sim_lr_genetic: Simulate Likelihood Ratios from Genetic Data

In mispitools: Missing Person Identification Tools

Simulate Likelihood Ratios from Genetic Data

Description

Simulates likelihood ratio (LR) distributions based on genetic (DNA) marker data. This function generates expected LR distributions under two hypotheses:

• H1 (Related): The unidentified person IS the missing person

• H2 (Unrelated): The unidentified person is NOT the missing person

This function wraps functionality from the forrel package to perform missing person LR calculations using pedigree structures.

Usage

sim_lr_genetic(reference, missing, numsims = 100, seed = 123, numCores = 1)

Arguments

reference	A pedigree object with attached genetic markers. Can be created using pedtools functions like linearPed(), nuclearPed(), etc., with markers attached via setMarkers().
missing	Character or numeric. The ID/label of the missing person in the pedigree.
numsims	Integer. Number of simulations to perform. Default: 100.
seed	Integer. Random seed for reproducibility. Default: 123.
numCores	Integer. Number of CPU cores for parallel processing. Default: 1 (no parallelization).

Details

The function performs two types of simulations:

1. H2 (Unrelated): Generates random genetic profiles for unrelated individuals using population allele frequencies, then calculates the LR for each profile.

2. H1 (Related): Simulates genetic profiles for the actual missing person based on the pedigree structure, then calculates the LR for each profile.

The LR is computed using forrel::missingPersonLR(), which calculates the ratio of likelihoods: P(data | POI is MP) / P(data | POI is unrelated).

Value

A list with two components:

• Unrelated: List of LR objects from simulations where POI is unrelated to the pedigree (H2 simulations)

• Related: List of LR objects from simulations where POI is the actual missing person (H1 simulations)

Use lr_to_dataframe to convert this to a data.frame for further analysis.

References

Marsico FL, Vigeland MD, Egeland T, Herrera Pinero F (2021). "Making decisions in missing person identification cases with low statistical power." Forensic Science International: Genetics, 52, 102519. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.fsigen.2021.102519")}

Vigeland MD, Egeland T (2021). "Joint DNA-based disaster victim identification." Forensic Science International: Genetics, 52, 102465.

See Also

lr_to_dataframe for converting output to dataframe, sim_lr_prelim for non-genetic LR simulations, plot_lr_distribution for visualizing LR distributions, decision_threshold for computing optimal thresholds.

Examples

library(forrel)
library(pedtools)

# Create a simple pedigree: grandparent-parent-child
x <- linearPed(2)
plot(x)

# Add genetic markers (using Norwegian frequencies as example)
x <- setMarkers(x, locusAttributes = NorwegianFrequencies[1:5])

# Simulate a profile for the reference person (ID 2)
x <- profileSim(x, N = 1, ids = 2)

# Simulate LRs (person 5 is missing)
lr_sims <- sim_lr_genetic(x, missing = 5, numsims = 50, seed = 123)

# Convert to dataframe for analysis
lr_df <- lr_to_dataframe(lr_sims)
head(lr_df)

# Visualize distributions
plot_lr_distribution(lr_df)

mispitools documentation built on Aug. 26, 2026, 1:08 a.m.