Compute conditional probabilities of the genotypes given a sequence of block markers

Usage

calc_genoprob_haplo(
  ploidy,
  n.mrk,
  n.ind,
  haplo,
  emit = NULL,
  rf_vec,
  ind.names,
  verbose = TRUE,
  highprec = FALSE
)

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
)

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, mmollin@ncsu.edu

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]))

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)

Arguments

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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)
  

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)

Arguments

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu and Guilherme Pereira, g.pereira@cgiar.org

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)

cat for phase information

Description

cat for phase information

Usage

cat_phase(
  input.seq,
  input.ph,
  all.ph,
  ct,
  seq.num,
  twopt.phase.number,
  hmm.phase.number
)

Checks the consistency of dataset (probability distribution)

Description

Checks the consistency of dataset (probability distribution)

Usage

check_data_dist_sanity(x)

Checks the consistency of dataset (dosage)

Description

Checks the consistency of dataset (dosage)

Usage

check_data_dose_sanity(x)

Data sanity check

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, mmollin@ncsu.edu

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)

Check if it is possible to estimate the recombination fraction between neighbor markers using two-point estimation

Description

Check if it is possible to estimate the recombination fraction between neighbor markers using two-point estimation

Usage

check_if_rf_is_possible(input.seq)

Compare a list of linkage phases and return the markers for which they are different.

Description

Compare a list of linkage phases and return the markers for which they are different.

Usage

check_ls_phase(ph)

Check if all pairwise combinations of elements of input.seq are contained in twopt

Description

Check if all pairwise combinations of elements of input.seq are contained in twopt

Usage

check_pairwise(input.seq, twopt)

Arguments

Value

If all pairwise combinations of elements of input.seq are contained in twopt, the function returns 0. Otherwise, returns the missing pairs.

Compare two polyploid haplotypes stored in list format

Description

Compare two polyploid haplotypes stored in list format

Usage

compare_haplotypes(ploidy, h1, h2)

Arguments

Value

a numeric vector of size ploidy indicating which homolog in h2 represents the homolog in h1. If there is no correspondence, i.e. different homolog, it returns NA for that homolog.

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(...)

Arguments

Value

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

Concatenate new marker

Description

Inserts a new marker at the end of the sequence, taking into account the two-point information

Usage

concatenate_new_marker(X = NULL, d, sh = NULL, seq.num = NULL, ploidy, mrk = 1)

Arguments

Value

a unique list of matrices representing linkage phases

concatenate two linkage phase lists

Description

concatenate two linkage phase lists

Usage

concatenate_ph_list(ph.list.1, ph.list.2)

Create a map with pseudomarkers at a given step

Description

Create a map with pseudomarkers at a given step

Usage

create_map(input.map, step = 0, phase.config = "best")

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
)

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, mmollin@ncsu.edu

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)
                                   
                                  

Detects which parent is informative

Description

Detects which parent is informative

Usage

detect_info_par(x)

Arguments

Returns the class with the highest probability in a genotype probability distribution

Description

Returns the class with the highest probability in a genotype probability distribution

Usage

dist_prob_to_class(geno, prob.thres = 0.9)

Arguments

Value

a matrix containing the doses of each genotype and marker. Markers are disposed in rows and individuals are disposed in columns. Missing data are represented by NAs

Examples

geno.dose <- dist_prob_to_class(hexafake.geno.dist$geno)
geno.dose$geno.dose[1:10,1:10]
   

Draw simple parental linkage phase configurations

Description

This function draws the parental map (including the linkage phase configuration) in a pdf output. This function is not to be directly called by the user

Usage

draw_cross(
  ploidy,
  dist.vec = NULL,
  hom.allele.p,
  hom.allele.q,
  file = NULL,
  width = 12,
  height = 6
)

Plot the linkage phase configuration given a list of homologous chromosomes

Description

Plot the linkage phase configuration given a list of homologous chromosomes

Usage

draw_phases(ploidy, hom.allele.p, hom.allele.q)

Arguments

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)

Arguments

Value

an object of class mappoly.map

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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)

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)

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, chtaniguti@tamu.edu

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)
 
 

