PONG2 – KIR Genotype Imputation & Model Training

PONG2 is an R package with C++ acceleration (via Rcpp) for high-accuracy imputation and training of Killer-cell Immunoglobulin-like Receptor (KIR) genotypes from SNP array data in the KIR locus (chromosome 19q13.4).

It is optimized for population genetics, immunogenetics, and large-scale biobank studies requiring reliable KIR allele calls across diverse ancestries.

Main CLI commands:

impute – predict KIR alleles from target PLINK files
train – build a new prediction model from reference genotypes + known KIR calls

Overview

PONG2 enables scalable and accurate KIR genotyping by combining:

Region-specific PLINK2 preprocessing
Optional local minimac4 pre-imputation for missing variants
Supervised allele prediction models tailored to the highly polymorphic KIR region

It supports both hg19 and hg38 assemblies and is particularly useful for studying immune response variation, HLA–KIR interactions, and disease associations in diverse populations.

Features

Multi-ancestry pre-trained models (EUR, AMR, AFR, EAS, SAS)
Automatic handling of hg19 / hg38 coordinate differences
Configurable SNP missingness threshold
Built-in local imputation fallback (--fill-missing) using minimac4
Support for external pre-imputation (e.g. Michigan Imputation Server)
Multi-threading via --threads
Automatic chunked prediction for large biobank datasets (>2,000 samples)
Force-run mode for low SNP match scenarios
Clean separation of preprocessing and prediction steps

Requirements

R version: ≥ 4.0

Required R packages (loaded at runtime):

readr
tidyverse
parallel

System tools (must be in PATH):

Tool	Version	Required
PLINK2	≥ 2.0	Always
minimac4	≥ 4.1.6	Only with `--fill-missing`
bgzip & tabix	HTSlib	Only with `--fill-missing`
Eagle2	≥ 2.4	Only for pre-phasing before `--fill-missing`

Installation

From GitHub (recommended)

# Install remotes if needed
if (!require("remotes", quietly = TRUE)) install.packages("remotes")

# Install PONG2 from GitHub
remotes::install_github("NormanLabUCD/PONG2")

From CRAN

install.packages("PONG2")

From source tarball

Download PONG2_1.0.0.tar.gz then:

# Standard install
R CMD INSTALL PONG2_1.0.0.tar.gz

# Custom library path
R CMD INSTALL --library=/your/custom/path PONG2_1.0.0.tar.gz

CLI Setup

After installation, add the pong2 command-line tool to your terminal PATH. Run the following once in your terminal:

# Run once after installation
PONG2_PATH=$(Rscript -e "cat(dirname(system.file('scripts', 'pong2', package='PONG2')))")
echo "export PATH=\"$PONG2_PATH:\$PATH\"" >> ~/.bashrc && source ~/.bashrc
pong2 --help

HPC users: Add the export PATH line to your job submission script or ~/.bashrc on the cluster to ensure pong2 is available in all sessions.

Note: You only need to do this once. After adding to ~/.bashrc, pong2 will be available in all future terminal sessions automatically.

Usage

pong2 <command> [options]

Help

pong2 --help              # General overview + list of commands
pong2 --help impute       # Detailed help for imputation
pong2 --help train        # Detailed help for training
pong2 version             # Show version number

impute command

Predict KIR alleles from a target PLINK dataset.

pong2 impute [options]

Required flags

Flag	Description	Example
`-i, --bfile`	PLINK bed/bim/fam prefix (chr19)	`data/chr19`
`-o, --output`	Output directory	`results/imputation`
`-l, --locus`	KIR locus to impute	`KIR3DL1`
`-a, --assembly`	Genome build	`hg19` or `hg38`

Optional flags

Flag	Default	Description
`--filter`	`0.005`	Allele frequency filter threshold (`0.005` or `0.01`)
`-t, --threads`	`4`	Number of CPU threads
`-f, --force`	`false`	Proceed even if SNP matching rate is low (<50%)
`--fill-missing`	`false`	Impute missing SNPs locally with minimac4 (requires `--vcf`)
`--vcf`	—	Pre-phased VCF file required when using `--fill-missing`

