Beck, A. T. | Search bioRxiv

bioRxiv2026

Exploring the role of local sequence context on patterns of germline mutation

Germline mutations occur at a non-uniform rate across the genome. While the influence of neighboring nucleotides is well known and accounted for in mutation models, the effective range of these flanking influences and the extent of their higher-order interactions remain insufficiently understood. Here, we leverage millions of singleton variants from the 1000 Genomes Project as a proxy for \textit{de novo} variants to systematically map the contextual influence of local nucleotide sequence context on germline mutation rates. We show that while nucleotides immediately adjacent to the mutation site exert the strongest effect, the influence of nucleotides at flanking positions does not decay monotonically with increased distance from the focal position. Notably, significant associations between flanking nucleotides and mutation status persist up to hundreds of base pairs from the focal site. Furthermore, higher-order interactions among flanking positions significantly modulate mutation rates, though their effect size diminishes as the order of interaction increases. Additionally, these mutational signatures are highly consistent across diverse ancestral groups. Together, these findings provide a comprehensive map of how local genomic architecture shapes the mutational landscape.

evolutionary biology↗

bioRxiv2025

A Benchmark of Modern Statistical Phasing Methods

Modern statistical phasing methods efficiently and accurately infer haplotype phase in large genomic samples, and their performance is critical for downstream analyses. However, rigorously evaluating phasing accuracy remains challenging. Here we demonstrate the use of synthetic diploids generated from male X chromosome sequences to benchmark and compare three commonly-used phasing methods: Beagle 5.4, SHAPEIT 4, and Eagle v2.4.1. Our evaluation reveals highly correlated error rates across all methods, although we observe distinct variation among methods within error types. Specifically, Eagle v2.4.1 is characterized by a higher frequency of switch errors, whereas SHAPEIT 4 displays a higher rate of flip (double-switch) errors. These patterns are consistent across diverse populations. Additionally, we observe an enrichment of errors at both CpG sites and rare variant sites, particularly for flip errors. Validating these results in autosomal trio data, we observe similar trends between the methods and error patterns, though with an increased flip error rate, a discrepancy that may be biased by genotype error.

genomics↗