Package 'mappoly'

Title: Genetic Linkage Maps in Autopolyploids
Description: Construction of genetic maps in autopolyploid full-sib populations. Uses pairwise recombination fraction estimation as the first source of information to sequentially position allelic variants in specific homologous chromosomes. For situations where pairwise analysis has limited power, the algorithm relies on the multilocus likelihood obtained through a hidden Markov model (HMM). For more detail, please see Mollinari and Garcia (2019) <doi:10.1534/g3.119.400378> and Mollinari et al. (2020) <doi:10.1534/g3.119.400620>.
Authors: Marcelo Mollinari [aut, cre] , Gabriel Gesteira [aut] , Cristiane Taniguti [aut] , Jeekin Lau [aut] , Oscar Riera-Lizarazu [ctb] , Guilhereme Pereira [ctb] , Augusto Garcia [ctb] , Zhao-Bang Zeng [ctb] , Katharine Preedy [ctb, cph] (MDS ordering algorithm), Robert Gentleman [cph] (C code for MLE optimization in src/pairwise_estimation.cpp), Ross Ihaka [cph] (C code for MLE optimization in src/pairwise_estimation.cpp), R Foundation [cph] (C code for MLE optimization in src/pairwise_estimation.cpp), R-core [cph] (C code for MLE optimization in src/pairwise_estimation.cpp)
Maintainer: Marcelo Mollinari <[email protected]>
License: GPL-3
Version: 0.4.1
Built: 2024-07-04 05:52:25 UTC
Source: https://github.com/mmollina/MAPpoly

Help Index


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

input.map

an object of class mappoly.map

mrk

the name of the marker to be inserted

pos

the name of the marker after which the new marker should be added. One also can inform the numeric position (between markers) were the new marker should be added. To insert a marker at the beginning of a map, use pos = 0

rf.matrix

an object of class mappoly.rf.matrix containing the recombination fractions and the number of homologues sharing alleles between pairwise markers on input.map. It is important that shared.alleles = TRUE in function rf_list_to_matrix when computing rf.matrix.

genoprob

an object of class mappoly.genoprob containing the genotype probabilities for all marker positions on input.map

phase.config

which phase configuration should be used. "best" (default) will choose the maximum likelihood configuration

tol

the desired accuracy (default = 10e-04)

extend.tail

the length of the chain's tail that should be used to calculate the likelihood of the map. If NULL (default), the function uses all markers positioned.

r.test

for internal use only

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

Details

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

Value

A list of class mappoly.map with two elements:

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

ploidy

the ploidy level

n.mrk

number of markers

seq.num

a vector containing the (ordered) indices of markers in the map, according to the input file

mrk.names

the names of markers in the map

seq.dose.p1

a vector containing the dosage in parent 1 for all markers in the map

seq.dose.p2

a vector containing the dosage in parent 2 for all markers in the map

chrom

a vector indicating the sequence (usually chromosome) each marker belongs as informed in the input file. If not available, chrom = NULL

genome.pos

physical position (usually in megabase) of the markers into the sequence

seq.ref

reference base used for each marker (i.e. A, T, C, G). If not available, seq.ref = NULL

seq.alt

alternative base used for each marker (i.e. A, T, C, G). If not available, seq.ref = NULL

chisq.pval

a vector containing p-values of the chi-squared test of Mendelian segregation for all markers in the map

data.name

name of the dataset of class mappoly.data

ph.thres

the LOD threshold used to define the linkage phase configurations to test

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

seq.num

a vector containing the (ordered) indices of markers in the map, according to the input file

seq.rf

a vector of size (n.mrk - 1) containing a sequence of recombination fraction between the adjacent markers in the map

seq.ph

linkage phase configuration for all markers in both parents

loglike

the hmm-based multipoint likelihood

Author(s)

Marcelo Mollinari, [email protected]

Examples

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

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

input.seq

an object of class mappoly.sequence

cached

If TRUE, access the counts for all linkage phase configurations in a internal file (default = FALSE)

cache.prev

an object of class cache.info containing pre-computed genotype frequencies, obtained with cache_counts_twopt (optional, default = NULL)

ncpus

Number of parallel processes to spawn (default = 1)

verbose

If TRUE (default), print the linkage phase configurations. If cached = TRUE, nothing is printed, since all linkage phase configurations will be cached.

joint.prob

If FALSE (default), returns the frequency of genotypes for transition probabilities (conditional probabilities). If TRUE returns the frequency for joint probabilities. The latter is especially important to compute the Fisher's Information for a pair of markers.

Value

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

Author(s)

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

References

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

Examples

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

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

input.map

An object of class mappoly.map

step

Maximum distance (in cM) between positions at which the genotype probabilities are calculated, though for step = 0, probabilities are calculated only at the marker locations.

phase.config

which phase configuration should be used. "best" (default) will choose the phase configuration associated with the maximum likelihood

verbose

if TRUE (default), current progress is shown; if FALSE, no output is produced

Value

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

Author(s)

Marcelo Mollinari, [email protected]

References

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

input.map

An object of class mappoly.map

dat.prob

an object of class mappoly.data containing the probability distribution of the genotypes

phase.config

which phase configuration should be used. "best" (default) will choose the phase configuration with the maximum likelihood

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

Value

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

Author(s)

Marcelo Mollinari, [email protected]

References

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

Examples

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

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

input.map

An object of class mappoly.map

step

Maximum distance (in cM) between positions at which the genotype probabilities are calculated, though for step = 0, probabilities are calculated only at the marker locations.

phase.config

which phase configuration should be used. "best" (default) will choose the maximum likelihood configuration

error

the assumed global error rate (default = 0.01)

th.prob

the threshold for using global error or genotype probability distribution contained in the dataset (default = 0.95)

restricted

if TRUE (default), restricts the prior to the possible classes under Mendelian non double-reduced segregation given the parental dosages

verbose

if TRUE (default), current progress is shown; if FALSE, no output is produced

Value

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

Author(s)

Marcelo Mollinari, [email protected]

References

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

Examples

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

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

input.map

An object of class mappoly.map (with exceptions)

step

Maximum distance (in cM) between positions at which the genotype probabilities are calculated, though for step = 0, probabilities are calculated only at the marker locations.

info.parent

index for informative parent

uninfo.parent

index for uninformative parent

global.err

the assumed global error rate (default = 0.0)

phase.config

which phase configuration should be used. "best" (default) will choose the phase configuration associated with the maximum likelihood

verbose

if TRUE (default), current progress is shown; if FALSE, no output is produced

Value

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

Author(s)

Marcelo Mollinari, [email protected]

References

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

Examples

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

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

input.genoprobs

an object of class mappoly.genoprob

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

Author(s)

Marcelo Mollinari, [email protected]

References

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

Examples

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

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

input.genoprobs

an object of class mappoly.genoprob

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

Author(s)

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

References

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)

Data sanity check

Description

Checks the consistency of a dataset

Usage

check_data_sanity(x)

Arguments

x

an object of class mappoly.data

Value

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

Author(s)

Marcelo Mollinari, [email protected]

References

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)

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

...

a list of objects of class mappoly.map

Value

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


Simulate an autopolyploid full-sib population

Description

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

Usage

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

Arguments

parental.phases

a list containing the linkage phase information for both parents

map.length

the map length

n.ind

number of individuals in the offspring

draw

if TRUE, draws a graphical representation of the parental map, including the linkage phase configuration, in a pdf output (default = FALSE)

file

name of the output file. It is ignored if draw = TRUE

prefix

prefix used in all marker names.

