Calculates individual significance level to be used to achieve a global alpha (with Bonferroni)

Description

It shows the alpha value to be used in each chi-square segregation test, in order to achieve a given global type I error. To do so, it uses Bonferroni's criteria.

Usage

Bonferroni_alpha(x, global.alpha = 0.05)

Arguments

Value

the alpha value for each test (numeric)

Examples

 data(onemap_example_bc) # Loads a fake backcross dataset installed with onemap
 Chi <- test_segregation(onemap_example_bc) # Performs the chi-square test for all markers
 print(Chi) # Shows the results of the Chi-square tests
 Bonferroni_alpha (Chi) # Shows the individual alpha level to be used

Perform gaussian sum

Description

Perform gaussian sum

Usage

acum(w)

Arguments

Creates a new sequence by adding markers.

Description

Creates a new sequence by adding markers from a predetermined one. The markers are added in the end of the sequence.

Usage

add_marker(input.seq, mrks)

Arguments

Value

An object of class sequence, which is a list containing the following components:

@author Marcelo Mollinari, mmollina@usp.br

See Also

drop_marker

Examples

data(onemap_example_out)
twopt <- rf_2pts(onemap_example_out)
all_mark <- make_seq(twopt,"all")
groups <- group(all_mark)
(LG1 <- make_seq(groups,1))
(LG.aug<-add_marker(LG1, c(4,7)))

Add the redundant markers removed by create_data_bins function

Description

Add the redundant markers removed by create_data_bins function

Usage

add_redundants(sequence, onemap.obj, bins)

Arguments

Value

New sequence object of class sequence, which is a list containing the following components:

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

See Also

find_bins

Onemap object sanity check

Description

Based on MAPpoly check_data_sanity function by Marcelo Mollinari

Usage

check_data(x)

Arguments

Value

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

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

Examples

data(onemap_example_bc)
check_data(onemap_example_bc)

Twopts object sanity check

Description

Based on MAPpoly check_data_sanity function by Marcelo Mollinari

Usage

check_twopts(x)

Arguments

Value

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

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

Examples

data(onemap_example_bc)
twopts <- rf_2pts(onemap_example_bc)
check_twopts(twopts)

Combine OneMap datasets

Description

Merge two or more OneMap datasets from the same cross type. Creates an object of class onemap.

Usage

combine_onemap(...)

Arguments

Details

Given a set of OneMap datasets, all from the same cross type (full-sib, backcross, F2 intercross or recombinant inbred lines obtained by self- or sib-mating), merges marker and phenotype information to create a single onemap object.

If sample IDs are present in all datasets (the standard new format), not all individuals need to be genotyped in all datasets - the merged dataset will contain all available information, with missing data elsewhere. If sample IDs are missing in at least one dataset, it is required that all datasets have the same number of individuals, and it is assumed that they are arranged in the same order in every dataset.

Value

An object of class onemap, i.e., a list with the following components:

Author(s)

Gabriel R A Margarido, gramarga@gmail.com

References

Lincoln, S. E., Daly, M. J. and Lander, E. S. (1993) Constructing genetic linkage maps with MAPMAKER/EXP Version 3.0: a tutorial and reference manual. A Whitehead Institute for Biomedical Research Technical Report.

Wu, R., Ma, C.-X., Painter, I. and Zeng, Z.-B. (2002) Simultaneous maximum likelihood estimation of linkage and linkage phases in outcrossing species. Theoretical Population Biology 61: 349-363.

See Also

read_onemap and read_mapmaker.

Examples

    
    data("onemap_example_out")
    data("vcf_example_out")
    combined_data <- combine_onemap(onemap_example_out, vcf_example_out)
  

Compare all possible orders (exhaustive search) for a given sequence of markers

Description

For a given sequence with n markers, computes the multipoint likelihood of all \frac{n!}{2} possible orders.

Usage

compare(input.seq, n.best = 50, tol = 0.001, verbose = FALSE)

Arguments

Details

Since the number \frac{n!}{2} is large even for moderate values of n, this function is to be used only for sequences with relatively few markers. If markers were genotyped in an outcross population, linkage phases need to be estimated and therefore more states need to be visited in the Markov chain; when segregation types are D1, D2 and C, computation can required a very long time (specially when markers linked in repulsion are involved), so we recommend to use this function up to 6 or 7 markers. For inbred-based populations, up to 10 or 11 markers can be ordered with this function, since linkage phase are known. The multipoint likelihood is calculated according to Wu et al. (2002b) (Eqs. 7a to 11), assuming that the recombination fraction is the same in both parents. Hidden Markov chain codes adapted from Broman et al. (2008) were used.

Value

An object of class compare, which is a list containing the following components:

Author(s)

Marcelo Mollinari, mmollina@usp.br

References

Broman, K. W., Wu, H., Churchill, G., Sen, S., Yandell, B. (2008) qtl: Tools for analyzing QTL experiments R package version 1.09-43

Jiang, C. and Zeng, Z.-B. (1997). Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines. Genetica 101: 47-58.

Lander, E. S., Green, P., Abrahamson, J., Barlow, A., Daly, M. J., Lincoln, S. E. and Newburg, L. (1987) MAPMAKER: An interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1: 174-181.

Mollinari, M., Margarido, G. R. A., Vencovsky, R. and Garcia, A. A. F. (2009) Evaluation of algorithms used to order markers on genetics maps. _Heredity_ 103: 494-502.

Wu, R., Ma, C.-X., Painter, I. and Zeng, Z.-B. (2002a) Simultaneous maximum likelihood estimation of linkage and linkage phases in outcrossing species. Theoretical Population Biology 61: 349-363.

Wu, R., Ma, C.-X., Wu, S. S. and Zeng, Z.-B. (2002b). Linkage mapping of sex-specific differences. Genetical Research 79: 85-96

See Also

marker_type for details about segregation types and make_seq.

Examples

  #outcrossing example
  data(onemap_example_out)
  twopt <- rf_2pts(onemap_example_out)
  markers <- make_seq(twopt,c(12,14,15,26,28))
  (markers.comp <- compare(markers))
  (markers.comp <- compare(markers,verbose=TRUE))

  #F2 example
  data(onemap_example_f2)
  twopt <- rf_2pts(onemap_example_f2)
  markers <- make_seq(twopt,c(17,26,29,30,44,46,55))
  (markers.comp <- compare(markers))
  (markers.comp <- compare(markers,verbose=TRUE))

New dataset based on bins

Description

Creates a new dataset based on onemap_bin object

Usage

create_data_bins(input.obj, bins)

Arguments

Details

Given a onemap_bin object, creates a new data set where the redundant markers are collapsed into bins and represented by the marker with the lower amount of missing data among those on the bin.

Value

An object of class onemap, i.e., a list with the following components:

Author(s)

Marcelo Mollinari, mmollina@usp.br

See Also

find_bins

Examples

  data("onemap_example_f2")
  (bins<-find_bins(onemap_example_f2, exact=FALSE))
  onemap_bins <- create_data_bins(onemap_example_f2, bins)

Create a dataframe suitable for a ggplot2 graphic

Description

An internal function that prepares a dataframe suitable for drawing a graphic of raw data using ggplot2, i. e., a data frame with long format

Usage

create_dataframe_for_plot_outcross(x)

Arguments

Value

a dataframe

Create database and ggplot graphic of allele reads depths

Description

Create database and ggplot graphic of allele reads depths

Usage

create_depths_profile(
  onemap.obj = NULL,
  vcfR.object = NULL,
  vcf = NULL,
  parent1 = NULL,
  parent2 = NULL,
  vcf.par = "AD",
  recovering = FALSE,
  mks = NULL,
  inds = NULL,
  GTfrom = "onemap",
  alpha = 1,
  rds.file = "data.rds",
  y_lim = NULL,
  x_lim = NULL,
  verbose = TRUE
)

Arguments

Value

an rds file and a ggplot graphic.

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

See Also

onemap_read_vcfR

Build genotype probabilities matrix for hmm

Description

The genotypes probabilities can be calculated considering a global error (default method) or considering a genotype error probability for each genotype. Furthermore, user can provide directly the genotype probability matrix.

Usage

create_probs(
  input.obj = NULL,
  global_error = NULL,
  genotypes_errors = NULL,
  genotypes_probs = NULL
)

Arguments

Details

The genotype probability matrix has number of individuals x number of markers rows and four columns (or two if considering backcross or RILs populations), one for each possible genotype of the population. This format follows the one proposed by MAPpoly.

The genotype probabilities come from SNP calling methods. If you do not have them, you can use a global error or a error value for each genotype. The OneMap until 2.1 version have only the global error option.

Value

An object of class onemap, i.e., a list with the following components:

Author(s)

Cristiane Taniguti chtaniguti@tamu.edu

References

Broman, K. W., Wu, H., Churchill, G., Sen, S., Yandell, B. (2008) qtl: Tools for analyzing QTL experiments R package version 1.09-43

See Also

make_seq

Examples

  data(onemap_example_out)
  new.data <- create_probs(onemap_example_out, global_error = 10^-5)
  

Draw a genetic map

Description

Provides a simple draw of a genetic map.

Usage

draw_map(
  map.list,
  horizontal = FALSE,
  names = FALSE,
  grid = FALSE,
  cex.mrk = 1,
  cex.grp = 0.75
)

Arguments

Value

figure with genetic map draw

Author(s)

Marcelo Mollinari, mmollina@usp.br

Examples

 #outcross example
  data(onemap_example_out)
  twopt <- rf_2pts(onemap_example_out)
  lg<-group(make_seq(twopt, "all"))
  maps<-vector("list", lg$n.groups)
  for(i in 1:lg$n.groups)
     maps[[i]]<- make_seq(order_seq(input.seq= make_seq(lg,i),twopt.alg =
   "rcd"), "force")
  draw_map(maps, grid=TRUE)
  draw_map(maps, grid=TRUE, horizontal=TRUE)

Draw a linkage map

Description

Provides a simple draw of a linkage map.

Usage

draw_map2(
  ...,
  tag = NULL,
  id = TRUE,
  pos = TRUE,
  cex.label = NULL,
  main = NULL,
  group.names = NULL,
  centered = FALSE,
  y.axis = TRUE,
  space = NULL,
  col.group = NULL,
  col.mark = NULL,
  col.tag = NULL,
  output = NULL,
  verbose = TRUE
)

Arguments

Value

ggplot graphic with genetic map draw

Author(s)

Getulio Caixeta Ferreira, getulio.caifer@gmail.com

Examples

data("onemap_example_out")
twopt <- rf_2pts(onemap_example_out)
lg<-group(make_seq(twopt, "all"))
seq1<-make_seq(order_seq(input.seq= make_seq(lg,1),twopt.alg = "rcd"), "force")
seq2<-make_seq(order_seq(input.seq= make_seq(lg,2),twopt.alg = "rcd"), "force")
seq3<-make_seq(order_seq(input.seq= make_seq(lg,3),twopt.alg = "rcd"), "force")
draw_map2(seq1,seq2,seq3,tag = c("M1","M2","M3","M4","M5"),
output = paste0(tempfile(), ".png"))

Creates a new sequence by dropping markers.

Description

Creates a new sequence by dropping markers from a predetermined one.