Examples

# Basic imputation
pong2 impute -i example/chr19 -o output -l KIR3DL1 -a hg19

train command

Build a new KIR prediction model from reference genotypes and known KIR calls.

pong2 train [options]

Required flags

Flag	Description	Example
`-i, --bfile`	Reference PLINK bed/bim/fam prefix	`data/chr19`
`-k, --kfile`	CSV with sample IDs and KIR allele calls	`data/kir_calls.csv`
`-o, --output`	Directory to save trained model	`models/KIR3DL1`
`-l, --locus`	KIR locus to train	`KIR3DL1`
`-a, --assembly`	Genome build	`hg19` or `hg38`

Optional flags

Flag	Default	Description
`-t, --threads`	`4`	Number of CPU threads
`--nclassifier`	`100`	Number of ensemble classifiers
`--split`	`0.7`	Train/validation split proportion
`--kirmaf`	`0.00`	Minimum KIR allele frequency filter
`--mac`	`3`	Minimum allele count for SNPs
`-r, --region`	Optimized default	Custom KIR region (e.g. `55281035-55295784`)

KIR file format

The KIR file (--kfile) must be a CSV with the following structure:

Sample	KIR3DL1_h1	KIR3DL1_h2	KIR2DL1_h1	KIR2DL1_h2
`HG00096`	`KIR3DL1*001`	`KIR3DL1*002`	`KIR2DL1*00302`	`KIR2DL1*00201`
`HG00097`	`KIR3DL1*005`	`KIR3DL1*015`	`KIR2DL1*00302`	`KIR2DL1*05101`

Example

pong2 train --bfile example/chr19 --kfile example/kir_call.csv --output test --locus KIR3DL1 --assembly hg19 --nclassifier 50

Improving Imputation Accuracy

NOTE:

KIR Region SNP Overlap between input data and 1KGP

Overlap rate is computed between your input data and the 1000 Genomes Project (1KGP) reference panel in the KIR region (chr19).

Overlap Rate Status Action

≥ 50% Pass Proceed with PONG2 directly

< 50% Fail Run Eagle2 + minimac4 pre-imputation first

If your SNP matching rate is below 50%, PONG2 provides two strategies:

Option A: Local pre-imputation with minimac4 (built-in)

Pre-phase your data with Eagle2, then run PONG2 with --fill-missing:

# Step 1: Pre-phase with Eagle2
eagle \
  --bfile=chr19 \
  --geneticMapFile=genetic_map_hg19.txt.gz \
  --outPrefix=chr19.phased \
  --chrom=19 \
  --numThreads=20 \
  --bpStart=55000000 \
  --bpEnd=55400000

# Step 2: Run PONG2 with fill-missing
pong2 impute \
  --vcf chr19.phased.vcf.gz \
  -o output \
  -l KIR3DL1 \
  -a hg19 \
  --fill-missing \
  -t 20

Note: A pre-phased VCF (--vcf) is required with --fill-missing.

Option B: External pre-imputation (recommended for highest accuracy)

Pre-impute your chr19 data using a public imputation server before running PONG2:

Michigan Imputation Server
TOPMed Imputation Server (recommended for diverse populations)

Input & Output Formats

Input

File	Format	Description
PLINK bfile	`.bed/.bim/.fam`	Genotype data for chr19
KIR file	`.csv`	Sample IDs + phased KIR allele calls (train only)
VCF	`.vcf.gz` (bgzipped + tabixed)	Pre-phased VCF (required with `--fill-missing`)

Output

File	Description
`KIR/<locus>.csv`	Predicted KIR alleles per sample
`KIR/<locus>.RData`	Full prediction object (alleles + probabilities)
`<locus>_model.RData`	Trained model object (train only)
`<locus>_test.RData`	Test genotypes (train only, when `--split < 1`)

Dependencies & External Tools

Tool	Purpose	Install
PLINK2	Genotype preprocessing	plink2
Eagle2	Pre-phasing for imputation	Eagle
minimac4	Local SNP imputation	minimac4
bgzip/tabix	VCF compression & indexing	HTSlib