seed

random number generator seed (default = NULL)

width

the width of the graphics region in inches (default = 12)

height

the height of the graphics region in inches (default = 6)

prob.P

a vector indicating the proportion of preferential pairing in parent P (currently ignored)

prob.Q

a vector indicating the proportion of preferential pairing in parent Q (currently ignored)

Details

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

Value

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

Author(s)

Marcelo Mollinari, [email protected]

References

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

Examples

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

Detects which parent is informative

Description

Detects which parent is informative

Usage

detect_info_par(x)

Arguments

x

an object of class mappoly.sequence or mappoly.map


Remove markers from a map

Description

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

Usage

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

Arguments

input.map

an object of class mappoly.map

mrk

a vector containing markers to be removed from the input map, identified by their names or positions

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

Value

an object of class mappoly.map

Author(s)

Marcelo Mollinari, [email protected]

Examples

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

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

input.seq

object of class mappoly.sequence with alternative order (not genomic order)

invert

vector of marker names to be inverted

remove

vector of marker names to be removed

Value

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

Author(s)

Cristiane Taniguti, [email protected]

Examples

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

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

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

Eliminate redundant markers

Description

Eliminate markers with identical dosage information for all individuals.

Usage

elim_redundant(input.seq, data = NULL)

Arguments

input.seq

an object of class mappoly.sequence

data

name of the dataset that contains sequence markers (optional, default = NULL)

Value

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

unique.seq

an object of class mappoly.sequence with the redundant markers removed

kept

a vector containing the name of the informative markers

eliminated

a vector containing the name of the non-informative (eliminated) markers

Author(s)

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

References

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

input.map

an object of class mappoly.map

error

the assumed global error rate (default = NULL)

tol

the desired accuracy (default = 10e-04)

restricted

if TRUE (default), restricts the prior to the possible classes under Mendelian, non double-reduced segregation given dosage of the parents

th.prob

the threshold for using global error or genotype probability distribution if present in the dataset (default = 0.95)

verbose

if TRUE, current progress is shown; if FALSE (default), no output is produced

Value

A list of class mappoly.map with two elements:

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

ploidy

the ploidy level

n.mrk

number of markers

seq.num

a vector containing the (ordered) indices of markers in the map, according to the input file

mrk.names

the names of markers in the map

seq.dose.p1

a vector containing the dosage in parent 1 for all markers in the map

seq.dose.p2

a vector containing the dosage in parent 2 for all markers in the map

chrom

a vector indicating the sequence (usually chromosome) each marker belongs as informed in the input file. If not available, chrom = NULL

genome.pos

physical position (usually in megabase) of the markers into the sequence

seq.ref

reference base used for each marker (i.e. A, T, C, G). If not available, seq.ref = NULL

seq.alt

alternative base used for each marker (i.e. A, T, C, G). If not available, seq.ref = NULL

chisq.pval

a vector containing p-values of the chi-squared test of Mendelian segregation for all markers in the map

data.name

name of the dataset of class mappoly.data

ph.thres

the LOD threshold used to define the linkage phase configurations to test

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

seq.num

a vector containing the (ordered) indices of markers in the map, according to the input file

seq.rf

a vector of size (n.mrk - 1) containing a sequence of recombination fraction between the adjacent markers in the map

seq.ph

linkage phase configuration for all markers in both parents

loglike

the hmm-based multipoint likelihood

Author(s)

Marcelo Mollinari, [email protected]

References

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

input.map

an object of class mappoly.map

dat.prob

an object of class mappoly.data containing the probability distribution of the genotypes

phase.config

which phase configuration should be used. "best" (default) will choose the maximum likelihood configuration

tol

the desired accuracy (default = 10e-04)

verbose

if TRUE, current progress is shown; if FALSE (default), no output is produced

Value

A list of class mappoly.map with two elements:

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

ploidy

the ploidy level

n.mrk

number of markers

seq.num

a vector containing the (ordered) indices of markers in the map, according to the input file

mrk.names

the names of markers in the map

seq.dose.p1

a vector containing the dosage in parent 1 for all markers in the map

seq.dose.p2

a vector containing the dosage in parent 2 for all markers in the map

chrom

a vector indicating the sequence (usually chromosome) each marker belongs as informed in the input file. If not available, chrom = NULL

genome.pos

physical position (usually in megabase) of the markers into the sequence

seq.ref

reference base used for each marker (i.e. A, T, C, G). If not available, seq.ref = NULL

seq.alt

alternative base used for each marker (i.e. A, T, C, G). If not available, seq.ref = NULL

chisq.pval

a vector containing p-values of the chi-squared test of Mendelian segregation for all markers in the map

data.name

name of the dataset of class mappoly.data

ph.thres

the LOD threshold used to define the linkage phase configurations to test

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

seq.num

a vector containing the (ordered) indices of markers in the map, according to the input file

seq.rf

a vector of size (n.mrk - 1) containing a sequence of recombination fraction between the adjacent markers in the map

seq.ph

linkage phase configuration for all markers in both parents

loglike

the hmm-based multipoint likelihood

Author(s)

Marcelo Mollinari, [email protected]

References

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")

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

input.seq

an object of class mappoly.sequence

count.cache

an object of class cache.info containing pre-computed genotype frequencies, obtained with cache_counts_twopt. If NULL (default), genotype frequencies are internally loaded.

count.matrix

similar to count.cache, but in matrix format. Mostly for internal use.

ncpus

Number of parallel processes (cores) to spawn (default = 1)

mrk.pairs

a matrix of dimensions 2*N, containing N pairs of markers to be analyzed. If NULL (default), all pairs are considered

n.batches

deprecated. Not available on MAPpoly 0.3.0 or higher

est.type

Indicates whether to use the discrete ("disc") or the probabilistic ("prob") dosage scoring when estimating the two-point recombination fractions.

verbose

If TRUE (default), current progress is shown; if FALSE, no output is produced

memory.warning

if TRUE, prints a memory warning if the number of markers is greater than 10000 for ploidy levels up to 4, and 3000 for ploidy levels > 4.

parallelization.type

one of the supported cluster types. This should be either PSOCK (default) or FORK.

tol

the desired accuracy. See optimize() for details

ll

will return log-likelihood instead of LOD scores. (for internal use)

Value

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

data.name

Name of the object of class mappoly.data containing the raw data.

n.mrk

Number of markers in the sequence.

seq.num

A vector containing the (ordered) indices of markers in the sequence, according to the input file.

pairwise

A list of size choose(length(input.seq$seq.num), 2), where each element is a matrix. The rows are named in the format x-y, where x and y indicate how many homologues share the same allelic variant in parents P and Q, respectively (see Mollinari and Garcia, 2019 for notation). The first column indicates the LOD Score for the most likely linkage phase configuration. The second column shows the estimated recombination fraction for each configuration, and the third column indicates the LOD Score for comparing the likelihood under no linkage (r = 0.5) with the estimated recombination fraction (evidence of linkage).

chisq.pval.thres

Threshold used to perform the segregation tests.

chisq.pval

P-values associated with the performed segregation tests.

