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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 achievea 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 predeterminedone. The markers are added in the end of the sequence.

Usage

add_marker(input.seq, mrks)

Arguments

Value

An object of classsequence, which is a listcontaining 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 classsequence, which is a list containing thefollowing 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, whereTRUE indicatesa 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, whereTRUE indicatesa 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 anobject of classonemap.

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 asingleonemap object.

If sample IDs are present in all datasets (the standard new format), notall individuals need to be genotyped in all datasets - the merged datasetwill contain all available information, with missing data elsewhere. Ifsample IDs are missing in at least one dataset, it is required that alldatasets have the same number of individuals, and it is assumed that theyare arranged in the same order in every dataset.

Value

An object of classonemap, i.e., a list with the followingcomponents:

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 tutorialand reference manual.A Whitehead Institute for Biomedical ResearchTechnical Report.

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

See Also

read_onemap andread_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 ofmarkers

Description

For a given sequence withn markers, computes the multipointlikelihood 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 valuesofn, this function is to be used only for sequences withrelatively few markers. If markers were genotyped in an outcross population,linkage phases need to be estimated and therefore more states need to bevisited in the Markov chain; when segregation types are D1, D2 and C,computation can required a very long time (specially when markers linked inrepulsion 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 thesame in both parents. Hidden Markov chain codes adapted from Broman et al.(2008) were used.

Value

An object of classcompare, which is a list containing thefollowing 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 version1.09-43

Jiang, C. and Zeng, Z.-B. (1997). Mapping quantitative trait loci withdominant 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 forconstructing primary genetic linkage maps of experimental and naturalpopulations.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 maximumlikelihood 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 ofsex-specific differences.Genetical Research 79: 85-96

See Also

marker_type for details about segregationtypes andmake_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 ononemap_bin object

Usage

create_data_bins(input.obj, bins)

Arguments

Details

Given aonemap_bin object,creates a new data set where the redundant markers arecollapsed into bins and represented by the marker with the loweramount of missing data among those on the bin.

Value

An object of classonemap, i.e., a list with the followingcomponents:

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 fordrawing a graphic of raw data using ggplot2, i. e., a data framewith 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 andfour columns (or two if considering backcross or RILs populations), one for each possible genotypeof 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 globalerror or a error value for each genotype. The OneMap until 2.1 version have only the global error option.

Value

An object of classonemap, i.e., a list with the followingcomponents:

Author(s)

Cristiane Tanigutichtaniguti@tamu.edu

References

Broman, K. W., Wu, H., Churchill, G., Sen, S., Yandell, B.(2008)qtl: Tools for analyzing QTL experiments R package version1.09-43

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 predeterminedone.

Usage

drop_marker(input.seq, mrks)

Arguments

Value

An object of classsequence, which is a listcontaining 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 classonemap, i.e., a list with the followingcomponents:

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 HiddenMarkov Models (HMM) to construct multipoint linkage maps inoutcrossing 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 recombinationfractions 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 withreference 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 classsequence, which is a list containing thefollowing 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 classonemap, i.e., a list with the followingcomponents:

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 classonemap, i.e., a list with the followingcomponents:

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 classonemap_bin, which is a list containing thefollowing 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 thetransitive 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., minimumLOD Score and maximum recombination fraction, areNULL (default),the values used are those contained in objectinput.seq. If notusingNULL, the new values override the ones in objectinput.seq.

Value

Returns an object of classgroup, which is a listcontaining the following components:

Author(s)

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

References

Lincoln, S. E., Daly, M. J. and Lander, E. S. (1993)Constructing genetic linkage maps with MAPMAKER/EXP Version3.0: a tutorial and reference manual.A WhiteheadInstitute for Biomedical Research Technical Report.

See Also

rf_2pts andmake_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 inputsequences objects withunlinked markers fromrf_2pts object. The results from two-point(pairwise) analysis and thetransitive property of linkage are used forgrouping, asgroup 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., minimumLOD Score and maximum recombination fraction, areNULL (default),the values used are those contained in objectinput.2pts. If notusingNULL, the new values override the ones in objectinput.2pts.

