Validate pedigrees and assign parentage with BIGpopA

Use a worked SNP dataset to clean a pedigree, validate recorded parent-offspring trios, and search candidate parents with BIGpopA.

Keywords

BIGpopA, pedigree cleaning, pedigree validation, parentage assignment, Mendelian error, SNP genotype, R

NoteSource and verification

This page is adapted from Josue Chinchilla-Vargas’s BIGpopA Tutorial: Pedigree cleaning, validation, and parentage assignment from SNP genotype data. The dataset, analysis sequence, function calls, and interpretations follow the supplied R Markdown and rendered HTML. Expected results were checked on July 16, 2026 with BIGpopA 1.0.5 from the official main branch at commit e98416e.

What you will learn

This tutorial demonstrates a typical pedigree workflow using BIGpopA, the analytical engine behind Familia. You will run three functions in sequence:

1. check_ped() to detect and correct structural pedigree errors;
2. validate_pedigree() to test recorded parent-offspring trios against SNP genotypes; and
3. find_parentage() to search a supplied candidate-parent pool.

Before starting, read Pedigree validation or parentage assignment? for the distinction between the two genotype-based tasks.

What you need

• R 4.4.0 or newer;
• permission to install R packages; and
• an internet connection for installation from GitHub.

Code execution is disabled during normal site builds. The displayed results come from running Josue’s complete example with R 4.5.1 and BIGpopA 1.0.5.

1. Install BIGpopA

The official BIGpopA repository currently describes the package as under development and provides a GitHub installation:

install.packages("remotes")
remotes::install_github(
  "Breeding-Insight/BIGpopA",
  dependencies = TRUE
)

library(BIGpopA)
packageVersion("BIGpopA")

The verified version for this page is:

[1] '1.0.5'

Report package problems through the BIGpopA issue tracker.

2. Create the example inputs

The example builds four objects directly in R:

Object	Required content in this example
genotypes	One id column followed by SNP genotypes coded 0, 1, or 2; NA represents missing data
pedigree	id, male_parent, and female_parent columns
parents	Candidate-parent id and sex coded M or F
progeny	IDs whose parentage will be searched

Run the supplied example code:

library(BIGpopA)

# SNP genotypes coded 0/1/2 (NA = missing); C_low is mostly missing
genotypes = data.frame(
  id    = c("M1","F1","M2","F2","C1","C2","C3","C_bad","C_low"),
  snp01 = c(  0,   0,   2,   2,   0,   2,   1,     0,     NA),
  snp02 = c(  0,   0,   2,   0,   0,   1,   0,     0,     NA),
  snp03 = c(  0,   2,   0,   2,   1,   1,   1,     1,     NA),
  snp04 = c(  0,   2,   0,   0,   1,   0,   0,     1,     NA),
  snp05 = c(  2,   0,   1,   1,   1,   1,   1,     1,     NA),
  snp06 = c(  2,   0,   1,   0,   1,   1,   1,     1,     NA),
  snp07 = c(  2,   2,   0,   1,   2,   0,   2,     2,     NA),
  snp08 = c(  2,   2,   2,   2,   2,   2,   2,     2,     NA),
  snp09 = c(  0,   1,   0,   2,   1,   1,   1,     1,      0),
  snp10 = c(  0,   1,   2,   0,   0,   1,   0,     0,      0),
  snp11 = c(  2,   1,   1,   2,   1,   2,   2,     1,      2),
  snp12 = c(  2,   1,   0,   1,   2,   1,   1,     2,      2),
  stringsAsFactors = FALSE
)

# Pedigree with founders coded 0, a misassigned trio (C_bad),
# and a duplicated row (C_missing)
pedigree = data.frame(
  id = c(
    "M1","F1","M2","F2","C1","C2","C3",
    "C_bad","C_low","C_missing","C_missing"
  ),
  male_parent = c(
    "0","0","0","0","M1","M2","M1",
    "M2","M1","M1","M1"
  ),
  female_parent = c(
    "0","0","0","0","F1","F2","F2",
    "F1","F1","F1","F1"
  ),
  stringsAsFactors = FALSE
)

# Candidate parents with sex and progeny to assign
parents = data.frame(
  id  = c("M1","M2","F1","F2"),
  sex = c("M", "M", "F", "F"),
  stringsAsFactors = FALSE
)

progeny = data.frame(
  id = c("C1","C2","C3","C_bad","C_low","C_missing"),
  stringsAsFactors = FALSE
)

pedigree
genotypes

The pedigree contains four founders, several crosses, a deliberately duplicated C_missing row, a misassigned C_bad trio, and a C_low sample with only four non-missing markers.