Author(s)

Marcelo Mollinari, [email protected]

References

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

input.seq

an object of class mappoly.sequence

ncpus

Number of parallel processes (cores) to spawn (default = 1)

mrk.pairs

a matrix of dimensions 2*N, containing N pairs of markers to be analyzed. If NULL (default), all pairs are considered

verbose

If TRUE (default), current progress is shown; if FALSE, no output is produced

tol

the desired accuracy. See optimize() for details

Details

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

Value

An object of class mappoly.twopt2

Author(s)

Marcelo Mollinari, [email protected]

References

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

input.seq

an object of class mappoly.sequence

input.ph

an object of class two.pts.linkage.phases. If not available (default = NULL), it will be computed

thres

LOD Score threshold used to determine if the linkage phases compared via two-point analysis should be considered. Smaller values will result in smaller number of linkage phase configurations to be evaluated by the multipoint algorithm.

twopt

an object of class mappoly.twopt containing two-point information

verbose

if TRUE, current progress is shown; if FALSE (default), no output is produced

tol

the desired accuracy (default = 1e-04)

est.given.0.rf

logical. If TRUE returns a map forcing all recombination fractions equals to 0 (1e-5, for internal use only. Default = FALSE)

reestimate.single.ph.configuration

logical. If TRUE returns a map without re-estimating the map parameters for cases where there is only one possible linkage phase configuration. This argument is intended to be used in a sequential map construction

high.prec

logical. If TRUE (default) uses high precision long double numbers in the HMM procedure

x

an object of the class mappoly.map

detailed

logical. if TRUE, prints the linkage phase configuration and the marker position for all maps. If FALSE (default), prints a map summary

...

currently ignored

left.lim

the left limit of the plot (in cM, default = 0).

right.lim

the right limit of the plot (in cM, default = Inf, i.e., will print the entire map)

phase

logical. If TRUE (default) plots the phase configuration for both parents

mrk.names

if TRUE, marker names are displayed (default = FALSE)

cex

The magnification to be used for marker names

config

should be 'best' or the position of the configuration to be plotted. If 'best', plot the configuration with the highest likelihood

P

a string containing the name of parent P

Q

a string containing the name of parent Q

xlim

range of the x-axis. If xlim = NULL (default) it uses the map range.

Details

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

Value

A list of class mappoly.map with two elements:

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

ploidy

the ploidy level

n.mrk

number of markers

seq.num

a vector containing the (ordered) indices of markers in the map, according to the input file

mrk.names

the names of markers in the map

seq.dose.p1

a vector containing the dosage in parent 1 for all markers in the map

seq.dose.p2

a vector containing the dosage in parent 2 for all markers in the map

chrom

a vector indicating the sequence (usually chromosome) each marker belongs as informed in the input file. If not available, chrom = NULL

genome.pos

physical position (usually in megabase) of the markers into the sequence

seq.ref

reference base used for each marker (i.e. A, T, C, G). If not available, seq.ref = NULL

seq.alt

alternative base used for each marker (i.e. A, T, C, G). If not available, seq.ref = NULL

chisq.pval

a vector containing p-values of the chi-squared test of Mendelian segregation for all markers in the map

data.name

name of the dataset of class mappoly.data

ph.thres

the LOD threshold used to define the linkage phase configurations to test

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

seq.num

a vector containing the (ordered) indices of markers in the map, according to the input file

seq.rf

a vector of size (n.mrk - 1) containing a sequence of recombination fraction between the adjacent markers in the map

seq.ph

linkage phase configuration for all markers in both parents

loglike

the hmm-based multipoint likelihood

Author(s)

Marcelo Mollinari, [email protected]

References

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

input.seq

an object of class mappoly.sequence

twopt

an object of class mappoly.twopt containing the two-point information

start.set

number of markers to start the phasing procedure (default = 4)

thres.twopt

the LOD threshold used to determine if the linkage phases compared via two-point analysis should be considered for the search space reduction (A.K.A. η\eta in Mollinari and Garcia (2019), default = 5)

thres.hmm

the LOD threshold used to determine if the linkage phases compared via hmm analysis should be evaluated in the next round of marker inclusion (default = 50)

extend.tail

the length of the chain's tail that should be used to calculate the likelihood of the map. If NULL (default), the function uses all markers positioned. Even if info.tail = TRUE, it uses at least extend.tail as the tail length

phase.number.limit

the maximum number of linkage phases of the sub-maps defined by arguments info.tail and extend.tail. Default is 20. If the size exceeds this limit, the marker will not be inserted. If Inf, then it will insert all markers.

sub.map.size.diff.limit

the maximum accepted length difference between the current and the previous sub-map defined by arguments info.tail and extend.tail. If the size exceeds this limit, the marker will not be inserted. If NULL(default), then it will insert all markers.

info.tail

if TRUE (default), it uses the complete informative tail of the chain (i.e. number of markers where all homologous (ploidyx2ploidy x 2) can be distinguished) to calculate the map likelihood

reestimate.single.ph.configuration

logical. If FALSE (default) returns a map without re-estimating the map parameters in cases where there are only one possible linkage phase configuration

tol

the desired accuracy during the sequential phase (default = 10e-02)

tol.final

the desired accuracy for the final map (default = 10e-04)

verbose

If TRUE (default), current progress is shown; if FALSE, no output is produced

detailed.verbose

If TRUE, the expansion of the current submap is shown;

high.prec

logical. If TRUE uses high precision (long double) numbers in the HMM procedure implemented in C++, which can take a long time to perform (default = FALSE)

Details

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

Value

A list of class mappoly.map with two elements:

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

ploidy

the ploidy level

n.mrk

number of markers

seq.num

a vector containing the (ordered) indices of markers in the map, according to the input file

mrk.names

the names of markers in the map

seq.dose.p1

a vector containing the dosage in parent 1 for all markers in the map

seq.dose.p2

a vector containing the dosage in parent 2 for all markers in the map

chrom

a vector indicating the sequence (usually chromosome) each marker belongs as informed in the input file. If not available, chrom = NULL

genome.pos

physical position (usually in megabase) of the markers into the sequence

seq.ref

reference base used for each marker (i.e. A, T, C, G). If not available, seq.ref = NULL

seq.alt

alternative base used for each marker (i.e. A, T, C, G). If not available, seq.ref = NULL

chisq.pval

a vector containing p-values of the chi-squared test of Mendelian segregation for all markers in the map

data.name

name of the dataset of class mappoly.data

ph.thres

the LOD threshold used to define the linkage phase configurations to test

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

seq.num

a vector containing the (ordered) indices of markers in the map, according to the input file

seq.rf

a vector of size (n.mrk - 1) containing a sequence of recombination fraction between the adjacent markers in the map

seq.ph

linkage phase configuration for all markers in both parents

loglike

the hmm-based multipoint likelihood

Author(s)

Marcelo Mollinari, [email protected]

References

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)

Export data to polymapR

Description

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

Usage

export_data_to_polymapR(data.in)

Arguments

data.in

an object of class mappoly.data

Value

a dosage matrix

Author(s)

Marcelo Mollinari, [email protected]


Export a genetic map to a CSV file

Description

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

Usage

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

Arguments

map.list

A list of objects or a single object of class mappoly.map

file

either a character string naming a file or a connection open for writing. "" indicates output to the console.