Value

Returns an object of classgroup_seq, which is a listcontaining the following components:

Author(s)

Cristiane Taniguti,chtaniguti@tamu.edu

See Also

make_seq andgroup

Examples

data(onemap_example_out) # load OneMap's fake dataset for a outcrossing populationdata(vcf_example_out) # load OneMap's fake dataset from a VCF file for a outcrossing populationcomb_example <- combine_onemap(onemap_example_out, vcf_example_out) # Combine datasetstwopts <- rf_2pts(comb_example)out_CHROM <- group_seq(twopts, seqs="CHROM", repeated=FALSE)out_CHROMseq1 <- 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 packagewritten by Marcelo Mollinari.

Usage

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

Arguments

Value

Returns an object of classgroup, which is a listcontaining the following components:

Author(s)

Marcelo Mollinari,mmollin@ncsu.edu

Cristiane Tanigutichtaniguti@tamu.edu

References

Mollinari, M., and Garcia, A. A. F. (2019) Linkageanalysis and haplotype phasing in experimental autopolyploidpopulations with high ploidy level using hidden Markovmodels, _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 classsequence, which is a list containing thefollowing 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 computerpackage for constructing primary genetic linkage maps of experimental andnatural populations.Genomics 1: 174-181.

See Also

compare,try_seq,order_seq andmap.

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

Value

An object of classsequence, which is a list containing thefollowing 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 version1.09-43

Jiang, C. and Zeng, Z.-B. (1997). Mapping quantitative trait loci withdominant 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 forconstructing primary genetic linkage maps of experimental and naturalpopulations.Genomics 1: 174-181.

Wu, R., Ma, C.-X., Painter, I. and Zeng, Z.-B. (2002a) Simultaneous maximumlikelihood 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 ofsex-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 classsequence, which is a list containing thefollowing 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 densitymarker sets. The mapping problem is reduced to a number of subsets (batches)which carry information forward in order to more accurately estimaterecombination fractions and phasing. It is a adapted version ofmap.overlapping.batches function of BatchMap package. The main differences arethat 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 linkagecriterias, 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 classsequence, which is a list containing thefollowing 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 (36co-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 howto useonemap output asR\qtl andQTL Cartographer input. Also, it is usedfor the analysis in the tutorial that comes with OneMap.

Examples

data(mapmaker_example_f2)# perform two-point analysestwopts <- 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 classsequence. For outcross populations it uses the notation byWuet al., 2002. For backcrosses, F2s and RILs, it uses thetraditional 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 thesequence 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 inoutcrossing 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 OneMapmultipoint 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 classsequence, which is a list containing thefollowing components:

Author(s)

Cristiane Taniguti,chtaniguti@tamu.edu

References

Preedy, K. F. and Hackett, C. A. (2016). A rapid marker ordering approach for high-densitygenetic linkage maps in experimental autotetraploid populations using multidimensionalscaling.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 maximumlikelihood 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 ofsex-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% ofmissing data. There is one quantitative phenotype to show howto useonemap output asR\qtl input.

Author(s)

Marcelo Mollinari,mmollina@usp.br

See Also

read_onemap andread_mapmaker.

Examples

data(onemap_example_bc)# perform two-point analysestwopts <- 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 (36co-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 howto useonemap output asR\qtl andQTL Cartographer input. Also, it is usedfor 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 bycrossing two non-homozygous parents.

Usage

data(onemap_example_out)

Format

An object of classonemap.

Details

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

Author(s)

Gabriel R A Margarido,gramarga@gmail.com

See Also

read_onemap for details about objects of classonemap.

Examples

data(onemap_example_out)# perform two-point analysestwopts <- rf_2pts(onemap_example_out)twopts

Simulated data from a RIL population produced by selfing.

Description

Simulated biallelic data set for anri 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 datacurrently contains only genotype information (no phenotypes). It isincluded to be used as a reference in order to understand how a datafile needs to be.