Usage

drop_marker(input.seq, mrks)

Arguments

Value

An object of class sequence, which is a list containing the following components:

@author Marcelo Mollinari, mmollina@usp.br

See Also

add_marker

Examples

data(onemap_example_out)
twopt <- rf_2pts(onemap_example_out)
all_mark <- make_seq(twopt,"all")
groups <- group(all_mark)
(LG1 <- make_seq(groups,1))
(LG.aug<-drop_marker(LG1, c(10,14)))

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_onemap(input.seq)

Arguments

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

Produce empty object to avoid code break. Function for internal purpose.

Description

Produce empty object to avoid code break. Function for internal purpose.

Usage

empty_onemap_obj(vcf, P1, P2, cross)

Arguments

Value

An empty object of class onemap, i.e., a list with the following components:

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

C++ routine for multipoint analysis in outcrossing populations

Description

It calls C++ routine that implements the methodology of Hidden Markov Models (HMM) to construct multipoint linkage maps in outcrossing species

Usage

est_map_hmm_out(
  geno,
  error,
  type,
  phase,
  rf.vec = NULL,
  verbose = TRUE,
  tol = 1e-06
)

Arguments

Value

a list containing the re-estimated vector of recombination fractions and the logarithm of the likelihood

Export genotype probabilities in MAPpoly format (input for QTLpoly)

Description

Export genotype probabilities in MAPpoly format (input for QTLpoly)

Usage

export_mappoly_genoprob(input.map)

Arguments

Value

object of class 'mappoly.genoprob'

Export OneMap maps to be visualized in VIEWpoly

Description

Export OneMap maps to be visualized in VIEWpoly

Usage

export_viewpoly(seqs.list)

Arguments

Value

object of class viewmap

Extract allele counts of progeny and parents of vcf file

Description

Uses vcfR package and onemap object to generates list of vectors with reference allele count and total counts for each marker and genotypes included in onemap object (only available for biallelic sites)

Usage

extract_depth(
  vcfR.object = NULL,
  onemap.object = NULL,
  vcf.par = c("GQ", "AD", "DPR, PL", "GL"),
  parent1 = "P1",
  parent2 = "P2",
  f1 = "F1",
  recovering = FALSE
)

Arguments

Value

list containing the following components:

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

Filter markers based on 2pts distance

Description

Filter markers based on 2pts distance

Usage

filter_2pts_gaps(input.seq, max.gap = 10)

Arguments

Value

New sequence object of class sequence, which is a list containing the following components:

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

Filter markers according with a missing data threshold

Description

Filter markers according with a missing data threshold

Usage

filter_missing(
  onemap.obj = NULL,
  threshold = 0.25,
  by = "markers",
  verbose = TRUE
)

Arguments

Value

An object of class onemap, i.e., a list with the following components:

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

Examples

  data(onemap_example_out)
  filt_obj <- filter_missing(onemap_example_out, threshold=0.25)
 

Function filter genotypes by genotype probability

Description

Function filter genotypes by genotype probability

Usage

filter_prob(onemap.obj = NULL, threshold = 0.8, verbose = TRUE)

Arguments

Value

An object of class onemap, i.e., a list with the following components:

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

Examples

  data(onemap_example_out)
  filt_obj <- filter_prob(onemap_example_out, threshold=0.8)
 

Allocate markers into bins

Description

Function to allocate markers with redundant information into bins. Within each bin, the pairwise recombination fraction between markers is zero.

Usage

find_bins(input.obj, exact = TRUE)

Arguments

Value

An object of class onemap_bin, which is a list containing the following components:

Author(s)

Marcelo Mollinari, mmollina@usp.br

See Also

create_data_bins

Examples

  data("vcf_example_out")
  (bins<-find_bins(vcf_example_out, exact=FALSE))

Function to divide the sequence in batches with user defined size

Description

Function to divide the sequence in batches with user defined size

Usage

generate_overlapping_batches(input.seq, size = 50, overlap = 15)

Arguments

Assign markers to linkage groups

Description

Identifies linkage groups of markers, using results from two-point (pairwise) analysis and the transitive property of linkage.

Usage

group(input.seq, LOD = NULL, max.rf = NULL, verbose = TRUE)

Arguments

Details

If the arguments specifying thresholds used to group markers, i.e., minimum LOD Score and maximum recombination fraction, are NULL (default), the values used are those contained in object input.seq. If not using NULL, the new values override the ones in object input.seq.

Value

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

Author(s)

Gabriel R A Margarido, gramarga@gmail.com and Marcelo Mollinari, mmollina@usp.br

References

Lincoln, S. E., Daly, M. J. and Lander, E. S. (1993) Constructing genetic linkage maps with MAPMAKER/EXP Version 3.0: a tutorial and reference manual. A Whitehead Institute for Biomedical Research Technical Report.

See Also

rf_2pts and make_seq

Examples

  data(onemap_example_out)
  twopts <- rf_2pts(onemap_example_out)

  all.data <- make_seq(twopts,"all")
  link_gr <- group(all.data)
  link_gr
  print(link_gr, details=FALSE) #omit the names of the markers

Assign markers to preexisting linkage groups

Description

Identifies linkage groups of markers combining input sequences objects with unlinked markers from rf_2pts object. The results from two-point (pairwise) analysis and the transitive property of linkage are used for grouping, as group function.

Usage

group_seq(
  input.2pts,
  seqs = "CHROM",
  unlink.mks = "all",
  repeated = FALSE,
  LOD = NULL,
  max.rf = NULL,
  min_mks = NULL
)

Arguments

Details

If the arguments specifying thresholds used to group markers, i.e., minimum LOD Score and maximum recombination fraction, are NULL (default), the values used are those contained in object input.2pts. If not using NULL, the new values override the ones in object input.2pts.

Value

Returns an object of class group_seq, which is a list containing the following components:

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

See Also

make_seq and group

Examples

data(onemap_example_out) # load OneMap's fake dataset for a outcrossing population
data(vcf_example_out) # load OneMap's fake dataset from a VCF file for a outcrossing population
comb_example <- combine_onemap(onemap_example_out, vcf_example_out) # Combine datasets
twopts <- rf_2pts(comb_example)

out_CHROM <- group_seq(twopts, seqs="CHROM", repeated=FALSE)
out_CHROM

seq1 <- make_seq(twopts, c(1,2,3,4,5,25,26))
seq2 <- make_seq(twopts, c(8,18))
seq3 <- make_seq(twopts, c(4,16,20,21,24,29))

out_seqs <- group_seq(twopts, seqs=list(seq1,seq2,seq3))
out_seqs

Assign markers to linkage groups

Description

Identifies linkage groups of markers using the results of two-point (pairwise) analysis and UPGMA method. Function adapted from MAPpoly package written by Marcelo Mollinari.

Usage

group_upgma(input.seq, expected.groups = NULL, inter = TRUE, comp.mat = FALSE)

Arguments

Value

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

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

Cristiane Taniguti chtaniguti@tamu.edu

References

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

Examples

 data("vcf_example_out")
 twopts <- rf_2pts(vcf_example_out)
 input.seq <- make_seq(twopts, "all")
 lgs <- group_upgma(input.seq, expected.groups = 3, comp.mat=TRUE, inter = FALSE)
 plot(lgs)

Apply Haldane mapping function

Description

Apply Haldane mapping function

Usage

haldane(rcmb)

Arguments

Value

vector with centimorgan values

Keep in the onemap and twopts object only markers in the sequences

Description

Keep in the onemap and twopts object only markers in the sequences

Usage

keep_only_selected_mks(list.sequences = NULL)

Arguments

Value

a list of objects 'sequences' with internal onemap and twopts objects reduced

Author(s)

Cristiane Taniguti

Apply Kosambi mapping function

Description

Apply Kosambi mapping function

Usage

kosambi(rcmb)

Arguments

Value

vector with centimorgan values

Load list of sequences saved by save_onemap_sequences

Description

Load list of sequences saved by save_onemap_sequences

Usage

load_onemap_sequences(filename)

Arguments

Create a sequence of markers based on other OneMap object types

Description

Makes a sequence of markers based on an object of another type.

Usage

make_seq(input.obj, arg = NULL, phase = NULL, data.name = NULL, twopt = NULL)

Arguments

Value

An object of class sequence, which is a list containing the following components:

Author(s)

Gabriel Margarido, gramarga@gmail.com

References

Lander, E. S., Green, P., Abrahamson, J., Barlow, A., Daly, M. J., Lincoln, S. E. and Newburg, L. (1987) MAPMAKER: An interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1: 174-181.

See Also

compare, try_seq, order_seq and map.

Examples

  data(onemap_example_out)
  twopt <- rf_2pts(onemap_example_out)

  all_mark <- make_seq(twopt,"all")
  all_mark <- make_seq(twopt,1:30) # same as above, for this data set
  groups <- group(all_mark)
  LG1 <- make_seq(groups,1)
  LG1.ord <- order_seq(LG1)
  (LG1.final <- make_seq(LG1.ord)) # safe order
  (LG1.final.all <- make_seq(LG1.ord,"force")) # forced order

  markers <- make_seq(twopt,c(2,3,12,14))
  markers.comp <- compare(markers)
  (base.map <- make_seq(markers.comp))
  base.map <- make_seq(markers.comp,1,1) # same as above
  (extend.map <- try_seq(base.map,30))
  (base.map <- make_seq(extend.map,5)) # fifth position is the best

Construct the linkage map for a sequence of markers

Description

Estimates the multipoint log-likelihood, linkage phases and recombination frequencies for a sequence of markers in a given order.

Usage

map(
  input.seq,
  tol = 1e-04,
  verbose = FALSE,
  rm_unlinked = FALSE,
  phase_cores = 1,
  parallelization.type = "PSOCK",
  global_error = NULL,
  genotypes_errors = NULL,
  genotypes_probs = NULL
)

Arguments

Details

Markers are mapped in the order defined in the object input.seq. If this object also contains a user-defined combination of linkage phases, recombination frequencies and log-likelihood are estimated for that particular case. Otherwise, the best linkage phase combination is also estimated. The multipoint likelihood is calculated according to Wu et al. (2002b)(Eqs. 7a to 11), assuming that the recombination fraction is the same in both parents. Hidden Markov chain codes adapted from Broman et al. (2008) were used.

Value

An object of class sequence, which is a list containing the following components:

Author(s)

Adapted from Karl Broman (package 'qtl') by Gabriel R A Margarido, gramarga@usp.br and Marcelo Mollinari, mmollina@gmail.com, with minor changes by Cristiane Taniguti and Bastian Schiffthaler

References

Broman, K. W., Wu, H., Churchill, G., Sen, S., Yandell, B. (2008) qtl: Tools for analyzing QTL experiments R package version 1.09-43

Jiang, C. and Zeng, Z.-B. (1997). Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines. Genetica 101: 47-58.

Lander, E. S., Green, P., Abrahamson, J., Barlow, A., Daly, M. J., Lincoln, S. E. and Newburg, L. (1987) MAPMAKER: An interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1: 174-181.

Wu, R., Ma, C.-X., Painter, I. and Zeng, Z.-B. (2002a) Simultaneous maximum likelihood estimation of linkage and linkage phases in outcrossing species. Theoretical Population Biology 61: 349-363.

