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Updated: Jan 17, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

IGD: a simple, efficient genotype data format.

Drew DeHaas¹, Xinzhu Wei¹

¹Department of Computational Biology, Cornell University, Ithaca, NY 14850, United States.

Bioinformatics Advances

|September 22, 2025

Summary

This summary is machine-generated.

Indexable Genotype Data (IGD) is a new, efficient file format for genotype data. It offers significant speed and size improvements over existing formats like VCF.gz for large-scale population genetics research.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Existing file formats for genotype data are often complex and inefficient.
Limited programming language support hinders research scalability.
There is a need for a simple, fast, and small file format for population genetics.

Purpose of the Study:

To introduce the Indexable Genotype Data (IGD) file format.
To provide a simple, efficient, and scalable solution for storing genotype data.
To facilitate research in population genetics and statistical genomics.

Main Methods:

Developed a new uncompressed binary file format: Indexable Genotype Data (IGD).
Implemented IGD reading/writing in Python with under 350 lines of code.
Created C++ library and conversion tools for VCF.gz to IGD.

Main Results:

IGD is over 100x faster than vcf.gz for biobank-scale whole-genome sequence data.
IGD files are 3.5x smaller than vcf.gz.
The Python implementation demonstrates the format's simplicity and ease of use.

Conclusions:

IGD offers a significant improvement in speed and size for genotype data storage.
The format's simplicity facilitates its adoption and integration into research workflows.
IGD supports highly scalable statistical and population genetics methods.