Package 'malan'

Description

Simulating genealogies backwards and imposing STR mutations forwards.

Details

See vignettes and manual for documentation.

Disclaimer: THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Author(s)

Maintainer: Mikkel Meyer Andersen [email protected]

References

Andersen MM, Balding DJ (2017) How convincing is a matching Y-chromosome profile? PLoS Genet 13(11): e1007028. doi:10.1371/journal.pgen.1007028.

See Also

Useful links:

• Report bugs at https://github.com/mikldk/malan/issues

Description

Get pedigree from malan_pedigreelist generated by build_pedigrees().

Usage

## S3 method for class 'malan_pedigreelist'
x[[...]]

Arguments

Value

Pedigree

Description

Get individual from population by pid

Usage

## S3 method for class 'malan_population'
x[[...]]

Arguments

Value

Individual

Description

Calculate LR-like quantities by haplotype counts.

Usage

analyse_mixture_result(
  mix_res,
  unique_haps_in_mixture,
  unique_haps_in_mixture_counts
)

Arguments

Details

NOTE: Only takes up to 9 contributors!

Value

A list with numeric quantities

Description

Refer to analyse_mixture_result() for details. Essentially, analyse_mixture_result() is run on each element of mixture_results.

Usage

analyse_mixture_results(
  mixture_results,
  unique_haps_in_mixture_list,
  unique_haps_in_mixture_counts_list
)

Arguments

Details

NOTE: Only takes up to 9 contributors!

Value

A list with lists of numeric quantities

Description

Get tidy graph object tbl_graph(), e.g. to plot with ggraph().

Usage

## S3 method for class 'malan_pedigreelist'
as_tbl_graph(x, ...)

Arguments

Value

tbl_graph() object

Description

Get individual's number of brothers that matches individual's haplotype

Usage

brothers_matching(individual)

Arguments

Value

Number of brothers that matches individual's haplotype

Description

Makes it possible to find all individuals' pid with a certain haplotype. Must be used with e.g. get_matching_pids_from_hashmap().

Usage

build_haplotype_hashmap(individuals, progress = TRUE)

Arguments

Value

External pointer to hashmap with haplotype as keys and vector of individuals' pid as value

See Also

get_matching_pids_from_hashmap().

Description

In a newly simulated population, each individual only knows its father and children. Using this information, this function builds pedigrees. This makes it easier to e.g. population haplotypes, find path between two individuals (if they are not in the same pedigree, they are not connected).

Usage

build_pedigrees(population, progress = TRUE)

Arguments

Value

An object with class malan_pedigreelist (an internal list of external pointers to pedigrees).

See Also

sample_geneology() and sample_geneology_varying_size() for simulating populations.

Examples

sim <- sample_geneology(100, 10)
str(sim, 1)
sim$population
peds <- build_pedigrees(sim$population)
peds

Description

Calculate conditional genotype cumulative probabilities with theta

Usage

calc_autosomal_genotype_conditional_cumdist(allele_dist, theta)

Arguments

Value

Matrix: row i: conditional cumulative distribution of alleles given allele i

Description

Calculate genotype probabilities with theta

Usage

calc_autosomal_genotype_probs(allele_dist, theta)

Arguments

Description

Construct M matrix

Usage

construct_M(meioses, mu_dw, mu_up)

Arguments

Description

Get individual's number of brothers

Usage

count_brothers(individual)

Arguments

Value

Number of brothers

See Also

get_brothers()

Description

Counts the number of types close to haplotype in individuals.

Usage

count_haplotype_near_matches_individuals(individuals, haplotype, max_dist)

Arguments

Value

Number of times that a haplotype within a radius of max_dist of haplotype occurred amongst individuals.

See Also

count_haplotype_occurrences_individuals(), pedigree_haplotype_matches_in_pedigree_meiosis_L1_dists().

Description

Counts the number of types haplotype appears in individuals.

Usage

count_haplotype_occurrences_individuals(individuals, haplotype)

Arguments

Value

Number of times that haplotype occurred amongst individuals.

See Also

pedigree_haplotype_matches_in_pedigree_meiosis_L1_dists(), count_haplotype_near_matches_individuals().