Author(s)

Cristiane Taniguti,chtaniguti@usp.br

See Also

read_onemap for details about objects of classonemap.

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. Forfull-sib progenies, both outbred parents must be genotyped. For backcrosses,F2 intercrosses and recombinant inbred lines, theoriginal inbredlines must be genotyped. Particularly for backcross progenies, therecurrent line must be provided as the first parent in the functionarguments.

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

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

Value

An object of classonemap, i.e., a list with the followingcomponents:

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 classsequence

Author(s)

Cristiane Taniguti

Search for the best order of markers combining compare and try_seqfunctions

Description

For a given sequence of markers, this function first uses thecompare function to create a framework for a subset of informativemarkers. Then, it tries to map remaining ones using thetry_seqfunction.

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 whichremaining markers will be used in thetry_seq step is given by thedegree of informativeness of markers (i.e markers of type A, B, C and D, inthis order).

For backcrosses, F2s or RILs, two methods can be used forchoosing the initial subset: i)"sample" randomly chooses a numberof markers, indicated byn.init, and calculates the multipointlog-likelihood of the\frac{n.init!}{2} possible orders.If the LOD Score of the second best order is greater thansubset.THRES, than it takes the best order to proceed with thetry_seq step. If not, the procedure is repeated. The maximum numberof times to repeat this procedure is given by thesubset.n.tryargument. ii)"twopt" uses a two-point based algorithm, given by theoption"twopt.alg", to construct a two-point based map. The optionsare"rec" for RECORD algorithm,"rcd" for Rapid ChainDelineation,"ser" for Seriation and"ug" for UnidirectionalGrowth. 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 thetry_seq step is given by marker types (i.e. co-dominant beforedominant) and by the missing information on each marker.

After running thecompare andtry_seq steps, which result ina "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 classorder, which is a list containing thefollowing components:

Author(s)

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

References

Broman, K. W., Wu, H., Churchill, G., Sen, S., Yandell, B.(2008)qtl: Tools for analyzing QTL experiments R package version1.09-43

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

Lander, E. S. and Green, P. (1987). Construction of multilocus geneticlinkage 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 forconstructing primary genetic linkage maps of experimental and naturalpopulations.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 maximumlikelihood 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 ofsex-specific differences.Genetical Research 79: 85-96

See Also

make_seq,compare andtry_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 inmap_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. Incase 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 classonemap, reads informationfrom genotypes from this object, converts it to a long dataframe formatusing function melt() from package reshape2() or internal function create_dataframe_for_plot_outcross(), converts numbers from the objectto genetic notation (according to the cross type), then plots the graphic.If there is more than 20 markers, removes y labelsFor outcross populations, it can show all markers together, or it can split them according the segregationpattern.

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 onemapplot(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 onemapplot(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 onemapplot(onemap_example_out) # This will show you the graph for all markersplot(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 thechi-square tests for the expected segregation patterns for all markers in a dataset.It includes a vertical line showing the threshold for declaring statistical significanceif Bonferroni's correction is considered, as well as the percentage of markers thatwill 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 classonemap.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 dataplot_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 objectof 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

adata.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 algorithmRapid 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 markerordering in linkage groups. It is not an exhaustive search method and,therefore, is not computationally intensive. However, it does not guaranteethat the best order is always found. The only requirement is a matrix withrecombination fractions between markers. Next is an excerpt from QTLCartographer Version 1.17 Manual describing theRCD algorithm(Basten et al., 2005):

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

After determining the order withRCD, the final map is constructedusing the multipoint approach (functionmap).

Value

An object of classsequence, which is a list containing thefollowing components:

Author(s)

Gabriel R A Margarido,gramarga@gmail.com

References

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

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 seeLincoln et al. (1993). Thecurrent version supports backcross, F2s and RIL populations. The filecan contain phenotypic data, but it will not be used in the analysis.

Value

An object of classonemap, i.e., a list with the followingcomponents:

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 columncontains data for a marker and each row represents an individual.

