bioRxivGeneticsPreprint
Why linkage disequilibrium measures disagree: Fisher geometry of rare common haplotype structure
Abstract
Conventional LD measures such as r2 perform poorly in the rare-common regime, particularly in asymmetric configurations such as nested haplotype structure. Because r2 is symmetric and quadratic, it removes directional structure in two ways: squaring discards the sign, or phase, retained by the signed LD coefficient D, while symmetric normalization hides the asymmetry between the conditional probabilities P(A|B) and P(B|A). Although D recovers the phase, it is locus-symmetric and unnormalized; its magnitude is hard to compare across frequency regimes and it does not by itself express which way the asymmetry runs. We therefore analyze the conditional-probability asymmetry {Delta} = P(A|B) - P(B|A), together with r2 and D, as distinct scalar functions on the haplotype simplex under the Fisher information metric. The conditional probabilities P(A|B) and P(B|A) are bounded in [0, 1], directly express carrier-set inclusion, and are more readily visualized than D. Moreover, their difference admits the exact decomposition {Delta} = M + C into a marginal-frequency term M and an LD-coupled term C. Prior work has characterized either the mathematical behavior of LD normalizations across allele-frequency space or the Fisher geometry of the haplotype simplex, but not their connection. We bridge this gap by showing that the geometric structure of the simplex explains why LD measures disagree in the rare-common regime and why symmetric normalizations such as r2 lose directional information. We show that the fixed-frequency leaf is intrinsically anisotropic, positively curved, and frequency-dependent under the Fisher metric. These geometric predictions are tested empirically -- in phased 1000 Genomes data1 and a two-locus Wright-Fisher model -- in a companion paper (Ichikawa, preprint2); the present note develops the geometry itself.
- assay: GWAS summary statistics
- research focus: benchmarking
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant effect prediction
- approach: statistical model
- approach: contrastive learning
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Formation, persistence, and breakdown of carrier-set topology in linkage disequilibrium: empirical structure in 1000 Genomes and a two locus Wright Fisher model
Yūki Ichikawa·bioRxiv·Jul 1, 2026
- The nonlinear structure of linkage disequilibrium
Reginald D. Smith·Theoretical Population BiologyPeer-reviewed·Mar 25, 2020
- Linkage disequilibrium - equivalence of the correlation and probability approaches
J A Sved, Igor J. Chybicki·bioRxiv·Sep 29, 2026
- How demography shapes linkage disequilibrium with or without recombination
E. Kerdoncuff, M. K. Ianni-Ravn, … G. Achaz·bioRxiv·Dec 13, 2023
- Exact Distribution of Linkage Disequilibrium in the Presence of Mutation, Selection or Minor Allele Frequency Filtering
Jiayi Qu, Stephen D. Kachman, … Hao Cheng·Frontiers in GeneticsPeer-reviewed·Apr 21, 2020
- Unbiased estimation of linkage disequilibrium from unphased data
Aaron P. Ragsdale, Simon Gravel·Molecular Biology and EvolutionPeer-reviewed·Nov 6, 2019