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

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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 comparisonCompare as many as four same-chromosome matrices over their shared variants, side by side or paired across the heatmap diagonal.
  • 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}
}