Wu, R., Ma, C.-X., Wu, S. S. and Zeng, Z.-B. (2002b). Linkage mapping of sex-specific differences. Genetical Research 79: 85-96

See Also

make_seq

Examples

  data(onemap_example_out)
  twopt <- rf_2pts(onemap_example_out)

  markers <- make_seq(twopt,c(30,12,3,14,2)) # correct phases
  map(markers)

  markers <- make_seq(twopt,c(30,12,3,14,2),phase=c(4,1,4,3)) # incorrect phases
  map(markers)
  

Repeat HMM if map find unlinked marker

Description

Repeat HMM if map find unlinked marker

Usage

map_avoid_unlinked(
  input.seq,
  size = NULL,
  overlap = NULL,
  phase_cores = 1,
  tol = 1e-04,
  parallelization.type = "PSOCK",
  max.gap = FALSE,
  global_error = NULL,
  genotypes_errors = NULL,
  genotypes_probs = NULL
)

Arguments

Value

An object of class sequence, which is a list containing the following components:

Examples

  data(onemap_example_out)
  twopt <- rf_2pts(onemap_example_out)

  markers <- make_seq(twopt,c(30,12,3,14,2)) # correct phases
  map_avoid_unlinked(markers)

  markers <- make_seq(twopt,c(30,12,3,14,2),phase=c(4,1,4,3)) # incorrect phases
  map_avoid_unlinked(markers)

   

Mapping overlapping batches

Description

Apply the batch mapping algorithm using overlapping windows.

Usage

map_overlapping_batches(
  input.seq,
  size = 50,
  overlap = 15,
  phase_cores = 1,
  verbose = FALSE,
  seeds = NULL,
  tol = 1e-04,
  rm_unlinked = TRUE,
  max.gap = FALSE,
  parallelization.type = "PSOCK"
)

Arguments

Details

This algorithm implements the overlapping batch maps for high density marker sets. The mapping problem is reduced to a number of subsets (batches) which carry information forward in order to more accurately estimate recombination fractions and phasing. It is a adapted version of map.overlapping.batches function of BatchMap package. The main differences are that this onemap version do not have the option to reorder the markers according to ripple algorithm and, if the it finds markers that do not reach the linkage criterias, the algorithm remove the problematic marker and repeat the analysis. Than, the output map can have few markers compared with the input.seq.

Value

An object of class sequence, which is a list containing the following components:

See Also

pick_batch_sizes, map

Perform map using background objects with only selected markers. It saves ram memory during the procedure. It is useful if dealing with many markers in total data set.

Description

Perform map using background objects with only selected markers. It saves ram memory during the procedure. It is useful if dealing with many markers in total data set.

Usage

map_save_ram(
  input.seq,
  tol = 1e-04,
  verbose = FALSE,
  rm_unlinked = FALSE,
  phase_cores = 1,
  size = NULL,
  overlap = NULL,
  parallelization.type = "PSOCK",
  max.gap = FALSE
)

Arguments

Simulated data from a F2 population

Description

Simulated data set from a F2 population.

Usage

data("mapmaker_example_f2")

Format

The format is: List of 8 $ geno : num [1:200, 1:66] 1 3 2 2 1 0 3 1 1 3 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:66] "M1" "M2" "M3" "M4" ... $ n.ind : num 200 $ n.mar : num 66 $ segr.type : chr [1:66] "A.H.B" "C.A" "D.B" "C.A" ... $ segr.type.num: num [1:66] 1 3 2 3 3 2 1 3 2 1 ... $ input : chr "/home/cristiane/R/x86_64-pc-linux-gnu-library/3.4/onemap/extdata/mapmaker_example_f2.raw" $ n.phe : num 1 $ pheno : num [1:200, 1] 37.6 36.4 37.2 35.8 37.1 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr "Trait_1" - attr(*, "class")= chr [1:2] "onemap" "f2"

Details

A total of 200 individuals were genotyped for 66 markers (36 co-dominant, i.e. a, ab or b and 30 dominant i.e. c or a and d or b) with 15% of missing data. There is one quantitative phenotype to show how to use onemap output as R\qtl and QTL Cartographer input. Also, it is used for the analysis in the tutorial that comes with OneMap.

Examples

data(mapmaker_example_f2)

# perform two-point analyses
twopts <- rf_2pts(mapmaker_example_f2)
twopts

Informs the segregation patterns of markers

Description

Informs the type of segregation of all markers from an object of class sequence. For outcross populations it uses the notation by Wu et al., 2002. For backcrosses, F2s and RILs, it uses the traditional notation from MAPMAKER i.e. AA, AB, BB, not AA and not BB.

Usage

marker_type(input.seq)

Arguments

Details

The segregation types are (Wu et al., 2002):

Value

data.frame with segregation types of all markers in the sequence are displayed on the screen.

Author(s)

Gabriel R A Margarido, gramarga@gmail.com

References

Wu, R., Ma, C.-X., Painter, I. and Zeng, Z.-B. (2002) Simultaneous maximum likelihood estimation of linkage and linkage phases in outcrossing species. Theoretical Population Biology 61: 349-363.

See Also

make_seq

Examples

 data(onemap_example_out)
 twopts <- rf_2pts(onemap_example_out)
 markers.ex <- make_seq(twopts,c(3,6,8,12,16,25))
 marker_type(input.seq = markers.ex) # segregation type for some markers

 data(onemap_example_f2)
 twopts <- rf_2pts(onemap_example_f2)
 all_mrk<-make_seq(twopts, "all")
 lgs<-group(all_mrk)
 lg1<-make_seq(lgs,1)
 marker_type(lg1) # segregation type for linkage group 1

OneMap interface with MDSMap package with option for multipoint distances estimation

Description

For a given sequence of markers, apply mds method described in Preedy and Hackett (2016) using MDSMap package to ordering markers and estimates the genetic distances with OneMap multipoint approach. Also gives MDSMap input file format for directly analysis in this package.

Usage

mds_onemap(
  input.seq,
  out.file = NULL,
  p = NULL,
  ispc = TRUE,
  displaytext = FALSE,
  weightfn = "lod2",
  mapfn = "haldane",
  ndim = 2,
  rm_unlinked = TRUE,
  size = NULL,
  overlap = NULL,
  phase_cores = 1,
  tol = 1e-05,
  hmm = TRUE,
  parallelization.type = "PSOCK"
)

Arguments

Details

For better description about MDS method, see MDSMap package vignette.

Value

An object of class sequence, which is a list containing the following components:

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

References

Preedy, K. F. and 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: 2117-2132

Mollinari, M., Margarido, G. R. A., Vencovsky, R. and Garcia, A. A. F. (2009) Evaluation of algorithms used to order markers on genetics maps. Heredity 103: 494-502.

Wu, R., Ma, C.-X., Painter, I. and Zeng, Z.-B. (2002a) Simultaneous maximum likelihood estimation of linkage and linkage phases in outcrossing species. Theoretical Population Biology 61: 349-363.

Wu, R., Ma, C.-X., Wu, S. S. and Zeng, Z.-B. (2002b). Linkage mapping of sex-specific differences. Genetical Research 79: 85-96

See Also

https://CRAN.R-project.org/package=MDSMap.

Simulated data from a backcross population

Description

Simulated data set from a backcross population.

Usage

data(onemap_example_bc)

Format

The format is: List of 10 $ geno : num [1:150, 1:67] 1 2 1 1 2 1 2 1 1 2 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:150] "ID1" "ID2" "ID3" "ID4" ... .. ..$ : chr [1:67] "M1" "M2" "M3" "M4" ... $ n.ind : int 150 $ n.mar : int 67 $ segr.type : chr [1:67] "A.H" "A.H" "A.H" "A.H" ... $ segr.type.num: logi [1:67] NA NA NA NA NA NA ... $ n.phe : int 1 $ pheno : num [1:150, 1] 40.8 39.5 37.9 34.2 38.9 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr "Trait_1" $ CHROM : NULL $ POS : NULL $ input : chr "onemap_example_bc.raw" - attr(*, "class")= chr [1:2] "onemap" "backcross"

Details

A total of 150 individuals were genotyped for 67 markers with 15% of missing data. There is one quantitative phenotype to show how to use onemap output as R\qtl input.

Author(s)

Marcelo Mollinari, mmollina@usp.br

See Also

read_onemap and read_mapmaker.

Examples

data(onemap_example_bc)

# perform two-point analyses
twopts <- rf_2pts(onemap_example_bc)
twopts

Simulated data from a F2 population

Description

Simulated data set from a F2 population.

Usage

data("onemap_example_f2")

Format

The format is: List of 10 $ geno : num [1:200, 1:66] 1 3 2 2 1 0 3 1 1 3 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:200] "IND1" "IND2" "IND3" "IND4" ... .. ..$ : chr [1:66] "M1" "M2" "M3" "M4" ... $ n.ind : int 200 $ n.mar : int 66 $ segr.type : chr [1:66] "A.H.B" "C.A" "D.B" "C.A" ... $ segr.type.num: num [1:66] 1 3 2 3 3 2 1 3 2 1 ... $ n.phe : int 1 $ pheno : num [1:200, 1] 37.6 36.4 37.2 35.8 37.1 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr "Trait_1" $ CHROM : NULL $ POS : NULL $ input : chr "/home/cristiane/R/x86_64-pc-linux-gnu-library/3.4/onemap/extdata/onemap_example_f2.raw" - attr(*, "class")= chr [1:2] "onemap" "f2"

Details

A total of 200 individuals were genotyped for 66 markers (36 co-dominant, i.e. a, ab or b and 30 dominant i.e. c or a and d or b) with 15% of missing data. There is one quantitative phenotype to show how to use onemap output as R\qtl and QTL Cartographer input. Also, it is used for the analysis in the tutorial that comes with OneMap.

Examples

data(onemap_example_f2)
plot(onemap_example_f2)

Data from a full-sib family derived from two outbred parents

Description

Simulated data set for an outcross, i.e., an F1 population obtained by crossing two non-homozygous parents.

Usage

data(onemap_example_out)

Format

An object of class onemap.

Details

A total of 100 F1 individuals were genotyped for 30 markers. The data currently contains only genotype information (no phenotypes). It is included to be used as a reference in order to understand how a data file needs to be. Also, it is used for the analysis in the tutorial that comes with OneMap.

Author(s)

Gabriel R A Margarido, gramarga@gmail.com

See Also

read_onemap for details about objects of class onemap.

Examples

data(onemap_example_out)

# perform two-point analyses
twopts <- rf_2pts(onemap_example_out)
twopts

Simulated data from a RIL population produced by selfing.

Description

Simulated biallelic data set for an ri self population.

Usage

data("onemap_example_riself")

Format

The format is: List of 10 $ geno : num [1:100, 1:68] 3 1 3 1 1 1 1 1 1 1 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:100] "ID1" "ID2" "ID3" "ID4" ... .. ..$ : chr [1:68] "M1" "M2" "M3" "M4" ... $ n.ind : int 100 $ n.mar : int 68 $ segr.type : chr [1:68] "A.B" "A.B" "A.B" "A.B" ... $ segr.type.num: logi [1:68] NA NA NA NA NA NA ... $ n.phe : int 0 $ pheno : NULL $ CHROM : NULL $ POS : NULL $ input : chr "onemap_example_riself.raw" - attr(*, "class")= chr [1:2] "onemap" "riself"

Details

A total of 100 F1 individuals were genotyped for 68 markers. The data currently contains only genotype information (no phenotypes). It is included to be used as a reference in order to understand how a data file needs to be.

Author(s)

Cristiane Taniguti, chtaniguti@usp.br

See Also

read_onemap for details about objects of class onemap.

Examples

data(onemap_example_riself)
plot(onemap_example_riself)

Convert vcf file to onemap object

Description

Converts data from a vcf file to onemap initial object, while identify the appropriate marker segregation patterns.