Author(s)

Marcelo Mollinari, [email protected]

References

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

input.genoprobs

an object of class mappoly.genoprob

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

Author(s)

Marcelo Mollinari, [email protected]

References

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

Examples

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

input.map

An object of class mappoly.map

phase.config

which phase configuration should be used. "best" (default) will choose the maximum likelihood configuration

Examples

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

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

input.data

name of input object (class mappoly.data)

aneuploid.info

data.frame with ploidy information by chromosome (columns) for each individual in progeny (rows). The chromosome and individuals names must match the ones in the file used as input in mappoly.

ploidy

main ploidy

rm_missing

remove also genotype information from chromosomes with missing data (NA) in the aneuploid.info file

Value

object of class mappoly.data

Author(s)

Cristiane Taniguti, [email protected]

Examples

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

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

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

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

input.data

name of input object (class mappoly.data)

ind.to.remove

individuals to be removed. If NULL it opens an interactive graphic to proceed with the individual selection

inter

if TRUE, expects user-input to proceed with filtering

type

A character string specifying the procedure to be used for detecting outlier offspring. Options include "Gmat", which utilizes the genomic kinship matrix, and "PCA", which employs principal component analysis on the dosage matrix.

coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated.

verbose

if TRUE (default), shows the filtered out individuals

Author(s)

Marcelo Mollinari, [email protected]


Filter missing genotypes

Description

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

Usage

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

Arguments

input.data

an object of class mappoly.data.

type

one of the following options:

  1. "marker": filter out markers based on their percentage of missing data (default).

  2. "individual": filter out individuals based on their percentage of missing data.

Please notice that removing individuals with certain amount of data can change some marker parameters (such as depth), and can also change the estimated genotypes for other individuals. So, be careful when removing individuals.

filter.thres

maximum percentage of missing data (default = 0.2).

inter

if TRUE, expects user-input to proceed with filtering.

Author(s)

Marcelo Mollinari, [email protected].

Examples

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

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

input.obj

name of input object (class mappoly.data)

chisq.pval.thres

p-value threshold used for chi-square tests (default = Bonferroni aproximation with global alpha of 0.05, i.e., 0.05/n.mrk)

inter

if TRUE (default), plots distorted vs. non-distorted markers

Value

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

keep

markers that follow Mendelian segregation pattern

exclude

markers with distorted segregation

chisq.pval.thres

threshold p-value used for chi-square tests

data.name

input dataset used to perform the chi-square tests

Author(s)

Marcelo Mollinari, [email protected]

Examples

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

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

input.seq

an object of class mappoly.sequence.

clustering.type

if 'rf', it uses UPGMA clusterization based on the recombination fraction matrix to assemble blocks. Linkage blocks are assembled by cutting the clusterization tree at rf.limit. If 'genome', it splits the marker sequence at neighbor markers morre than 'genome.block.threshold' apart.

rf.limit

the maximum value to consider linked markers in case of 'clustering.type = rf'

genome.block.threshold

the threshold to assume markers are in the same linkage block. to be considered when allocating markers into blocks in case of 'clustering.type = genomee'

rf.mat

an object of class mappoly.rf.matrix.

ncpus

Number of parallel processes to spawn

ph.thres

the threshold used to sequentially phase markers. Used in thres.twopt and thres.hmm. See est_rf_hmm_sequential for details.

phase.number.limit

the maximum number of linkage phases of the sub-maps. The default is 10. See est_rf_hmm_sequential for details.

error

the assumed global genotyping error rate. If NULL (default) it does not include an error in the block estimation.

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced.

tol

tolerance for the C routine, i.e., the value used to evaluate convergence.

tol.err

tolerance for the C routine, i.e., the value used to evaluate convergence, including the global genotyping error in the model.

Value

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

Author(s)

Marcelo Mollinari, [email protected]

Examples

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

## End(Not run)

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

input.seq

object of class mappoly.sequence

twopt

object of class mappoly.twopt

start.set

number of markers to start the phasing procedure (default = 4)

thres.twopt

the LOD threshold used to determine if the linkage phases compared via two-point analysis should be considered for the search space reduction (default = 5)

thres.hmm

the LOD threshold used to determine if the linkage phases compared via hmm analysis should be evaluated in the next round of marker inclusion (default = 50)

extend.tail

the length of the chain's tail that should be used to calculate the likelihood of the map. If NULL (default), the function uses all markers positioned. Even if info.tail = TRUE, it uses at least extend.tail as the tail length

inflation.lim.p1

the maximum accepted length difference between the current and the previous parent 1 sub-map defined by arguments info.tail and extend.tail. If the size exceeds this limit, the marker will not be inserted. If NULL(default), then it will insert all markers.

inflation.lim.p2

same as 'inflation.lim.p1' but for parent 2 sub-map.

phase.number.limit

the maximum number of linkage phases of the sub-maps defined by arguments info.tail and extend.tail. Default is 20. If the size exceeds this limit, the marker will not be inserted. If Inf, then it will insert all markers.

tol

the desired accuracy during the sequential phase of each parental map (default = 10e-02)

tol.final

the desired accuracy for the final parental map (default = 10e-04)

verbose

If TRUE (default), current progress is shown; if FALSE, no output is produced

method

indicates whether to use 'hmm' (Hidden Markov Models), 'ols' (Ordinary Least Squares) to re-estimate the recombination fractions while merging the parental maps (default:hmm)

Value

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

Author(s)

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


Genetic Mapping Functions

Description

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

Usage

mf_k(d)

mf_h(d)

mf_m(d)

imf_k(r)

imf_h(r)

imf_m(r)

Arguments

d

Numeric or numeric vector, representing genetic distances in centiMorgans (cM) for direct functions (mf_k, mf_h, mf_m).

r

Numeric or numeric vector, representing recombination fractions for inverse functions (imf_k, imf_h, imf_m).

Details

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

  • mf_k: Kosambi mapping function.

  • mf_h: Haldane mapping function.

  • mf_m: Morgan mapping function.

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

  • imf_k: Inverse Kosambi mapping function.

  • imf_h: Inverse Haldane mapping function.

  • imf_m: Inverse Morgan mapping function.

References

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


Get the genomic position of markers in a sequence

Description

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

Usage

get_genomic_order(input.seq, verbose = TRUE)

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

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

Arguments

input.seq

a sequence object of class mappoly.sequence

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

x

an object of the class mappoly.geno.ord

...

currently ignored

Author(s)

Marcelo Mollinari, [email protected]

Examples

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

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

input.map

An object of class mappoly.map

mrk.pos

positions of the markers that should be considered in the new map. This can be in any order

phase.config

which phase configuration should be used. "best" (default) will choose the configuration associated with the maximum likelihood

reestimate.rf

logical. If TRUE (default) the recombination fractions between markers are re-estimated

reestimate.phase

logical. If TRUE, the linkage phase configurations are re-estimated (default = FALSE)

thres.twopt

the LOD threshold used to determine if the linkage phases compared via two-point analysis should be considered (default = 5)

thres.hmm

the threshold used to determine if the linkage phases compared via hmm analysis should be considered (default = 3)

extend.tail

the length of the tail of the chain that should be used to calculate the likelihood of the linkage phases. If info.tail = TRUE, the function uses at least extend.tail as the length of the tail (default = 50)

tol

the desired accuracy during the sequential phase (default = 0.1)

tol.final

the desired accuracy for the final map (default = 10e-04)

use.high.precision

logical. If TRUE uses high precision (long double) numbers in the HMM procedure implemented in C++, which can take a long time to perform (default = FALSE)

verbose

If TRUE (default), current progress is shown; if FALSE, no output is produced

Author(s)

Marcelo Mollinari, [email protected]

References

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

x

an object of class mappoly.map

Author(s)

Gabriel Gesteira, [email protected]