Examples

sim <- sample_geneology(100, 10)
peds <- build_pedigrees(sim$population)
pedigrees_all_populate_haplotypes(peds, 2, c(0, 0))
count_haplotype_occurrences_individuals(sim$end_generation_individuals, c(0, 0))

Description

Counts the number of types haplotype appears in pedigree.

Usage

count_haplotype_occurrences_pedigree(
  pedigree,
  haplotype,
  generation_upper_bound_in_result = -1L
)

Arguments

Value

Number of times that haplotype occurred in pedigree.

See Also

pedigree_haplotype_matches_in_pedigree_meiosis_L1_dists().

Description

Get individual's number of uncles

Usage

count_uncles(individual)

Arguments

Value

Number of uncles

See Also

get_uncles()

Description

Delete hashmap made by build_haplotype_hashmap().

Usage

delete_haplotypeids_hashmap(hashmap)

Arguments

See Also

get_matching_pids_from_hashmap() and build_haplotype_hashmap().

Description

Estimate autosomal theta for one subpopulation given a sample of genotypes.

Usage

estimate_autotheta_1subpop_genotypes(genotypes, return_estimation_info = FALSE)

Arguments

Details

Assumes that pedigrees_all_populate_autosomal() was used first to populate autosomal genotypes.

Value

List:

• theta

  • estimate: Vector of length 1 containing estimate of theta or NA if it could not be estimated

  • error: true if an error happened, false otherwise

  • details: contains description if an error happened

  • estimation_info: If return_estimation_info = true: a list with information used to estimate theta. Else NULL.

Description

Estimate autosomal theta for one subpopulation given a list of individuals.

Usage

estimate_autotheta_1subpop_individuals(
  individuals,
  return_estimation_info = FALSE
)

Arguments

Details

Assumes that pedigrees_all_populate_autosomal() was used first to populate autosomal genotypes.

Value

List:

• theta

  • estimate: Vector of length 1 containing estimate of theta or NA if it could not be estimated

  • error: true if an error happened, false otherwise

  • details: contains description if an error happened

  • estimation_info: If return_estimation_info = true: a list with information used to estimate theta. Else NULL.

Description

Estimates autosomal F, theta, and f for a number of subpopulations given a list of genotypes.

Usage

estimate_autotheta_subpops_genotypes(subpops, subpops_sizes)

Arguments

Details

Assumes that pedigrees_all_populate_autosomal() was used first to populate autosomal genotypes.

Based on Bruce S Weir, Genetic Data Analysis 2, 1996. (GDA2).

Value

Estimates of autosomal F, theta, and f as well as additional information

Description

Estimates autosomal F, theta, and f for a number of subpopulations given a list of individuals.

Usage

estimate_autotheta_subpops_individuals(subpops, subpops_sizes)

Arguments

Details

Assumes that pedigrees_all_populate_autosomal() was used first to populate autosomal genotypes.

Based on Bruce S Weir, Genetic Data Analysis 2, 1996. (GDA2).

Value

Estimates of autosomal F, theta, and f as well as additional information

Description

Estimates autosomal F, theta, and f for a number of subpopulations given a list of pids (individual ids).

Usage

estimate_autotheta_subpops_pids(population, subpops, subpops_sizes)

Arguments

Details

Assumes that pedigrees_all_populate_autosomal() was used first to populate autosomal genotypes.

Based on Bruce S Weir, Genetic Data Analysis 2, 1996. (GDA2).

Value

Estimates of autosomal F, theta, and f as well as additional information

Description

Estimates unweighted autosomal theta for a number of subpopulations given a list of subpopulations of genotypes.

Usage

estimate_autotheta_subpops_unweighted_genotypes(subpops, assume_HWE)

Arguments

Details

Assumes that pedigrees_all_populate_autosomal() was used first to populate autosomal genotypes.

Based on Weir and Goudet, Genetics 2017: http://www.genetics.org/content/early/2017/05/26/genetics.116.198424

Value

Estimate of autosomal theta

Description

Estimates unweighted autosomal theta for a number of subpopulations given a list of pids (individual ids).

Usage

estimate_autotheta_subpops_unweighted_pids(population, subpops, assume_HWE)

Arguments

Details

Assumes that pedigrees_all_populate_autosomal() was used first to populate autosomal genotypes.