Eliminate configurations using two-point information

Description

Drops unlikely configuration phases given the two-point information and a LOD threshold

Usage

elim_conf_using_two_pts(input.seq, twopt, thres)

Arguments

Value

a unique list of matrices representing linkage phases

Eliminates equivalent linkage phase configurations

Description

Drop equivalent linkage phase configurations, i.e. the ones which have permuted homologous chromosomes

Usage

elim_equiv(Z)

Arguments

Value

a unique list of matrices

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

Eliminate redundant markers

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, mmollin@ncsu.edu, with minor modifications by Gabriel Gesteira, gdesiqu@ncsu.edu

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)
   

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
)

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, mmollin@ncsu.edu

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")

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
)

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, mmollin@ncsu.edu

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")

Estimate a genetic map given a sequence of block markers

Description

Estimate a genetic map given a sequence of block markers

Usage

est_haplo_hmm(
  ploidy,
  n.mrk,
  n.ind,
  haplo,
  emit = NULL,
  rf_vec,
  verbose = TRUE,
  use_H0 = FALSE,
  highprec = FALSE,
  tol = 0.001
)

Estimate a genetic map given a sequence of block markers given the conditional probabilities of the genotypes

Description

Estimate a genetic map given a sequence of block markers given the conditional probabilities of the genotypes

Usage

est_map_haplo_given_genoprob(map.list, genoprob.list, tol = 1e-04)

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
)

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, mmollin@ncsu.edu

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)

Pairwise two-point analysis - RcppParallel version

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, mmollin@ncsu.edu

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)
  

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,
  ...
)

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, mmollin@ncsu.edu

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)

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
)

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, mmollin@ncsu.edu

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)
   

Multipoint analysis using Hidden Markov Models (single phase)

Description

Multipoint analysis using Hidden Markov Models (single phase)

Usage

est_rf_hmm_single_phase(
  input.seq,
  input.ph.single,
  rf.temp = NULL,
  tol,
  verbose = FALSE,
  ret.map.no.rf.estimation = FALSE,
  high.prec = TRUE,
  max.rf.to.break.EM = 0.5
)

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

Multilocus analysis using Hidden Markov Models (single parent, single phase)

Description

Multilocus analysis using Hidden Markov Models (single parent, single phase)

Usage

est_rf_hmm_single_phase_single_parent(
  input.seq,
  input.ph.single,
  info.parent = 1,
  uninfo.parent = 2,
  rf.vec = NULL,
  global.err = 0,
  tol = 0.001,
  verbose = FALSE,
  ret.map.no.rf.estimation = FALSE
)

Export data to polymapR

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, mmollin@ncsu.edu

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")

Arguments

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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 = "")

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)

Arguments

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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)
  

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")

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)")

Filter aneuploid chromosomes from progeny individuals

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, chtaniguti@tamu.edu

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)

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
)

Arguments

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

Filter MAPpoly Map Configurations by Loglikelihood Threshold

Description

This function filters configurations within a '"mappoly.map"' object based on a specified log-likelihood threshold.

Usage

filter_map_at_hmm_thres(map, thres.hmm)

Arguments

Value

Returns the modified '"mappoly.map"' object with configurations filtered based on the log-likelihood threshold.

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
)

Arguments

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu.

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)

Filter individuals based on missing genotypes

Description

Filter individuals based on missing genotypes

Usage

filter_missing_ind(input.data, filter.thres = 0.2, inter = TRUE)

Arguments

Filter markers based on missing genotypes

Description

Filter markers based on missing genotypes

Usage

filter_missing_mrk(input.data, filter.thres = 0.2, inter = TRUE)

Arguments

Filter non-conforming classes in F1, non double reduced population.

Description

Filter non-conforming classes in F1, non double reduced population.

Usage

filter_non_conforming_classes(input.data, prob.thres = NULL)

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)

Arguments

Value

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

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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)

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
)

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, mmollin@ncsu.edu

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)

Format results from pairwise two-point estimation in C++

Description

Format results from pairwise two-point estimation in C++

Usage

format_rf(res)

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"
)

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, mmollin@ncsu.edu with documentation and minor modifications by Cristiane Taniguti chtaniguti@tamu.edu

Generate all possible linkage phases in matrix form given the dose and the number of shared alleles between a inserted marker and a pre-computed linkage configuration.