Author(s)

Adapted from Karl Broman (packageqtl) 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 version1.09-43

Lincoln, S. E., Daly, M. J. and Lander, E. S. (1993) Constructing geneticlinkage maps with MAPMAKER/EXP Version 3.0: a tutorial and referencemanual.A Whitehead Institute for Biomedical Research TechnicalReport.

See Also

mapmaker_example_bc andmapmaker_example_f2 raw files in thepackage 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 inbredparents (backcross, F2 intercross and recombinant inbred lines obtainedby self- or sib-mating). Creates an object of classonemap.

Usage

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

Arguments

Details

The file format is similar to that used byMAPMAKER/EXP(Lincoln et al., 1993). The first line indicates the cross typeand is structured asdata type {cross}, wherecrossmust be one of"outcross","f2 intercross","f2 backcross","ri self" or"ri sib". The second linecontains five integers: i) the number of individuals; ii) the number ofmarkers; iii) an indicator variable taking the value 1 if there is CHROMinformation, i.e., if markers are anchored on any reference sequence, and0 otherwise; iv) a similar 1/0 variable indicating whether there is POSinformation 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 inputdatasets to be merged.

Next comes the genotype data for all markers. Each new marker is initiatedwith a “*” (without the quotes) followed by the marker name, withoutany space between them. Each marker name is followed by the correspondingsegregation 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 [seemarker_type andWu 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 inbredlines.

After the segregation type comes the genotype data for thecorresponding marker. Depending on the segregation type, genotypes may bedenoted byac,ad,bc,bd,a,ba,b,bc,ab ando, in several possiblecombinations. To make things easier, we have followedexactly thenotation used byWu et al. (2002). Allowed values for backcrossesarea andab; for F2 crosses they area,ab andb; for RILs they may bea andb. Genotypesmustbe separated by a space. Missing values are denoted by"-".

If there is physical information for markers, i.e., if they are anchored atspecific positions in reference sequences (usually chromosomes), this isincluded immediately after the marker data. These lines start with specialkeywords*CHROM and*POS and containstrings andintegers, respectively, indicating the reference sequence andposition for each marker. These also need to be separated by spaces.

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

Theexample directory in the package distribution contains anexample data file to be read with this function. Further instructions canbe found at the tutorial distributed along with this package.

Value

An object of classonemap, i.e., a list with the followingcomponents:

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 tutorialand reference manual.A Whitehead Institute for Biomedical ResearchTechnical Report.

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

See Also

combine_onemap and theexampledirectory 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 algorithmRecombination Counting andOrdering (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 algorithmfor marker ordering in linkage groups. It is not an exhaustive searchmethod and, therefore, is not computationally intensive. However, it doesnot guarantee that the best order is always found. The only requirement isa matrix with recombination fractions between markers.

After determining the order withRECORD, the final map isconstructed using the multipoint approach (functionmap).

Value

An object of classsequence, which is a list containing thefollowing 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 geneticsmaps.Heredity 103: 494-502.

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

See Also

make_seq andmap

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 classonemap without the selected individualsif onemap object is used as input, or a list of objects of classsequencewithout 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 byWu 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

Forn markers, there are

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

pairs ofmarkers to be analyzed. Therefore, completion of the two-point analyses cantake a long time.

Value

An object of classrf_2pts, which is a list containing thefollowing components:

Note

The thresholds used forLOD andmax.rf will be used insubsequent analyses, but can be overriden.

Author(s)

Gabriel R A Margaridogramarga@gmail.com and Marcelo Mollinarimmollina@usp.br

References

Wu, R., Ma, C.-X., Painter, I. and Zeng, Z.-B. (2002)Simultaneous maximum likelihood estimation of linkage and linkage phases inoutcrossing 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 orLOD Scores using a color scale. Any value of thematrix 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 informationabout corresponding markers for that cell (line (y)\times column (x)). They are:i) the name of the markers;ii) the number ofthe markers on the data set;iii) the segregation types;iv)the recombination fraction between the markers andv) the LOD-Scorefor each possible linkage phase calculated via two-point analysis. Forneighbor markers, the multipoint recombination fraction is printed;otherwise, the two-point recombination fraction is printed. For markers oftypeD1 andD2, it is impossible to calculate recombinationfraction via two-point analysis and, therefore, the corresponding cell willbe empty (white color). For cells on the diagonal of the matrix, the name, the number andthe type of the marker are printed, as well as the percentage of missingdata 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 classsequence, which is a list containing thefollowing 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 linkagegroup