Based on Weir and Goudet, Genetics 2017: http://www.genetics.org/content/early/2017/05/26/genetics.116.198424

Value

Estimate of autosomal theta

Description

Does the father have the same profile as individual?

Usage

father_matches(individual)

Arguments

Value

Whether father has the same profile as individual or not

Description

Convert igraph to population

Usage

from_igraph(x, ...)

Arguments

Value

A population

Examples

g <- igraph::graph_from_literal( 2 +- 1 -+ 3, 4 -+ 5 )
plot(g)
pop <- from_igraph(g)
peds <- build_pedigrees(pop, progress = FALSE)
plot(peds)
infer_generations(peds)
get_generation(get_individual(pop, 1))
get_generation(get_individual(pop, 2))
get_generation(get_individual(pop, 3))
get_generation(get_individual(pop, 4))
get_generation(get_individual(pop, 5))

Description

Generate paternal brothers population

Usage

from_igraph_rcpp(vertices, edges)

Arguments

Value

An external pointer to the population.

Description

Generate a function to simulate pedigree founder haplotype based on a haplotype databasep

Usage

generate_get_founder_haplotype_db(db)

Arguments

Description

Generate a function to simulate pedigree founder haplotype based on ladder information

Usage

generate_get_founder_haplotype_ladder(ladder_min, ladder_max)

Arguments

Description

Assumes that pedigrees_all_populate_autosomal() was used first to populate autosomal genotypes.

Usage

get_allele_counts_genotypes(subpops)

Arguments

Value

Matrix with allele counts

Description

Assumes that pedigrees_all_populate_autosomal() was used first to populate autosomal genotypes.

Usage

get_allele_counts_pids(population, subpops)

Arguments

Value

Matrix with allele counts

Description

Get individual's brothers

Usage

get_brothers(individual)

Arguments

Value

List with brothers

See Also

get_father(), get_uncles(), get_children(), get_cousins()

Description

Get individual's children

Usage

get_children(individual)

Arguments

Value

List with children

See Also

get_father(), get_brothers(), get_uncles(), get_cousins()

Description

Get individual's cousins

Usage

get_cousins(individual)

Arguments

Value

List with cousins

See Also

get_brothers(), get_uncles(), get_children()

Description

Get individual's family information

Usage

get_family_info(individual)

Arguments

Value

List with family information

Description

Get individual's father

Usage

get_father(individual)

Arguments

Value

Father

See Also

get_brothers(), get_uncles(), get_children(), get_cousins()

Description

Note that generation 0 is final, end generation. 1 is second last generation etc.

Usage

get_generation(individual)

Arguments

Value

generation

Examples

sim <- sample_geneology(100, 10)
indv <- get_individual(sim$population, 1)
get_generation(indv)

Description

Requires that haplotypes are first populated, e.g. with pedigrees_all_populate_haplotypes(), pedigrees_all_populate_haplotypes_custom_founders(), or pedigrees_all_populate_haplotypes_ladder_bounded().

Usage

get_haplotype(individual)

Arguments

Value

Haplotype for individual.

See Also

get_haplotypes_individuals() and get_haplotypes_pids().

Examples

sim <- sample_geneology(100, 10)
peds <- build_pedigrees(sim$population)
pedigrees_all_populate_haplotypes(peds, 2, c(1, 1))
get_haplotype(sim$end_generation_individuals[[1]])

Description

Get haplotypes in pedigree

Usage

get_haplotypes_in_pedigree(ped)

Arguments

Value

List with haplotypes

Examples

sim <- sample_geneology(100, 10)
peds <- build_pedigrees(sim$population)
pedigrees_all_populate_haplotypes(peds, 2, c(1, 1))
get_haplotypes_in_pedigree(peds[[1]])

Description

Requires that haplotypes are first populated, e.g. with pedigrees_all_populate_haplotypes(), pedigrees_all_populate_haplotypes_custom_founders(), or pedigrees_all_populate_haplotypes_ladder_bounded().

Usage

get_haplotypes_individuals(individuals)

Arguments

Value

Matrix of haplotypes where row i is the haplotype of individuals[[i]].

See Also

get_haplotypes_pids().

