Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.09.24.754106

Linkage disequilibrium - equivalence of the correlation and probability approaches

Sved, J. A.·Chybicki, I. J.
View original source ↗Export metadata ↓

Abstract

Amongst measures of linkage disequilibrium, the correlation measure r2, due originally to Sewall Wright, has several advantages. Although basically a haploid statistic, it can readily be estimated using covariance and correlation from diploid data. Its population expectation can also be found from probability methods. We show that the probability of linked identity by descent, LIBD, directly estimates the expectation for r2 for Wright-Fisher models. The simplest such model for two loci is the haploid model, where each haplotype is chosen independently from haplotypes of the previous generation. This gives an equilibrium between recombination (recombination frequency c) and drift (population size N) where the expected value of r2 is approximately 1/(1+4Nc). For diploid models, the expectation is similar for low values of c, but contains a term in c2 and rises to double the haploid value for the limiting value of c = 0.5. An increase in r2 is also expected for the model of monogamous mating, as opposed to random mating for each offspring. The expectation for r2 is unaffected by the use of diploid rather than haploid data, an exception being the case of monogamous mating. Computer simulation shows close agreement of r2 with probability expectations, with minor discrepancies observed for unlinked loci. Using the probabilistic definition, r2 can be intuitively understood as a parameter shaping the effective sample size in genome-wide association studies.

Explore related subjects

geneticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sved, J. A., Chybicki, I. J.. 2026-09-29. Linkage disequilibrium - equivalence of the correlation and probability approaches. https://doi.org/10.64898/2026.09.24.754106

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Utilizing single-cell data for per-cell type eQTL mapping in the human pancreas

Aims/hypothesis The human pancreas is a central organ for metabolic regulation that is comprised of diverse cell types that uniquely contribute to its function. Previous studies have performed expression quantitative trail loci (eQTL) discovery in either whole pancreas or in pancreatic islets, but due to differences between pancreatic cell types, this approach does not reveal cell type-specific effects. In this study, we sought to either implicate the cell type of action for known eQTLs or identify new eQTLs that may have been masked in bulk studies by performing eQTL discovery in individual pancreatic cell types. Methods We clustered 153,018 single-cell RNA sequencing (scRNA-seq) data from 71 pancreatic islet donors from the Human Pancreas Analysis Program (HPAP). We performed eQTL discovery in six pancreatic cell types using this resource directly. We further utilized this single cell resource as a reference to deconvolute bulk pancreatic RNA sequencing data from 305 Genotype Tissue Expression (GTEx) project donors and performed eQTL discovery in four pancreatic cell types. Finally, we performed fine-mapping and co-localization of pancreatic cell type eQTLs with metabolic GWAS to connect our findings to metabolic disease risk. Results From analyzing 71 individuals with single cell profiles, we identified 112 unique eGenes across six pancreatic cell types, 99 of which had been identified previously and 13 unique to this study. From the deconvoluted eQTLs, we identified 3,134 unique eGenes across four pancreatic cell types, 116 of which were unique to our study. Fine-mapping and co-localization of eQTLs with metabolic GWAS yielded key leads that warrant further investigation, such as the association of rs2168101 with LMO1 expression in alpha cells. Conclusions/interpretation We identified new signals that were previously not found in bulk pancreatic eQTL studies and potential cell type of action for several signals that were identified previously. Although there are limitations to the power, and therefore, discoverability of this study, it provides insights into how individual pancreatic cells differently contribute to metabolic disease.

genetics↗
bioRxiv2026

Snurportin-1 maintains muscle niche integrity and myogenic progenitor homeostasis

Loss-of-function variants in SNUPN, encoding the nuclear import factor Snurportin-1 (SPN1) required for spliceosomal small nuclear ribonucleoprotein (snRNP) transport, cause a recently described form of limb-girdle muscular dystrophy (LGMD). However, the role of SPN1 in skeletal muscle homeostasis remains poorly understood, in part due to the lack of a suitable in vivo model. Here, we generated a zebrafish snupn loss-of-function model that recapitulates key features of the skeletal muscle phenotype observed in patients. Mutant larvae developed severe locomotor impairment by 6 days post-fertilization (dpf), accompanied by sarcomeric disorganization and impaired muscle fiber integrity. Transcriptomic profiling at 6 dpf revealed widespread alternative splicing and transcriptional dysregulation, with prominent alterations in extracellular matrix and basement membrane components, together with upregulation of stress- and inflammation-associated genes. Notably, these late-stage abnormalities were preceded by disruption of the muscle progenitor population at 2 dpf, with reduced Pax7 progenitor abundance and myogenic gene expression together with altered muscle differentiation and organization. Together, these findings identify SPN1 as a key regulator of skeletal muscle homeostasis linking RNA processing to extracellular niche integrity and myogenic progenitor maintenance. This zebrafish model provides an in vivo platform for dissecting LGMD-associated disease mechanisms and developing therapeutic strategies aimed at restoring muscle function and regenerative capacity.

genetics↗
bioRxiv2026

MOD-scTWAS: Leveraging gene co-expression for single-cell transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) provide an effective framework for identifying genes associated with complex traits. Population-scale single-cell transcriptomic data enable genetically regulated expression (GReX) prediction and TWAS analyses at cell-type resolution, but the predictive performance of existing single-cell TWAS methods remains limited. Here, we develop MOD-scTWAS, a module-based method that jointly models GReX for genes within co-expression modules to borrow information across genes. Starting from a generative model for single-cell gene expression, MOD-scTWAS accounts for the heteroscedasticity and cross-gene correlation of individual-level pseudobulk expression in joint GReX prediction. In cross-validation analyses of the OneK1K dataset, MOD-scTWAS achieved higher mean GReX prediction accuracy than scTWAS across all 14 cell types and increased the number of imputable genes. When applied to TWAS analyses of UK Biobank quantitative hematological traits, MOD-scTWAS identified more significant cell type-gene-trait associations than scTWAS. These results demonstrate the potential of leveraging gene co-expression through joint modeling to improve cell-type-specific GReX prediction and TWAS discovery.

genetics↗