Description

Generate all possible linkage phases in matrix form given the dose and the number of shared alleles between a inserted marker and a pre-computed linkage configuration.

Usage

generate_all_link_phase_elim_equivalent(X, d, sh, ploidy, k1, k2)

Arguments

Value

a unique list of matrices representing linkage phases

Eliminate equivalent linkage phases

Description

Generates all possible linkage phases between two blocks of markers (or a block and a marker), eliminating equivalent configurations, i.e. configurations with the same likelihood and also considering the two-point information (shared alleles)

Usage

generate_all_link_phases_elim_equivalent_haplo(
  block1,
  block2,
  rf.matrix,
  ploidy,
  max.inc = NULL
)

Arguments

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu and Gabriel Gesteira, gdesiqu@ncsu.edu

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)

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.

Prior probability for genotyping error

Description

If restricted = TRUE, it restricts the prior to the possible classes under Mendelian, non double-reduced segregation given dosage of the parents

Usage

genotyping_global_error(
  x,
  ploidy,
  restricted = TRUE,
  error = 0.01,
  th.prob = 0.95
)

Extract the LOD Scores in a 'mappoly.map' object

Description

Extract the LOD Scores in a 'mappoly.map' object

Usage

get_LOD(x, sorted = TRUE)

Arguments

Value

a numeric vector containing the LOD Scores

Access a remote server to get Counts for recombinant classes

Description

Access a remote server to get Counts for recombinant classes

Usage

get_cache_two_pts_from_web(
  ploidy,
  url.address = NULL,
  joint.prob = TRUE,
  verbose = FALSE
)

Counts for recombinant classes

Description

Counts for recombinant classes

Usage

get_counts(
  ploidy,
  P.k = NULL,
  P.k1 = NULL,
  Q.k = NULL,
  Q.k1 = NULL,
  verbose = FALSE,
  make.names = FALSE,
  joint.prob = FALSE
)

Counts for recombinant classes

Description

return the counts of each recombinant class (for two loci) in polyploid cross. The results of this function contains several matrices each one corresponding to one possible linkage phase. The associated names in the matrices indicates the number of shared homologous chromosomes. The row names indicates the dosage in loci k and k+1 respectively

Usage

get_counts_all_phases(
  x,
  ploidy,
  verbose = FALSE,
  make.names = FALSE,
  joint.prob = FALSE
)

Counts for recombinant classes in a polyploid parent.

Description

The conditional probability of a genotype at locus k+1 given the genotype at locus k is ...

Usage

get_counts_single_parent(
  ploidy,
  gen.par.mk1,
  gen.par.mk2,
  gen.prog.mk1,
  gen.prog.mk2
)

Arguments

Value

S3 object; a list consisting of

Counts for recombinant classes

Description

Counts for recombinant classes

Usage

get_counts_two_parents(
  x = c(2, 2),
  ploidy,
  p.k,
  p.k1,
  q.k,
  q.k1,
  verbose = FALSE,
  joint.prob = FALSE
)

Get Dosage Type in a Sequence

Description

Analyzes a genomic sequence object to categorize markers based on their dosage type. The function calculates the dosage type by comparing the dosage of two parental sequences (p1 and p2) against the ploidy level. It categorizes markers into simplex for parent 1 (simplex.p), simplex for parent 2 (simplex.q), double simplex (ds), and multiplex based on the calculated dosages.

Usage

get_dosage_type(input.seq)

Arguments

Value

A list with four components categorizing marker names into:

simplex.p

Markers with a simplex dosage from parent 1.

simplex.q

Markers with a simplex dosage from parent 2.

double.simplex

Markers with a double simplex dosage.

multiplex

Markers not fitting into the above categories, indicating a multiplex dosage.

Get the tail of a marker sequence up to the point where the markers provide no additional information.

Description

Get the tail of a marker sequence up to the point where the markers provide no additional information.

Usage

get_full_info_tail(input.obj, extend = NULL)

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, ...)