Usage

onemap_read_vcfR(
  vcf = NULL,
  vcfR.object = NULL,
  cross = NULL,
  parent1 = NULL,
  parent2 = NULL,
  f1 = NULL,
  only_biallelic = TRUE,
  output_info_rds = NULL,
  verbose = TRUE
)

Arguments

Details

Only biallelic SNPs and indels for diploid variant sites are considered.

Genotype information on the parents is required for all cross types. For full-sib progenies, both outbred parents must be genotyped. For backcrosses, F2 intercrosses and recombinant inbred lines, the original inbred lines must be genotyped. Particularly for backcross progenies, the recurrent line must be provided as the first parent in the function arguments.

Marker type is determined based on parental genotypes. Variants for which parent genotypes cannot be determined are discarded.

Reference sequence ID and position for each variant site are also stored.

Value

An object of class onemap, i.e., a list with the following components:

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

See Also

read_onemap for a description of the output object of class onemap.

Examples

data <- onemap_read_vcfR(vcf=system.file("extdata/vcf_example_out.vcf.gz", package = "onemap"),
                 cross="outcross",
                 parent1=c("P1"),
                 parent2=c("P2"))

                

Order the markers in a sequence using the genomic position

Description

Order the markers in a sequence using the genomic position

Usage

ord_by_geno(input.seq)

Arguments

Value

An object of class sequence

Author(s)

Cristiane Taniguti

Search for the best order of markers combining compare and try_seq functions

Description

For a given sequence of markers, this function first uses the compare function to create a framework for a subset of informative markers. Then, it tries to map remaining ones using the try_seq function.

Usage

order_seq(
  input.seq,
  n.init = 5,
  subset.search = c("twopt", "sample"),
  subset.n.try = 30,
  subset.THRES = 3,
  twopt.alg = c("rec", "rcd", "ser", "ug"),
  THRES = 3,
  touchdown = FALSE,
  tol = 0.1,
  rm_unlinked = FALSE,
  verbose = FALSE
)

Arguments

Details

For outcrossing populations, the initial subset and the order in which remaining markers will be used in the try_seq step is given by the degree of informativeness of markers (i.e markers of type A, B, C and D, in this order).

For backcrosses, F2s or RILs, two methods can be used for choosing the initial subset: i) "sample" randomly chooses a number of markers, indicated by n.init, and calculates the multipoint log-likelihood of the \frac{n.init!}{2} possible orders. If the LOD Score of the second best order is greater than subset.THRES, than it takes the best order to proceed with the try_seq step. If not, the procedure is repeated. The maximum number of times to repeat this procedure is given by the subset.n.try argument. ii) "twopt" uses a two-point based algorithm, given by the option "twopt.alg", to construct a two-point based map. The options are "rec" for RECORD algorithm, "rcd" for Rapid Chain Delineation, "ser" for Seriation and "ug" for Unidirectional Growth. Then, equally spaced markers are taken from this map. The "compare" step will then be applied on this subset of markers.

In both cases, the order in which the other markers will be used in the try_seq step is given by marker types (i.e. co-dominant before dominant) and by the missing information on each marker.

After running the compare and try_seq steps, which result in a "safe" order, markers that could not be mapped are "forced" into the map, resulting in a map with all markers positioned.

Value

An object of class order, which is a list containing the following components:

Author(s)

Gabriel R A Margarido, gramarga@usp.br and Marcelo Mollinari, mmollina@gmail.com

References

Broman, K. W., Wu, H., Churchill, G., Sen, S., Yandell, B. (2008) qtl: Tools for analyzing QTL experiments R package version 1.09-43

Jiang, C. and Zeng, Z.-B. (1997). Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines. Genetica 101: 47-58.

Lander, E. S. and Green, P. (1987). Construction of multilocus genetic linkage maps in humans. Proc. Natl. Acad. Sci. USA 84: 2363-2367.

Lander, E. S., Green, P., Abrahamson, J., Barlow, A., Daly, M. J., Lincoln, S. E. and Newburg, L. (1987) MAPMAKER: An interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1: 174-181.

Mollinari, M., Margarido, G. R. A., Vencovsky, R. and Garcia, A. A. F. (2009) Evaluation of algorithms used to order markers on genetics maps. Heredity 103: 494-502.

Wu, R., Ma, C.-X., Painter, I. and Zeng, Z.-B. (2002a) Simultaneous maximum likelihood estimation of linkage and linkage phases in outcrossing species. Theoretical Population Biology 61: 349-363.

Wu, R., Ma, C.-X., Wu, S. S. and Zeng, Z.-B. (2002b). Linkage mapping of sex-specific differences. Genetical Research 79: 85-96

See Also

make_seq, compare and try_seq.

Examples

  #outcross example
  data(onemap_example_out)
  twopt <- rf_2pts(onemap_example_out)
  all_mark <- make_seq(twopt,"all")
  groups <- group(all_mark)
  LG2 <- make_seq(groups,2)
  LG2.ord <- order_seq(LG2,touchdown=TRUE)
  LG2.ord
  make_seq(LG2.ord) # get safe sequence
  make_seq(LG2.ord,"force") # get forced sequence

Generates data.frame with parents estimated haplotypes

Description

Generates data.frame with parents estimated haplotypes

Usage

parents_haplotypes(
  ...,
  group_names = NULL,
  map.function = "kosambi",
  ref_alt_alleles = FALSE
)

Arguments

Value

data.frame with group ID (group), marker number (mk.number) and names (mk.names), position in centimorgan (dist) and parents haplotypes (P1_1, P1_2, P2_1, P2_2)

Author(s)

Getulio Caixeta Ferreira, getulio.caifer@gmail.com

Cristiane Taniguti, chtaniguti@tamu.edu

Examples

data("onemap_example_out")
twopts <- rf_2pts(onemap_example_out)
lg1 <- make_seq(twopts, 1:5)
lg1.map <- map(lg1)
parents_haplotypes(lg1.map)

Picking optimal batch size values

Description

Suggest an optimal batch size value for use in map_overlapping_batches

Usage

pick_batch_sizes(input.seq, size = 50, overlap = 15, around = 5)

Arguments

Value

An integer value for the size which most evenly divides batches. In case of ties, bigger batch sizes are preferred.

Author(s)

Bastian Schiffthaler, bastian.schiffthaler@umu.se

See Also

map_overlapping_batches

Examples

  LG <- structure(list(seq.num = seq(1,800)), class = "sequence")
  batchsize <- pick_batch_sizes(LG, 50, 19)

Show the results of grouping procedure

Description

It shows the linkage groups as well as the unlinked markers.

Usage

## S3 method for class 'group.upgma'
plot(x, ...)

Arguments

Value

NULL

Draw a graphic of raw data for any OneMap population

Description

Shows a heatmap (in ggplot2, a graphic of geom "tile") for raw data. Lines correspond to markers and columns to individuals. The function can plot a graph for all marker types, depending of the cross type (dominant/codominant markers, in all combinations). The function receives a onemap object of class onemap, reads information from genotypes from this object, converts it to a long dataframe format using function melt() from package reshape2() or internal function create_dataframe_for_plot_outcross(), converts numbers from the object to genetic notation (according to the cross type), then plots the graphic. If there is more than 20 markers, removes y labels For outcross populations, it can show all markers together, or it can split them according the segregation pattern.

Usage

## S3 method for class 'onemap'
plot(x, all = TRUE, ...)

Arguments

Value

a ggplot graphic

Examples

# library(ggplot2)
data(onemap_example_bc) # Loads a fake backcross dataset installed with onemap
plot(onemap_example_bc) # This will show you the graph

# You can store the graphic in an object, then save it with a number of properties
# For details, see the help of ggplot2's function ggsave()
g <- plot(onemap_example_bc)

data(onemap_example_f2) # Loads a fake backcross dataset installed with onemap
plot(onemap_example_f2) # This will show you the graph

# You can store the graphic in an object, then save it with a number of properties
# For details, see the help of ggplot2's function ggsave()
g <- plot(onemap_example_f2)

data(onemap_example_out) # Loads a fake full-sib dataset installed with onemap
plot(onemap_example_out) # This will show you the graph for all markers
plot(onemap_example_out, all=FALSE) # This will show you the graph splitted for marker types

# You can store the graphic in an object, then save it.
# For details, see the help of ggplot2's function ggsave()
g <- plot(onemap_example_out, all=FALSE)

Plots progeny haplotypes

Description

Figure is generated with the haplotypes for each selected individual. As a representation, the recombination breakpoints are here considered to be in the mean point of the distance between two markers. It is important to highlight that it did not reflects the exact breakpoint position, specially if the genetic map have low resolution.

Usage

## S3 method for class 'onemap_progeny_haplotypes'
plot(
  x,
  col = NULL,
  position = "stack",
  show_markers = TRUE,
  main = "Genotypes",
  ncol = 4,
  ...
)

Arguments

Value

a ggplot graphic

Author(s)

Getulio Caixeta Ferreira, getulio.caifer@gmail.com

Cristiane Taniguti, chtaniguti@tamu.edu

Examples

 
data("onemap_example_out")
twopts <- rf_2pts(onemap_example_out)
lg1 <- make_seq(twopts, 1:5)
lg1.map <- map(lg1)
plot(progeny_haplotypes(lg1.map))

Plot recombination breakpoints counts for each individual

Description

Plot recombination breakpoints counts for each individual

Usage

## S3 method for class 'onemap_progeny_haplotypes_counts'
plot(x, by_homolog = FALSE, n.graphics = NULL, ncol = NULL, ...)

Arguments

Value

a ggplot graphic

Examples

data("onemap_example_out")
twopts <- rf_2pts(onemap_example_out)
lg1 <- make_seq(twopts, 1:5)
lg1.map <- map(lg1)
prog.haplo <- progeny_haplotypes(lg1.map, most_likely = TRUE)
plot(progeny_haplotypes_counts(prog.haplo))

Plot p-values for chi-square tests of expected segregation

Description

Draw a graphic showing the p-values (re-scaled to -log10(p-values)) associated with the chi-square tests for the expected segregation patterns for all markers in a dataset. It includes a vertical line showing the threshold for declaring statistical significance if Bonferroni's correction is considered, as well as the percentage of markers that will be discarded if this criterion is used.

Usage

## S3 method for class 'onemap_segreg_test'
plot(x, order = TRUE, ...)

Arguments

Value

a ggplot graphic

Examples

 data(onemap_example_bc) # load OneMap's fake dataset for a backcross population
 BC.seg <- test_segregation(onemap_example_bc) # Applies chi-square tests
 print(BC.seg) # Shows the results
 plot(BC.seg) # Plot the graph, ordering the p-values
 plot(BC.seg, order=FALSE) # Plot the graph showing the results keeping the order in the dataset

 data(onemap_example_out) # load OneMap's fake dataset for an outcrossing population
 Out.seg <- test_segregation(onemap_example_out) # Applies chi-square tests
 print(Out.seg) # Shows the results
 plot(Out.seg) # Plot the graph, ordering the p-values
 plot(Out.seg, order=FALSE) # Plot the graph showing the results keeping the order in the dataset

Draw a graphic showing the number of markers of each segregation pattern.

Description

The function receives an object of class onemap. For outcrossing populations, it can show detailed information (all 18 possible categories), or a simplified version.

