Calibration improves estimation of linkage disequilibrium on low sample sizes
Abstract
Linkage disequilibrium is a central statistic in population genetic studies, commonly measured by the squared correlation between pairs of genetic variants. An important drawback of this measure is its upward bias caused by a finite sample size. To handle this, different methods exist that correct for sample-size bias. However, because the correlation consists of a ratio, there is no unbiased method to compute it. In this work, we present a procedure to calibrate those methods using a nonparametric approach with simulated data. This is done with forward modeling to generate genotype matrices with known parameters, followed by an inverse mapping to recover estimates of the underlying parameters. Then, a mean-centering calibration is applied to the recovered estimate of the true parameter. This approach is applied to real and simulated human data, showing consistent improvement in accuracy compared to other sample-size-aware methods. Furthermore, to study the effects on downstream analyses, we analyze the classification performance on LD pruning, where we also observe an improvement, particularly in extreme cases with low sample sizes of 5 or 10 individuals.
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BibTeX
@article{bercovich2026calibration,
title = {{Calibration improves estimation of linkage disequilibrium on low sample sizes}},
author = {Ulises Bercovich and Carsten Wiuf and Anders Albrechtsen},
journal = {G3: Genes | Genomes | Genetics},
year = {2026},
month = aug,
doi = {10.1093/g3journal/jkag239},
url = {https://doi.org/10.1093/g3journal/jkag239},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Calibration improves estimation of linkage disequilibrium on low sample sizes
AU - Ulises Bercovich
AU - Carsten Wiuf
AU - Anders Albrechtsen
PY - 2026
DA - 2026/08/27
JO - G3: Genes | Genomes | Genetics
DO - 10.1093/g3journal/jkag239
UR - https://doi.org/10.1093/g3journal/jkag239
AB - Linkage disequilibrium is a central statistic in population genetic studies, commonly measured by the squared correlation between pairs of genetic variants. An important drawback of this measure is its upward bias caused by a finite sample size. To handle this, different methods exist that correct for sample-size bias. However, because the correlation consists of a ratio, there is no unbiased method to compute it. In this work, we present a procedure to calibrate those methods using a nonparametric approach with simulated data. This is done with forward modeling to generate genotype matrices with known parameters, followed by an inverse mapping to recover estimates of the underlying parameters. Then, a mean-centering calibration is applied to the recovered estimate of the true parameter. This approach is applied to real and simulated human data, showing consistent improvement in accuracy compared to other sample-size-aware methods. Furthermore, to study the effects on downstream analyses, we analyze the classification performance on LD pruning, where we also observe an improvement, particularly in extreme cases with low sample sizes of 5 or 10 individuals.
SN - 2160-1836
ER -