Assign markers to linkage groups

Description

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

Usage

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

Arguments

input.mat

an object of class mappoly.rf.matrix

expected.groups

when available, inform the number of expected linkage groups (i.e. chromosomes) for the species

inter

if TRUE (default), plots a dendrogram highlighting the expected groups before continue

comp.mat

if TRUE, shows a comparison between the reference based and the linkage based grouping, if the chromosome information is available (default = FALSE)

LODweight

if TRUE, clusterization is weighted by the square of the LOD Score

verbose

logical. If TRUE (default), current progress is shown; if FALSE, no output is produced

Value

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

data.name

the referred dataset name

hc.snp

a list containing information related to the UPGMA grouping method

expected.groups

the number of expected linkage groups

groups.snp

the groups to which each of the markers belong

seq.vs.grouped.snp

comparison between the genomic group information (when available) and the groups provided by group_mappoly

chisq.pval.thres

the threshold used on the segregation test when reading the dataset

chisq.pval

the p-values associated with the segregation test for all markers in the sequence

Author(s)

Marcelo Mollinari, [email protected]

References

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

input.data

a polymapR dataset

ploidy

the ploidy level

parent1

a character string containing the name (or pattern of genotype IDs) of parent 1

parent2

a character string containing the name (or pattern of genotype IDs) of parent 2

input.type

Indicates whether the input is discrete ("disc") or probabilistic ("prob")

prob.thres

threshold probability to assign a dosage to offspring. If the probability is smaller than thresh.parent.geno, the data point is converted to 'NA'.

pardose

matrix of dimensions (n.mrk x 3) containing the name of the markers in the first column, and the dosage of parents 1 and 2 in columns 2 and 3. (see polymapR vignette)

offspring

a character string containing the name (or pattern of genotype IDs) of the offspring individuals. If NULL (default) it considers all individuals as offsprings, except parent1 and parent2.

filter.non.conforming

if TRUE exclude samples with non expected genotypes under no double reduction. Since markers were already filtered in polymapR, the default is FALSE.

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

Details

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

Author(s)

Marcelo Mollinari [email protected]

References

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

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

object

the name of the object of class multidog

prob.thres

probability threshold to associate a marker call to a dosage. Markers with maximum genotype probability smaller than 'prob.thres' are considered as missing data for the dosage calling purposes

filter.non.conforming

if TRUE (default) exclude samples with non expected genotypes under random chromosome pairing and no double reduction

chrom

a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence

genome.pos

vector with physical position of the markers into the sequence

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

Value

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

ploidy

ploidy level

n.ind

number individuals

n.mrk

total number of markers

ind.names

the names of the individuals

mrk.names

the names of the markers

dosage.p1

a vector containing the dosage in parent P for all n.mrk markers

dosage.p2

a vector containing the dosage in parent Q for all n.mrk markers

chrom

a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence

genome.pos

physical position of the markers into the sequence

prob.thres

probability threshold to associate a marker call to a dosage. Markers with maximum genotype probability smaller than 'prob.thres' were considered as missing data in the 'geno.dose' matrix

geno.dose

a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by ploidy_level + 1

geno

a data.frame containing the probability distribution for each combination of marker and offspring. The first two columns represent the marker and the offspring, respectively. The remaining elements represent the probability associated to each one of the possible dosages. Missing data are converted from NA to the expected segregation ratio using function segreg_poly

n.phen

number of phenotypic traits

phen

a matrix containing the phenotypic data. The rows correspond to the traits and the columns correspond to the individuals

chisq.pval

a vector containing p-values related to the chi-squared test of Mendelian segregation performed for all markers

Author(s)

Gabriel Gesteira, [email protected]

References

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

maplist

a list of phased maps obtained using function create_phased_maplist from package polymapR

mappoly.data

a dataset used to obtain maplist, converted into class mappoly.data

ploidy

the ploidy level

Details

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

Author(s)

Marcelo Mollinari [email protected]

References

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

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


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

input.map

An object of class mappoly.map

input.data

An object of class mappoly.data, which was used to generate input.map

verbose

If TRUE, map information is shown; if FALSE(default), no output is produced

Author(s)

Marcelo Mollinari, [email protected]

References

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

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

input.mat

an object of class mappoly.rf.matrix

input.seq

an object of class mappoly.sequence, with a sequence of markers contained in input.mat

Value

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

Author(s)

Marcelo Mollinari, [email protected]

References

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

input.twopt

an object of class mappoly.twopt

input.seq

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

Value

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

Author(s)

Marcelo Mollinari, [email protected]

References

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

input.obj

An object belonging to one of the specified classes: mappoly.data, mappoly.map, mappoly.sequence, mappoly.group, mappoly.unique.seq, mappoly.pcmap, mappoly.pcmap3d, mappoly.geno.ord, or mappoly.edit.order.

arg

Specifies the markers to include in the sequence, accepting several formats: a string 'all' for all markers; a string or vector of strings 'seqx' where x is the sequence number (0 for unassigned markers); a vector of integers indicating specific markers; or a vector of integers representing linkage group numbers if input.obj is of class mappoly.group. For certain classes (mappoly.pcmap, mappoly.pcmap3d, mappoly.unique.seq, or mappoly.geno.ord), arg can be NULL.

data.name

Name of the mappoly.data class object.

info.parent

Selection criteria based on parental information: 'all' for all dosage combinations, 'P1' for markers informative in parent 1, or 'P2' for markers informative in parent 2. Default is 'all'.

genomic.info

Optional and applicable only to mappoly.group objects. Specifies the use of genomic information in sequence creation. With NULL (default), all markers defined by the grouping function are included. Numeric values indicate the use of specific sequences from genomic information, aiming to match the maximum number of markers with the group. Supports single values or vectors for multiple sequence consideration.

x

An object of class mappoly.sequence.

...

Currently ignored.

Value

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

"seq.num"

Ordered vector of marker indices according to the input.

"seq.phases"

List of linkage phases between markers; -1 for undefined phases.

"seq.rf"

Vector of recombination frequencies; -1 for not estimated frequencies.

"loglike"

Log-likelihood of the linkage map.

"data.name"

Name of the 'mappoly.data' object with raw data.

"twopt"

Name of the 'mappoly.twopt' object with 2-point analyses; -1 if not computed.

Author(s)

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

References

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

Examples

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

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

input.mat

an object of class mappoly.input.matrix

p

integer. The smoothing parameter for the principal curve. If NULL (default) this will be done using the leave-one-out cross validation

n

vector of integers or strings containing loci to be omitted from the analysis

ndim

number of dimensions to be considered in the multidimensional scaling procedure (default = 2)

weight.exponent

the exponent that should be used in the LOD score values to weight the MDS procedure (default = 2)

verbose

if TRUE (default), display information about the analysis

x

an object of class mappoly.mds

...

currently ignored

Value

A list containing:

M

the input distance map

sm

the unconstrained MDS results

pc

the principal curve results

distmap

a matrix of pairwise distances between loci where the columns are in the estimated order

locimap

a data frame of the loci containing the name and position of each locus in order of increasing distance

length

integer giving the total length of the segment

removed

a vector of the names of loci removed from the analysis

scale

the scaling factor from the MDS

locikey

a data frame showing the number associated with each locus name for interpreting the MDS configuration plot

confplotno

a data frame showing locus name associated with each number on the MDS configuration plots

Author(s)