Arguments

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

Examples

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

Given a pair of character indicating the numbers i and j : 'i-j', returns a numeric pair c(i,j)

Description

Given a pair of character indicating the numbers i and j : 'i-j', returns a numeric pair c(i,j)

Usage

get_ij(w)

Arguments

Value

a numeric pair c(i,j)

Get the indices of selected linkage phases given a threshold

Description

Get the indices of selected linkage phases given a threshold

Usage

get_indices_from_selected_phases(x, thres)

Arguments

Value

a list of indices for both parents

Get weighted ordinary least squared map give a sequence and rf matrix

Description

Get weighted ordinary least squared map give a sequence and rf matrix

Usage

get_ols_map(input.seq, input.mat, weight = TRUE)

Given a homology group in matrix form, it returns the number shared homologous for all pairs of markers in this group

Description

Given a homology group in matrix form, it returns the number shared homologous for all pairs of markers in this group

Usage

get_ph_conf_ret_sh(M)

Arguments

Value

a vector containing the number of shared homologous for all pairs of markers

subset of a linkage phase list

Description

subset of a linkage phase list

Usage

get_ph_list_subset(ph.list, seq.num, conf)

Get the recombination fraction for a sequence of markers given an object of class mappoly.twopt and a list containing the linkage phase configuration. This list can be found in any object of class two.pts.linkage.phases, in x$config.to.test$'Conf-i', where x is the object of class two.pts.linkage.phases and i is one of the possible configurations.

Description

Get the recombination fraction for a sequence of markers given an object of class mappoly.twopt and a list containing the linkage phase configuration. This list can be found in any object of class two.pts.linkage.phases, in x$config.to.test$'Conf-i', where x is the object of class two.pts.linkage.phases and i is one of the possible configurations.

Usage

get_rf_from_list(twopt, ph.list)

Arguments

Value

a vector with the recombination fraction between markers present in ph.list, for that specific order.

Get recombination fraction from a matrix

Description

Get recombination fraction from a matrix

Usage

get_rf_from_mat(M)

Get states and emission in one informative parent

Description

Get states and emission in one informative parent

Usage

get_states_and_emission_single_parent(ploidy, ph, global.err, D, dose.notinf.P)

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
)

Arguments

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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)
  

Get table of dosage combinations

Description

Internal function

Usage

get_tab_mrks(x)

Arguments

Author(s)

Gabriel Gesteira, gdesiqu@ncsu.edu

Get the number of bivalent configurations

Description

Get the number of bivalent configurations

Usage

get_w_m(ploidy)

Color pallet ggplot-like

Description

Color pallet ggplot-like

Usage

gg_color_hue(n)

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
)

Arguments

Value

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

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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)
   

Simulated autohexaploid dataset.

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

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

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
)

Arguments

Details

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

Author(s)

Marcelo Mollinari mmollin@ncsu.edu

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

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
)

Arguments

Value

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

Author(s)

Gabriel Gesteira, gdesiqu@ncsu.edu

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)
}

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)

Arguments

Details

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

Author(s)

Marcelo Mollinari mmollin@ncsu.edu

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

Check if Object is a Probability Dataset in MAPpoly

Description

Determines whether the specified object is a probability dataset by checking for the existence of the 'geno' component within a '"mappoly.data"' object.

Usage

is.prob.data(x)

Arguments

Value

A logical value: ‘TRUE' if the ’geno' component exists within 'x', indicating it is a valid probability dataset for genetic analysis; 'FALSE' otherwise.

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)

Arguments

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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
 
 

List of linkage phases

Description

Returns a list of possible linkage phase configurations using the two-point information contained in the object mappoly.twopt as elimination criteria

Usage

ls_linkage_phases(input.seq, thres, twopt, mrk.to.add = NULL, prev.info = NULL)

## S3 method for class 'two.pts.linkage.phases'
print(x, ...)

## S3 method for class 'two.pts.linkage.phases'
plot(x, ...)

Arguments

Value

An object of class two.pts.linkage.phases which contains the following structure:

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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

seq.all.mrk <- make_seq_mappoly(hexafake, 'all')
id <- get_genomic_order(seq.all.mrk)
s <- make_seq_mappoly(id)
seq10 <- make_seq_mappoly(hexafake, s$seq.mrk.names[1:10])
twopt <- est_pairwise_rf(seq10)

## Using the first 10 markers 
l10.seq.3.0 <- ls_linkage_phases(input.seq = seq10, thres = 3, twopt = twopt)
l10.seq.3.0
plot(l10.seq.3.0)
l10.seq.2.0 <- ls_linkage_phases(input.seq = seq10, thres = 2.0, twopt = twopt)
l10.seq.2.0
plot(l10.seq.2.0)
l10.seq.1.0 <- ls_linkage_phases(input.seq = seq10, thres = 1.0, twopt = twopt)
l10.seq.1.0
plot(l10.seq.1.0)

## Using the first 5 markers 
seq5 <- make_seq_mappoly(hexafake, s$seq.mrk.names[1:5])
l5.seq.5.0 <- ls_linkage_phases(input.seq = seq5, thres = 5, twopt = twopt)
l5.seq.5.0
plot(l5.seq.5.0)
l5.seq.3.0 <- ls_linkage_phases(input.seq = seq5, thres = 3, twopt = twopt)
l5.seq.3.0
plot(l5.seq.3.0)
l5.seq.1.0 <- ls_linkage_phases(input.seq = seq5, thres = 1, twopt = twopt)
l5.seq.1.0
plot(l5.seq.1.0)

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)

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, mmollin@ncsu.edu

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)
   

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)

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, mmollin@ncsu.edu

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")
   

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, ...)

Arguments

Value

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

Author(s)

Marcelo Mollinari mmollin@ncsu.edu, with modifications by Gabriel Gesteira gdesiqu@ncsu.edu

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)

MAPpoly Color Palettes

Description

Provides a set of color palettes designed for use with MAPpoly, a package for genetic mapping in polyploids. These palettes are intended to enhance the visual representation of genetic data.

Usage

mp_pallet1(n)

Details

The available palettes are:

mp_pallet1

A palette with warm colors ranging from yellow to dark red and brown.

mp_pallet2

A palette with cool colors, including purples, blues, and green.

mp_pallet3

A comprehensive palette that combines colors from both mp_pallet1 and mp_pallet2, offering a broad range of colors.

Each palette function returns a function that can generate color vectors of variable length, suitable for mapping or plotting functions in R.

Examples

# Generate a palette of 5 colors from mp_pallet1
pal1 <- mp_pallet1(5)
plot(1:5, pch=19, col=pal1)

# Generate a palette of 10 colors from mp_pallet2
pal2 <- mp_pallet2(10)
plot(1:10, pch=19, col=pal2)

# Generate a palette of 15 colors from mp_pallet3
pal3 <- mp_pallet3(15)
plot(1:15, pch=19, col=pal3)

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.

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, ...)

Arguments

Value

A list containing:

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu mostly adapted from MDSmap codes, written by Katharine F. Preedy, katharine.preedy@bioss.ac.uk

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")

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)

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, gdesiqu@ncsu.edu

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)

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
)

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, mmollin@ncsu.edu

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])

Build merged parental maps

Description

Build merged parental maps

Usage

merge_parental_maps(
  map.p1,
  map.p2,
  full.seq,
  full.mat,
  method = c("ols", "hmm"),
  verbose = TRUE
)

Arguments

Value

object of class mappoly.map with both parents information

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu with documentation and minor modifications by Cristiane Taniguti chtaniguti@tamu.edu

Chi-square test

Description

Chi-square test

Usage

mrk_chisq_test(x, ploidy)

Msg function

Description

Msg function

Usage

msg(text, line = 1)

Parallel Pairwise Discrete Estimation

Description

This function performs parallel pairwise estimation of recombination fractions using discrete dosage scoring via a C++ backend.

Usage

paralell_pairwise_discrete(
  mrk.pairs,
  input.seq,
  geno,
  dP,
  dQ,
  count.cache,
  tol = .Machine$double.eps^0.25,
  ll = ll
)

Arguments

Value

Depending on the ll parameter, returns either log-likelihood values or formatted LOD scores from pairwise recombination fraction estimation.

