Help for package BIGpopA

Title:

Pedigree Validation Genetic Composition of Diploids & Polyploids

Version:

1.0.5

Maintainer:

Josue Chinchilla-Vargas <josue.chinchilla@ufl.edu>

Description:

Tools for pedigree quality control and genomic breed/line composition estimation in diploid and polyploid breeding populations. 'BIGpopA' provides functions to check and correct common pedigree errors, assign parentage from SNP genotype data using Mendelian error rates, validate parent-offspring trios, and estimate genome-wide breed or line composition using quadratic programming. Supports both diploid and polyploid species. For more details about the included 'breedTools' functions, see Funkhouser et al. (2017) <doi:10.2527/tas2016.0003>.

License:

Apache License (≥ 2)

URL:

https://github.com/Breeding-Insight/BIGpopA

BugReports:

https://github.com/Breeding-Insight/BIGpopA/issues

Encoding:

UTF-8

Depends:

R (≥ 4.4.0)

Imports:

dplyr, janitor, quadprog, data.table, ggplot2

Suggests:

covr, knitr (≥ 1.10), rmarkdown, testthat (≥ 3.0.0)

Config/roxygen2/version:

8.0.0

NeedsCompilation:

Packaged:

2026-06-18 20:36:44 UTC; josue.chinchilla

Author:

Josue Chinchilla-Vargas [cre, aut], Alexander Sandercock [aut], University of Florida [cph] (Breeding Insight)

Repository:

CRAN

Date/Publication:

2026-06-24 08:20:24 UTC

Compute Allele Frequencies for Populations

Description

Computes allele frequencies for specified populations given SNP array data.

Usage

allele_freq_poly(geno, populations, ploidy = 2)

Arguments

geno

matrix of genotypes coded as the dosage of allele B (0, 1, 2, ..., ploidy) with individuals in rows (named) and SNPs in columns (named).

populations

list of named populations. Each population has a vector of IDs that belong to the population. Allele frequencies will be derived from all animals in each population.

ploidy

integer indicating the ploidy level (default is 2 for diploid).

Value

A matrix of allele frequencies with SNPs in rows and populations in columns.

References

Funkhouser SA, Bates RO, Ernst CW, Newcom D, Steibel JP. Estimation of genome-wide and locus-specific breed composition in pigs. Transl Anim Sci. 2017 Feb 1;1(1):36-44.

Examples

geno_matrix <- matrix(
  c(4, 1, 4, 0,
    2, 2, 1, 3,
    0, 4, 0, 4,
    3, 3, 2, 2,
    1, 4, 2, 3),
  nrow = 4, ncol = 5, byrow = FALSE,
  dimnames = list(paste0("Ind", 1:4), paste0("S", 1:5))
)

pop_list <- list(
  PopA = c("Ind1", "Ind2"),
  PopB = c("Ind3", "Ind4")
)

allele_freqs <- allele_freq_poly(geno = geno_matrix,
                                 populations = pop_list,
                                 ploidy = 4)
print(allele_freqs)

Check and Correct Common Pedigree Errors

Description

Reads a 3-column pedigree file (id, male_parent, female_parent) and performs quality checks, optionally correcting detected errors. Exact duplicates and missing parents are always corrected. Conflicting trios and inconsistent sex roles are corrected when their respective arguments are TRUE. Cycles are reported only and must be resolved manually.

Usage

check_ped(
  ped.file,
  seed = NULL,
  verbose = TRUE,
  correct_conflicting_trios = TRUE,
  correct_inconsistent_sex_roles = TRUE
)

Arguments

ped.file

Path to the pedigree text file (TSV/CSV/TXT), OR a data.frame / data.table with columns: id, male_parent, female_parent.

seed

Optional integer seed for reproducibility. Pass NULL (default) to skip setting a seed.

verbose

Logical. If TRUE (default), prints the report to the console.

correct_conflicting_trios

Logical. If TRUE (default), sets conflicting male_parent and female_parent to 0 and collapses to one row per ID.

correct_inconsistent_sex_roles

Logical. If TRUE (default), sets male_parent and female_parent to 0 for rows involving IDs found as both, then removes any resulting exact duplicates.