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

References

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

Examples

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

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

dat.1

the first dataset of class mappoly.data to be merged

dat.2

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

Value

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

ploidy

ploidy level

n.ind

number individuals

n.mrk

total number of markers

ind.names

the names of the individuals

mrk.names

the names of the markers

dosage.p1

a vector containing the dosage in parent P for all n.mrk markers

dosage.p2

a vector containing the dosage in parent Q for all n.mrk markers

chrom

a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence

genome.pos

Physical position of the markers into the sequence

seq.ref

if one or both datasets originated from read_vcf, it keeps reference alleles from sequencing platform, otherwise is NULL

seq.alt

if one or both datasets originated from read_vcf, it keeps alternative alleles from sequencing platform, otherwise is NULL

all.mrk.depth

if one or both datasets originated from read_vcf, it keeps marker read depths from sequencing, otherwise is NULL

prob.thres

(unused field)

geno.dose

a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by ploidy_level + 1

geno

if both datasets contain genotype distribution information, the final object will contain 'geno'. This is set to NULL otherwise

nphen

(0)

phen

(NULL)

chisq.pval

a vector containing p-values related to the chi-squared test of Mendelian segregation performed for all markers in both datasets

kept

if elim.redundant = TRUE when reading any dataset, holds all non-redundant markers

elim.correspondence

if elim.redundant = TRUE when reading any dataset, holds all non-redundant markers and its equivalence to the redundant ones

Author(s)

Gabriel Gesteira, [email protected]

References

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

map.list

a list of objects of class mappoly.map to be merged.

twopt

an object of class mappoly.twopt containing the two-point information for all pairs of markers present in the original maps

thres.twopt

the threshold used to determine if the linkage phases compared via two-point analysis should be considered for the search space reduction (default = 3)

genoprob.list

a list of objects of class mappoly.genoprob containing the genotype probabilities for the maps to be merged. If NULL (default), the probabilities are computed.

thres.hmm

the threshold used to determine which linkage phase configurations should be returned when merging two maps. If "best" (default), returns only the best linkage phase configuration. NOTE: if merging multiple maps, it always uses the "best" linkage phase configuration at each block insertion.

tol

the desired accuracy (default = 10e-04)

Details

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

Value

A list of class mappoly.map with two elements:

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

ploidy

the ploidy level

n.mrk

number of markers

seq.num

a vector containing the (ordered) indices of markers in the map, according to the input file

mrk.names

the names of markers in the map

seq.dose.p1

a vector containing the dosage in parent 1 for all markers in the map

seq.dose.p2

a vector containing the dosage in parent 2 for all markers in the map

chrom

a vector indicating the sequence (usually chromosome) each marker belongs as informed in the input file. If not available, chrom = NULL

genome.pos

physical position (usually in megabase) of the markers into the sequence

seq.ref

reference base used for each marker (i.e. A, T, C, G). If not available, seq.ref = NULL

seq.alt

alternative base used for each marker (i.e. A, T, C, G). If not available, seq.ref = NULL

chisq.pval

a vector containing p-values of the chi-squared test of Mendelian segregation for all markers in the map

data.name

name of the dataset of class mappoly.data

ph.thres

the LOD threshold used to define the linkage phase configurations to test

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

seq.num

a vector containing the (ordered) indices of markers in the map, according to the input file

seq.rf

a vector of size (n.mrk - 1) containing a sequence of recombination fraction between the adjacent markers in the map

seq.ph

linkage phase configuration for all markers in both parents

loglike

the hmm-based multipoint likelihood

Author(s)

Marcelo Mollinari, [email protected]

Examples

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

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

map.list

A list or a single object of class mappoly.map

phase.config

A vector containing which phase configuration should be plotted. If 'best' (default), plots the configuration with the highest likelihood for all elements in 'map.list'

same.ch.lg

Logical. If TRUE displays only the scatterplots between the chromosomes and linkage groups with the same number. Default is FALSE.

alpha

transparency factor for SNPs points

size

size of the SNP points

Author(s)

Marcelo Mollinari, [email protected]

References

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)

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

hprobs

an object of class mappoly.homoprob

P

a string containing the name of parent P

Q

a string containing the name of parent Q

Examples

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

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

map.list

A list of objects or a single object of class mappoly.map

horiz

logical. If FALSE, the maps are plotted vertically with the first map to the left. If TRUE (default), the maps are plotted horizontally with the first at the bottom

col

a vector of colors for each linkage group. (default = 'lightgray') ggstyle produces maps using the default ggplot color palette.

title

a title (string) for the maps (default = 'Linkage group')

Value

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

Author(s)

Marcelo Mollinari, [email protected]

References

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

x

object of class mappoly.map2


Plot marker information

Description

Plots summary statistics for a given marker

Usage

plot_mrk_info(input.data, mrk)

Arguments

input.data

an object of class mappoly.data

mrk

marker name or position in the dataset

Author(s)

Marcelo Mollinari, [email protected]

References

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")

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

map_list

a list of multiple mappoly.map.list

error

error rate used in global error in the 'calc_genoprob_error()'

verbose

if TRUE (default), current progress is shown; if FALSE, no output is produced

output_corrected

logical. if FALSE only the ggplot of the changed dosage is printed, if TRUE then a new corrected dosage matrix is output.

Value

A ggplot of the changed and imputed genotypic dosages

Author(s)

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

Examples

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

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

x

an object of class mappoly.homoprob

stack

logical. If TRUE, probability profiles of all homologues are stacked in the plot (default = FALSE)

lg

indicates which linkage group should be plotted. If NULL (default), it plots the first linkage group. If "all", it plots all linkage groups

ind

indicates which individuals should be plotted. It can be the position of the individuals in the dataset or it's name. If NULL (default), the function plots the first individual

use.plotly

if TRUE (default), it uses plotly interactive graphic

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

...

unused arguments


Plots mappoly.prefpair.profiles

Description

Plots mappoly.prefpair.profiles

Usage

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

Arguments

x

an object of class mappoly.prefpair.profiles

type

a character string indicating which type of graphic is plotted: "pair.configs" (default) plots the preferential pairing profile for the pairing configurations or "hom.pairs" plots the preferential pairing profile for the homolog pairs

min.y.prof

lower bound for y axis on the probability profile graphic (default = 0)

max.y.prof

upper bound for y axis on the probability profile graphic (default = 1)

thresh

threshold for chi-square test (default = 0.01)

P1

a string containing the name of parent P1

P2

a string containing the name of parent P2

...

unused arguments


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

file.in

a character string with the name of (or full path to) the input file

ploidy

the ploidy level

parent1

a character string containing the name (or pattern of genotype IDs) of parent 1

parent2

a character string containing the name (or pattern of genotype IDs) of parent 2

offspring

a character string containing the name (or pattern of genotype IDs) of the offspring individuals. If NULL (default) it considers all individuals as offsprings, except parent1 and parent2.

filter.non.conforming

if TRUE (default) converts data points with unexpected genotypes (i.e. no double reduction) to 'NA'. See function segreg_poly for information on expected classes and their respective frequencies.

elim.redundant

logical. If TRUE (default), removes redundant markers during map construction, keeping them annotated to in order to include them in the final map.

parent.geno

indicates whether to use the joint probability 'joint' (default) or the maximum probability of multiple replicates (if available) to assign dosage to parents. If there is one observation per parent, both options will yield the same results.

thresh.parent.geno

threshold probability to assign a dosage to parents. If the probability is smaller than thresh.parent.geno, the marker is discarded.

prob.thres

threshold probability to assign a dosage to offspring. If the probability is smaller than prob.thres, the data point is converted to 'NA'.

file.type

indicates whether the characters in the input file are separated by 'white spaces' ("table") or by commas ("csv").