Examples

sim <- sample_geneology(100, 10)
peds <- build_pedigrees(sim$population)
pedigrees_all_populate_haplotypes(peds, 2, c(1, 1))
get_haplotypes_individuals(sim$end_generation_individuals)

Description

Requires that haplotypes are first populated, e.g. with pedigrees_all_populate_haplotypes(), pedigrees_all_populate_haplotypes_custom_founders(), or pedigrees_all_populate_haplotypes_ladder_bounded().

Usage

get_haplotypes_pids(population, pids)

Arguments

Value

Matrix of haplotypes where row i is the haplotype of individuals[[i]].

See Also

get_haplotypes_individuals().

Description

Get individual by pid

Usage

get_individual(population, pid)

Arguments

Value

Individual

Examples

sim <- sample_geneology(100, 10)
indv <- get_individual(sim$population, 1)
get_pid(indv)

Description

Get all individuals in population

Usage

get_individuals(population)

Arguments

Description

By using hashmap made by build_haplotype_hashmap(), it is easy to get all individuals with a certain haplotype id.

Usage

get_matching_pids_from_hashmap(hashmap, haplotype)

Arguments

Value

List of individuals with a given haplotype id

See Also

build_haplotype_hashmap().

Description

Get nodes and edges in malan_pedigreelist. For example to plot via as_tbl_graph().

Usage

get_nodes_edges(x, ...)

Arguments

Value

List with entries nodes and edges

Description

Get pedigree information as graph (mainly intended for plotting)

Usage

get_pedigree_as_graph(ped)

Arguments

Description

Get pedigree from individual

Usage

get_pedigree_from_individual(individual)

Arguments

Value

pedigree

Description

Get pedigree id

Usage

get_pedigree_id(ped)

Arguments

Examples

sim <- sample_geneology(100, 10)
peds <- build_pedigrees(sim$population)
get_pedigree_id(peds[[1]])

Description

Get pedigree ids from pids

Usage

get_pedigree_id_from_pid(population, pids)

Arguments

Value

Vector with pedigree ids

Description

Get pedigrees information in tidy format

Usage

get_pedigrees_tidy(pedigrees)

Arguments

Description

Get pid from individual

Usage

get_pid(individual)

Arguments

Value

pid

Examples

sim <- sample_geneology(100, 10)
indv <- get_individual(sim$population, 1)
get_pid(indv)

Description

Get pids in pedigree

Usage

get_pids_in_pedigree(ped)

Arguments

Examples

sim <- sample_geneology(100, 10)
peds <- build_pedigrees(sim$population)
get_pids_in_pedigree(peds[[1]])

Description

Get individual's uncles

Usage

get_uncles(individual)

Arguments

Value

List with uncles

See Also

get_brothers(), get_children(), get_cousins()

Description

Generate a function to generate the zero haplotype

Usage

get_zero_haplotype_generator(loci)

Arguments

Description

Does the frandfather have the same profile as individual?

Usage

grandfather_matches(individual)

Arguments

Value

Whether grandfather has the same profile as individual or not

Description

Get the indvididuals that matches haplotype in individuals.

Usage

haplotype_matches_individuals(individuals, haplotype)

Arguments

Value

List of individuals that matches haplotype amongst individuals.

See Also

pedigree_haplotype_matches_in_pedigree_meiosis_L1_dists().

Description

Get the indvididuals that partially matches haplotype in individuals.

Usage

haplotype_partially_matches_individuals(
  individuals,
  haplotype,
  ignore_loci = as.integer(c())
)

Arguments

Value

List of individuals that partially matches haplotype amongst individuals.

Description

Individuals with the same haplotype will have the same hash (integer) and individuals with different haplotypes will have different hashes (integers).

Usage

haplotypes_to_hashes(population, pids)

Arguments

Details

This can be useful if for example using haplotypes to define groups and the haplotype itself is not of interest.

Value

Integer vector with haplotype hashes

Description

Takes as input final generation, then moves up in pedigree and increments generation number.

Usage

infer_generation(final_generation)

Arguments

Details

Note: Only works when all final generation individuals are provided.

Description

Infer generation numbers from pedigrees

Usage

infer_generations(peds)

Arguments

Value

Nothing

Description

Note that individuals loaded this way does not have information about generation.

Usage

load_haplotypes(population, pid, haplotypes, progress = TRUE)

Arguments

Description

Note that individuals loaded this way does not have information about generation.

