Linkage disequilibrium (LD) Browser

Explore population-scale correlation structure between genetic variants, directly in your browser.

Segregating variationMeiosis I
CT AG variant 1C / T variant 2A / G

Alleles inherited together result in a correlation structure (LD) between two genetic variants.

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Open an LD matrix

Zarr v2 · Reads stay chunked and local

Local storage

Open from this computer

Select a Zarr directory or ZIP archive. Files are read locally and are never uploaded.

Cloud storage

Stream a remote matrix

Hugging Face Available AWS S3 Available Google Cloud Planned

Hosted UK Biobank LD matrices

Use a custom cloud sourceHF, S3, or HTTPS
Connection options

Tokens remain in this tab and are only sent to Hugging Face.

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Image snapshot

Save LD heatmap

Choose the output resolution. PNG snapshots are lossless; higher scales produce sharper labels and larger image dimensions.

Output resolution

About

Linkage disequilibrium (LD) Browser

This browser supports interactive exploration of linkage disequilibrium patterns from human genetic variation data.

What is linkage disequilibrium?

Linkage disequilibrium (LD) is the non-random association of alleles at different genomic positions in a population. Variants that are inherited together tend to be correlated, producing local blocks of LD across the genome.

Why is LD useful?

LD helps researchers interpret genetic association signals, identify tag variants, fine-map candidate causal variants, prune correlated markers, and build statistical models such as polygenic scores.

Browser features

  • Regional heatmapsExplore LD across base-pair intervals without loading the whole genome-wide matrix.
  • Population comparisonView as many as four same-chromosome matrices side by side across one shared interval.
  • Flexible matrix viewsSwitch instantly between upper-triangular and symmetric views.
  • Variant toolsFind tagging variants, prune correlated sets, filter by pasted rsIDs, and query individual pairs.
  • Variant metadataReview genomic positions, alleles, frequencies, and stored LD scores.
  • Portable outputsExport heatmaps, correlation blocks, independent sets, and tagging variants.

The reader implements the compact LD matrix format proposed by Zabad et al. (2025), with support for both cloud-hosted and local Zarr data.

Private local exploration

Local LD files stay on your computer. They are read directly by the browser without being uploaded anywhere.

Cloud and local access

Stream cloud datasets when convenient, or use local files for a faster and fully private workflow.

References

How to cite

Zabad, S., Haryan, C. A., Gravel, S., Misra, S., & Li, Y. (2025). Toward whole-genome inference of polygenic scores with fast and memory-efficient algorithms. The American Journal of Human Genetics, 112(7), 1528–1546. https://doi.org/10.1016/j.ajhg.2025.05.002

View paper ↗
Show BibTeX
@article{ZABAD20251528,
    title = {Toward whole-genome inference of polygenic scores with fast and memory-efficient algorithms},
    journal = {The American Journal of Human Genetics},
    volume = {112},
    number = {7},
    pages = {1528-1546},
    year = {2025},
    issn = {0002-9297},
    doi = {https://doi.org/10.1016/j.ajhg.2025.05.002},
    url = {https://www.sciencedirect.com/science/article/pii/S000292972500182X},
    author = {Shadi Zabad and Chirayu Anant Haryan and Simon Gravel and Sanchit Misra and Yue Li},
}

Zabad, S., Gravel, S., & Li, Y. (2023). Fast and accurate Bayesian polygenic risk modeling with variational inference. The American Journal of Human Genetics, 110(5), 741–761. https://doi.org/10.1016/j.ajhg.2023.03.009

View paper ↗
Show BibTeX
@article{ZABAD2023741,
    title = {Fast and accurate Bayesian polygenic risk modeling with variational inference},
    journal = {The American Journal of Human Genetics},
    volume = {110},
    number = {5},
    pages = {741-761},
    year = {2023},
    issn = {0002-9297},
    doi = {https://doi.org/10.1016/j.ajhg.2023.03.009},
    url = {https://www.sciencedirect.com/science/article/pii/S0002929723000939},
    author = {Shadi Zabad and Simon Gravel and Yue Li}
}