Value

An invisible named list of data frames:

exact_duplicates: Exact duplicate rows found in the input.
conflicting_trios: IDs with conflicting male_parent or female_parent assignments.
inconsistent_sex_roles: Rows where a conflicting ID appears as male_parent or female_parent.
missing_parents: Parent IDs absent from id, added as founders.
dependencies: Cycles detected in the pedigree. Must be resolved manually.
corrected_pedigree: Corrected pedigree table.

Author(s)

Josue Chinchilla-Vargas

Examples

# Self-contained example using a data.frame
ped_df <- data.frame(
  id            = c("A", "B", "C", "C", "D"),
  male_parent   = c("0", "0", "A", "A", "B"),
  female_parent = c("0", "0", "B", "B", "C"),
  stringsAsFactors = FALSE
)
ped_errors <- check_ped(ped.file = ped_df, seed = 101919, verbose = FALSE)
names(ped_errors)
head(ped_errors$corrected_pedigree)


library(data.table)
ped_dt <- data.table(id = c("A", "B", "C"),
                     male_parent   = c("0", "0", "A"),
                     female_parent = c("0", "0", "B"))
ped_errors <- check_ped(ped.file = ped_dt, verbose = FALSE)

Find Parentage Assignments for Progeny

Description

Assigns the most likely parent(s) to each progeny from SNP genotype data using Mendelian error rates or homozygous mismatch rates. Parents or progeny absent from the genotype file are removed with a warning.

Usage

find_parentage(
  genotypes_file,
  parents_file,
  progeny_file,
  method = "best_pair",
  min_markers = 10,
  error_threshold = 5,
  show_ties = TRUE,
  allow_parent_selfing = FALSE,
  exclude_self_match = TRUE,
  verbose = TRUE,
  plot_results = TRUE
)

Arguments

genotypes_file

Path to a TSV/CSV/TXT file, OR a data.frame / data.table with an 'id' column followed by marker columns coded as 0, 1, 2.

parents_file

Path to a TSV/CSV/TXT file, OR a data.frame / data.table with an 'id' column and an optional 'sex' column ('M', 'F', or 'A'). If absent, all parents are treated as ambiguous.

progeny_file

Path to a TSV/CSV/TXT file, OR a data.frame / data.table with an 'id' column.

method

Character. One of "best_male_parent", "best_female_parent", "best_match", or "best_pair" (default).

min_markers

Integer. Minimum markers required; fewer flags low_markers (default: 10).

error_threshold

Numeric. Maximum mismatch percentage; exceeded values flag high_error (default: 5.0). Must be between 0 and 100.

show_ties

Logical. If TRUE, tied best pairs are appended as suffix columns. Default is TRUE.

allow_parent_selfing

Logical. If FALSE, candidate pairs with identical male and female parent IDs are excluded. Applies only when method is "best_pair". Default is FALSE.

exclude_self_match

Logical. If TRUE, each progeny ID is excluded from its own candidate parent set, preventing self-matches when progeny are also present in the parents file. Default is TRUE.

verbose

Logical. If TRUE, prints progress and summary. Default is TRUE.

plot_results

Logical. If TRUE, plots the Mendelian error distribution. Requires ggplot2. Default is TRUE.

Value

A named list (returned invisibly) with elements:

pass: Progeny with a confident parentage assignment.
high_error: Progeny whose best assignment exceeds the error threshold.
low_markers: Progeny with insufficient markers for a valid assignment.
full_results: Complete data.table with all progeny and all output columns.
plot: ggplot object if plot_results = TRUE, otherwise NULL.