Usage

load_individuals(pid, pid_dad, progress = TRUE, error_on_pid_not_found = TRUE)

Arguments

Description

Get the distribution of number of meioses from individual to all individuals in individual's pedigree. Note the generation_upper_bound_in_result parameter.

Usage

meioses_generation_distribution(
  individual,
  generation_upper_bound_in_result = -1L
)

Arguments

Description

Get the number of meioses between two individuals. Note, that pedigrees must first have been inferred by build_pedigrees().

Usage

meiotic_dist(ind1, ind2)

Arguments

Value

Number of meioses between ind1 and ind2 if they are in the same pedigree, else -1.

Description

Get the number of meioses between two individuals. Note, that pedigrees must first have been inferred by build_pedigrees().

Usage

meiotic_dist_threshold(ind1, ind2, threshold)

Arguments

Value

Number of meioses between ind1 and ind2 if they are in the same pedigree, else -1.

Description

Get all individual IDs within a meiotic radius Note, that pedigrees must first have been inferred by build_pedigrees().

Usage

meiotic_radius(ind, radius)

Arguments

Value

Matrix with ID and meiotic radius

Description

Mixture information about 2 persons' mixture of donor1 and donor2.

Usage

mixture_info_by_individuals_2pers(
  individuals,
  donor1,
  donor2,
  include_genealogy_info = TRUE
)

Arguments

Value

A list with mixture information about the mixture donor1+donor2+donor3 from individuals

See Also

mixture_info_by_individuals_3pers, mixture_info_by_individuals_4pers, mixture_info_by_individuals_5pers

Description

Mixture information about 3 persons' mixture of donor1, donor2 and donor3.

Usage

mixture_info_by_individuals_3pers(individuals, donor1, donor2, donor3)

Arguments

Value

A list with mixture information about the mixture donor1+donor2+donor3 from individuals

See Also

mixture_info_by_individuals_2pers, mixture_info_by_individuals_4pers, mixture_info_by_individuals_5pers

Description

Mixture information about 4 persons' mixture of donor1, donor2, donor3 and donor4.

Usage

mixture_info_by_individuals_4pers(individuals, donor1, donor2, donor3, donor4)

Arguments

Value

A list with mixture information about the mixture donor1+donor2+donor3 from individuals

See Also

mixture_info_by_individuals_2pers, mixture_info_by_individuals_3pers, mixture_info_by_individuals_5pers

Description

Mixture information about 5 persons' mixture of donor1, donor2, donor3, donor4 and donor5.

Usage

mixture_info_by_individuals_5pers(
  individuals,
  donor1,
  donor2,
  donor3,
  donor4,
  donor5
)

Arguments

Value

A list with mixture information about the mixture donor1+donor2+donor3 from individuals

See Also

mixture_info_by_individuals_2pers, mixture_info_by_individuals_3pers, mixture_info_by_individuals_4pers

Description

Convert pedigree to igraph

Usage

pedigree_as_igraph(x, ...)

Arguments

Value

igraph object

Description

Gives information about all individuals in pedigree that matches an individual. Just as count_haplotype_occurrences_individuals() counts the number of occurrences amongst a list of individuals, this gives detailed information about matching individuals in the pedigree, e.g. meiotic distances and maximum L1 distance on the path as some of these matches may have (back)mutations between in between them (but often this will be 0).

Usage

pedigree_haplotype_matches_in_pedigree_meiosis_L1_dists(
  suspect,
  generation_upper_bound_in_result = -1L,
  error_on_no_haplotype = TRUE
)

Arguments

Value

Matrix with information about matching individuals. Columns in order: meioses (meiotic distance to suspect), max_L1 (on the path between the matching individual and suspect, what is the maximum L1 distance between the suspect's profile and the profiles of the individuals on the path), pid (pid of matching individual)

See Also

count_haplotype_occurrences_individuals().

Description

Gives information about all individuals in pedigree that almost matches an individual. Just as count_haplotype_near_matches_individuals() counts the number of occurrences amongst a list of individuals, this gives detailed information about almost matching individuals in the pedigree: for now, the meiotic distances.

Usage

pedigree_haplotype_near_matches_meiosis(
  suspect,
  max_dist,
  generation_upper_bound_in_result = -1L
)

Arguments

Value

Matrix with information about matching individuals. Columns in order: 1) meioses (meiotic distance to suspect), 2) haplotype distance, 3) pid (pid of matching individual)

See Also

count_haplotype_near_matches_individuals().