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

Value

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

ploidy

ploidy level

n.ind

number individuals

n.mrk

total number of markers

ind.names

the names of the individuals

mrk.names

the names of the markers

dosage.p1

a vector containing the dosage in parent P for all n.mrk markers

dosage.p2

a vector containing the dosage in parent Q for all n.mrk markers

chrom

a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence

genome.pos

Physical position of the markers into the sequence

seq.ref

NULL (unused in this type of data)

seq.alt

NULL (unused in this type of data)

all.mrk.depth

NULL (unused in this type of data)

geno.dose

a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by ploidy_level + 1

n.phen

number of phenotypic traits

phen

a matrix containing the phenotypic data. The rows correspond to the traits and the columns correspond to the individuals

kept

if elim.redundant = TRUE, holds all non-redundant markers

elim.correspondence

if elim.redundant = TRUE, holds all non-redundant markers and its equivalence to the redundant ones

Author(s)

Marcelo Mollinari, [email protected]

References

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

Examples

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

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

file.in

a character string with the name of (or full path to) the input file which contains the data to be read

filter.non.conforming

if TRUE (default) converts data points with unexpected genotypes (i.e. no double reduction) to 'NA'. See function segreg_poly for information on expected classes and their respective frequencies.

elim.redundant

logical. If TRUE (default), removes redundant markers during map construction, keeping them annotated to export to the final map.

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

x

an object of class mappoly.data

detailed

if available, print the number of markers per sequence (default = FALSE)

...

currently ignored

thresh.line

position of a threshold line for p values of the segregation test (default = 10e-06)

Details

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

Value

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

ploidy

ploidy level

n.ind

number individuals

n.mrk

total number of markers

ind.names

the names of the individuals

mrk.names

the names of the markers

dosage.p1

a vector containing the dosage in parent P for all n.mrk markers

dosage.p2

a vector containing the dosage in parent Q for all n.mrk markers

chrom

a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence

genome.pos

Physical position of the markers into the sequence

seq.ref

NULL (unused in this type of data)

seq.alt

NULL (unused in this type of data)

all.mrk.depth

NULL (unused in this type of data)

geno.dose

a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by ploidy_level + 1

n.phen

number of phenotypic traits

phen

a matrix containing the phenotypic data. The rows correspond to the traits and the columns correspond to the individuals

kept

if elim.redundant = TRUE, holds all non-redundant markers

elim.correspondence

if elim.redundant = TRUE, holds all non-redundant markers and its equivalence to the redundant ones

Author(s)

Marcelo Mollinari, [email protected]

References

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

file.in

a character string with the name of (or full path to) the input file containing the data to be read

ploidy

the ploidy level

filter.non.conforming

if TRUE (default) converts data points with unexpected genotypes (i.e. no double reduction) to 'NA'. See function segreg_poly for information on expected classes and their respective frequencies.

elim.redundant

logical. If TRUE (default), removes redundant markers during map construction, keeping them annotated to export to the final map.

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

Details

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

Value

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

ploidy

ploidy level

n.ind

number individuals

n.mrk

total number of markers

ind.names

the names of the individuals

mrk.names

the names of the markers

dosage.p1

a vector containing the dosage in parent P for all n.mrk markers

dosage.p2

a vector containing the dosage in parent Q for all n.mrk markers

chrom

a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence

genome.pos

Physical position of the markers into the sequence

seq.ref

NULL (unused in this type of data)

seq.alt

NULL (unused in this type of data)

all.mrk.depth

NULL (unused in this type of data)

geno.dose

a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by ploidy_level + 1

n.phen

number of phenotypic traits

phen

a matrix containing the phenotypic data. The rows correspond to the traits and the columns correspond to the individuals

kept

if elim.redundant = TRUE, holds all non-redundant markers

elim.correspondence

if elim.redundant = TRUE, holds all non-redundant markers and its equivalence to the redundant ones

Author(s)

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

References

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

file.in

a character string with the name of (or full path to) the input file which contains the data to be read

prob.thres

probability threshold to associate a marker call to a dosage. Markers with maximum genotype probability smaller than prob.thres are considered as missing data for the dosage calling purposes (default = 0.95)

filter.non.conforming

if TRUE (default) converts data points with unexpected genotypes (i.e. no double reduction) to 'NA'. See function segreg_poly for information on expected classes and their respective frequencies.

elim.redundant

logical. If TRUE (default), removes redundant markers during map construction, keeping them annotated to export to the final map.

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

Details

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

Value

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

ploidy

ploidy level

n.ind

number individuals

n.mrk

total number of markers

ind.names

the names of the individuals

mrk.names

the names of the markers

dosage.p1

a vector containing the dosage in parent P for all n.mrk markers

dosage.p2

a vector containing the dosage in parent Q for all n.mrk markers

chrom

a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence

genome.pos

physical position of the markers into the sequence

seq.ref

NULL (unused in this type of data)

seq.alt

NULL (unused in this type of data)

all.mrk.depth

NULL (unused in this type of data)

prob.thres

probability threshold to associate a marker call to a dosage. Markers with maximum genotype probability smaller than 'prob.thres' were considered as missing data in the 'geno.dose' matrix

geno.dose

a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by ploidy_level + 1

geno

a data.frame containing the probability distribution for each combination of marker and offspring. The first two columns represent the marker and the offspring, respectively. The remaining elements represent the probability associated to each one of the possible dosages. Missing data are converted from NA to the expected segregation ratio using function segreg_poly

n.phen

number of phenotypic traits

phen

a matrix containing the phenotypic data. The rows correspond to the traits and the columns correspond to the individuals

chisq.pval

a vector containing p-values related to the chi-squared test of Mendelian segregation performed for all markers

kept

if elim.redundant = TRUE, holds all non-redundant markers

elim.correspondence

if elim.redundant = TRUE, holds all non-redundant markers and its equivalence to the redundant ones

Author(s)

Marcelo Mollinari, [email protected]

References

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

file.in

a character string with the name of (or full path to) the input file which contains the data (VCF format)

parent.1

a character string containing the name of parent 1

parent.2

a character string containing the name of parent 2

ploidy

the species ploidy (optional, it will be automatically detected)

filter.non.conforming

if TRUE (default) converts data points with unexpected genotypes (i.e. no double reduction) to 'NA'. See function segreg_poly for information on expected classes and their respective frequencies.

thresh.line

threshold used for p-values on segregation test (default = 0.05)

min.gt.depth

minimum genotype depth to keep information. If the genotype depth is below min.gt.depth, it will be replaced with NA (default = 0)

min.av.depth

minimum average depth to keep markers (default = 0)

max.missing

maximum proportion of missing data to keep markers (range = 0-1; default = 1)

elim.redundant

logical. If TRUE (default), removes redundant markers during map construction, keeping them annotated to export to the final map.