Author(s)

Josue Chinchilla-Vargas

Examples


geno_df <- data.frame(
  id  = c("P1", "P2", "P3", "Off1", "Off2"),
  S1  = c(0L, 2L, 0L, 1L, 0L),
  S2  = c(2L, 0L, 2L, 1L, 2L),
  S3  = c(0L, 2L, 0L, 1L, 0L),
  S4  = c(2L, 0L, 2L, 1L, 2L),
  S5  = c(0L, 2L, 0L, 1L, 0L),
  S6  = c(2L, 0L, 2L, 1L, 2L),
  S7  = c(0L, 2L, 0L, 1L, 0L),
  S8  = c(2L, 0L, 2L, 1L, 2L),
  S9  = c(0L, 2L, 0L, 1L, 0L),
  S10 = c(2L, 0L, 2L, 1L, 2L)
)

parents_df <- data.frame(
  id  = c("P1", "P2", "P3"),
  sex = c("M",  "F",  "F"),
  stringsAsFactors = FALSE
)

progeny_df <- data.frame(
  id = c("Off1", "Off2"),
  stringsAsFactors = FALSE
)

results <- find_parentage(
  genotypes_file = geno_df,
  parents_file   = parents_df,
  progeny_file   = progeny_df,
  method         = "best_pair",
  verbose        = FALSE,
  plot_results   = FALSE
)
print(results$full_results)

Compute Genome-Wide Breed Composition

Description

Computes genome-wide breed/ancestry composition using quadratic programming on a batch of animals.

Usage

solve_composition_poly(
  Y,
  X,
  ped = NULL,
  groups = NULL,
  mia = FALSE,
  sire = FALSE,
  dam = FALSE,
  ploidy = 2
)

Arguments

Y

numeric matrix of genotypes (columns) from all animals (rows) in the population, coded as dosage of allele B (0, 1, 2, ..., ploidy).

X

numeric matrix of allele frequencies (rows) from each reference panel (columns). Frequencies are relative to allele B.

ped

data.frame giving pedigree information. Must be formatted with columns: ID, Sire, Dam.

groups

list of IDs categorized by breed/population. If specified, output will be a list of results categorized by breed/population.

mia

logical. Only applies if ped argument is supplied. If TRUE, returns a data.frame containing the inferred maternally inherited allele for each locus for each animal instead of breed composition results.

sire

logical. Only applies if ped argument is supplied. If TRUE, returns a data.frame containing sire genotypes for each locus for each animal instead of breed composition results.

dam

logical. Only applies if ped argument is supplied. If TRUE, returns a data.frame containing dam genotypes for each locus for each animal instead of breed composition results.

ploidy

integer. The ploidy level of the species (e.g., 2 for diploid, 3 for triploid).

Value

A data.frame, or a list of data.frames when groups is not NULL, containing breed/ancestry composition results.

References

Funkhouser SA, Bates RO, Ernst CW, Newcom D, Steibel JP. Estimation of genome-wide and locus-specific breed composition in pigs. Transl Anim Sci. 2017 Feb 1;1(1):36-44.

Examples

allele_freqs_matrix <- matrix(
  c(0.625, 0.500,
    0.500, 0.500,
    0.500, 0.500,
    0.750, 0.500,
    0.625, 0.625),
  nrow = 5, ncol = 2, byrow = TRUE,
  dimnames = list(paste0("SNP", 1:5), c("VarA", "VarB"))
)

val_geno_matrix <- matrix(
  c(2, 1, 2, 3, 4,
    3, 4, 2, 3, 0),
  nrow = 2, ncol = 5, byrow = TRUE,
  dimnames = list(paste0("Test", 1:2), paste0("SNP", 1:5))
)

composition <- solve_composition_poly(Y = val_geno_matrix,
                                      X = allele_freqs_matrix,
                                      ploidy = 4)
print(composition)

Validate Pedigree Trios Using Mendelian Error Analysis

Description

Validates parent-offspring trios against SNP genotype data using Mendelian error rates. Identifies incorrect parentage assignments, suggests best-matching replacements, and outputs a corrected pedigree. Founder trios (both parents coded as 0) are preserved unchanged if a founders file is supplied. Trios absent from the genotype file are retained as no_genotype_data.