Wrapper function to discrete-based pairwise two-point estimation in C++

Description

Wrapper function to discrete-based pairwise two-point estimation in C++

Usage

paralell_pairwise_discrete_rcpp(
  mrk.pairs,
  m,
  geno,
  dP,
  dQ,
  count.vector,
  count.phases,
  count.matrix.rownames,
  count.matrix.number,
  count.matrix.pos,
  count.matrix.length,
  tol = .Machine$double.eps^0.25
)

Parallel Pairwise Probability Estimation

Description

This function performs parallel pairwise estimation of recombination fractions using probability-based dosage scoring via a C++ backend.

Usage

paralell_pairwise_probability(
  mrk.pairs,
  input.seq,
  geno,
  dP,
  dQ,
  count.cache,
  tol = .Machine$double.eps^0.25,
  ll = ll
)

Arguments

Value

Depending on the ll parameter, returns either log-likelihood values or formatted LOD scores from pairwise recombination fraction estimation.

Auxiliary function to estimate a map in a block of markers using parallel processing

Description

Auxiliary function to estimate a map in a block of markers using parallel processing

Usage

parallel_block(
  mrk.vec,
  dat.name,
  ph.thres = 3,
  tol = 0.01,
  phase.number.limit = 20,
  error = 0.05,
  tol.err = 0.001,
  verbose = FALSE
)

N!/2 combination

Description

N!/2 combination

Usage

perm_pars(v)

N! combination

Description

N! combination

Usage

perm_tot(v)

Linkage phase format conversion: list to matrix

Description

This function converts linkage phase configurations from list to matrix form

Usage

ph_list_to_matrix(L, ploidy)

Arguments

Value

a matrix whose columns represent homologous chromosomes and the rows represent markers

Linkage phase format conversion: matrix to list

Description

This function converts linkage phase configurations from matrix form to list

Usage

ph_matrix_to_list(M)

Arguments

Value

a list of linkage phase configurations

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,
  ...
)

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",
  ...
)

Arguments

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")

Arguments

Examples

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

Plot Two Overlapped Haplotypes

Description

This function plots two sets of haplotypes for comparison, allowing for visual inspection of homologous allele patterns across two groups or conditions. It is designed to handle and display genetic data for organisms with varying ploidy levels.

Usage

plot_compare_haplotypes(
  ploidy,
  hom.allele.p1,
  hom.allele.q1,
  hom.allele.p2 = NULL,
  hom.allele.q2 = NULL
)

Arguments

Details

The function creates a graphical representation of haplotypes, where each marker's alleles are plotted along a line for each parent ('p' and 'q'). If provided, the second set of haplotypes (for comparison) are overlaid on the same plot. This allows for direct visual comparison of allele presence or absence across the two sets. Different colors are used to distinguish between the first and second sets of haplotypes.

The function uses several internal helper functions ('ph_list_to_matrix' and 'ph_matrix_to_list') to manipulate haplotype data. These functions should correctly handle the conversion between list and matrix representations of haplotype data.

Value

Invisible. The function primarily generates a plot for visual analysis and does not return any data.

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
)

Arguments

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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)
 

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"
)

Arguments

Value

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

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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)
 

Plot object mappoly.map2

Description

Plot object mappoly.map2

Usage

plot_mappoly.map2(x)

Arguments

Plot marker information

Description

Plots summary statistics for a given marker

Usage

plot_mrk_info(input.data, mrk)

Arguments

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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")
    

plot a single linkage group with no phase

Description

plot a single linkage group with no phase

Usage

plot_one_map(x, i = 0, horiz = FALSE, col = "lightgray")

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
)

Arguments

Value

A ggplot of the changed and imputed genotypic dosages

Author(s)

Jeekin Lau, jzl0026@tamu.edu, with optimization by Cristiane Taniguti, chtaniguti@tamu.edu

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

Estimate genetic map using as input the probability distribution of genotypes (wrapper function to C++)

Description

Estimate genetic map using as input the probability distribution of genotypes (wrapper function to C++)

Usage

poly_hmm_est(
  ploidy,
  n.mrk,
  n.ind,
  p,
  dp,
  q,
  dq,
  g,
  rf,
  verbose = TRUE,
  tol = 0.001
)

prepare maps for plot

Description

prepare maps for plot

Usage

prepare_map(input.map, config = "best")

Summary of a set of markers

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)

cat for graphical representation of the phases

Description

cat for graphical representation of the phases

Usage

print_ph(input.ph)

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
)

Arguments

Value

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

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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)

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, ...)