Description

For a given sequence of ordered markers, computes the multipoint likelihoodof alternative orders, by shuffling subsets (windows) of markers within thesequence. 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 ifthere are many partially informative markers.

Value

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

Author(s)

Gabriel R A Margarido,gramarga@gmail.com andMarcelo 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 version1.09-43

Jiang, C. and Zeng, Z.-B. (1997). Mapping quantitative trait loci withdominant 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 forconstructing primary genetic linkage maps of experimental and naturalpopulations.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 maximumlikelihood 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 ofsex-specific differences.Genetical Research 79: 85-96

See Also

make_seq,compare,try_seqandorder_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 exampledata(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.ordmake_seq(LG3.ord) # get safe sequenceord.1<-make_seq(LG3.ord,"force") # get forced sequenceripple_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 classonemap, i.e., a list with the followingcomponents:

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 Rwhen 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 recombinationfrequencies 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 objectinput.seq. Thebest combination of linkage phases is also estimated starting from the firstposition not in the given seeds.The multipoint likelihood is calculatedaccording to Wu et al. (2002b)(Eqs. 7a to 11), assuming that therecombination fraction is the same in both parents. Hidden Markov chaincodes adapted from Broman et al. (2008) were used.

Value

An object of classsequence, which is a list containing thefollowing 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 Schiffthalerbastian.schiffthaler@umu.se

References

Broman, K. W., Wu, H., Churchill, G., Sen, S., Yandell, B.(2008)qtl: Tools for analyzing QTL experiments R package version1.09-43

Jiang, C. and Zeng, Z.-B. (1997). Mapping quantitative trait loci withdominant 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 forconstructing primary genetic linkage maps of experimental and naturalpopulations.Genomics 1: 174-181.

Wu, R., Ma, C.-X., Painter, I. and Zeng, Z.-B. (2002a) Simultaneous maximumlikelihood 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 ofsex-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 isapplied 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 classsequence with selected marker type, which is a list containing thefollowing components:

Author(s)

Cristiane Taniguti,chtaniguti@tamu.edu

See Also

make_seq

Seriation

Description

Implements the marker ordering algorithmSeriation (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. Itis not an exhaustive search method and, therefore, is not computationallyintensive. However, it does not guarantee that the best order is alwaysfound. The only requirement is a matrix with recombination fractionsbetween markers.

NOTE: When there are to many pairs of markers with the same value in therecombination fraction matrix, it can result in ties during the ordinationprocess and theSeriation algorithm may not work properly. This isparticularly relevant for outcrossing populations with mixture of markersof typeD1 andD2. When this occurs, the function shows thefollowing error message:There are too many ties in the ordinationprocess - please, consider using another ordering algorithm.

After determining the order withSeriation, the final map isconstructed using the multipoint approach (functionmap).

Value

An object of classsequence, which is a list containing thefollowing components:

Author(s)

Gabriel R A Margarido,gramarga@gmail.com

References

Buetow, K. H. and Chakravarti, A. (1987) Multipoint genemapping using seriation. I. General methods.American Journal ofHuman 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 throughthe 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 recombinationvalues.Annuaire of Eugenetics 12: 172-175.

Author(s)

Marcelo Mollinari,mmollina@usp.br

References

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

See Also

kosambi andhaldane

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 andread_mapmaker.

Examples

data(simu_example_bc)# perform two-point analysestwopts <- 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 andread_mapmaker.

Examples

data(simu_example_f2)# perform two-point analysestwopts <- 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 andread_mapmaker.

