Preprint · not peer-reviewed Researchers Educators

Preprint proposes carrier-set topology framework to characterise linkage disequilibrium structure

Researchers argue that standard scalar LD measures such as r² and D' can obscure structurally distinct patterns of haplotype sharing, and propose a geometric framework grounded in 1000 Genomes data.

Published · AI-drafted summary based on 1 public source
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A preprint deposited on bioRxiv introduces a geometric framework for characterising the topology of carrier sets at pairs of biallelic loci — describing whether carriers of one variant are fully nested within, partially overlapping with, or entirely disjoint from carriers of another variant. The authors argue that widely used scalar summaries of linkage disequilibrium (LD), such as r² and D', conflate structurally distinct patterns that have different implications for association mapping, fine-mapping, and population-genetic inference.

The framework is grounded empirically in data from the 1000 Genomes Project and is also explored through a two-locus Wright–Fisher model. The authors describe how carrier-set topology forms, persists across generations, and breaks down under recombination and drift.

The work is relevant to statistical geneticists developing fine-mapping and colocalisation methods, to population geneticists studying haplotype structure, and to researchers working on rare-variant association analyses where haplotype nesting patterns can strongly influence power and interpretation. The preprint has not yet been peer-reviewed.

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  1. Primary sourcePreprint bioRxiv (Cold Spring Harbor Laboratory) · 2026-07-01
    Formation, persistence, and breakdown of carrier-set topology in linkage disequilibrium: empirical structure in 1000 Genomes and a two locus Wright Fisher model

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linkage-disequilibrium haplotype-structure statistical-genetics population-genetics 1000-genomes fine-mapping preprint
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Genetic Current is the news section of Evagene, an academic, research, and educational pedigree-modelling platform. Stories are AI-drafted summaries of items from trusted public sources, written for researchers, clinicians, educators, students, genealogists, and patients with an interest in genetics. Summaries are for educational and research purposes only and are not medical advice.

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