Troubleshooting

Error	Likely Cause	Fix
`--vcf is required with --fill-missing`	No VCF provided	Supply pre-phased VCF with `--vcf`
`High missing rate`	SNP overlap < 50%	Run Eagle2 + minimac4, or use `--force`
`No model found for locus`	Unsupported locus or wrong filter	Check locus name and `--filter` value
`incorrect number of dimensions`	Too few training samples	Verify sample overlap between KIR and PLINK files
`plink2 not found`	Not in PATH	Add plink2 to PATH
`pong2: command not found`	CLI not in PATH	Re-run CLI Setup steps above

License

PONG2 is licensed under the GNU General Public License v3.0 (GPL-3.0).

You are free to use, modify, and distribute PONG2, provided that derivative works are distributed under the same license. See LICENSE for details.

Citation

If you use PONG2.0 in your research, please cite:

Sadeeq SA, Leaton LA, Kichula KM, Farias TDJ, Font-Porterias N, Pollock NR, the Colorado Center for Personalized Medicine, Collora CE, Castelli EC, Gignoux CR, Norman PJ. PONG 2.0: allele imputation for the killer cell immunoglobulin-like receptors. Human Molecular Genetics. 2026;35(17):ddag075. doi:10.1093/hmg/ddag075

BibTeX

@article{sadeeq2026pong2,
  author  = {Sadeeq, Suraju A and Leaton, Laura A and Kichula, Katherine M and
             Farias, Ticiana D J and Font-Porterias, Neus and Pollock, Nicholas R and
             {the Colorado Center for Personalized Medicine} and Collora, Christopher E and
             Castelli, Erick C and Gignoux, Christopher R and Norman, Paul J},
  title   = {{PONG} 2.0: allele imputation for the killer cell immunoglobulin-like receptors},
  journal = {Human Molecular Genetics},
  volume  = {35},
  number  = {17},
  pages   = {ddag075},
  year    = {2026},
  doi     = {10.1093/hmg/ddag075}
}

Contact & Support

Tutorial (basic, imputation, train)
GitHub Issues (preferred for bug reports, feature requests, questions): https://github.com/NormanLabUCD/PONG2/issues
Email (for collaboration or private inquiries): paul.norman@cuanschutz.edu
Lab / Institution: Norman Lab | University of Colorado Anschutz Medical Campus | Department of Biomedical Informatics | Immunology & Microbiology

We aim to respond to issues and emails within 1–3 business days. Thank you for using PONG2 — happy KIR analysis! 🧬

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
R		R
data		data
docs		docs
example		example
inst		inst
man		man
src		src
vignettes		vignettes
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
LICENSE		LICENSE
NAMESPACE		NAMESPACE
README.md		README.md
cran-comments.md		cran-comments.md

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PONG2 – KIR Genotype Imputation & Model Training

Table of Contents

Overview

Features

Requirements

Installation

From GitHub (recommended)

From CRAN

From source tarball

CLI Setup

Usage

Help

impute command

Required flags

Optional flags

Examples

train command

Required flags

Optional flags

KIR file format

Example

Improving Imputation Accuracy

KIR Region SNP Overlap between input data and 1KGP

Option A: Local pre-imputation with minimac4 (built-in)

Option B: External pre-imputation (recommended for highest accuracy)

Input & Output Formats

Input

Output

Dependencies & External Tools

Troubleshooting

License

Citation

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Overlap Rate	Status	Action
≥ 50%	Pass	Proceed with PONG2 directly
< 50%	Fail	Run Eagle2 + minimac4 pre-imputation first

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PONG2 – KIR Genotype Imputation & Model Training

Table of Contents

Overview

Features

Requirements

Installation

From GitHub (recommended)

From CRAN

From source tarball

CLI Setup

Usage

Help

impute command

Required flags

Optional flags

Examples

train command

Required flags

Optional flags

KIR file format

Example

Improving Imputation Accuracy

KIR Region SNP Overlap between input data and 1KGP

Option A: Local pre-imputation with minimac4 (built-in)

Option B: External pre-imputation (recommended for highest accuracy)

Input & Output Formats

Input

Output

Dependencies & External Tools

Troubleshooting

License

Citation

Contact & Support

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