Biology subjects

Bercovich Szulmajster, U.

Publications and source records attributed to Bercovich Szulmajster, U..

3 recordsLinked to original sources

bioRxiv2026

Calibration improves estimation of linkage disequilibrium on low sample sizes

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 non-parametric 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 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.

bioinformatics↗

bioRxiv2025

LD Matrix Approximations for Scalable Analysis of High-dimensional Genetic Data

Linkage disequilibrium (LD) matrices are an essential part of many statistical genetics methods. However, their high dimensionality makes their computation and storage impractical for large genomic data. Common sparse approximations, such as banded matrices, come at the expense of losing the positive semi-definite (PSD) property, a critical quality that ensures numerical stability of many downstream analyses. Conversely, methods that guarantee a PSD approximation, like block-diagonal approaches, require coarse approximations of the LD structure. In this work, we present a novel method to approximate an LD matrix with a sparse, banded matrix that is guaranteed to be PSD while preserving the correlation structure within the band. This is done via a reformulation of the nearest correlation matrix problem using the Cholesky decomposition, which implicitly imposes the PSD property in a highly scalable parallel approach. On whole-chromosome data from the 1000 Genomes Project and the UK Biobank, our method builds sparse positive semi-definiteness that are more more accurate than either block-diagonal or shrinkage estimators.

genetics↗

bioRxiv2024

Measuring linkage disequilibrium and improvement of pruning and clumping in structured populations

Standard measures of linkage disequilibrium (LD) are affected by admixture and population structure, such that loci that are not in LD within each ancestral population appear linked when considered jointly. The influence of population structure on LD can cause problems for downstream analysis methods, in particular those that rely on LD pruning or clumping. To address this issue, we propose a measure of LD that accommodates population structure using the top inferred principal components. We estimate LD from the correlation of geno-type residuals and prove that this LD measure remains unaffected by population structure when analyzing multiple populations jointly, even with admixed individuals. Based on this adjusted measure of LD, we can perform LD pruning to remove the correlation between markers for downstream analysis. Traditional LD pruning is more likely to remove markers with high differences in allele frequencies between populations, which biases measures for genetic differentiation and removes markers that are not in LD in the ancestral populations. Using data from moderately differentiated human populations and highly differentiated giraffe populations we show that traditional LD pruning biases FST and PCA but that this can be alleviated with the adjusted LD measure. In addition, we show the adjusted LD leads to better PCA when pruning and that LD clumping retains more sites and the retained sites have stronger associations.

genetics↗