Examples

data(simu_example_out)# perform two-point analysestwopts <- 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 classonemap, i.e., a list with the followingcomponents:

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 classrf_2pts with only the selected markers, which is a list containing thefollowing 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 outputis 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 testsfor 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 thatwill be performed for all markers in the data set, and then using this to calculatethe 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 takenand then converted to LOD Score.

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

Value

the suggested LOD to be used for testing linkage

Examples

data(onemap_example_bc) # Loads a fake backcross dataset installed with onemapsuggest_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 followingthe 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-valuesand the % of individuals genotyped. To see the object, it is necessary to printit.

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 theexpected 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 itdefines the H0 hypothesis, and then tests it, together with percentage ofmissing 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 onemaptest_segregation_of_a_marker(onemap_example_bc,1)data(onemap_example_out) # Loads a fake outcross dataset installed with onemaptest_segregation_of_a_marker(onemap_example_out,1)

Try to map a marker into every possible position between markersin a given map

Description

For a given linkage map, tries do add an additional unpositionedmarker. This function estimates parameters for all possible mapsincluding the new marker in all possible positions, while keepingthe original linkage map unaltered.

Usage

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

Arguments

Value

An object of classtry, which is a list containingthe 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 QTLexperiments R package version 1.09-43

Jiang, C. and Zeng, Z.-B. (1997). Mapping quantitative trait lociwith dominant and missing markers in various crosses from twoinbred 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 interactivecomputer package for constructing primary genetic linkage mapsof 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 ordermarkers 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 andlinkage phases in outcrossing species.TheoreticalPopulation Biology 61: 349-363.

Wu, R., Ma, C.-X., Wu, S. S. and Zeng, Z.-B. (2002b). Linkagemapping of sex-specific differences.Genetical Research79: 85-96

See Also

make_seq andcompare.

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 classsequence, which is a list containing thefollowing components:

Unidirectional Growth

Description

Implements the marker ordering algorithmUnidirectional 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 markerordering in linkage groups. It is not an exhaustive search method and,therefore, is not computationally intensive. However, it does not guaranteethat the best order is always found. The only requirement is a matrix withrecombination fractions between markers.

After determining the order withUG, the final map is constructedusing the multipoint approach (functionmap).

Value

An object of classsequence, which is a list containing thefollowing 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 geneticsmaps.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 classonemap.

Details

A total of 142 backcross individuals were genotyped with 25 markers. The datawas generated from a VCF file. It contains chromossome and positioninformations for each marker. It is included to be used as a example inorder to understand how to convert VCF file to OneMap input data with the functionsvcf2raw andonemap_read_vcfR.

Author(s)

Cristiane Hayumi Taniguti,chaytaniguti@gmail.com

See Also

read_onemap for details about objects of classonemap.

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 classonemap.

Details

A total of 192 F2 individuals were genotyped with 25 markers. The datawas generated from a VCF file. It contains chromossome and positioninformations for each marker. It is included to be used as a reference inorder 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 classonemap.

Examples

data(vcf_example_f2)# plot markers informationsplot(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 bycrossing two non-homozygous parents.

Usage

data(vcf_example_out)

Format

An object of classonemap.

Details

A total of 92 F1 individuals were genotyped with 27 markers. The datawas generated from a VCF file. It contains chromossome and positioninformations for each marker. It is included to be used as a reference inorder 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 classonemap.

Examples

data(vcf_example_out)# plot markers informationsplot(vcf_example_out)

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

Description

Simulated biallelic data set for anri 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 datawas generated from a VCF file. It contains chromossome and positioninformations for each marker. It is included to be used as a example inorder to understand how to convert VCF file to OneMap input data with the functionsvcf2raw andonemap_read_vcfR.

Author(s)

Cristiane Hayumi Taniguti,chaytaniguti@gmail.com

See Also

read_onemap for details about objects of classonemap.

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. Theoutput file can be used as an input to perform QTL mapping using the packageR/qtl. It is also possible to create an output to be used withQTLCartographer 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 version1.09-43

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"))