Usage

validate_pedigree(
  pedigree_file,
  genotypes_file,
  founders_file = NULL,
  trio_error_threshold = 5,
  min_markers = 10,
  single_parent_error_threshold = 2,
  verbose = TRUE,
  plot_results = TRUE
)

Arguments

pedigree_file

Path to the pedigree file (TSV/CSV/TXT), OR a data.frame / data.table with columns: id, male_parent, female_parent.

genotypes_file

Path to the genotypes file (TSV/CSV/TXT), OR a data.frame / data.table with an id column followed by marker columns coded as 0, 1, 2.

founders_file

Character, optional. Path to a one-column file listing founder IDs. Founders with both parents coded as 0 are left unchanged. Defaults to NULL.

trio_error_threshold

Numeric. Maximum Mendelian error percentage to classify a trio as pass (default: 5.0). Must be between 0 and 100.

min_markers

Integer. Minimum non-missing markers required to evaluate a trio (default: 10).

single_parent_error_threshold

Numeric. Maximum homozygous-marker mismatch percentage for a parent to be considered acceptable (default: 2.0). Must be between 0 and 100.

verbose

Logical. If TRUE, prints progress, summary, and results to the console (default: TRUE).

plot_results

Logical. If TRUE, prints a histogram of trio Mendelian error percentages with a threshold line (default: TRUE).

Value

An invisible named list with the following elements:

pass: Trios that passed the Mendelian error threshold.
fail: Trios that failed the Mendelian error threshold.
low_markers: Trios with insufficient markers for evaluation.
no_genotype_data: Trios absent from the genotype file.
founders: Trios identified as founders.
missing_parents: Trios with one or both parents coded as 0 (non-founders).
full_results: Complete data.table with all trios and all output columns.
corrected_pedigree: Pedigree table after applying recommended corrections.
plot: ggplot object if plot_results = TRUE, otherwise NULL.

Author(s)

Josue Chinchilla-Vargas

Examples


geno_df <- data.frame(
  id  = c("P1", "P2", "P3", "Off1", "Off2"),
  S1  = c(0L, 2L, 0L, 1L, 0L),
  S2  = c(2L, 0L, 2L, 1L, 2L),
  S3  = c(0L, 2L, 0L, 1L, 0L),
  S4  = c(2L, 0L, 2L, 1L, 2L),
  S5  = c(0L, 2L, 0L, 1L, 0L),
  S6  = c(2L, 0L, 2L, 1L, 2L),
  S7  = c(0L, 2L, 0L, 1L, 0L),
  S8  = c(2L, 0L, 2L, 1L, 2L),
  S9  = c(0L, 2L, 0L, 1L, 0L),
  S10 = c(2L, 0L, 2L, 1L, 2L)
)

ped_df <- data.frame(
  id            = c("Off1", "Off2"),
  male_parent   = c("P1",   "P1"),
  female_parent = c("P2",   "P3"),
  stringsAsFactors = FALSE
)

results <- validate_pedigree(
  pedigree_file  = ped_df,
  genotypes_file = geno_df,
  verbose        = FALSE,
  plot_results   = FALSE
)
print(results$full_results)

Package {BIGpopA}

Compute Allele Frequencies for Populations

Description

Usage

Arguments

Value

References

Examples

Check and Correct Common Pedigree Errors

Description

Usage

Arguments

Value

Author(s)

Examples

Find Parentage Assignments for Progeny

Description

Usage

Arguments

Value

Author(s)

Examples

Compute Genome-Wide Breed Composition

Description

Usage

Arguments

Value

References

Examples

Validate Pedigree Trios Using Mendelian Error Analysis

Description

Usage

Arguments

Value

Author(s)

Examples