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Fast and Memory-Efficient Dynamic Programming Approach for Large-Scale EHH-Based Selection Scans.

Amatur Rahman¹, T Quinn Smith¹, Zachary A Szpiech¹

¹Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA.

Molecular Biology and Evolution

|October 27, 2025

Summary

This summary is machine-generated.

This study introduces a new dynamic programming algorithm to speed up calculations for extended haplotype homozygosity (EHH), a key metric in population genetics for detecting positive selection. The optimized method significantly reduces computational time and memory usage for large genomic datasets.

Keywords:

haplotypes population genetics positive selection software

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Area of Science:

Population Genetics
Computational Biology
Genomics

Background:

Haplotype-based statistics are essential for identifying genomic regions under positive selection.
Extended haplotype homozygosity (EHH) is a core statistic, but its computation is computationally intensive.
Existing tools struggle to scale with large population datasets like the UK Biobank.

Purpose of the Study:

To develop a computationally efficient algorithm for calculating EHH.
To improve the scalability of haplotype-based selection scans for large-scale genomic data.
To optimize runtime and memory usage for analyzing population genetics datasets.

Main Methods:

A novel dynamic programming algorithm was developed to compute EHH.
The algorithm was tested on both real phased and simulated genomic data.
Performance was evaluated against existing computational tools for haplotype analysis.

Main Results:

The new algorithm achieved a 5-50x speedup on real phased data with minimal memory usage.
Simulations demonstrated up to 15x speedup and 46x memory reduction on large populations.
EHH statistics for unphased genotypes showed an order of magnitude speedup, with multi-parameter support yielding a 20x runtime improvement.

Conclusions:

The proposed dynamic programming approach significantly enhances the efficiency of EHH computation.
This optimization enables scalable analysis of large population genomic datasets for detecting positive selection.
The new method, implemented in selscan v2.1, provides a powerful tool for population genetics research.