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

read.geno.prob

If genotypic probabilities are available (PL field), generates a probability-based dataframe (default = FALSE).

prob.thres

probability threshold to associate a marker call to a dosage. Markers with maximum genotype probability smaller than prob.thres are considered as missing data for the dosage calling purposes (default = 0.95)

Details

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

Value

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

ploidy

ploidy level

n.ind

number individuals

n.mrk

total number of markers

ind.names

the names of the individuals

mrk.names

the names of the markers

dosage.p1

a vector containing the dosage in parent P for all n.mrk markers

dosage.p2

a vector containing the dosage in parent Q for all n.mrk markers

chrom

a vector indicating which sequence each marker belongs. Zero indicates that the marker was not assigned to any sequence

genome.pos

Physical position of the markers into the sequence

seq.ref

Reference base used for each marker (i.e. A, T, C, G)

seq.alt

Alternative base used for each marker (i.e. A, T, C, G)

prob.thres

(unused field)

geno.dose

a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by ploidy_level + 1

geno

a dataframe containing all genotypic probabilities columns for each marker and individual combination (rows). Missing data are represented by ploidy_level + 1

nphen

(unused field)

phen

(unused field)

all.mrk.depth

DP information for all markers on VCF file

chisq.pval

a vector containing p-values related to the chi-squared test of Mendelian segregation performed for all markers

kept

if elim.redundant = TRUE, holds all non-redundant markers

elim.correspondence

if elim.redundant = TRUE, holds all non-redundant markers and its equivalence to the redundant ones

Author(s)

Gabriel Gesteira, [email protected]

References

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

input.map

An object of class mappoly.map

input.mat

An object of class mappoly.rf.matrix

tol

tolerance for determining convergence (default = 10e-03)

phase.config

which phase configuration should be used. "best" (default) will choose the maximum likelihood configuration

method

indicates whether to use 'hmm' (Hidden Markov Models), 'ols' (Ordinary Least Squares) or 'wMDS_to_1D_pc' (weighted MDS followed by fitting a one dimensional principal curve) to re-estimate the recombination fractions.

weight

if TRUE (default), it uses the LOD scores to perform a weighted regression when the Ordinary Least Squares is chosen

verbose

if TRUE (default), current progress is shown; if FALSE, no output is produced

high.prec

logical. If TRUE uses high precision (long double) numbers in the HMM procedure implemented in C++, which can take a long time to perform (default = FALSE)

max.rf.to.break.EM

for internal use only.

input.mds

An object of class mappoly.map

Value

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

References

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

input.map

an object of class mappoly.map

Author(s)

Marcelo Mollinari, [email protected]

Examples

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

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

input.twopt

an object of class mappoly.twopt or mappoly.twopt2

thresh.LOD.ph

LOD score threshold for linkage phase configurations (default = 0)

thresh.LOD.rf

LOD score threshold for recombination fractions (default = 0)

thresh.rf

the threshold used for recombination fraction filtering (default = 0.5)

ncpus

number of parallel processes (i.e. cores) to spawn (default = 1)

shared.alleles

if TRUE, computes two matrices (for both parents) indicating the number of homologues that share alleles (default = FALSE)

verbose

if TRUE (default), current progress is shown; if FALSE, no output is produced

x

an object of class mappoly.rf.matrix

...

currently ignored

type

type of matrix that should be printed. Can be one of the following: "rf", for recombination fraction or "lod" for LOD Score

ord

the order in which the markers should be plotted (default = NULL)

rem

which markers should be removed from the heatmap (default = NULL)

main.text

a character string as the title of the heatmap (default = NULL)

index

logical should the name of the markers be printed in the diagonal of the heatmap? (default = FALSE)

fact

positive integer. factor expressed as number of cells to be aggregated (default = 1, no aggregation)

Details

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

Value

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

Author(s)

Marcelo Mollinari, [email protected]

References

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

input.twopt

an object of class mappoly.twopt

thresh.LOD.ph

LOD score threshold for linkage phase configuration (default = 5)

thresh.LOD.rf

LOD score threshold for recombination fraction (default = 5)

thresh.rf

threshold for recombination fractions (default = 0.15)

probs

indicates the probability corresponding to the filtering quantiles. (default = c(0.05, 1))

diag.markers

A window where marker pairs should be considered. If NULL (default), all markers are considered.

mrk.order

marker order. Only has effect if 'diag.markers' is not NULL

ncpus

number of parallel processes (i.e. cores) to spawn (default = 1)

diagnostic.plot

if TRUE produces a diagnostic plot

breaks

number of cells for the histogram

Details

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

Value

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

Author(s)

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

References

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")

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

ploidy

the ploidy level

dP

the dosage in parent P

dQ

the dosage in parent Q

Value

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

Author(s)

Marcelo Mollinari, [email protected]

References

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)

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

ploidy

ploidy level. Must be an even number

n.mrk

number of markers

prob.dose

a vector indicating the proportion of markers for different dosage to be simulated (default = NULL)

seed

random number generator seed

Details

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

Value

a list containing the following components:

hom.allele.p

a list of vectors containing linkage phase configurations. Each vector contains the numbers of the homologous chromosomes in which the alleles are located. For instance, a vector containing (1,3,4)(1,3,4) means that the marker has three doses located in the chromosomes 1, 3 and 4. For zero doses, use 0

p

contains the indices of the starting positions of the dosages, considering that the vectors contained in p are concatenated. Markers with no doses (zero doses are also considered)

hom.allele.q

Analogously to hom.allele.p

q

Analogously to p

ploidy

ploidy level

Author(s)

Marcelo Mollinari, [email protected]

References

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

input.map

an object of class mappoly.map

twopt

an object of class mappoly.twopt containing the two-point information for the markers contained in input.map

gap.threshold

distance threshold of neighboring markers where the map should be spitted. The default value is 5 cM

size.rem.cluster

the size of the marker cluster (in number of markers) from which the cluster should be removed. The default value is 1

phase.config

which phase configuration should be used. "best" (default) will choose the maximum likelihood phase configuration

thres.twopt

the threshold used to determine if the linkage phases compared via two-point analysis should be considered for the search space reduction (default = 3)

thres.hmm

the threshold used to determine which linkage phase configurations should be returned when merging two maps. If "best" (default), returns only the best linkage phase configuration. NOTE: if merging multiple maps, it always uses the "best" linkage phase configuration at each block insertion.

tol.merge

the desired accuracy for merging maps (default = 10e-04)

tol.final

the desired accuracy for the final map (default = 10e-04)

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

Value

An object of class mappoly.map

Author(s)

Marcelo Mollinari, [email protected]

References

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")

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

map.list

a list of objects of class mappoly.map

verbose

if TRUE (default), the current progress is shown; if FALSE, no output is produced

Value

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

Author(s)

Gabriel Gesteira, [email protected]

Examples

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

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

input.map.list

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

input.seq

object of class mappoly.sequence containing all markers for specific group

twopt

object of class mappoly.twopt

thres.twopt

the LOD threshold used to determine if the linkage phases compared via two-point analysis should be considered for the search space reduction (default = 5)

init.LOD

the LOD threshold used to determine if the marker will be included or not after hmm analysis (default = 30)

verbose

If TRUE (default), current progress is shown; if FALSE, no output is produced

method

indicates whether to use 'hmm' (Hidden Markov Models), 'ols' (Ordinary Least Squares) or 'wMDS_to_1D_pc' (weighted MDS followed by fitting a one dimensional principal curve) to re-estimate the recombination fractions after adding markers

input.mds

An object of class mappoly.map

max.rounds

integer defining number of times to try to fit the remaining markers in the sequence

size.rem.cluster

threshold for number of markers that must contain in a segment after a gap is removed to keep this segment in the sequence

gap.threshold

threshold for gap size

Value

object of class mappoly.map2

Author(s)

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


Update map

Description

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

Usage

update_map(input.maps, verbose = TRUE)

Arguments

input.maps

a single map or a list of maps of class mappoly.map

verbose

if TRUE (default), shows information about each update process

Value

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

Author(s)

Gabriel Gesteira, [email protected]

Examples

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