Description

Get pedigree size

Usage

pedigree_size(ped)

Arguments

Examples

sim <- sample_geneology(100, 10)
peds <- build_pedigrees(sim$population)
pedigree_size(peds[[1]])

Description

Get the size of the pedigree. Note the generation_upper_bound_in_result parameter.

Usage

pedigree_size_generation(pedigree, generation_upper_bound_in_result = -1L)

Arguments

Description

Populate 1-locus autosomal DNA profile from founder and down in all pedigrees. Note, that only alleles from ladder is assigned and that all founders draw type randomly.

Usage

pedigrees_all_populate_autosomal(
  pedigrees,
  allele_dist,
  theta,
  mutation_rate,
  progress = TRUE
)

Arguments

Details

Note, that pedigrees must first have been inferred by build_pedigrees().

See Also

pedigrees_all_populate_haplotypes_custom_founders() and pedigrees_all_populate_haplotypes_ladder_bounded().

Description

Populate haplotypes from founder and down in all pedigrees. Note, that haplotypes are unbounded and that all founders get haplotype rep(0L, loci).

Usage

pedigrees_all_populate_haplotypes(
  pedigrees,
  loci,
  mutation_rates,
  prob_two_step = 0,
  prob_genealogical_error = 0,
  progress = TRUE
)

Arguments

Details

Note, that pedigrees must first have been inferred by build_pedigrees().

See Also

pedigrees_all_populate_haplotypes_custom_founders() and pedigrees_all_populate_haplotypes_ladder_bounded().

Examples

sim <- sample_geneology(100, 10)
peds <- build_pedigrees(sim$population)
pedigrees_all_populate_haplotypes(peds, 2, c(1, 1))
get_haplotype(sim$end_generation_individuals[[1]])

Description

Populate haplotypes from founder and down in all pedigrees. Note, that haplotypes are unbounded. All founders get a haplotype from calling the user provided function get_founder_haplotype().

Usage

pedigrees_all_populate_haplotypes_custom_founders(
  pedigrees,
  mutation_rates,
  get_founder_haplotype = NULL,
  prob_two_step = 0,
  prob_genealogical_error = 0,
  progress = TRUE
)

Arguments

Details

Note, that pedigrees must first have been inferred by build_pedigrees().

See Also

pedigrees_all_populate_haplotypes() and pedigrees_all_populate_haplotypes_ladder_bounded().

Examples

sim <- sample_geneology(100, 10)
peds <- build_pedigrees(sim$population)
pedigrees_all_populate_haplotypes_custom_founders(
  peds, c(1, 1), function(x) c(10, 10))
get_haplotype(sim$end_generation_individuals[[1]])

Description

Populate haplotypes from founder and down in all pedigrees. Note, that haplotypes are bounded by ladder_min and ladder_max. All founders get a haplotype from calling the user provided function get_founder_haplotype().

Usage

pedigrees_all_populate_haplotypes_ladder_bounded(
  pedigrees,
  mutation_rates,
  ladder_min,
  ladder_max,
  get_founder_haplotype = NULL,
  prob_two_step = 0,
  prob_genealogical_error = 0,
  progress = TRUE
)

Arguments

Details

Given that a two step mutation should happen (probability specified by prob_two_step): With distances >= 2 to ladder bounds, mutations happen as usual. At distance = 0 or 1 to a ladder bound, the mutation is forced to move away from the boundary.

Note, that pedigrees must first have been inferred by build_pedigrees().

See Also

pedigrees_all_populate_haplotypes() and pedigrees_all_populate_haplotypes_custom_founders().

Examples

sim <- sample_geneology(100, 10)
peds <- build_pedigrees(sim$population)
pedigrees_all_populate_haplotypes_ladder_bounded(
  peds, c(1, 1), c(0L, 0L), c(10L, 10L), 
  function(x) c(10, 10))
get_haplotype(sim$end_generation_individuals[[1]])

Description

Get number of pedigrees

Usage

pedigrees_count(pedigrees)

Arguments

Examples

sim <- sample_geneology(100, 10)
peds <- build_pedigrees(sim$population)
pedigrees_count(peds)

Description

Get distribution of pedigree sizes

Usage

pedigrees_table(pedigrees)

Arguments

Examples

sim <- sample_geneology(100, 10)
peds <- build_pedigrees(sim$population)
pedigrees_table(peds)

Description

Plot malan_pedigree.