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, mmollin@ncsu.edu

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)

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
)

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, mmollin@ncsu.edu, with minor changes by Gabriel Gesteira, gdesiqu@ncsu.edu

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)

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
)

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 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:

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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/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
)

Arguments

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:

Author(s)

Gabriel Gesteira, gdesiqu@ncsu.edu

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

## 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
)

Arguments

Value

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

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

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)

Arguments

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

Examples

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,
  ...
)

Arguments

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, mmollin@ncsu.edu

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, 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
)

Arguments

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, mmollin@ncsu.edu with updates by Gabriel Gesteira, gdesiqu@ncsu.edu

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, 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")

Random sampling of dataset

Description

Random sampling of dataset

Usage

sample_data(
  input.data,
  n = NULL,
  percentage = NULL,
  type = c("individual", "marker"),
  selected.ind = NULL,
  selected.mrk = NULL
)

Arguments

Value

an object of class mappoly.data

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)

Arguments

Value

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

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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

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)

Select rf and lod based on thresholds

Description

Select rf and lod based on thresholds

Usage

select_rf(x, thresh.LOD.ph, thresh.LOD.rf, thresh.rf, shared.alleles = FALSE)

Simulate mapping population (one parent)

Description

This function simulates a polyploid mapping population under random chromosome segregation with one informative parent. This function is not to be directly called by the user

Usage

sim_cross_one_informative_parent(
  ploidy,
  n.mrk,
  rf.vec,
  hom.allele,
  n.ind,
  seed = NULL,
  prob = NULL
)

Simulate mapping population (tow parents)

Description

Simulate mapping population (tow parents)

Usage

sim_cross_two_informative_parents(
  ploidy,
  n.mrk,
  rf.vec,
  n.ind,
  hom.allele.p,
  hom.allele.q,
  prob.P = NULL,
  prob.Q = NULL,
  seed = NULL
)

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)

Arguments

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:

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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)

Resulting maps from tetra.solcap

Description

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

Usage

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

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

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

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
)

Arguments

Value

An object of class mappoly.map

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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

 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")

Split map into sub maps given a gap threshold

Description

Split map into sub maps given a gap threshold

Usage

split_mappoly(
  input.map,
  gap.threshold = 5,
  size.rem.cluster = 1,
  phase.config = "best",
  tol.final = 0.001,
  verbose = TRUE
)

Summary maps

Description

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

Usage

summary_maps(map.list, verbose = TRUE)

Arguments

Value

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

Author(s)

Gabriel Gesteira, gdesiqu@ncsu.edu

Examples

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

Conversion of data.frame to mappoly.data

Description

Conversion of data.frame to mappoly.data

Usage

table_to_mappoly(
  dat,
  ploidy,
  filter.non.conforming = TRUE,
  elim.redundant = TRUE,
  verbose = TRUE
)

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

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

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. Although this dataset contains the probability distribution of the genotypes, it is essentially the same dataset found in tetra.solcap

Usage

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
)

Arguments

Value

object of class mappoly.map2

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu with documentation and minor modifications by Cristiane Taniguti chtaniguti@tamu.edu

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)

Arguments

Value

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

Author(s)

Gabriel Gesteira, gdesiqu@ncsu.edu

Examples

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

Update missing information

Description

Update missing information

Usage

update_missing(input.data, prob.thres = 0.95)

makes a phase list from map, selecting only configurations under a certain threshold

Description

makes a phase list from map, selecting only configurations under a certain threshold

Usage

update_ph_list_at_hmm_thres(map, thres.hmm)

Conversion: vector to matrix

Description

Conversion: vector to matrix

Usage

v_2_m(x, n)