Usage

plot_by_segreg_type(x, subcateg = TRUE)

Arguments

Value

a ggplot graphic

Examples

data(onemap_example_out) #Outcrossing data
plot_by_segreg_type(onemap_example_out)
plot_by_segreg_type(onemap_example_out, subcateg=FALSE)

data(onemap_example_bc)
plot_by_segreg_type(onemap_example_bc)

data(mapmaker_example_f2)
plot_by_segreg_type(mapmaker_example_f2)

Draws a physical vs cM map

Description

Provides simple genetic to physical ggplot.

Usage

plot_genome_vs_cm(map.list, mapping_function = "kosambi", group.names = NULL)

Arguments

Value

ggplot with cM on x-axis and physical position on y-axis

Author(s)

Jeekin Lau, jeekinlau@gmail.com

print method for object class 'compare'

Description

print method for object class 'compare'

Usage

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

Arguments

Value

compare object description

Show the results of grouping procedure

Description

It shows the linkage groups as well as the unlinked markers.

Usage

## S3 method for class 'group'
print(x, detailed = TRUE, ...)

Arguments

Value

NULL

Show the results of grouping procedure

Description

It shows the linkage groups as well as the unlinked markers.

Usage

## S3 method for class 'group.upgma'
print(x, ...)

Arguments

Value

NULL

Show the results of grouping markers to preexisting sequence

Description

It shows the groups sequences, the repeated markers, as well as the unlinked markers.

Usage

## S3 method for class 'group_seq'
print(x, detailed = TRUE, ...)

Arguments

Value

No return value, called for side effects

Print method for object class 'onemap'

Description

Print method for object class 'onemap'

Usage

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

Arguments

Value

printed information about onemap object

print method for object class 'onemap_bin'

Description

print method for object class 'onemap_bin'

Usage

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

Arguments

Value

No return value, called for side effects

Show the results of segregation tests

Description

It shows the results of Chisquare tests performed for all markers in a onemap object of cross type outcross, backcross, F2 intercross or recombinant inbred lines.

Usage

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

Arguments

Value

a dataframe with marker name, H0 hypothesis, chi-square statistics, p-values, and

Examples

 data(onemap_example_out) # Loads a fake outcross dataset installed with onemap
 Chi <- test_segregation(onemap_example_out) # Performs the chi-square test for all markers
 print(Chi) # Shows the results

Print order_seq object

Description

Print order_seq object

Usage

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

Arguments

Value

printed information about order_seq object

Print method for object class 'rf_2pts'

Description

It shows the linkage groups as well as the unlinked markers.

Usage

## S3 method for class 'rf_2pts'
print(x, mrk = NULL, ...)

Arguments

Value

NULL

Print method for object class 'sequence'

Description

Print method for object class 'sequence'

Usage

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

Arguments

Value

printed information about sequence object

Print method for object class 'try'

Description

Print method for object class 'try'

Usage

## S3 method for class 'try'
print(x, j = NULL, ...)

Arguments

Value

NULL

Generate data.frame with genotypes estimated by HMM and its probabilities

Description

Generate data.frame with genotypes estimated by HMM and its probabilities

Usage

progeny_haplotypes(..., ind = 1, group_names = NULL, most_likely = FALSE)

Arguments

Value

a data.frame information: individual (ind) and marker ID, group ID (grp), position in centimorgan (pos), genotypes probabilities (prob), parents, and the parents homologs and the allele IDs.

Author(s)

Getulio Caixeta Ferreira, getulio.caifer@gmail.com

Cristiane Taniguti, chtaniguti@tamu.edu

Examples

data("onemap_example_out")
twopts <- rf_2pts(onemap_example_out)
lg1 <- make_seq(twopts, 1:5)
lg1.map <- map(lg1)
progeny_haplotypes(lg1.map)

Plot number of breakpoints by individuals

Description

Generate graphic with the number of break points for each individual considering the most likely genotypes estimated by the HMM. Genotypes with same probability for two genotypes are removed. By now, only available for outcrossing and f2 intercross.

Usage

progeny_haplotypes_counts(x)

Arguments

Value

a data.frame with columns individuals ID (ind), group ID (grp), homolog (homolog) and counts of breakpoints

Examples

data("onemap_example_out")
twopts <- rf_2pts(onemap_example_out)
lg1 <- make_seq(twopts, 1:5)
lg1.map <- map(lg1)
progeny_haplotypes_counts(progeny_haplotypes(lg1.map, most_likely = TRUE))

Rapid Chain Delineation

Description

Implements the marker ordering algorithm Rapid Chain Delineation (Doerge, 1996).

Usage

rcd(
  input.seq,
  LOD = 0,
  max.rf = 0.5,
  tol = 1e-04,
  rm_unlinked = TRUE,
  size = NULL,
  overlap = NULL,
  phase_cores = 1,
  hmm = TRUE,
  parallelization.type = "PSOCK",
  verbose = TRUE
)

Arguments

Details

Rapid Chain Delineation (RCD) is an algorithm for marker ordering in linkage groups. It is not an exhaustive search method and, therefore, is not computationally intensive. However, it does not guarantee that the best order is always found. The only requirement is a matrix with recombination fractions between markers. Next is an excerpt from QTL Cartographer Version 1.17 Manual describing the RCD algorithm (Basten et al., 2005):

The linkage group is initiated with the pair of markers having the smallest recombination fraction. The remaining markers are placed in a “pool” awaiting placement on the map. The linkage group is extended by adding markers from the pool of unlinked markers. Each terminal marker of the linkage group is a candidate for extension of the chain: The unlinked marker that has the smallest recombination fraction with either is added to the chain subject to the provision that the recombination fraction is statistically significant at a prespecified level. This process is repeated as long as markers can be added to the chain.

After determining the order with RCD, the final map is constructed using the multipoint approach (function map).

Value

An object of class sequence, which is a list containing the following components:

Author(s)

Gabriel R A Margarido, gramarga@gmail.com

References

Basten, C. J., Weir, B. S. and Zeng, Z.-B. (2005) QTL Cartographer Version 1.17: A Reference Manual and Tutorial for QTL Mapping.

Doerge, R. W. (1996) Constructing genetic maps by rapid chain delineation. Journal of Quantitative Trait Loci 2: 121-132.

Mollinari, M., Margarido, G. R. A., Vencovsky, R. and Garcia, A. A. F. (2009) Evaluation of algorithms used to order markers on genetics maps. Heredity 103: 494-502.

See Also

make_seq, map

Examples

  #outcross example
  data(onemap_example_out)
  twopt <- rf_2pts(onemap_example_out)
  all_mark <- make_seq(twopt,"all")
  groups <- group(all_mark)
  LG1 <- make_seq(groups,1)
  LG1.rcd <- rcd(LG1, hmm = FALSE)

  #F2 example
  data(onemap_example_f2)
  twopt <- rf_2pts(onemap_example_f2)
  all_mark <- make_seq(twopt,"all")
  groups <- group(all_mark)
  LG1 <- make_seq(groups,1)
  LG1.rcd <- rcd(LG1, hmm = FALSE)
  LG1.rcd

Read data from a Mapmaker raw file

Description

Imports data from a Mapmaker raw file.

Usage

read_mapmaker(file = NULL, dir = NULL, verbose = TRUE)

Arguments

Details

For details about MAPMAKER files see Lincoln et al. (1993). The current version supports backcross, F2s and RIL populations. The file can contain phenotypic data, but it will not be used in the analysis.

Value

An object of class onemap, i.e., a list with the following components:

MAPMAKER/EXP fashion, i.e., 1, 2, 3: AA, AB, BB, respectively; 3, 4: BB, not BB, respectively; 1, 5: AA, not AA, respectively. Each column contains data for a marker and each row represents an individual.

Author(s)

Adapted from Karl Broman (package qtl) by Marcelo Mollinari, mmollina@usp.br

References

Broman, K. W., Wu, H., Churchill, G., Sen, S., Yandell, B. (2008) qtl: Tools for analyzing QTL experiments R package version 1.09-43

Lincoln, S. E., Daly, M. J. and Lander, E. S. (1993) Constructing genetic linkage maps with MAPMAKER/EXP Version 3.0: a tutorial and reference manual. A Whitehead Institute for Biomedical Research Technical Report.

See Also

mapmaker_example_bc and mapmaker_example_f2 raw files in the package source.

Examples

 map_data <-read_mapmaker(file=system.file("extdata/mapmaker_example_f2.raw", package = "onemap"))
 #Checking 'mapmaker_example_f2'
 data(mapmaker_example_f2)
 names(mapmaker_example_f2)

Read data from all types of progenies supported by OneMap

Description

Imports data derived from outbred parents (full-sib family) or inbred parents (backcross, F2 intercross and recombinant inbred lines obtained by self- or sib-mating). Creates an object of class onemap.

Usage

read_onemap(inputfile = NULL, dir = NULL, verbose = TRUE)

Arguments

Details

The file format is similar to that used by MAPMAKER/EXP (Lincoln et al., 1993). The first line indicates the cross type and is structured as data type {cross}, where cross must be one of "outcross", "f2 intercross", "f2 backcross", "ri self" or "ri sib". The second line contains five integers: i) the number of individuals; ii) the number of markers; iii) an indicator variable taking the value 1 if there is CHROM information, i.e., if markers are anchored on any reference sequence, and 0 otherwise; iv) a similar 1/0 variable indicating whether there is POS information for markers; and v) the number of phenotypic traits.

The next line contains sample IDs, separated by empty spaces or tabs. Addition of this sample ID requirement makes it possible for separate input datasets to be merged.

Next comes the genotype data for all markers. Each new marker is initiated with a “*” (without the quotes) followed by the marker name, without any space between them. Each marker name is followed by the corresponding segregation type, which may be: "A.1", "A.2", "A.3", "A.4", "B1.5", "B2.6", "B3.7", "C.8", "D1.9", "D1.10", "D1.11", "D1.12", "D1.13", "D2.14", "D2.15", "D2.16", "D2.17" or "D2.18" (without quotes), for full-sibs [see marker_type and Wu et al. (2002) for details]. Other cross types have special marker types: "A.H" for backcrosses; "A.H.B" for F2 intercrosses; and "A.B" for recombinant inbred lines.

After the segregation type comes the genotype data for the corresponding marker. Depending on the segregation type, genotypes may be denoted by ac, ad, bc, bd, a, ba, b, bc, ab and o, in several possible combinations. To make things easier, we have followed exactly the notation used by Wu et al. (2002). Allowed values for backcrosses are a and ab; for F2 crosses they are a, ab and b; for RILs they may be a and b. Genotypes must be separated by a space. Missing values are denoted by "-".

If there is physical information for markers, i.e., if they are anchored at specific positions in reference sequences (usually chromosomes), this is included immediately after the marker data. These lines start with special keywords *CHROM and *POS and contain strings and integers, respectively, indicating the reference sequence and position for each marker. These also need to be separated by spaces.

Finally, if there is phenotypic data, it will be added just after the marker or CHROM/POS data. They need to be separated by spaces as well, using the same symbol for missing information.

The example directory in the package distribution contains an example data file to be read with this function. Further instructions can be found at the tutorial distributed along with this package.

Value

An object of class onemap, i.e., a list with the following components:

Author(s)

Gabriel R A Margarido, gramarga@gmail.com

References

Lincoln, S. E., Daly, M. J. and Lander, E. S. (1993) Constructing genetic linkage maps with MAPMAKER/EXP Version 3.0: a tutorial and reference manual. A Whitehead Institute for Biomedical Research Technical Report.