3. Clean the pedigree structure

check_ped() checks the columns id, male_parent, and female_parent. According to the BIGpopA documentation, exact duplicates and missing parent records are corrected automatically. Conflicting trios and inconsistent parent-sex roles are corrected by default. Cycles are reported for manual resolution.

clean_ped_results = check_ped(
  pedigree,
  verbose = FALSE
)

clean_ped = clean_ped_results$corrected_pedigree
clean_ped

The repeated C_missing row is removed, leaving ten pedigree records:

id	male_parent	female_parent
M1	0	0
F1	0	0
M2	0	0
F2	0	0
C1	M1	F1
C2	M2	F2
C3	M1	F2
C_bad	M2	F1
C_low	M1	F1
C_missing	M1	F1

Inspect individual issue tables when you need the records behind a correction:

clean_ped_results$exact_duplicates
clean_ped_results$conflicting_trios
clean_ped_results$inconsistent_sex_roles
clean_ped_results$missing_parents
clean_ped_results$dependencies

4. Validate the recorded parents

validate_pedigree() compares each recorded trio with the genotype matrix and calculates a Mendelian-error percentage.

ped_validate_results = validate_pedigree(
  clean_ped,
  genotypes,
  verbose = FALSE,
  plot_results = TRUE
)

ped_validate_report = ped_validate_results$full_results
ped_validate_report

Recorded-trio Mendelian-error results from BIGpopA 1.0.5.

The verified result is:

id	Error (%)	Markers tested	status	recommended_correction
M1	NA	0	missing_both_parents	none
F1	NA	0	missing_both_parents	none
M2	NA	0	missing_both_parents	none
F2	NA	0	missing_both_parents	none
C1	0.00	12	pass	none
C2	0.00	12	pass	none
C3	0.00	12	pass	none
C_bad	41.67	12	fail	remove_male_parent
C_low	0.00	4	low_markers	low_markers_remove_female_parent
C_missing	NA	0	no_genotype_data	none

The four founders appear as missing_both_parents in this run because no founders_file was supplied. For C_bad, the recorded male parent has an 83.33 percent homozygous-mismatch rate and the recorded female parent has zero percent, leading to remove_male_parent. C_low is not evaluated as a passing trio because only four markers are available.

The settings used above are the BIGpopA 1.0.5 defaults:

Argument	Default	Function meaning
trio_error_threshold	5.0	Maximum Mendelian-error percentage classified as pass
min_markers	10	Minimum non-missing markers required to evaluate a trio
single_parent_error_threshold	2.0	Maximum homozygous-marker mismatch percentage for a parent to be considered acceptable

ImportantDefaults are not universal recommendations

These values document how BIGpopA 1.0.5 classified this supplied example. They are exposed as adjustable function arguments in the official package documentation.

5. Assign parents from the candidate pool

When parentage is unknown, find_parentage() searches the supplied candidate parents. Josue’s example uses method = "best_pair".

find_parentage_results = find_parentage(
  genotypes,
  parents,
  progeny,
  method = "best_pair",
  verbose = FALSE,
  plot_results = TRUE
)

find_parentage_report = find_parentage_results$full_results
find_parentage_report

Best-pair parentage results from BIGpopA 1.0.5.

BIGpopA warns that C_missing is not in the genotype data and removes it from the parentage search:

The following progeny IDs were not in the genotype file and will not be analyzed: C_missing

The verified report contains:

id	male_parent	female_parent	Error (%)	Markers tested	status
C1	M1	F1	0.00	12	pass
C2	M2	F2	0.00	12	pass
C3	M1	F2	0.00	12	pass
C_bad	M1	F1	0.00	12	pass
C_low	M1	F1	0.00	4	low_markers

C_bad failed validation against its recorded pair of M2 and F1. In the candidate-parent search, the example recovers M1 and F1 with zero percent Mendelian error. C_low has the same best pair but remains low_markers.

The package documents four search methods:

method	Search performed
"best_pair"	Best male and female parent pair
"best_male_parent"	Best male parent
"best_female_parent"	Best female parent
"best_match"	Best parent without using a parent-sex role

Other documented controls include error_threshold, min_markers, show_ties, allow_parent_selfing, and exclude_self_match. Their defaults describe package behavior and should not be treated as dataset-independent recommendations.

6. Put the three steps together

The complete sequence from Josue’s tutorial is:

clean_ped = check_ped(pedigree)$corrected_pedigree

validated = validate_pedigree(
  clean_ped,
  genotypes
)$full_results

assigned = find_parentage(
  genotypes,
  parents,
  progeny,
  method = "best_pair"
)$full_results

Pedigree validation and parentage assignment answer different questions. The companion concept page explains how to read their outputs.

7. Record the software session

Record the versions used for an analysis:

sessionInfo()

This page was verified with:

R version 4.5.1
BIGpopA_1.0.5

8. Cite BIGpopA

Run the package citation command for the current reference:

citation("BIGpopA")

For version 1.0.5, the installed package returns:

Chinchilla-Vargas J, Sandercock A, University of Florida. 2026. BIGpopA: Pedigree Validation Genetic Composition of Diploids & Polyploids. R package version 1.0.5. https://github.com/Breeding-Insight/BIGpopA.

Acknowledgments

Developed as part of Breeding Insight, an initiative providing open-source, data-driven tools for modern breeding programs.

Continue learning

Review validation and assignment Choose a genomic analysis path

Sources

• Chinchilla-Vargas J. 2026. BIGpopA Tutorial: Pedigree cleaning, validation, and parentage assignment from SNP genotype data. Source R Markdown, R script, and rendered HTML supplied for adaptation to Software Hub.
• BIGpopA 1.0.5 source and function documentation