Usage

## S3 method for class 'malan_pedigree'
plot(
  x,
  ids = TRUE,
  haplotypes = FALSE,
  locus_sep = " ",
  mark_pids = NULL,
  label_color = "black",
  node_color = "lightgray",
  mark_color = "orange",
  ...
)

Arguments

Description

Plot malan_pedigreelist generated by build_pedigrees().

Usage

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

Arguments

Description

Populate 1-locus autosomal DNA profile from founder and down in all pedigrees. Note, that all founders have type 0 to begin with.

Usage

population_populate_autosomal_infinite_alleles(
  population,
  mutation_rate,
  progress = TRUE
)

Arguments

Details

The maternal allele is taken by random from the 2*N[g] alleles in the previous generation consisting of N[g] males with descendants in the live population.

This is also why this is not using pedigrees but instead the population.

Note, that pedigrees need not be inferred.

See Also

pedigrees_all_populate_haplotypes_custom_founders() and pedigrees_all_populate_haplotypes_ladder_bounded().

Description

Get the size of the population. Note the generation_upper_bound_in_result parameter.

Usage

population_size_generation(population, generation_upper_bound_in_result = -1L)

Arguments

Description

Print individual

Usage

print_individual(individual)

Arguments

Examples

sim <- sample_geneology(100, 10)
indv <- get_individual(sim$population, 1)
print_individual(indv)

Description

Print pedigree

Usage

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

Arguments

Description

Print malan_pedigreelist generated by build_pedigrees().

Usage

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

Arguments

Description

Print malan_population generated by sample_geneology() or sample_geneology_varying_size().

Usage

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

Arguments

Description

Print malan_population_abort

Usage

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

Arguments

Description

Calculate distribution of allele difference after m meioses.

Usage

relationship_allele_diff_dist(meioses, mu_dw, mu_up, method = "explicit")

Arguments

Value

data.frame with columns d (allele difference) and p (prob)

Description

Calculate distribution of allele difference after m meioses.

Usage

relationship_allele_diff_dist_sym(meioses, mu_updw, method = "explicit")

Arguments

Value

data.frame with columns d (allele difference) and p (prob)

Description

Sample genotype with theta

Usage

sample_autosomal_genotype(allele_dist, theta)

Arguments

Description

This function simulates a geneology where the last generation has population_size individuals.

Usage

sample_geneology(
  population_size,
  generations,
  generations_full = 1L,
  generations_return = 3L,
  enable_gamma_variance_extension = FALSE,
  gamma_parameter_shape = 5,
  gamma_parameter_scale = 1/5,
  progress = TRUE,
  verbose_result = FALSE
)

Arguments

Details

By the backwards simulating process of the Wright-Fisher model, individuals with no descendants in the end population are not simulated. If for some reason additional full generations should be simulated, the number can be specified via the generations_full parameter. This can for example be useful if one wants to simulate the final 3 generations although some of these may not get (male) children.

Let $\alpha$ be the parameter of a symmetric Dirichlet distribution specifying each man's probability to be the father of an arbitrary male in the next generation. When $\alpha = 5$, a man's relative probability to be the father has 95\ constant 1 under the standard Wright-Fisher model and the standard deviation in the number of male offspring per man is 1.10 (standard Wright-Fisher = 1).

This symmetric Dirichlet distribution is implemented by drawing father (unscaled) probabilities from a Gamma distribution with parameters gamma_parameter_shape and gamma_parameter_scale that are then normalised to sum to 1. To obtain a symmetric Dirichlet distribution with parameter $\alpha$, the following must be used: $`gamma_parameter_shape` = \alpha$ and $`gamma_parameter_scale` = 1/\alpha$.

Value

A malan_simulation / list with the following entries:

• population. An external pointer to the population.

• generations. Generations actually simulated, mostly useful when parameter generations = -1.

• founders. Number of founders after the simulated generations.

• growth_type. Growth type model.

• sdo_type. Standard deviation in a man's number of male offspring. StandardWF or GammaVariation depending on enable_gamma_variance_extension.