Wu, R., Ma, C.-X., Painter, I. and Zeng, Z.-B. (2002) Simultaneous maximum likelihood estimation of linkage and linkage phases in outcrossing species. Theoretical Population Biology 61: 349-363.

See Also

combine_onemap and the example directory in the package source.

Examples

 outcr_data <- read_onemap(inputfile= 
 system.file("extdata/onemap_example_out.raw", package= "onemap"))

Recombination Counting and Ordering

Description

Implements the marker ordering algorithm Recombination Counting and Ordering (Van Os et al., 2005).

Usage

record(
  input.seq,
  times = 10,
  LOD = 0,
  max.rf = 0.5,
  tol = 1e-04,
  rm_unlinked = TRUE,
  size = NULL,
  overlap = NULL,
  phase_cores = 1,
  hmm = TRUE,
  parallelization.type = "PSOCK",
  verbose = TRUE
)

Arguments

Details

Recombination Counting and Ordering (RECORD) is an algorithm for marker ordering in linkage groups. It is not an exhaustive search method and, therefore, is not computationally intensive. However, it does not guarantee that the best order is always found. The only requirement is a matrix with recombination fractions between markers.

After determining the order with RECORD, the final map is constructed using the multipoint approach (function map).

Value

An object of class sequence, which is a list containing the following components:

Author(s)

Marcelo Mollinari, mmollina@usp.br

References

Mollinari, M., Margarido, G. R. A., Vencovsky, R. and Garcia, A. A. F. (2009) Evaluation of algorithms used to order markers on genetics maps. Heredity 103: 494-502.

Van Os, H., Stam, P., Visser, R.G.F. and Van Eck, H.J. (2005) RECORD: a novel method for ordering loci on a genetic linkage map. Theoretical and Applied Genetics 112: 30-40.

See Also

make_seq and map

Examples

  ##outcross example
  data(onemap_example_out)
  twopt <- rf_2pts(onemap_example_out)
  all_mark <- make_seq(twopt,"all")
  groups <- group(all_mark)
  LG1 <- make_seq(groups,1)
  LG1.rec <- record(LG1, hmm = FALSE)

  ##F2 example
  data(onemap_example_f2)
  twopt <- rf_2pts(onemap_example_f2)
  all_mark <- make_seq(twopt,"all")
  groups <- group(all_mark)
  LG1 <- make_seq(groups,1)
  LG1.rec <- record(LG1, hmm = FALSE)
  LG1.rec

Remove individuals from the onemap object

Description

Remove individuals from the onemap object

Usage

remove_inds(onemap.obj = NULL, rm.ind = NULL, list.seqs = NULL)

Arguments

Value

An object of class onemap without the selected individuals if onemap object is used as input, or a list of objects of class sequence without the selected individuals if a list of sequences objects is use as input

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

Two-point analysis between genetic markers

Description

Performs the two-point (pairwise) analysis proposed by Wu et al. (2002) between all pairs of markers.

Usage

rf_2pts(input.obj, LOD = 3, max.rf = 0.5, verbose = TRUE, rm_mks = FALSE)

Arguments

Details

For n markers, there are

\frac{n(n-1)}{2}

pairs of markers to be analyzed. Therefore, completion of the two-point analyses can take a long time.

Value

An object of class rf_2pts, which is a list containing the following components:

Note

The thresholds used for LOD and max.rf will be used in subsequent analyses, but can be overriden.

Author(s)

Gabriel R A Margarido gramarga@gmail.com and Marcelo Mollinari mmollina@usp.br

References

Wu, R., Ma, C.-X., Painter, I. and Zeng, Z.-B. (2002) Simultaneous maximum likelihood estimation of linkage and linkage phases in outcrossing species. Theoretical Population Biology 61: 349-363.

Examples

  data(onemap_example_out)

  twopts <- rf_2pts(onemap_example_out,LOD=3,max.rf=0.5) # perform two-point analyses
  twopts

  print(twopts,c("M1","M2")) # detailed results for markers 1 and 2

  

Plots pairwise recombination fractions and LOD Scores in a heatmap

Description

Plots a matrix of pairwise recombination fraction or LOD Scores using a color scale. Any value of the matrix can be easily accessed using an interactive plotly-html interface, helping users to check for possible problems.

Usage

rf_graph_table(
  input.seq,
  graph.LOD = FALSE,
  main = NULL,
  inter = FALSE,
  html.file = NULL,
  mrk.axis = "numbers",
  lab.xy = NULL,
  n.colors = 4,
  display = TRUE
)

Arguments

Details

The color scale varies from red (small distances or big LODs) to purple. When hover on a cell, a dialog box is displayed with some information about corresponding markers for that cell (line (y) \times column (x)). They are: i) the name of the markers; ii) the number of the markers on the data set; iii) the segregation types; iv) the recombination fraction between the markers and v) the LOD-Score for each possible linkage phase calculated via two-point analysis. For neighbor markers, the multipoint recombination fraction is printed; otherwise, the two-point recombination fraction is printed. For markers of type D1 and D2, it is impossible to calculate recombination fraction via two-point analysis and, therefore, the corresponding cell will be empty (white color). For cells on the diagonal of the matrix, the name, the number and the type of the marker are printed, as well as the percentage of missing data for that marker.

Value

a ggplot graphic

Author(s)

Rodrigo Amadeu, rramadeu@gmail.com

Examples

##outcross example
  data(onemap_example_out)
  twopt <- rf_2pts(onemap_example_out)
  all_mark <- make_seq(twopt,"all")
  groups <- group(all_mark)
  LG1 <- make_seq(groups,1)
  LG1.rcd <- rcd(LG1)
  rf_graph_table(LG1.rcd, inter=FALSE)


  ##F2 example
  data(onemap_example_f2)
  twopt <- rf_2pts(onemap_example_f2)
  all_mark <- make_seq(twopt,"all")
  groups <- group(all_mark)

  ##"pre-allocate" an empty list of length groups$n.groups (3, in this case)
  maps.list<-vector("list", groups$n.groups)

  for(i in 1:groups$n.groups){
    ##create linkage group i
    LG.cur <- make_seq(groups,i)
    ##ordering
    map.cur<-order_seq(LG.cur, subset.search = "sample")
    ##assign the map of the i-th group to the maps.list
    maps.list[[i]]<-make_seq(map.cur, "force")
  }

Filter markers according with a two-points recombination fraction and LOD threshold. Adapted from MAPpoly.

Description

Filter markers according with a two-points recombination fraction and LOD threshold. Adapted from MAPpoly.

Usage

rf_snp_filter_onemap(
  input.seq,
  thresh.LOD.rf = 5,
  thresh.rf = 0.15,
  probs = c(0.05, 1)
)

Arguments

Value

An object of class sequence, which is a list containing the following components:

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

Examples

 data("vcf_example_out")
 twopts <- rf_2pts(vcf_example_out)
 seq1 <- make_seq(twopts, which(vcf_example_out$CHROM == "1"))
filt_seq <- rf_snp_filter_onemap(seq1, 20, 0.5, c(0.5,1))

Compares and displays plausible alternative orders for a given linkage group

Description

For a given sequence of ordered markers, computes the multipoint likelihood of alternative orders, by shuffling subsets (windows) of markers within the sequence. For each position of the window, all possible (ws)! orders are compared.

Usage

ripple_seq(input.seq, ws = 4, ext.w = NULL, LOD = 3, tol = 0.1, verbose = TRUE)

Arguments

Details

Large values for the window size make computations very slow, specially if there are many partially informative markers.

Value

This function does not return any value; it just produces text output to suggest alternative orders.

Author(s)

Gabriel R A Margarido, gramarga@gmail.com and Marcelo Mollinari, mmollina@usp.br

References

Broman, K. W., Wu, H., Churchill, G., Sen, S., Yandell, B. (2008) qtl: Tools for analyzing QTL experiments R package version 1.09-43

Jiang, C. and Zeng, Z.-B. (1997). Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines. Genetica 101: 47-58.

Lander, E. S., Green, P., Abrahamson, J., Barlow, A., Daly, M. J., Lincoln, S. E. and Newburg, L. (1987) MAPMAKER: An interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1: 174-181.

Mollinari, M., Margarido, G. R. A., Vencovsky, R. and Garcia, A. A. F. (2009) Evaluation of algorithms used to order markers on genetics maps. Heredity 103: 494-502.

Wu, R., Ma, C.-X., Painter, I. and Zeng, Z.-B. (2002a) Simultaneous maximum likelihood estimation of linkage and linkage phases in outcrossing species. Theoretical Population Biology 61: 349-363.

Wu, R., Ma, C.-X., Wu, S. S. and Zeng, Z.-B. (2002b). Linkage mapping of sex-specific differences. Genetical Research 79: 85-96

See Also

make_seq, compare, try_seq and order_seq.

Examples

#Outcross example
 data(onemap_example_out)
 twopt <- rf_2pts(onemap_example_out)
 markers <- make_seq(twopt,c(27,16,20,4,19,21,23,9,24,29))
 markers.map <- map(markers)
 ripple_seq(markers.map)

#F2 example
data(onemap_example_f2)
twopt <- rf_2pts(onemap_example_f2)
all_mark <- make_seq(twopt,"all")
groups <- group(all_mark)
LG3 <- make_seq(groups,1)
LG3.ord <- order_seq(LG3, subset.search = "twopt", twopt.alg = "rcd", touchdown=TRUE)
LG3.ord
make_seq(LG3.ord) # get safe sequence
ord.1<-make_seq(LG3.ord,"force") # get forced sequence
ripple_seq(ord.1, ws=5)

Remove duplicated markers keeping the one with less missing data

Description

Remove duplicated markers keeping the one with less missing data

Usage

rm_dupli_mks(onemap.obj)

Arguments

Value

An empty object of class onemap, i.e., a list with the following components:

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

Save a list of onemap sequence objects

Description

The onemap sequence object contains everything users need to reproduce the complete analysis: the input onemap object, the rf_2pts result, and the sequence genetic distance and marker order. Therefore, a list of sequences is the only object users need to save to be able to recover all analysis. But simple saving the list of sequences will save many redundant objects. This redundancy is only considered by R when saving the object. For example, one input object and the rf_2pts result will be saved for every sequence.

Usage

save_onemap_sequences(sequences.list, filename)

Arguments

Construct the linkage map for a sequence of markers after seeding phases

Description

Estimates the multipoint log-likelihood, linkage phases and recombination frequencies for a sequence of markers in a given order using seeded phases.

Usage

seeded_map(
  input.seq,
  tol = 1e-04,
  phase_cores = 1,
  seeds,
  verbose = FALSE,
  rm_unlinked = FALSE,
  parallelization.type = "PSOCK"
)

Arguments

Details

Markers are mapped in the order defined in the object input.seq. The best combination of linkage phases is also estimated starting from the first position not in the given seeds.The multipoint likelihood is calculated according to Wu et al. (2002b)(Eqs. 7a to 11), assuming that the recombination fraction is the same in both parents. Hidden Markov chain codes adapted from Broman et al. (2008) were used.

Value

An object of class sequence, which is a list containing the following components:

Author(s)

Adapted from Karl Broman (package 'qtl') by Gabriel R A Margarido, gramarga@usp.br and Marcelo Mollinari, mmollina@gmail.com. Modified to use seeded phases by Bastian Schiffthaler bastian.schiffthaler@umu.se

References

Broman, K. W., Wu, H., Churchill, G., Sen, S., Yandell, B. (2008) qtl: Tools for analyzing QTL experiments R package version 1.09-43

Jiang, C. and Zeng, Z.-B. (1997). Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines. Genetica 101: 47-58.

Lander, E. S., Green, P., Abrahamson, J., Barlow, A., Daly, M. J., Lincoln, S. E. and Newburg, L. (1987) MAPMAKER: An interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1: 174-181.

Wu, R., Ma, C.-X., Painter, I. and Zeng, Z.-B. (2002a) Simultaneous maximum likelihood estimation of linkage and linkage phases in outcrossing species. Theoretical Population Biology 61: 349-363.

Wu, R., Ma, C.-X., Wu, S. S. and Zeng, Z.-B. (2002b). Linkage mapping of sex-specific differences. Genetical Research 79: 85-96

See Also

make_seq

Examples

  data(onemap_example_out)
  twopt <- rf_2pts(onemap_example_out)

  markers <- make_seq(twopt,c(30,12,3,14,2))
  seeded_map(markers, seeds = c(4,2))

Show markers with/without segregation distortion

Description

A function to shows which marker have segregation distortion if Bonferroni's correction is applied for the Chi-square tests of mendelian segregation.

Usage

select_segreg(x, distorted = FALSE, numbers = FALSE, threshold = NULL)

Arguments

Value

a vector with marker names or numbers, according to the option for "distorted" and "numbers"

Examples

 # Loads a fake backcross dataset installed with onemap
 data(onemap_example_out)
 # Performs the chi-square test for all markers
 Chi <- test_segregation(onemap_example_out)
 # To show non-distorted markers
 select_segreg(Chi)
 # To show markers with segregation distortion
 select_segreg(Chi, distorted=TRUE)
 # To show the numbers of the markers with segregation distortion
 select_segreg(Chi, distorted=TRUE, numbers=TRUE)

Extract marker number by name

Description

Extract marker number by name

Usage

seq_by_type(sequence, mk_type)

Arguments

Value

New sequence object of class sequence with selected marker type, which is a list containing the following components:

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

See Also

make_seq

Seriation

Description

Implements the marker ordering algorithm Seriation (Buetow & Chakravarti, 1987).

Usage

seriation(
  input.seq,
  LOD = 0,
  max.rf = 0.5,
  tol = 1e-04,
  rm_unlinked = TRUE,
  size = NULL,
  overlap = NULL,
  phase_cores = 1,
  hmm = TRUE,
  parallelization.type = "PSOCK",
  verbose = TRUE
)

Arguments

Details

Seriation is an algorithm for marker ordering in linkage groups. It is not an exhaustive search method and, therefore, is not computationally intensive. However, it does not guarantee that the best order is always found. The only requirement is a matrix with recombination fractions between markers.

NOTE: When there are to many pairs of markers with the same value in the recombination fraction matrix, it can result in ties during the ordination process and the Seriation algorithm may not work properly. This is particularly relevant for outcrossing populations with mixture of markers of type D1 and D2. When this occurs, the function shows the following error message: There are too many ties in the ordination process - please, consider using another ordering algorithm.

After determining the order with Seriation, the final map is constructed using the multipoint approach (function map).

Value

An object of class sequence, which is a list containing the following components:

Author(s)

Gabriel R A Margarido, gramarga@gmail.com

References

Buetow, K. H. and Chakravarti, A. (1987) Multipoint gene mapping using seriation. I. General methods. American Journal of Human Genetics 41: 180-188.

Mollinari, M., Margarido, G. R. A., Vencovsky, R. and Garcia, A. A. F. (2009) Evaluation of algorithms used to order markers on genetics maps. Heredity 103: 494-502.

See Also

make_seq, map

Examples

  ##outcross example
  data(onemap_example_out)
  twopt <- rf_2pts(onemap_example_out)
  all_mark <- make_seq(twopt,"all")
  groups <- group(all_mark)
  LG3 <- make_seq(groups,3)
  LG3.ser <- seriation(LG3)

Defines the default mapping function

Description

Defines the function that should be used to display the genetic map through the analysis.

Usage

set_map_fun(type = c("kosambi", "haldane"))

Arguments

Value

No return value, called for side effects

Kosambi, D. D. (1944) The estimation of map distance from recombination values. Annuaire of Eugenetics 12: 172-175.

Author(s)

Marcelo Mollinari, mmollina@usp.br

References

Haldane, J. B. S. (1919) The combination of linkage values and the calculation of distance between the loci of linked factors. Journal of Genetics 8: 299-309.

See Also

kosambi and haldane

Simulated data from a backcross population

Description

Simulated data set from a backcross population.

Usage

data(simu_example_bc)

Format

The format is: List of 11 $ geno : num [1:200, 1:54] 1 2 1 1 2 2 2 1 1 2 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:200] "BC_001" "BC_002" "BC_003" "BC_004" ... .. ..$ : chr [1:54] "M001" "M002" "M003" "M004" ... $ n.ind : int 200 $ n.mar : int 54 $ segr.type : chr [1:54] "A.H" "A.H" "A.H" "A.H" ... $ segr.type.num: num [1:54] 8 8 8 8 8 8 8 8 8 8 ... $ n.phe : int 0 $ pheno : NULL $ CHROM : NULL $ POS : NULL $ input : chr "simu_example_bc.raw" $ error : num [1:10800, 1:2] 1 1 1 1 1 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:10800] "M001_BC_001" "M002_BC_001" "M003_BC_001" "M004_BC_001" ... .. ..$ : NULL - attr(*, "class")= chr [1:2] "onemap" "backcross"

Details

A simulation of a backcross population of 200 individuals genotyped with 54 markers. There are no missing data. There are two groups, one (Chr01) with a total of 100 cM and the other (Chr10) with 150 cM. The markers are positioned equidistant from each other.

Author(s)

Cristiane Taniguti, chtaniguti@usp.br

See Also

read_onemap and read_mapmaker.

Examples

data(simu_example_bc)

# perform two-point analyses
twopts <- rf_2pts(simu_example_bc)
twopts

Simulated data from a f2 intercross population

Description

Simulated data set from a f2 intercross population.

Usage

data(simu_example_f2)

Format

The format is: List of 11 $ geno : num [1:200, 1:54] 1 2 1 1 2 2 1 1 1 2 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:200] "F2_001" "F2_002" "F2_003" "F2_004" ... .. ..$ : chr [1:54] "M001" "M002" "M003" "M004" ... $ n.ind : int 200 $ n.mar : int 54 $ segr.type : chr [1:54] "C.A" "C.A" "C.A" "C.A" ... $ segr.type.num: num [1:54] 7 7 7 7 4 4 7 4 4 4 ... $ n.phe : int 0 $ pheno : NULL $ CHROM : NULL $ POS : NULL $ input : chr "simu_example_f2.raw" $ error : num [1:10800, 1:4] 1 1 1 1 1 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:10800] "M001_F2_001" "M002_F2_001" "M003_F2_001" "M004_F2_001" ... .. ..$ : NULL - attr(*, "class")= chr [1:2] "onemap" "f2"

Details

A simulation of a f2 intercross population of 200 individuals genotyped with 54 markers. There are no missing data. There are two groups, one (Chr01) with a total of 100 cM and the other (Chr10) with 150 cM. The markers are positioned equidistant from each other.

Author(s)

Cristiane Taniguti, chtaniguti@usp.br

See Also

read_onemap and read_mapmaker.

Examples

data(simu_example_f2)

# perform two-point analyses
twopts <- rf_2pts(simu_example_f2)
twopts

Simulated data from a outcrossing population

Description

Simulated data set from a outcrossing population.

Usage

data(simu_example_out)

Format

The format is: List of 11 $ geno : num [1:200, 1:54] 2 1 2 1 1 2 2 2 1 1 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:200] "F1_001" "F1_002" "F1_003" "F1_004" ... .. ..$ : chr [1:54] "M001" "M002" "M003" "M004" ... $ n.ind : int 200 $ n.mar : int 54 $ segr.type : chr [1:54] "D2.16" "D2.17" "D2.17" "D1.9" ... $ segr.type.num: num [1:54] 7 7 7 6 1 3 3 1 7 6 ... $ n.phe : int 0 $ pheno : NULL $ CHROM : NULL $ POS : NULL $ input : chr "simu_example_out.raw" $ error : num [1:10800, 1:4] 1.00e-05 1.00e-05 1.00e-05 1.00 3.33e-06 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:10800] "M001_F1_001" "M002_F1_001" "M003_F1_001" "M004_F1_001" ... .. ..$ : NULL - attr(*, "class")= chr [1:2] "onemap" "outcross"

Details

A simulation of a outcrossing population of 200 individuals genotyped with 54 markers. There are no missing data. There are two groups, one (Chr01) with a total of 100 cM and the other (Chr10) with 150 cM. The markers are positioned equidistant from each other.

Author(s)

Cristiane Taniguti, chtaniguti@usp.br

See Also

read_onemap and read_mapmaker.

Examples

data(simu_example_out)

# perform two-point analyses
twopts <- rf_2pts(simu_example_out)
twopts

Sort markers in onemap object by their position in reference genome

Description

Sort markers in onemap object by their position in reference genome

Usage

sort_by_pos(onemap.obj)

Arguments

Value

An object of class onemap, i.e., a list with the following components:

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

Split rf_2pts object by markers

Description

Split rf_2pts object by markers

Usage

split_2pts(twopts.obj, mks)

Arguments

Value

An object of class rf_2pts with only the selected markers, which is a list containing the following components:

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

Split onemap data sets

Description

Receives one onemap object and a vector with markers names to be removed from the input onemap object and inserted in a new one. The output is a list containing the two onemap objects.

Usage

split_onemap(onemap.obj = NULL, mks = NULL)

Arguments

Value

a list containing in first level the original onemap object without the indicated markers and the second level the new onemap object with only the indicated markers

Suggests a LOD Score for two point tests

Description

It suggests a LOD Score for declaring statistical significance for two-point tests for linkage between all pairs of markers, considering that multiple tests are being performed.

Usage

suggest_lod(x)

Arguments

Details

In a somehow naive approach, the function calculates the number of two-point tests that will be performed for all markers in the data set, and then using this to calculate the global alpha required to control type I error using Bonferroni's correction.

From this global alpha, the corresponding quantile from the chi-square distribution is taken and then converted to LOD Score.

This can be seen as just an initial approximation to help users to select a LOD Score for two point tests.

Value

the suggested LOD to be used for testing linkage

Examples

data(onemap_example_bc) # Loads a fake backcross dataset installed with onemap
suggest_lod(onemap_example_bc) # An value that should be used to start the analysis

Create table with summary information about the linkage map

Description

Create table with summary information about the linkage map

Usage

summary_maps_onemap(map.list, mapping_function = "kosambi")

Arguments

Value

data.frame with basic summary statistics

Author(s)

Jeekin Lau, jeekinlau@gmail.com

test_segregation

Description

Using OneMap internal function test_segregation_of_a_marker(), performs the Chi-square test to check if all markers in a dataset are following the expected segregation pattern, i. e., 1:1:1:1 (A), 1:2:1 (B), 3:1 (C) and 1:1 (D) according to OneMap's notation.

Usage

test_segregation(x, simulate.p.value = FALSE)

Arguments

Details

First, it identifies the correct segregation pattern and corresponding H0 hypothesis, and then tests it.

Value

an object of class onemap_segreg_test, which is a list with marker name, H0 hypothesis being tested, the chi-square statistics, the associated p-values and the % of individuals genotyped. To see the object, it is necessary to print it.