• end_generation_individuals. Pointers to individuals in end generation.

• individuals_generations. Pointers to individuals in last generations_return generation (if generations_return = 3, then individuals in the last three generations are returned).

If verbose_result is true, then these additional components are also returned:

• individual_pids. A matrix with pid (person id) for each individual.

• father_pids. A matrix with pid (person id) for each individual's father.

• father_indices. A matrix with indices for fathers.

See Also

sample_geneology_varying_size().

Examples

sim <- sample_geneology(100, 10)
str(sim, 1)
sim$population
peds <- build_pedigrees(sim$population)
peds

Description

This function simulates a geneology with varying population size specified by a vector of population sizes, one for each generation.

Usage

sample_geneology_varying_size(
  population_sizes,
  generations_full = 1L,
  generations_return = 3L,
  enable_gamma_variance_extension = FALSE,
  gamma_parameter_shape = 5,
  gamma_parameter_scale = 1/5,
  progress = TRUE
)

Arguments

Details

By the backwards simulating process of the Wright-Fisher model, individuals with no descendants in the end population are not simulated If for some reason additional full generations should be simulated, the number can be specified via the generations_full parameter. This can for example be useful if one wants to simulate the final 3 generations although some of these may not get (male) children.

Let $\alpha$ be the parameter of a symmetric Dirichlet distribution specifying each man's probability to be the father of an arbitrary male in the next generation. When $\alpha = 5$, a man's relative probability to be the father has 95\ constant 1 under the standard Wright-Fisher model and the standard deviation in the number of male offspring per man is 1.10 (standard Wright-Fisher = 1).

This symmetric Dirichlet distribution is implemented by drawing father (unscaled) probabilities from a Gamma distribution with parameters gamma_parameter_shape and gamma_parameter_scale that are then normalised to sum to 1. To obtain a symmetric Dirichlet distribution with parameter $\alpha$, the following must be used: $`gamma_parameter_shape` = \alpha$ and $`gamma_parameter_scale` = 1/\alpha$.

Value

A malan_simulation / list with the following entries:

• population. An external pointer to the population.

• generations. Generations actually simulated, mostly useful when parameter generations = -1.

• founders. Number of founders after the simulated generations.

• growth_type. Growth type model.

• sdo_type. Standard deviation in a man's number of male offspring. StandardWF or GammaVariation depending on enable_gamma_variance_extension.

• end_generation_individuals. Pointers to individuals in end generation.

• individuals_generations. Pointers to individuals in last generations_return generation (if generations_return = 3, then individuals in the last three generations are returned).

See Also

sample_geneology().

Examples

sim <- sample_geneology_varying_size(10*(1:10))
str(sim, 1)
sim$population
peds <- build_pedigrees(sim$population)
peds

Description

Note that generation 0 is final, end generation. 1 is second last generation etc.

Usage

set_generation(individual, generation)

Arguments

Examples

sim <- sample_geneology(100, 10)
indv <- get_individual(sim$population, 1)
get_generation(indv)
set_generation(indv, 100)
get_generation(indv)

Description

Individuals with the same haplotype will be in the same group and individuals with different haplotypes will be in different groups.

Usage

split_by_haplotypes(population, pids)

Arguments

Value

List of integer vector, element i is an IntegerVector with all pids from pids with the same haplotype

Description

Generate test population

Usage

test_create_population()

Value

An external pointer to the population.

Description

A dataset containing information about the Y chromosomal short tandem repeat (Y-STR) markers that are present in the kit.

Usage

ystr_kits

Format

A data frame with 88 rows and 2 variables:

Marker

name of Y-STR marker

Kit

name of Y-STR kit

Source

https://www.yhrd.org

Description

A dataset from yhrd.org (and their sources) containing mutational information about Y chromosomal short tandem repeat (Y-STR) markers used in forensic genetics.

Usage

ystr_markers

Format

A data frame with 29 rows and 5 variables:

Marker

name of Y-STR marker

Meioses

number of meioses observed

Mutations

number of mutations observed in the corresponding number of Meioses

MutProb

point estimate of mutation probability, MutProb = Mutations/Meioses

Alleles

observed alleles

Details

Note, that loci with duplications (DYS385a/b as well as DYF387S1a/b have been split into two loci).

Source

https://www.yhrd.org