Examples

 data(onemap_example_out) # Loads a fake outcross dataset installed with onemap
 Chi <- test_segregation(onemap_example_out) # Performs the chi-square test for all markers
 print(Chi) # Shows the results

test_segregation_of_a_marker

Description

Applies the chi-square test to check if markers are following the expected segregation pattern, i. e., 1:1:1:1 (A), 1:2:1 (B), 3:1 (C) and 1:1 (D) according to OneMap's notation. It does not use Yate's correction.

Usage

test_segregation_of_a_marker(x, marker, simulate.p.value = FALSE)

Arguments

Details

First, the function selects the correct segregation pattern, then it defines the H0 hypothesis, and then tests it, together with percentage of missing data.

Value

a list with the H0 hypothesis being tested, the chi-square statistics, the associated p-values, and the % of individuals genotyped.

Examples

data(onemap_example_bc) # Loads a fake backcross dataset installed with onemap
test_segregation_of_a_marker(onemap_example_bc,1)

data(onemap_example_out) # Loads a fake outcross dataset installed with onemap
test_segregation_of_a_marker(onemap_example_out,1)

Try to map a marker into every possible position between markers in a given map

Description

For a given linkage map, tries do add an additional unpositioned marker. This function estimates parameters for all possible maps including the new marker in all possible positions, while keeping the original linkage map unaltered.

Usage

try_seq(input.seq, mrk, tol = 0.1, pos = NULL, verbose = FALSE)

Arguments

Value

An object of class try, which is a list containing the following components:

Author(s)

Marcelo Mollinari, mmollina@usp.br

References

Broman, K. W., Wu, H., Churchill, G., Sen, S., Yandell, B. (2008) qtl: Tools for analyzing QTL experiments R package version 1.09-43

Jiang, C. and Zeng, Z.-B. (1997). Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines. Genetica 101: 47-58.

Lander, E. S., Green, P., Abrahamson, J., Barlow, A., Daly, M. J., Lincoln, S. E. and Newburg, L. (1987) MAPMAKER: An interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1: 174-181.

Mollinari, M., Margarido, G. R. A., Vencovsky, R. and Garcia, A. A. F. (2009) Evaluation of algorithms used to order markers on genetic maps. Heredity 103: 494-502

Wu, R., Ma, C.-X., Painter, I. and Zeng, Z.-B. (2002a) Simultaneous maximum likelihood estimation of linkage and linkage phases in outcrossing species. Theoretical Population Biology 61: 349-363.

Wu, R., Ma, C.-X., Wu, S. S. and Zeng, Z.-B. (2002b). Linkage mapping of sex-specific differences. Genetical Research 79: 85-96

See Also

make_seq and compare.

Examples

  #outcrossing example
  data(onemap_example_out)
  twopt <- rf_2pts(onemap_example_out)
  markers <- make_seq(twopt,c(2,3,12,14))
  markers.comp <- compare(markers)
  base.map <- make_seq(markers.comp,1)

  extend.map <- try_seq(base.map,30)
  extend.map
  print(extend.map,5) # best position
  print(extend.map,4) # second best position

Run try_seq considering previous sequence

Description

It uses try_seq function repeatedly trying to positioned each marker in a vector of markers into a already ordered sequence. Each marker in the vector "markers" is kept in the sequence if the difference of LOD and total group size of the models with and without the marker are below the thresholds "lod.thr" and "cM.thr".

Usage

try_seq_by_seq(sequence, markers, cM.thr = 10, lod.thr = -10, verbose = TRUE)

Arguments

Value

An object of class sequence, which is a list containing the following components:

Unidirectional Growth

Description

Implements the marker ordering algorithm Unidirectional Growth (Tan & Fu, 2006).

Usage

ug(
  input.seq,
  LOD = 0,
  max.rf = 0.5,
  tol = 1e-04,
  rm_unlinked = TRUE,
  size = NULL,
  overlap = NULL,
  phase_cores = 1,
  hmm = TRUE,
  parallelization.type = "PSOCK",
  verbose = TRUE
)

Arguments

Details

Unidirectional Growth (UG) is an algorithm for marker ordering in linkage groups. It is not an exhaustive search method and, therefore, is not computationally intensive. However, it does not guarantee that the best order is always found. The only requirement is a matrix with recombination fractions between markers.

After determining the order with UG, the final map is constructed using the multipoint approach (function map).

Value

An object of class sequence, which is a list containing the following components:

Author(s)

Marcelo Mollinari, mmollina@usp.br

References

Mollinari, M., Margarido, G. R. A., Vencovsky, R. and Garcia, A. A. F. (2009) Evaluation of algorithms used to order markers on genetics maps. Heredity 103: 494-502.

Tan, Y. and Fu, Y. (2006) A novel method for estimating linkage maps. Genetics 173: 2383-2390.

See Also

make_seq, map

Examples

  #outcross example
  data(onemap_example_out)
  twopt <- rf_2pts(onemap_example_out)
  all_mark <- make_seq(twopt,"all")
  groups <- group(all_mark)
  LG1 <- make_seq(groups,1)
  LG1.ug <- ug(LG1)

  #F2 example
  data(mapmaker_example_f2)
  twopt <- rf_2pts(mapmaker_example_f2)
  all_mark <- make_seq(twopt,"all")
  groups <- group(all_mark)
  LG1 <- make_seq(groups,1)
  LG1.ug <- ug(LG1)
  LG1.ug

These functions are defunct and no longer available.

Description

These functions are defunct and no longer available.

Usage

vcf2raw()

Value

No return value, called for side effects

Data generated from VCF file with biallelic markers from a f2 backcross population

Description

Simulated biallelic data set for an backcross population

Usage

data("vcf_example_bc")

Format

An object of class onemap.

Details

A total of 142 backcross individuals were genotyped with 25 markers. The data was generated from a VCF file. It contains chromossome and position informations for each marker. It is included to be used as a example in order to understand how to convert VCF file to OneMap input data with the functions vcf2raw and onemap_read_vcfR.

Author(s)

Cristiane Hayumi Taniguti, chaytaniguti@gmail.com

See Also

read_onemap for details about objects of class onemap.

Examples

data(vcf_example_bc)
plot(vcf_example_bc) 

Data generated from VCF file with biallelic markers from a f2 intercross population

Description

Simulated biallelic data set for an f2 population

Usage

data(vcf_example_f2)

Format

An object of class onemap.

Details

A total of 192 F2 individuals were genotyped with 25 markers. The data was generated from a VCF file. It contains chromossome and position informations for each marker. It is included to be used as a reference in order to understand how to convert VCF file to OneMap input data. Also, it is used for the analysis in the tutorial that comes with OneMap.

Author(s)

Cristiane Hayumi Taniguti, chaytaniguti@gmail.com

See Also

read_onemap for details about objects of class onemap.

Examples

data(vcf_example_f2)

# plot markers informations
plot(vcf_example_f2)

Data generated from VCF file with biallelic markers from a full-sib family derived from two outbred parents

Description

Simulated biallelic data set for an outcross, i.e., an F1 population obtained by crossing two non-homozygous parents.

Usage

data(vcf_example_out)

Format

An object of class onemap.

Details

A total of 92 F1 individuals were genotyped with 27 markers. The data was generated from a VCF file. It contains chromossome and position informations for each marker. It is included to be used as a reference in order to understand how to convert VCF file to OneMap input data. Also, it is used for the analysis in the tutorial that comes with OneMap.

Author(s)

Cristiane Hayumi Taniguti, chaytaniguti@gmail.com

See Also

read_onemap for details about objects of class onemap.

Examples

data(vcf_example_out)

# plot markers informations
plot(vcf_example_out)

Data generated from VCF file with biallelic markers from a RIL population produced by selfing

Description

Simulated biallelic data set for an ri self population.

Usage

data("vcf_example_riself")

Format

The format is: List of 10 $ geno : num [1:92, 1:25] 3 3 1 3 1 3 3 1 3 1 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:92] "ID1" "ID3" "ID4" "ID5" ... .. ..$ : chr [1:25] "SNP16" "SNP12" "SNP17" "SNP10" ... $ n.ind : int 92 $ n.mar : int 25 $ segr.type : chr [1:25] "A.B" "A.B" "A.B" "A.B" ... $ segr.type.num: logi [1:25] NA NA NA NA NA NA ... $ n.phe : int 0 $ pheno : NULL $ CHROM : chr [1:25] "1" "1" "1" "1" ... $ POS : int [1:25] 1791 6606 9001 11326 11702 15533 17151 18637 19146 19220 ... $ input : chr "vcf_example_riself.raw" - attr(*, "class")= chr [1:2] "onemap" "riself"

Details

A total of 92 rils individuals were genotyped with 25 markers. The data was generated from a VCF file. It contains chromossome and position informations for each marker. It is included to be used as a example in order to understand how to convert VCF file to OneMap input data with the functions vcf2raw and onemap_read_vcfR.

Author(s)

Cristiane Hayumi Taniguti, chaytaniguti@gmail.com

See Also

read_onemap for details about objects of class onemap.

Examples

data(vcf_example_riself)
plot(vcf_example_riself)

Write a genetic map to a file

Description

Write a genetic map to a file, base on a given map, or a list of maps. The output file can be used as an input to perform QTL mapping using the package R/qtl. It is also possible to create an output to be used with QTLCartographer program.

Usage

write_map(map.list, file.out)

Arguments

Details

This function is available only for backcross, F2 and RILs.

Value

file with genetic map information

Wang S., Basten, C. J. and Zeng Z.-B. (2010) Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC.

Author(s)

Marcelo Mollinari, mmollina@usp.br

References

Broman, K. W., Wu, H., Churchill, G., Sen, S., Yandell, B. (2008) qtl: Tools for analyzing QTL experiments R package version 1.09-43

Examples

data(mapmaker_example_f2)
twopt<-rf_2pts(mapmaker_example_f2)
lg<-group(make_seq(twopt, "all"))

##"pre-allocate" an empty list of length lg$n.groups (3, in this case)
maps.list<-vector("list", lg$n.groups)

for(i in 1:lg$n.groups){
  ##create linkage group i
  LG.cur <- make_seq(lg,i)
  ##ordering
  map.cur<-order_seq(LG.cur, subset.search = "sample")
  ##assign the map of the i-th group to the maps.list
  maps.list[[i]]<-make_seq(map.cur, "force")

  ##write maps.list to ".map" file
  write_map(maps.list, tempfile(fileext = ".map"))

}

Convert onemap object to onemap raw file

Description

Converts onemap R object to onemap input file. The input file brings information about the mapping population: First line: cross type, it can be "outcrossing", "f2 intercross", "f2 backcross", "ri self" or "ri sib". Second line: number of individuals, number of markers, presence (1) or absence (0) of chromossome and position of the markers, and number of phenotypes mesured. Third line: Individuals/sample names; Followed lines: marker name, marker type and genotypes. One line for each marker. Final lines: chromossome, position and phenotypes informations. See more about input file format at vignettes.

Usage

write_onemap_raw(onemap.obj = NULL, file.name = NULL)

Arguments

Value

a onemap input file

Author(s)

Cristiane Taniguti, chtaniguti@tamu.edu

See Also

read_onemap for a description of the output object of class onemap.

Examples

data(onemap_example_out)
write_onemap_raw(onemap_example_out, file.name = paste0(tempfile(), ".raw"))